diff --git a/.github/workflows/pytest_imputation_beta.yml b/.github/workflows/pytest_imputation_beta.yml index e7f3efe7..82c61a5b 100644 --- a/.github/workflows/pytest_imputation_beta.yml +++ b/.github/workflows/pytest_imputation_beta.yml @@ -35,7 +35,6 @@ jobs: python -m pytest ./tests/test_imputation_tkcm.py python -m pytest ./tests/test_imputation_deepmvi.py python -m pytest ./tests/test_imputation_brits.py - python -m pytest ./tests/test_imputation_mpin.py python -m pytest ./tests/test_imputation_pristi.py python -m pytest ./tests/test_imputation_grin.py python -m pytest ./tests/test_imputation_gain.py diff --git a/.idea/imputegap.iml b/.idea/imputegap.iml index 5fe0bf86..a5ca65ce 100644 --- a/.idea/imputegap.iml +++ b/.idea/imputegap.iml @@ -2,7 +2,7 @@ - + diff --git a/.idea/misc.xml b/.idea/misc.xml index 3b46d595..8c3a99c1 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -3,5 +3,5 @@ - + \ No newline at end of file diff --git a/README.md b/README.md index 8dff3735..d015aa6e 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ Access to commonly used datasets in time series research (Datasets).
![Python](https://img.shields.io/badge/Python-v3.12-blue) -![Release](https://img.shields.io/badge/Release-v1.0.6-brightgreen) +![Release](https://img.shields.io/badge/Release-v1.0.7-brightgreen) ![License](https://img.shields.io/badge/License-GPLv3-blue?style=flat&logo=gnu) ![Coverage](https://img.shields.io/badge/Coverage-93%25-brightgreen) ![PyPI](https://img.shields.io/pypi/v/imputegap?label=PyPI&color=blue) @@ -40,13 +40,13 @@ Access to commonly used datasets in time series research (Datasets). # List of available imputation algorithms | **Family** | **Algorithm** | **Venue -- Year** | |--------------------|---------------------------|------------------------------| -| Deep Learning | BITGraph [[32]](#ref32) | ICLR -- 2024 | +| Deep Learning | BitGraph [[32]](#ref32) | ICLR -- 2024 | | Deep Learning | BayOTIDE [[30]](#ref30) | PMLR -- 2024 | -| Deep Learning | MPIN [[25]](#ref25) | PVLDB -- 2024 | | Deep Learning | MissNet [[27]](#ref27) | KDD -- 2024 | -| Deep Learning | PriSTI [[26]](#ref26) | ICDE -- 2023 | +| Deep Learning | MPIN [[25]](#ref25) | PVLDB -- 2024 | +| Deep Learning | PRISTI [[26]](#ref26) | ICDE -- 2023 | | Deep Learning | GRIN [[29]](#ref29) | ICLR -- 2022 | -| Deep Learning | HKMF-T [[31]](#ref31) | TKDE -- 2021 | +| Deep Learning | HKMF_T [[31]](#ref31) | TKDE -- 2021 | | Deep Learning | DeepMVI [[24]](#ref24) | PVLDB -- 2021 | | Deep Learning | MRNN [[22]](#ref22) | IEEE Trans on BE -- 2019 | | Deep Learning | BRITS [[23]](#ref23) | NeurIPS -- 2018 | @@ -60,18 +60,18 @@ Access to commonly used datasets in time series research (Datasets). | Matrix Completion | SPIRIT [[5]](#ref5) | VLDB -- 2005 | | Matrix Completion | IterativeSVD [[2]](#ref2) | BIOINFORMATICS -- 2001 | | Pattern Search | TKCM [[11]](#ref11) | EDBT -- 2017 | -| Pattern Search | ST-MVL [[9]](#ref9) | IJCAI -- 2016 | +| Pattern Search | STMVL [[9]](#ref9) | IJCAI -- 2016 | | Pattern Search | DynaMMo [[10]](#ref10) | KDD -- 2009 | | Machine Learning | IIM [[12]](#ref12) | ICDE -- 2019 | -| Machine Learning | XGBI [[13]](#ref13) | KDD -- 2016 | -| Machine Learning | Mice [[14]](#ref14) | Statistical Software -- 2011 | +| Machine Learning | XGBOOST [[13]](#ref13) | KDD -- 2016 | +| Machine Learning | MICE [[14]](#ref14) | Statistical Software -- 2011 | | Machine Learning | MissForest [[15]](#ref15) | BioInformatics -- 2011 | | Statistics | KNNImpute | - | | Statistics | Interpolation | - | -| Statistics | Min Impute | - | -| Statistics | Zero Impute | - | -| Statistics | Mean Impute | - | -| Statistics | Mean Impute By Series | - | +| Statistics | MinImpute | - | +| Statistics | ZeroImpute | - | +| Statistics | MeanImpute | - | +| Statistics | MeanImputeBySeries | - | --- @@ -155,7 +155,7 @@ ts.normalize(normalizer="z_score") # plot and print a subset of time series ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") -ts.print(nbr_series=9, nbr_val=100) +ts.print(nbr_series=9, nbr_val=20) ``` --- @@ -235,8 +235,7 @@ imputer.score(ts.data, imputer.recov_data) ts.print_results(imputer.metrics) # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, - save_path="./imputegap_assets") +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets") ``` --- @@ -270,9 +269,16 @@ imputer = Imputation.MatrixCompletion.CDRec(ts_m) # use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) -# compute and print the imputation metrics +# compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics) + +# compute the imputation metrics with default parameter values +imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() +imputer_def.score(ts.data, imputer_def.recov_data) + +# print the imputation metrics with default and optimized parameter values +ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") +ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, @@ -357,7 +363,7 @@ imputer = Imputation.MatrixCompletion.CDRec(ts_m) imputer.impute() # compute and print the downstream results -downstream_config = {"task": "forecast", "model": "hw-add"} +downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) ``` @@ -369,7 +375,7 @@ ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) ## Benchmark -ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms[[33]](#ref33) . Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. +ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms[[33]](#ref33) . Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. The default metrics evaluated include "RMSE", "MAE", "MI", "Pearson", and the runtime. The documentation for the benchmark is described [here](https://imputegap.readthedocs.io/en/latest/benchmark.html). @@ -381,22 +387,27 @@ The benchmarking module can be utilized as follows: ```python from imputegap.recovery.benchmark import Benchmark -save_dir = "./analysis" -nbr_run = 2 +save_dir = "./imputegap_assets/benchmark" +nbr_runs = 1 -datasets = ["eeg-alcohol", "eeg-reading"] +datasets = ["eeg-alcohol"] -optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} -optimizers = [optimizer] +optimizers = ["default_params"] -algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"] +algorithms = ["SoftImpute", "KNNImpute"] patterns = ["mcar"] range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] # launch the evaluation -list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) +list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_runs) +``` + +You can change the optimizer using the following command: +```python +optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} +optimizers = [optimizer] ``` --- @@ -407,20 +418,6 @@ To add your own imputation algorithm in Python or C++, please refer to the detai --- - -## Articles - - -Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, Philippe Cudre-Mauroux: Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series. Proc. VLDB Endow. 13(5): 768-782 (2020) - -Mourad Khayati, Quentin Nater, Jacques Pasquier: ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data. Proc. VLDB Endow. 17(12): 4329-4332 (2024) - ---- - - - - - ## Citing If you use ImputeGAP in your research, please cite the paper: diff --git a/build/lib/imputegap/__init__.py b/build/lib/imputegap/__init__.py index 222a4c12..84ce1414 100644 --- a/build/lib/imputegap/__init__.py +++ b/build/lib/imputegap/__init__.py @@ -1 +1 @@ -__version__ = "1.0.5" \ No newline at end of file +__version__ = "1.0.7" \ No newline at end of file diff --git a/build/lib/imputegap/algorithms/bayotide.py b/build/lib/imputegap/algorithms/bayotide.py index e843a583..63e6e85c 100644 --- a/build/lib/imputegap/algorithms/bayotide.py +++ b/build/lib/imputegap/algorithms/bayotide.py @@ -66,6 +66,6 @@ def bay_otide(incomp_data, K_trend=20, K_season=2, n_season=5, K_bias=1, time_sc end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation bay_otide - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation bay_otide - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/bit_graph.py b/build/lib/imputegap/algorithms/bit_graph.py index 9f00d15d..b32cc2eb 100644 --- a/build/lib/imputegap/algorithms/bit_graph.py +++ b/build/lib/imputegap/algorithms/bit_graph.py @@ -64,6 +64,6 @@ def bit_graph(incomp_data, node_number=-1, kernel_set=[1], dropout=0.1, subgraph end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation bit graph - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation bit graph - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/brits.py b/build/lib/imputegap/algorithms/brits.py index 346ab9ba..4bc69301 100644 --- a/build/lib/imputegap/algorithms/brits.py +++ b/build/lib/imputegap/algorithms/brits.py @@ -49,6 +49,6 @@ def brits(incomp_data, model="brits", epoch=10, batch_size=7, nbr_features=1, hi end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation brits - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation brits - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/cdrec.py b/build/lib/imputegap/algorithms/cdrec.py index bcad9d95..9d6fb16d 100644 --- a/build/lib/imputegap/algorithms/cdrec.py +++ b/build/lib/imputegap/algorithms/cdrec.py @@ -29,7 +29,7 @@ def native_cdrec(__py_matrix, __py_rank, __py_epsilon, __py_iterations): Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7 """ - shared_lib = utils.load_share_lib("lib_cdrec.so") + shared_lib = utils.load_share_lib("lib_cdrec") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -87,7 +87,7 @@ def cdrec(incomp_data, truncation_rank, iterations, epsilon, logs=True, lib_path """ - print(f"\t\t\t\t(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, " + print(f"(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, " f"epsilon {epsilon}, and iterations {iterations}...") start_time = time.time() # Record start time @@ -98,6 +98,6 @@ def cdrec(incomp_data, truncation_rank, iterations, epsilon, logs=True, lib_path end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/cpp_integration.py b/build/lib/imputegap/algorithms/cpp_integration.py index 30ef7656..7985ad6b 100644 --- a/build/lib/imputegap/algorithms/cpp_integration.py +++ b/build/lib/imputegap/algorithms/cpp_integration.py @@ -25,7 +25,7 @@ def native_algo(__py_matrix, __py_param): """ - shared_lib = utils.load_share_lib("to_adapt.so") + shared_lib = utils.load_share_lib("to_adapt") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -79,6 +79,6 @@ def your_algo(contamination, param, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation algo - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation algo - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/deep_mvi.py b/build/lib/imputegap/algorithms/deep_mvi.py index 3317aabb..8fab88f7 100644 --- a/build/lib/imputegap/algorithms/deep_mvi.py +++ b/build/lib/imputegap/algorithms/deep_mvi.py @@ -41,6 +41,6 @@ def deep_mvi(incomp_data, max_epoch=1000, patience=2, lr=0.001, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation deep mvi - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation deep mvi - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/dynammo.py b/build/lib/imputegap/algorithms/dynammo.py index 205f1bf4..acc9f6ff 100644 --- a/build/lib/imputegap/algorithms/dynammo.py +++ b/build/lib/imputegap/algorithms/dynammo.py @@ -29,7 +29,7 @@ def native_dynammo(__py_matrix, __py_h, __py_maxIter, __py_fast): L. Li, J. McCann, N. S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pages 507–516, 2009. """ - shared_lib = utils.load_share_lib("lib_dynammo.so") + shared_lib = utils.load_share_lib("lib_dynammo") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -92,6 +92,6 @@ def dynammo(incomp_data, h, max_iteration, approximation, logs=True, lib_path=No end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation DynaMMo - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation DynaMMo - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/gain.py b/build/lib/imputegap/algorithms/gain.py index 0d0a1917..912e8ed4 100644 --- a/build/lib/imputegap/algorithms/gain.py +++ b/build/lib/imputegap/algorithms/gain.py @@ -44,6 +44,6 @@ def gain(incomp_data, batch_size=32, hint_rate=0.9, alpha=10, epoch=100, logs=Tr end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation gain - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation gain - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/grin.py b/build/lib/imputegap/algorithms/grin.py index 6de5ead0..f6626d29 100644 --- a/build/lib/imputegap/algorithms/grin.py +++ b/build/lib/imputegap/algorithms/grin.py @@ -60,6 +60,6 @@ def grin(incomp_data, d_hidden=32, lr=0.001, batch_size=32, window=10, alpha=10. end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation grin - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation grin - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/grouse.py b/build/lib/imputegap/algorithms/grouse.py index e90f6833..e72d2636 100644 --- a/build/lib/imputegap/algorithms/grouse.py +++ b/build/lib/imputegap/algorithms/grouse.py @@ -24,7 +24,7 @@ def native_grouse(__py_matrix, __py_rank): D. Zhang and L. Balzano. Global convergence of a grassmannian gradient descent algorithm for subspace estimation. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, pages 1460–1468, 2016. """ - shared_lib = utils.load_share_lib("lib_grouse.so") + shared_lib = utils.load_share_lib("lib_grouse") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -80,6 +80,6 @@ def grouse(incomp_data, max_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation GROUSE - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation GROUSE - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/hkmf_t.py b/build/lib/imputegap/algorithms/hkmf_t.py index ab38585f..b6d3f819 100644 --- a/build/lib/imputegap/algorithms/hkmf_t.py +++ b/build/lib/imputegap/algorithms/hkmf_t.py @@ -48,6 +48,6 @@ def hkmf_t(incomp_data, tags=None, data_names=None, epoch=10, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation hkmf_t - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation hkmf_t - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/iim.py b/build/lib/imputegap/algorithms/iim.py index 38b258f4..afbdb5be 100644 --- a/build/lib/imputegap/algorithms/iim.py +++ b/build/lib/imputegap/algorithms/iim.py @@ -45,6 +45,6 @@ def iim(incomp_data, number_neighbor, algo_code, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/interpolation.py b/build/lib/imputegap/algorithms/interpolation.py index a1230012..fe1d93c5 100644 --- a/build/lib/imputegap/algorithms/interpolation.py +++ b/build/lib/imputegap/algorithms/interpolation.py @@ -30,7 +30,7 @@ def interpolation(incomp_data, method="linear", poly_order=2, logs=True): """ - print(f"\t\t\t\t(PYTHON) interpolation : ({incomp_data.shape[0]},{incomp_data.shape[1]}) for method {method}" + print(f"(PYTHON) interpolation : ({incomp_data.shape[0]},{incomp_data.shape[1]}) for method {method}" f", and polynomial order {poly_order}...") start_time = time.time() # Record start time @@ -70,6 +70,6 @@ def interpolation(incomp_data, method="linear", poly_order=2, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation with interpolation - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation with interpolation - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/iterative_svd.py b/build/lib/imputegap/algorithms/iterative_svd.py index faed1b02..18b43585 100644 --- a/build/lib/imputegap/algorithms/iterative_svd.py +++ b/build/lib/imputegap/algorithms/iterative_svd.py @@ -25,7 +25,7 @@ def native_iterative_svd(__py_matrix, __py_rank): Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman, Missing value estimation methods for DNA microarrays , Bioinformatics, Volume 17, Issue 6, June 2001, Pages 520–525, https://doi.org/10.1093/bioinformatics/17.6.520 """ - shared_lib = utils.load_share_lib("lib_iterative_svd.so") + shared_lib = utils.load_share_lib("lib_iterative_svd") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -82,6 +82,6 @@ def iterative_svd(incomp_data, truncation_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation iterative svd - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation iterative svd - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/knn.py b/build/lib/imputegap/algorithms/knn.py index 97f35693..146fc32c 100644 --- a/build/lib/imputegap/algorithms/knn.py +++ b/build/lib/imputegap/algorithms/knn.py @@ -29,7 +29,7 @@ def knn(incomp_data, k=5, weights="uniform", logs=True): """ - print(f"\t\t\t\t(PYTHON) KNN: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for k {k}, " + print(f"(PYTHON) KNNImpute: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for k {k}, " f", and weights {weights}...") start_time = time.time() # Record start time @@ -74,6 +74,6 @@ def knn(incomp_data, k=5, weights="uniform", logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation knn - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation knn_impute - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/lib/lib_cdrec.dylib b/build/lib/imputegap/algorithms/lib/lib_cdrec.dylib new file mode 100644 index 00000000..4996676c Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_cdrec.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_dynammo.dylib b/build/lib/imputegap/algorithms/lib/lib_dynammo.dylib new file mode 100644 index 00000000..d74632c7 Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_dynammo.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_grouse.dylib b/build/lib/imputegap/algorithms/lib/lib_grouse.dylib new file mode 100644 index 00000000..f2a2faed Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_grouse.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_iterative_svd.dylib b/build/lib/imputegap/algorithms/lib/lib_iterative_svd.dylib new file mode 100644 index 00000000..4476ac45 Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_iterative_svd.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_rosl.dylib b/build/lib/imputegap/algorithms/lib/lib_rosl.dylib new file mode 100644 index 00000000..0fbc1996 Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_rosl.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_soft_impute.dylib b/build/lib/imputegap/algorithms/lib/lib_soft_impute.dylib new file mode 100644 index 00000000..45bb6e3e Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_soft_impute.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_spirit.dylib b/build/lib/imputegap/algorithms/lib/lib_spirit.dylib new file mode 100644 index 00000000..ed900873 Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_spirit.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_stmvl.dylib b/build/lib/imputegap/algorithms/lib/lib_stmvl.dylib new file mode 100644 index 00000000..0b84a73f Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_stmvl.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_svt.dylib b/build/lib/imputegap/algorithms/lib/lib_svt.dylib new file mode 100644 index 00000000..7dbffb24 Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_svt.dylib differ diff --git a/build/lib/imputegap/algorithms/lib/lib_tkcm.dylib b/build/lib/imputegap/algorithms/lib/lib_tkcm.dylib new file mode 100644 index 00000000..4d90ae1c Binary files /dev/null and b/build/lib/imputegap/algorithms/lib/lib_tkcm.dylib differ diff --git a/build/lib/imputegap/algorithms/mean_impute_by_series.py b/build/lib/imputegap/algorithms/mean_impute_by_series.py index bb6f7c4a..0276e974 100644 --- a/build/lib/imputegap/algorithms/mean_impute_by_series.py +++ b/build/lib/imputegap/algorithms/mean_impute_by_series.py @@ -38,7 +38,7 @@ def mean_impute_by_series(incomp_data, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mean impute (by series) - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mean impute (by series) - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/mice.py b/build/lib/imputegap/algorithms/mice.py index ab73e320..d394078a 100644 --- a/build/lib/imputegap/algorithms/mice.py +++ b/build/lib/imputegap/algorithms/mice.py @@ -39,7 +39,7 @@ def mice(incomp_data, max_iter=3, tol=0.001, initial_strategy='mean', seed=42, l https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer """ - print("\t\t(PYTHON) MICE : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for max_iter ", + print("(PYTHON) MICE : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for max_iter ", max_iter, ", tol ", tol, " initial_strategy ", initial_strategy, ", and seed ", seed, "...\n\n\t\t\t" "Careful, this imputation algorithm might take a while to compute.") @@ -50,6 +50,6 @@ def mice(incomp_data, max_iter=3, tol=0.001, initial_strategy='mean', seed=42, l end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation MICE - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation MICE - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/miss_forest.py b/build/lib/imputegap/algorithms/miss_forest.py index 3dd56645..0a5145d6 100644 --- a/build/lib/imputegap/algorithms/miss_forest.py +++ b/build/lib/imputegap/algorithms/miss_forest.py @@ -45,7 +45,7 @@ def miss_forest(incomp_data, n_estimators=10, max_iter=3, max_features='sqrt', s https://pypi.org/project/MissForest/ """ - print("\t\t(PYTHON) MISS FOREST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for n_estimators ", + print("(PYTHON) MISS FOREST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for n_estimators ", n_estimators, ", max_iter ", max_iter, " max_features ", max_features, ", and seed ", seed, "...") # Convert numpy array to pandas DataFrame if needed @@ -64,6 +64,6 @@ def miss_forest(incomp_data, n_estimators=10, max_iter=3, max_features='sqrt', s end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation MISS FOREST - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation MISS FOREST - Execution Time: {(end_time - start_time):.4f} seconds\n") return np.array(recov_data) diff --git a/build/lib/imputegap/algorithms/miss_net.py b/build/lib/imputegap/algorithms/miss_net.py index a636065a..e120c7ba 100644 --- a/build/lib/imputegap/algorithms/miss_net.py +++ b/build/lib/imputegap/algorithms/miss_net.py @@ -46,7 +46,7 @@ def miss_net(incomp_data, alpha, beta, L, n_cl, max_iteration, tol, random_init, https://github.com/KoheiObata/MissNet/tree/main """ - print("\t\t(PYTHON) MISS NET: Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") " + print("(PYTHON) MISS NET: Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") " "for alpha ", alpha, ", beta ", beta, ", L ", L, ", n_cl ", n_cl, ", max_iteration ", max_iteration, "tol ", tol, " random_init ", random_init, "...") @@ -62,6 +62,6 @@ def miss_net(incomp_data, alpha, beta, L, n_cl, max_iteration, tol, random_init, recov_data[nan_mask] = incomp_data[nan_mask] if logs: - print(f"\n\t\t> logs, imputation miss_net - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation miss_net - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/mpin.py b/build/lib/imputegap/algorithms/mpin.py index 41f7e385..8e544153 100644 --- a/build/lib/imputegap/algorithms/mpin.py +++ b/build/lib/imputegap/algorithms/mpin.py @@ -52,6 +52,6 @@ def mpin(incomp_data=None, incre_mode="alone", window=2, k=10, lr=0.01, weight_d end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mpin - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mpin - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/mrnn.py b/build/lib/imputegap/algorithms/mrnn.py index 53b8640d..e8ed7716 100644 --- a/build/lib/imputegap/algorithms/mrnn.py +++ b/build/lib/imputegap/algorithms/mrnn.py @@ -48,6 +48,6 @@ def mrnn(incomp_data, hidden_dim, learning_rate, iterations, sequence_length, lo end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/pristi.py b/build/lib/imputegap/algorithms/pristi.py index ce00d3e0..a86967f1 100644 --- a/build/lib/imputegap/algorithms/pristi.py +++ b/build/lib/imputegap/algorithms/pristi.py @@ -44,6 +44,6 @@ def pristi(incomp_data, target_strategy="hybrid", unconditional=True, seed=42, d end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation priSTI - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation priSTI - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/rosl.py b/build/lib/imputegap/algorithms/rosl.py index 41dd2c92..9ef33122 100644 --- a/build/lib/imputegap/algorithms/rosl.py +++ b/build/lib/imputegap/algorithms/rosl.py @@ -31,7 +31,7 @@ def native_rosl(__py_matrix, __py_rank, __py_regularization): ---------- X. Shu, F. Porikli, and N. Ahuja. Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 3874–3881, 2014. """ - shared_lib = utils.load_share_lib("lib_rosl.so") + shared_lib = utils.load_share_lib("lib_rosl") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -96,6 +96,6 @@ def rosl(incomp_data, rank, regularization, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation ROSL - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation ROSL - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/soft_impute.py b/build/lib/imputegap/algorithms/soft_impute.py index df64c67c..980a1b3b 100644 --- a/build/lib/imputegap/algorithms/soft_impute.py +++ b/build/lib/imputegap/algorithms/soft_impute.py @@ -24,7 +24,7 @@ def native_soft_impute(__py_matrix, __py_max_rank): R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11:2287–2322, 2010. """ - shared_lib = utils.load_share_lib("lib_soft_impute.so") + shared_lib = utils.load_share_lib("lib_soft_impute") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -81,6 +81,6 @@ def soft_impute(incomp_data, max_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation Soft Impute - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation Soft Impute - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/spirit.py b/build/lib/imputegap/algorithms/spirit.py index 3d168954..65aaadc3 100644 --- a/build/lib/imputegap/algorithms/spirit.py +++ b/build/lib/imputegap/algorithms/spirit.py @@ -31,7 +31,7 @@ def native_spirit(__py_matrix, __py_k, __py_w, __py_lambda): S. Papadimitriou, J. Sun, and C. Faloutsos. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30 - September 2, 2005, pages 697–708, 2005. """ - shared_lib = utils.load_share_lib("lib_spirit.so") + shared_lib = utils.load_share_lib("lib_spirit") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -98,6 +98,6 @@ def spirit(incomp_data, k, w, lambda_value, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation SPIRIT - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation SPIRIT - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/stmvl.py b/build/lib/imputegap/algorithms/stmvl.py index bd75f55d..9eef0f14 100644 --- a/build/lib/imputegap/algorithms/stmvl.py +++ b/build/lib/imputegap/algorithms/stmvl.py @@ -39,7 +39,7 @@ def native_stmvl(__py_matrix, __py_window, __py_gamma, __py_alpha): School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. """ - shared_lib = utils.load_share_lib("lib_stmvl.so") + shared_lib = utils.load_share_lib("lib_stmvl") __py_sizen = len(__py_matrix); __py_sizem = len(__py_matrix[0]); @@ -84,6 +84,9 @@ def stmvl(incomp_data, window_size, gamma, alpha, logs=True): :return: recov_data, metrics : all time series with imputation data and their metrics """ + print(f"(PYTHON) ST-MVL: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for window_size {window_size}, " + f"gamma {gamma}, and alpha {alpha}...") + start_time = time.time() # Record start time # Call the C++ function to perform recovery @@ -91,6 +94,6 @@ def stmvl(incomp_data, window_size, gamma, alpha, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/svt.py b/build/lib/imputegap/algorithms/svt.py index 12d5256d..ea8522bc 100644 --- a/build/lib/imputegap/algorithms/svt.py +++ b/build/lib/imputegap/algorithms/svt.py @@ -25,7 +25,7 @@ def native_svt(__py_matrix, __py_tau): J. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. [8] J. Cambronero, J. K. Feser, M. J. Smith, and """ - shared_lib = utils.load_share_lib("lib_svt.so") + shared_lib = utils.load_share_lib("lib_svt") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -80,6 +80,6 @@ def svt(incomp_data, tau, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation SVT - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation SVT - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/tkcm.py b/build/lib/imputegap/algorithms/tkcm.py index 713fe644..59b35b5a 100644 --- a/build/lib/imputegap/algorithms/tkcm.py +++ b/build/lib/imputegap/algorithms/tkcm.py @@ -24,7 +24,7 @@ def native_tkcm(__py_matrix, __py_rank): K. Wellenzohn, M. H. Böhlen, A. Dignös, J. Gamper, and H. Mitterer. Continuous imputation of missing values in streams of pattern-determining time series. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017., pages 330–341, 2017. """ - shared_lib = utils.load_share_lib("lib_tkcm.so") + shared_lib = utils.load_share_lib("lib_tkcm") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -81,6 +81,6 @@ def tkcm(incomp_data, rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation TKCM - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation TKCM - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/trmf.py b/build/lib/imputegap/algorithms/trmf.py index a80d46a7..db3cfe9d 100644 --- a/build/lib/imputegap/algorithms/trmf.py +++ b/build/lib/imputegap/algorithms/trmf.py @@ -57,6 +57,6 @@ def trmf(incomp_data, lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_ite end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation trmf - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation trmf - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/build/lib/imputegap/algorithms/xgboost.py b/build/lib/imputegap/algorithms/xgboost.py index 29834896..bb6bc0e2 100644 --- a/build/lib/imputegap/algorithms/xgboost.py +++ b/build/lib/imputegap/algorithms/xgboost.py @@ -37,7 +37,7 @@ def xgboost(incomp_data, n_estimators=10, seed=42, logs=True): https://medium.com/@tzhaonj/imputing-missing-data-using-xgboost-802757cace6d """ - print("\t\t(PYTHON) XGBOOST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ")" + print("(PYTHON) XGBOOST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ")" " for n_estimators ", n_estimators, ", and seed ", seed, "...") if isinstance(incomp_data, np.ndarray): @@ -68,6 +68,6 @@ def xgboost(incomp_data, n_estimators=10, seed=42, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation XGBOOST - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation XGBOOST - Execution Time: {(end_time - start_time):.4f} seconds\n") return np.array(recov_data) diff --git a/build/lib/imputegap/env/default_values.toml b/build/lib/imputegap/env/default_values.toml index e422936d..299eb3d4 100644 --- a/build/lib/imputegap/env/default_values.toml +++ b/build/lib/imputegap/env/default_values.toml @@ -87,7 +87,7 @@ approximation = true rank = 4 # ALGORITHM DEFAULT VALUES : STATISTICS -[knn] +[knn_impute] k = 5 weights = "uniform" diff --git a/imputegap/imputegap_assets/shap/.gitkeep b/build/lib/imputegap/imputegap_assets/benchmark/.gitkeep similarity index 100% rename from imputegap/imputegap_assets/shap/.gitkeep rename to build/lib/imputegap/imputegap_assets/benchmark/.gitkeep diff --git a/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/eegalcohol_mcar_default_params_CORRELATION.jpg b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/eegalcohol_mcar_default_params_CORRELATION.jpg new file mode 100644 index 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b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/eegalcohol_mcar_metrics_subplot.jpg differ diff --git a/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.txt b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.txt new file mode 100644 index 00000000..2ff471db --- /dev/null +++ b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.txt @@ -0,0 +1,83 @@ +Report for Dataset: eeg-alcohol +Generated on: 2025-03-21 15:54:48 +Run number: 0 +======================================================================================================================== + +RMSE: Root Mean Square Error - Measures the average magnitude of error. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | RMSE | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.4359915238 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.3665001858 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.3983300622 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.4355910162 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.4500113662 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.4655442240 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.2410259540 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.2889085181 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.3252384253 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.3382458476 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.3656435952 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.4985193265 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +MAE: Mean Absolute Error - Measures the average absolute error. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | MAE | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.3725965559 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.2989983613 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.3082464402 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.3335144216 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.3380858657 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.3508926604 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.1898483284 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.2299862373 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.2398150375 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.2509197576 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.2663072460 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.3727407178 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +MI: Mutual Information - Indicates dependency between variables. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | MI | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 1.4169232776 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.9078918431 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.8483406827 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.7286325588 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.6481512577 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.6150677913 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 1.5477828628 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.9934511614 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 1.0141220941 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.9534836632 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.7933983583 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.5872198066 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +CORRELATION: Correlation Coefficient - Indicates linear relationship between variables. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | CORRELATION | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.9530448037 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.9049909723 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.9161465703 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.9021032587 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.8893263437 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.8791443563 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.9810468571 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.9429448316 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.9441733384 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.9418540692 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.9285706344 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.8588039019 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +Dictionary of Results: +{'eegalcohol': {'mcar': {'SoftImpute': {'default_params': {'0.05': {'scores': {'RMSE': 0.4359915238078244, 'MAE': 0.3725965559420608, 'MI': 1.4169232775678364, 'CORRELATION': 0.9530448037164908}, 'times': {'contamination': 0.00027561187744140625, 'optimization': 0, 'imputation': 0.15315723419189453, 'log_imputation': -0.8148624850120225}}, '0.1': {'scores': {'RMSE': 0.3665001858394363, 'MAE': 0.2989983612840734, 'MI': 0.9078918430616858, 'CORRELATION': 0.9049909722894052}, 'times': {'contamination': 0.0004036426544189453, 'optimization': 0, 'imputation': 0.10621070861816406, 'log_imputation': -0.9738316936187855}}, '0.2': {'scores': {'RMSE': 0.39833006221984, 'MAE': 0.30824644022807457, 'MI': 0.8483406827418594, 'CORRELATION': 0.9161465703422209}, 'times': {'contamination': 0.0015327930450439453, 'optimization': 0, 'imputation': 0.08952975273132324, 'log_imputation': -1.048032614966155}}, '0.4': {'scores': {'RMSE': 0.435591016228979, 'MAE': 0.3335144215651955, 'MI': 0.7286325588353783, 'CORRELATION': 0.9021032587324183}, 'times': {'contamination': 0.03658604621887207, 'optimization': 0, 'imputation': 0.12557768821716309, 'log_imputation': -0.9010875162113435}}, '0.6': {'scores': {'RMSE': 0.4500113661547204, 'MAE': 0.338085865703361, 'MI': 0.6481512576687939, 'CORRELATION': 0.8893263437029546}, 'times': {'contamination': 0.08436226844787598, 'optimization': 0, 'imputation': 0.11329030990600586, 'log_imputation': -0.945807235187996}}, '0.8': {'scores': {'RMSE': 0.46554422402146944, 'MAE': 0.3508926604243284, 'MI': 0.6150677913271478, 'CORRELATION': 0.8791443563129441}, 'times': {'contamination': 0.13154006004333496, 'optimization': 0, 'imputation': 0.33798837661743164, 'log_imputation': -0.4710982348043248}}}}, 'KNNImpute': {'default_params': {'0.05': {'scores': {'RMSE': 0.24102595399583507, 'MAE': 0.18984832836399548, 'MI': 1.547782862758484, 'CORRELATION': 0.9810468571465141}, 'times': {'contamination': 0.0008156299591064453, 'optimization': 0, 'imputation': 0.051076412200927734, 'log_imputation': -1.291779616813155}}, '0.1': {'scores': {'RMSE': 0.28890851809839135, 'MAE': 0.22998623733608023, 'MI': 0.9934511613817691, 'CORRELATION': 0.942944831550703}, 'times': {'contamination': 0.0005509853363037109, 'optimization': 0, 'imputation': 0.02433300018310547, 'log_imputation': -1.6138043406113736}}, '0.2': {'scores': {'RMSE': 0.32523842533021824, 'MAE': 0.2398150375225743, 'MI': 1.0141220941333857, 'CORRELATION': 0.9441733384102147}, 'times': {'contamination': 0.0013000965118408203, 'optimization': 0, 'imputation': 0.03016376495361328, 'log_imputation': -1.5205144520419687}}, '0.4': {'scores': {'RMSE': 0.33824584758611187, 'MAE': 0.2509197576351218, 'MI': 0.9534836631617412, 'CORRELATION': 0.9418540692188737}, 'times': {'contamination': 0.005934953689575195, 'optimization': 0, 'imputation': 0.08333516120910645, 'log_imputation': -1.0791717201157554}}, '0.6': {'scores': {'RMSE': 0.3656435952078159, 'MAE': 0.26630724595251076, 'MI': 0.7933983583413302, 'CORRELATION': 0.9285706343976159}, 'times': {'contamination': 0.03005695343017578, 'optimization': 0, 'imputation': 0.09784293174743652, 'log_imputation': -1.0094705426576025}}, '0.8': {'scores': {'RMSE': 0.49851932645867886, 'MAE': 0.3727407177987301, 'MI': 0.5872198065951101, 'CORRELATION': 0.8588039019214768}, 'times': {'contamination': 0.06295108795166016, 'optimization': 0, 'imputation': 0.1400442123413086, 'log_imputation': -0.8537348347173945}}}}}}} diff --git a/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.xlsx b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.xlsx new file mode 100644 index 00000000..202a6c52 Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/benchmark/_details/run_0/eeg-alcohol/report_eeg-alcohol.xlsx differ diff --git a/build/lib/imputegap/imputegap_assets/benchmark/_heatmap/.gitkeep b/build/lib/imputegap/imputegap_assets/benchmark/_heatmap/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/build/lib/imputegap/imputegap_assets/benchmark/_heatmap/benchmarking_rmse.jpg b/build/lib/imputegap/imputegap_assets/benchmark/_heatmap/benchmarking_rmse.jpg new file mode 100644 index 00000000..107ada01 Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/benchmark/_heatmap/benchmarking_rmse.jpg differ diff --git a/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/eegalcohol_mcar_metrics_subplot.jpg b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/eegalcohol_mcar_metrics_subplot.jpg new file mode 100644 index 00000000..cc67e9dc Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/eegalcohol_mcar_metrics_subplot.jpg differ diff --git a/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.txt b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.txt new file mode 100644 index 00000000..62b5d211 --- /dev/null +++ b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.txt @@ -0,0 +1,82 @@ +Report for Dataset: eegalcohol +Generated on: 2025-03-21 15:54:56 +======================================================================================================================== + +RMSE: Root Mean Square Error - Measures the average magnitude of error. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | RMSE | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.4359915238 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.3665001858 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.3983300622 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.4355910162 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.4500113662 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.4655442240 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.2410259540 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.2889085181 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.3252384253 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.3382458476 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.3656435952 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.4985193265 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +MAE: Mean Absolute Error - Measures the average absolute error. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | MAE | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.3725965559 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.2989983613 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.3082464402 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.3335144216 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.3380858657 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.3508926604 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.1898483284 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.2299862373 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.2398150375 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.2509197576 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.2663072460 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.3727407178 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +MI: Mutual Information - Indicates dependency between variables. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | MI | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 1.4169232776 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.9078918431 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.8483406827 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.7286325588 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.6481512577 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.6150677913 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 1.5477828628 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.9934511614 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 1.0141220941 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.9534836632 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.7933983583 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.5872198066 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +CORRELATION: Correlation Coefficient - Indicates linear relationship between variables. + ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| Dataset | Pattern | Algorithm | Optimizer | X Value | CORRELATION | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ +| eegalcohol | mcar | SoftImpute | default_params | 0.05 | 0.9530448037 | +| eegalcohol | mcar | SoftImpute | default_params | 0.1 | 0.9049909723 | +| eegalcohol | mcar | SoftImpute | default_params | 0.2 | 0.9161465703 | +| eegalcohol | mcar | SoftImpute | default_params | 0.4 | 0.9021032587 | +| eegalcohol | mcar | SoftImpute | default_params | 0.6 | 0.8893263437 | +| eegalcohol | mcar | SoftImpute | default_params | 0.8 | 0.8791443563 | +| eegalcohol | mcar | KNNImpute | default_params | 0.05 | 0.9810468571 | +| eegalcohol | mcar | KNNImpute | default_params | 0.1 | 0.9429448316 | +| eegalcohol | mcar | KNNImpute | default_params | 0.2 | 0.9441733384 | +| eegalcohol | mcar | KNNImpute | default_params | 0.4 | 0.9418540692 | +| eegalcohol | mcar | KNNImpute | default_params | 0.6 | 0.9285706344 | +| eegalcohol | mcar | KNNImpute | default_params | 0.8 | 0.8588039019 | ++-----------------+-----------------+-----------------+--------------------+--------------+---------------------------+ + +Dictionary of Results: +{'eegalcohol': {'mcar': {'SoftImpute': {'default_params': {'0.05': {'scores': {'RMSE': 0.4359915238078244, 'MAE': 0.3725965559420608, 'MI': 1.4169232775678364, 'CORRELATION': 0.9530448037164908}, 'times': {'contamination': 0.00027561187744140625, 'optimization': 0.0, 'imputation': 0.15315723419189453, 'log_imputation': -0.8148624850120225}}, '0.1': {'scores': {'RMSE': 0.3665001858394363, 'MAE': 0.2989983612840734, 'MI': 0.9078918430616858, 'CORRELATION': 0.9049909722894052}, 'times': {'contamination': 0.0004036426544189453, 'optimization': 0.0, 'imputation': 0.10621070861816406, 'log_imputation': -0.9738316936187855}}, '0.2': {'scores': {'RMSE': 0.39833006221984, 'MAE': 0.30824644022807457, 'MI': 0.8483406827418594, 'CORRELATION': 0.9161465703422209}, 'times': {'contamination': 0.0015327930450439453, 'optimization': 0.0, 'imputation': 0.08952975273132324, 'log_imputation': -1.048032614966155}}, '0.4': {'scores': {'RMSE': 0.435591016228979, 'MAE': 0.3335144215651955, 'MI': 0.7286325588353783, 'CORRELATION': 0.9021032587324183}, 'times': {'contamination': 0.03658604621887207, 'optimization': 0.0, 'imputation': 0.12557768821716309, 'log_imputation': -0.9010875162113435}}, '0.6': {'scores': {'RMSE': 0.4500113661547204, 'MAE': 0.338085865703361, 'MI': 0.6481512576687939, 'CORRELATION': 0.8893263437029546}, 'times': {'contamination': 0.08436226844787598, 'optimization': 0.0, 'imputation': 0.11329030990600586, 'log_imputation': -0.945807235187996}}, '0.8': {'scores': {'RMSE': 0.46554422402146944, 'MAE': 0.3508926604243284, 'MI': 0.6150677913271478, 'CORRELATION': 0.8791443563129441}, 'times': {'contamination': 0.13154006004333496, 'optimization': 0.0, 'imputation': 0.33798837661743164, 'log_imputation': -0.4710982348043248}}}}, 'KNNImpute': {'default_params': {'0.05': {'scores': {'RMSE': 0.24102595399583507, 'MAE': 0.18984832836399548, 'MI': 1.547782862758484, 'CORRELATION': 0.9810468571465141}, 'times': {'contamination': 0.0008156299591064453, 'optimization': 0.0, 'imputation': 0.051076412200927734, 'log_imputation': -1.291779616813155}}, '0.1': {'scores': {'RMSE': 0.28890851809839135, 'MAE': 0.22998623733608023, 'MI': 0.9934511613817691, 'CORRELATION': 0.942944831550703}, 'times': {'contamination': 0.0005509853363037109, 'optimization': 0.0, 'imputation': 0.02433300018310547, 'log_imputation': -1.6138043406113736}}, '0.2': {'scores': {'RMSE': 0.32523842533021824, 'MAE': 0.2398150375225743, 'MI': 1.0141220941333857, 'CORRELATION': 0.9441733384102147}, 'times': {'contamination': 0.0013000965118408203, 'optimization': 0.0, 'imputation': 0.03016376495361328, 'log_imputation': -1.5205144520419687}}, '0.4': {'scores': {'RMSE': 0.33824584758611187, 'MAE': 0.2509197576351218, 'MI': 0.9534836631617412, 'CORRELATION': 0.9418540692188737}, 'times': {'contamination': 0.005934953689575195, 'optimization': 0.0, 'imputation': 0.08333516120910645, 'log_imputation': -1.0791717201157554}}, '0.6': {'scores': {'RMSE': 0.3656435952078159, 'MAE': 0.26630724595251076, 'MI': 0.7933983583413302, 'CORRELATION': 0.9285706343976159}, 'times': {'contamination': 0.03005695343017578, 'optimization': 0.0, 'imputation': 0.09784293174743652, 'log_imputation': -1.0094705426576025}}, '0.8': {'scores': {'RMSE': 0.49851932645867886, 'MAE': 0.3727407177987301, 'MI': 0.5872198065951101, 'CORRELATION': 0.8588039019214768}, 'times': {'contamination': 0.06295108795166016, 'optimization': 0.0, 'imputation': 0.1400442123413086, 'log_imputation': -0.8537348347173945}}}}}}} diff --git a/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.xlsx b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.xlsx new file mode 100644 index 00000000..ec8cf72c Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/benchmark/eegalcohol/report_eegalcohol.xlsx differ diff --git a/build/lib/imputegap/imputegap_assets/downstream/.gitkeep b/build/lib/imputegap/imputegap_assets/downstream/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_all.png b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_all.png new file mode 100644 index 00000000..312bc77c Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_all.png differ diff --git a/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_cat.png b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_cat.png new file mode 100644 index 00000000..530f0517 Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_shap_cat.png differ diff --git a/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_values.txt b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_values.txt new file mode 100644 index 00000000..85a91920 --- /dev/null +++ b/build/lib/imputegap/imputegap_assets/shap/eeg-alcohol_CDRec_pycatch_values.txt @@ -0,0 +1,22 @@ +Feature : 5 CDRec with a score of 31.1 Correlation Time reversibility CO_trev_1_num +Feature : 10 CDRec with a score of 20.85 Geometry Goodness of exponential fit to embedding distance distribution CO_Embed2_Dist_tau_d_expfit_meandiff +Feature : 2 CDRec with a score of 12.12 Correlation First 1/e crossing of the ACF CO_f1ecac +Feature : 21 CDRec with a score of 9.13 Trend Error of 3-point rolling mean forecast FC_LocalSimple_mean3_stderr +Feature : 8 CDRec with a score of 5.16 Geometry Transition matrix column variance SB_TransitionMatrix_3ac_sumdiagcov +Feature : 15 CDRec with a score of 4.71 Transformation Power in the lowest 20% of frequencies SP_Summaries_welch_rect_area_5_1 +Feature : 17 CDRec with a score of 4.04 Trend Entropy of successive pairs in symbolized series SB_MotifThree_quantile_hh +Feature : 1 CDRec with a score of 3.42 Geometry 10-bin histogram mode DN_HistogramMode_10 +Feature : 6 CDRec with a score of 2.09 Geometry Proportion of high incremental changes in the series MD_hrv_classic_pnn40 +Feature : 4 CDRec with a score of 2.07 Correlation Histogram-based automutual information (lag 2, 5 bins) CO_HistogramAMI_even_2_5 +Feature : 20 CDRec with a score of 1.61 Transformation Centroid frequency SP_Summaries_welch_rect_centroid +Feature : 13 CDRec with a score of 1.27 Geometry Positive outlier timing DN_OutlierInclude_p_001_mdrmd +Feature : 0 CDRec with a score of 1.05 Geometry 5-bin histogram mode DN_HistogramMode_5 +Feature : 18 CDRec with a score of 0.63 Geometry Rescaled range fluctuation analysis (low-scale scaling) SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 +Feature : 12 CDRec with a score of 0.41 Correlation Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat +Feature : 14 CDRec with a score of 0.33 Geometry Negative outlier timing DN_OutlierInclude_n_001_mdrmd +Feature : 3 CDRec with a score of 0.0 Correlation First minimum of the ACF CO_FirstMin_ac +Feature : 7 CDRec with a score of 0.0 Geometry Longest stretch of above-mean values SB_BinaryStats_mean_longstretch1 +Feature : 9 CDRec with a score of 0.0 Trend Wangs periodicity metric PD_PeriodicityWang_th0_01 +Feature : 11 CDRec with a score of 0.0 Correlation First minimum of the AMI function IN_AutoMutualInfoStats_40_gaussian_fmmi +Feature : 16 CDRec with a score of 0.0 Geometry Longest stretch of decreasing values SB_BinaryStats_diff_longstretch0 +Feature : 19 CDRec with a score of 0.0 Geometry Detrended fluctuation analysis (low-scale scaling) SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 diff --git a/build/lib/imputegap/imputegap_assets/shap/grouped/.gitkeep b/build/lib/imputegap/imputegap_assets/shap/grouped/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/build/lib/imputegap/imputegap_assets/shap/grouped/eeg-alcohol_CDRec_pycatch_DTL_Beeswarm.png b/build/lib/imputegap/imputegap_assets/shap/grouped/eeg-alcohol_CDRec_pycatch_DTL_Beeswarm.png new file mode 100644 index 00000000..519bad13 Binary files /dev/null and b/build/lib/imputegap/imputegap_assets/shap/grouped/eeg-alcohol_CDRec_pycatch_DTL_Beeswarm.png differ diff --git a/build/lib/imputegap/imputegap_assets/shap/grouped/eeg-alcohol_CDRec_pycatch_DTL_Waterfall.png b/build/lib/imputegap/imputegap_assets/shap/grouped/eeg-alcohol_CDRec_pycatch_DTL_Waterfall.png new file mode 100644 index 00000000..4c5fb5ce Binary files /dev/null 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b/build/lib/imputegap/recovery/benchmark.py @@ -4,7 +4,6 @@ import time import numpy as np import matplotlib.pyplot as plt - import xlsxwriter from imputegap.tools import utils @@ -187,9 +186,9 @@ def avg_results(self, *datasets): for j, algo in enumerate(algorithms_list): comprehensive_matrix[i, j] = average_rmse_matrix[dataset].get(algo, np.nan) - print("\nVisualization of datasets:", datasets_list) - print("Visualization of algorithms:", algorithms_list) - print("Visualization of matrix:\n", comprehensive_matrix, "\n\n") + print("\nvisualization of datasets:", *datasets_list) + print("visualization of algorithms:", *algorithms_list) + print(f"visualization of aggregate matrix :\n {comprehensive_matrix}\n\n") return comprehensive_matrix, algorithms_list, datasets_list @@ -215,6 +214,7 @@ def generate_heatmap(self, scores_list, algos, sets, save_dir="./reports", displ Bool True if the matrix has been generated """ + save_dir = save_dir + "/_heatmap/" if not os.path.exists(save_dir): os.makedirs(save_dir) @@ -226,6 +226,7 @@ def generate_heatmap(self, scores_list, algos, sets, save_dir="./reports", displ y_size = cell_size*nbr_datasets fig, ax = plt.subplots(figsize=(x_size, y_size)) + fig.canvas.manager.set_window_title("benchmark heatmap") cmap = plt.cm.Greys norm = plt.Normalize(vmin=0, vmax=2) # Normalizing values between 0 and 2 (RMSE) @@ -307,21 +308,28 @@ def generate_reports_txt(self, runs_plots_scores, save_dir="./reports", dataset= "MI": "Mutual Information - Indicates dependency between variables.", "CORRELATION": "Correlation Coefficient - Indicates linear relationship between variables." } + first_metric = True for metric, description in metrics.items(): # Write the metric description file.write(f"{metric}: {description}\n\n") - column_widths = [15, 15, 15, 15, 12, 25] + column_widths = [15, 15, 15, 18, 12, 25] # Create a table header - headers = ["Dataset", "Algorithm", "Optimizer", "Pattern", "X Value", metric] + headers = ["Dataset", "Pattern", "Algorithm", "Optimizer", "Rate", metric] header_row = "|".join(f" {header:^{width}} " for header, width in zip(headers, column_widths)) separator_row = "+" + "+".join(f"{'-' * (width + 2)}" for width in column_widths) + "+" file.write(f"{separator_row}\n") file.write(f"|{header_row}|\n") file.write(f"{separator_row}\n") + if first_metric and run ==-1 : + print(f"\n{metric}: {description}\n") + print(separator_row) + print(f"|{header_row}|") + print(separator_row) + # Extract and write results for the current metric for dataset, algo_items in runs_plots_scores.items(): for algorithm, optimizer_items in algo_items.items(): @@ -335,12 +343,17 @@ def generate_reports_txt(self, runs_plots_scores, save_dir="./reports", dataset= row = "|".join( f" {value:^{width}} " for value, width in zip(row_values, column_widths)) file.write(f"|{row}|\n") + if first_metric and run ==-1 : + print(f"|{row}|") file.write(f"{separator_row}\n\n") + if first_metric and run ==-1 : + print(separator_row + "\n") + first_metric = False file.write("Dictionary of Results:\n") file.write(str(runs_plots_scores) + "\n") - print(f"\nReport recorded in {save_path}") + print(f"\nreports recorded in the following directory : {save_path}") def generate_reports_excel(self, runs_plots_scores, save_dir="./reports", dataset="", run=-1): """ @@ -440,9 +453,8 @@ def generate_reports_excel(self, runs_plots_scores, save_dir="./reports", datase # Close the workbook workbook.close() - print(f"\nExcel report recorded in {save_path}") - def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports"): + def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports", display=False): """ Generate and save plots for each metric and pattern based on provided scores. @@ -456,6 +468,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save If True, generates a single figure with subplots for all metrics (default is False). save_dir : str, optional Directory to save generated plots (default is "./reports"). + display : bool, optional + Display or not the plots (default is False). Returns ------- @@ -475,6 +489,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save if subplot: fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(x_size*1.90, y_size*2.90)) # Adjusted figsize + fig.canvas.manager.set_window_title("benchmark analysis") + axes = axes.ravel() # Flatten the 2D array of axes to a 1D array # Iterate over each metric, generating separate plots, including new timing metrics @@ -522,8 +538,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save # Save plot only if there is data to display if has_data: ylabel_metric = { - "imputation_time": "Imputation Time (sec)", - "log_imputation": "Imputation Time (log)", + "imputation_time": "Runtime Linear Scale (sec)", + "log_imputation": "Runtime Log Scale", }.get(metric, metric) ax.set_title(metric) @@ -534,8 +550,10 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save # Set y-axis limits with padding below 0 for visibility if metric == "imputation_time": ax.set_ylim(-10, 90) + ax.set_title("Runtime Linear Scale") elif metric == "log_imputation": ax.set_ylim(-4.5, 2.5) + ax.set_title("Runtime Log Scale") elif metric == "MAE": ax.set_ylim(-0.1, 2.4) elif metric == "MI": @@ -544,12 +562,13 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save ax.set_ylim(-0.1, 2.6) elif metric == "CORRELATION": ax.set_ylim(-0.75, 1.1) + ax.set_title("Pearson Correlation") # Customize x-axis ticks ax.set_xticks(ticks) ax.set_xticklabels([f"{int(tick * 100)}%" for tick in ticks]) ax.grid(True, zorder=0) - ax.legend(loc='upper left', bbox_to_anchor=(1, 1)) + ax.legend(loc='upper left', fontsize=7, frameon=True, fancybox=True, framealpha=0.8) if not subplot: filename = f"{dataset}_{pattern}_{optimizer}_{metric}.jpg" @@ -562,12 +581,14 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save filename = f"{dataset}_{pattern}_metrics_subplot.jpg" filepath = os.path.join(save_dir, filename) plt.savefig(filepath) - plt.close() - print("\nAll plots recorded in", save_dir) + if display: + plt.show() + + print("\nplots recorded in the following directory : ", save_dir) def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"], - x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["user_def"], save_dir="./reports", runs=1): + x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["default_params"], save_dir="./reports", runs=1): """ Execute a comprehensive evaluation of imputation algorithms over multiple datasets and patterns. @@ -603,6 +624,9 @@ def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"] not_optimized = ["none"] mean_group = ["mean", "MeanImpute", "min", "MinImpute", "zero", "ZeroImpute", "MeanImputeBySeries"] + if "mpin" in algorithms or "MPIN" in algorithms: + raise ValueError("The 'mpin' algorithm is not compatible with this setup.") + for i_run in range(0, abs(runs)): for dataset in datasets: runs_plots_scores = {} @@ -714,33 +738,34 @@ def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"] "times": dic_timing } - save_dir_runs = save_dir + "/run_" + str(i_run) + "/" + dataset - print("\n\truns saved in : ", save_dir_runs) + save_dir_runs = save_dir + "/_details/run_" + str(i_run) + "/" + dataset + print("\nruns saved in : ", save_dir_runs) self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_runs) self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_runs) self.generate_reports_txt(runs_plots_scores, save_dir_runs, dataset, i_run) self.generate_reports_excel(runs_plots_scores, save_dir_runs, dataset, i_run) run_storage.append(runs_plots_scores) - print("============================================================================\n\n\n\n\n\n") + print("\n\n\n\n\n\n\n\n\n\n\n\n=end_of_the_evaluation===============================================" + "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nresults of the analysis:\n") scores_list, algos, sets = self.avg_results(*run_storage) - _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=False) + _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=True) run_averaged = self.average_runs_by_names(run_storage) - save_dir_agg = save_dir + "/aggregation" - print("\n\n\taggragation of results saved in : ", save_dir_agg) + print("\n\nthe results of the analysis has been saved in : ", save_dir, "\n\n") for scores in run_averaged: all_keys = list(scores.keys()) dataset_name = str(all_keys[0]) - save_dir_agg_set = save_dir_agg + "/" + dataset_name + save_dir_agg_set = save_dir + "/" + dataset_name - self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set) - self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set) self.generate_reports_txt(scores, save_dir_agg_set, dataset_name, -1) + self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set) + # self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set) self.generate_reports_excel(scores, save_dir_agg_set, dataset_name, -1) + print("\n\n") return run_averaged, scores_list diff --git a/build/lib/imputegap/recovery/downstream.py b/build/lib/imputegap/recovery/downstream.py index eafbddca..0d7e6cf4 100644 --- a/build/lib/imputegap/recovery/downstream.py +++ b/build/lib/imputegap/recovery/downstream.py @@ -6,14 +6,6 @@ from imputegap.tools import utils -from darts import TimeSeries -from darts.metrics import mae as darts_mae, mse as darts_mse -from sklearn.metrics import mean_absolute_error, mean_squared_error -from sktime.forecasting.base import ForecastingHorizon - - - - class Downstream: @@ -53,7 +45,7 @@ class Downstream: - def __init__(self, input_data, recov_data, incomp_data, downstream): + def __init__(self, input_data, recov_data, incomp_data, algorithm, downstream): """ Initialize the Downstream class @@ -65,6 +57,8 @@ def __init__(self, input_data, recov_data, incomp_data, downstream): The imputed time series. incomp_data : numpy.ndarray The time series with contamination (NaN values). + algorithm : str + Name of the algorithm to analyse. downstream : dict Information about the model to launch with its parameters """ @@ -72,6 +66,7 @@ def __init__(self, input_data, recov_data, incomp_data, downstream): self.recov_data = recov_data self.incomp_data = incomp_data self.downstream = downstream + self.algorithm = algorithm self.split = 0.8 self.sktime_models = utils.list_of_downstreams_sktime() @@ -92,31 +87,39 @@ def downstream_analysis(self): model = self.downstream.get("model", "naive") params = self.downstream.get("params", None) plots = self.downstream.get("plots", True) - + comparator = self.downstream.get("comparator", None) + model = model.lower() evaluator = evaluator.lower() if not params: - print("\n\t\t\t\tThe params for model of downstream analysis are empty or missing. Default ones loaded...") + print("\n\t(DOWNSTREAM) The params for model of downstream analysis are empty or missing. Default ones loaded...") loader = "forecaster-" + str(model) params = utils.load_parameters(query="default", algorithm=loader) - print("\n\t\t\t\tDownstream analysis launched for <", evaluator, "> on the model <", model, + print("\n(DOWNSTREAM) Analysis launched for <", evaluator, "> on the model <", model, "> with parameters :\n\t\t\t\t\t", params, " \n\n") if evaluator in ["forecast", "forecaster", "forecasting"]: y_train_all, y_test_all, y_pred_all = [], [], [] - mae, mse = [], [] + mae, mse, smape = [], [], [] for x in range(3): # Iterate over recov_data, input_data, and mean_impute if x == 0: - data = self.recov_data - elif x == 1: data = self.input_data + elif x == 1: + data = self.recov_data elif x == 2: from imputegap.recovery.imputation import Imputation - zero_impute = Imputation.Statistics.ZeroImpute(self.incomp_data).impute() - data = zero_impute.recov_data + + if comparator is not None: + impt = utils.config_impute_algorithm(self.incomp_data, algorithm=comparator) + impt.impute() + data = impt.recov_data + else: + comparator = "zero-impute" + zero_impute = Imputation.Statistics.ZeroImpute(self.incomp_data).impute() + data = zero_impute.recov_data data_len = data.shape[1] train_len = int(data_len * self.split) @@ -128,6 +131,10 @@ def downstream_analysis(self): if model in self.sktime_models: # --- SKTIME APPROACH --- + from sklearn.metrics import mean_absolute_error, mean_squared_error + from sktime.forecasting.base import ForecastingHorizon + from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError + y_pred = np.zeros_like(y_test) for series_idx in range(data.shape[0]): @@ -146,10 +153,17 @@ def downstream_analysis(self): # Compute metrics using sktime mae.append(mean_absolute_error(y_test, y_pred)) mse.append(mean_squared_error(y_test, y_pred)) + scoring_m = MeanAbsolutePercentageError(symmetric=True) + smape.append(scoring_m.evaluate(y_test, y_pred)*100) # Compute SMAPE + else: # --- DARTS APPROACH --- # Convert entire matrix to a Darts multivariate TimeSeries object + from darts import TimeSeries + from darts.metrics import mae as darts_mae, mse as darts_mse + from darts.metrics import smape as darts_smape + y_train_ts = TimeSeries.from_values(y_train.T) # Shape: (time_steps, n_series) y_test_ts = TimeSeries.from_values(y_test.T) # Shape: (time_steps, n_series) @@ -175,11 +189,12 @@ def downstream_analysis(self): # Compute metrics safely mae_score = darts_mae(y_test_ts, y_pred_ts) mse_score = darts_mse(y_test_ts, y_pred_ts) - + smape_score = darts_smape(y_test_ts, y_pred_ts) # Compute metrics using Darts mae.append(mae_score) mse.append(mse_score) + smape.append(smape_score) # Store for plotting y_train_all.append(y_train) @@ -188,23 +203,24 @@ def downstream_analysis(self): if plots: # Global plot with all rows and columns - self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, model, evaluator) + self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, self.algorithm, comparator, model, evaluator) # Save metrics in a dictionary - metrics = {"DOWNSTREAM-RECOV-MAE": mae[0], "DOWNSTREAM-INPUT-MAE": mae[1], - "DOWNSTREAM-MEANI-MAE": mae[2], "DOWNSTREAM-RECOV-MSE": mse[0], - "DOWNSTREAM-INPUT-MSE": mse[1], "DOWNSTREAM-MEANI-MSE": mse[2]} - - print("\n\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n") + al_name = "DOWNSTREAM-" + self.algorithm.upper() + "-MSE" + al_name_s = "DOWNSTREAM-" + self.algorithm.upper() + "-SMAPE" + al_name_c = "DOWNSTREAM-" + comparator.upper() + "-MSE" + al_name_cs = "DOWNSTREAM-" + comparator.upper() + "-SMAPE" + metrics = {"DOWNSTREAM-ORIGIN-MSE": mse[0], al_name: mse[1], al_name_c: mse[2], + "DOWNSTREAM-ORIGIN-SMAPE": smape[0], al_name_s: smape[1], al_name_cs: smape[2] } return metrics else: - print("\t\t\t\tNo evaluator found... list possible : 'forecaster'" + "*" * 30 + "\n") + print("\tNo evaluator found... list possible : 'forecaster'" + "*" * 30 + "\n") return None @staticmethod - def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None, title="Ground Truth vs Predictions", max_series=1, save_path="./imputegap_assets"): + def _plot_downstream(y_train, y_test, y_pred, incomp_data, algorithm, comparison, model=None, type=None, title="", max_series=1, save_path="./imputegap_assets/downstream"): """ Plot ground truth vs. predictions for contaminated series (series with NaN values). @@ -220,6 +236,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None Incomplete data array of shape (n_series, total_len), used to identify contaminated series. model : str Name of the current model used + algorithm : str + Name of the current algorithm used + comparison : str + Name of the current algorithm used as comparison type : str Name of the current type used title : str @@ -235,6 +255,7 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None x_size = 24 fig, axs = plt.subplots(3, max_series, figsize=(x_size, 15)) + fig.canvas.manager.set_window_title("downstream evaluation") fig.suptitle(title, fontsize=16) # Iterate over the three data types (recov_data, input_data, mean_impute) @@ -258,40 +279,41 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None full_series = np.concatenate([s_y_train[series_idx], s_y_test[series_idx]]) # Plot training data - ax.plot(range(len(s_y_train[series_idx])), s_y_train[series_idx], label="Training Data", color="green") + ax.plot(range(len(s_y_train[series_idx])), s_y_train[series_idx], color="green") # Plot ground truth (testing data) ax.plot( range(len(s_y_train[series_idx]), len(full_series)), s_y_test[series_idx], - label="Ground Truth", + label="ground truth", color="green" ) + label = type + " " + model # Plot forecasted data ax.plot( range(len(s_y_train[series_idx]), len(full_series)), s_y_pred[series_idx], - label="Forecast", + label=label, linestyle="--", marker=None, color="red" ) # Add a vertical line at the split point - ax.axvline(x=len(s_y_train[series_idx]), color="orange", linestyle="--", label="Split Point") + ax.axvline(x=len(s_y_train[series_idx]), color="orange", linestyle="--") # Add labels, title, and grid if row_idx == 0: - ax.set_title(f"IMPUTATED DATA (RECOVERY), series {series_idx}") + ax.set_title(f"original data, series_{series_idx}") elif row_idx == 1: - ax.set_title(f"ORIGINAL DATA (GROUND TRUTH), series {series_idx}") + ax.set_title(f"{algorithm.lower()} imputation, series_{series_idx}") else: - ax.set_title(f"BAD IMPUTER (ZERO IMP), series {series_idx}") + ax.set_title(f"{comparison.lower()} imputation, series_{series_idx}") ax.set_xlabel("Timestamp") ax.set_ylabel("Value") - ax.legend() + ax.legend(loc='upper left', fontsize=7, frameon=True, fancybox=True, framealpha=0.8) ax.grid() # Adjust layout diff --git a/build/lib/imputegap/recovery/evaluation.py b/build/lib/imputegap/recovery/evaluation.py index ea2e449a..47fe912d 100644 --- a/build/lib/imputegap/recovery/evaluation.py +++ b/build/lib/imputegap/recovery/evaluation.py @@ -1,7 +1,4 @@ import numpy as np -from sklearn.metrics import mutual_info_score -from scipy.stats import pearsonr - class Evaluation: """ @@ -126,6 +123,8 @@ def compute_mi(self): float The mutual information (MI) score for NaN positions in the contamination dataset. """ + from sklearn.metrics import mutual_info_score + nan_locations = np.isnan(self.incomp_data) # Discretize the continuous data into bins @@ -151,6 +150,8 @@ def compute_correlation(self): float The Pearson correlation coefficient for NaN positions in the contamination dataset. """ + from scipy.stats import pearsonr + nan_locations = np.isnan(self.incomp_data) input_data_values = self.input_data[nan_locations] imputed_values = self.recov_data[nan_locations] diff --git a/build/lib/imputegap/recovery/explainer.py b/build/lib/imputegap/recovery/explainer.py index efb4c1c2..1fda9a7f 100644 --- a/build/lib/imputegap/recovery/explainer.py +++ b/build/lib/imputegap/recovery/explainer.py @@ -13,7 +13,6 @@ from matplotlib import pyplot as plt from sklearn.ensemble import RandomForestRegressor -from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils @@ -366,7 +365,7 @@ def convert_results(tmp, file, algo, descriptions, features, categories, mean_fe result_display = sorted(result_display, key=lambda tup: (tup[1], tup[2]), reverse=True) - with open(to_save + "_results.txt", 'w') as file_output: + with open(to_save + "_values.txt", 'w') as file_output: for (x, algo, rate, description, feature, category, mean_features) in result_display: file_output.write( f"Feature : {x:<5} {algo:<10} with a score of {rate:<10} {category:<18} {description:<65} {feature}\n") @@ -410,8 +409,12 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl plots_categories = config[extractor]['categories'] path_file = "./imputegap_assets/shap/" - if not os.path.exists(path_file): - path_file = "./imputegap" + path_file[1:] + path_file_details = "./imputegap_assets/shap/grouped/" + path_file_categories = "./imputegap_assets/shap/per_categories/" + + os.makedirs(path_file, exist_ok=True) + os.makedirs(path_file_details, exist_ok=True) + os.makedirs(path_file_categories, exist_ok=True) x_features, x_categories, x_descriptions = [], [], [] x_fs, x_cs, x_ds, alphas = [], [], [], [] @@ -451,8 +454,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl print("\t SHAP_MODEL >> descriptions shape:", x_descriptions.shape, "\n") print("\t SHAP_MODEL >> features OK:", np.all(np.all(x_features == x_features[0, :], axis=1))) print("\t SHAP_MODEL >> categories OK:", np.all(np.all(x_categories == x_categories[0, :], axis=1))) - print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)), - "\n\n") + print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)), "\n\n") model = RandomForestRegressor() model.fit(x_train, y_train) @@ -470,7 +472,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl series_names.append("Series " + str(names + np.array(x_train).shape[0])) shap.summary_plot(shval, x_test, plot_size=(25, 10), feature_names=optimal_display, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_plot.png") + alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_all.png") plt.title("SHAP Details Results") os.makedirs(path_file, exist_ok=True) plt.savefig(alpha) @@ -478,14 +480,14 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.summary_plot(np.array(shval).T, np.array(x_test).T, feature_names=series_names, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_reverse_plot.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_reverse.png") plt.title("SHAP Features by Series") plt.savefig(alpha) plt.close() alphas.append(alpha) shap.plots.waterfall(shval_x[0], show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png") plt.title("SHAP Waterfall Results") fig = plt.gcf() # Get the current figure created by SHAP fig.set_size_inches(20, 10) # Ensure the size is correct @@ -494,7 +496,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.plots.beeswarm(shval_x, show=display, plot_size=(22, 10)) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png") plt.title("SHAP Beeswarm Results") plt.savefig(alpha) plt.close() @@ -550,7 +552,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl mean_features = np.array(mean_features) shap.summary_plot(np.array(geometry).T, np.array(geometryT).T, plot_size=(20, 10), feature_names=geometryDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + ".png") plt.title("SHAP details of " + plots_categories[0].lower()) plt.savefig(alpha) plt.close() @@ -558,7 +560,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(transformation).T, np.array(transformationT).T, plot_size=(20, 10), feature_names=transformationDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + ".png") plt.title("SHAP details of " + plots_categories[1].lower()) plt.savefig(alpha) plt.close() @@ -566,7 +568,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(correlation).T, np.array(correlationT).T, plot_size=(20, 10), feature_names=correlationDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + ".png") plt.title("SHAP details of " + plots_categories[1].lower()) plt.savefig(alpha) plt.close() @@ -574,7 +576,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(trend).T, np.array(trendT).T, plot_size=(20, 8), feature_names=trendDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + ".png") plt.title("SHAP details of " + plots_categories[3].lower()) plt.savefig(alpha) plt.close() @@ -594,7 +596,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl aggregation_test = np.array(aggregation_test).T shap.summary_plot(aggregation_features, aggregation_test, feature_names=plots_categories, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_plot.png") + alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_cat.png") plt.title("SHAP Aggregation Results") plt.gca().axes.get_xaxis().set_visible(False) plt.savefig(alpha) @@ -602,7 +604,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.summary_plot(np.array(aggregation_features).T, np.array(aggregation_test).T, feature_names=series_names, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_reverse_plot.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_agg_reverse.png") plt.title("SHAP Aggregation Features by Series") plt.savefig(alpha) plt.close() diff --git a/build/lib/imputegap/recovery/imputation.py b/build/lib/imputegap/recovery/imputation.py index 6a3a1a23..70842d99 100644 --- a/build/lib/imputegap/recovery/imputation.py +++ b/build/lib/imputegap/recovery/imputation.py @@ -1,43 +1,11 @@ import re - -from imputegap.algorithms.bayotide import bay_otide -from imputegap.algorithms.bit_graph import bit_graph -from imputegap.algorithms.brits import brits -from imputegap.algorithms.deep_mvi import deep_mvi -from imputegap.algorithms.dynammo import dynammo -from imputegap.algorithms.gain import gain -from imputegap.algorithms.grin import grin -from imputegap.algorithms.grouse import grouse -from imputegap.algorithms.hkmf_t import hkmf_t -from imputegap.algorithms.interpolation import interpolation -from imputegap.algorithms.iterative_svd import iterative_svd -from imputegap.algorithms.knn import knn -from imputegap.algorithms.mean_impute import mean_impute -from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series -from imputegap.algorithms.mice import mice -from imputegap.algorithms.miss_forest import miss_forest -from imputegap.algorithms.miss_net import miss_net -from imputegap.algorithms.mpin import mpin -from imputegap.algorithms.pristi import pristi -from imputegap.algorithms.rosl import rosl -from imputegap.algorithms.soft_impute import soft_impute -from imputegap.algorithms.spirit import spirit -from imputegap.algorithms.svt import svt -from imputegap.algorithms.tkcm import tkcm -from imputegap.algorithms.trmf import trmf -from imputegap.algorithms.xgboost import xgboost +from imputegap.tools import utils from imputegap.recovery.downstream import Downstream from imputegap.recovery.evaluation import Evaluation -from imputegap.algorithms.cdrec import cdrec -from imputegap.algorithms.iim import iim -from imputegap.algorithms.min_impute import min_impute -from imputegap.algorithms.mrnn import mrnn -from imputegap.algorithms.stmvl import stmvl -from imputegap.algorithms.zero_impute import zero_impute -from imputegap.tools import utils - -not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt", "tkcm", "deep_mvi", "brits", "mpin", "pristi"] +not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt", + "tkcm", "deep_mvi", "brits", "mpin", "pristi", "bay_otide", "bit_graph", "gain", "grin", "hkmf_t", + "mice", "miss_forest", "miss_net", "trmf", "xgboost"] class BaseImputer: @@ -108,12 +76,17 @@ def score(self, input_data, recov_data=None, downstream=None): Returns ------- None + + Example + ------- + >>> imputer.score(ts.data, imputer.recov_data) # upstream + >>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream """ if self.recov_data is None: self.recov_data = recov_data if isinstance(downstream, dict) and downstream is not None: - self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, downstream).downstream_analysis() + self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, self.algorithm, downstream).downstream_analysis() else: self.metrics = Evaluation(input_data, self.recov_data, self.incomp_data).compute_all_metrics() @@ -182,7 +155,7 @@ def _optimize(self, parameters={}): raise ValueError( f"\n\tThis algorithm '{self.algorithm}' is not optimized for this optimizer. " f"\n\tPlease use `run_tune` to optimize the hyperparameters for:\n\t\t {', '.join(not_optimized)}" - "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts_1.data, 'optimizer': 'ray_tune'})" + "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts.data, 'optimizer': 'ray_tune'})" ) input_data = ( @@ -376,8 +349,8 @@ class Statistics: Imputation method that replaces missing values with the minimum value of the ground truth by series. Interpolation : Imputation method that replaces missing values with the Interpolation - KNN : - Imputation method that replaces missing values with KNN logic + KNNImpute : + Imputation method that replaces missing values with KNNImpute logic """ class ZeroImpute(BaseImputer): @@ -406,6 +379,8 @@ def impute(self, params=None): self : ZeroImpute The object with `recov_data` set. """ + from imputegap.algorithms.zero_impute import zero_impute + self.recov_data = zero_impute(self.incomp_data, params) return self @@ -436,6 +411,8 @@ def impute(self, params=None): self : MinImpute The object with `recov_data` set. """ + from imputegap.algorithms.mean_impute import mean_impute + self.recov_data = mean_impute(self.incomp_data, params) return self @@ -466,6 +443,8 @@ def impute(self, params=None): self : MinImpute The object with `recov_data` set. """ + from imputegap.algorithms.min_impute import min_impute + self.recov_data = min_impute(self.incomp_data, params) return self @@ -490,6 +469,8 @@ def impute(self): self : MeanImputeBySeries The object with `recov_data` set. """ + from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series + self.recov_data = mean_impute_by_series(self.incomp_data, logs=self.logs) return self @@ -526,9 +507,11 @@ def impute(self, user_def=True, params=None): >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data) >>> interpolation_imputer.impute() # default parameters for imputation > or >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2}) # user-defined > or - >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = interpolation_imputer.recov_data """ + from imputegap.algorithms.interpolation import interpolation + if params is not None: method, poly_order = self._check_params(user_def, params) else: @@ -539,16 +522,16 @@ def impute(self, user_def=True, params=None): return self - class KNN(BaseImputer): + class KNNImpute(BaseImputer): """ - KNN class to impute missing values with K-Nearest Neighbor algorithm + KNNImpute class to impute missing values with K-Nearest Neighbor algorithm Methods ------- impute(self, params=None): Perform imputation by replacing missing values with K-Nearest Neighbor """ - algorithm = "knn" + algorithm = "knn_impute" def impute(self, user_def=True, params=None): """ @@ -559,7 +542,7 @@ def impute(self, user_def=True, params=None): user_def : bool, optional Whether to use user-defined or default parameters (default is True). params : dict, optional - Parameters of the KNN algorithm, if None, default ones are loaded. + Parameters of the KNNImpute algorithm, if None, default ones are loaded. **Algorithm parameters:** k : int, optional @@ -569,17 +552,19 @@ def impute(self, user_def=True, params=None): Returns ------- - self : KNN + self : KNNImpute The object with `recov_data` set. Example ------- - >>> knn_imputer = Imputation.Statistics.KNN(incomp_data) + >>> knn_imputer = Imputation.Statistics.KNNImpute(incomp_data) >>> knn_imputer.impute() # default parameters for imputation > or >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"}) # user-defined > or - >>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> knn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = knn_imputer.recov_data """ + from imputegap.algorithms.knn import knn + if params is not None: k, weights = self._check_params(user_def, params) else: @@ -719,13 +704,14 @@ def impute(self, user_def=True, params=None): >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data) >>> cdrec_imputer.impute() # default parameters for imputation > or >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100}) # user-defined > or - >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = cdrec_imputer.recov_data References ---------- Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7 """ + from imputegap.algorithms.cdrec import cdrec if params is not None: rank, epsilon, iterations = self._check_params(user_def, params) @@ -777,13 +763,14 @@ def impute(self, user_def=True, params=None): >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data) >>> i_svd_imputer.impute() # default parameters for imputation > or >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined > or - >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = i_svd_imputer.recov_data References ---------- Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman, Missing value estimation methods for DNA microarrays , Bioinformatics, Volume 17, Issue 6, June 2001, Pages 520–525, https://doi.org/10.1093/bioinformatics/17.6.520 """ + from imputegap.algorithms.iterative_svd import iterative_svd if params is not None: rank = self._check_params(user_def, params)[0] @@ -834,13 +821,14 @@ def impute(self, user_def=True, params=None): >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data) >>> grouse_imputer.impute() # default parameters for imputation > or >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined > or - >>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> grouse_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = grouse_imputer.recov_data References ---------- D. Zhang and L. Balzano. Global convergence of a grassmannian gradient descent algorithm for subspace estimation. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, pages 1460–1468, 2016. """ + from imputegap.algorithms.grouse import grouse if params is not None: max_rank = self._check_params(user_def, params)[0] @@ -894,13 +882,15 @@ def impute(self, user_def=True, params=None): >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data) >>> rosl_imputer.impute() # default parameters for imputation > or >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined > or - >>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> rosl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = rosl_imputer.recov_data References ---------- X. Shu, F. Porikli, and N. Ahuja. Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 3874–3881, 2014. """ + from imputegap.algorithms.rosl import rosl + if params is not None: rank, regularization = self._check_params(user_def, params) else: @@ -950,13 +940,15 @@ def impute(self, user_def=True, params=None): >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data) >>> soft_impute_imputer.impute() # default parameters for imputation > or >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined > or - >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = soft_impute_imputer.recov_data References ---------- R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11:2287–2322, 2010. """ + from imputegap.algorithms.soft_impute import soft_impute + if params is not None: max_rank = self._check_params(user_def, params)[0] else: @@ -1013,13 +1005,15 @@ def impute(self, user_def=True, params=None): >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data) >>> spirit_imputer.impute() # default parameters for imputation > or >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined > or - >>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> spirit_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = spirit_imputer.recov_data References ---------- S. Papadimitriou, J. Sun, and C. Faloutsos. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30 - September 2, 2005, pages 697–708, 2005. """ + from imputegap.algorithms.spirit import spirit + if params is not None: k, w, lambda_value = self._check_params(user_def, params) else: @@ -1070,13 +1064,15 @@ def impute(self, user_def=True, params=None): >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data) >>> svt_imputer.impute() # default parameters for imputation > or >>> svt_imputer.impute(params={'tau': 1}) # user-defined > or - >>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> svt_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = svt_imputer.recov_data References ---------- J. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. [8] J. Cambronero, J. K. Feser, M. J. Smith, and """ + from imputegap.algorithms.svt import svt + if params is not None: tau = self._check_params(user_def, params)[0] else: @@ -1143,13 +1139,15 @@ def impute(self, user_def=True, params=None): >>> trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data) >>> trmf_imputer.impute() >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100}) - >>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) + >>> trmf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) >>> recov_data = trmf_imputer.recov_data References ---------- H.-F. Yu, N. Rao, and I. S. Dhillon, "Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction," in *Advances in Neural Information Processing Systems*, vol. 29, 2016. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf """ + from imputegap.algorithms.trmf import trmf + if params is not None: lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = self._check_params(user_def, params) else: @@ -1229,7 +1227,7 @@ def impute(self, user_def=True, params=None): >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data) >>> mf_imputer.impute() # default parameters for imputation > or >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42}) # user defined > or - >>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mf_imputer.recov_data References @@ -1238,6 +1236,8 @@ def impute(self, user_def=True, params=None): https://github.com/yuenshingyan/MissForest https://pypi.org/project/MissForest/ """ + from imputegap.algorithms.miss_forest import miss_forest + if params is not None: n_estimators, max_iter, max_features, seed = self._check_params(user_def, params) else: @@ -1291,7 +1291,7 @@ def impute(self, user_def=True, params=None): >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data) >>> mice_imputer.impute() # default parameters for imputation > or >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42}) # user defined > or - >>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mice_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mice_imputer.recov_data References @@ -1301,6 +1301,8 @@ def impute(self, user_def=True, params=None): S. F. Buck, (1960). “A Method of Estimation of Missing Values in Multivariate Data Suitable for use with an Electronic Computer”. Journal of the Royal Statistical Society 22(2): 302-306. https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer """ + from imputegap.algorithms.mice import mice + if params is not None: max_iter, tol, initial_strategy, seed = self._check_params(user_def, params) else: @@ -1349,7 +1351,7 @@ def impute(self, user_def=True, params=None): >>> mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data) >>> mxgboost_imputer.impute() # default parameters for imputation > or >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42}) # user defined > or - >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mxgboost_imputer.recov_data References @@ -1358,6 +1360,8 @@ def impute(self, user_def=True, params=None): https://dl.acm.org/doi/10.1145/2939672.2939785 https://medium.com/@tzhaonj/imputing-missing-data-using-xgboost-802757cace6d """ + from imputegap.algorithms.xgboost import xgboost + if params is not None: n_estimators, seed = self._check_params(user_def, params) else: @@ -1404,7 +1408,7 @@ def impute(self, user_def=True, params=None): >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data) >>> iim_imputer.impute() # default parameters for imputation > or >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10}) # user-defined > or - >>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> iim_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = iim_imputer.recov_data References @@ -1412,6 +1416,8 @@ def impute(self, user_def=True, params=None): A. Zhang, S. Song, Y. Sun and J. Wang, "Learning Individual Models for Imputation," 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China, 2019, pp. 160-171, doi: 10.1109/ICDE.2019.00023. keywords: {Data models;Adaptation models;Computational modeling;Predictive models;Numerical models;Aggregates;Regression tree analysis;Missing values;Data imputation} """ + from imputegap.algorithms.iim import iim + if params is not None: learning_neighbours, algo_code = self._check_params(user_def, params) else: @@ -1481,7 +1487,7 @@ def impute(self, user_def=True, params=None): >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data) >>> stmvl_imputer.impute() # default parameters for imputation > or >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7}) # user-defined > or - >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = stmvl_imputer.recov_data References @@ -1489,6 +1495,8 @@ def impute(self, user_def=True, params=None): Yi, X., Zheng, Y., Zhang, J., & Li, T. ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data. School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. """ + from imputegap.algorithms.stmvl import stmvl + if params is not None: window_size, gamma, alpha = self._check_params(user_def, params) else: @@ -1541,13 +1549,15 @@ def impute(self, user_def=True, params=None): >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data) >>> dynammo_imputer.impute() # default parameters for imputation > or >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined > or - >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = dynammo_imputer.recov_data References ---------- L. Li, J. McCann, N. S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pages 507–516, 2009. """ + from imputegap.algorithms.dynammo import dynammo + if params is not None: h, max_iteration, approximation = self._check_params(user_def, params) else: @@ -1596,13 +1606,15 @@ def impute(self, user_def=True, params=None): >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data) >>> tkcm_imputer.impute() # default parameters for imputation > or >>> tkcm_imputer.impute(params={'rank': 5}) # user-defined > or - >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = tkcm_imputer.recov_data References ---------- K. Wellenzohn, M. H. Böhlen, A. Dignös, J. Gamper, and H. Mitterer. Continuous imputation of missing values in streams of pattern-determining time series. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017., pages 330–341, 2017. """ + from imputegap.algorithms.tkcm import tkcm + if params is not None: rank = self._check_params(user_def, params)[0] else: @@ -1683,13 +1695,15 @@ def impute(self, user_def=True, params=None): >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data) >>> mrnn_imputer.impute() # default parameters for imputation > or >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7}) # user-defined > or - >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = mrnn_imputer.recov_data References ---------- J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp. 1477-1490, May 2019, doi: 10.1109/TBME.2018.2874712. keywords: {Time measurement;Interpolation;Estimation;Medical diagnostic imaging;Correlation;Recurrent neural networks;Biomedical measurement;Missing data;temporal data streams;imputation;recurrent neural nets} """ + from imputegap.algorithms.mrnn import mrnn + if params is not None: hidden_dim, learning_rate, iterations, sequence_length = self._check_params(user_def, params) else: @@ -1746,13 +1760,15 @@ def impute(self, user_def=True, params=None): >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data) >>> brits_imputer.impute() # default parameters for imputation > or >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64}) # user-defined > or - >>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> brits_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = brits_imputer.recov_data References ---------- Cao, W., Wang, D., Li, J., Zhou, H., Li, L. & Li, Y. BRITS: Bidirectional Recurrent Imputation for Time Series. Advances in Neural Information Processing Systems, 31 (2018). https://proceedings.neurips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf """ + from imputegap.algorithms.brits import brits + if params is not None: model, epoch, batch_size, nbr_features, hidden_layer = self._check_params(user_def, params) else: @@ -1804,7 +1820,7 @@ def impute(self, user_def=True, params=None): >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data) >>> deep_mvi_imputer.impute() # default parameters for imputation > or >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2}) # user-defined > or - >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = deep_mvi_imputer.recov_data References @@ -1812,6 +1828,8 @@ def impute(self, user_def=True, params=None): P. Bansal, P. Deshpande, and S. Sarawagi. Missing value imputation on multidimensional time series. arXiv preprint arXiv:2103.01600, 2023 https://github.com/pbansal5/DeepMVI """ + from imputegap.algorithms.deep_mvi import deep_mvi + if params is not None: max_epoch, patience, lr = self._check_params(user_def, params) else: @@ -1823,6 +1841,7 @@ def impute(self, user_def=True, params=None): class MPIN(BaseImputer): """ MPIN class to impute missing values using Multi-attribute Sensor Data Streams via Message Propagation algorithm. + Need torch-cluster to work. Methods ------- @@ -1834,6 +1853,7 @@ class MPIN(BaseImputer): def impute(self, user_def=True, params=None): """ Perform imputation using the MPIN algorithm. + Need torch-cluster to work. Parameters ---------- @@ -1874,7 +1894,7 @@ def impute(self, user_def=True, params=None): >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data) >>> mpin_imputer.impute() # default parameters for imputation > or >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"}) # user-defined > or - >>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mpin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mpin_imputer.recov_data References @@ -1882,6 +1902,8 @@ def impute(self, user_def=True, params=None): Li, X., Li, H., Lu, H., Jensen, C.S., Pandey, V. & Markl, V. Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version). arXiv (2023). https://arxiv.org/abs/2311.07344 https://github.com/XLI-2020/MPIN """ + from imputegap.algorithms.mpin import mpin + if params is not None: incre_mode, window, k, learning_rate, weight_decay, epochs, num_of_iteration, threshold, base = self._check_params(user_def, params) else: @@ -1935,7 +1957,7 @@ def impute(self, user_def=True, params=None): >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data) >>> pristi_imputer.impute() # default parameters for imputation > or >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"}) # user-defined > or - >>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> pristi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = pristi_imputer.recov_data References @@ -1943,6 +1965,8 @@ def impute(self, user_def=True, params=None): M. Liu, H. Huang, H. Feng, L. Sun, B. Du and Y. Fu, "PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation," 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, USA, 2023, pp. 1927-1939, doi: 10.1109/ICDE55515.2023.00150. https://github.com/LMZZML/PriSTI """ + from imputegap.algorithms.pristi import pristi + if params is not None: target_strategy, unconditional, seed, device = self._check_params(user_def, params) else: @@ -2002,7 +2026,7 @@ def impute(self, user_def=True, params=None): >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data) >>> miss_net_imputer.impute() # default parameters for imputation > or >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False}) # user-defined > or - >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = miss_net_imputer.recov_data References @@ -2010,6 +2034,8 @@ def impute(self, user_def=True, params=None): Kohei Obata, Koki Kawabata, Yasuko Matsubara, and Yasushi Sakurai. 2024. Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24). Association for Computing Machinery, New York, NY, USA, 2296–2306. https://doi.org/10.1145/3637528.3671760 https://github.com/KoheiObata/MissNet/tree/main """ + from imputegap.algorithms.miss_net import miss_net + if params is not None: alpha, beta, L, n_cl, max_iteration, tol, random_init = self._check_params(user_def, params) else: @@ -2069,7 +2095,7 @@ def impute(self, user_def=True, params=None): >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data) >>> gain_imputer.impute() # default parameters for imputation > or >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100}) # user defined> or - >>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> gain_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = gain_imputer.recov_data References @@ -2077,6 +2103,8 @@ def impute(self, user_def=True, params=None): J. Yoon, J. Jordon, and M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," CoRR, vol. abs/1806.02920, 2018. Available: http://arxiv.org/abs/1806.02920. """ + from imputegap.algorithms.gain import gain + if params is not None: batch_size, hint_rate, alpha, epoch = self._check_params(user_def, params) else: @@ -2148,7 +2176,7 @@ def impute(self, user_def=True, params=None): >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data) >>> grin_imputer.impute() # default parameters for imputation > or >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2}) # user defined> or - >>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> grin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = grin_imputer.recov_data References @@ -2156,6 +2184,7 @@ def impute(self, user_def=True, params=None): A. Cini, I. Marisca, and C. Alippi, "Multivariate Time Series Imputation by Graph Neural Networks," CoRR, vol. abs/2108.00298, 2021 https://github.com/Graph-Machine-Learning-Group/grin """ + from imputegap.algorithms.grin import grin if params is not None: d_hidden, lr, batch_size, window, alpha, patience, epochs, workers = self._check_params(user_def, params) @@ -2233,7 +2262,7 @@ def impute(self, user_def=True, params=None): >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data) >>> bay_otide_imputer.impute() # default parameters for imputation > or >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5}) # user defined> or - >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = bay_otide_imputer.recov_data References @@ -2241,6 +2270,7 @@ def impute(self, user_def=True, params=None): S. Fang, Q. Wen, Y. Luo, S. Zhe, and L. Sun, "BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition," CoRR, vol. abs/2308.14906, 2024. [Online]. Available: https://arxiv.org/abs/2308.14906. https://github.com/xuangu-fang/BayOTIDE """ + from imputegap.algorithms.bayotide import bay_otide if params is not None: K_trend, K_season, n_season, K_bias, time_scale, a0, b0, v = self._check_params(user_def, params) @@ -2299,7 +2329,7 @@ def impute(self, user_def=True, params=None): >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data) >>> hkmf_t_imputer.impute() # default parameters for imputation > or >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5}) # user defined> or - >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = hkmf_t_imputer.recov_data References @@ -2307,6 +2337,7 @@ def impute(self, user_def=True, params=None): L. Wang, S. Wu, T. Wu, X. Tao and J. Lu, "HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 11, pp. 3582-3593, 1 Nov. 2021, doi: 10.1109/TKDE.2020.2971190. keywords: {Time series analysis;Matrix decomposition;Market research;Meteorology;Sparse matrices;Indexes;Software;Tagged time series;missing value imputation;blackouts;hankel matrix factorization} https://github.com/wangliang-cs/hkmf-t?tab=readme-ov-file """ + from imputegap.algorithms.hkmf_t import hkmf_t if params is not None: tags, data_names, epoch = self._check_params(user_def, params) @@ -2386,7 +2417,7 @@ def impute(self, user_def=True, params=None): >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data) >>> bit_graph_imputer.impute() # default parameters for imputation > or >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42}) # user defined> or - >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = bit_graph_imputer.recov_data References @@ -2395,6 +2426,8 @@ def impute(self, user_def=True, params=None): https://github.com/chenxiaodanhit/BiTGraph """ + from imputegap.algorithms.bit_graph import bit_graph + if params is not None: node_number, kernel_set, dropout, subgraph_size, node_dim, seq_len, lr, epoch, seed = self._check_params(user_def, params) else: diff --git a/build/lib/imputegap/recovery/manager.py b/build/lib/imputegap/recovery/manager.py index de9ace68..cb62be1a 100644 --- a/build/lib/imputegap/recovery/manager.py +++ b/build/lib/imputegap/recovery/manager.py @@ -1,28 +1,45 @@ import datetime import os +import platform import time import numpy as np import matplotlib -from scipy.stats import zscore -from sklearn.preprocessing import MinMaxScaler import importlib.resources -from scipy.stats import norm - from imputegap.tools import utils -# Use Agg backend if in a headless or CI environment -if os.getenv('DISPLAY') is None or os.getenv('CI') is not None: - matplotlib.use("Agg") - print("Running in a headless environment or CI. Using Agg backend.") -else: - try: - matplotlib.use("TkAgg") - if importlib.util.find_spec("tkinter") is None: - print("tkinter is not available.") - except (ImportError, RuntimeError): +from matplotlib import pyplot as plt # type: ignore + + +def select_backend(): + system = platform.system() + headless = os.getenv('DISPLAY') is None or os.getenv('CI') is not None + + if headless: matplotlib.use("Agg") + return + + if system == "Darwin": # macOS + try: + matplotlib.use("MacOSX") + except (ImportError, RuntimeError): + try: + if importlib.util.find_spec("tkinter") is not None: + matplotlib.use("TkAgg") + else: + raise ImportError + except (ImportError, RuntimeError): + matplotlib.use("Agg") + else: # Windows or Linux + try: + if importlib.util.find_spec("tkinter") is not None: + matplotlib.use("TkAgg") + else: + raise ImportError + except (ImportError, RuntimeError): + matplotlib.use("Agg") -from matplotlib import pyplot as plt # type: ignore +# Call the backend selector +select_backend() class TimeSeries: @@ -132,6 +149,11 @@ def load_series(self, data, nbr_series=None, nbr_val=None, header=False, replace ------- TimeSeries The TimeSeries object with the loaded data. + + Example + ------- + >>> ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=50, nbr_val=100) + """ if data is not None: @@ -184,11 +206,11 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): ------- None """ - print("\nTime Series set :") - to_print = self.data nbr_tot_series, nbr_tot_values = to_print.shape - print_col, print_row = "Timestamp", "Series" + print_col, print_row = "timestamp", "Series" + + print(f"\nshape of {self.name} : {self.data.shape}\n\tnumber of series = { nbr_tot_series}\n\tnumber of values = {nbr_tot_values}\n") if nbr_val == -1: nbr_val = to_print.shape[1] @@ -198,7 +220,7 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): if not view_by_series: to_print = to_print.T - print_col, print_row = "Series", "Timestamp" + print_col, print_row = "Series", "timestamp" header_format = "{:<15}" # Fixed size for headers value_format = "{:>15.10f}" # Fixed size for values @@ -210,16 +232,13 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): # Print each limited series with fixed size for i, series in enumerate(to_print): - print(header_format.format(f"{print_row} {i + 1}"), end="") + print(header_format.format(f"{print_row}_{i + 1}"), end="") print("".join([value_format.format(elem) for elem in series])) if nbr_series < nbr_tot_series: print("...") - print("\nshape of the time series :", self.data.shape, "\n\tnumber of series =", nbr_tot_series, - "\n\tnumber of values =", nbr_tot_values, "\n\n") - - def print_results(self, metrics, algorithm="", text="Imputation Results of"): + def print_results(self, metrics, algorithm="", text="Results of the analysis"): """ Prints the results of the imputation process. @@ -235,6 +254,10 @@ def print_results(self, metrics, algorithm="", text="Imputation Results of"): Returns ------- None + + Example + ------- + >>> ts.print_results(imputer.metrics, imputer.algorithm) """ if algorithm != "": @@ -262,6 +285,10 @@ def normalize(self, normalizer="z_score"): ------- numpy.ndarray The normalized time series data. + + Example + ------- + >>> ts.normalize(normalizer="z_score") """ print("Normalization of the original time series dataset with ", normalizer) self.data = self.data.T @@ -282,6 +309,8 @@ def normalize(self, normalizer="z_score"): end_time = time.time() elif normalizer == "z_lib": + from scipy.stats import zscore + start_time = time.time() # Record start time self.data = zscore(self.data, axis=0) @@ -289,6 +318,8 @@ def normalize(self, normalizer="z_score"): end_time = time.time() elif normalizer == "m_lib": + from sklearn.preprocessing import MinMaxScaler + start_time = time.time() # Record start time scaler = MinMaxScaler() @@ -311,10 +342,10 @@ def normalize(self, normalizer="z_score"): self.data = self.data.T - print(f"\n\t\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n") def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None, - subplot=False, size=(16, 8), save_path="./imputegap_assets", display=True): + subplot=False, size=(16, 8), algorithm=None, save_path="./imputegap_assets", display=True): """ Plot the time series data, including raw, contaminated, or imputed data. @@ -336,6 +367,8 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n Print one time series by subplot or all in the same plot. size : tuple, optional Size of the plot in inches. Default is (16, 8). + algorithm : str, optional + Name of the algorithm used for imputation. save_path : str, optional Path to save the plot locally. display : bool, optional @@ -345,8 +378,17 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n ------- str or None The file path of the saved plot, if applicable. + + Example + ------- + >>> ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") # plain data + >>> ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets") # contamination + >>> ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") # imputation """ number_of_series = 0 + if algorithm is None: + algorithm = "imputegap" + algorithm.lower() if nbr_series is None or nbr_series == -1: nbr_series = input_data.shape[0] @@ -381,6 +423,7 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n y_size = y_size_screen fig, axes = plt.subplots(n_rows, n_cols, figsize=(x_size, y_size), squeeze=False) + fig.canvas.manager.set_window_title(algorithm) axes = axes.flatten() else: plt.figure(figsize=size) @@ -407,28 +450,28 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n if incomp_data is None and recov_data is None: # plot only raw matrix ax.plot(timestamps, input_data[i, :nbr_val], linewidth=2.5, - color=color, linestyle='-', label=f'TS {i + 1}') + color=color, linestyle='-', label=f'Series {i + 1}') if incomp_data is not None and recov_data is None: # plot infected matrix if np.isnan(incomp_data[i, :]).any(): ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5, - color=color, linestyle='--', label=f'TS-INCOMP {i + 1}') + color=color, linestyle='--', label=f'Missing Data {i + 1}') if np.isnan(incomp_data[i, :]).any() or not subplot: ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val], - color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}') + color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}') if recov_data is not None: # plot imputed matrix if np.isnan(incomp_data[i, :]).any(): ax.plot(np.arange(min(recov_data.shape[1], nbr_val)), recov_data[i, :nbr_val], - linestyle='-', color="r", label=f'TS-RECOV {i + 1}') + linestyle='-', color="r", label=f'Imputed Data {i + 1}') ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5, - linestyle='--', color=color, label=f'TS-INCOM {i + 1}') + linestyle='--', color=color, label=f'Missing Data {i + 1}') if np.isnan(incomp_data[i, :]).any() or not subplot: ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val], - color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}') + color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}') # Label and legend for subplot if subplot: @@ -466,7 +509,7 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n now = datetime.datetime.now() current_time = now.strftime("%y_%m_%d_%H_%M_%S") - file_path = os.path.join(save_path + "/" + current_time + "_plot.jpg") + file_path = os.path.join(save_path + "/" + current_time + "_" + algorithm + "_plot.jpg") plt.savefig(file_path, bbox_inches='tight') print("plots saved in ", file_path) @@ -481,13 +524,13 @@ class Contamination: Methods ------- - missing_completely_at_random(ts, series_rate=0.2, missing_rate=0.2, block_size=10, offset=0.1, seed=True, explainer=False) : + mcar(ts, series_rate=0.2, missing_rate=0.2, block_size=10, offset=0.1, seed=True, explainer=False) : Apply Missing Completely at Random (MCAR) contamination to the time series data. - missing_percentage(ts, series_rate=0.2, missing_rate=0.2, offset=0.1) : + aligned(ts, series_rate=0.2, missing_rate=0.2, offset=0.1) : Apply missing percentage contamination to the time series data. - missing_percentage_at_random(ts, series_rate=0.2, missing_rate=0.2, offset=0.1, seed=True) : + missing_percentage_at_random(ts, series_rate=0.2, missing_rate=0.2, offset=0.1, seed=True) : Apply missing percentage contamination at random to the time series data. blackout(ts, missing_rate=0.2, offset=0.1) : @@ -506,7 +549,7 @@ class Contamination: Apply Overlapping contamination to the time series data. """ - def missing_completely_at_random(input_data, rate_dataset=0.2, rate_series=0.2, block_size=10, offset=0.1, seed=True, explainer=False): + def mcar(input_data, rate_dataset=0.2, rate_series=0.2, block_size=10, offset=0.1, seed=True, explainer=False): """ Apply Missing Completely at Random (MCAR) contamination to the time series data. @@ -531,6 +574,14 @@ def missing_completely_at_random(input_data, rate_dataset=0.2, rate_series=0.2, ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.mcar(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ if seed: @@ -555,16 +606,16 @@ def missing_completely_at_random(input_data, rate_dataset=0.2, rate_series=0.2, values_nbr = int(NS * rate_series) if not explainer: - print(f"\n\n\tMCAR contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\ta block size of {block_size}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tthis selection of series {series_selected}\n\n") + print(f"\n\nMCAR contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\ta block size of {block_size}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tthis selection of series {series_selected}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -604,9 +655,9 @@ def missing_completely_at_random(input_data, rate_dataset=0.2, rate_series=0.2, return ts_contaminated - def missing_percentage(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1): + def aligned(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1): """ - Apply missing percentage contamination to the time series data. + Apply aligned missing blocks contamination to the time series data. Parameters ---------- @@ -623,6 +674,14 @@ def missing_percentage(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1 ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.aligned(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ ts_contaminated = input_data.copy() @@ -637,13 +696,13 @@ def missing_percentage(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1 values_nbr = int(NS * rate_series) - print("\n\n\tMISSING PERCENTAGE contamination has been called with :" - "\n\t\ta number of series impacted ", rate_dataset * 100, "%", - "\n\t\ta missing rate of ", rate_series * 100, "%", - "\n\t\ta starting position at ", offset, - "\n\t\tshape of the set ", ts_contaminated.shape, - "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted, - "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") + print("\n\nALIGNED (missing percentage) contamination has been called with :" + "\n\ta number of series impacted ", rate_dataset * 100, "%", + "\n\ta missing rate of ", rate_series * 100, "%", + "\n\ta starting position at ", offset, + "\n\tshape of the set ", ts_contaminated.shape, + "\n\tthis selection of series : ", 1, "->", nbr_series_impacted, + "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") if offset_nbr + values_nbr > NS: @@ -664,7 +723,7 @@ def missing_percentage(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1 return ts_contaminated - def percentage_shift(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1, seed=True): + def scattered(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1, seed=True): """ Apply percentage shift contamination with random starting position to the time series data. @@ -685,6 +744,14 @@ def percentage_shift(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1, ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.scattered(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ if seed: @@ -703,13 +770,13 @@ def percentage_shift(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1, values_nbr = int(NS * rate_series) - print("\n\n\tMISSING PERCENTAGE AT RANDOM contamination has been called with :" - "\n\t\ta number of series impacted ", rate_dataset * 100, "%", - "\n\t\ta missing rate of ", rate_series * 100, "%", - "\n\t\ta starting position at ", offset, - "\n\t\tshape of the set ", ts_contaminated.shape, - "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted, - "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") + print("\n\nSCATTER (missing percentage AT RANDOM) contamination has been called with :" + "\n\ta number of series impacted ", rate_dataset * 100, "%", + "\n\ta missing rate of ", rate_series * 100, "%", + "\n\ta starting position at ", offset, + "\n\tshape of the set ", ts_contaminated.shape, + "\n\tthis selection of series : ", 1, "->", nbr_series_impacted, + "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") if offset_nbr + values_nbr > NS: @@ -750,8 +817,16 @@ def blackout(input_data, series_rate=0.2, offset=0.1): ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.blackout(ts.data, series_rate=0.2) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ - return TimeSeries.Contamination.missing_percentage(input_data, rate_dataset=1, rate_series=series_rate, offset=offset) + return TimeSeries.Contamination.aligned(input_data, rate_dataset=1, rate_series=series_rate, offset=offset) def gaussian(input_data, rate_dataset=0.2, rate_series=0.2, std_dev=0.2, offset=0.1, seed=True): """ @@ -776,7 +851,16 @@ def gaussian(input_data, rate_dataset=0.2, rate_series=0.2, std_dev=0.2, offset= ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.gaussian(ts.data, rate_series=0.2, std_dev=0.2, offset=0.1, seed=True) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ + from scipy.stats import norm ts_contaminated = input_data.copy() M, NS = ts_contaminated.shape @@ -795,16 +879,16 @@ def gaussian(input_data, rate_dataset=0.2, rate_series=0.2, std_dev=0.2, offset= offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tGAUSSIAN contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tGaussian std_dev {std_dev}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tthis selection of series {nbr_series_impacted}\n\n") + print(f"\n\nGAUSSIAN contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tGaussian std_dev {std_dev}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tthis selection of series {nbr_series_impacted}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -859,6 +943,14 @@ def distribution(input_data, rate_dataset=0.2, rate_series=0.2, probabilities=No ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.distribution(ts.data, rate_dataset=0.2, rate_series=0.2, probabilities=probabilities, offset=0.1, seed=True) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ ts_contaminated = input_data.copy() @@ -878,16 +970,16 @@ def distribution(input_data, rate_dataset=0.2, rate_series=0.2, probabilities=No offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tGAUSSIAN contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tprobabilities list {np.array(probabilities).shape}" - f"\n\t\tthis selection of series {nbr_series_impacted}\n\n") + print(f"\n\nDISTRIBUTION contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tprobabilities list {np.array(probabilities).shape}" + f"\n\tthis selection of series {nbr_series_impacted}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -932,6 +1024,14 @@ def disjoint(input_data, rate_series=0.1, limit=1, offset=0.1): ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.disjoint(ts.data, rate_series=0.1, limit=1, offset=0.1) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ ts_contaminated = input_data.copy() M, NS = ts_contaminated.shape @@ -943,12 +1043,12 @@ def disjoint(input_data, rate_series=0.1, limit=1, offset=0.1): values_nbr = int(NS * rate_series) - print(f"\n\n\tDISJOINT contamination has been called with :" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\tlimit to stop {limit}" - f"\n\t\tshape of the set {ts_contaminated.shape}") + print(f"\n\nDISJOINT contamination has been called with :" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\tlimit to stop {limit}" + f"\n\tshape of the set {ts_contaminated.shape}") if offset_nbr + values_nbr > NS: @@ -998,6 +1098,14 @@ def overlap(input_data, rate_series=0.2, limit=1, shift=0.05, offset=0.1): ------- numpy.ndarray The contaminated time series data. + + Example + ------- + >>> ts_m = ts.Contamination.overlap(ts.data, rate_series=0.1, limit=1, shift=0.05, offset=0.1) + + References + ---------- + https://imputegap.readthedocs.io/en/latest/patterns.html """ ts_contaminated = input_data.copy() M, NS = ts_contaminated.shape @@ -1008,15 +1116,15 @@ def overlap(input_data, rate_series=0.2, limit=1, shift=0.05, offset=0.1): offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tOVERLAP contamination has been called with :" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta offset of {offset*100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\ta shift overlap of {shift * 100} %" - f"\n\t\ta shift in number {int(shift * NS)}" - f"\n\t\tlimit to stop {limit}" - f"\n\t\tshape of the set {ts_contaminated.shape}") + print(f"\n\nOVERLAP contamination has been called with :" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta offset of {offset*100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\ta shift overlap of {shift * 100} %" + f"\n\ta shift in number {int(shift * NS)}" + f"\n\tlimit to stop {limit}" + f"\n\tshape of the set {ts_contaminated.shape}") if offset_nbr + values_nbr > NS: raise ValueError( @@ -1050,3 +1158,8 @@ def overlap(input_data, rate_series=0.2, limit=1, shift=0.05, offset=0.1): return ts_contaminated + missing_completely_at_random = mcar + mp = aligned + missing_percentage = aligned + + diff --git a/build/lib/imputegap/recovery/optimization.py b/build/lib/imputegap/recovery/optimization.py index 77496a31..8e11bcb9 100644 --- a/build/lib/imputegap/recovery/optimization.py +++ b/build/lib/imputegap/recovery/optimization.py @@ -1,25 +1,11 @@ -import os import time from itertools import product import numpy as np - from imputegap.recovery.imputation import Imputation from imputegap.tools import utils from imputegap.tools.algorithm_parameters import SEARCH_SPACES, ALL_ALGO_PARAMS, PARAM_NAMES, SEARCH_SPACES_PSO, RAYTUNE_PARAMS import imputegap.tools.algorithm_parameters as sh_params -# RAY TUNE IMPORT -from ray import tune -import ray - -# PSO IMPORT -from functools import partial -import pyswarms as ps - -# BAYESIAN IMPORT -import skopt -from skopt.space import Integer - from pyswarms.utils.reporter import Reporter reporter = Reporter() @@ -289,6 +275,10 @@ def optimize(self, input_data, incomp_data, metrics=["RMSE"], algorithm="cdrec", tuple A tuple containing the best parameters and their corresponding score. """ + # BAYESIAN IMPORT + import skopt + from skopt.space import Integer + start_time = time.time() # Record start time search_spaces = SEARCH_SPACES @@ -416,6 +406,9 @@ def optimize(self, input_data, incomp_data, metrics, algorithm, n_particles, c1, tuple A tuple containing the best parameters and their corresponding score. """ + from functools import partial + import pyswarms as ps + start_time = time.time() # Record start time if not isinstance(metrics, list): @@ -620,6 +613,9 @@ def optimize(self, input_data, incomp_data, metrics=["RMSE"], algorithm="cdrec", tuple A tuple containing the best parameters and their corresponding score. """ + from ray import tune + import ray + if not ray.is_initialized(): ray.init() used_metric = metrics[0] diff --git a/build/lib/imputegap/runner_benchmark.py b/build/lib/imputegap/runner_benchmark.py index baed387b..740e808d 100644 --- a/build/lib/imputegap/runner_benchmark.py +++ b/build/lib/imputegap/runner_benchmark.py @@ -1,45 +1,17 @@ -import numpy as np - from imputegap.recovery.benchmark import Benchmark -# define analysis global variables -reconstruction = False -save_dir = "./analysis" +save_dir = "./imputegap_assets/benchmark" nbr_run = 1 -# define the datasets to evaluate -datasets_full = ["eeg-alcohol", "eeg-reading", "fmri-objectviewing", "fmri-stoptask", "chlorine", "drift"] -datasets = ["chlorine", "eeg-reading"] +datasets = ["eeg-alcohol"] -# define the optimizer to fine-tine the algorithms -optimiser_bayesian = {"optimizer": "bayesian", "options": {"n_calls": 2, "n_random_starts": 50, "acq_func": "gp_hedge", "metrics": "RMSE"}} -optimiser_greedy = {"optimizer": "greedy", "options": {"n_calls": 250, "metrics": "RMSE"}} -optimiser_pso = {"optimizer": "pso", "options": {"n_particles": 50, "iterations": 10, "metrics": "RMSE"}} -optimiser_sh = {"optimizer": "sh", "options": {"num_configs": 10, "num_iterations": 5, "metrics": "RMSE"}} -optimiser_ray = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} -optimizers = [optimiser_ray] +optimizers = ["default_params"] -# define the algorithms for the imputation -algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"] +algorithms = ["SoftImpute", "KNNImpute"] -# define the missing pattern to contaminate the time series -patterns_full = ["mcar", "mp", "blackout", "disjoint", "overlap", "gaussian"] patterns = ["mcar"] -# define missing values percentages to see the evolution of the imputation range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] - -if not reconstruction: - # launch the analysis - list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) -else: - test_plots = {'eegreading': {'mp': {'mean': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.9585345039764159, 'MAE': 0.71318962961796, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0001308917999267578, 'optimization': 0.0, 'imputation': 0.0005505084991455078, 'log_imputation': -3.25923597169041}}, '0.1': {'scores': {'RMSE': 1.158802114694506, 'MAE': 0.9654264194724749, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.00028133392333984375, 'optimization': 0.0, 'imputation': 0.0002911090850830078, 'log_imputation': -3.5359442406627037}}, '0.2': {'scores': {'RMSE': 0.9366041090001302, 'MAE': 0.713304743455646, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.00025653839111328125, 'optimization': 0.0, 'imputation': 0.0002605915069580078, 'log_imputation': -3.5840397426578834}}, '0.4': {'scores': {'RMSE': 1.0599636170074507, 'MAE': 0.8381968262587581, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0006632804870605469, 'optimization': 0.0, 'imputation': 0.00026726722717285156, 'log_imputation': -3.573054292012613}}, '0.6': {'scores': {'RMSE': 1.0562417941470534, 'MAE': 0.8508551190781458, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0013227462768554688, 'optimization': 0.0, 'imputation': 0.0002796649932861328, 'log_imputation': -3.553361892492057}}, '0.7': {'scores': {'RMSE': 1.040204405772873, 'MAE': 0.8335213108852025, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0017957687377929688, 'optimization': 0.0, 'imputation': 0.00025272369384765625, 'log_imputation': -3.597354039342816}}}}, 'cdrec': {'ray_tune': {'0.05': {'scores': {'RMSE': 1.1697500835978993, 'MAE': 0.9591122423282666, 'MI': 0.874795673555792, 'CORRELATION': -0.6435768999074719}, 'times': {'contamination': 0.00013375282287597656, 'optimization': 7.705925226211548, 'imputation': 0.03410458564758301, 'log_imputation': -1.4671872225457172}}, '0.1': {'scores': {'RMSE': 1.160748195036071, 'MAE': 0.9801100319738002, 'MI': 0.24843228556023436, 'CORRELATION': -0.1457168801420723}, 'times': {'contamination': 0.0010766983032226562, 'optimization': 7.705925226211548, 'imputation': 0.0187838077545166, 'log_imputation': -1.7262163652567906}}, '0.2': {'scores': {'RMSE': 0.9258702230012604, 'MAE': 0.7412912028091162, 'MI': 0.09990051537425793, 'CORRELATION': 0.018312853013798043}, 'times': {'contamination': 0.0012714862823486328, 'optimization': 7.705925226211548, 'imputation': 0.06531453132629395, 'log_imputation': -1.184990185145398}}, '0.4': {'scores': {'RMSE': 1.0158290954098952, 'MAE': 0.7699069240021237, 'MI': 0.043509996942918995, 'CORRELATION': 0.026854101443570477}, 'times': {'contamination': 0.002095460891723633, 'optimization': 7.705925226211548, 'imputation': 0.06960487365722656, 'log_imputation': -1.1573603504995333}}, '0.6': {'scores': {'RMSE': 1.074841191421588, 'MAE': 0.8305870491121395, 'MI': 0.04052775735888883, 'CORRELATION': 0.02240964134518767}, 'times': {'contamination': 0.003614664077758789, 'optimization': 7.705925226211548, 'imputation': 0.11719846725463867, 'log_imputation': -0.9310780680722405}}, '0.7': {'scores': {'RMSE': 1.028461165376999, 'MAE': 0.8037932945848046, 'MI': 0.04862782947355959, 'CORRELATION': 0.28310309461657}, 'times': {'contamination': 0.004042148590087891, 'optimization': 7.705925226211548, 'imputation': 0.14756202697753906, 'log_imputation': -0.8310253877423743}}}}, 'stmvl': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.7277001471585042, 'MAE': 0.6187129093081218, 'MI': 0.849293361425945, 'CORRELATION': 0.6050898026864381}, 'times': {'contamination': 0.0003566741943359375, 'optimization': 401.3516755104065, 'imputation': 13.703130722045898, 'log_imputation': 1.1368198007314763}}, '0.1': {'scores': {'RMSE': 0.9169700690065775, 'MAE': 0.7874056676843385, 'MI': 0.3666479681879978, 'CORRELATION': 0.5010483466574436}, 'times': {'contamination': 0.00020051002502441406, 'optimization': 401.3516755104065, 'imputation': 13.661174535751343, 'log_imputation': 1.1354880399360694}}, '0.2': {'scores': {'RMSE': 0.7183875117312635, 'MAE': 0.5896797263562054, 'MI': 0.24774404354441104, 'CORRELATION': 0.46263398990236104}, 'times': {'contamination': 0.0002193450927734375, 'optimization': 401.3516755104065, 'imputation': 13.761161088943481, 'log_imputation': 1.1386550787575944}}, '0.4': {'scores': {'RMSE': 0.8742119003541577, 'MAE': 0.6297161236774441, 'MI': 0.12171661757737935, 'CORRELATION': 0.2830622046394087}, 'times': {'contamination': 0.0006110668182373047, 'optimization': 401.3516755104065, 'imputation': 14.14597225189209, 'log_imputation': 1.1506328018545884}}, '0.6': {'scores': {'RMSE': 20.12875114987835, 'MAE': 3.972851513364007, 'MI': 0.005760162876990282, 'CORRELATION': -0.0020763310368411936}, 'times': {'contamination': 0.001154184341430664, 'optimization': 401.3516755104065, 'imputation': 8.714907884597778, 'log_imputation': 0.9402628010361087}}, '0.7': {'scores': {'RMSE': 0.9089996638242581, 'MAE': 0.7155219794334919, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.001634359359741211, 'optimization': 401.3516755104065, 'imputation': 8.389863967895508, 'log_imputation': 0.9237549192945785}}}}, 'iim': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.9917499844047692, 'MAE': 0.6835294570359077, 'MI': 1.0383833294554503, 'CORRELATION': 0.39342144548050073}, 'times': {'contamination': 0.00010991096496582031, 'optimization': 87.12388730049133, 'imputation': 0.1563720703125, 'log_imputation': -0.8058408138870692}}, '0.1': {'scores': {'RMSE': 0.5640999644452854, 'MAE': 0.436875650594336, 'MI': 0.9382015646212765, 'CORRELATION': 0.8446597946056835}, 'times': {'contamination': 0.000209808349609375, 'optimization': 87.12388730049133, 'imputation': 1.0088622570037842, 'log_imputation': 0.003831874753497683}}, '0.2': {'scores': {'RMSE': 0.5524062619653164, 'MAE': 0.4527739222276905, 'MI': 0.4682825796414235, 'CORRELATION': 0.734130718955202}, 'times': {'contamination': 0.00021195411682128906, 'optimization': 87.12388730049133, 'imputation': 6.273438930511475, 'log_imputation': 0.7975056746615543}}, '0.4': {'scores': {'RMSE': 0.6703666990440234, 'MAE': 0.5167364139824571, 'MI': 0.25478017143468495, 'CORRELATION': 0.6160285049199379}, 'times': {'contamination': 0.0006020069122314453, 'optimization': 87.12388730049133, 'imputation': 48.24830389022827, 'log_imputation': 1.683482050859879}}, '0.6': {'scores': {'RMSE': 0.846371619650148, 'MAE': 0.6521374554849746, 'MI': 0.10898611984678795, 'CORRELATION': 0.37877848480193976}, 'times': {'contamination': 0.0012598037719726562, 'optimization': 87.12388730049133, 'imputation': 148.6065971851349, 'log_imputation': 2.1720380897576836}}, '0.7': {'scores': {'RMSE': 0.939708165479016, 'MAE': 0.7342180002582285, 'MI': 0.0747388622783703, 'CORRELATION': 0.3115372974410071}, 'times': {'contamination': 0.0016543865203857422, 'optimization': 87.12388730049133, 'imputation': 240.74761581420898, 'log_imputation': 2.3815619948874964}}}}, 'mrnn': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.5647614331747706, 'MAE': 0.4956801381604213, 'MI': 0.602588078427742, 'CORRELATION': 0.7892828777307545}, 'times': {'contamination': 0.00017881393432617188, 'optimization': 23.75439763069153, 'imputation': 9.659311532974243, 'log_imputation': 0.9849461731975306}}, '0.1': {'scores': {'RMSE': 1.4942930218243802, 'MAE': 1.2039603558089516, 'MI': 0.47511351289726134, 'CORRELATION': -0.24282448220213368}, 'times': {'contamination': 0.00033783912658691406, 'optimization': 23.75439763069153, 'imputation': 9.660546064376831, 'log_imputation': 0.985001675695301}}, '0.2': {'scores': {'RMSE': 1.493030946284299, 'MAE': 1.2759205639542883, 'MI': 0.14247255829853323, 'CORRELATION': 0.17585501858419272}, 'times': {'contamination': 0.0004456043243408203, 'optimization': 23.75439763069153, 'imputation': 9.614870071411133, 'log_imputation': 0.982943419864104}}, '0.4': {'scores': {'RMSE': 1.6610979347034713, 'MAE': 1.32902912214158, 'MI': 0.07259583818478979, 'CORRELATION': -0.04241780698399349}, 'times': {'contamination': 0.0007512569427490234, 'optimization': 23.75439763069153, 'imputation': 9.778327465057373, 'log_imputation': 0.9902645771992502}}, '0.6': {'scores': {'RMSE': 1.6248154262874046, 'MAE': 1.3587893211292972, 'MI': 0.04243232622955761, 'CORRELATION': -0.05349772421574814}, 'times': {'contamination': 0.0013408660888671875, 'optimization': 23.75439763069153, 'imputation': 9.961318492889404, 'log_imputation': 0.9983168260028805}}, '0.7': {'scores': {'RMSE': 1.3781088381679387, 'MAE': 1.102321907630172, 'MI': 0.013439658692967605, 'CORRELATION': -0.01874660352385742}, 'times': {'contamination': 0.0019338130950927734, 'optimization': 23.75439763069153, 'imputation': 9.934946298599243, 'log_imputation': 0.9971655239586622}}}}, 'knn': {'ray_tune': {'0.05': {'scores': {'RMSE': 1.0891820888848962, 'MAE': 0.8741423177191895, 'MI': 1.0726621001615337, 'CORRELATION': 0.23766946742758965}, 'times': {'contamination': 0.0002646446228027344, 'optimization': 5.975560188293457, 'imputation': 0.004753589630126953, 'log_imputation': -2.3229783129452315}}, '0.1': {'scores': {'RMSE': 0.6835893631062424, 'MAE': 0.5234507579018903, 'MI': 0.7407074666713884, 'CORRELATION': 0.7645134370284421}, 'times': {'contamination': 0.00014519691467285156, 'optimization': 5.975560188293457, 'imputation': 0.006691694259643555, 'log_imputation': -2.174463909966519}}, '0.2': {'scores': {'RMSE': 0.5992380715293445, 'MAE': 0.4859085244932665, 'MI': 0.4086948138880703, 'CORRELATION': 0.6965923314128772}, 'times': {'contamination': 0.0002143383026123047, 'optimization': 5.975560188293457, 'imputation': 0.013432025909423828, 'log_imputation': -1.8718584791459945}}, '0.4': {'scores': {'RMSE': 0.6868836297408376, 'MAE': 0.5093608359137556, 'MI': 0.2868485602164113, 'CORRELATION': 0.6240763887663399}, 'times': {'contamination': 0.0005633831024169922, 'optimization': 5.975560188293457, 'imputation': 0.039768218994140625, 'log_imputation': -1.4004638583058664}}, '0.6': {'scores': {'RMSE': 0.7785967363211405, 'MAE': 0.6158636946887723, 'MI': 0.19729389232186453, 'CORRELATION': 0.5576154866890136}, 'times': {'contamination': 0.001161813735961914, 'optimization': 5.975560188293457, 'imputation': 0.0798337459564209, 'log_imputation': -1.0978134922993485}}, '0.7': {'scores': {'RMSE': 0.867427846592856, 'MAE': 0.6717590115075687, 'MI': 0.1554022092659651, 'CORRELATION': 0.5020021909165999}, 'times': {'contamination': 0.0016968250274658203, 'optimization': 5.975560188293457, 'imputation': 0.1084587574005127, 'log_imputation': -0.9647353755339024}}}}, 'interpolation': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.652962872644497, 'MAE': 0.5073890992493721, 'MI': 0.8457271995908064, 'CORRELATION': 0.8055318132299937}, 'times': {'contamination': 8.559226989746094e-05, 'optimization': 5.141337633132935, 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'imputation': 50.53941249847412, 'log_imputation': 1.7036301891043986}}}}, 'mpin': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.9925272461717889, 'MAE': 0.7461083558118482, 'MI': 0.5348692435514593, 'CORRELATION': -0.007667736330282676}, 'times': {'contamination': 0.00036644935607910156, 'optimization': 7.812181711196899, 'imputation': 0.5112149715423584, 'log_imputation': -0.29139643582966285}}, '0.1': {'scores': {'RMSE': 1.1680441592588726, 'MAE': 0.9781717371399381, 'MI': 0.11371116243625422, 'CORRELATION': -0.016646509270680725}, 'times': {'contamination': 0.0002243518829345703, 'optimization': 7.812181711196899, 'imputation': 0.4404561519622803, 'log_imputation': -0.35609731973472436}}, '0.2': {'scores': {'RMSE': 0.9408439748172953, 'MAE': 0.7285930425690125, 'MI': 0.04098822429350485, 'CORRELATION': 0.04181051886506206}, 'times': {'contamination': 0.00023174285888671875, 'optimization': 7.812181711196899, 'imputation': 0.4183802604675293, 'log_imputation': -0.37842881403623974}}, '0.4': {'scores': {'RMSE': 1.0052976858981615, 'MAE': 0.786965308522399, 'MI': 0.01344178556294245, 'CORRELATION': 0.024681873992244058}, 'times': {'contamination': 0.0006501674652099609, 'optimization': 7.812181711196899, 'imputation': 0.40064024925231934, 'log_imputation': -0.3972454226158039}}, '0.6': {'scores': {'RMSE': 0.9548251319263584, 'MAE': 0.7511442207051872, 'MI': 0.00930135623468233, 'CORRELATION': -0.01575182086616044}, 'times': {'contamination': 0.0012600421905517578, 'optimization': 7.812181711196899, 'imputation': 0.4070866107940674, 'log_imputation': -0.39031318146702354}}, '0.7': {'scores': {'RMSE': 0.9446318662376141, 'MAE': 0.744122264330064, 'MI': 0.007539213289227095, 'CORRELATION': -0.018240287271113223}, 'times': {'contamination': 0.0017695426940917969, 'optimization': 7.812181711196899, 'imputation': 0.39906764030456543, 'log_imputation': -0.3989534869665162}}}}, 'pristi': {'ray_tune': {'0.05': {'scores': {'RMSE': 55.960116992995445, 'MAE': 46.4096472319626, 'MI': 0.42064699601068534, 'CORRELATION': 0.04507016986244122}, 'times': {'contamination': 0.00011730194091796875, 'optimization': 76.60794115066528, 'imputation': 29.775820016860962, 'log_imputation': 1.47386373065503}}, '0.1': {'scores': {'RMSE': 57.538277532977055, 'MAE': 46.82173605706819, 'MI': 0.08824583086697571, 'CORRELATION': 0.0434011572132245}, 'times': {'contamination': 0.00023126602172851562, 'optimization': 76.60794115066528, 'imputation': 30.05645751953125, 'log_imputation': 1.4779377929381963}}, '0.2': {'scores': {'RMSE': 60.808746540345666, 'MAE': 48.85156592826631, 'MI': 0.03497257877394178, 'CORRELATION': -0.028840216845075817}, 'times': {'contamination': 0.00027680397033691406, 'optimization': 76.60794115066528, 'imputation': 30.277715921401978, 'log_imputation': 1.4811231099249287}}, '0.4': {'scores': {'RMSE': 61.76763817066328, 'MAE': 49.5988990990065, 'MI': 0.007898084001455433, 'CORRELATION': 0.0030498998829152878}, 'times': {'contamination': 0.0006997585296630859, 'optimization': 76.60794115066528, 'imputation': 28.5592999458313, 'log_imputation': 1.4557475576773615}}, '0.6': {'scores': {'RMSE': 61.98612564576895, 'MAE': 49.755647879167334, 'MI': 0.004322458488836388, 'CORRELATION': 0.01233963956767782}, 'times': {'contamination': 0.0013575553894042969, 'optimization': 76.60794115066528, 'imputation': 32.24260687828064, 'log_imputation': 1.5084301481170062}}, '0.7': {'scores': {'RMSE': 62.379540899173236, 'MAE': 50.156074239740796, 'MI': 0.0031404425022982423, 'CORRELATION': 0.009111022593762223}, 'times': {'contamination': 0.0018019676208496094, 'optimization': 76.60794115066528, 'imputation': 35.09843707084656, 'log_imputation': 1.545287777815695}}}}}}} - Benchmark().generate_plots(runs_plots_scores=test_plots, ticks=range, subplot=False, y_size=max(4, int(len(algorithms)*0.28)), save_dir=save_dir) - Benchmark().generate_plots(runs_plots_scores=test_plots, ticks=range, subplot=True, y_size=max(4, int(len(algorithms)*0.28)),save_dir=save_dir) - Benchmark().generate_reports_txt(runs_plots_scores=test_plots, save_dir=save_dir, dataset="chlorine", run=0) - Benchmark().generate_reports_excel(runs_plots_scores=test_plots, save_dir=save_dir, dataset="chlorine", run=0) - - Benchmark().generate_heatmap(np.array([[1.12395851, 0.6470281, 0.67607129, 0.52176976, 0.52304288, 0.36444636, 0.89733918, 0.35040938, 0.35273418, 0.96177331, 0.53492822, 1.20178291, 54.63150047, 0.89519623, 0.36984999, 100., 0.91834629, 0.4267107, 100.], [1.10116065, 1.06258333, 0.95263409, 0.67823842, 2.30571521, 0.76078378, 0.87041159, 1.77119164, 0.78415296, 1.03505842, 1.00102834, 1.36935127, 60.07340763, 0.88553276, 1.73914404, 100., 4.04583674, 0.67136028, 100.]]), - np.array(['brits', 'cdrec', 'deep_mvi', 'dynammo', 'grouse', 'iim', 'interpolation', 'iter_svd', 'knn', 'mean', 'mpin', 'mrnn', 'pristi', 'rosl', 'soft_imp', 'spirit', 'stmvl', 'svt', 'tkcm']), - np.array(['eegalcohol', 'eegreading']), save_dir=save_dir, display=False) +# launch the evaluation +list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) \ No newline at end of file diff --git a/build/lib/imputegap/runner_contamination.py b/build/lib/imputegap/runner_contamination.py index 2da2b2c2..cd9ec977 100644 --- a/build/lib/imputegap/runner_contamination.py +++ b/build/lib/imputegap/runner_contamination.py @@ -1,11 +1,11 @@ from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"Missingness patterns : {ts.patterns}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") diff --git a/build/lib/imputegap/runner_downstream.py b/build/lib/imputegap/runner_downstream.py index 4aeb6c7d..e7cdecfb 100644 --- a/build/lib/imputegap/runner_downstream.py +++ b/build/lib/imputegap/runner_downstream.py @@ -2,11 +2,11 @@ from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("forecast-economy")) ts.normalize(normalizer="min_max") @@ -17,7 +17,7 @@ imputer = Imputation.MatrixCompletion.CDRec(ts_m) imputer.impute() -# compute print the downstream results -downstream_config = {"task": "forecast", "model": "hw-add"} +# compute and print the downstream results +downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) -ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) \ No newline at end of file +ts.print_results(imputer.downstream_metrics, algorithm="hw-add") \ No newline at end of file diff --git a/build/lib/imputegap/runner_explainer.py b/build/lib/imputegap/runner_explainer.py index 3ba45d1d..e7d67bab 100644 --- a/build/lib/imputegap/runner_explainer.py +++ b/build/lib/imputegap/runner_explainer.py @@ -2,10 +2,10 @@ from imputegap.recovery.explainer import Explainer from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") diff --git a/build/lib/imputegap/runner_imputation.py b/build/lib/imputegap/runner_imputation.py index f0539772..db2ad428 100644 --- a/build/lib/imputegap/runner_imputation.py +++ b/build/lib/imputegap/runner_imputation.py @@ -2,11 +2,11 @@ from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"Imputation algorithms : {ts.algorithms}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") @@ -15,11 +15,11 @@ # impute the contaminated series imputer = Imputation.MatrixCompletion.CDRec(ts_m) -imputer.impute() # could also use a dictionary for params: params={"rank": 5, "epsilon": 0.01, "iterations": 100} +imputer.impute() # compute and print the imputation metrics imputer.score(ts.data, imputer.recov_data) ts.print_results(imputer.metrics) # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") \ No newline at end of file +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets") \ No newline at end of file diff --git a/build/lib/imputegap/runner_loading.py b/build/lib/imputegap/runner_loading.py index 22c24023..894aad36 100644 --- a/build/lib/imputegap/runner_loading.py +++ b/build/lib/imputegap/runner_loading.py @@ -1,16 +1,14 @@ from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() +# initialize the time series object ts = TimeSeries() print(f"ImputeGAP datasets : {ts.datasets}") - -# load the timeseries from file or from the code +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) +ts.normalize(normalizer="z_score") -# plot a subset of time series -ts.plot(input_data=ts.data, save_path="./imputegap_assets") - -# print a subset of time series -ts.print(nbr_series=9, nbr_val=20) +# plot and print a subset of time series +ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") +ts.print(nbr_series=9, nbr_val=20) \ No newline at end of file diff --git a/build/lib/imputegap/runner_optimization.py b/build/lib/imputegap/runner_optimization.py index f0ccd167..47c16066 100644 --- a/build/lib/imputegap/runner_optimization.py +++ b/build/lib/imputegap/runner_optimization.py @@ -2,11 +2,10 @@ from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() -print(f"AutoML Optimizers : {ts.optimizers}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") @@ -17,12 +16,19 @@ # use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) -# compute and print the imputation metrics +# compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics) + +# compute the imputation metrics with default parameter values +imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() +imputer_def.score(ts.data, imputer_def.recov_data) + +# print the imputation metrics with default and optimized parameter values +ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") +ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True) # save hyperparameters -utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune") \ No newline at end of file +utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune") diff --git a/build/lib/imputegap/tools/algorithm_parameters.py b/build/lib/imputegap/tools/algorithm_parameters.py index 5d8f3b1a..296d451a 100644 --- a/build/lib/imputegap/tools/algorithm_parameters.py +++ b/build/lib/imputegap/tools/algorithm_parameters.py @@ -61,7 +61,7 @@ 'cdrec': { "rank": tune.grid_search([i for i in range(2, 16, 1)]), "eps": tune.loguniform(1e-6, 1), - "iters": tune.grid_search([i * 100 for i in range(1, 3)]) + "iters": tune.grid_search([i * 50 for i in range(1, 4)]) }, "iim": { "learning_neighbors": tune.grid_search([i for i in range(1, 20)]) # Up to 100 learning neighbors @@ -157,6 +157,12 @@ "device": tune.choice(["cpu"]) # Allow switching between CPU and GPU }, + "knn_impute": { + "k": tune.grid_search([1, 12, 1]), + "weights": tune.choice(["uniform", "distance"]) + }, + + "knn": { "k": tune.grid_search([1, 12, 1]), "weights": tune.choice(["uniform", "distance"]) diff --git a/build/lib/imputegap/tools/utils.py b/build/lib/imputegap/tools/utils.py index 3bc09422..13bfab9b 100644 --- a/build/lib/imputegap/tools/utils.py +++ b/build/lib/imputegap/tools/utils.py @@ -2,9 +2,8 @@ import os import toml import importlib.resources -import numpy as __numpy_import; - - +import numpy as __numpy_import +import platform def config_impute_algorithm(incomp_data, algorithm): """ @@ -62,8 +61,8 @@ def config_impute_algorithm(incomp_data, algorithm): imputer = Imputation.DeepLearning.PRISTI(incomp_data) # 3rd generation - elif algorithm == "knn" or algorithm == "KNN": - imputer = Imputation.Statistics.KNN(incomp_data) + elif algorithm == "knn" or algorithm == "KNN" or algorithm == "knn_impute" or algorithm == "KNNImpute": + imputer = Imputation.Statistics.KNNImpute(incomp_data) elif algorithm == "interpolation" or algorithm == "Interpolation": imputer = Imputation.Statistics.Interpolation(incomp_data) elif algorithm == "mean_series" or algorithm == "MeanImputeBySeries": @@ -120,9 +119,9 @@ def config_contamination(ts, pattern, dataset_rate=0.4, series_rate=0.4, block_s """ if pattern == "mcar" or pattern == "missing_completely_at_random": incomp_data = ts.Contamination.mcar(input_data=ts.data, rate_dataset=dataset_rate, rate_series=series_rate, block_size=block_size, offset=offset, seed=seed, explainer=explainer) - elif pattern == "mp" or pattern == "missing_percentage": + elif pattern == "mp" or pattern == "missing_percentage" or pattern == "aligned": incomp_data = ts.Contamination.aligned(input_data=ts.data, rate_dataset=dataset_rate, rate_series=series_rate, offset=offset) - elif pattern == "ps" or pattern == "percentage_shift": + elif pattern == "ps" or pattern == "percentage_shift" or pattern == "scattered": incomp_data = ts.Contamination.scattered(input_data=ts.data, rate_dataset=dataset_rate, rate_series=series_rate, offset=offset, seed=seed) elif pattern == "disjoint": incomp_data = ts.Contamination.disjoint(input_data=ts.data, rate_series=dataset_rate, limit=1, offset=offset) @@ -364,7 +363,7 @@ def load_parameters(query: str = "default", algorithm: str = "cdrec", dataset: s with open(filepath, "r") as _: config = toml.load(filepath) - print("\n\t\t\t\t(SYS) Inner files loaded : ", filepath, "\n") + print("\n(SYS) Inner files loaded : ", filepath, "\n") if algorithm == "cdrec": truncation_rank = int(config[algorithm]['rank']) @@ -450,7 +449,7 @@ def load_parameters(query: str = "default", algorithm: str = "cdrec", dataset: s seed = int(config[algorithm]['seed']) device = str(config[algorithm]['device']) return (target_strategy, unconditional, seed, device) - elif algorithm == "knn": + elif algorithm == "knn" or algorithm == "knn_impute": k = int(config[algorithm]['k']) weights = str(config[algorithm]['weights']) return (k, weights) @@ -755,18 +754,25 @@ def load_share_lib(name="lib_cdrec", lib=True): ctypes.CDLL The loaded shared library object. """ + system = platform.system() + if system == "Windows": + ext = ".so" + elif system == "Darwin": + ext = ".dylib" # macOS uses .dylib for dynamic libraries + else: + ext = ".so" if lib: - lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name)) + lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name) + ext) else: - local_path_lin = './algorithms/lib/' + name + '.so' + local_path_lin = './algorithms/lib/' + name + ext if not os.path.exists(local_path_lin): - local_path_lin = './imputegap/algorithms/lib/' + name + '.so' + local_path_lin = './imputegap/algorithms/lib/' + name + ext lib_path = os.path.join(local_path_lin) - print("\t\t(SYS) lib loaded from:", lib_path) + print("\n(SYS) Wrapper files loaded for C++ : ", lib_path, "\n") return ctypes.CDLL(lib_path) @@ -895,7 +901,7 @@ def save_optimization(optimal_params, algorithm="cdrec", dataset="", optimizer=" "seed": 42, # Default seed "device": "cpu" # Default device } - elif algorithm == "knn": + elif algorithm == "knn" or algorithm == "knn_impute": params_to_save = { "k": int(optimal_params[0]), "weights": str(optimal_params[1]) @@ -988,9 +994,9 @@ def save_optimization(optimal_params, algorithm="cdrec", dataset="", optimizer=" try: with open(file_name, 'w') as file: toml.dump(params_to_save, file) - print(f"\n\t\t(SYS) Optimization parameters successfully saved to {file_name}") + print(f"\n(SYS) Optimization parameters successfully saved to {file_name}") except Exception as e: - print(f"\n\t\t(SYS) An error occurred while saving the file: {e}") + print(f"\n(SYS) An error occurred while saving the file: {e}") def list_of_algorithms(): @@ -1010,7 +1016,7 @@ def list_of_algorithms(): "XGBOOST", "MICE", "MissForest", - "KNN", + "KNNImpute", "Interpolation", "MinImpute", "MeanImpute", @@ -1031,9 +1037,9 @@ def list_of_algorithms(): def list_of_patterns(): return sorted([ - "missing_completely_at_random", - "missing_percentage", - "percentage_shift", + "mcar", + "aligned", + "scattered", "disjoint", "overlap", "gaussian", diff --git a/build/lib/imputegap/wrapper/AlgoCollection/Makefile b/build/lib/imputegap/wrapper/AlgoCollection/Makefile index 10938eea..3dfccf58 100644 --- a/build/lib/imputegap/wrapper/AlgoCollection/Makefile +++ b/build/lib/imputegap/wrapper/AlgoCollection/Makefile @@ -13,6 +13,7 @@ all: libAlgoCollection.so AlgoCollection.dll +# ================================================================================================================================== libAlgoCollection.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o libAlgoCollection.so -Wall -Werror -Wextra -pedantic \ @@ -23,6 +24,7 @@ libAlgoCollection.so: Algebra/CentroidDecomposition.cpp Algebra/RSVD.cpp Stats/Correlation.cpp shared/SharedLibFunctions.cpp \ -lopenblas -larpack -lmlpack +# ================================================================================================================================== libSTMVL.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_stmvl.so -Wall -Werror -Wextra -pedantic \ @@ -30,18 +32,44 @@ libSTMVL.so: Algorithms/ST_MVL.cpp shared/SharedSTMVL.cpp \ -lopenblas -larpack -lmlpack +libSTMVL.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_stmvl.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/ST_MVL.cpp shared/SharedSTMVL.cpp \ + -larmadillo -lopenblas -larpack +# ================================================================================================================================== + libIterativeSVD.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_iterative_svd.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/RSVD.cpp Algorithms/IterativeSVD.cpp shared/SharedLibIterativeSVD.cpp \ -lopenblas -larpack +libIterativeSVD.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_iterative_svd.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/RSVD.cpp Algorithms/IterativeSVD.cpp shared/SharedLibIterativeSVD.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libGROUSE.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_grouse.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/GROUSE.cpp shared/SharedLibGROUSE.cpp \ -lopenblas -larpack +libGROUSE.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_grouse.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/GROUSE.cpp shared/SharedLibGROUSE.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libDynaMMo.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_dynammo.so -Wall -Werror -Wextra -pedantic \ @@ -49,48 +77,122 @@ libDynaMMo.so: Algebra/Auxiliary.cpp Algorithms/DynaMMo.cpp shared/SharedLibDynaMMo.cpp \ -lopenblas -larpack +libDynaMMo.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_dynammo.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/Auxiliary.cpp Algorithms/DynaMMo.cpp shared/SharedLibDynaMMo.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libNMF.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_rosl.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -I/usr/include/ensmallen -std=gnu++14 \ Algorithms/NMFMissingValueRecovery.cpp shared/SharedLibNMF.cpp \ -lopenblas -larpack +libNMF.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_nmf.dylib \ + -I/opt/homebrew/include -I/opt/homebrew/include/ensmallen \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/NMFMissingValueRecovery.cpp shared/SharedLibNMF.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libROSL.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_rosl.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/Auxiliary.cpp Algorithms/ROSL.cpp shared/SharedLibROSL.cpp \ -lopenblas -larpack +libROSL.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_rosl.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/Auxiliary.cpp Algorithms/ROSL.cpp shared/SharedLibROSL.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSoftImpute.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_soft_impute.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/RSVD.cpp Algorithms/SoftImpute.cpp shared/SharedLibSoftImpute.cpp \ -lopenblas -larpack +libSoftImpute.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_soft_impute.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/RSVD.cpp Algorithms/SoftImpute.cpp shared/SharedLibSoftImpute.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSPIRIT.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_spirit.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/SPIRIT.cpp shared/SharedLibSPIRIT.cpp \ -lopenblas -larpack +libSPIRIT.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_spirit.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/SPIRIT.cpp shared/SharedLibSPIRIT.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSVT.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_svt.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/SVT.cpp shared/SharedLibSVT.cpp \ -lopenblas -larpack +libSVT.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_svt.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/SVT.cpp shared/SharedLibSVT.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libTKCM.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_tkcm.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/TKCM.cpp shared/SharedLibTKCM.cpp \ -lopenblas -larpack +libTKCM.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_tkcm.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/TKCM.cpp shared/SharedLibTKCM.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== libCDREC.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_cdrec.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Stats/Correlation.cpp Algorithms/CDMissingValueRecovery.cpp Algebra/Auxiliary.cpp \ Algebra/CentroidDecomposition.cpp shared/SharedLibCDREC.cpp \ -lopenblas -larpack -lmlpack + +libCDREC.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_cdrec.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib \ + -L/opt/homebrew/opt/openblas/lib \ + Stats/Correlation.cpp Algorithms/CDMissingValueRecovery.cpp Algebra/Auxiliary.cpp \ + Algebra/CentroidDecomposition.cpp shared/SharedLibCDREC.cpp \ + -larmadillo -lopenblas -larpack +# ================================================================================================================================== + + libAlgoCollection.dll: g++ -O3 -D ARMA_DONT_USE_WRAPPER -shared -o libAlgoCollection.dll -Wall -Werror -Wextra -pedantic -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 -fPIC \ diff --git a/build/lib/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt b/build/lib/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt new file mode 100644 index 00000000..51ad7318 Binary files /dev/null and b/build/lib/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt differ diff --git a/build/lib/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547910.diufpc048844.345185.0 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a/docs/generation/build/doctrees/imputegap.manager.doctree and b/docs/generation/build/doctrees/imputegap.manager.doctree differ diff --git a/docs/generation/build/doctrees/tutorials.doctree b/docs/generation/build/doctrees/tutorials.doctree index dc2b083b..b174a4b8 100644 Binary files a/docs/generation/build/doctrees/tutorials.doctree and b/docs/generation/build/doctrees/tutorials.doctree differ diff --git a/docs/generation/build/html/.buildinfo b/docs/generation/build/html/.buildinfo index 307ef32b..391dab99 100644 --- a/docs/generation/build/html/.buildinfo +++ b/docs/generation/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file records the configuration used when building these files. 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@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation
diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.cdrec.html b/docs/generation/build/html/autosummary/imputegap.algorithms.cdrec.html index 0b98d003..09e108e6 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.cdrec.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.cdrec.html @@ -6,7 +6,7 @@ - imputegap.algorithms.cdrec - imputegap 1.0.5 documentation + imputegap.algorithms.cdrec - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -318,7 +318,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.html b/docs/generation/build/html/autosummary/imputegap.algorithms.html index f0012ef6..2a0efffa 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.html @@ -6,7 +6,7 @@ - imputegap.algorithms - imputegap 1.0.5 documentation + imputegap.algorithms - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -305,7 +305,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.iim.html b/docs/generation/build/html/autosummary/imputegap.algorithms.iim.html index 3d56c0ac..53b3d3f0 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.iim.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.iim.html @@ -6,7 +6,7 @@ - imputegap.algorithms.iim - imputegap 1.0.5 documentation + imputegap.algorithms.iim - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.mean_impute.html b/docs/generation/build/html/autosummary/imputegap.algorithms.mean_impute.html index 8def0a54..f7ab1533 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.mean_impute.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.mean_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.mean_impute - imputegap 1.0.5 documentation + imputegap.algorithms.mean_impute - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.min_impute.html b/docs/generation/build/html/autosummary/imputegap.algorithms.min_impute.html index f8fc2a0f..75820896 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.min_impute.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.min_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.min_impute - imputegap 1.0.5 documentation + imputegap.algorithms.min_impute - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.mrnn.html b/docs/generation/build/html/autosummary/imputegap.algorithms.mrnn.html index ca20f875..7e122a5a 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.mrnn.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.mrnn.html @@ -6,7 +6,7 @@ - imputegap.algorithms.mrnn - imputegap 1.0.5 documentation + imputegap.algorithms.mrnn - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.stmvl.html b/docs/generation/build/html/autosummary/imputegap.algorithms.stmvl.html index ca622a00..eb03d550 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.stmvl.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.stmvl.html @@ -6,7 +6,7 @@ - imputegap.algorithms.stmvl - imputegap 1.0.5 documentation + imputegap.algorithms.stmvl - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -318,7 +318,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.algorithms.zero_impute.html b/docs/generation/build/html/autosummary/imputegap.algorithms.zero_impute.html index 588e3f58..427c0ee7 100644 --- a/docs/generation/build/html/autosummary/imputegap.algorithms.zero_impute.html +++ b/docs/generation/build/html/autosummary/imputegap.algorithms.zero_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.zero_impute - imputegap 1.0.5 documentation + imputegap.algorithms.zero_impute - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.benchmark.html b/docs/generation/build/html/autosummary/imputegap.recovery.benchmark.html index cf147572..f2042f23 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.benchmark.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.benchmark.html @@ -6,7 +6,7 @@ - imputegap.recovery.benchmark - imputegap 1.0.5 documentation + imputegap.recovery.benchmark - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.benchmarking.html b/docs/generation/build/html/autosummary/imputegap.recovery.benchmarking.html index 5126d6ae..9e76c227 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.benchmarking.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.benchmarking.html @@ -6,7 +6,7 @@ - imputegap.recovery.benchmark - imputegap 1.0.5 documentation + imputegap.recovery.benchmark - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -312,7 +312,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.evaluation.html b/docs/generation/build/html/autosummary/imputegap.recovery.evaluation.html index 7ee1f731..8dbe3bc6 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.evaluation.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.evaluation.html @@ -6,7 +6,7 @@ - imputegap.recovery.evaluation - imputegap 1.0.5 documentation + imputegap.recovery.evaluation - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.explainer.html b/docs/generation/build/html/autosummary/imputegap.recovery.explainer.html index 7ed9d685..a818fdff 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.explainer.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.explainer.html @@ -6,7 +6,7 @@ - imputegap.recovery.explainer - imputegap 1.0.5 documentation + imputegap.recovery.explainer - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.imputation.html b/docs/generation/build/html/autosummary/imputegap.recovery.imputation.html index 310e1f0a..73111386 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.imputation.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.imputation.html @@ -6,7 +6,7 @@ - imputegap.recovery.imputation - imputegap 1.0.5 documentation + imputegap.recovery.imputation - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -318,7 +318,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.manager.html b/docs/generation/build/html/autosummary/imputegap.recovery.manager.html index b6b52ec8..a5f6a607 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.manager.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.manager.html @@ -6,7 +6,7 @@ - imputegap.recovery.manager - imputegap 1.0.5 documentation + imputegap.recovery.manager - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -315,7 +315,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.recovery.optimization.html b/docs/generation/build/html/autosummary/imputegap.recovery.optimization.html index 9c7981dc..8fff3134 100644 --- a/docs/generation/build/html/autosummary/imputegap.recovery.optimization.html +++ b/docs/generation/build/html/autosummary/imputegap.recovery.optimization.html @@ -6,7 +6,7 @@ - imputegap.recovery.optimization - imputegap 1.0.5 documentation + imputegap.recovery.optimization - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -318,7 +318,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.tools.algorithm_parameters.html b/docs/generation/build/html/autosummary/imputegap.tools.algorithm_parameters.html index 4ee6e212..a4b89182 100644 --- a/docs/generation/build/html/autosummary/imputegap.tools.algorithm_parameters.html +++ b/docs/generation/build/html/autosummary/imputegap.tools.algorithm_parameters.html @@ -6,7 +6,7 @@ - imputegap.tools.algorithm_parameters - imputegap 1.0.5 documentation + imputegap.tools.algorithm_parameters - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -305,7 +305,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.tools.html b/docs/generation/build/html/autosummary/imputegap.tools.html index e9c9f81b..c9a187cb 100644 --- a/docs/generation/build/html/autosummary/imputegap.tools.html +++ b/docs/generation/build/html/autosummary/imputegap.tools.html @@ -6,7 +6,7 @@ - imputegap.tools - imputegap 1.0.5 documentation + imputegap.tools - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -305,7 +305,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.tools.utils.html b/docs/generation/build/html/autosummary/imputegap.tools.utils.html index 07373f8a..f359cd8c 100644 --- a/docs/generation/build/html/autosummary/imputegap.tools.utils.html +++ b/docs/generation/build/html/autosummary/imputegap.tools.utils.html @@ -6,7 +6,7 @@ - imputegap.tools.utils - imputegap 1.0.5 documentation + imputegap.tools.utils - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -336,7 +336,7 @@
- + diff --git a/docs/generation/build/html/autosummary/imputegap.wrapper.html b/docs/generation/build/html/autosummary/imputegap.wrapper.html index 9db9aca1..a55c8a22 100644 --- a/docs/generation/build/html/autosummary/imputegap.wrapper.html +++ b/docs/generation/build/html/autosummary/imputegap.wrapper.html @@ -6,7 +6,7 @@ - imputegap.wrapper - imputegap 1.0.5 documentation + imputegap.wrapper - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -305,7 +305,7 @@
- + diff --git a/docs/generation/build/html/benchmark.html b/docs/generation/build/html/benchmark.html index 7c0ea959..a83b47fc 100644 --- a/docs/generation/build/html/benchmark.html +++ b/docs/generation/build/html/benchmark.html @@ -6,7 +6,7 @@ - Benchmark - imputegap 1.0.5 documentation + Benchmark - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -272,26 +272,25 @@

Benchmark

-

ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]. Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates.

+

ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]. Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. The default metrics evaluated include “RMSE”, “MAE”, “MI”, “Pearson”, and the runtime.

The benchmarking module can be utilized as follows:

from imputegap.recovery.benchmark import Benchmark
 
-save_dir = "./analysis"
-nbr_run = 2
+save_dir = "./imputegap_assets/benchmark"
+nbr_runs = 1
 
-datasets = ["eeg-alcohol", "eeg-reading"]
+datasets = ["eeg-alcohol"]
 
-optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}}
-optimizers = [optimizer]
+optimizers = ["default_params"]
 
-algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"]
+algorithms = ["SoftImpute", "KNNImpute"]
 
 patterns = ["mcar"]
 
 range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8]
 
 # launch the evaluation
-list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run)
+list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_runs)
diff --git a/docs/generation/build/html/contributors.html b/docs/generation/build/html/contributors.html index 6ef9a6d3..baa3df48 100644 --- a/docs/generation/build/html/contributors.html +++ b/docs/generation/build/html/contributors.html @@ -6,7 +6,7 @@ - Contributors - imputegap 1.0.5 documentation + Contributors - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -337,7 +337,7 @@

Core Contributors +

diff --git a/docs/generation/build/html/datasets.html b/docs/generation/build/html/datasets.html index 7b56eda7..e5bc2238 100644 --- a/docs/generation/build/html/datasets.html +++ b/docs/generation/build/html/datasets.html @@ -6,7 +6,7 @@ - Datasets - imputegap 1.0.5 documentation + Datasets - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -350,7 +350,7 @@

Datasets

- + diff --git a/docs/generation/build/html/downstream.html b/docs/generation/build/html/downstream.html index 91d29316..d2748667 100644 --- a/docs/generation/build/html/downstream.html +++ b/docs/generation/build/html/downstream.html @@ -6,7 +6,7 @@ - Downstream Evaluation - imputegap 1.0.5 documentation + Downstream Evaluation - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -293,7 +293,7 @@

Downstream Evaluationimputer.impute() # compute and print the downstream results -downstream_config = {"task": "forecast", "model": "hw-add"} +downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)

@@ -355,7 +355,7 @@

Downstream Evaluation - + diff --git a/docs/generation/build/html/explainer.html b/docs/generation/build/html/explainer.html index b5791fd5..28bf88b0 100644 --- a/docs/generation/build/html/explainer.html +++ b/docs/generation/build/html/explainer.html @@ -6,7 +6,7 @@ - Explainer - imputegap 1.0.5 documentation + Explainer - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -195,7 +195,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -343,7 +343,7 @@

Explainer +

diff --git a/docs/generation/build/html/genindex.html b/docs/generation/build/html/genindex.html index 524fef29..b7fdebbe 100644 --- a/docs/generation/build/html/genindex.html +++ b/docs/generation/build/html/genindex.html @@ -4,7 +4,7 @@ - Index - imputegap 1.0.5 documentation + Index - imputegap 1.0.7 documentation @@ -166,7 +166,7 @@
-
imputegap 1.0.5 documentation
 +
imputegap 1.0.7 documentation
@@ -193,7 +193,7 @@
- imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -328,7 +328,7 @@

_

  • (imputegap.recovery.imputation.Imputation.Statistics.Interpolation method)
    • -
  • (imputegap.recovery.imputation.Imputation.Statistics.KNN method) +
  • (imputegap.recovery.imputation.Imputation.Statistics.KNNImpute method)
  • (imputegap.recovery.imputation.Imputation.Statistics.MeanImpute method)
    • @@ -404,7 +404,7 @@

    A

  • (imputegap.recovery.imputation.Imputation.Statistics.Interpolation attribute)
    • -
  • (imputegap.recovery.imputation.Imputation.Statistics.KNN attribute) +
  • (imputegap.recovery.imputation.Imputation.Statistics.KNNImpute attribute)
  • (imputegap.recovery.imputation.Imputation.Statistics.MeanImpute attribute)
    • @@ -611,7 +611,7 @@

    I

  • Imputation.Statistics.Interpolation (class in imputegap.recovery.imputation)
    • -
  • Imputation.Statistics.KNN (class in imputegap.recovery.imputation) +
  • Imputation.Statistics.KNNImpute (class in imputegap.recovery.imputation)
  • Imputation.Statistics.MeanImpute (class in imputegap.recovery.imputation)
    • @@ -678,7 +678,7 @@

    I

  • (imputegap.recovery.imputation.Imputation.Statistics.Interpolation method)
    • -
  • (imputegap.recovery.imputation.Imputation.Statistics.KNN method) +
  • (imputegap.recovery.imputation.Imputation.Statistics.KNNImpute method)
  • (imputegap.recovery.imputation.Imputation.Statistics.MeanImpute method)
    • @@ -911,7 +911,7 @@

    L

  • (imputegap.recovery.imputation.Imputation.Statistics.Interpolation attribute)
    • -
  • (imputegap.recovery.imputation.Imputation.Statistics.KNN attribute) +
  • (imputegap.recovery.imputation.Imputation.Statistics.KNNImpute attribute)
  • (imputegap.recovery.imputation.Imputation.Statistics.MeanImpute attribute)
    • @@ -935,6 +935,10 @@

    M

  • mean_impute() (in module imputegap.algorithms.mean_impute)
  • min_impute() (in module imputegap.algorithms.min_impute) +
    • +
  • missing_completely_at_random() (imputegap.recovery.manager.TimeSeries.Contamination method) +
    • +
  • missing_percentage() (imputegap.recovery.manager.TimeSeries.Contamination method)
  • module @@ -981,6 +985,8 @@

    M

    • @@ -1126,7 +1132,7 @@

    S

  • (imputegap.recovery.imputation.Imputation.Statistics.Interpolation method)
    • -
  • (imputegap.recovery.imputation.Imputation.Statistics.KNN method) +
  • (imputegap.recovery.imputation.Imputation.Statistics.KNNImpute method)
  • (imputegap.recovery.imputation.Imputation.Statistics.MeanImpute method)
    • @@ -1140,6 +1146,8 @@

    S

    - + diff --git a/docs/generation/build/html/getting_started.html b/docs/generation/build/html/getting_started.html index c6812c3b..9f700600 100644 --- a/docs/generation/build/html/getting_started.html +++ b/docs/generation/build/html/getting_started.html @@ -6,7 +6,7 @@ - Getting Started - imputegap 1.0.5 documentation + Getting Started - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -367,7 +367,7 @@

    Troubleshooting +

    diff --git a/docs/generation/build/html/imputegap.algorithm_parameters.html b/docs/generation/build/html/imputegap.algorithm_parameters.html index 85340c89..d00fd340 100644 --- a/docs/generation/build/html/imputegap.algorithm_parameters.html +++ b/docs/generation/build/html/imputegap.algorithm_parameters.html @@ -6,7 +6,7 @@ - imputegap.tools.algorithm_parameters package - imputegap 1.0.5 documentation + imputegap.tools.algorithm_parameters package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -346,7 +346,7 @@

    Submodule Documentation

    - + diff --git a/docs/generation/build/html/imputegap.algorithms.html b/docs/generation/build/html/imputegap.algorithms.html index 8cb820f2..b6bbda12 100644 --- a/docs/generation/build/html/imputegap.algorithms.html +++ b/docs/generation/build/html/imputegap.algorithms.html @@ -6,7 +6,7 @@ - Submodules - imputegap 1.0.5 documentation + Submodules - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -314,7 +314,7 @@

    Submodule Documentation

    - + diff --git a/docs/generation/build/html/imputegap.benchmark.html b/docs/generation/build/html/imputegap.benchmark.html index 115e706d..4dcd3f63 100644 --- a/docs/generation/build/html/imputegap.benchmark.html +++ b/docs/generation/build/html/imputegap.benchmark.html @@ -6,7 +6,7 @@ - imputegap.recovery.benchmark package - imputegap 1.0.5 documentation + imputegap.recovery.benchmark package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -353,7 +353,7 @@

    Returns
    -eval(algorithms=['cdrec'], datasets=['eeg-alcohol'], patterns=['mcar'], x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=['user_def'], save_dir='./reports', runs=1)[source]
    +eval(algorithms=['cdrec'], datasets=['eeg-alcohol'], patterns=['mcar'], x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=['default_params'], save_dir='./reports', runs=1)[source]

    Execute a comprehensive evaluation of imputation algorithms over multiple datasets and patterns.

    Parameters

    @@ -417,7 +417,7 @@

    Returns
    -generate_plots(runs_plots_scores, ticks, subplot=False, y_size=4, save_dir='./reports')[source]
    +generate_plots(runs_plots_scores, ticks, subplot=False, y_size=4, save_dir='./reports', display=False)[source]

    Generate and save plots for each metric and pattern based on provided scores.

    Parameters

    @@ -430,6 +430,8 @@

    Parameters¶<

    save_dirstr, optional

    Directory to save generated plots (default is “./reports”).

    +
    displaybool, optional

    Display or not the plots (default is False).

    +

    @@ -570,7 +572,7 @@

    Returns
    -eval(algorithms=['cdrec'], datasets=['eeg-alcohol'], patterns=['mcar'], x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=['user_def'], save_dir='./reports', runs=1)[source]
    +eval(algorithms=['cdrec'], datasets=['eeg-alcohol'], patterns=['mcar'], x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=['default_params'], save_dir='./reports', runs=1)[source]

    Execute a comprehensive evaluation of imputation algorithms over multiple datasets and patterns.

    Parameters

    @@ -634,7 +636,7 @@

    Returns
    -generate_plots(runs_plots_scores, ticks, subplot=False, y_size=4, save_dir='./reports')[source]
    +generate_plots(runs_plots_scores, ticks, subplot=False, y_size=4, save_dir='./reports', display=False)[source]

    Generate and save plots for each metric and pattern based on provided scores.

    Parameters

    @@ -647,6 +649,8 @@

    Parameters

    save_dirstr, optional

    Directory to save generated plots (default is “./reports”).

    +
    displaybool, optional

    Display or not the plots (default is False).

    +

    @@ -792,7 +796,7 @@

    Notes<

    - + diff --git a/docs/generation/build/html/imputegap.cdrec.html b/docs/generation/build/html/imputegap.cdrec.html index be82e9bf..51bcf400 100644 --- a/docs/generation/build/html/imputegap.cdrec.html +++ b/docs/generation/build/html/imputegap.cdrec.html @@ -6,7 +6,7 @@ - imputegap.algorithms.cdrec package - imputegap 1.0.5 documentation + imputegap.algorithms.cdrec package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -421,7 +421,7 @@

    References +

    diff --git a/docs/generation/build/html/imputegap.evaluation.html b/docs/generation/build/html/imputegap.evaluation.html index 582a256a..d31f5e82 100644 --- a/docs/generation/build/html/imputegap.evaluation.html +++ b/docs/generation/build/html/imputegap.evaluation.html @@ -6,7 +6,7 @@ - imputegap.recovery.evaluation package - imputegap 1.0.5 documentation + imputegap.recovery.evaluation package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -574,7 +574,7 @@

    Returns

    - + diff --git a/docs/generation/build/html/imputegap.explainer.html b/docs/generation/build/html/imputegap.explainer.html index b7f28292..85ab8275 100644 --- a/docs/generation/build/html/imputegap.explainer.html +++ b/docs/generation/build/html/imputegap.explainer.html @@ -6,7 +6,7 @@ - imputegap.recovery.explainer package - imputegap 1.0.5 documentation + imputegap.recovery.explainer package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -690,7 +690,7 @@

    Notes

    - + diff --git a/docs/generation/build/html/imputegap.html b/docs/generation/build/html/imputegap.html index 8cf090b3..3590e383 100644 --- a/docs/generation/build/html/imputegap.html +++ b/docs/generation/build/html/imputegap.html @@ -6,7 +6,7 @@ - ImputeGAP Package - imputegap 1.0.5 documentation + ImputeGAP Package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -289,6 +289,7 @@

    SubpackagesTimeSeries.print_results() +
  • select_backend()
    • @@ -545,7 +546,7 @@

    Submodules +

    diff --git a/docs/generation/build/html/imputegap.iim.html b/docs/generation/build/html/imputegap.iim.html index 685f82df..3f6a8141 100644 --- a/docs/generation/build/html/imputegap.iim.html +++ b/docs/generation/build/html/imputegap.iim.html @@ -6,7 +6,7 @@ - imputegap.algorithms.iim package - imputegap 1.0.5 documentation + imputegap.algorithms.iim package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -397,7 +397,7 @@

    References +

    diff --git a/docs/generation/build/html/imputegap.imputation.html b/docs/generation/build/html/imputegap.imputation.html index 70dcd155..107742ec 100644 --- a/docs/generation/build/html/imputegap.imputation.html +++ b/docs/generation/build/html/imputegap.imputation.html @@ -6,7 +6,7 @@ - imputegap.recovery.imputation package - imputegap 1.0.5 documentation + imputegap.recovery.imputation package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -362,7 +362,7 @@

    Returns

    Example

    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -497,7 +497,7 @@

    Example 
    >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)
 >>> brits_imputer.impute()  # default parameters for imputation > or
 >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64})  # user-defined > or
->>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> brits_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = brits_imputer.recov_data
    @@ -536,7 +536,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -624,7 +624,7 @@
    Example
    >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)
 >>> bay_otide_imputer.impute()  # default parameters for imputation > or
 >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5})  # user defined> or
->>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = bay_otide_imputer.recov_data
    @@ -664,7 +664,7 @@

    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -751,7 +751,7 @@
    Example
    >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)
 >>> bit_graph_imputer.impute()  # default parameters for imputation > or
 >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42})  # user defined> or
->>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = bit_graph_imputer.recov_data
    @@ -791,7 +791,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -867,7 +867,7 @@
    Example
    >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)
 >>> deep_mvi_imputer.impute()  # default parameters for imputation > or
 >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2})  # user-defined > or
->>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = deep_mvi_imputer.recov_data
    @@ -907,7 +907,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -982,7 +982,7 @@
    Example
    >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)
 >>> gain_imputer.impute()  # default parameters for imputation > or
 >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100})  # user defined> or
->>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> gain_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = gain_imputer.recov_data
    @@ -1023,7 +1023,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1107,7 +1107,7 @@
    Example
    >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)
 >>> grin_imputer.impute()  # default parameters for imputation > or
 >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2})  # user defined> or
->>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> grin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = grin_imputer.recov_data
    @@ -1147,7 +1147,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1226,7 +1226,7 @@
    Example
    >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)
 >>> hkmf_t_imputer.impute()  # default parameters for imputation > or
 >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5})  # user defined> or
->>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = hkmf_t_imputer.recov_data
    @@ -1266,7 +1266,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1279,7 +1279,8 @@
    Example class MPIN(incomp_data)[source]

    Bases: BaseImputer

    -

    MPIN class to impute missing values using Multi-attribute Sensor Data Streams via Message Propagation algorithm.

    +

    MPIN class to impute missing values using Multi-attribute Sensor Data Streams via Message Propagation algorithm. +Need torch-cluster to work.

    Methods
    @@ -1307,7 +1308,8 @@
    Parameters
    impute(user_def=True, params=None)[source]
    -

    Perform imputation using the MPIN algorithm.

    +

    Perform imputation using the MPIN algorithm. +Need torch-cluster to work.

    Parameters
    @@ -1350,7 +1352,7 @@
    Example
    >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)
 >>> mpin_imputer.impute()  # default parameters for imputation > or
 >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"})  # user-defined > or
->>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> mpin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = mpin_imputer.recov_data
    @@ -1390,7 +1392,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1478,7 +1480,7 @@
    Example
    >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)
 >>> mrnn_imputer.impute()  # default parameters for imputation > or
 >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7})  # user-defined > or
->>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+>>> mrnn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
 >>> recov_data = mrnn_imputer.recov_data
    @@ -1519,7 +1521,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1607,7 +1609,7 @@
    Example
    >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)
 >>> miss_net_imputer.impute()  # default parameters for imputation > or
 >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False})  # user-defined > or
->>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+>>> miss_net_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
 >>> recov_data = miss_net_imputer.recov_data
    @@ -1647,7 +1649,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1724,7 +1726,7 @@
    Example
    >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)
 >>> pristi_imputer.impute()  # default parameters for imputation > or
 >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"})  # user-defined > or
->>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> pristi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = pristi_imputer.recov_data
    @@ -1764,7 +1766,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1859,7 +1861,7 @@
    Example
    >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data)
 >>> iim_imputer.impute()  # default parameters for imputation > or
 >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10})  # user-defined  > or
->>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+>>> iim_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
 >>> recov_data = iim_imputer.recov_data
    @@ -1899,7 +1901,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -1975,7 +1977,7 @@
    Example
    >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data)
 >>> mice_imputer.impute()  # default parameters for imputation > or
 >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42})  # user defined > or
->>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> mice_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = mice_imputer.recov_data
    @@ -2017,7 +2019,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2098,7 +2100,7 @@
    Example
    >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)
 >>> mf_imputer.impute()  # default parameters for imputation > or
 >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42})  # user defined > or
->>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> mf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = mf_imputer.recov_data
    @@ -2139,7 +2141,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2209,7 +2211,7 @@
    Example
    >>> mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data)
 >>> mxgboost_imputer.impute()  # default parameters for imputation > or
 >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42})  # user defined > or
->>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = mxgboost_imputer.recov_data
    @@ -2250,7 +2252,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2478,7 +2480,7 @@
    Example
    >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)
 >>> cdrec_imputer.impute()  # default parameters for imputation > or
 >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100})  # user-defined > or
->>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+>>> cdrec_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
 >>> recov_data = cdrec_imputer.recov_data
    @@ -2517,7 +2519,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2588,7 +2590,7 @@
    Example
    >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)
 >>> grouse_imputer.impute()  # default parameters for imputation > or
 >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined  > or
->>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> grouse_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = grouse_imputer.recov_data
    @@ -2629,7 +2631,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2704,7 +2706,7 @@
    Example
    >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)
 >>> i_svd_imputer.impute()  # default parameters for imputation > or
 >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined  > or
->>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> i_svd_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = i_svd_imputer.recov_data
    @@ -2743,7 +2745,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2817,7 +2819,7 @@
    Example
    >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)
 >>> rosl_imputer.impute()  # default parameters for imputation > or
 >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined  > or
->>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> rosl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = rosl_imputer.recov_data
    @@ -2858,7 +2860,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -2933,7 +2935,7 @@
    Example
    >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)
 >>> spirit_imputer.impute()  # default parameters for imputation > or
 >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined  > or
->>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> spirit_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = spirit_imputer.recov_data
    @@ -2974,7 +2976,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3043,7 +3045,7 @@
    Example
    >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)
 >>> svt_imputer.impute()  # default parameters for imputation > or
 >>> svt_imputer.impute(params={'tau': 1}) # user-defined  > or
->>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> svt_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = svt_imputer.recov_data
    @@ -3084,7 +3086,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3155,7 +3157,7 @@
    Example
    >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)
 >>> soft_impute_imputer.impute()  # default parameters for imputation > or
 >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined  > or
->>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = soft_impute_imputer.recov_data
    @@ -3196,7 +3198,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3281,7 +3283,7 @@
    Example
    >>> trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data)
 >>> trmf_imputer.impute()
 >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100})
->>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})
+>>> trmf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})
 >>> recov_data = trmf_imputer.recov_data
    @@ -3320,7 +3322,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3412,7 +3414,7 @@
    Example
    >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)
 >>> dynammo_imputer.impute()  # default parameters for imputation > or
 >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined  > or
->>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> dynammo_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = dynammo_imputer.recov_data
    @@ -3453,7 +3455,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3533,7 +3535,7 @@
    Example
    >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)
 >>> stmvl_imputer.impute()  # default parameters for imputation > or
 >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7})  # user-defined  > or
->>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+>>> stmvl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
 >>> recov_data = stmvl_imputer.recov_data
    @@ -3573,7 +3575,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3641,7 +3643,7 @@
    Example
    >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)
 >>> tkcm_imputer.impute()  # default parameters for imputation > or
 >>> tkcm_imputer.impute(params={'rank': 5})  # user-defined > or
->>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> tkcm_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = tkcm_imputer.recov_data
    @@ -3682,7 +3684,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3710,7 +3712,7 @@

    Subclasses

    Interpolation :

    Imputation method that replaces missing values with the Interpolation

    -
    KNN :

    Imputation method that replaces missing values with KNN logic

    +
    KNNImpute :

    Imputation method that replaces missing values with KNNImpute logic

    @@ -3767,7 +3769,7 @@
    Example
    >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)
 >>> interpolation_imputer.impute()  # default parameters for imputation > or
 >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2})  # user-defined > or
->>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> interpolation_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = interpolation_imputer.recov_data
    @@ -3802,7 +3804,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3812,10 +3814,10 @@
    Example
    -
    -class KNN(incomp_data)[source]
    +
    +class KNNImpute(incomp_data)[source]

    Bases: BaseImputer

    -

    KNN class to impute missing values with K-Nearest Neighbor algorithm

    +

    KNNImpute class to impute missing values with K-Nearest Neighbor algorithm

    Methods
    @@ -3823,8 +3825,8 @@
    Methods
    -
    -__init__(incomp_data)
    +
    +__init__(incomp_data)

    Initialize the BaseImputer with an infected time series matrix.

    Parameters
    @@ -3836,20 +3838,20 @@
    Parameters
    -
    -algorithm = 'knn'
    +
    +algorithm = 'knn_impute'
    -
    -impute(user_def=True, params=None)[source]
    +
    +impute(user_def=True, params=None)[source]

    Impute missing values by replacing them with the K-Nearest Neighbor value

    Parameters
    user_defbool, optional

    Whether to use user-defined or default parameters (default is True).

    -
    paramsdict, optional

    Parameters of the KNN algorithm, if None, default ones are loaded.

    +
    paramsdict, optional

    Parameters of the KNNImpute algorithm, if None, default ones are loaded.

    Algorithm parameters: k : int, optional

    @@ -3865,16 +3867,16 @@
    Parameters
    Returns
    -
    selfKNN

    The object with recov_data set.

    +
    selfKNNImpute

    The object with recov_data set.

    Example
    -
    >>> knn_imputer = Imputation.Statistics.KNN(incomp_data)
+
    >>> knn_imputer = Imputation.Statistics.KNNImpute(incomp_data)
 >>> knn_imputer.impute()  # default parameters for imputation > or
 >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"})  # user-defined > or
->>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+>>> knn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
 >>> recov_data = knn_imputer.recov_data
 
    
 
    
@@ -3882,13 +3884,13 @@ 
    Example
    -
    -logs = True
    +
    +logs = True
    -
    -score(input_data, recov_data=None, downstream=None)
    +
    +score(input_data, recov_data=None, downstream=None)

    Compute evaluation metrics for the imputed time series.

    Parameters
    @@ -3909,7 +3911,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -3996,7 +3998,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -4075,7 +4077,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -4162,7 +4164,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -4249,7 +4251,7 @@
    Returns
    Example
    >>> imputer.score(ts.data, imputer.recov_data) # upstream
->>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+>>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
    @@ -4589,12 +4591,12 @@

    ReturnsImputation.Statistics.Interpolation.score() -
  • Imputation.Statistics.KNN

    • -
    -overlap(rate_series=0.2, limit=1, shift=0.05, offset=0.1)[source]
    -

    Apply overlap contamination to the time series data.

    +
    +missing_completely_at_random(rate_dataset=0.2, rate_series=0.2, block_size=10, offset=0.1, seed=True, explainer=False)
    +

    Apply Missing Completely at Random (MCAR) contamination to the time series data.

    Parameters
    input_datanumpy.ndarray

    The time series dataset to contaminate.

    -
    rate_seriesfloat, optional

    Percentage of missing values per series (default is 0.2).

    +
    rate_datasetfloat, optional

    Percentage of series to contaminate (default is 0.2).

    -
    limitfloat, optional

    Percentage expressing the limit index of the end of the contamination (default is 1: all length).

    +
    rate_seriesfloat, optional

    Percentage of missing values per series (default is 0.2).

    -
    shiftfloat, optional

    Percentage of shift inside each the last disjoint contamination.

    +
    block_sizeint, optional

    Size of the block of missing data (default is 10).

    offsetfloat, optional

    Size of the uncontaminated section at the beginning of the series (default is 0.1).

    +
    seedbool, optional

    Whether to use a seed for reproducibility (default is True).

    +
    +
    explainerbool, optional

    Whether to apply MCAR to specific series for explanation purposes (default is False).

    +
    @@ -598,7 +602,7 @@
    Returns
    Example
    -
    >>> ts_m = ts.Contamination.overlap(ts.data, rate_series=0.1, limit=1, shift=0.05, offset=0.1)
+
    >>> ts_m = ts.Contamination.mcar(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
    @@ -611,9 +615,9 @@
    References
    -
    -scattered(rate_dataset=0.2, rate_series=0.2, offset=0.1, seed=True)[source]
    -

    Apply percentage shift contamination with random starting position to the time series data.

    +
    +missing_percentage(rate_dataset=0.2, rate_series=0.2, offset=0.1)
    +

    Apply aligned missing blocks contamination to the time series data.

    Parameters
    @@ -625,8 +629,6 @@
    Parameters
    offsetfloat, optional

    Size of the uncontaminated section at the beginning of the series (default is 0.1).

    -
    seedbool, optional

    Whether to use a seed for reproducibility (default is True).

    -
    @@ -638,7 +640,7 @@
    Returns
    Example
    -
    >>> ts_m = ts.Contamination.scattered(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1)
+
    >>> ts_m = ts.Contamination.aligned(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1)
    @@ -650,6 +652,124 @@
    References
    +
    +
    +mp(rate_dataset=0.2, rate_series=0.2, offset=0.1)
    +

    Apply aligned missing blocks contamination to the time series data.

    +
    +
    Parameters
    +
    +
    input_datanumpy.ndarray

    The time series dataset to contaminate.

    +
    +
    rate_datasetfloat, optional

    Percentage of series to contaminate (default is 0.2).

    +
    +
    rate_seriesfloat, optional

    Percentage of missing values per series (default is 0.2).

    +
    +
    offsetfloat, optional

    Size of the uncontaminated section at the beginning of the series (default is 0.1).

    +
    +
    +
    +
    +
    Returns
    +
    +
    numpy.ndarray

    The contaminated time series data.

    +
    +
    +
    +
    +
    Example
    +
    >>> ts_m = ts.Contamination.aligned(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1)
+
    +
    +
    +
    +
    References
    +
    +
    +
    +
    + +
    +
    +overlap(rate_series=0.2, limit=1, shift=0.05, offset=0.1)[source]
    +

    Apply overlap contamination to the time series data.

    +
    +
    Parameters
    +
    +
    input_datanumpy.ndarray

    The time series dataset to contaminate.

    +
    +
    rate_seriesfloat, optional

    Percentage of missing values per series (default is 0.2).

    +
    +
    limitfloat, optional

    Percentage expressing the limit index of the end of the contamination (default is 1: all length).

    +
    +
    shiftfloat, optional

    Percentage of shift inside each the last disjoint contamination.

    +
    +
    offsetfloat, optional

    Size of the uncontaminated section at the beginning of the series (default is 0.1).

    +
    +
    +
    +
    +
    Returns
    +
    +
    numpy.ndarray

    The contaminated time series data.

    +
    +
    +
    +
    +
    Example
    +
    >>> ts_m = ts.Contamination.overlap(ts.data, rate_series=0.1, limit=1, shift=0.05, offset=0.1)
+
    +
    +
    +
    +
    References
    +
    +
    +
    +
    + +
    +
    +scattered(rate_dataset=0.2, rate_series=0.2, offset=0.1, seed=True)[source]
    +

    Apply percentage shift contamination with random starting position to the time series data.

    +
    +
    Parameters
    +
    +
    input_datanumpy.ndarray

    The time series dataset to contaminate.

    +
    +
    rate_datasetfloat, optional

    Percentage of series to contaminate (default is 0.2).

    +
    +
    rate_seriesfloat, optional

    Percentage of missing values per series (default is 0.2).

    +
    +
    offsetfloat, optional

    Size of the uncontaminated section at the beginning of the series (default is 0.1).

    +
    +
    seedbool, optional

    Whether to use a seed for reproducibility (default is True).

    +
    +
    +
    +
    +
    Returns
    +
    +
    numpy.ndarray

    The contaminated time series data.

    +
    +
    +
    +
    +
    Example
    +
    >>> ts_m = ts.Contamination.scattered(ts.data, rate_dataset=0.2, rate_series=0.4, offset=0.1)
+
    +
    +
    +
    +
    References
    +
    +
    +
    +
    + @@ -659,15 +779,15 @@
    References

    Imports a matrix of time series data.

    The data can be provided as a list or a NumPy array. The format is (Series, Values), where series are separated by space, and values are separated by newline characters.

    -
    -

    Parameters

    +
    +

    Parameters

    datalist or numpy.ndarray, optional

    The matrix of time series data to import.

    -
    -

    Returns

    +
    +

    Returns

    TimeSeries

    The TimeSeries object with the imported data.

    @@ -681,8 +801,8 @@

    Returns

    Loads time series data from a file or predefined dataset.

    The data is loaded as a matrix of shape (Values, Series). You can limit the number of series or values per series for computational efficiency.

    -
    -

    Parameters

    +
    +

    Parameters

    datastr

    The file path or name of a predefined dataset (e.g., ‘bafu.txt’).

    @@ -696,15 +816,15 @@

    Parameters

    -
    -

    Returns

    +
    +

    Returns

    TimeSeries

    The TimeSeries object with the loaded data.

    -
    -

    Example

    +
    +

    Example

    >>> ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=50, nbr_val=100)
    @@ -717,22 +837,22 @@

    Example

    Normalize the time series dataset.

    Supported normalization techniques are “z_score” and “min_max”. The method also logs the execution time for the normalization process.

    -
    -

    Parameters

    +
    +

    Parameters

    normalizerstr, optional

    The normalization technique to use. Options are “z_score” or “min_max”. Default is “z_score”.

    -
    -

    Returns

    +
    +

    Returns

    numpy.ndarray

    The normalized time series data.

    -
    -

    Example

    +
    +

    Example

    >>> ts.normalize(normalizer="z_score")
    @@ -741,10 +861,10 @@

    Example
    -plot(input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None, subplot=False, size=(16, 8), save_path='./imputegap_assets', display=True)[source]
    +plot(input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None, subplot=False, size=(16, 8), algorithm=None, save_path='./imputegap_assets', display=True)[source]

    Plot the time series data, including raw, contaminated, or imputed data.

    -
    -

    Parameters

    +
    +

    Parameters

    input_datanumpy.ndarray

    The original time series data without contamination.

    @@ -762,21 +882,23 @@

    Parameters

    sizetuple, optional

    Size of the plot in inches. Default is (16, 8).

    +
    algorithmstr, optional

    Name of the algorithm used for imputation.

    +
    save_pathstr, optional

    Path to save the plot locally.

    displaybool, optional

    Whether to display the plot. Default is True.

    -
    -

    Returns

    +
    +

    Returns

    str or None

    The file path of the saved plot, if applicable.

    -
    -

    Example

    +
    +

    Example

    >>> ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") # plain data
 >>> ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets") # contamination
 >>> ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") # imputation
@@ -789,8 +911,8 @@ 
    Example
 print(nbr_val=10, nbr_series=7, view_by_series=False)[source]
 
    Prints a limited number of time series from the dataset.
    
-
    
-
    Parameters
    
+
    
+
    Parameters
    
 
    nbr_val : int, optional
 The number of timestamps to print. Default is 15. Use -1 for no restriction.
 nbr_series : int, optional
@@ -798,18 +920,18 @@ 
    Parameters¶
 view_by_series : bool, optional
 Whether to view by series (True) or by values (False).
    
 
    
-
    
-
    Returns
    
+
    
+
    Returns
    
 
    None
    
 
    
 
    
 
 
    
 
    
-print_results(metrics, algorithm='', text='Imputation Results of')[source]
    
+print_results(metrics, algorithm='', text='Results of the analysis')[source]
 
    Prints the results of the imputation process.
    
-
    
-
    Parameters
    
+
    
+
    Parameters
    
 
    
 
    metricsdict
    A dictionary containing the imputation metrics to display.
    
 
    
@@ -819,12 +941,12 @@ 
    Parameters
    -
    -

    Returns

    +
    +

    Returns

    None

    -
    -

    Example

    +
    +

    Example

    >>> ts.print_results(imputer.metrics, imputer.algorithm)
    @@ -834,6 +956,11 @@

    Example +
    +
    +imputegap.recovery.manager.select_backend()[source]
    +
    +

    @@ -903,6 +1030,9 @@

    ExampleTimeSeries.Contamination.distribution()
  • TimeSeries.Contamination.gaussian()
  • TimeSeries.Contamination.mcar()
    • +
  • TimeSeries.Contamination.missing_completely_at_random()
    • +
  • TimeSeries.Contamination.missing_percentage()
    • +
  • TimeSeries.Contamination.mp()
  • TimeSeries.Contamination.overlap()
  • TimeSeries.Contamination.scattered()
    • @@ -915,6 +1045,7 @@

    ExampleTimeSeries.print_results() +
  • select_backend()
    • @@ -928,7 +1059,7 @@

    Example - + diff --git a/docs/generation/build/html/imputegap.mean_impute.html b/docs/generation/build/html/imputegap.mean_impute.html index 8a463c78..d3046dff 100644 --- a/docs/generation/build/html/imputegap.mean_impute.html +++ b/docs/generation/build/html/imputegap.mean_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.mean_impute package - imputegap 1.0.5 documentation + imputegap.algorithms.mean_impute package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -390,7 +390,7 @@

    Example

    - + diff --git a/docs/generation/build/html/imputegap.min_impute.html b/docs/generation/build/html/imputegap.min_impute.html index 4ee78a5f..9261e7a2 100644 --- a/docs/generation/build/html/imputegap.min_impute.html +++ b/docs/generation/build/html/imputegap.min_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.min_impute package - imputegap 1.0.5 documentation + imputegap.algorithms.min_impute package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -390,7 +390,7 @@

    Example

    - + diff --git a/docs/generation/build/html/imputegap.mrnn.html b/docs/generation/build/html/imputegap.mrnn.html index dbda5f6d..f64bcd13 100644 --- a/docs/generation/build/html/imputegap.mrnn.html +++ b/docs/generation/build/html/imputegap.mrnn.html @@ -6,7 +6,7 @@ - imputegap.algorithms.mrnn package - imputegap 1.0.5 documentation + imputegap.algorithms.mrnn package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -401,7 +401,7 @@

    References +

    diff --git a/docs/generation/build/html/imputegap.optimization.html b/docs/generation/build/html/imputegap.optimization.html index d92c576e..c4943b54 100644 --- a/docs/generation/build/html/imputegap.optimization.html +++ b/docs/generation/build/html/imputegap.optimization.html @@ -6,7 +6,7 @@ - imputegap.recovery.optimization package - imputegap 1.0.5 documentation + imputegap.recovery.optimization package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -641,7 +641,7 @@

    Returns

    - + diff --git a/docs/generation/build/html/imputegap.stmvl.html b/docs/generation/build/html/imputegap.stmvl.html index e8b2b998..26b66c42 100644 --- a/docs/generation/build/html/imputegap.stmvl.html +++ b/docs/generation/build/html/imputegap.stmvl.html @@ -6,7 +6,7 @@ - imputegap.algorithms.stmvl package - imputegap 1.0.5 documentation + imputegap.algorithms.stmvl package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -418,7 +418,7 @@

    References +

    diff --git a/docs/generation/build/html/imputegap.tools.html b/docs/generation/build/html/imputegap.tools.html index a0a18d83..5f73a072 100644 --- a/docs/generation/build/html/imputegap.tools.html +++ b/docs/generation/build/html/imputegap.tools.html @@ -6,7 +6,7 @@ - imputegap.tools package - imputegap 1.0.5 documentation + imputegap.tools package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -336,7 +336,7 @@

    Submodule Documentation

    - + diff --git a/docs/generation/build/html/imputegap.utils.html b/docs/generation/build/html/imputegap.utils.html index b9d3bdb3..cfed2d10 100644 --- a/docs/generation/build/html/imputegap.utils.html +++ b/docs/generation/build/html/imputegap.utils.html @@ -6,7 +6,7 @@ - imputegap.tools.utils package - imputegap 1.0.5 documentation + imputegap.tools.utils package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -643,7 +643,7 @@

    Notes

    - + diff --git a/docs/generation/build/html/imputegap.wrapper.html b/docs/generation/build/html/imputegap.wrapper.html index b7d7b734..2787830f 100644 --- a/docs/generation/build/html/imputegap.wrapper.html +++ b/docs/generation/build/html/imputegap.wrapper.html @@ -6,7 +6,7 @@ - imputegap.wrapper package - imputegap 1.0.5 documentation + imputegap.wrapper package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -338,7 +338,7 @@

    Submodules +

    diff --git a/docs/generation/build/html/imputegap.zero_impute.html b/docs/generation/build/html/imputegap.zero_impute.html index c8e4cd10..cc7ec139 100644 --- a/docs/generation/build/html/imputegap.zero_impute.html +++ b/docs/generation/build/html/imputegap.zero_impute.html @@ -6,7 +6,7 @@ - imputegap.algorithms.zero_impute package - imputegap 1.0.5 documentation + imputegap.algorithms.zero_impute package - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -394,7 +394,7 @@

    Example

    - + diff --git a/docs/generation/build/html/index.html b/docs/generation/build/html/index.html index 4117b866..2d8def95 100644 --- a/docs/generation/build/html/index.html +++ b/docs/generation/build/html/index.html @@ -6,7 +6,7 @@ - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -432,7 +432,7 @@

    - + diff --git a/docs/generation/build/html/modules.html b/docs/generation/build/html/modules.html index d1383199..2971ef25 100644 --- a/docs/generation/build/html/modules.html +++ b/docs/generation/build/html/modules.html @@ -6,7 +6,7 @@ - imputegap - imputegap 1.0.5 documentation + imputegap - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -386,7 +386,7 @@

    imputegap +

    diff --git a/docs/generation/build/html/modules/imputegap/algorithms/cdrec.html b/docs/generation/build/html/modules/imputegap/algorithms/cdrec.html index 7598ce59..00679a17 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/cdrec.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/cdrec.html @@ -5,7 +5,7 @@ - imputegap.algorithms.cdrec - imputegap 1.0.5 documentation + imputegap.algorithms.cdrec - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -297,7 +297,7 @@

    Source code for imputegap.algorithms.cdrec

         Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7
     """
 
-    shared_lib = utils.load_share_lib("lib_cdrec.so")
+    shared_lib = utils.load_share_lib("lib_cdrec")
 
     __py_n = len(__py_matrix);
     __py_m = len(__py_matrix[0]);
@@ -358,7 +358,7 @@ 
    Source code for imputegap.algorithms.cdrec
     
     """
 
-    print(f"\t\t\t\t(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, "
+    print(f"(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, "
           f"epsilon {epsilon}, and iterations {iterations}...")
 
     start_time = time.time()  # Record start time
@@ -369,7 +369,7 @@ 
    Source code for imputegap.algorithms.cdrec
         end_time = time.time()
 
     if logs:
-        print(f"\n\t\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n")
+        print(f"\n\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n")
 
     return recov_data
    
 
@@ -405,7 +405,7 @@ 
    Source code for imputegap.algorithms.cdrec
           
     
   
    
-
    
+
    diff --git a/docs/generation/build/html/modules/imputegap/algorithms/iim.html b/docs/generation/build/html/modules/imputegap/algorithms/iim.html index 16494ef6..a2f4af83 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/iim.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/iim.html @@ -5,7 +5,7 @@ - imputegap.algorithms.iim - imputegap 1.0.5 documentation + imputegap.algorithms.iim - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -313,7 +313,7 @@

    Source code for imputegap.algorithms.iim

     
     end_time = time.time()
     if logs:
-        print(f"\n\t\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n")
+        print(f"\n\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n")
 
     return recov_data
    @@ -349,7 +349,7 @@

    Source code for imputegap.algorithms.iim
    -
    + diff --git a/docs/generation/build/html/modules/imputegap/algorithms/mean_impute.html b/docs/generation/build/html/modules/imputegap/algorithms/mean_impute.html index 31858fc8..bde45be0 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/mean_impute.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/mean_impute.html @@ -5,7 +5,7 @@ - imputegap.algorithms.mean_impute - imputegap 1.0.5 documentation + imputegap.algorithms.mean_impute - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -340,7 +340,7 @@

    Source code for imputegap.algorithms.mean_impute

    -
    + diff --git a/docs/generation/build/html/modules/imputegap/algorithms/min_impute.html b/docs/generation/build/html/modules/imputegap/algorithms/min_impute.html index 404cf45b..655a0e96 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/min_impute.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/min_impute.html @@ -5,7 +5,7 @@ - imputegap.algorithms.min_impute - imputegap 1.0.5 documentation + imputegap.algorithms.min_impute - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -340,7 +340,7 @@

    Source code for imputegap.algorithms.min_impute

    <
    -
    + diff --git a/docs/generation/build/html/modules/imputegap/algorithms/mrnn.html b/docs/generation/build/html/modules/imputegap/algorithms/mrnn.html index d64b105d..a4ecc248 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/mrnn.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/mrnn.html @@ -5,7 +5,7 @@ - imputegap.algorithms.mrnn - imputegap 1.0.5 documentation + imputegap.algorithms.mrnn - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -316,7 +316,7 @@

    Source code for imputegap.algorithms.mrnn

     
     end_time = time.time()
     if logs:
-        print(f"\n\t\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n")
+        print(f"\n\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n")
 
     return recov_data
    @@ -352,7 +352,7 @@

    Source code for imputegap.algorithms.mrnn
    -
    + diff --git a/docs/generation/build/html/modules/imputegap/algorithms/stmvl.html b/docs/generation/build/html/modules/imputegap/algorithms/stmvl.html index 5c3c6cb6..fefbf6d6 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/stmvl.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/stmvl.html @@ -5,7 +5,7 @@ - imputegap.algorithms.stmvl - imputegap 1.0.5 documentation + imputegap.algorithms.stmvl - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -307,7 +307,7 @@

    Source code for imputegap.algorithms.stmvl

         School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
     """
 
-    shared_lib = utils.load_share_lib("lib_stmvl.so")
+    shared_lib = utils.load_share_lib("lib_stmvl")
 
     __py_sizen = len(__py_matrix);
     __py_sizem = len(__py_matrix[0]);
@@ -355,6 +355,9 @@ 
    Source code for imputegap.algorithms.stmvl
         :return: recov_data, metrics : all time series with imputation data and their metrics
 
     """
+    print(f"(PYTHON) ST-MVL: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for window_size {window_size}, "
+          f"gamma {gamma}, and alpha {alpha}...")
+
     start_time = time.time()  # Record start time
 
     # Call the C++ function to perform recovery
@@ -362,7 +365,7 @@ 
    Source code for imputegap.algorithms.stmvl
     
     end_time = time.time()
     if logs:
-        print(f"\n\t\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n")
+        print(f"\n\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n")
 
     return recov_data
    
 
@@ -398,7 +401,7 @@ 
    Source code for imputegap.algorithms.stmvl
           
     
   
    
-
    
+
    diff --git a/docs/generation/build/html/modules/imputegap/algorithms/zero_impute.html b/docs/generation/build/html/modules/imputegap/algorithms/zero_impute.html index 17b77ffc..34a2f05f 100644 --- a/docs/generation/build/html/modules/imputegap/algorithms/zero_impute.html +++ b/docs/generation/build/html/modules/imputegap/algorithms/zero_impute.html @@ -5,7 +5,7 @@ - imputegap.algorithms.zero_impute - imputegap 1.0.5 documentation + imputegap.algorithms.zero_impute - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -335,7 +335,7 @@

    Source code for imputegap.algorithms.zero_impute

    -
    + diff --git a/docs/generation/build/html/modules/imputegap/recovery/benchmark.html b/docs/generation/build/html/modules/imputegap/recovery/benchmark.html index 4d8b06df..c60736e2 100644 --- a/docs/generation/build/html/modules/imputegap/recovery/benchmark.html +++ b/docs/generation/build/html/modules/imputegap/recovery/benchmark.html @@ -5,7 +5,7 @@ - imputegap.recovery.benchmark - imputegap 1.0.5 documentation + imputegap.recovery.benchmark - imputegap 1.0.7 documentation @@ -167,7 +167,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -194,7 +194,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -270,7 +270,6 @@

    Source code for imputegap.recovery.benchmark

    import time
 import numpy as np
 import matplotlib.pyplot as plt
-
 import xlsxwriter
 
 from imputegap.tools import utils
@@ -460,9 +459,9 @@ 
    Source code for imputegap.recovery.benchmark
    for j, algo in enumerate(algorithms_list):
                 comprehensive_matrix[i, j] = average_rmse_matrix[dataset].get(algo, np.nan)
 
-        print("\nVisualization of datasets:", datasets_list)
-        print("Visualization of algorithms:", algorithms_list)
-        print("Visualization of matrix:\n", comprehensive_matrix, "\n\n")
+        print("\nvisualization of datasets:", *datasets_list)
+        print("visualization of algorithms:", *algorithms_list)
+        print(f"visualization of aggregate matrix :\n {comprehensive_matrix}\n\n")
 
         return comprehensive_matrix, algorithms_list, datasets_list
    
 
@@ -491,6 +490,7 @@ 
    Source code for imputegap.recovery.benchmark
            Bool
             True if the matrix has been generated
         """
+        save_dir = save_dir + "/_heatmap/"
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)
 
@@ -502,6 +502,7 @@ 
    Source code for imputegap.recovery.benchmark
    y_size = cell_size*nbr_datasets
 
         fig, ax = plt.subplots(figsize=(x_size, y_size))
+        fig.canvas.manager.set_window_title("benchmark heatmap")
         cmap = plt.cm.Greys
         norm = plt.Normalize(vmin=0, vmax=2)  # Normalizing values between 0 and 2 (RMSE)
 
@@ -586,21 +587,28 @@ 
    Source code for imputegap.recovery.benchmark
    "MI": "Mutual Information - Indicates dependency between variables.",
                 "CORRELATION": "Correlation Coefficient - Indicates linear relationship between variables."
             }
+            first_metric = True
 
             for metric, description in metrics.items():
                 # Write the metric description
                 file.write(f"{metric}: {description}\n\n")
 
-                column_widths = [15, 15, 15, 15, 12, 25]
+                column_widths = [15, 15, 15, 18, 12, 25]
 
                 # Create a table header
-                headers = ["Dataset", "Algorithm", "Optimizer", "Pattern", "X Value", metric]
+                headers = ["Dataset", "Pattern", "Algorithm", "Optimizer", "Rate", metric]
                 header_row = "|".join(f" {header:^{width}} " for header, width in zip(headers, column_widths))
                 separator_row = "+" + "+".join(f"{'-' * (width + 2)}" for width in column_widths) + "+"
                 file.write(f"{separator_row}\n")
                 file.write(f"|{header_row}|\n")
                 file.write(f"{separator_row}\n")
 
+                if first_metric and run ==-1 :
+                    print(f"\n{metric}: {description}\n")
+                    print(separator_row)
+                    print(f"|{header_row}|")
+                    print(separator_row)
+
                 # Extract and write results for the current metric
                 for dataset, algo_items in runs_plots_scores.items():
                     for algorithm, optimizer_items in algo_items.items():
@@ -614,12 +622,17 @@ 
    Source code for imputegap.recovery.benchmark
    row = "|".join(
                                             f" {value:^{width}} " for value, width in zip(row_values, column_widths))
                                         file.write(f"|{row}|\n")
+                                        if first_metric and run ==-1 :
+                                            print(f"|{row}|")
                 file.write(f"{separator_row}\n\n")
+                if first_metric and run ==-1 :
+                    print(separator_row + "\n")
+                    first_metric = False
 
             file.write("Dictionary of Results:\n")
             file.write(str(runs_plots_scores) + "\n")
 
-        print(f"\nReport recorded in {save_path}")
    
+        print(f"\nreports recorded in the following directory : {save_path}")
    
 
 
 
    
@@ -720,14 +733,13 @@ 
    Source code for imputegap.recovery.benchmark
    row += 1
 
         # Close the workbook
-        workbook.close()
+        workbook.close()
    
 
-        print(f"\nExcel report recorded in {save_path}")
    
 
 
 
    
 [docs]
-    def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports"):
+    def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports", display=False):
         """
         Generate and save plots for each metric and pattern based on provided scores.
 
@@ -741,6 +753,8 @@ 
    Source code for imputegap.recovery.benchmark
                If True, generates a single figure with subplots for all metrics (default is False).
         save_dir : str, optional
             Directory to save generated plots (default is "./reports").
+        display : bool, optional
+            Display or not the plots (default is False).
 
         Returns
         -------
@@ -760,6 +774,8 @@ 
    Source code for imputegap.recovery.benchmark
    if subplot:
                     fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(x_size*1.90, y_size*2.90))  # Adjusted figsize
+                    fig.canvas.manager.set_window_title("benchmark analysis")
+
                     axes = axes.ravel()  # Flatten the 2D array of axes to a 1D array
 
                 # Iterate over each metric, generating separate plots, including new timing metrics
@@ -807,8 +823,8 @@ 
    Source code for imputegap.recovery.benchmark
    # Save plot only if there is data to display
                     if has_data:
                         ylabel_metric = {
-                            "imputation_time": "Imputation Time (sec)",
-                            "log_imputation": "Imputation Time (log)",
+                            "imputation_time": "Runtime Linear Scale (sec)",
+                            "log_imputation": "Runtime Log Scale",
                         }.get(metric, metric)
 
                         ax.set_title(metric)
@@ -819,8 +835,10 @@ 
    Source code for imputegap.recovery.benchmark
    # Set y-axis limits with padding below 0 for visibility
                         if metric == "imputation_time":
                             ax.set_ylim(-10, 90)
+                            ax.set_title("Runtime Linear Scale")
                         elif metric == "log_imputation":
                             ax.set_ylim(-4.5, 2.5)
+                            ax.set_title("Runtime Log Scale")
                         elif metric == "MAE":
                             ax.set_ylim(-0.1, 2.4)
                         elif metric == "MI":
@@ -829,12 +847,13 @@ 
    Source code for imputegap.recovery.benchmark
    ax.set_ylim(-0.1, 2.6)
                         elif metric == "CORRELATION":
                             ax.set_ylim(-0.75, 1.1)
+                            ax.set_title("Pearson Correlation")
 
                         # Customize x-axis ticks
                         ax.set_xticks(ticks)
                         ax.set_xticklabels([f"{int(tick * 100)}%" for tick in ticks])
                         ax.grid(True, zorder=0)
-                        ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
+                        ax.legend(loc='upper left', fontsize=7, frameon=True, fancybox=True, framealpha=0.8)
 
                     if not subplot:
                         filename = f"{dataset}_{pattern}_{optimizer}_{metric}.jpg"
@@ -847,15 +866,17 @@ 
    Source code for imputegap.recovery.benchmark
    filename = f"{dataset}_{pattern}_metrics_subplot.jpg"
                     filepath = os.path.join(save_dir, filename)
                     plt.savefig(filepath)
-                    plt.close()
 
-        print("\nAll plots recorded in", save_dir)
    
+                    if display:
+                        plt.show()
+
+        print("\nplots recorded in the following directory : ", save_dir)
    
 
 
 
    
 [docs]
     def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"],
-             x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["user_def"], save_dir="./reports", runs=1):
+             x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["default_params"], save_dir="./reports", runs=1):
         """
         Execute a comprehensive evaluation of imputation algorithms over multiple datasets and patterns.
 
@@ -891,6 +912,9 @@ 
    Source code for imputegap.recovery.benchmark
    not_optimized = ["none"]
         mean_group = ["mean", "MeanImpute", "min", "MinImpute", "zero", "ZeroImpute", "MeanImputeBySeries"]
 
+        if "mpin" in algorithms or "MPIN" in algorithms:
+            raise ValueError("The 'mpin' algorithm is not compatible with this setup.")
+
         for i_run in range(0, abs(runs)):
             for dataset in datasets:
                 runs_plots_scores = {}
@@ -1002,34 +1026,35 @@ 
    Source code for imputegap.recovery.benchmark
    "times": dic_timing
                                 }
 
-                save_dir_runs = save_dir + "/run_" + str(i_run) + "/" + dataset
-                print("\n\truns saved in : ", save_dir_runs)
+                save_dir_runs = save_dir + "/_details/run_" + str(i_run) + "/" + dataset
+                print("\nruns saved in : ", save_dir_runs)
                 self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_runs)
                 self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_runs)
                 self.generate_reports_txt(runs_plots_scores, save_dir_runs, dataset, i_run)
                 self.generate_reports_excel(runs_plots_scores, save_dir_runs, dataset, i_run)
                 run_storage.append(runs_plots_scores)
 
-                print("============================================================================\n\n\n\n\n\n")
+                print("\n\n\n\n\n\n\n\n\n\n\n\n=end_of_the_evaluation==============================================="
+                      "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nresults of the analysis:\n")
 
         scores_list, algos, sets = self.avg_results(*run_storage)
-        _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=False)
+        _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=True)
 
         run_averaged = self.average_runs_by_names(run_storage)
 
-        save_dir_agg = save_dir + "/aggregation"
-        print("\n\n\taggragation of results saved in : ", save_dir_agg)
+        print("\n\nthe results of the analysis has been saved in : ", save_dir, "\n\n")
 
         for scores in run_averaged:
             all_keys = list(scores.keys())
             dataset_name = str(all_keys[0])
 
-            save_dir_agg_set = save_dir_agg + "/" + dataset_name
+            save_dir_agg_set = save_dir + "/" + dataset_name
 
-            self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set)
-            self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set)
             self.generate_reports_txt(scores, save_dir_agg_set, dataset_name, -1)
+            self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set)
+            # self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set)
             self.generate_reports_excel(scores, save_dir_agg_set, dataset_name, -1)
+            print("\n\n")
 
         return run_averaged, scores_list
    
 
    
@@ -1066,7 +1091,7 @@ 
    Source code for imputegap.recovery.benchmark
       
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/recovery/evaluation.html b/docs/generation/build/html/modules/imputegap/recovery/evaluation.html
index cc0a9b27..37a88929 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/evaluation.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/evaluation.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.recovery.evaluation - imputegap 1.0.5 documentation
+        imputegap.recovery.evaluation - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -265,9 +265,6 @@
         
    
           
    Source code for imputegap.recovery.evaluation
     import numpy as np
-from sklearn.metrics import mutual_info_score
-from scipy.stats import pearsonr
-
 
 
    
 [docs]
@@ -405,6 +402,8 @@ 
    Source code for imputegap.recovery.evaluation
            float
             The mutual information (MI) score for NaN positions in the contamination dataset.
         """
+        from sklearn.metrics import mutual_info_score
+
         nan_locations = np.isnan(self.incomp_data)
 
         # Discretize the continuous data into bins
@@ -433,6 +432,8 @@ 
    Source code for imputegap.recovery.evaluation
            float
             The Pearson correlation coefficient for NaN positions in the contamination dataset.
         """
+        from scipy.stats import pearsonr
+
         nan_locations = np.isnan(self.incomp_data)
         input_data_values = self.input_data[nan_locations]
         imputed_values = self.recov_data[nan_locations]
@@ -483,7 +484,7 @@ 
    Source code for imputegap.recovery.evaluation
    
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/recovery/explainer.html b/docs/generation/build/html/modules/imputegap/recovery/explainer.html
index 6dd2fe8e..d550c30a 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/explainer.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/explainer.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.recovery.explainer - imputegap 1.0.5 documentation
+        imputegap.recovery.explainer - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -279,7 +279,6 @@ 
    Source code for imputegap.recovery.explainer
    from matplotlib import pyplot as plt
 from sklearn.ensemble import RandomForestRegressor
 
-from imputegap.recovery.imputation import Imputation
 from imputegap.recovery.manager import TimeSeries
 from imputegap.tools import utils
 
@@ -651,7 +650,7 @@ 
    Source code for imputegap.recovery.explainer
    result_display = sorted(result_display, key=lambda tup: (tup[1], tup[2]), reverse=True)
 
-        with open(to_save + "_results.txt", 'w') as file_output:
+        with open(to_save + "_values.txt", 'w') as file_output:
             for (x, algo, rate, description, feature, category, mean_features) in result_display:
                 file_output.write(
                     f"Feature : {x:<5} {algo:<10} with a score of {rate:<10} {category:<18} {description:<65} {feature}\n")
@@ -698,8 +697,12 @@ 
    Source code for imputegap.recovery.explainer
    plots_categories = config[extractor]['categories']
 
         path_file = "./imputegap_assets/shap/"
-        if not os.path.exists(path_file):
-            path_file = "./imputegap" + path_file[1:]
+        path_file_details = "./imputegap_assets/shap/grouped/"
+        path_file_categories = "./imputegap_assets/shap/per_categories/"
+
+        os.makedirs(path_file, exist_ok=True)
+        os.makedirs(path_file_details, exist_ok=True)
+        os.makedirs(path_file_categories, exist_ok=True)
 
         x_features, x_categories, x_descriptions = [], [], []
         x_fs, x_cs, x_ds, alphas = [], [], [], []
@@ -739,8 +742,7 @@ 
    Source code for imputegap.recovery.explainer
    print("\t SHAP_MODEL >> descriptions shape:", x_descriptions.shape, "\n")
             print("\t SHAP_MODEL >> features OK:", np.all(np.all(x_features == x_features[0, :], axis=1)))
             print("\t SHAP_MODEL >> categories OK:", np.all(np.all(x_categories == x_categories[0, :], axis=1)))
-            print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)),
-                  "\n\n")
+            print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)), "\n\n")
 
         model = RandomForestRegressor()
         model.fit(x_train, y_train)
@@ -758,7 +760,7 @@ 
    Source code for imputegap.recovery.explainer
    series_names.append("Series " + str(names + np.array(x_train).shape[0]))
 
         shap.summary_plot(shval, x_test, plot_size=(25, 10), feature_names=optimal_display, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_plot.png")
+        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_all.png")
         plt.title("SHAP Details Results")
         os.makedirs(path_file, exist_ok=True)
         plt.savefig(alpha)
@@ -766,14 +768,14 @@ 
    Source code for imputegap.recovery.explainer
    alphas.append(alpha)
 
         shap.summary_plot(np.array(shval).T, np.array(x_test).T, feature_names=series_names, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_reverse_plot.png")
+        alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_reverse.png")
         plt.title("SHAP Features by Series")
         plt.savefig(alpha)
         plt.close()
         alphas.append(alpha)
 
         shap.plots.waterfall(shval_x[0], show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png")
+        alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png")
         plt.title("SHAP Waterfall Results")
         fig = plt.gcf()  # Get the current figure created by SHAP
         fig.set_size_inches(20, 10)  # Ensure the size is correct
@@ -782,7 +784,7 @@ 
    Source code for imputegap.recovery.explainer
    alphas.append(alpha)
 
         shap.plots.beeswarm(shval_x, show=display, plot_size=(22, 10))
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png")
+        alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png")
         plt.title("SHAP Beeswarm Results")
         plt.savefig(alpha)
         plt.close()
@@ -838,7 +840,7 @@ 
    Source code for imputegap.recovery.explainer
    mean_features = np.array(mean_features)
 
         shap.summary_plot(np.array(geometry).T, np.array(geometryT).T, plot_size=(20, 10), feature_names=geometryDesc, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + "_plot.png")
+        alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + ".png")
         plt.title("SHAP details of " + plots_categories[0].lower())
         plt.savefig(alpha)
         plt.close()
@@ -846,7 +848,7 @@ 
    Source code for imputegap.recovery.explainer
    shap.summary_plot(np.array(transformation).T, np.array(transformationT).T, plot_size=(20, 10),
                           feature_names=transformationDesc, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + "_plot.png")
+        alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + ".png")
         plt.title("SHAP details of " + plots_categories[1].lower())
         plt.savefig(alpha)
         plt.close()
@@ -854,7 +856,7 @@ 
    Source code for imputegap.recovery.explainer
    shap.summary_plot(np.array(correlation).T, np.array(correlationT).T, plot_size=(20, 10),
                           feature_names=correlationDesc, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + "_plot.png")
+        alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + ".png")
         plt.title("SHAP details of " + plots_categories[1].lower())
         plt.savefig(alpha)
         plt.close()
@@ -862,7 +864,7 @@ 
    Source code for imputegap.recovery.explainer
    shap.summary_plot(np.array(trend).T, np.array(trendT).T, plot_size=(20, 8), feature_names=trendDesc,
                           show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + "_plot.png")
+        alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + ".png")
         plt.title("SHAP details of " + plots_categories[3].lower())
         plt.savefig(alpha)
         plt.close()
@@ -882,7 +884,7 @@ 
    Source code for imputegap.recovery.explainer
    aggregation_test = np.array(aggregation_test).T
 
         shap.summary_plot(aggregation_features, aggregation_test, feature_names=plots_categories, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_plot.png")
+        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_cat.png")
         plt.title("SHAP Aggregation Results")
         plt.gca().axes.get_xaxis().set_visible(False)
         plt.savefig(alpha)
@@ -890,7 +892,7 @@ 
    Source code for imputegap.recovery.explainer
    alphas.append(alpha)
 
         shap.summary_plot(np.array(aggregation_features).T, np.array(aggregation_test).T, feature_names=series_names, show=display)
-        alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_reverse_plot.png")
+        alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_agg_reverse.png")
         plt.title("SHAP Aggregation Features by Series")
         plt.savefig(alpha)
         plt.close()
@@ -1096,7 +1098,7 @@ 
    Source code for imputegap.recovery.explainer
       
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/recovery/imputation.html b/docs/generation/build/html/modules/imputegap/recovery/imputation.html
index 85eedf97..5e31d9ca 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/imputation.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/imputation.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.recovery.imputation - imputegap 1.0.5 documentation
+        imputegap.recovery.imputation - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -265,45 +265,13 @@
         
    
           
    Source code for imputegap.recovery.imputation
     import re
-
-from imputegap.algorithms.bayotide import bay_otide
-from imputegap.algorithms.bit_graph import bit_graph
-from imputegap.algorithms.brits import brits
-from imputegap.algorithms.deep_mvi import deep_mvi
-from imputegap.algorithms.dynammo import dynammo
-from imputegap.algorithms.gain import gain
-from imputegap.algorithms.grin import grin
-from imputegap.algorithms.grouse import grouse
-from imputegap.algorithms.hkmf_t import hkmf_t
-from imputegap.algorithms.interpolation import interpolation
-from imputegap.algorithms.iterative_svd import iterative_svd
-from imputegap.algorithms.knn import knn
-from imputegap.algorithms.mean_impute import mean_impute
-from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series
-from imputegap.algorithms.mice import mice
-from imputegap.algorithms.miss_forest import miss_forest
-from imputegap.algorithms.miss_net import miss_net
-from imputegap.algorithms.mpin import mpin
-from imputegap.algorithms.pristi import pristi
-from imputegap.algorithms.rosl import rosl
-from imputegap.algorithms.soft_impute import soft_impute
-from imputegap.algorithms.spirit import spirit
-from imputegap.algorithms.svt import svt
-from imputegap.algorithms.tkcm import tkcm
-from imputegap.algorithms.trmf import trmf
-from imputegap.algorithms.xgboost import xgboost
+from imputegap.tools import utils
 from imputegap.recovery.downstream import Downstream
 from imputegap.recovery.evaluation import Evaluation
-from imputegap.algorithms.cdrec import cdrec
-from imputegap.algorithms.iim import iim
-from imputegap.algorithms.min_impute import min_impute
-from imputegap.algorithms.mrnn import mrnn
-from imputegap.algorithms.stmvl import stmvl
-from imputegap.algorithms.zero_impute import zero_impute
-from imputegap.tools import utils
-
-not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt", "tkcm", "deep_mvi", "brits", "mpin", "pristi"]
 
+not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt",
+                 "tkcm", "deep_mvi", "brits", "mpin", "pristi", "bay_otide", "bit_graph", "gain", "grin", "hkmf_t",
+                 "mice", "miss_forest", "miss_net", "trmf", "xgboost"]
 
 
 
    
@@ -388,13 +356,13 @@ 
    Source code for imputegap.recovery.imputation
            Example
         -------
             >>> imputer.score(ts.data, imputer.recov_data) # upstream
-            >>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream
+            >>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream
         """
         if self.recov_data is None:
             self.recov_data = recov_data
 
         if isinstance(downstream, dict) and downstream is not None:
-            self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, downstream).downstream_analysis()
+            self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, self.algorithm, downstream).downstream_analysis()
         else:
             self.metrics = Evaluation(input_data, self.recov_data, self.incomp_data).compute_all_metrics()
    
 
@@ -464,7 +432,7 @@ 
    Source code for imputegap.recovery.imputation
    raise ValueError(
                 f"\n\tThis algorithm '{self.algorithm}' is not optimized for this optimizer. "
                 f"\n\tPlease use `run_tune` to optimize the hyperparameters for:\n\t\t {', '.join(not_optimized)}"
-                "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts_1.data, 'optimizer': 'ray_tune'})"
+                "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts.data, 'optimizer': 'ray_tune'})"
             )
 
         input_data = (
@@ -666,8 +634,8 @@ 
    Source code for imputegap.recovery.imputation
                Imputation method that replaces missing values with the minimum value of the ground truth by series.
         Interpolation :
             Imputation method that replaces missing values with the Interpolation
-        KNN :
-            Imputation method that replaces missing values with KNN logic
+        KNNImpute :
+            Imputation method that replaces missing values with KNNImpute logic
         """
 
 
    
@@ -700,6 +668,8 @@ 
    Source code for imputegap.recovery.imputation
                    self : ZeroImpute
                     The object with `recov_data` set.
                 """
+                from imputegap.algorithms.zero_impute import zero_impute
+
                 self.recov_data = zero_impute(self.incomp_data, params)
 
                 return self
    
@@ -736,6 +706,8 @@ 
    Source code for imputegap.recovery.imputation
                    self : MinImpute
                     The object with `recov_data` set.
                 """
+                from imputegap.algorithms.mean_impute import mean_impute
+
                 self.recov_data = mean_impute(self.incomp_data, params)
 
                 return self
    
@@ -772,6 +744,8 @@ 
    Source code for imputegap.recovery.imputation
                    self : MinImpute
                     The object with `recov_data` set.
                 """
+                from imputegap.algorithms.min_impute import min_impute
+
                 self.recov_data = min_impute(self.incomp_data, params)
 
                 return self
    
@@ -802,6 +776,8 @@ 
    Source code for imputegap.recovery.imputation
                    self : MeanImputeBySeries
                     The object with `recov_data` set.
                 """
+                from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series
+
                 self.recov_data = mean_impute_by_series(self.incomp_data, logs=self.logs)
 
                 return self
    
@@ -844,9 +820,11 @@ 
    Source code for imputegap.recovery.imputation
                        >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data)
                     >>> interpolation_imputer.impute()  # default parameters for imputation > or
                     >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2})  # user-defined > or
-                    >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = interpolation_imputer.recov_data
                 """
+                from imputegap.algorithms.interpolation import interpolation
+
                 if params is not None:
                     method, poly_order = self._check_params(user_def, params)
                 else:
@@ -859,21 +837,21 @@ 
    Source code for imputegap.recovery.imputation
    
-[docs]
-        class KNN(BaseImputer):
+
    
+[docs]
+        class KNNImpute(BaseImputer):
             """
-            KNN class to impute missing values with K-Nearest Neighbor algorithm
+            KNNImpute class to impute missing values with K-Nearest Neighbor algorithm
 
             Methods
             -------
             impute(self, params=None):
                 Perform imputation by replacing missing values with K-Nearest Neighbor
             """
-            algorithm = "knn"
+            algorithm = "knn_impute"
 
-
    
-[docs]
+
    
+[docs]
             def impute(self, user_def=True, params=None):
                 """
                 Impute missing values by replacing them with the K-Nearest Neighbor value
@@ -883,7 +861,7 @@ 
    Source code for imputegap.recovery.imputation
                    user_def : bool, optional
                     Whether to use user-defined or default parameters (default is True).
                 params : dict, optional
-                    Parameters of the KNN algorithm, if None, default ones are loaded.
+                    Parameters of the KNNImpute algorithm, if None, default ones are loaded.
 
                     **Algorithm parameters:**
                     k : int, optional
@@ -893,17 +871,19 @@ 
    Source code for imputegap.recovery.imputation
                    Returns
                 -------
-                self : KNN
+                self : KNNImpute
                     The object with `recov_data` set.
 
                 Example
                 -------
-                    >>> knn_imputer = Imputation.Statistics.KNN(incomp_data)
+                    >>> knn_imputer = Imputation.Statistics.KNNImpute(incomp_data)
                     >>> knn_imputer.impute()  # default parameters for imputation > or
                     >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"})  # user-defined > or
-                    >>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> knn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = knn_imputer.recov_data
                 """
+                from imputegap.algorithms.knn import knn
+
                 if params is not None:
                     k, weights = self._check_params(user_def, params)
                 else:
@@ -1052,13 +1032,14 @@ 
    Source code for imputegap.recovery.imputation
                        >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data)
                     >>> cdrec_imputer.impute()  # default parameters for imputation > or
                     >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100})  # user-defined > or
-                    >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
                     >>> recov_data = cdrec_imputer.recov_data
 
                 References
                 ----------
                 Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7
                 """
+                from imputegap.algorithms.cdrec import cdrec
 
                 if params is not None:
                     rank, epsilon, iterations = self._check_params(user_def, params)
@@ -1116,13 +1097,14 @@ 
    Source code for imputegap.recovery.imputation
                        >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data)
                     >>> i_svd_imputer.impute()  # default parameters for imputation > or
                     >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined  > or
-                    >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = i_svd_imputer.recov_data
 
                 References
                 ----------
                 Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman, Missing value estimation methods for DNA microarrays , Bioinformatics, Volume 17, Issue 6, June 2001, Pages 520–525, https://doi.org/10.1093/bioinformatics/17.6.520
                 """
+                from imputegap.algorithms.iterative_svd import iterative_svd
 
                 if params is not None:
                     rank  = self._check_params(user_def, params)[0]
@@ -1179,13 +1161,14 @@ 
    Source code for imputegap.recovery.imputation
                        >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data)
                     >>> grouse_imputer.impute()  # default parameters for imputation > or
                     >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined  > or
-                    >>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> grouse_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = grouse_imputer.recov_data
 
                 References
                 ----------
                 D. Zhang and L. Balzano. Global convergence of a grassmannian gradient descent algorithm for subspace estimation. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, pages 1460–1468, 2016.
                 """
+                from imputegap.algorithms.grouse import grouse
 
                 if params is not None:
                     max_rank  = self._check_params(user_def, params)[0]
@@ -1245,13 +1228,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data)
                     >>> rosl_imputer.impute()  # default parameters for imputation > or
                     >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined  > or
-                    >>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> rosl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = rosl_imputer.recov_data
 
                 References
                 ----------
                 X. Shu, F. Porikli, and N. Ahuja. Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 3874–3881, 2014.
                 """
+                from imputegap.algorithms.rosl import rosl
+
                 if params is not None:
                     rank, regularization = self._check_params(user_def, params)
                 else:
@@ -1307,13 +1292,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data)
                     >>> soft_impute_imputer.impute()  # default parameters for imputation > or
                     >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined  > or
-                    >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = soft_impute_imputer.recov_data
 
                 References
                 ----------
                 R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11:2287–2322, 2010.
                 """
+                from imputegap.algorithms.soft_impute import soft_impute
+
                 if params is not None:
                     max_rank = self._check_params(user_def, params)[0]
                 else:
@@ -1376,13 +1363,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data)
                     >>> spirit_imputer.impute()  # default parameters for imputation > or
                     >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined  > or
-                    >>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> spirit_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = spirit_imputer.recov_data
 
                 References
                 ----------
                 S. Papadimitriou, J. Sun, and C. Faloutsos. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30 - September 2, 2005, pages 697–708, 2005.
                 """
+                from imputegap.algorithms.spirit import spirit
+
                 if params is not None:
                     k, w, lambda_value = self._check_params(user_def, params)
                 else:
@@ -1439,13 +1428,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data)
                     >>> svt_imputer.impute()  # default parameters for imputation > or
                     >>> svt_imputer.impute(params={'tau': 1}) # user-defined  > or
-                    >>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> svt_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = svt_imputer.recov_data
 
                 References
                 ----------
                 J. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. [8] J. Cambronero, J. K. Feser, M. J. Smith, and
                 """
+                from imputegap.algorithms.svt import svt
+
                 if params is not None:
                     tau = self._check_params(user_def, params)[0]
                 else:
@@ -1518,13 +1509,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data)
                     >>> trmf_imputer.impute()
                     >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100})
-                    >>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})
+                    >>> trmf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})
                     >>> recov_data = trmf_imputer.recov_data
 
                 References
                 ----------
                 H.-F. Yu, N. Rao, and I. S. Dhillon, "Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction," in *Advances in Neural Information Processing Systems*, vol. 29, 2016. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf
                 """
+                from imputegap.algorithms.trmf import trmf
+
                 if params is not None:
                     lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = self._check_params(user_def, params)
                 else:
@@ -1613,7 +1606,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data)
                     >>> mf_imputer.impute()  # default parameters for imputation > or
                     >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42})  # user defined > or
-                    >>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> mf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = mf_imputer.recov_data
 
                 References
@@ -1622,6 +1615,8 @@ 
    Source code for imputegap.recovery.imputation
                    https://github.com/yuenshingyan/MissForest
                 https://pypi.org/project/MissForest/
                 """
+                from imputegap.algorithms.miss_forest import miss_forest
+
                 if params is not None:
                     n_estimators, max_iter, max_features, seed = self._check_params(user_def, params)
                 else:
@@ -1681,7 +1676,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data)
                     >>> mice_imputer.impute()  # default parameters for imputation > or
                     >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42})  # user defined > or
-                    >>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> mice_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = mice_imputer.recov_data
 
                 References
@@ -1691,6 +1686,8 @@ 
    Source code for imputegap.recovery.imputation
                    S. F. Buck, (1960). “A Method of Estimation of Missing Values in Multivariate Data Suitable for use with an Electronic Computer”. Journal of the Royal Statistical Society 22(2): 302-306.
                 https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer
                 """
+                from imputegap.algorithms.mice import mice
+
                 if params is not None:
                     max_iter, tol, initial_strategy, seed = self._check_params(user_def, params)
                 else:
@@ -1745,7 +1742,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data)
                     >>> mxgboost_imputer.impute()  # default parameters for imputation > or
                     >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42})  # user defined > or
-                    >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = mxgboost_imputer.recov_data
 
                 References
@@ -1754,6 +1751,8 @@ 
    Source code for imputegap.recovery.imputation
                    https://dl.acm.org/doi/10.1145/2939672.2939785
                 https://medium.com/@tzhaonj/imputing-missing-data-using-xgboost-802757cace6d
                 """
+                from imputegap.algorithms.xgboost import xgboost
+
                 if params is not None:
                     n_estimators, seed = self._check_params(user_def, params)
                 else:
@@ -1806,7 +1805,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data)
                     >>> iim_imputer.impute()  # default parameters for imputation > or
                     >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10})  # user-defined  > or
-                    >>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> iim_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
                     >>> recov_data = iim_imputer.recov_data
 
                 References
@@ -1814,6 +1813,8 @@ 
    Source code for imputegap.recovery.imputation
                    A. Zhang, S. Song, Y. Sun and J. Wang, "Learning Individual Models for Imputation," 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China, 2019, pp. 160-171, doi: 10.1109/ICDE.2019.00023.
                 keywords: {Data models;Adaptation models;Computational modeling;Predictive models;Numerical models;Aggregates;Regression tree analysis;Missing values;Data imputation}
                 """
+                from imputegap.algorithms.iim import iim
+
                 if params is not None:
                     learning_neighbours, algo_code = self._check_params(user_def, params)
                 else:
@@ -1892,7 +1893,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data)
                     >>> stmvl_imputer.impute()  # default parameters for imputation > or
                     >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7})  # user-defined  > or
-                    >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
                     >>> recov_data = stmvl_imputer.recov_data
 
                 References
@@ -1900,6 +1901,8 @@ 
    Source code for imputegap.recovery.imputation
                    Yi, X., Zheng, Y., Zhang, J., & Li, T. ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data.
                 School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
                 """
+                from imputegap.algorithms.stmvl import stmvl
+
                 if params is not None:
                     window_size, gamma, alpha = self._check_params(user_def, params)
                 else:
@@ -1958,13 +1961,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data)
                     >>> dynammo_imputer.impute()  # default parameters for imputation > or
                     >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined  > or
-                    >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = dynammo_imputer.recov_data
 
                 References
                 ----------
                 L. Li, J. McCann, N. S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pages 507–516, 2009.
                 """
+                from imputegap.algorithms.dynammo import dynammo
+
                 if params is not None:
                     h, max_iteration, approximation = self._check_params(user_def, params)
                 else:
@@ -2019,13 +2024,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data)
                     >>> tkcm_imputer.impute()  # default parameters for imputation > or
                     >>> tkcm_imputer.impute(params={'rank': 5})  # user-defined > or
-                    >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = tkcm_imputer.recov_data
 
                 References
                 ----------
                 K. Wellenzohn, M. H. Böhlen, A. Dignös, J. Gamper, and H. Mitterer. Continuous imputation of missing values in streams of pattern-determining time series. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017., pages 330–341, 2017.
                 """
+                from imputegap.algorithms.tkcm import tkcm
+
                 if params is not None:
                     rank = self._check_params(user_def, params)[0]
                 else:
@@ -2115,13 +2122,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data)
                     >>> mrnn_imputer.impute()  # default parameters for imputation > or
                     >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7})  # user-defined > or
-                    >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
+                    >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}})  # automl with bayesian
                     >>> recov_data = mrnn_imputer.recov_data
 
                 References
                 ----------
                 J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp. 1477-1490, May 2019, doi: 10.1109/TBME.2018.2874712. keywords: {Time measurement;Interpolation;Estimation;Medical diagnostic imaging;Correlation;Recurrent neural networks;Biomedical measurement;Missing data;temporal data streams;imputation;recurrent neural nets}
                 """
+                from imputegap.algorithms.mrnn import mrnn
+
                 if params is not None:
                     hidden_dim, learning_rate, iterations, sequence_length = self._check_params(user_def, params)
                 else:
@@ -2184,13 +2193,15 @@ 
    Source code for imputegap.recovery.imputation
                        >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data)
                     >>> brits_imputer.impute()  # default parameters for imputation > or
                     >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64})  # user-defined > or
-                    >>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> brits_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = brits_imputer.recov_data
 
                 References
                 ----------
                 Cao, W., Wang, D., Li, J., Zhou, H., Li, L. & Li, Y. BRITS: Bidirectional Recurrent Imputation for Time Series. Advances in Neural Information Processing Systems, 31 (2018). https://proceedings.neurips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf
                 """
+                from imputegap.algorithms.brits import brits
+
                 if params is not None:
                     model, epoch, batch_size, nbr_features, hidden_layer = self._check_params(user_def, params)
                 else:
@@ -2248,7 +2259,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data)
                     >>> deep_mvi_imputer.impute()  # default parameters for imputation > or
                     >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2})  # user-defined > or
-                    >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = deep_mvi_imputer.recov_data
 
                 References
@@ -2256,6 +2267,8 @@ 
    Source code for imputegap.recovery.imputation
                    P. Bansal, P. Deshpande, and S. Sarawagi. Missing value imputation on multidimensional time series. arXiv preprint arXiv:2103.01600, 2023
                 https://github.com/pbansal5/DeepMVI
                 """
+                from imputegap.algorithms.deep_mvi import deep_mvi
+
                 if params is not None:
                     max_epoch, patience, lr = self._check_params(user_def, params)
                 else:
@@ -2271,6 +2284,7 @@ 
    Source code for imputegap.recovery.imputation
    class MPIN(BaseImputer):
             """
             MPIN class to impute missing values using Multi-attribute Sensor Data Streams via Message Propagation algorithm.
+            Need torch-cluster to work.
 
             Methods
             -------
@@ -2284,6 +2298,7 @@ 
    Source code for imputegap.recovery.imputation
    def impute(self, user_def=True, params=None):
                 """
                 Perform imputation using the MPIN algorithm.
+                Need torch-cluster to work.
 
                 Parameters
                 ----------
@@ -2324,7 +2339,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data)
                     >>> mpin_imputer.impute()  # default parameters for imputation > or
                     >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"})  # user-defined > or
-                    >>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> mpin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = mpin_imputer.recov_data
 
                 References
@@ -2332,6 +2347,8 @@ 
    Source code for imputegap.recovery.imputation
                    Li, X., Li, H., Lu, H., Jensen, C.S., Pandey, V. & Markl, V. Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version). arXiv (2023). https://arxiv.org/abs/2311.07344
                 https://github.com/XLI-2020/MPIN
                 """
+                from imputegap.algorithms.mpin import mpin
+
                 if params is not None:
                     incre_mode, window, k, learning_rate, weight_decay, epochs, num_of_iteration, threshold, base = self._check_params(user_def, params)
                 else:
@@ -2391,7 +2408,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data)
                     >>> pristi_imputer.impute()  # default parameters for imputation > or
                     >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"})  # user-defined > or
-                    >>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # automl with ray_tune
+                    >>> pristi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # automl with ray_tune
                     >>> recov_data = pristi_imputer.recov_data
 
                 References
@@ -2399,6 +2416,8 @@ 
    Source code for imputegap.recovery.imputation
                    M. Liu, H. Huang, H. Feng, L. Sun, B. Du and Y. Fu, "PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation," 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, USA, 2023, pp. 1927-1939, doi: 10.1109/ICDE55515.2023.00150.
                 https://github.com/LMZZML/PriSTI
                 """
+                from imputegap.algorithms.pristi import pristi
+
                 if params is not None:
                     target_strategy, unconditional, seed, device = self._check_params(user_def, params)
                 else:
@@ -2464,7 +2483,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data)
                     >>> miss_net_imputer.impute()  # default parameters for imputation > or
                     >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False})  # user-defined > or
-                    >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = miss_net_imputer.recov_data
 
                 References
@@ -2472,6 +2491,8 @@ 
    Source code for imputegap.recovery.imputation
                    Kohei Obata, Koki Kawabata, Yasuko Matsubara, and Yasushi Sakurai. 2024. Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24). Association for Computing Machinery, New York, NY, USA, 2296–2306. https://doi.org/10.1145/3637528.3671760
                 https://github.com/KoheiObata/MissNet/tree/main
                 """
+                from imputegap.algorithms.miss_net import miss_net
+
                 if params is not None:
                     alpha, beta, L, n_cl, max_iteration, tol, random_init = self._check_params(user_def, params)
                 else:
@@ -2537,7 +2558,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data)
                     >>> gain_imputer.impute()  # default parameters for imputation > or
                     >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100})  # user defined> or
-                    >>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> gain_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = gain_imputer.recov_data
 
                 References
@@ -2545,6 +2566,8 @@ 
    Source code for imputegap.recovery.imputation
                    J. Yoon, J. Jordon, and M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," CoRR, vol. abs/1806.02920, 2018. Available: http://arxiv.org/abs/1806.02920.
                 """
 
+                from imputegap.algorithms.gain import gain
+
                 if params is not None:
                     batch_size, hint_rate, alpha, epoch = self._check_params(user_def, params)
                 else:
@@ -2622,7 +2645,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data)
                     >>> grin_imputer.impute()  # default parameters for imputation > or
                     >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2})  # user defined> or
-                    >>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> grin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = grin_imputer.recov_data
 
                 References
@@ -2630,6 +2653,7 @@ 
    Source code for imputegap.recovery.imputation
                    A. Cini, I. Marisca, and C. Alippi, "Multivariate Time Series Imputation by Graph Neural Networks," CoRR, vol. abs/2108.00298, 2021
                 https://github.com/Graph-Machine-Learning-Group/grin
                 """
+                from imputegap.algorithms.grin import grin
 
                 if params is not None:
                     d_hidden, lr, batch_size, window, alpha, patience, epochs, workers = self._check_params(user_def, params)
@@ -2713,7 +2737,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data)
                     >>> bay_otide_imputer.impute()  # default parameters for imputation > or
                     >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5})  # user defined> or
-                    >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = bay_otide_imputer.recov_data
 
                 References
@@ -2721,6 +2745,7 @@ 
    Source code for imputegap.recovery.imputation
                    S. Fang, Q. Wen, Y. Luo, S. Zhe, and L. Sun, "BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition," CoRR, vol. abs/2308.14906, 2024. [Online]. Available: https://arxiv.org/abs/2308.14906.
                 https://github.com/xuangu-fang/BayOTIDE
                 """
+                from imputegap.algorithms.bayotide import bay_otide
 
                 if params is not None:
                     K_trend, K_season, n_season, K_bias, time_scale, a0, b0, v = self._check_params(user_def, params)
@@ -2785,7 +2810,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data)
                     >>> hkmf_t_imputer.impute()  # default parameters for imputation > or
                     >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5})  # user defined> or
-                    >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = hkmf_t_imputer.recov_data
 
                 References
@@ -2793,6 +2818,7 @@ 
    Source code for imputegap.recovery.imputation
                    L. Wang, S. Wu, T. Wu, X. Tao and J. Lu, "HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 11, pp. 3582-3593, 1 Nov. 2021, doi: 10.1109/TKDE.2020.2971190. keywords: {Time series analysis;Matrix decomposition;Market research;Meteorology;Sparse matrices;Indexes;Software;Tagged time series;missing value imputation;blackouts;hankel matrix factorization}
                 https://github.com/wangliang-cs/hkmf-t?tab=readme-ov-file
                 """
+                from imputegap.algorithms.hkmf_t import hkmf_t
 
                 if params is not None:
                     tags, data_names, epoch = self._check_params(user_def, params)
@@ -2878,7 +2904,7 @@ 
    Source code for imputegap.recovery.imputation
                        >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data)
                     >>> bit_graph_imputer.impute()  # default parameters for imputation > or
                     >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42})  # user defined> or
-                    >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
+                    >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})  # auto-ml with ray_tune
                     >>> recov_data = bit_graph_imputer.recov_data
 
                 References
@@ -2887,6 +2913,8 @@ 
    Source code for imputegap.recovery.imputation
                    https://github.com/chenxiaodanhit/BiTGraph
                 """
 
+                from imputegap.algorithms.bit_graph import bit_graph
+
                 if params is not None:
                     node_number, kernel_set, dropout, subgraph_size, node_dim, seq_len, lr, epoch, seed = self._check_params(user_def, params)
                 else:
@@ -2939,7 +2967,7 @@ 
    Source code for imputegap.recovery.imputation
    
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/recovery/manager.html b/docs/generation/build/html/modules/imputegap/recovery/manager.html
index ae281a0e..61ad3360 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/manager.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/manager.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.recovery.manager - imputegap 1.0.5 documentation
+        imputegap.recovery.manager - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -266,29 +266,49 @@
           
    Source code for imputegap.recovery.manager
     import datetime
 import os
+import platform
 import time
 import numpy as np
 import matplotlib
-from scipy.stats import zscore
-from sklearn.preprocessing import MinMaxScaler
 import importlib.resources
-from scipy.stats import norm
-
 from imputegap.tools import utils
 
-# Use Agg backend if in a headless or CI environment
-if os.getenv('DISPLAY') is None or os.getenv('CI') is not None:
-    matplotlib.use("Agg")
-    print("Running in a headless environment or CI. Using Agg backend.")
-else:
-    try:
-        matplotlib.use("TkAgg")
-        if importlib.util.find_spec("tkinter") is None:
-            print("tkinter is not available.")
-    except (ImportError, RuntimeError):
+from matplotlib import pyplot as plt  # type: ignore
+
+
+
    
+[docs]
+def select_backend():
+    system = platform.system()
+    headless = os.getenv('DISPLAY') is None or os.getenv('CI') is not None
+
+    if headless:
         matplotlib.use("Agg")
+        return
+
+    if system == "Darwin":  # macOS
+        try:
+            matplotlib.use("MacOSX")
+        except (ImportError, RuntimeError):
+            try:
+                if importlib.util.find_spec("tkinter") is not None:
+                    matplotlib.use("TkAgg")
+                else:
+                    raise ImportError
+            except (ImportError, RuntimeError):
+                matplotlib.use("Agg")
+    else:  # Windows or Linux
+        try:
+            if importlib.util.find_spec("tkinter") is not None:
+                matplotlib.use("TkAgg")
+            else:
+                raise ImportError
+        except (ImportError, RuntimeError):
+            matplotlib.use("Agg")
    
 
-from matplotlib import pyplot as plt  # type: ignore
+
+# Call the backend selector
+select_backend()
 
 
 
    
@@ -465,11 +485,11 @@ 
    Source code for imputegap.recovery.manager
             -------
         None
         """
-        print("\nTime Series set :")
-
         to_print = self.data
         nbr_tot_series, nbr_tot_values = to_print.shape
-        print_col, print_row = "Timestamp", "Series"
+        print_col, print_row = "timestamp", "Series"
+
+        print(f"\nshape of {self.name} : {self.data.shape}\n\tnumber of series = { nbr_tot_series}\n\tnumber of values = {nbr_tot_values}\n")
 
         if nbr_val == -1:
             nbr_val = to_print.shape[1]
@@ -479,7 +499,7 @@ 
    Source code for imputegap.recovery.manager
     
         if not view_by_series:
             to_print = to_print.T
-            print_col, print_row = "Series", "Timestamp"
+            print_col, print_row = "Series", "timestamp"
 
         header_format = "{:<15}"  # Fixed size for headers
         value_format = "{:>15.10f}"  # Fixed size for values
@@ -491,19 +511,16 @@ 
    Source code for imputegap.recovery.manager
     
         # Print each limited series with fixed size
         for i, series in enumerate(to_print):
-            print(header_format.format(f"{print_row} {i + 1}"), end="")
+            print(header_format.format(f"{print_row}_{i + 1}"), end="")
             print("".join([value_format.format(elem) for elem in series]))
 
         if nbr_series < nbr_tot_series:
-            print("...")
-
-        print("\nshape of the time series :", self.data.shape, "\n\tnumber of series =", nbr_tot_series,
-              "\n\tnumber of values =", nbr_tot_values, "\n\n")
    
+            print("...")
    
 
 
 
    
 [docs]
-    def print_results(self, metrics, algorithm="", text="Imputation Results of"):
+    def print_results(self, metrics, algorithm="", text="Results of the analysis"):
         """
         Prints the results of the imputation process.
 
@@ -577,6 +594,8 @@ 
    Source code for imputegap.recovery.manager
     
             end_time = time.time()
         elif normalizer == "z_lib":
+            from scipy.stats import zscore
+
             start_time = time.time()  # Record start time
 
             self.data = zscore(self.data, axis=0)
@@ -584,6 +603,8 @@ 
    Source code for imputegap.recovery.manager
                 end_time = time.time()
 
         elif normalizer == "m_lib":
+            from sklearn.preprocessing import MinMaxScaler
+
             start_time = time.time()  # Record start time
 
             scaler = MinMaxScaler()
@@ -606,13 +627,13 @@ 
    Source code for imputegap.recovery.manager
     
         self.data = self.data.T
 
-        print(f"\n\t\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n")
    
+        print(f"\n\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n")
    
 
 
 
    
 [docs]
     def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None,
-             subplot=False, size=(16, 8), save_path="./imputegap_assets", display=True):
+             subplot=False, size=(16, 8), algorithm=None, save_path="./imputegap_assets", display=True):
         """
         Plot the time series data, including raw, contaminated, or imputed data.
 
@@ -634,6 +655,8 @@ 
    Source code for imputegap.recovery.manager
                 Print one time series by subplot or all in the same plot.
         size : tuple, optional
             Size of the plot in inches. Default is (16, 8).
+        algorithm : str, optional
+            Name of the algorithm used for imputation.
         save_path : str, optional
             Path to save the plot locally.
         display : bool, optional
@@ -651,6 +674,9 @@ 
    Source code for imputegap.recovery.manager
                 >>> ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") # imputation
         """
         number_of_series = 0
+        if algorithm is None:
+            algorithm = "imputegap"
+        algorithm.lower()
 
         if nbr_series is None or nbr_series == -1:
             nbr_series = input_data.shape[0]
@@ -685,6 +711,7 @@ 
    Source code for imputegap.recovery.manager
                     y_size = y_size_screen
 
             fig, axes = plt.subplots(n_rows, n_cols, figsize=(x_size, y_size), squeeze=False)
+            fig.canvas.manager.set_window_title(algorithm)
             axes = axes.flatten()
         else:
             plt.figure(figsize=size)
@@ -711,28 +738,28 @@ 
    Source code for imputegap.recovery.manager
     
                 if incomp_data is None and recov_data is None:  # plot only raw matrix
                     ax.plot(timestamps, input_data[i, :nbr_val], linewidth=2.5,
-                            color=color, linestyle='-', label=f'TS {i + 1}')
+                            color=color, linestyle='-', label=f'Series {i + 1}')
 
                 if incomp_data is not None and recov_data is None:  # plot infected matrix
                     if np.isnan(incomp_data[i, :]).any():
                         ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5,
-                                color=color, linestyle='--', label=f'TS-INCOMP {i + 1}')
+                                color=color, linestyle='--', label=f'Missing Data {i + 1}')
 
                     if np.isnan(incomp_data[i, :]).any() or not subplot:
                         ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val],
-                                color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}')
+                                color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}')
 
                 if recov_data is not None:  # plot imputed matrix
                     if np.isnan(incomp_data[i, :]).any():
                         ax.plot(np.arange(min(recov_data.shape[1], nbr_val)), recov_data[i, :nbr_val],
-                                linestyle='-', color="r", label=f'TS-RECOV {i + 1}')
+                                linestyle='-', color="r", label=f'Imputed Data {i + 1}')
 
                         ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5,
-                                linestyle='--', color=color, label=f'TS-INCOM {i + 1}')
+                                linestyle='--', color=color, label=f'Missing Data {i + 1}')
 
                     if np.isnan(incomp_data[i, :]).any() or not subplot:
                         ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val],
-                                color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}')
+                                color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}')
 
                 # Label and legend for subplot
                 if subplot:
@@ -770,7 +797,7 @@ 
    Source code for imputegap.recovery.manager
     
             now = datetime.datetime.now()
             current_time = now.strftime("%y_%m_%d_%H_%M_%S")
-            file_path = os.path.join(save_path + "/" + current_time + "_plot.jpg")
+            file_path = os.path.join(save_path + "/" + current_time + "_" + algorithm + "_plot.jpg")
             plt.savefig(file_path, bbox_inches='tight')
             print("plots saved in ", file_path)
 
@@ -872,16 +899,16 @@ 
    Source code for imputegap.recovery.manager
                 values_nbr = int(NS * rate_series)
 
             if not explainer:
-                print(f"\n\n\tMCAR contamination has been called with :"
-                      f"\n\t\ta number of series impacted {rate_dataset * 100}%"
-                      f"\n\t\ta missing rate of {rate_series * 100}%"
-                      f"\n\t\ta starting position at {offset_nbr}"
-                      f"\n\t\ta block size of {block_size}"
-                      f"\n\t\tvalues to remove by series {values_nbr}"
-                      f"\n\t\twith a seed option set to {seed}"
-                      f"\n\t\twith a seed value set to {seed_value}"
-                      f"\n\t\tshape of the set {ts_contaminated.shape}"
-                      f"\n\t\tthis selection of series {series_selected}\n\n")
+                print(f"\n\nMCAR contamination has been called with :"
+                      f"\n\ta number of series impacted {rate_dataset * 100}%"
+                      f"\n\ta missing rate of {rate_series * 100}%"
+                      f"\n\ta starting position at {offset_nbr}"
+                      f"\n\ta block size of {block_size}"
+                      f"\n\tvalues to remove by series {values_nbr}"
+                      f"\n\twith a seed option set to {seed}"
+                      f"\n\twith a seed value set to {seed_value}"
+                      f"\n\tshape of the set {ts_contaminated.shape}"
+                      f"\n\tthis selection of series {series_selected}\n\n")
 
             if offset_nbr + values_nbr > NS:
                 raise ValueError(
@@ -965,13 +992,13 @@ 
    Source code for imputegap.recovery.manager
                 values_nbr = int(NS * rate_series)
 
 
-            print("\n\n\tALIGNED (missing percentage) contamination has been called with :"
-                  "\n\t\ta number of series impacted ", rate_dataset * 100, "%",
-                  "\n\t\ta missing rate of ", rate_series * 100, "%",
-                  "\n\t\ta starting position at ", offset,
-                  "\n\t\tshape of the set ", ts_contaminated.shape,
-                  "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted,
-                  "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n")
+            print("\n\nALIGNED (missing percentage) contamination has been called with :"
+                  "\n\ta number of series impacted ", rate_dataset * 100, "%",
+                  "\n\ta missing rate of ", rate_series * 100, "%",
+                  "\n\ta starting position at ", offset,
+                  "\n\tshape of the set ", ts_contaminated.shape,
+                  "\n\tthis selection of series : ", 1, "->", nbr_series_impacted,
+                  "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n")
 
 
             if offset_nbr + values_nbr > NS:
@@ -1042,13 +1069,13 @@ 
    Source code for imputegap.recovery.manager
                 values_nbr = int(NS * rate_series)
 
 
-            print("\n\n\tSCATTER (missing percentage AT RANDOM) contamination has been called with :"
-                  "\n\t\ta number of series impacted ", rate_dataset * 100, "%",
-                  "\n\t\ta missing rate of ", rate_series * 100, "%",
-                  "\n\t\ta starting position at ", offset,
-                  "\n\t\tshape of the set ", ts_contaminated.shape,
-                  "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted,
-                  "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n")
+            print("\n\nSCATTER (missing percentage AT RANDOM) contamination has been called with :"
+                  "\n\ta number of series impacted ", rate_dataset * 100, "%",
+                  "\n\ta missing rate of ", rate_series * 100, "%",
+                  "\n\ta starting position at ", offset,
+                  "\n\tshape of the set ", ts_contaminated.shape,
+                  "\n\tthis selection of series : ", 1, "->", nbr_series_impacted,
+                  "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n")
 
 
             if offset_nbr + values_nbr > NS:
@@ -1138,6 +1165,7 @@ 
    Source code for imputegap.recovery.manager
                 ----------
                 https://imputegap.readthedocs.io/en/latest/patterns.html
             """
+            from scipy.stats import norm
 
             ts_contaminated = input_data.copy()
             M, NS = ts_contaminated.shape
@@ -1156,16 +1184,16 @@ 
    Source code for imputegap.recovery.manager
                 offset_nbr = int(offset * NS)
             values_nbr = int(NS * rate_series)
 
-            print(f"\n\n\tGAUSSIAN contamination has been called with :"
-                  f"\n\t\ta number of series impacted {rate_dataset * 100}%"
-                  f"\n\t\ta missing rate of {rate_series * 100}%"
-                  f"\n\t\ta starting position at {offset_nbr}"
-                  f"\n\t\tvalues to remove by series {values_nbr}"
-                  f"\n\t\twith a seed option set to {seed}"
-                  f"\n\t\twith a seed value set to {seed_value}"
-                  f"\n\t\tGaussian std_dev {std_dev}"
-                  f"\n\t\tshape of the set {ts_contaminated.shape}"
-                  f"\n\t\tthis selection of series {nbr_series_impacted}\n\n")
+            print(f"\n\nGAUSSIAN contamination has been called with :"
+                  f"\n\ta number of series impacted {rate_dataset * 100}%"
+                  f"\n\ta missing rate of {rate_series * 100}%"
+                  f"\n\ta starting position at {offset_nbr}"
+                  f"\n\tvalues to remove by series {values_nbr}"
+                  f"\n\twith a seed option set to {seed}"
+                  f"\n\twith a seed value set to {seed_value}"
+                  f"\n\tGaussian std_dev {std_dev}"
+                  f"\n\tshape of the set {ts_contaminated.shape}"
+                  f"\n\tthis selection of series {nbr_series_impacted}\n\n")
 
             if offset_nbr + values_nbr > NS:
                 raise ValueError(
@@ -1250,16 +1278,16 @@ 
    Source code for imputegap.recovery.manager
                 offset_nbr = int(offset * NS)
             values_nbr = int(NS * rate_series)
 
-            print(f"\n\n\tDISTRIBUTION contamination has been called with :"
-                  f"\n\t\ta number of series impacted {rate_dataset * 100}%"
-                  f"\n\t\ta missing rate of {rate_series * 100}%"
-                  f"\n\t\ta starting position at {offset_nbr}"
-                  f"\n\t\tvalues to remove by series {values_nbr}"
-                  f"\n\t\twith a seed option set to {seed}"
-                  f"\n\t\twith a seed value set to {seed_value}"
-                  f"\n\t\tshape of the set {ts_contaminated.shape}"
-                  f"\n\t\tprobabilities list {np.array(probabilities).shape}"
-                  f"\n\t\tthis selection of series {nbr_series_impacted}\n\n")
+            print(f"\n\nDISTRIBUTION contamination has been called with :"
+                  f"\n\ta number of series impacted {rate_dataset * 100}%"
+                  f"\n\ta missing rate of {rate_series * 100}%"
+                  f"\n\ta starting position at {offset_nbr}"
+                  f"\n\tvalues to remove by series {values_nbr}"
+                  f"\n\twith a seed option set to {seed}"
+                  f"\n\twith a seed value set to {seed_value}"
+                  f"\n\tshape of the set {ts_contaminated.shape}"
+                  f"\n\tprobabilities list {np.array(probabilities).shape}"
+                  f"\n\tthis selection of series {nbr_series_impacted}\n\n")
 
             if offset_nbr + values_nbr > NS:
                 raise ValueError(
@@ -1326,12 +1354,12 @@ 
    Source code for imputegap.recovery.manager
                 values_nbr = int(NS * rate_series)
 
 
-            print(f"\n\n\tDISJOINT contamination has been called with :"
-                  f"\n\t\ta missing rate of {rate_series * 100}%"
-                  f"\n\t\ta starting position at {offset_nbr}"
-                  f"\n\t\tvalues to remove by series {values_nbr}"
-                  f"\n\t\tlimit to stop {limit}"
-                  f"\n\t\tshape of the set {ts_contaminated.shape}")
+            print(f"\n\nDISJOINT contamination has been called with :"
+                  f"\n\ta missing rate of {rate_series * 100}%"
+                  f"\n\ta starting position at {offset_nbr}"
+                  f"\n\tvalues to remove by series {values_nbr}"
+                  f"\n\tlimit to stop {limit}"
+                  f"\n\tshape of the set {ts_contaminated.shape}")
 
 
             if offset_nbr + values_nbr > NS:
@@ -1402,15 +1430,15 @@ 
    Source code for imputegap.recovery.manager
                 offset_nbr = int(offset * NS)
             values_nbr = int(NS * rate_series)
 
-            print(f"\n\n\tOVERLAP contamination has been called with :"
-                  f"\n\t\ta missing rate of {rate_series * 100}%"
-                  f"\n\t\ta offset of {offset*100}%"
-                  f"\n\t\ta starting position at {offset_nbr}"
-                  f"\n\t\tvalues to remove by series {values_nbr}"
-                  f"\n\t\ta shift overlap of {shift * 100} %"
-                  f"\n\t\ta shift in number {int(shift * NS)}"
-                  f"\n\t\tlimit to stop {limit}"
-                  f"\n\t\tshape of the set {ts_contaminated.shape}")
+            print(f"\n\nOVERLAP contamination has been called with :"
+                  f"\n\ta missing rate of {rate_series * 100}%"
+                  f"\n\ta offset of {offset*100}%"
+                  f"\n\ta starting position at {offset_nbr}"
+                  f"\n\tvalues to remove by series {values_nbr}"
+                  f"\n\ta shift overlap of {shift * 100} %"
+                  f"\n\ta shift in number {int(shift * NS)}"
+                  f"\n\tlimit to stop {limit}"
+                  f"\n\tshape of the set {ts_contaminated.shape}")
 
             if offset_nbr + values_nbr > NS:
                 raise ValueError(
@@ -1443,8 +1471,13 @@ 
    Source code for imputegap.recovery.manager
                     S = S + 1
 
             return ts_contaminated
    
+
+
+        missing_completely_at_random = mcar
+        mp = aligned
+        missing_percentage = aligned
    
 
    
-
    
+
 
 
 
    
@@ -1479,7 +1512,7 @@ 
    Source code for imputegap.recovery.manager
           
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/recovery/optimization.html b/docs/generation/build/html/modules/imputegap/recovery/optimization.html
index 5dec4d59..fc01a1ea 100644
--- a/docs/generation/build/html/modules/imputegap/recovery/optimization.html
+++ b/docs/generation/build/html/modules/imputegap/recovery/optimization.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.recovery.optimization - imputegap 1.0.5 documentation
+        imputegap.recovery.optimization - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -264,28 +264,14 @@
         
    
         
    
           
    Source code for imputegap.recovery.optimization
    -import os
-import time
+import time
 from itertools import product
 import numpy as np
-
 from imputegap.recovery.imputation import Imputation
 from imputegap.tools import utils
 from imputegap.tools.algorithm_parameters import SEARCH_SPACES, ALL_ALGO_PARAMS, PARAM_NAMES, SEARCH_SPACES_PSO, RAYTUNE_PARAMS
 import imputegap.tools.algorithm_parameters as sh_params
 
-# RAY TUNE IMPORT
-from ray import tune
-import ray
-
-# PSO IMPORT
-from functools import partial
-import pyswarms as ps
-
-# BAYESIAN IMPORT
-import skopt
-from skopt.space import Integer
-
 from pyswarms.utils.reporter import Reporter
 reporter = Reporter()
 
@@ -573,6 +559,10 @@ 
    Source code for imputegap.recovery.optimization
    <
             tuple
                 A tuple containing the best parameters and their corresponding score.
             """
+            # BAYESIAN IMPORT
+            import skopt
+            from skopt.space import Integer
+
             start_time = time.time()  # Record start time
 
             search_spaces = SEARCH_SPACES
@@ -706,6 +696,9 @@ 
    Source code for imputegap.recovery.optimization
    <
             tuple
                 A tuple containing the best parameters and their corresponding score.
             """
+            from functools import partial
+            import pyswarms as ps
+
             start_time = time.time()  # Record start time
 
             if not isinstance(metrics, list):
@@ -922,6 +915,9 @@ 
    Source code for imputegap.recovery.optimization
    <
             tuple
                 A tuple containing the best parameters and their corresponding score.
             """
+            from ray import tune
+            import ray
+
             if not ray.is_initialized():
                 ray.init()
             used_metric = metrics[0]
@@ -1007,7 +1003,7 @@ 
    Source code for imputegap.recovery.optimization
    <
       
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/imputegap/tools/utils.html b/docs/generation/build/html/modules/imputegap/tools/utils.html
index a5520678..73971a64 100644
--- a/docs/generation/build/html/modules/imputegap/tools/utils.html
+++ b/docs/generation/build/html/modules/imputegap/tools/utils.html
@@ -5,7 +5,7 @@
     
 
     
-        imputegap.tools.utils - imputegap 1.0.5 documentation
+        imputegap.tools.utils - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -268,9 +268,8 @@ 
    Source code for imputegap.tools.utils
     import os
 import toml
 import importlib.resources
-import numpy as __numpy_import;
-
-
+import numpy as __numpy_import
+import platform
 
 
    
 [docs]
@@ -330,8 +329,8 @@ 
    Source code for imputegap.tools.utils
             imputer = Imputation.DeepLearning.PRISTI(incomp_data)
 
     # 3rd generation
-    elif algorithm == "knn" or algorithm == "KNN":
-        imputer = Imputation.Statistics.KNN(incomp_data)
+    elif algorithm == "knn" or algorithm == "KNN" or algorithm == "knn_impute" or algorithm == "KNNImpute":
+        imputer = Imputation.Statistics.KNNImpute(incomp_data)
     elif algorithm == "interpolation" or algorithm == "Interpolation":
         imputer = Imputation.Statistics.Interpolation(incomp_data)
     elif algorithm == "mean_series" or algorithm == "MeanImputeBySeries":
@@ -647,7 +646,7 @@ 
    Source code for imputegap.tools.utils
         with open(filepath, "r") as _:
         config = toml.load(filepath)
 
-    print("\n\t\t\t\t(SYS) Inner files loaded : ", filepath, "\n")
+    print("\n(SYS) Inner files loaded : ", filepath, "\n")
 
     if algorithm == "cdrec":
         truncation_rank = int(config[algorithm]['rank'])
@@ -733,7 +732,7 @@ 
    Source code for imputegap.tools.utils
             seed = int(config[algorithm]['seed'])
         device = str(config[algorithm]['device'])
         return (target_strategy, unconditional, seed, device)
-    elif algorithm == "knn":
+    elif algorithm == "knn" or algorithm == "knn_impute":
         k = int(config[algorithm]['k'])
         weights = str(config[algorithm]['weights'])
         return (k, weights)
@@ -1044,18 +1043,25 @@ 
    Source code for imputegap.tools.utils
         ctypes.CDLL
         The loaded shared library object.
     """
+    system = platform.system()
+    if system == "Windows":
+        ext = ".so"
+    elif system == "Darwin":
+        ext = ".dylib"  # macOS uses .dylib for dynamic libraries
+    else:
+        ext = ".so"
 
     if lib:
-        lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name))
+        lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name) + ext)
     else:
-        local_path_lin = './algorithms/lib/' + name + '.so'
+        local_path_lin = './algorithms/lib/' + name + ext
 
         if not os.path.exists(local_path_lin):
-            local_path_lin = './imputegap/algorithms/lib/' + name + '.so'
+            local_path_lin = './imputegap/algorithms/lib/' + name + ext
 
         lib_path = os.path.join(local_path_lin)
-        print("\t\t(SYS) lib loaded from:", lib_path)
 
+    print("\n(SYS) Wrapper files loaded for C++ : ", lib_path, "\n")
 
     return ctypes.CDLL(lib_path)
    
 
@@ -1187,7 +1193,7 @@ 
    Source code for imputegap.tools.utils
                 "seed": 42,  # Default seed
             "device": "cpu"  # Default device
         }
-    elif algorithm == "knn":
+    elif algorithm == "knn" or algorithm == "knn_impute":
         params_to_save = {
             "k": int(optimal_params[0]),
             "weights": str(optimal_params[1])
@@ -1280,9 +1286,9 @@ 
    Source code for imputegap.tools.utils
         try:
         with open(file_name, 'w') as file:
             toml.dump(params_to_save, file)
-        print(f"\n\t\t(SYS) Optimization parameters successfully saved to {file_name}")
+        print(f"\n(SYS) Optimization parameters successfully saved to {file_name}")
     except Exception as e:
-        print(f"\n\t\t(SYS) An error occurred while saving the file: {e}")
    
+        print(f"\n(SYS) An error occurred while saving the file: {e}")
    
 
 
 
@@ -1305,7 +1311,7 @@ 
    Source code for imputegap.tools.utils
             "XGBOOST",
         "MICE",
         "MissForest",
-        "KNN",
+        "KNNImpute",
         "Interpolation",
         "MinImpute",
         "MeanImpute",
@@ -1448,7 +1454,7 @@ 
    Source code for imputegap.tools.utils
           
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/modules/index.html b/docs/generation/build/html/modules/index.html
index 98a85e30..9aed8ce7 100644
--- a/docs/generation/build/html/modules/index.html
+++ b/docs/generation/build/html/modules/index.html
@@ -5,7 +5,7 @@
     
 
     
-        Overview: module code - imputegap 1.0.5 documentation
+        Overview: module code - imputegap 1.0.7 documentation
       
     
     
@@ -167,7 +167,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -194,7 +194,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -310,7 +310,7 @@ 
    All modules for which code is available
    
       
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/objects.inv b/docs/generation/build/html/objects.inv
index e64de181..afb7c640 100644
Binary files a/docs/generation/build/html/objects.inv and b/docs/generation/build/html/objects.inv differ
diff --git a/docs/generation/build/html/patterns.html b/docs/generation/build/html/patterns.html
index fd011578..f478b51c 100644
--- a/docs/generation/build/html/patterns.html
+++ b/docs/generation/build/html/patterns.html
@@ -6,7 +6,7 @@
 
 
     
-        Patterns - imputegap 1.0.5 documentation
+        Patterns - imputegap 1.0.7 documentation
       
     
     
@@ -168,7 +168,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -195,7 +195,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -433,7 +433,7 @@ 
    Patterns
       
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/preprocessing.html b/docs/generation/build/html/preprocessing.html
index d4502a98..53a7ee15 100644
--- a/docs/generation/build/html/preprocessing.html
+++ b/docs/generation/build/html/preprocessing.html
@@ -6,7 +6,7 @@
 
 
     
-        Preprocessing - imputegap 1.0.5 documentation
+        Preprocessing - imputegap 1.0.7 documentation
       
     
     
@@ -168,7 +168,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -195,7 +195,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -342,7 +342,7 @@ 
    Preprocessing
+
    
     
     
     
diff --git a/docs/generation/build/html/py-modindex.html b/docs/generation/build/html/py-modindex.html
index da288845..743495f4 100644
--- a/docs/generation/build/html/py-modindex.html
+++ b/docs/generation/build/html/py-modindex.html
@@ -4,7 +4,7 @@
     
     
 
-    Python Module Index - imputegap 1.0.5 documentation
+    Python Module Index - imputegap 1.0.7 documentation
 
     
     
@@ -166,7 +166,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -193,7 +193,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -422,7 +422,7 @@ 
    Python Module Index
    
       
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/search.html b/docs/generation/build/html/search.html
index 10456326..19be4ec6 100644
--- a/docs/generation/build/html/search.html
+++ b/docs/generation/build/html/search.html
@@ -7,7 +7,7 @@
 
     
 
-Search - imputegap 1.0.5 documentation
+Search - imputegap 1.0.7 documentation
     
     
     
@@ -168,7 +168,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -195,7 +195,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -307,7 +307,7 @@
       
     
   
    
-
    
+
    
     
     
     
diff --git a/docs/generation/build/html/searchindex.js b/docs/generation/build/html/searchindex.js
index cdc345b6..55e0c22c 100644
--- a/docs/generation/build/html/searchindex.js
+++ b/docs/generation/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/generation/build/html/sources/algorithms.rst.txt b/docs/generation/build/html/sources/algorithms.rst.txt
index 9f5fe3c4..37d4f16f 100644
--- a/docs/generation/build/html/sources/algorithms.rst.txt
+++ b/docs/generation/build/html/sources/algorithms.rst.txt
@@ -25,20 +25,20 @@ To list all the available algorithms and their optimizers, you can use this comm
    * - **Family**
      - **Algorithm**
      - **Venue -- Year**
-   * - Deep Learning
-     - MPIN [25]_
-     - PVLDB -- 2024
    * - Deep Learning
      - MissNet [27]_
      - KDD -- 2024
    * - Deep Learning
-     - BITGraph [32]_
+     - BitGraph [32]_
      - ICLR -- 2024
    * - Deep Learning
      - BayOTIDE [30]_
      - PMLR -- 2024
    * - Deep Learning
-     - PriSTI [26]_
+     - MPIN* [25]_
+     - PVLDB -- 2024
+   * - Deep Learning
+     - PRISTI [26]_
      - ICDE -- 2023
    * - Deep Learning
      - GRIN [29]_
@@ -47,7 +47,7 @@ To list all the available algorithms and their optimizers, you can use this comm
      - DeepMVI [24]_
      - PVLDB -- 2021
    * - Deep Learning
-     - HKMF-T [31]_
+     - HKMF_T [31]_
      - TKDE -- 2021
    * - Deep Learning
      - MRNN [22]_
@@ -86,7 +86,7 @@ To list all the available algorithms and their optimizers, you can use this comm
      - TKCM [11]_
      - EDBT -- 2017
    * - Pattern Search
-     - ST-MVL [9]_
+     - STMVL [9]_
      - IJCAI -- 2016
    * - Pattern Search
      - DynaMMo [10]_
@@ -95,10 +95,10 @@ To list all the available algorithms and their optimizers, you can use this comm
      - IIM [12]_
      - ICDE -- 2019
    * - Machine Learning
-     - XGBI [13]_
+     - XGBOOST [13]_
      - KDD -- 2016
    * - Machine Learning
-     - Mice [14]_
+     - MICE [14]_
      - Statistical Software -- 2011
    * - Machine Learning
      - MissForest [15]_
@@ -180,3 +180,5 @@ References
 .. [31] Liang Wang, Simeng Wu, Tianheng Wu, Xianping Tao, Jian Lu: HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization. IEEE Trans. Knowl. Data Eng. 33(11): 3582-3593 (2021)
 
 .. [32] Xiaodan Chen, Xiucheng Li, Bo Liu, Zhijun Li: Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values. ICLR 2024
+
+* need torch-cluster to work
diff --git a/docs/generation/build/html/sources/benchmark.rst.txt b/docs/generation/build/html/sources/benchmark.rst.txt
index f9a16b39..e2ab3950 100644
--- a/docs/generation/build/html/sources/benchmark.rst.txt
+++ b/docs/generation/build/html/sources/benchmark.rst.txt
@@ -2,7 +2,7 @@
 Benchmark
 =========
 
-ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]_.  Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates.
+ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]_.  Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. The default metrics evaluated include "RMSE", "MAE", "MI", "Pearson", and the runtime.
 
 
 The benchmarking module can be utilized as follows:
@@ -11,22 +11,21 @@ The benchmarking module can be utilized as follows:
 
     from imputegap.recovery.benchmark import Benchmark
 
-    save_dir = "./analysis"
-    nbr_run = 2
+    save_dir = "./imputegap_assets/benchmark"
+    nbr_runs = 1
 
-    datasets = ["eeg-alcohol", "eeg-reading"]
+    datasets = ["eeg-alcohol"]
 
-    optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}}
-    optimizers = [optimizer]
+    optimizers = ["default_params"]
 
-    algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"]
+    algorithms = ["SoftImpute", "KNNImpute"]
 
     patterns = ["mcar"]
 
     range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8]
 
     # launch the evaluation
-    list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run)
+    list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_runs)
 
 
 
diff --git a/docs/generation/build/html/sources/downstream.rst.txt b/docs/generation/build/html/sources/downstream.rst.txt
index 5dfc924b..fb1aff01 100644
--- a/docs/generation/build/html/sources/downstream.rst.txt
+++ b/docs/generation/build/html/sources/downstream.rst.txt
@@ -31,7 +31,7 @@ Below is an example of how to call the downstream process for the model by defin
     imputer.impute()
 
     # compute and print the downstream results
-    downstream_config = {"task": "forecast", "model": "hw-add"}
+    downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"}
     imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
     ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)
 
diff --git a/docs/generation/build/html/sources/tutorials.rst.txt b/docs/generation/build/html/sources/tutorials.rst.txt
index 1c53eeb7..615dcd2a 100644
--- a/docs/generation/build/html/sources/tutorials.rst.txt
+++ b/docs/generation/build/html/sources/tutorials.rst.txt
@@ -27,7 +27,7 @@ alcoholism. The dataset contains measurements from 64 electrodes placed on subje
 
     # plot and print a subset of time series
     ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
-    ts.print(nbr_series=9, nbr_val=100)
+    ts.print(nbr_series=9, nbr_val=20)
 
 
 .. _contamination:
@@ -104,7 +104,7 @@ Let's illustrate the imputation using the CDRec Algorithm from the Matrix Comple
     ts.print_results(imputer.metrics)
 
     # plot the recovered time series
-    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets")
+    ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets")
 
 
 
@@ -136,9 +136,16 @@ The Optimizer component manages algorithm configuration and hyperparameter tunin
     # use Ray Tune to fine tune the imputation algorithm
     imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})
 
-    # compute and print the imputation metrics
+    # compute the imputation metrics with optimized parameter values
     imputer.score(ts.data, imputer.recov_data)
-    ts.print_results(imputer.metrics)
+
+    # compute the imputation metrics with default parameter values
+    imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute()
+    imputer_def.score(ts.data, imputer_def.recov_data)
+
+    # print the imputation metrics with default and optimized parameter values
+    ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values")
+    ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values")
 
     # plot the recovered time series
     ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True)
diff --git a/docs/generation/build/html/static/documentation_options.js b/docs/generation/build/html/static/documentation_options.js
index d0fab5f7..3046a46e 100644
--- a/docs/generation/build/html/static/documentation_options.js
+++ b/docs/generation/build/html/static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '1.0.5',
+    VERSION: '1.0.7',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/generation/build/html/tutorials.html b/docs/generation/build/html/tutorials.html
index 7a728f4f..dae22fff 100644
--- a/docs/generation/build/html/tutorials.html
+++ b/docs/generation/build/html/tutorials.html
@@ -6,7 +6,7 @@
 
 
     
-        Tutorials - imputegap 1.0.5 documentation
+        Tutorials - imputegap 1.0.7 documentation
       
     
     
@@ -168,7 +168,7 @@
       
     
    
     
    
-      
    imputegap 1.0.5 documentation
    
+      
    imputegap 1.0.7 documentation
    
     
    
     
    
       
    
@@ -195,7 +195,7 @@
     
   
    
   
-  imputegap 1.0.5 documentation
+  imputegap 1.0.7 documentation
   
 
   
@@ -289,7 +289,7 @@ 
    Tutorials# plot and print a subset of time series
 ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
-ts.print(nbr_series=9, nbr_val=100)
+ts.print(nbr_series=9, nbr_val=20)

    @@ -347,7 +347,7 @@

    Tutorialsts.print_results(imputer.metrics) # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets")

    @@ -372,9 +372,16 @@

    Tutorials# use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) -# compute and print the imputation metrics +# compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics) + +# compute the imputation metrics with default parameter values +imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() +imputer_def.score(ts.data, imputer_def.recov_data) + +# print the imputation metrics with default and optimized parameter values +ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") +ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True) @@ -457,7 +464,7 @@

    Tutorials + diff --git a/docs/generation/build/html/tutorials_distribution.html b/docs/generation/build/html/tutorials_distribution.html index 03c7eda6..b2aa1bea 100644 --- a/docs/generation/build/html/tutorials_distribution.html +++ b/docs/generation/build/html/tutorials_distribution.html @@ -6,7 +6,7 @@ - Distribution - imputegap 1.0.5 documentation + Distribution - imputegap 1.0.7 documentation @@ -168,7 +168,7 @@
    -
    imputegap 1.0.5 documentation
     +
    imputegap 1.0.7 documentation
    @@ -195,7 +195,7 @@
    - imputegap 1.0.5 documentation + imputegap 1.0.7 documentation @@ -325,7 +325,7 @@

    Distribution +

    diff --git a/docs/generation/report.log b/docs/generation/report.log index 81d75c92..4b641c4f 100644 --- a/docs/generation/report.log +++ b/docs/generation/report.log @@ -77,3 +77,6 @@ 2025-03-20 14:21:35,116 - numba.cuda.cudadrv.driver - INFO - init 2025-03-20 14:52:39,925 - numba.cuda.cudadrv.driver - INFO - init 2025-03-20 15:01:15,050 - numba.cuda.cudadrv.driver - INFO - init +2025-03-21 09:46:24,117 - numba.cuda.cudadrv.driver - INFO - init +2025-03-21 15:58:44,042 - numba.cuda.cudadrv.driver - INFO - init +2025-03-21 17:48:19,571 - numba.cuda.cudadrv.driver - INFO - init diff --git a/docs/generation/source/algorithms.rst b/docs/generation/source/algorithms.rst index 9f5fe3c4..37d4f16f 100644 --- a/docs/generation/source/algorithms.rst +++ b/docs/generation/source/algorithms.rst @@ -25,20 +25,20 @@ To list all the available algorithms and their optimizers, you can use this comm * - **Family** - **Algorithm** - **Venue -- Year** - * - Deep Learning - - MPIN [25]_ - - PVLDB -- 2024 * - Deep Learning - MissNet [27]_ - KDD -- 2024 * - Deep Learning - - BITGraph [32]_ + - BitGraph [32]_ - ICLR -- 2024 * - Deep Learning - BayOTIDE [30]_ - PMLR -- 2024 * - Deep Learning - - PriSTI [26]_ + - MPIN* [25]_ + - PVLDB -- 2024 + * - Deep Learning + - PRISTI [26]_ - ICDE -- 2023 * - Deep Learning - GRIN [29]_ @@ -47,7 +47,7 @@ To list all the available algorithms and their optimizers, you can use this comm - DeepMVI [24]_ - PVLDB -- 2021 * - Deep Learning - - HKMF-T [31]_ + - HKMF_T [31]_ - TKDE -- 2021 * - Deep Learning - MRNN [22]_ @@ -86,7 +86,7 @@ To list all the available algorithms and their optimizers, you can use this comm - TKCM [11]_ - EDBT -- 2017 * - Pattern Search - - ST-MVL [9]_ + - STMVL [9]_ - IJCAI -- 2016 * - Pattern Search - DynaMMo [10]_ @@ -95,10 +95,10 @@ To list all the available algorithms and their optimizers, you can use this comm - IIM [12]_ - ICDE -- 2019 * - Machine Learning - - XGBI [13]_ + - XGBOOST [13]_ - KDD -- 2016 * - Machine Learning - - Mice [14]_ + - MICE [14]_ - Statistical Software -- 2011 * - Machine Learning - MissForest [15]_ @@ -180,3 +180,5 @@ References .. [31] Liang Wang, Simeng Wu, Tianheng Wu, Xianping Tao, Jian Lu: HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization. IEEE Trans. Knowl. Data Eng. 33(11): 3582-3593 (2021) .. [32] Xiaodan Chen, Xiucheng Li, Bo Liu, Zhijun Li: Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values. ICLR 2024 + +* need torch-cluster to work diff --git a/docs/generation/source/benchmark.rst b/docs/generation/source/benchmark.rst index f9a16b39..e2ab3950 100644 --- a/docs/generation/source/benchmark.rst +++ b/docs/generation/source/benchmark.rst @@ -2,7 +2,7 @@ Benchmark ========= -ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]_. Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. +ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms [33]_. Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. The default metrics evaluated include "RMSE", "MAE", "MI", "Pearson", and the runtime. The benchmarking module can be utilized as follows: @@ -11,22 +11,21 @@ The benchmarking module can be utilized as follows: from imputegap.recovery.benchmark import Benchmark - save_dir = "./analysis" - nbr_run = 2 + save_dir = "./imputegap_assets/benchmark" + nbr_runs = 1 - datasets = ["eeg-alcohol", "eeg-reading"] + datasets = ["eeg-alcohol"] - optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} - optimizers = [optimizer] + optimizers = ["default_params"] - algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"] + algorithms = ["SoftImpute", "KNNImpute"] patterns = ["mcar"] range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] # launch the evaluation - list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) + list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_runs) diff --git a/docs/generation/source/conf.py b/docs/generation/source/conf.py index 7d915772..a476798d 100644 --- a/docs/generation/source/conf.py +++ b/docs/generation/source/conf.py @@ -46,8 +46,8 @@ html_css_files = ['custom.css'] # Set the version and release info -version = '1.0.6' -release = '1.0.6' +version = '1.0.7' +release = '1.0.7' html_theme_options = { diff --git a/docs/generation/source/downstream.rst b/docs/generation/source/downstream.rst index 5dfc924b..fb1aff01 100644 --- a/docs/generation/source/downstream.rst +++ b/docs/generation/source/downstream.rst @@ -31,7 +31,7 @@ Below is an example of how to call the downstream process for the model by defin imputer.impute() # compute and print the downstream results - downstream_config = {"task": "forecast", "model": "hw-add"} + downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) diff --git a/docs/generation/source/tutorials.rst b/docs/generation/source/tutorials.rst index 1c53eeb7..615dcd2a 100644 --- a/docs/generation/source/tutorials.rst +++ b/docs/generation/source/tutorials.rst @@ -27,7 +27,7 @@ alcoholism. The dataset contains measurements from 64 electrodes placed on subje # plot and print a subset of time series ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") - ts.print(nbr_series=9, nbr_val=100) + ts.print(nbr_series=9, nbr_val=20) .. _contamination: @@ -104,7 +104,7 @@ Let's illustrate the imputation using the CDRec Algorithm from the Matrix Comple ts.print_results(imputer.metrics) # plot the recovered time series - ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") + ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets") @@ -136,9 +136,16 @@ The Optimizer component manages algorithm configuration and hyperparameter tunin # use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) - # compute and print the imputation metrics + # compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) - ts.print_results(imputer.metrics) + + # compute the imputation metrics with default parameter values + imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() + imputer_def.score(ts.data, imputer_def.recov_data) + + # print the imputation metrics with default and optimized parameter values + ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") + ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True) diff --git a/imputegap.egg-info/PKG-INFO b/imputegap.egg-info/PKG-INFO index ce34969b..ad8baa6d 100644 --- a/imputegap.egg-info/PKG-INFO +++ b/imputegap.egg-info/PKG-INFO @@ -1,12 +1,12 @@ -Metadata-Version: 2.2 +Metadata-Version: 2.4 Name: imputegap -Version: 1.0.5 +Version: 1.0.7 Summary: A Library of Imputation Techniques for Time Series Data Home-page: https://github.com/eXascaleInfolab/ImputeGAP Author: Quentin Nater Author-email: quentin.nater@unifr.ch License: MIT License -Project-URL: Documentation, https://imputegap.readthedocs.io/ +Project-URL: Documentation, https://exascaleinfolab.github.io/ImputeGAP/ Project-URL: Source, https://github.com/eXascaleInfolab/ImputeGAP Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers @@ -21,7 +21,6 @@ Requires-Dist: matplotlib==3.7.5 Requires-Dist: toml==0.10.2 Requires-Dist: scikit-learn>=1.5.0 Requires-Dist: scipy==1.14.1 -Requires-Dist: setuptools==75.1.0 Requires-Dist: tensorflow==2.17.0 Requires-Dist: shap==0.44.1 Requires-Dist: pycatch22==0.4.4 @@ -56,7 +55,6 @@ Requires-Dist: ray[tune]==2.41.0 Requires-Dist: fire==0.7.0 Requires-Dist: fastdtw==0.3.4 Requires-Dist: torch-geometric==2.6.1 -Requires-Dist: torch-cluster==1.6.3 Requires-Dist: pytorch-lightning==2.5.0 Requires-Dist: torchmetrics==1.6.1 Requires-Dist: lightning==2.5.0 @@ -67,6 +65,7 @@ Dynamic: description Dynamic: description-content-type Dynamic: home-page Dynamic: license +Dynamic: license-file Dynamic: project-url Dynamic: requires-dist Dynamic: requires-python @@ -82,18 +81,19 @@ ImputeGAP is a comprehensive Python library for imputation of missing values in In detail, the package provides: Access to commonly used datasets in time series research (Datasets). - - Configurable contamination module that simulates real-world missingness patterns. - - Automated preprocessing with built-in methods for normalizing time series. - - Parameterized state-of-the-art time series imputation algorithms. - - Modular tools to analyze the behavior of these algorithms and assess their impact on key downstream tasks in time series analysis. - - Experiment benchmarking, fostering research reproducibility in time series. - - Fine-grained analysis of the impact of time series features on imputation results. + - Access to commonly used datasets in time series research ([Datasets](https://imputegap.readthedocs.io/en/latest/datasets.html)). + - Automated preprocessing with built-in methods for normalizing time series ([PreProcessing](https://imputegap.readthedocs.io/en/latest/preprocessing.html)). + - Configurable contamination module that simulates real-world missingness patterns ([Patterns](https://imputegap.readthedocs.io/en/latest/patterns.html)). + - Parameterizable state-of-the-art time series imputation algorithms ([Algorithms](https://imputegap.readthedocs.io/en/latest/algorithms.html)). + - Benchmarking to foster reproducibility in time series imputation ([Benchmark](https://imputegap.readthedocs.io/en/latest/benchmark.html)). + - Modular tools to analyze the behavior of imputation algorithms and assess their impact on key downstream tasks in time series analysis ([Downstream](https://imputegap.readthedocs.io/en/latest/downstream.html)). + - Fine-grained analysis of the impact of time series features on imputation results ([Explainer](https://imputegap.readthedocs.io/en/latest/explainer.html)). - Plug-and-play integration of new datasets and algorithms in various languages such as Python, C++, Matlab, Java, and R.
    ![Python](https://img.shields.io/badge/Python-v3.12-blue) -![Release](https://img.shields.io/badge/Release-v1.0.5-brightgreen) +![Release](https://img.shields.io/badge/Release-v1.0.7-brightgreen) ![License](https://img.shields.io/badge/License-GPLv3-blue?style=flat&logo=gnu) ![Coverage](https://img.shields.io/badge/Coverage-93%25-brightgreen) ![PyPI](https://img.shields.io/pypi/v/imputegap?label=PyPI&color=blue) @@ -108,6 +108,46 @@ Access to commonly used datasets in time series research (Datasets). - **Datasets**: [Repository](https://github.com/eXascaleInfolab/ImputeGAP/tree/main/imputegap/dataset) - --- + + +# List of available imputation algorithms +| **Family** | **Algorithm** | **Venue -- Year** | +|--------------------|---------------------------|------------------------------| +| Deep Learning | BitGraph [[32]](#ref32) | ICLR -- 2024 | +| Deep Learning | BayOTIDE [[30]](#ref30) | PMLR -- 2024 | +| Deep Learning | MissNet [[27]](#ref27) | KDD -- 2024 | +| Deep Learning | MPIN [[25]](#ref25) | PVLDB -- 2024 | +| Deep Learning | PRISTI [[26]](#ref26) | ICDE -- 2023 | +| Deep Learning | GRIN [[29]](#ref29) | ICLR -- 2022 | +| Deep Learning | HKMF_T [[31]](#ref31) | TKDE -- 2021 | +| Deep Learning | DeepMVI [[24]](#ref24) | PVLDB -- 2021 | +| Deep Learning | MRNN [[22]](#ref22) | IEEE Trans on BE -- 2019 | +| Deep Learning | BRITS [[23]](#ref23) | NeurIPS -- 2018 | +| Deep Learning | GAIN [[28]](#ref28) | ICML -- 2018 | +| Matrix Completion | CDRec [[1]](#ref1) | KAIS -- 2020 | +| Matrix Completion | TRMF [[8]](#ref8) | NeurIPS -- 2016 | +| Matrix Completion | GROUSE [[3]](#ref3) | PMLR -- 2016 | +| Matrix Completion | ROSL [[4]](#ref4) | CVPR -- 2014 | +| Matrix Completion | SoftImpute [[6]](#ref6) | JMLR -- 2010 | +| Matrix Completion | SVT [[7]](#ref7) | SIAM J. OPTIM -- 2010 | +| Matrix Completion | SPIRIT [[5]](#ref5) | VLDB -- 2005 | +| Matrix Completion | IterativeSVD [[2]](#ref2) | BIOINFORMATICS -- 2001 | +| Pattern Search | TKCM [[11]](#ref11) | EDBT -- 2017 | +| Pattern Search | STMVL [[9]](#ref9) | IJCAI -- 2016 | +| Pattern Search | DynaMMo [[10]](#ref10) | KDD -- 2009 | +| Machine Learning | IIM [[12]](#ref12) | ICDE -- 2019 | +| Machine Learning | XGBOOST [[13]](#ref13) | KDD -- 2016 | +| Machine Learning | MICE [[14]](#ref14) | Statistical Software -- 2011 | +| Machine Learning | MissForest [[15]](#ref15) | BioInformatics -- 2011 | +| Statistics | KNNImpute | - | +| Statistics | Interpolation | - | +| Statistics | MinImpute | - | +| Statistics | ZeroImpute | - | +| Statistics | MeanImpute | - | +| Statistics | MeanImputeBySeries | - | + +--- + ### **Quick Navigation** - **Deployment** @@ -131,45 +171,6 @@ Access to commonly used datasets in time series research (Datasets). - [Core Contributors](#core-contributors) ---- - -## Families of Algorithms -# Algorithms Table -| **Family** | **Algorithm** | **Venue -- Year** | -|--------------------|---------------------------|------------------------------| -| Matrix Completion | CDRec [[1]](#ref1) | KAIS -- 2020 | -| Matrix Completion | TRMF [[8]](#ref8) | NeurIPS -- 2016 | -| Matrix Completion | GROUSE [[3]](#ref3) | PMLR -- 2016 | -| Matrix Completion | ROSL [[4]](#ref4) | CVPR -- 2014 | -| Matrix Completion | SoftImpute [[6]](#ref6) | JMLR -- 2010 | -| Matrix Completion | SVT [[7]](#ref7) | SIAM J. OPTIM -- 2010 | -| Matrix Completion | SPIRIT [[5]](#ref5) | VLDB -- 2005 | -| Matrix Completion | IterativeSVD [[2]](#ref2) | BIOINFORMATICS -- 2001 | -| Pattern Search | TKCM [[11]](#ref11) | EDBT -- 2017 | -| Pattern Search | ST-MVL [[9]](#ref9) | IJCAI -- 2016 | -| Pattern Search | DynaMMo [[10]](#ref10) | KDD -- 2009 | -| Machine Learning | IIM [[12]](#ref12) | ICDE -- 2019 | -| Machine Learning | XGBI [[13]](#ref13) | KDD -- 2016 | -| Machine Learning | Mice [[14]](#ref14) | Statistical Software -- 2011 | -| Machine Learning | MissForest [[15]](#ref15) | BioInformatics -- 2011 | -| Deep Learning | BITGraph [[32]](#ref32) | ICLR -- 2024 | -| Deep Learning | BayOTIDE [[30]](#ref30) | PMLR -- 2024 | -| Deep Learning | MPIN [[25]](#ref25) | PVLDB -- 2024 | -| Deep Learning | MissNet [[27]](#ref27) | KDD -- 2024 | -| Deep Learning | PriSTI [[26]](#ref26) | ICDE -- 2023 | -| Deep Learning | GRIN [[29]](#ref29) | ICLR -- 2022 | -| Deep Learning | HKMF-T [[31]](#ref31) | TKDE -- 2021 | -| Deep Learning | DeepMVI [[24]](#ref24) | PVLDB -- 2021 | -| Deep Learning | MRNN [[22]](#ref22) | IEEE Trans on BE -- 2019 | -| Deep Learning | BRITS [[23]](#ref23) | NeurIPS -- 2018 | -| Deep Learning | GAIN [[28]](#ref28) | ICML -- 2018 | -| Statistics | KNNImpute | - | -| Statistics | Interpolation | - | -| Statistics | Min Impute | - | -| Statistics | Zero Impute | - | -| Statistics | Mean Impute | - | -| Statistics | Mean Impute By Series | - | - --- @@ -205,7 +206,7 @@ pip install -e . --- ## Loading and Preprocessing -ImputeGAP comes with several time series datasets. You can find them inside the submodule ``ts.datasets``. +ImputeGAP comes with several time series datasets. The list of datasets is described [here](https://imputegap.readthedocs.io/en/latest/datasets.html). As an example, we start by using eeg-alcohol, a standard dataset composed of individuals with a genetic predisposition to alcoholism. The dataset contains measurements from 64 electrodes placed on subject’s scalps, sampled at 256 Hz (3.9-ms epoch) for 1 second. The dimensions of the dataset are 64 series, each containing 256 values. @@ -217,29 +218,26 @@ You can find this example in the file [`runner_loading.py`](https://github.com/e from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() +# initialize the time series object ts = TimeSeries() print(f"ImputeGAP datasets : {ts.datasets}") -# load the timeseries from file or from the code +# load and normalize the dataset from file or from the code ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") -# plot a subset of time series +# plot and print a subset of time series ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") - -# print a subset of time series -ts.print(nbr_series=6, nbr_val=20) - +ts.print(nbr_series=9, nbr_val=20) ``` --- ## Contamination -We now describe how to simulate missing values in the loaded dataset. ImputeGAP implements eight different missingness patterns. You can find them inside the module ``ts.patterns``. +We now describe how to simulate missing values in the loaded dataset. ImputeGAP implements eight different missingness patterns. -For more details, please refer to the documentation in this     page. +For more details, please refer to the documentation in this [page](https://imputegap.readthedocs.io/en/latest/patterns.html).

    ### Example Contamination @@ -252,16 +250,16 @@ As example, we show how to contaminate the eeg-alcohol dataset with the MCAR pat from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"Missingness patterns : {ts.patterns}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") # contaminate the time series with MCAR pattern -ts_m = ts.Contamination.missing_completely_at_random(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True) +ts_m = ts.Contamination.mcar(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True) # [OPTIONAL] plot the contaminated time series ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets") @@ -272,7 +270,7 @@ ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets ## Imputation In this section, we will illustrate how to impute the contaminated time series. Our library implements five families of imputation algorithms. Statistical, Machine Learning, Matrix Completion, Deep Learning, and Pattern Search Methods. -You can find the list of algorithms inside the module ``ts.algorithms``. +The list of algorithms and their optimizers is described [here](https://imputegap.readthedocs.io/en/latest/algorithms.html). ### Example Imputation You can find this example in the file [`runner_imputation.py`](https://github.com/eXascaleInfolab/ImputeGAP/blob/main/imputegap/runner_imputation.py). @@ -290,16 +288,16 @@ from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"Imputation algorithms : {ts.algorithms}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") # contaminate the time series -ts_m = ts.Contamination.missing_completely_at_random(ts.data) +ts_m = ts.Contamination.mcar(ts.data) # impute the contaminated series imputer = Imputation.MatrixCompletion.CDRec(ts_m) @@ -310,7 +308,7 @@ imputer.score(ts.data, imputer.recov_data) ts.print_results(imputer.metrics) # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets") ``` --- @@ -329,30 +327,39 @@ from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"AutoML Optimizers : {ts.optimizers}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") # contaminate and impute the time series -ts_m = ts.Contamination.missing_completely_at_random(ts.data) +ts_m = ts.Contamination.mcar(ts.data) imputer = Imputation.MatrixCompletion.CDRec(ts_m) # use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) -# compute and print the imputation metrics +# compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics) + +# compute the imputation metrics with default parameter values +imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() +imputer_def.score(ts.data, imputer_def.recov_data) + +# print the imputation metrics with default and optimized parameter values +ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") +ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True) +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, + save_path="./imputegap_assets", display=True) # save hyperparameters -utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune") +utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", + optimizer="ray_tune") ``` --- @@ -361,7 +368,7 @@ utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.alg ## Explainer ImputeGAP provides insights into the algorithm’s behavior by identifying the features that impact the most the imputation results. It trains a regression model to predict imputation results across various methods and uses SHapley Additive exPlanations ([SHAP](https://shap.readthedocs.io/en/latest/)) to reveal how different time series features influence the model’s predictions. - +The documentation for the explainer is described [here](https://imputegap.readthedocs.io/en/latest/explainer.html). ### Example Explainer @@ -375,17 +382,17 @@ from imputegap.recovery.manager import TimeSeries from imputegap.recovery.explainer import Explainer from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) ts.normalize(normalizer="z_score") # configure the explanation shap_values, shap_details = Explainer.shap_explainer(input_data=ts.data, extractor="pycatch", - pattern="missing_completely_at_random", + pattern="mcar", file_name=ts.name, algorithm="CDRec") @@ -398,6 +405,7 @@ Explainer.print(shap_values, shap_details) ## Downstream ImputeGAP includes a dedicated module for systematically evaluating the impact of data imputation on downstream tasks. Currently, forecasting is the primary supported task, with plans to expand to additional applications in the future. The example below demonstrates how to define the forecasting task and specify Prophet as the predictive model +The documentation for the downstream evaluation is described [here](https://imputegap.readthedocs.io/en/latest/downstream.html). Below is an example of how to call the downstream process for the model Prophet by defining a dictionary for the evaluator and selecting the model: @@ -412,23 +420,23 @@ from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils -# initialize the TimeSeries() object +# initialize the time series object ts = TimeSeries() print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}") -# load and normalize the timeseries +# load and normalize the dataset ts.load_series(utils.search_path("forecast-economy")) ts.normalize(normalizer="min_max") # contaminate the time series -ts_m = ts.Contamination.missing_percentage(ts.data, rate_series=0.8) +ts_m = ts.Contamination.aligned(ts.data, rate_series=0.8) # define and impute the contaminated series imputer = Imputation.MatrixCompletion.CDRec(ts_m) imputer.impute() -# compute print the downstream results -downstream_config = {"task": "forecast", "model": "hw-add"} +# compute and print the downstream results +downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) ``` @@ -440,7 +448,9 @@ ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) ## Benchmark -ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms[[33]](#ref33) . Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. +ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms[[33]](#ref33) . Users have full control over the benchmark by customizing various parameters, including the list of datasets to evaluate, the algorithms to compare, the choice of optimizer to fine-tune the algorithms on the chosen datasets, the missingness patterns, and the range of missing rates. The default metrics evaluated include "RMSE", "MAE", "MI", "Pearson", and the runtime. +The documentation for the benchmark is described [here](https://imputegap.readthedocs.io/en/latest/benchmark.html). + ### Example Benchmark You can find this example in the file [`runner_benchmark.py`](https://github.com/eXascaleInfolab/ImputeGAP/blob/main/imputegap/runner_benchmark.py). @@ -450,24 +460,29 @@ The benchmarking module can be utilized as follows: ```python from imputegap.recovery.benchmark import Benchmark -save_dir = "./analysis" -nbr_run = 2 +save_dir = "./imputegap_assets/benchmark" +nbr_run = 1 -datasets = ["eeg-alcohol", "eeg-reading"] +datasets = ["eeg-alcohol"] -optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} -optimizers = [optimizer] +optimizers = ["default_params"] -algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"] +algorithms = ["SoftImpute", "KNNImpute"] -patterns = ["missing_completely_at_random"] +patterns = ["mcar"] range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] -# launch the analysis +# launch the evaluation list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) ``` +You can change the optimizer using the following command: +```python +optimizer = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} +optimizers = [optimizer] +``` + --- ## Integration @@ -476,56 +491,49 @@ To add your own imputation algorithm in Python or C++, please refer to the detai --- +## Citing -## Articles - - -Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, Philippe Cudre-Mauroux: Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series. Proc. VLDB Endow. 13(5): 768-782 (2020) - -Mourad Khayati, Quentin Nater, Jacques Pasquier: ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data. Proc. VLDB Endow. 17(12): 4329-4332 (2024) +If you use ImputeGAP in your research, please cite the paper: +``` +@article{nater2025imputegap, + title = {ImputeGAP: A Comprehensive Library for Time Series Imputation}, + author = {Nater, Quentin and Khayati, Mourad and Pasquier, Jacques}, + year = {2025}, + eprint = {2503.15250}, + archiveprefix = {arXiv}, + primaryclass = {cs.LG}, + url = {https://arxiv.org/abs/2503.15250} +} +``` --- - ## Core Contributors
    - Quentin Nater - ImputeGAP + + Quentin Nater - ImputeGAP + - Mourad Khayati - ImputeGAP + + Mourad Khayati - ImputeGAP +
    Quentin Nater - quentin.nater@unifr.ch - Mourad Khayati - mourad.khayati@unifr.ch + Mourad Khayati
    ---- - - -## Citing - -If you use ImputeGAP in your research, please cite the library -```bash -@software{ImputeGAP_2025, - author = {Nater, Quentin and Khayati, Mourad}, - license = {MIT}, - title = {{ImputeGAP Library}}, - url = {https://github.com/eXascaleInfolab/ImputeGAP}, - year = {2025} -} -``` --- diff --git a/imputegap.egg-info/SOURCES.txt b/imputegap.egg-info/SOURCES.txt index cd5eb3f3..1d344f8e 100644 --- a/imputegap.egg-info/SOURCES.txt +++ b/imputegap.egg-info/SOURCES.txt @@ -57,17 +57,25 @@ imputegap/algorithms/xgboost.py imputegap/algorithms/zero_impute.py imputegap/algorithms/lib/lib_algo.dll imputegap/algorithms/lib/lib_algo.so +imputegap/algorithms/lib/lib_cdrec.dylib imputegap/algorithms/lib/lib_cdrec.so +imputegap/algorithms/lib/lib_dynammo.dylib imputegap/algorithms/lib/lib_dynammo.so +imputegap/algorithms/lib/lib_grouse.dylib imputegap/algorithms/lib/lib_grouse.so -imputegap/algorithms/lib/lib_grouse_old.so +imputegap/algorithms/lib/lib_iterative_svd.dylib imputegap/algorithms/lib/lib_iterative_svd.so +imputegap/algorithms/lib/lib_rosl.dylib imputegap/algorithms/lib/lib_rosl.so -imputegap/algorithms/lib/lib_rosl_old.so +imputegap/algorithms/lib/lib_soft_impute.dylib imputegap/algorithms/lib/lib_soft_impute.so +imputegap/algorithms/lib/lib_spirit.dylib imputegap/algorithms/lib/lib_spirit.so +imputegap/algorithms/lib/lib_stmvl.dylib imputegap/algorithms/lib/lib_stmvl.so +imputegap/algorithms/lib/lib_svt.dylib imputegap/algorithms/lib/lib_svt.so +imputegap/algorithms/lib/lib_tkcm.dylib imputegap/algorithms/lib/lib_tkcm.so imputegap/assets/logo_imputegab.png imputegap/dataset/README.md @@ -184,8 +192,11 @@ imputegap/dataset/docs/temperature/features_temperature.txt imputegap/env/default_explainer.toml imputegap/env/default_values.toml imputegap/imputegap_assets/.gitkeep -imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg -imputegap/imputegap_assets/shap/.gitkeep +imputegap/imputegap_assets/benchmark/.gitkeep +imputegap/imputegap_assets/benchmark/_heatmap/.gitkeep +imputegap/imputegap_assets/downstream/.gitkeep +imputegap/imputegap_assets/shap/grouped/.gitkeep +imputegap/imputegap_assets/shap/per_categories/.gitkeep imputegap/params/optimal_parameters_b_bafu_iim.toml imputegap/params/optimal_parameters_b_bafu_mrnn.toml imputegap/params/optimal_parameters_b_bafu_stmvl.toml @@ -400,6 +411,10 @@ imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/epoch=19-step imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/model/version_0/events.out.tfevents.1741892881.diufpc048844.135606.2 imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/model/version_0/events.out.tfevents.1741892891.diufpc048844.135606.3 imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/model/version_0/hparams.yaml +imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt +imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547910.diufpc048844.345185.0 +imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547938.diufpc048844.345185.1 +imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/hparams.yaml imputegap/wrapper/AlgoPython/HKMF_T/callback.py imputegap/wrapper/AlgoPython/HKMF_T/dataloder.py imputegap/wrapper/AlgoPython/HKMF_T/hankel_methods.py @@ -445,6 +460,7 @@ imputegap/wrapper/AlgoPython/MissNet/data/conversion.py imputegap/wrapper/AlgoPython/MissNet/data/generate.py imputegap/wrapper/AlgoPython/MissNet/data/original/dynammo.zip imputegap/wrapper/AlgoPython/MissNet/data/original/motes.zip +imputegap/wrapper/AlgoPython/MissNet/temp/model.pkl imputegap/wrapper/AlgoPython/TIDER/TIDER.pt imputegap/wrapper/AlgoPython/TIDER/TIDER.py imputegap/wrapper/AlgoPython/TIDER/__init__.py diff --git a/imputegap.egg-info/requires.txt b/imputegap.egg-info/requires.txt index 3b9b5c49..4ed6e1ef 100644 --- a/imputegap.egg-info/requires.txt +++ b/imputegap.egg-info/requires.txt @@ -3,7 +3,6 @@ matplotlib==3.7.5 toml==0.10.2 scikit-learn>=1.5.0 scipy==1.14.1 -setuptools==75.1.0 tensorflow==2.17.0 shap==0.44.1 pycatch22==0.4.4 @@ -38,7 +37,6 @@ ray[tune]==2.41.0 fire==0.7.0 fastdtw==0.3.4 torch-geometric==2.6.1 -torch-cluster==1.6.3 pytorch-lightning==2.5.0 torchmetrics==1.6.1 lightning==2.5.0 diff --git a/imputegap/__init__.py b/imputegap/__init__.py index 010279ae..84ce1414 100644 --- a/imputegap/__init__.py +++ b/imputegap/__init__.py @@ -1 +1 @@ -__version__ = "1.0.6" \ No newline at end of file +__version__ = "1.0.7" \ No newline at end of file diff --git a/imputegap/__pycache__/__init__.cpython-312.pyc b/imputegap/__pycache__/__init__.cpython-312.pyc index 0ed9d1b9..e458f40d 100644 Binary files a/imputegap/__pycache__/__init__.cpython-312.pyc and 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differ diff --git a/imputegap/algorithms/bayotide.py b/imputegap/algorithms/bayotide.py index e843a583..63e6e85c 100644 --- a/imputegap/algorithms/bayotide.py +++ b/imputegap/algorithms/bayotide.py @@ -66,6 +66,6 @@ def bay_otide(incomp_data, K_trend=20, K_season=2, n_season=5, K_bias=1, time_sc end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation bay_otide - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation bay_otide - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/bit_graph.py b/imputegap/algorithms/bit_graph.py index 9f00d15d..b32cc2eb 100644 --- a/imputegap/algorithms/bit_graph.py +++ b/imputegap/algorithms/bit_graph.py @@ -64,6 +64,6 @@ def bit_graph(incomp_data, node_number=-1, kernel_set=[1], dropout=0.1, subgraph end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation bit graph - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation bit graph - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/brits.py b/imputegap/algorithms/brits.py index 346ab9ba..4bc69301 100644 --- a/imputegap/algorithms/brits.py +++ b/imputegap/algorithms/brits.py @@ -49,6 +49,6 @@ def brits(incomp_data, model="brits", epoch=10, batch_size=7, nbr_features=1, hi end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation brits - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation brits - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/cdrec.py b/imputegap/algorithms/cdrec.py index bcad9d95..9d6fb16d 100644 --- a/imputegap/algorithms/cdrec.py +++ b/imputegap/algorithms/cdrec.py @@ -29,7 +29,7 @@ def native_cdrec(__py_matrix, __py_rank, __py_epsilon, __py_iterations): Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7 """ - shared_lib = utils.load_share_lib("lib_cdrec.so") + shared_lib = utils.load_share_lib("lib_cdrec") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -87,7 +87,7 @@ def cdrec(incomp_data, truncation_rank, iterations, epsilon, logs=True, lib_path """ - print(f"\t\t\t\t(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, " + print(f"(PYTHON) CDRec: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for rank {truncation_rank}, " f"epsilon {epsilon}, and iterations {iterations}...") start_time = time.time() # Record start time @@ -98,6 +98,6 @@ def cdrec(incomp_data, truncation_rank, iterations, epsilon, logs=True, lib_path end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/cpp_integration.py b/imputegap/algorithms/cpp_integration.py index 30ef7656..7985ad6b 100644 --- a/imputegap/algorithms/cpp_integration.py +++ b/imputegap/algorithms/cpp_integration.py @@ -25,7 +25,7 @@ def native_algo(__py_matrix, __py_param): """ - shared_lib = utils.load_share_lib("to_adapt.so") + shared_lib = utils.load_share_lib("to_adapt") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -79,6 +79,6 @@ def your_algo(contamination, param, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation algo - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation algo - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/deep_mvi.py b/imputegap/algorithms/deep_mvi.py index 3317aabb..8fab88f7 100644 --- a/imputegap/algorithms/deep_mvi.py +++ b/imputegap/algorithms/deep_mvi.py @@ -41,6 +41,6 @@ def deep_mvi(incomp_data, max_epoch=1000, patience=2, lr=0.001, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation deep mvi - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation deep mvi - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/dynammo.py b/imputegap/algorithms/dynammo.py index 205f1bf4..acc9f6ff 100644 --- a/imputegap/algorithms/dynammo.py +++ b/imputegap/algorithms/dynammo.py @@ -29,7 +29,7 @@ def native_dynammo(__py_matrix, __py_h, __py_maxIter, __py_fast): L. Li, J. McCann, N. S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pages 507–516, 2009. """ - shared_lib = utils.load_share_lib("lib_dynammo.so") + shared_lib = utils.load_share_lib("lib_dynammo") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -92,6 +92,6 @@ def dynammo(incomp_data, h, max_iteration, approximation, logs=True, lib_path=No end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation DynaMMo - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation DynaMMo - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/gain.py b/imputegap/algorithms/gain.py index 0d0a1917..912e8ed4 100644 --- a/imputegap/algorithms/gain.py +++ b/imputegap/algorithms/gain.py @@ -44,6 +44,6 @@ def gain(incomp_data, batch_size=32, hint_rate=0.9, alpha=10, epoch=100, logs=Tr end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation gain - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation gain - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/grin.py b/imputegap/algorithms/grin.py index 6de5ead0..f6626d29 100644 --- a/imputegap/algorithms/grin.py +++ b/imputegap/algorithms/grin.py @@ -60,6 +60,6 @@ def grin(incomp_data, d_hidden=32, lr=0.001, batch_size=32, window=10, alpha=10. end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation grin - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation grin - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/grouse.py b/imputegap/algorithms/grouse.py index e90f6833..e72d2636 100644 --- a/imputegap/algorithms/grouse.py +++ b/imputegap/algorithms/grouse.py @@ -24,7 +24,7 @@ def native_grouse(__py_matrix, __py_rank): D. Zhang and L. Balzano. Global convergence of a grassmannian gradient descent algorithm for subspace estimation. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, pages 1460–1468, 2016. """ - shared_lib = utils.load_share_lib("lib_grouse.so") + shared_lib = utils.load_share_lib("lib_grouse") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -80,6 +80,6 @@ def grouse(incomp_data, max_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation GROUSE - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation GROUSE - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/hkmf_t.py b/imputegap/algorithms/hkmf_t.py index ab38585f..b6d3f819 100644 --- a/imputegap/algorithms/hkmf_t.py +++ b/imputegap/algorithms/hkmf_t.py @@ -48,6 +48,6 @@ def hkmf_t(incomp_data, tags=None, data_names=None, epoch=10, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation hkmf_t - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation hkmf_t - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/iim.py b/imputegap/algorithms/iim.py index 38b258f4..afbdb5be 100644 --- a/imputegap/algorithms/iim.py +++ b/imputegap/algorithms/iim.py @@ -45,6 +45,6 @@ def iim(incomp_data, number_neighbor, algo_code, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/interpolation.py b/imputegap/algorithms/interpolation.py index a1230012..fe1d93c5 100644 --- a/imputegap/algorithms/interpolation.py +++ b/imputegap/algorithms/interpolation.py @@ -30,7 +30,7 @@ def interpolation(incomp_data, method="linear", poly_order=2, logs=True): """ - print(f"\t\t\t\t(PYTHON) interpolation : ({incomp_data.shape[0]},{incomp_data.shape[1]}) for method {method}" + print(f"(PYTHON) interpolation : ({incomp_data.shape[0]},{incomp_data.shape[1]}) for method {method}" f", and polynomial order {poly_order}...") start_time = time.time() # Record start time @@ -70,6 +70,6 @@ def interpolation(incomp_data, method="linear", poly_order=2, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation with interpolation - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation with interpolation - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/iterative_svd.py b/imputegap/algorithms/iterative_svd.py index faed1b02..18b43585 100644 --- a/imputegap/algorithms/iterative_svd.py +++ b/imputegap/algorithms/iterative_svd.py @@ -25,7 +25,7 @@ def native_iterative_svd(__py_matrix, __py_rank): Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman, Missing value estimation methods for DNA microarrays , Bioinformatics, Volume 17, Issue 6, June 2001, Pages 520–525, https://doi.org/10.1093/bioinformatics/17.6.520 """ - shared_lib = utils.load_share_lib("lib_iterative_svd.so") + shared_lib = utils.load_share_lib("lib_iterative_svd") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -82,6 +82,6 @@ def iterative_svd(incomp_data, truncation_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation iterative svd - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation iterative svd - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/knn.py b/imputegap/algorithms/knn.py index 97f35693..146fc32c 100644 --- a/imputegap/algorithms/knn.py +++ b/imputegap/algorithms/knn.py @@ -29,7 +29,7 @@ def knn(incomp_data, k=5, weights="uniform", logs=True): """ - print(f"\t\t\t\t(PYTHON) KNN: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for k {k}, " + print(f"(PYTHON) KNNImpute: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for k {k}, " f", and weights {weights}...") start_time = time.time() # Record start time @@ -74,6 +74,6 @@ def knn(incomp_data, k=5, weights="uniform", logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation knn - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation knn_impute - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/lib/lib_cdrec.dylib b/imputegap/algorithms/lib/lib_cdrec.dylib new file mode 100644 index 00000000..4996676c Binary files /dev/null and b/imputegap/algorithms/lib/lib_cdrec.dylib differ diff --git a/imputegap/algorithms/lib/lib_dynammo.dylib b/imputegap/algorithms/lib/lib_dynammo.dylib new file mode 100644 index 00000000..d74632c7 Binary files /dev/null and b/imputegap/algorithms/lib/lib_dynammo.dylib differ diff --git a/imputegap/algorithms/lib/lib_grouse.dylib b/imputegap/algorithms/lib/lib_grouse.dylib new file mode 100644 index 00000000..f2a2faed Binary files /dev/null and b/imputegap/algorithms/lib/lib_grouse.dylib differ diff --git a/imputegap/algorithms/lib/lib_grouse_old.so b/imputegap/algorithms/lib/lib_grouse_old.so deleted file mode 100644 index faca87aa..00000000 Binary files a/imputegap/algorithms/lib/lib_grouse_old.so and /dev/null differ diff --git a/imputegap/algorithms/lib/lib_iterative_svd.dylib b/imputegap/algorithms/lib/lib_iterative_svd.dylib new file mode 100644 index 00000000..4476ac45 Binary files /dev/null and b/imputegap/algorithms/lib/lib_iterative_svd.dylib differ diff --git a/imputegap/algorithms/lib/lib_rosl.dylib b/imputegap/algorithms/lib/lib_rosl.dylib new file mode 100644 index 00000000..0fbc1996 Binary files /dev/null and b/imputegap/algorithms/lib/lib_rosl.dylib differ diff --git a/imputegap/algorithms/lib/lib_rosl_old.so b/imputegap/algorithms/lib/lib_rosl_old.so deleted file mode 100644 index 0cc7e086..00000000 Binary files a/imputegap/algorithms/lib/lib_rosl_old.so and /dev/null differ diff --git a/imputegap/algorithms/lib/lib_soft_impute.dylib b/imputegap/algorithms/lib/lib_soft_impute.dylib new file mode 100644 index 00000000..45bb6e3e Binary files /dev/null and b/imputegap/algorithms/lib/lib_soft_impute.dylib differ diff --git a/imputegap/algorithms/lib/lib_spirit.dylib b/imputegap/algorithms/lib/lib_spirit.dylib new file mode 100644 index 00000000..ed900873 Binary files /dev/null and b/imputegap/algorithms/lib/lib_spirit.dylib differ diff --git a/imputegap/algorithms/lib/lib_stmvl.dylib b/imputegap/algorithms/lib/lib_stmvl.dylib new file mode 100644 index 00000000..0b84a73f Binary files /dev/null and b/imputegap/algorithms/lib/lib_stmvl.dylib differ diff --git a/imputegap/algorithms/lib/lib_svt.dylib b/imputegap/algorithms/lib/lib_svt.dylib new file mode 100644 index 00000000..7dbffb24 Binary files /dev/null and b/imputegap/algorithms/lib/lib_svt.dylib differ diff --git a/imputegap/algorithms/lib/lib_tkcm.dylib b/imputegap/algorithms/lib/lib_tkcm.dylib new file mode 100644 index 00000000..4d90ae1c Binary files /dev/null and b/imputegap/algorithms/lib/lib_tkcm.dylib differ diff --git a/imputegap/algorithms/mean_impute_by_series.py b/imputegap/algorithms/mean_impute_by_series.py index bb6f7c4a..0276e974 100644 --- a/imputegap/algorithms/mean_impute_by_series.py +++ b/imputegap/algorithms/mean_impute_by_series.py @@ -38,7 +38,7 @@ def mean_impute_by_series(incomp_data, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mean impute (by series) - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mean impute (by series) - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/mice.py b/imputegap/algorithms/mice.py index ab73e320..d394078a 100644 --- a/imputegap/algorithms/mice.py +++ b/imputegap/algorithms/mice.py @@ -39,7 +39,7 @@ def mice(incomp_data, max_iter=3, tol=0.001, initial_strategy='mean', seed=42, l https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer """ - print("\t\t(PYTHON) MICE : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for max_iter ", + print("(PYTHON) MICE : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for max_iter ", max_iter, ", tol ", tol, " initial_strategy ", initial_strategy, ", and seed ", seed, "...\n\n\t\t\t" "Careful, this imputation algorithm might take a while to compute.") @@ -50,6 +50,6 @@ def mice(incomp_data, max_iter=3, tol=0.001, initial_strategy='mean', seed=42, l end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation MICE - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation MICE - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/miss_forest.py b/imputegap/algorithms/miss_forest.py index 3dd56645..0a5145d6 100644 --- a/imputegap/algorithms/miss_forest.py +++ b/imputegap/algorithms/miss_forest.py @@ -45,7 +45,7 @@ def miss_forest(incomp_data, n_estimators=10, max_iter=3, max_features='sqrt', s https://pypi.org/project/MissForest/ """ - print("\t\t(PYTHON) MISS FOREST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for n_estimators ", + print("(PYTHON) MISS FOREST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") for n_estimators ", n_estimators, ", max_iter ", max_iter, " max_features ", max_features, ", and seed ", seed, "...") # Convert numpy array to pandas DataFrame if needed @@ -64,6 +64,6 @@ def miss_forest(incomp_data, n_estimators=10, max_iter=3, max_features='sqrt', s end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation MISS FOREST - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation MISS FOREST - Execution Time: {(end_time - start_time):.4f} seconds\n") return np.array(recov_data) diff --git a/imputegap/algorithms/miss_net.py b/imputegap/algorithms/miss_net.py index a636065a..e120c7ba 100644 --- a/imputegap/algorithms/miss_net.py +++ b/imputegap/algorithms/miss_net.py @@ -46,7 +46,7 @@ def miss_net(incomp_data, alpha, beta, L, n_cl, max_iteration, tol, random_init, https://github.com/KoheiObata/MissNet/tree/main """ - print("\t\t(PYTHON) MISS NET: Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") " + print("(PYTHON) MISS NET: Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ") " "for alpha ", alpha, ", beta ", beta, ", L ", L, ", n_cl ", n_cl, ", max_iteration ", max_iteration, "tol ", tol, " random_init ", random_init, "...") @@ -62,6 +62,6 @@ def miss_net(incomp_data, alpha, beta, L, n_cl, max_iteration, tol, random_init, recov_data[nan_mask] = incomp_data[nan_mask] if logs: - print(f"\n\t\t> logs, imputation miss_net - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation miss_net - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/mpin.py b/imputegap/algorithms/mpin.py index 41f7e385..8e544153 100644 --- a/imputegap/algorithms/mpin.py +++ b/imputegap/algorithms/mpin.py @@ -52,6 +52,6 @@ def mpin(incomp_data=None, incre_mode="alone", window=2, k=10, lr=0.01, weight_d end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mpin - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mpin - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/mrnn.py b/imputegap/algorithms/mrnn.py index 53b8640d..e8ed7716 100644 --- a/imputegap/algorithms/mrnn.py +++ b/imputegap/algorithms/mrnn.py @@ -48,6 +48,6 @@ def mrnn(incomp_data, hidden_dim, learning_rate, iterations, sequence_length, lo end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/pristi.py b/imputegap/algorithms/pristi.py index ce00d3e0..a86967f1 100644 --- a/imputegap/algorithms/pristi.py +++ b/imputegap/algorithms/pristi.py @@ -44,6 +44,6 @@ def pristi(incomp_data, target_strategy="hybrid", unconditional=True, seed=42, d end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation priSTI - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation priSTI - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/rosl.py b/imputegap/algorithms/rosl.py index 41dd2c92..9ef33122 100644 --- a/imputegap/algorithms/rosl.py +++ b/imputegap/algorithms/rosl.py @@ -31,7 +31,7 @@ def native_rosl(__py_matrix, __py_rank, __py_regularization): ---------- X. Shu, F. Porikli, and N. Ahuja. Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 3874–3881, 2014. """ - shared_lib = utils.load_share_lib("lib_rosl.so") + shared_lib = utils.load_share_lib("lib_rosl") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -96,6 +96,6 @@ def rosl(incomp_data, rank, regularization, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation ROSL - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation ROSL - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/soft_impute.py b/imputegap/algorithms/soft_impute.py index df64c67c..980a1b3b 100644 --- a/imputegap/algorithms/soft_impute.py +++ b/imputegap/algorithms/soft_impute.py @@ -24,7 +24,7 @@ def native_soft_impute(__py_matrix, __py_max_rank): R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11:2287–2322, 2010. """ - shared_lib = utils.load_share_lib("lib_soft_impute.so") + shared_lib = utils.load_share_lib("lib_soft_impute") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -81,6 +81,6 @@ def soft_impute(incomp_data, max_rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation Soft Impute - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation Soft Impute - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/spirit.py b/imputegap/algorithms/spirit.py index 3d168954..65aaadc3 100644 --- a/imputegap/algorithms/spirit.py +++ b/imputegap/algorithms/spirit.py @@ -31,7 +31,7 @@ def native_spirit(__py_matrix, __py_k, __py_w, __py_lambda): S. Papadimitriou, J. Sun, and C. Faloutsos. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30 - September 2, 2005, pages 697–708, 2005. """ - shared_lib = utils.load_share_lib("lib_spirit.so") + shared_lib = utils.load_share_lib("lib_spirit") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -98,6 +98,6 @@ def spirit(incomp_data, k, w, lambda_value, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation SPIRIT - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation SPIRIT - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/stmvl.py b/imputegap/algorithms/stmvl.py index bd75f55d..9eef0f14 100644 --- a/imputegap/algorithms/stmvl.py +++ b/imputegap/algorithms/stmvl.py @@ -39,7 +39,7 @@ def native_stmvl(__py_matrix, __py_window, __py_gamma, __py_alpha): School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. """ - shared_lib = utils.load_share_lib("lib_stmvl.so") + shared_lib = utils.load_share_lib("lib_stmvl") __py_sizen = len(__py_matrix); __py_sizem = len(__py_matrix[0]); @@ -84,6 +84,9 @@ def stmvl(incomp_data, window_size, gamma, alpha, logs=True): :return: recov_data, metrics : all time series with imputation data and their metrics """ + print(f"(PYTHON) ST-MVL: ({incomp_data.shape[0]},{incomp_data.shape[1]}) for window_size {window_size}, " + f"gamma {gamma}, and alpha {alpha}...") + start_time = time.time() # Record start time # Call the C++ function to perform recovery @@ -91,6 +94,6 @@ def stmvl(incomp_data, window_size, gamma, alpha, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/svt.py b/imputegap/algorithms/svt.py index 12d5256d..ea8522bc 100644 --- a/imputegap/algorithms/svt.py +++ b/imputegap/algorithms/svt.py @@ -25,7 +25,7 @@ def native_svt(__py_matrix, __py_tau): J. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. [8] J. Cambronero, J. K. Feser, M. J. Smith, and """ - shared_lib = utils.load_share_lib("lib_svt.so") + shared_lib = utils.load_share_lib("lib_svt") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -80,6 +80,6 @@ def svt(incomp_data, tau, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation SVT - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation SVT - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/tkcm.py b/imputegap/algorithms/tkcm.py index 713fe644..59b35b5a 100644 --- a/imputegap/algorithms/tkcm.py +++ b/imputegap/algorithms/tkcm.py @@ -24,7 +24,7 @@ def native_tkcm(__py_matrix, __py_rank): K. Wellenzohn, M. H. Böhlen, A. Dignös, J. Gamper, and H. Mitterer. Continuous imputation of missing values in streams of pattern-determining time series. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017., pages 330–341, 2017. """ - shared_lib = utils.load_share_lib("lib_tkcm.so") + shared_lib = utils.load_share_lib("lib_tkcm") __py_n = len(__py_matrix); __py_m = len(__py_matrix[0]); @@ -81,6 +81,6 @@ def tkcm(incomp_data, rank, logs=True, lib_path=None): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation TKCM - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation TKCM - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/trmf.py b/imputegap/algorithms/trmf.py index a80d46a7..db3cfe9d 100644 --- a/imputegap/algorithms/trmf.py +++ b/imputegap/algorithms/trmf.py @@ -57,6 +57,6 @@ def trmf(incomp_data, lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_ite end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation trmf - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation trmf - Execution Time: {(end_time - start_time):.4f} seconds\n") return recov_data diff --git a/imputegap/algorithms/xgboost.py b/imputegap/algorithms/xgboost.py index 29834896..bb6bc0e2 100644 --- a/imputegap/algorithms/xgboost.py +++ b/imputegap/algorithms/xgboost.py @@ -37,7 +37,7 @@ def xgboost(incomp_data, n_estimators=10, seed=42, logs=True): https://medium.com/@tzhaonj/imputing-missing-data-using-xgboost-802757cace6d """ - print("\t\t(PYTHON) XGBOOST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ")" + print("(PYTHON) XGBOOST : Matrix Shape: (", incomp_data.shape[0], ", ", incomp_data.shape[1], ")" " for n_estimators ", n_estimators, ", and seed ", seed, "...") if isinstance(incomp_data, np.ndarray): @@ -68,6 +68,6 @@ def xgboost(incomp_data, n_estimators=10, seed=42, logs=True): end_time = time.time() if logs: - print(f"\n\t\t> logs, imputation XGBOOST - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, imputation XGBOOST - Execution Time: {(end_time - start_time):.4f} seconds\n") return np.array(recov_data) diff --git a/imputegap/env/default_values.toml b/imputegap/env/default_values.toml index e422936d..299eb3d4 100644 --- a/imputegap/env/default_values.toml +++ b/imputegap/env/default_values.toml @@ -87,7 +87,7 @@ approximation = true rank = 4 # ALGORITHM DEFAULT VALUES : STATISTICS -[knn] +[knn_impute] k = 5 weights = "uniform" diff --git a/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg b/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg deleted file mode 100644 index e0560938..00000000 Binary files a/imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg and /dev/null differ diff --git a/imputegap/imputegap_assets/benchmark/.gitkeep b/imputegap/imputegap_assets/benchmark/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/imputegap/imputegap_assets/benchmark/_heatmap/.gitkeep b/imputegap/imputegap_assets/benchmark/_heatmap/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/imputegap/imputegap_assets/downstream/.gitkeep b/imputegap/imputegap_assets/downstream/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/imputegap/imputegap_assets/shap/grouped/.gitkeep b/imputegap/imputegap_assets/shap/grouped/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/imputegap/imputegap_assets/shap/per_categories/.gitkeep b/imputegap/imputegap_assets/shap/per_categories/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc b/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc index 6eb0c663..f51f0384 100644 Binary files a/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc and b/imputegap/recovery/__pycache__/benchmark.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/downstream.cpython-312.pyc b/imputegap/recovery/__pycache__/downstream.cpython-312.pyc index 86f4b575..d2ca269b 100644 Binary files a/imputegap/recovery/__pycache__/downstream.cpython-312.pyc and b/imputegap/recovery/__pycache__/downstream.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/evaluation.cpython-312.pyc b/imputegap/recovery/__pycache__/evaluation.cpython-312.pyc index 8d1fe371..98092fc6 100644 Binary files a/imputegap/recovery/__pycache__/evaluation.cpython-312.pyc and b/imputegap/recovery/__pycache__/evaluation.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/explainer.cpython-312.pyc b/imputegap/recovery/__pycache__/explainer.cpython-312.pyc index 1d5bfae7..00d577a6 100644 Binary files a/imputegap/recovery/__pycache__/explainer.cpython-312.pyc and b/imputegap/recovery/__pycache__/explainer.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/imputation.cpython-312.pyc b/imputegap/recovery/__pycache__/imputation.cpython-312.pyc index 7dccd467..36e5e416 100644 Binary files a/imputegap/recovery/__pycache__/imputation.cpython-312.pyc and b/imputegap/recovery/__pycache__/imputation.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/manager.cpython-312.pyc b/imputegap/recovery/__pycache__/manager.cpython-312.pyc index 5c5ec9bb..86c3fc1d 100644 Binary files a/imputegap/recovery/__pycache__/manager.cpython-312.pyc and b/imputegap/recovery/__pycache__/manager.cpython-312.pyc differ diff --git a/imputegap/recovery/__pycache__/optimization.cpython-312.pyc b/imputegap/recovery/__pycache__/optimization.cpython-312.pyc index e90f32ed..deaef965 100644 Binary files a/imputegap/recovery/__pycache__/optimization.cpython-312.pyc and b/imputegap/recovery/__pycache__/optimization.cpython-312.pyc differ diff --git a/imputegap/recovery/benchmark.py b/imputegap/recovery/benchmark.py index e25cc722..cef2dbc9 100644 --- a/imputegap/recovery/benchmark.py +++ b/imputegap/recovery/benchmark.py @@ -4,7 +4,6 @@ import time import numpy as np import matplotlib.pyplot as plt - import xlsxwriter from imputegap.tools import utils @@ -187,9 +186,9 @@ def avg_results(self, *datasets): for j, algo in enumerate(algorithms_list): comprehensive_matrix[i, j] = average_rmse_matrix[dataset].get(algo, np.nan) - print("\nVisualization of datasets:", datasets_list) - print("Visualization of algorithms:", algorithms_list) - print("Visualization of matrix:\n", comprehensive_matrix, "\n\n") + print("\nvisualization of datasets:", *datasets_list) + print("visualization of algorithms:", *algorithms_list) + print(f"visualization of aggregate matrix :\n {comprehensive_matrix}\n\n") return comprehensive_matrix, algorithms_list, datasets_list @@ -215,6 +214,7 @@ def generate_heatmap(self, scores_list, algos, sets, save_dir="./reports", displ Bool True if the matrix has been generated """ + save_dir = save_dir + "/_heatmap/" if not os.path.exists(save_dir): os.makedirs(save_dir) @@ -226,6 +226,7 @@ def generate_heatmap(self, scores_list, algos, sets, save_dir="./reports", displ y_size = cell_size*nbr_datasets fig, ax = plt.subplots(figsize=(x_size, y_size)) + fig.canvas.manager.set_window_title("benchmark heatmap") cmap = plt.cm.Greys norm = plt.Normalize(vmin=0, vmax=2) # Normalizing values between 0 and 2 (RMSE) @@ -307,21 +308,28 @@ def generate_reports_txt(self, runs_plots_scores, save_dir="./reports", dataset= "MI": "Mutual Information - Indicates dependency between variables.", "CORRELATION": "Correlation Coefficient - Indicates linear relationship between variables." } + first_metric = True for metric, description in metrics.items(): # Write the metric description file.write(f"{metric}: {description}\n\n") - column_widths = [15, 15, 15, 15, 12, 25] + column_widths = [15, 15, 15, 18, 12, 25] # Create a table header - headers = ["Dataset", "Algorithm", "Optimizer", "Pattern", "X Value", metric] + headers = ["Dataset", "Pattern", "Algorithm", "Optimizer", "Rate", metric] header_row = "|".join(f" {header:^{width}} " for header, width in zip(headers, column_widths)) separator_row = "+" + "+".join(f"{'-' * (width + 2)}" for width in column_widths) + "+" file.write(f"{separator_row}\n") file.write(f"|{header_row}|\n") file.write(f"{separator_row}\n") + if first_metric and run ==-1 : + print(f"\n{metric}: {description}\n") + print(separator_row) + print(f"|{header_row}|") + print(separator_row) + # Extract and write results for the current metric for dataset, algo_items in runs_plots_scores.items(): for algorithm, optimizer_items in algo_items.items(): @@ -335,12 +343,17 @@ def generate_reports_txt(self, runs_plots_scores, save_dir="./reports", dataset= row = "|".join( f" {value:^{width}} " for value, width in zip(row_values, column_widths)) file.write(f"|{row}|\n") + if first_metric and run ==-1 : + print(f"|{row}|") file.write(f"{separator_row}\n\n") + if first_metric and run ==-1 : + print(separator_row + "\n") + first_metric = False file.write("Dictionary of Results:\n") file.write(str(runs_plots_scores) + "\n") - print(f"\nReport recorded in {save_path}") + print(f"\nreports recorded in the following directory : {save_path}") def generate_reports_excel(self, runs_plots_scores, save_dir="./reports", dataset="", run=-1): """ @@ -440,9 +453,8 @@ def generate_reports_excel(self, runs_plots_scores, save_dir="./reports", datase # Close the workbook workbook.close() - print(f"\nExcel report recorded in {save_path}") - def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports"): + def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save_dir="./reports", display=False): """ Generate and save plots for each metric and pattern based on provided scores. @@ -456,6 +468,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save If True, generates a single figure with subplots for all metrics (default is False). save_dir : str, optional Directory to save generated plots (default is "./reports"). + display : bool, optional + Display or not the plots (default is False). Returns ------- @@ -475,6 +489,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save if subplot: fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(x_size*1.90, y_size*2.90)) # Adjusted figsize + fig.canvas.manager.set_window_title("benchmark analysis") + axes = axes.ravel() # Flatten the 2D array of axes to a 1D array # Iterate over each metric, generating separate plots, including new timing metrics @@ -522,8 +538,8 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save # Save plot only if there is data to display if has_data: ylabel_metric = { - "imputation_time": "Imputation Time (sec)", - "log_imputation": "Imputation Time (log)", + "imputation_time": "Runtime Linear Scale (sec)", + "log_imputation": "Runtime Log Scale", }.get(metric, metric) ax.set_title(metric) @@ -534,8 +550,10 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save # Set y-axis limits with padding below 0 for visibility if metric == "imputation_time": ax.set_ylim(-10, 90) + ax.set_title("Runtime Linear Scale") elif metric == "log_imputation": ax.set_ylim(-4.5, 2.5) + ax.set_title("Runtime Log Scale") elif metric == "MAE": ax.set_ylim(-0.1, 2.4) elif metric == "MI": @@ -544,12 +562,13 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save ax.set_ylim(-0.1, 2.6) elif metric == "CORRELATION": ax.set_ylim(-0.75, 1.1) + ax.set_title("Pearson Correlation") # Customize x-axis ticks ax.set_xticks(ticks) ax.set_xticklabels([f"{int(tick * 100)}%" for tick in ticks]) ax.grid(True, zorder=0) - ax.legend(loc='upper left', bbox_to_anchor=(1, 1)) + ax.legend(loc='upper left', fontsize=7, frameon=True, fancybox=True, framealpha=0.8) if not subplot: filename = f"{dataset}_{pattern}_{optimizer}_{metric}.jpg" @@ -562,12 +581,14 @@ def generate_plots(self, runs_plots_scores, ticks, subplot=False, y_size=4, save filename = f"{dataset}_{pattern}_metrics_subplot.jpg" filepath = os.path.join(save_dir, filename) plt.savefig(filepath) - plt.close() - print("\nAll plots recorded in", save_dir) + if display: + plt.show() + + print("\nplots recorded in the following directory : ", save_dir) def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"], - x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["user_def"], save_dir="./reports", runs=1): + x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], optimizers=["default_params"], save_dir="./reports", runs=1): """ Execute a comprehensive evaluation of imputation algorithms over multiple datasets and patterns. @@ -603,6 +624,9 @@ def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"] not_optimized = ["none"] mean_group = ["mean", "MeanImpute", "min", "MinImpute", "zero", "ZeroImpute", "MeanImputeBySeries"] + if "mpin" in algorithms or "MPIN" in algorithms: + raise ValueError("The 'mpin' algorithm is not compatible with this setup.") + for i_run in range(0, abs(runs)): for dataset in datasets: runs_plots_scores = {} @@ -714,33 +738,34 @@ def eval(self, algorithms=["cdrec"], datasets=["eeg-alcohol"], patterns=["mcar"] "times": dic_timing } - save_dir_runs = save_dir + "/run_" + str(i_run) + "/" + dataset - print("\n\truns saved in : ", save_dir_runs) + save_dir_runs = save_dir + "/_details/run_" + str(i_run) + "/" + dataset + print("\nruns saved in : ", save_dir_runs) self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_runs) self.generate_plots(runs_plots_scores=runs_plots_scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_runs) self.generate_reports_txt(runs_plots_scores, save_dir_runs, dataset, i_run) self.generate_reports_excel(runs_plots_scores, save_dir_runs, dataset, i_run) run_storage.append(runs_plots_scores) - print("============================================================================\n\n\n\n\n\n") + print("\n\n\n\n\n\n\n\n\n\n\n\n=end_of_the_evaluation===============================================" + "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nresults of the analysis:\n") scores_list, algos, sets = self.avg_results(*run_storage) - _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=False) + _ = self.generate_heatmap(scores_list, algos, sets, save_dir=save_dir, display=True) run_averaged = self.average_runs_by_names(run_storage) - save_dir_agg = save_dir + "/aggregation" - print("\n\n\taggragation of results saved in : ", save_dir_agg) + print("\n\nthe results of the analysis has been saved in : ", save_dir, "\n\n") for scores in run_averaged: all_keys = list(scores.keys()) dataset_name = str(all_keys[0]) - save_dir_agg_set = save_dir_agg + "/" + dataset_name + save_dir_agg_set = save_dir + "/" + dataset_name - self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set) - self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set) self.generate_reports_txt(scores, save_dir_agg_set, dataset_name, -1) + self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=True, y_size=y_p_size, save_dir=save_dir_agg_set) + # self.generate_plots(runs_plots_scores=scores, ticks=x_axis, subplot=False, y_size=y_p_size, save_dir=save_dir_agg_set) self.generate_reports_excel(scores, save_dir_agg_set, dataset_name, -1) + print("\n\n") return run_averaged, scores_list diff --git a/imputegap/recovery/downstream.py b/imputegap/recovery/downstream.py index eafbddca..0d7e6cf4 100644 --- a/imputegap/recovery/downstream.py +++ b/imputegap/recovery/downstream.py @@ -6,14 +6,6 @@ from imputegap.tools import utils -from darts import TimeSeries -from darts.metrics import mae as darts_mae, mse as darts_mse -from sklearn.metrics import mean_absolute_error, mean_squared_error -from sktime.forecasting.base import ForecastingHorizon - - - - class Downstream: @@ -53,7 +45,7 @@ class Downstream: - def __init__(self, input_data, recov_data, incomp_data, downstream): + def __init__(self, input_data, recov_data, incomp_data, algorithm, downstream): """ Initialize the Downstream class @@ -65,6 +57,8 @@ def __init__(self, input_data, recov_data, incomp_data, downstream): The imputed time series. incomp_data : numpy.ndarray The time series with contamination (NaN values). + algorithm : str + Name of the algorithm to analyse. downstream : dict Information about the model to launch with its parameters """ @@ -72,6 +66,7 @@ def __init__(self, input_data, recov_data, incomp_data, downstream): self.recov_data = recov_data self.incomp_data = incomp_data self.downstream = downstream + self.algorithm = algorithm self.split = 0.8 self.sktime_models = utils.list_of_downstreams_sktime() @@ -92,31 +87,39 @@ def downstream_analysis(self): model = self.downstream.get("model", "naive") params = self.downstream.get("params", None) plots = self.downstream.get("plots", True) - + comparator = self.downstream.get("comparator", None) + model = model.lower() evaluator = evaluator.lower() if not params: - print("\n\t\t\t\tThe params for model of downstream analysis are empty or missing. Default ones loaded...") + print("\n\t(DOWNSTREAM) The params for model of downstream analysis are empty or missing. Default ones loaded...") loader = "forecaster-" + str(model) params = utils.load_parameters(query="default", algorithm=loader) - print("\n\t\t\t\tDownstream analysis launched for <", evaluator, "> on the model <", model, + print("\n(DOWNSTREAM) Analysis launched for <", evaluator, "> on the model <", model, "> with parameters :\n\t\t\t\t\t", params, " \n\n") if evaluator in ["forecast", "forecaster", "forecasting"]: y_train_all, y_test_all, y_pred_all = [], [], [] - mae, mse = [], [] + mae, mse, smape = [], [], [] for x in range(3): # Iterate over recov_data, input_data, and mean_impute if x == 0: - data = self.recov_data - elif x == 1: data = self.input_data + elif x == 1: + data = self.recov_data elif x == 2: from imputegap.recovery.imputation import Imputation - zero_impute = Imputation.Statistics.ZeroImpute(self.incomp_data).impute() - data = zero_impute.recov_data + + if comparator is not None: + impt = utils.config_impute_algorithm(self.incomp_data, algorithm=comparator) + impt.impute() + data = impt.recov_data + else: + comparator = "zero-impute" + zero_impute = Imputation.Statistics.ZeroImpute(self.incomp_data).impute() + data = zero_impute.recov_data data_len = data.shape[1] train_len = int(data_len * self.split) @@ -128,6 +131,10 @@ def downstream_analysis(self): if model in self.sktime_models: # --- SKTIME APPROACH --- + from sklearn.metrics import mean_absolute_error, mean_squared_error + from sktime.forecasting.base import ForecastingHorizon + from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError + y_pred = np.zeros_like(y_test) for series_idx in range(data.shape[0]): @@ -146,10 +153,17 @@ def downstream_analysis(self): # Compute metrics using sktime mae.append(mean_absolute_error(y_test, y_pred)) mse.append(mean_squared_error(y_test, y_pred)) + scoring_m = MeanAbsolutePercentageError(symmetric=True) + smape.append(scoring_m.evaluate(y_test, y_pred)*100) # Compute SMAPE + else: # --- DARTS APPROACH --- # Convert entire matrix to a Darts multivariate TimeSeries object + from darts import TimeSeries + from darts.metrics import mae as darts_mae, mse as darts_mse + from darts.metrics import smape as darts_smape + y_train_ts = TimeSeries.from_values(y_train.T) # Shape: (time_steps, n_series) y_test_ts = TimeSeries.from_values(y_test.T) # Shape: (time_steps, n_series) @@ -175,11 +189,12 @@ def downstream_analysis(self): # Compute metrics safely mae_score = darts_mae(y_test_ts, y_pred_ts) mse_score = darts_mse(y_test_ts, y_pred_ts) - + smape_score = darts_smape(y_test_ts, y_pred_ts) # Compute metrics using Darts mae.append(mae_score) mse.append(mse_score) + smape.append(smape_score) # Store for plotting y_train_all.append(y_train) @@ -188,23 +203,24 @@ def downstream_analysis(self): if plots: # Global plot with all rows and columns - self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, model, evaluator) + self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, self.algorithm, comparator, model, evaluator) # Save metrics in a dictionary - metrics = {"DOWNSTREAM-RECOV-MAE": mae[0], "DOWNSTREAM-INPUT-MAE": mae[1], - "DOWNSTREAM-MEANI-MAE": mae[2], "DOWNSTREAM-RECOV-MSE": mse[0], - "DOWNSTREAM-INPUT-MSE": mse[1], "DOWNSTREAM-MEANI-MSE": mse[2]} - - print("\n\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n") + al_name = "DOWNSTREAM-" + self.algorithm.upper() + "-MSE" + al_name_s = "DOWNSTREAM-" + self.algorithm.upper() + "-SMAPE" + al_name_c = "DOWNSTREAM-" + comparator.upper() + "-MSE" + al_name_cs = "DOWNSTREAM-" + comparator.upper() + "-SMAPE" + metrics = {"DOWNSTREAM-ORIGIN-MSE": mse[0], al_name: mse[1], al_name_c: mse[2], + "DOWNSTREAM-ORIGIN-SMAPE": smape[0], al_name_s: smape[1], al_name_cs: smape[2] } return metrics else: - print("\t\t\t\tNo evaluator found... list possible : 'forecaster'" + "*" * 30 + "\n") + print("\tNo evaluator found... list possible : 'forecaster'" + "*" * 30 + "\n") return None @staticmethod - def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None, title="Ground Truth vs Predictions", max_series=1, save_path="./imputegap_assets"): + def _plot_downstream(y_train, y_test, y_pred, incomp_data, algorithm, comparison, model=None, type=None, title="", max_series=1, save_path="./imputegap_assets/downstream"): """ Plot ground truth vs. predictions for contaminated series (series with NaN values). @@ -220,6 +236,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None Incomplete data array of shape (n_series, total_len), used to identify contaminated series. model : str Name of the current model used + algorithm : str + Name of the current algorithm used + comparison : str + Name of the current algorithm used as comparison type : str Name of the current type used title : str @@ -235,6 +255,7 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None x_size = 24 fig, axs = plt.subplots(3, max_series, figsize=(x_size, 15)) + fig.canvas.manager.set_window_title("downstream evaluation") fig.suptitle(title, fontsize=16) # Iterate over the three data types (recov_data, input_data, mean_impute) @@ -258,40 +279,41 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, model=None, type=None full_series = np.concatenate([s_y_train[series_idx], s_y_test[series_idx]]) # Plot training data - ax.plot(range(len(s_y_train[series_idx])), s_y_train[series_idx], label="Training Data", color="green") + ax.plot(range(len(s_y_train[series_idx])), s_y_train[series_idx], color="green") # Plot ground truth (testing data) ax.plot( range(len(s_y_train[series_idx]), len(full_series)), s_y_test[series_idx], - label="Ground Truth", + label="ground truth", color="green" ) + label = type + " " + model # Plot forecasted data ax.plot( range(len(s_y_train[series_idx]), len(full_series)), s_y_pred[series_idx], - label="Forecast", + label=label, linestyle="--", marker=None, color="red" ) # Add a vertical line at the split point - ax.axvline(x=len(s_y_train[series_idx]), color="orange", linestyle="--", label="Split Point") + ax.axvline(x=len(s_y_train[series_idx]), color="orange", linestyle="--") # Add labels, title, and grid if row_idx == 0: - ax.set_title(f"IMPUTATED DATA (RECOVERY), series {series_idx}") + ax.set_title(f"original data, series_{series_idx}") elif row_idx == 1: - ax.set_title(f"ORIGINAL DATA (GROUND TRUTH), series {series_idx}") + ax.set_title(f"{algorithm.lower()} imputation, series_{series_idx}") else: - ax.set_title(f"BAD IMPUTER (ZERO IMP), series {series_idx}") + ax.set_title(f"{comparison.lower()} imputation, series_{series_idx}") ax.set_xlabel("Timestamp") ax.set_ylabel("Value") - ax.legend() + ax.legend(loc='upper left', fontsize=7, frameon=True, fancybox=True, framealpha=0.8) ax.grid() # Adjust layout diff --git a/imputegap/recovery/evaluation.py b/imputegap/recovery/evaluation.py index ea2e449a..47fe912d 100644 --- a/imputegap/recovery/evaluation.py +++ b/imputegap/recovery/evaluation.py @@ -1,7 +1,4 @@ import numpy as np -from sklearn.metrics import mutual_info_score -from scipy.stats import pearsonr - class Evaluation: """ @@ -126,6 +123,8 @@ def compute_mi(self): float The mutual information (MI) score for NaN positions in the contamination dataset. """ + from sklearn.metrics import mutual_info_score + nan_locations = np.isnan(self.incomp_data) # Discretize the continuous data into bins @@ -151,6 +150,8 @@ def compute_correlation(self): float The Pearson correlation coefficient for NaN positions in the contamination dataset. """ + from scipy.stats import pearsonr + nan_locations = np.isnan(self.incomp_data) input_data_values = self.input_data[nan_locations] imputed_values = self.recov_data[nan_locations] diff --git a/imputegap/recovery/explainer.py b/imputegap/recovery/explainer.py index efb4c1c2..1fda9a7f 100644 --- a/imputegap/recovery/explainer.py +++ b/imputegap/recovery/explainer.py @@ -13,7 +13,6 @@ from matplotlib import pyplot as plt from sklearn.ensemble import RandomForestRegressor -from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries from imputegap.tools import utils @@ -366,7 +365,7 @@ def convert_results(tmp, file, algo, descriptions, features, categories, mean_fe result_display = sorted(result_display, key=lambda tup: (tup[1], tup[2]), reverse=True) - with open(to_save + "_results.txt", 'w') as file_output: + with open(to_save + "_values.txt", 'w') as file_output: for (x, algo, rate, description, feature, category, mean_features) in result_display: file_output.write( f"Feature : {x:<5} {algo:<10} with a score of {rate:<10} {category:<18} {description:<65} {feature}\n") @@ -410,8 +409,12 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl plots_categories = config[extractor]['categories'] path_file = "./imputegap_assets/shap/" - if not os.path.exists(path_file): - path_file = "./imputegap" + path_file[1:] + path_file_details = "./imputegap_assets/shap/grouped/" + path_file_categories = "./imputegap_assets/shap/per_categories/" + + os.makedirs(path_file, exist_ok=True) + os.makedirs(path_file_details, exist_ok=True) + os.makedirs(path_file_categories, exist_ok=True) x_features, x_categories, x_descriptions = [], [], [] x_fs, x_cs, x_ds, alphas = [], [], [], [] @@ -451,8 +454,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl print("\t SHAP_MODEL >> descriptions shape:", x_descriptions.shape, "\n") print("\t SHAP_MODEL >> features OK:", np.all(np.all(x_features == x_features[0, :], axis=1))) print("\t SHAP_MODEL >> categories OK:", np.all(np.all(x_categories == x_categories[0, :], axis=1))) - print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)), - "\n\n") + print("\t SHAP_MODEL >> descriptions OK:", np.all(np.all(x_descriptions == x_descriptions[0, :], axis=1)), "\n\n") model = RandomForestRegressor() model.fit(x_train, y_train) @@ -470,7 +472,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl series_names.append("Series " + str(names + np.array(x_train).shape[0])) shap.summary_plot(shval, x_test, plot_size=(25, 10), feature_names=optimal_display, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_plot.png") + alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_all.png") plt.title("SHAP Details Results") os.makedirs(path_file, exist_ok=True) plt.savefig(alpha) @@ -478,14 +480,14 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.summary_plot(np.array(shval).T, np.array(x_test).T, feature_names=series_names, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_reverse_plot.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_reverse.png") plt.title("SHAP Features by Series") plt.savefig(alpha) plt.close() alphas.append(alpha) shap.plots.waterfall(shval_x[0], show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Waterfall.png") plt.title("SHAP Waterfall Results") fig = plt.gcf() # Get the current figure created by SHAP fig.set_size_inches(20, 10) # Ensure the size is correct @@ -494,7 +496,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.plots.beeswarm(shval_x, show=display, plot_size=(22, 10)) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_DTL_Beeswarm.png") plt.title("SHAP Beeswarm Results") plt.savefig(alpha) plt.close() @@ -550,7 +552,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl mean_features = np.array(mean_features) shap.summary_plot(np.array(geometry).T, np.array(geometryT).T, plot_size=(20, 10), feature_names=geometryDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[0].lower() + ".png") plt.title("SHAP details of " + plots_categories[0].lower()) plt.savefig(alpha) plt.close() @@ -558,7 +560,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(transformation).T, np.array(transformationT).T, plot_size=(20, 10), feature_names=transformationDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[2].lower() + ".png") plt.title("SHAP details of " + plots_categories[1].lower()) plt.savefig(alpha) plt.close() @@ -566,7 +568,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(correlation).T, np.array(correlationT).T, plot_size=(20, 10), feature_names=correlationDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[1].lower() + ".png") plt.title("SHAP details of " + plots_categories[1].lower()) plt.savefig(alpha) plt.close() @@ -574,7 +576,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl shap.summary_plot(np.array(trend).T, np.array(trendT).T, plot_size=(20, 8), feature_names=trendDesc, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + "_plot.png") + alpha = os.path.join(path_file_categories + file + "_" + algorithm + "_" + extractor + "_shap_" + plots_categories[3].lower() + ".png") plt.title("SHAP details of " + plots_categories[3].lower()) plt.savefig(alpha) plt.close() @@ -594,7 +596,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl aggregation_test = np.array(aggregation_test).T shap.summary_plot(aggregation_features, aggregation_test, feature_names=plots_categories, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_plot.png") + alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_cat.png") plt.title("SHAP Aggregation Results") plt.gca().axes.get_xaxis().set_visible(False) plt.savefig(alpha) @@ -602,7 +604,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl alphas.append(alpha) shap.summary_plot(np.array(aggregation_features).T, np.array(aggregation_test).T, feature_names=series_names, show=display) - alpha = os.path.join(path_file + file + "_" + algorithm + "_" + extractor + "_shap_aggregate_reverse_plot.png") + alpha = os.path.join(path_file_details + file + "_" + algorithm + "_" + extractor + "_shap_agg_reverse.png") plt.title("SHAP Aggregation Features by Series") plt.savefig(alpha) plt.close() diff --git a/imputegap/recovery/imputation.py b/imputegap/recovery/imputation.py index 3c283bfd..70842d99 100644 --- a/imputegap/recovery/imputation.py +++ b/imputegap/recovery/imputation.py @@ -1,43 +1,11 @@ import re - -from imputegap.algorithms.bayotide import bay_otide -from imputegap.algorithms.bit_graph import bit_graph -from imputegap.algorithms.brits import brits -from imputegap.algorithms.deep_mvi import deep_mvi -from imputegap.algorithms.dynammo import dynammo -from imputegap.algorithms.gain import gain -from imputegap.algorithms.grin import grin -from imputegap.algorithms.grouse import grouse -from imputegap.algorithms.hkmf_t import hkmf_t -from imputegap.algorithms.interpolation import interpolation -from imputegap.algorithms.iterative_svd import iterative_svd -from imputegap.algorithms.knn import knn -from imputegap.algorithms.mean_impute import mean_impute -from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series -from imputegap.algorithms.mice import mice -from imputegap.algorithms.miss_forest import miss_forest -from imputegap.algorithms.miss_net import miss_net -from imputegap.algorithms.mpin import mpin -from imputegap.algorithms.pristi import pristi -from imputegap.algorithms.rosl import rosl -from imputegap.algorithms.soft_impute import soft_impute -from imputegap.algorithms.spirit import spirit -from imputegap.algorithms.svt import svt -from imputegap.algorithms.tkcm import tkcm -from imputegap.algorithms.trmf import trmf -from imputegap.algorithms.xgboost import xgboost +from imputegap.tools import utils from imputegap.recovery.downstream import Downstream from imputegap.recovery.evaluation import Evaluation -from imputegap.algorithms.cdrec import cdrec -from imputegap.algorithms.iim import iim -from imputegap.algorithms.min_impute import min_impute -from imputegap.algorithms.mrnn import mrnn -from imputegap.algorithms.stmvl import stmvl -from imputegap.algorithms.zero_impute import zero_impute -from imputegap.tools import utils - -not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt", "tkcm", "deep_mvi", "brits", "mpin", "pristi"] +not_optimized = ["knn", "interpolation", "iterative_svd", "grouse", "dynammo", "rosl", "soft_impute", "spirit", "svt", + "tkcm", "deep_mvi", "brits", "mpin", "pristi", "bay_otide", "bit_graph", "gain", "grin", "hkmf_t", + "mice", "miss_forest", "miss_net", "trmf", "xgboost"] class BaseImputer: @@ -112,13 +80,13 @@ def score(self, input_data, recov_data=None, downstream=None): Example ------- >>> imputer.score(ts.data, imputer.recov_data) # upstream - >>> imputer.score(ts.data, imputer.recov_data, downstream={"task": "forecast", "model": "hw-add"}) # downstream + >>> imputer.score(ts.data, imputer.recov_data, {"task": "forecast", "model": "hw-add", "comparator": "ZeroImputation"}) # downstream """ if self.recov_data is None: self.recov_data = recov_data if isinstance(downstream, dict) and downstream is not None: - self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, downstream).downstream_analysis() + self.downstream_metrics = Downstream(input_data, self.recov_data, self.incomp_data, self.algorithm, downstream).downstream_analysis() else: self.metrics = Evaluation(input_data, self.recov_data, self.incomp_data).compute_all_metrics() @@ -187,7 +155,7 @@ def _optimize(self, parameters={}): raise ValueError( f"\n\tThis algorithm '{self.algorithm}' is not optimized for this optimizer. " f"\n\tPlease use `run_tune` to optimize the hyperparameters for:\n\t\t {', '.join(not_optimized)}" - "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts_1.data, 'optimizer': 'ray_tune'})" + "\n\tPlease use update your call :\n\t\t.impute(user_def=False, params={'input_data': ts.data, 'optimizer': 'ray_tune'})" ) input_data = ( @@ -381,8 +349,8 @@ class Statistics: Imputation method that replaces missing values with the minimum value of the ground truth by series. Interpolation : Imputation method that replaces missing values with the Interpolation - KNN : - Imputation method that replaces missing values with KNN logic + KNNImpute : + Imputation method that replaces missing values with KNNImpute logic """ class ZeroImpute(BaseImputer): @@ -411,6 +379,8 @@ def impute(self, params=None): self : ZeroImpute The object with `recov_data` set. """ + from imputegap.algorithms.zero_impute import zero_impute + self.recov_data = zero_impute(self.incomp_data, params) return self @@ -441,6 +411,8 @@ def impute(self, params=None): self : MinImpute The object with `recov_data` set. """ + from imputegap.algorithms.mean_impute import mean_impute + self.recov_data = mean_impute(self.incomp_data, params) return self @@ -471,6 +443,8 @@ def impute(self, params=None): self : MinImpute The object with `recov_data` set. """ + from imputegap.algorithms.min_impute import min_impute + self.recov_data = min_impute(self.incomp_data, params) return self @@ -495,6 +469,8 @@ def impute(self): self : MeanImputeBySeries The object with `recov_data` set. """ + from imputegap.algorithms.mean_impute_by_series import mean_impute_by_series + self.recov_data = mean_impute_by_series(self.incomp_data, logs=self.logs) return self @@ -531,9 +507,11 @@ def impute(self, user_def=True, params=None): >>> interpolation_imputer = Imputation.Statistics.Interpolation(incomp_data) >>> interpolation_imputer.impute() # default parameters for imputation > or >>> interpolation_imputer.impute(user_def=True, params={"method":"linear", "poly_order":2}) # user-defined > or - >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> interpolation_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = interpolation_imputer.recov_data """ + from imputegap.algorithms.interpolation import interpolation + if params is not None: method, poly_order = self._check_params(user_def, params) else: @@ -544,16 +522,16 @@ def impute(self, user_def=True, params=None): return self - class KNN(BaseImputer): + class KNNImpute(BaseImputer): """ - KNN class to impute missing values with K-Nearest Neighbor algorithm + KNNImpute class to impute missing values with K-Nearest Neighbor algorithm Methods ------- impute(self, params=None): Perform imputation by replacing missing values with K-Nearest Neighbor """ - algorithm = "knn" + algorithm = "knn_impute" def impute(self, user_def=True, params=None): """ @@ -564,7 +542,7 @@ def impute(self, user_def=True, params=None): user_def : bool, optional Whether to use user-defined or default parameters (default is True). params : dict, optional - Parameters of the KNN algorithm, if None, default ones are loaded. + Parameters of the KNNImpute algorithm, if None, default ones are loaded. **Algorithm parameters:** k : int, optional @@ -574,17 +552,19 @@ def impute(self, user_def=True, params=None): Returns ------- - self : KNN + self : KNNImpute The object with `recov_data` set. Example ------- - >>> knn_imputer = Imputation.Statistics.KNN(incomp_data) + >>> knn_imputer = Imputation.Statistics.KNNImpute(incomp_data) >>> knn_imputer.impute() # default parameters for imputation > or >>> knn_imputer.impute(user_def=True, params={'k': 5, 'weights': "uniform"}) # user-defined > or - >>> knn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> knn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = knn_imputer.recov_data """ + from imputegap.algorithms.knn import knn + if params is not None: k, weights = self._check_params(user_def, params) else: @@ -724,13 +704,14 @@ def impute(self, user_def=True, params=None): >>> cdrec_imputer = Imputation.MatrixCompletion.CDRec(incomp_data) >>> cdrec_imputer.impute() # default parameters for imputation > or >>> cdrec_imputer.impute(user_def=True, params={'rank': 5, 'epsilon': 0.01, 'iterations': 100}) # user-defined > or - >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> cdrec_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = cdrec_imputer.recov_data References ---------- Khayati, M., Cudré-Mauroux, P. & Böhlen, M.H. Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl Inf Syst 62, 2257–2280 (2020). https://doi.org/10.1007/s10115-019-01421-7 """ + from imputegap.algorithms.cdrec import cdrec if params is not None: rank, epsilon, iterations = self._check_params(user_def, params) @@ -782,13 +763,14 @@ def impute(self, user_def=True, params=None): >>> i_svd_imputer = Imputation.MatrixCompletion.IterativeSVD(incomp_data) >>> i_svd_imputer.impute() # default parameters for imputation > or >>> i_svd_imputer.impute(params={'rank': 5}) # user-defined > or - >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> i_svd_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = i_svd_imputer.recov_data References ---------- Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman, Missing value estimation methods for DNA microarrays , Bioinformatics, Volume 17, Issue 6, June 2001, Pages 520–525, https://doi.org/10.1093/bioinformatics/17.6.520 """ + from imputegap.algorithms.iterative_svd import iterative_svd if params is not None: rank = self._check_params(user_def, params)[0] @@ -839,13 +821,14 @@ def impute(self, user_def=True, params=None): >>> grouse_imputer = Imputation.MatrixCompletion.GROUSE(incomp_data) >>> grouse_imputer.impute() # default parameters for imputation > or >>> grouse_imputer.impute(params={'max_rank': 5}) # user-defined > or - >>> grouse_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> grouse_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = grouse_imputer.recov_data References ---------- D. Zhang and L. Balzano. Global convergence of a grassmannian gradient descent algorithm for subspace estimation. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016, pages 1460–1468, 2016. """ + from imputegap.algorithms.grouse import grouse if params is not None: max_rank = self._check_params(user_def, params)[0] @@ -899,13 +882,15 @@ def impute(self, user_def=True, params=None): >>> rosl_imputer = Imputation.MatrixCompletion.ROSL(incomp_data) >>> rosl_imputer.impute() # default parameters for imputation > or >>> rosl_imputer.impute(params={'rank': 5, 'regularization': 10}) # user-defined > or - >>> rosl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> rosl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = rosl_imputer.recov_data References ---------- X. Shu, F. Porikli, and N. Ahuja. Robust orthonormal subspace learning: Efficient recovery of corrupted low-rank matrices. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 3874–3881, 2014. """ + from imputegap.algorithms.rosl import rosl + if params is not None: rank, regularization = self._check_params(user_def, params) else: @@ -955,13 +940,15 @@ def impute(self, user_def=True, params=None): >>> soft_impute_imputer = Imputation.MatrixCompletion.SoftImpute(incomp_data) >>> soft_impute_imputer.impute() # default parameters for imputation > or >>> soft_impute_imputer.impute(params={'max_rank': 5}) # user-defined > or - >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> soft_impute_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = soft_impute_imputer.recov_data References ---------- R. Mazumder, T. Hastie, and R. Tibshirani. Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11:2287–2322, 2010. """ + from imputegap.algorithms.soft_impute import soft_impute + if params is not None: max_rank = self._check_params(user_def, params)[0] else: @@ -1018,13 +1005,15 @@ def impute(self, user_def=True, params=None): >>> spirit_imputer = Imputation.MatrixCompletion.SPIRIT(incomp_data) >>> spirit_imputer.impute() # default parameters for imputation > or >>> spirit_imputer.impute(params={'k': 2, 'w': 5, 'lambda_value': 0.85}) # user-defined > or - >>> spirit_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> spirit_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = spirit_imputer.recov_data References ---------- S. Papadimitriou, J. Sun, and C. Faloutsos. Streaming pattern discovery in multiple time-series. In Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30 - September 2, 2005, pages 697–708, 2005. """ + from imputegap.algorithms.spirit import spirit + if params is not None: k, w, lambda_value = self._check_params(user_def, params) else: @@ -1075,13 +1064,15 @@ def impute(self, user_def=True, params=None): >>> svt_imputer = Imputation.MatrixCompletion.SVT(incomp_data) >>> svt_imputer.impute() # default parameters for imputation > or >>> svt_imputer.impute(params={'tau': 1}) # user-defined > or - >>> svt_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> svt_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = svt_imputer.recov_data References ---------- J. Cai, E. J. Candès, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4):1956–1982, 2010. [8] J. Cambronero, J. K. Feser, M. J. Smith, and """ + from imputegap.algorithms.svt import svt + if params is not None: tau = self._check_params(user_def, params)[0] else: @@ -1148,13 +1139,15 @@ def impute(self, user_def=True, params=None): >>> trmf_imputer = Imputation.MatrixCompletion.TRMF(incomp_data) >>> trmf_imputer.impute() >>> trmf_imputer.impute(params={"lags":[], "K":-1, "lambda_f":1.0, "lambda_x":1.0, "lambda_w":1.0, "eta":1.0, "alpha":1000.0, "max_iter":100}) - >>> trmf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) + >>> trmf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) >>> recov_data = trmf_imputer.recov_data References ---------- H.-F. Yu, N. Rao, and I. S. Dhillon, "Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction," in *Advances in Neural Information Processing Systems*, vol. 29, 2016. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf """ + from imputegap.algorithms.trmf import trmf + if params is not None: lags, K, lambda_f, lambda_x, lambda_w, eta, alpha, max_iter = self._check_params(user_def, params) else: @@ -1234,7 +1227,7 @@ def impute(self, user_def=True, params=None): >>> mf_imputer = Imputation.MachineLearning.MissForest(incomp_data) >>> mf_imputer.impute() # default parameters for imputation > or >>> mf_imputer.impute(user_def=True, params={"n_estimators":10, "max_iter":3, "max_features":"sqrt", "seed": 42}) # user defined > or - >>> mf_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mf_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mf_imputer.recov_data References @@ -1243,6 +1236,8 @@ def impute(self, user_def=True, params=None): https://github.com/yuenshingyan/MissForest https://pypi.org/project/MissForest/ """ + from imputegap.algorithms.miss_forest import miss_forest + if params is not None: n_estimators, max_iter, max_features, seed = self._check_params(user_def, params) else: @@ -1296,7 +1291,7 @@ def impute(self, user_def=True, params=None): >>> mice_imputer = Imputation.MachineLearning.MICE(incomp_data) >>> mice_imputer.impute() # default parameters for imputation > or >>> mice_imputer.impute(user_def=True, params={"max_iter":3, "tol":0.001, "initial_strategy":"mean", "seed": 42}) # user defined > or - >>> mice_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mice_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mice_imputer.recov_data References @@ -1306,6 +1301,8 @@ def impute(self, user_def=True, params=None): S. F. Buck, (1960). “A Method of Estimation of Missing Values in Multivariate Data Suitable for use with an Electronic Computer”. Journal of the Royal Statistical Society 22(2): 302-306. https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer """ + from imputegap.algorithms.mice import mice + if params is not None: max_iter, tol, initial_strategy, seed = self._check_params(user_def, params) else: @@ -1354,7 +1351,7 @@ def impute(self, user_def=True, params=None): >>> mxgboost_imputer = Imputation.MachineLearning.XGBOOST(incomp_data) >>> mxgboost_imputer.impute() # default parameters for imputation > or >>> mxgboost_imputer.impute(user_def=True, params={"n_estimators":3, "seed": 42}) # user defined > or - >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mxgboost_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mxgboost_imputer.recov_data References @@ -1363,6 +1360,8 @@ def impute(self, user_def=True, params=None): https://dl.acm.org/doi/10.1145/2939672.2939785 https://medium.com/@tzhaonj/imputing-missing-data-using-xgboost-802757cace6d """ + from imputegap.algorithms.xgboost import xgboost + if params is not None: n_estimators, seed = self._check_params(user_def, params) else: @@ -1409,7 +1408,7 @@ def impute(self, user_def=True, params=None): >>> iim_imputer = Imputation.MachineLearning.IIM(incomp_data) >>> iim_imputer.impute() # default parameters for imputation > or >>> iim_imputer.impute(user_def=True, params={'learning_neighbors': 10}) # user-defined > or - >>> iim_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> iim_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = iim_imputer.recov_data References @@ -1417,6 +1416,8 @@ def impute(self, user_def=True, params=None): A. Zhang, S. Song, Y. Sun and J. Wang, "Learning Individual Models for Imputation," 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China, 2019, pp. 160-171, doi: 10.1109/ICDE.2019.00023. keywords: {Data models;Adaptation models;Computational modeling;Predictive models;Numerical models;Aggregates;Regression tree analysis;Missing values;Data imputation} """ + from imputegap.algorithms.iim import iim + if params is not None: learning_neighbours, algo_code = self._check_params(user_def, params) else: @@ -1486,7 +1487,7 @@ def impute(self, user_def=True, params=None): >>> stmvl_imputer = Imputation.PatternSearch.STMVL(incomp_data) >>> stmvl_imputer.impute() # default parameters for imputation > or >>> stmvl_imputer.impute(user_def=True, params={'window_size': 7, 'learning_rate':0.01, 'gamma':0.85, 'alpha': 7}) # user-defined > or - >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> stmvl_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = stmvl_imputer.recov_data References @@ -1494,6 +1495,8 @@ def impute(self, user_def=True, params=None): Yi, X., Zheng, Y., Zhang, J., & Li, T. ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data. School of Information Science and Technology, Southwest Jiaotong University; Microsoft Research; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. """ + from imputegap.algorithms.stmvl import stmvl + if params is not None: window_size, gamma, alpha = self._check_params(user_def, params) else: @@ -1546,13 +1549,15 @@ def impute(self, user_def=True, params=None): >>> dynammo_imputer = Imputation.PatternSearch.DynaMMo(incomp_data) >>> dynammo_imputer.impute() # default parameters for imputation > or >>> dynammo_imputer.impute(params={'h': 5, 'max_iteration': 100, 'approximation': True}) # user-defined > or - >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> dynammo_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = dynammo_imputer.recov_data References ---------- L. Li, J. McCann, N. S. Pollard, and C. Faloutsos. Dynammo: mining and summarization of coevolving sequences with missing values. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009, pages 507–516, 2009. """ + from imputegap.algorithms.dynammo import dynammo + if params is not None: h, max_iteration, approximation = self._check_params(user_def, params) else: @@ -1601,13 +1606,15 @@ def impute(self, user_def=True, params=None): >>> tkcm_imputer = Imputation.PatternSearch.TKCM(incomp_data) >>> tkcm_imputer.impute() # default parameters for imputation > or >>> tkcm_imputer.impute(params={'rank': 5}) # user-defined > or - >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> tkcm_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = tkcm_imputer.recov_data References ---------- K. Wellenzohn, M. H. Böhlen, A. Dignös, J. Gamper, and H. Mitterer. Continuous imputation of missing values in streams of pattern-determining time series. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, March 21-24, 2017., pages 330–341, 2017. """ + from imputegap.algorithms.tkcm import tkcm + if params is not None: rank = self._check_params(user_def, params)[0] else: @@ -1688,13 +1695,15 @@ def impute(self, user_def=True, params=None): >>> mrnn_imputer = Imputation.DeepLearning.MRNN(incomp_data) >>> mrnn_imputer.impute() # default parameters for imputation > or >>> mrnn_imputer.impute(user_def=True, params={'hidden_dim': 10, 'learning_rate':0.01, 'iterations':50, 'sequence_length': 7}) # user-defined > or - >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian + >>> mrnn_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "bayesian", "options": {"n_calls": 2}}) # automl with bayesian >>> recov_data = mrnn_imputer.recov_data References ---------- J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks," in IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp. 1477-1490, May 2019, doi: 10.1109/TBME.2018.2874712. keywords: {Time measurement;Interpolation;Estimation;Medical diagnostic imaging;Correlation;Recurrent neural networks;Biomedical measurement;Missing data;temporal data streams;imputation;recurrent neural nets} """ + from imputegap.algorithms.mrnn import mrnn + if params is not None: hidden_dim, learning_rate, iterations, sequence_length = self._check_params(user_def, params) else: @@ -1751,13 +1760,15 @@ def impute(self, user_def=True, params=None): >>> brits_imputer = Imputation.DeepLearning.BRITS(incomp_data) >>> brits_imputer.impute() # default parameters for imputation > or >>> brits_imputer.impute(params={"model": "brits", "epoch": 2, "batch_size": 10, "nbr_features": 1, "hidden_layer": 64}) # user-defined > or - >>> brits_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> brits_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = brits_imputer.recov_data References ---------- Cao, W., Wang, D., Li, J., Zhou, H., Li, L. & Li, Y. BRITS: Bidirectional Recurrent Imputation for Time Series. Advances in Neural Information Processing Systems, 31 (2018). https://proceedings.neurips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf """ + from imputegap.algorithms.brits import brits + if params is not None: model, epoch, batch_size, nbr_features, hidden_layer = self._check_params(user_def, params) else: @@ -1809,7 +1820,7 @@ def impute(self, user_def=True, params=None): >>> deep_mvi_imputer = Imputation.DeepLearning.DeepMVI(incomp_data) >>> deep_mvi_imputer.impute() # default parameters for imputation > or >>> deep_mvi_imputer.impute(params={"max_epoch": 10, "patience": 2}) # user-defined > or - >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> deep_mvi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = deep_mvi_imputer.recov_data References @@ -1817,6 +1828,8 @@ def impute(self, user_def=True, params=None): P. Bansal, P. Deshpande, and S. Sarawagi. Missing value imputation on multidimensional time series. arXiv preprint arXiv:2103.01600, 2023 https://github.com/pbansal5/DeepMVI """ + from imputegap.algorithms.deep_mvi import deep_mvi + if params is not None: max_epoch, patience, lr = self._check_params(user_def, params) else: @@ -1828,6 +1841,7 @@ def impute(self, user_def=True, params=None): class MPIN(BaseImputer): """ MPIN class to impute missing values using Multi-attribute Sensor Data Streams via Message Propagation algorithm. + Need torch-cluster to work. Methods ------- @@ -1839,6 +1853,7 @@ class MPIN(BaseImputer): def impute(self, user_def=True, params=None): """ Perform imputation using the MPIN algorithm. + Need torch-cluster to work. Parameters ---------- @@ -1879,7 +1894,7 @@ def impute(self, user_def=True, params=None): >>> mpin_imputer = Imputation.DeepLearning.MPIN(incomp_data) >>> mpin_imputer.impute() # default parameters for imputation > or >>> mpin_imputer.impute(params={"incre_mode": "data+state", "window": 1, "k": 15, "learning_rate": 0.001, "weight_decay": 0.2, "epochs": 6, "num_of_iteration": 6, "threshold": 0.50, "base": "GCN"}) # user-defined > or - >>> mpin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> mpin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = mpin_imputer.recov_data References @@ -1887,6 +1902,8 @@ def impute(self, user_def=True, params=None): Li, X., Li, H., Lu, H., Jensen, C.S., Pandey, V. & Markl, V. Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version). arXiv (2023). https://arxiv.org/abs/2311.07344 https://github.com/XLI-2020/MPIN """ + from imputegap.algorithms.mpin import mpin + if params is not None: incre_mode, window, k, learning_rate, weight_decay, epochs, num_of_iteration, threshold, base = self._check_params(user_def, params) else: @@ -1940,7 +1957,7 @@ def impute(self, user_def=True, params=None): >>> pristi_imputer = Imputation.DeepLearning.PRISTI(incomp_data) >>> pristi_imputer.impute() # default parameters for imputation > or >>> pristi_imputer.impute(params={"target_strategy":"hybrid", "unconditional":True, "seed":42, "device":"cpu"}) # user-defined > or - >>> pristi_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # automl with ray_tune + >>> pristi_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # automl with ray_tune >>> recov_data = pristi_imputer.recov_data References @@ -1948,6 +1965,8 @@ def impute(self, user_def=True, params=None): M. Liu, H. Huang, H. Feng, L. Sun, B. Du and Y. Fu, "PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation," 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, USA, 2023, pp. 1927-1939, doi: 10.1109/ICDE55515.2023.00150. https://github.com/LMZZML/PriSTI """ + from imputegap.algorithms.pristi import pristi + if params is not None: target_strategy, unconditional, seed, device = self._check_params(user_def, params) else: @@ -2007,7 +2026,7 @@ def impute(self, user_def=True, params=None): >>> miss_net_imputer = Imputation.DeepLearning.MissNet(incomp_data) >>> miss_net_imputer.impute() # default parameters for imputation > or >>> miss_net_imputer.impute(user_def=True, params={'alpha': 0.5, 'beta':0.1, 'L':10, 'n_cl': 1, 'max_iteration':20, 'tol':5, 'random_init':False}) # user-defined > or - >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> miss_net_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = miss_net_imputer.recov_data References @@ -2015,6 +2034,8 @@ def impute(self, user_def=True, params=None): Kohei Obata, Koki Kawabata, Yasuko Matsubara, and Yasushi Sakurai. 2024. Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24). Association for Computing Machinery, New York, NY, USA, 2296–2306. https://doi.org/10.1145/3637528.3671760 https://github.com/KoheiObata/MissNet/tree/main """ + from imputegap.algorithms.miss_net import miss_net + if params is not None: alpha, beta, L, n_cl, max_iteration, tol, random_init = self._check_params(user_def, params) else: @@ -2074,7 +2095,7 @@ def impute(self, user_def=True, params=None): >>> gain_imputer = Imputation.DeepLearning.GAIN(incomp_data) >>> gain_imputer.impute() # default parameters for imputation > or >>> gain_imputer.impute(user_def=True, params={"batch_size":32, "hint_rate":0.9, "alpha":10, "epoch":100}) # user defined> or - >>> gain_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> gain_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = gain_imputer.recov_data References @@ -2082,6 +2103,8 @@ def impute(self, user_def=True, params=None): J. Yoon, J. Jordon, and M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," CoRR, vol. abs/1806.02920, 2018. Available: http://arxiv.org/abs/1806.02920. """ + from imputegap.algorithms.gain import gain + if params is not None: batch_size, hint_rate, alpha, epoch = self._check_params(user_def, params) else: @@ -2153,7 +2176,7 @@ def impute(self, user_def=True, params=None): >>> grin_imputer = Imputation.DeepLearning.GRIN(incomp_data) >>> grin_imputer.impute() # default parameters for imputation > or >>> grin_imputer.impute(user_def=True, params={"d_hidden":32, "lr":0.001, "batch_size":32, "window":1, "alpha":10.0, "patience":4, "epochs":20, "workers":2}) # user defined> or - >>> grin_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> grin_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = grin_imputer.recov_data References @@ -2161,6 +2184,7 @@ def impute(self, user_def=True, params=None): A. Cini, I. Marisca, and C. Alippi, "Multivariate Time Series Imputation by Graph Neural Networks," CoRR, vol. abs/2108.00298, 2021 https://github.com/Graph-Machine-Learning-Group/grin """ + from imputegap.algorithms.grin import grin if params is not None: d_hidden, lr, batch_size, window, alpha, patience, epochs, workers = self._check_params(user_def, params) @@ -2238,7 +2262,7 @@ def impute(self, user_def=True, params=None): >>> bay_otide_imputer = Imputation.DeepLearning.BayOTIDE(incomp_data) >>> bay_otide_imputer.impute() # default parameters for imputation > or >>> bay_otide_imputer.impute(user_def=True, params={"K_trend":20, "K_season":2, "n_season":5, "K_bias":1, "time_scale":1, "a0":0.6, "b0":2.5, "v":0.5}) # user defined> or - >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> bay_otide_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = bay_otide_imputer.recov_data References @@ -2246,6 +2270,7 @@ def impute(self, user_def=True, params=None): S. Fang, Q. Wen, Y. Luo, S. Zhe, and L. Sun, "BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition," CoRR, vol. abs/2308.14906, 2024. [Online]. Available: https://arxiv.org/abs/2308.14906. https://github.com/xuangu-fang/BayOTIDE """ + from imputegap.algorithms.bayotide import bay_otide if params is not None: K_trend, K_season, n_season, K_bias, time_scale, a0, b0, v = self._check_params(user_def, params) @@ -2304,7 +2329,7 @@ def impute(self, user_def=True, params=None): >>> hkmf_t_imputer = Imputation.DeepLearning.HKMF_T(incomp_data) >>> hkmf_t_imputer.impute() # default parameters for imputation > or >>> hkmf_t_imputer.impute(user_def=True, params={"tags":None, "data_names":None, "epoch":5}) # user defined> or - >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> hkmf_t_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = hkmf_t_imputer.recov_data References @@ -2312,6 +2337,7 @@ def impute(self, user_def=True, params=None): L. Wang, S. Wu, T. Wu, X. Tao and J. Lu, "HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 11, pp. 3582-3593, 1 Nov. 2021, doi: 10.1109/TKDE.2020.2971190. keywords: {Time series analysis;Matrix decomposition;Market research;Meteorology;Sparse matrices;Indexes;Software;Tagged time series;missing value imputation;blackouts;hankel matrix factorization} https://github.com/wangliang-cs/hkmf-t?tab=readme-ov-file """ + from imputegap.algorithms.hkmf_t import hkmf_t if params is not None: tags, data_names, epoch = self._check_params(user_def, params) @@ -2391,7 +2417,7 @@ def impute(self, user_def=True, params=None): >>> bit_graph_imputer = Imputation.DeepLearning.BitGraph(incomp_data) >>> bit_graph_imputer.impute() # default parameters for imputation > or >>> bit_graph_imputer.impute(user_def=True, params={"node_number":-1, "kernel_set":[1], "dropout":0.1, "subgraph_size":5, "node_dim":3, "seq_len":1, "lr":0.001, "epoch":10, "seed":42}) # user defined> or - >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts_1.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune + >>> bit_graph_imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) # auto-ml with ray_tune >>> recov_data = bit_graph_imputer.recov_data References @@ -2400,6 +2426,8 @@ def impute(self, user_def=True, params=None): https://github.com/chenxiaodanhit/BiTGraph """ + from imputegap.algorithms.bit_graph import bit_graph + if params is not None: node_number, kernel_set, dropout, subgraph_size, node_dim, seq_len, lr, epoch, seed = self._check_params(user_def, params) else: diff --git a/imputegap/recovery/manager.py b/imputegap/recovery/manager.py index 2193ede0..cb62be1a 100644 --- a/imputegap/recovery/manager.py +++ b/imputegap/recovery/manager.py @@ -1,28 +1,45 @@ import datetime import os +import platform import time import numpy as np import matplotlib -from scipy.stats import zscore -from sklearn.preprocessing import MinMaxScaler import importlib.resources -from scipy.stats import norm - from imputegap.tools import utils -# Use Agg backend if in a headless or CI environment -if os.getenv('DISPLAY') is None or os.getenv('CI') is not None: - matplotlib.use("Agg") - print("Running in a headless environment or CI. Using Agg backend.") -else: - try: - matplotlib.use("TkAgg") - if importlib.util.find_spec("tkinter") is None: - print("tkinter is not available.") - except (ImportError, RuntimeError): +from matplotlib import pyplot as plt # type: ignore + + +def select_backend(): + system = platform.system() + headless = os.getenv('DISPLAY') is None or os.getenv('CI') is not None + + if headless: matplotlib.use("Agg") + return + + if system == "Darwin": # macOS + try: + matplotlib.use("MacOSX") + except (ImportError, RuntimeError): + try: + if importlib.util.find_spec("tkinter") is not None: + matplotlib.use("TkAgg") + else: + raise ImportError + except (ImportError, RuntimeError): + matplotlib.use("Agg") + else: # Windows or Linux + try: + if importlib.util.find_spec("tkinter") is not None: + matplotlib.use("TkAgg") + else: + raise ImportError + except (ImportError, RuntimeError): + matplotlib.use("Agg") -from matplotlib import pyplot as plt # type: ignore +# Call the backend selector +select_backend() class TimeSeries: @@ -189,11 +206,11 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): ------- None """ - print("\nTime Series set :") - to_print = self.data nbr_tot_series, nbr_tot_values = to_print.shape - print_col, print_row = "Timestamp", "Series" + print_col, print_row = "timestamp", "Series" + + print(f"\nshape of {self.name} : {self.data.shape}\n\tnumber of series = { nbr_tot_series}\n\tnumber of values = {nbr_tot_values}\n") if nbr_val == -1: nbr_val = to_print.shape[1] @@ -203,7 +220,7 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): if not view_by_series: to_print = to_print.T - print_col, print_row = "Series", "Timestamp" + print_col, print_row = "Series", "timestamp" header_format = "{:<15}" # Fixed size for headers value_format = "{:>15.10f}" # Fixed size for values @@ -215,16 +232,13 @@ def print(self, nbr_val=10, nbr_series=7, view_by_series=False): # Print each limited series with fixed size for i, series in enumerate(to_print): - print(header_format.format(f"{print_row} {i + 1}"), end="") + print(header_format.format(f"{print_row}_{i + 1}"), end="") print("".join([value_format.format(elem) for elem in series])) if nbr_series < nbr_tot_series: print("...") - print("\nshape of the time series :", self.data.shape, "\n\tnumber of series =", nbr_tot_series, - "\n\tnumber of values =", nbr_tot_values, "\n\n") - - def print_results(self, metrics, algorithm="", text="Imputation Results of"): + def print_results(self, metrics, algorithm="", text="Results of the analysis"): """ Prints the results of the imputation process. @@ -295,6 +309,8 @@ def normalize(self, normalizer="z_score"): end_time = time.time() elif normalizer == "z_lib": + from scipy.stats import zscore + start_time = time.time() # Record start time self.data = zscore(self.data, axis=0) @@ -302,6 +318,8 @@ def normalize(self, normalizer="z_score"): end_time = time.time() elif normalizer == "m_lib": + from sklearn.preprocessing import MinMaxScaler + start_time = time.time() # Record start time scaler = MinMaxScaler() @@ -324,10 +342,10 @@ def normalize(self, normalizer="z_score"): self.data = self.data.T - print(f"\n\t\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n") + print(f"\n\t> logs, normalization {normalizer} - Execution Time: {(end_time - start_time):.4f} seconds\n") def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, nbr_val=None, series_range=None, - subplot=False, size=(16, 8), save_path="./imputegap_assets", display=True): + subplot=False, size=(16, 8), algorithm=None, save_path="./imputegap_assets", display=True): """ Plot the time series data, including raw, contaminated, or imputed data. @@ -349,6 +367,8 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n Print one time series by subplot or all in the same plot. size : tuple, optional Size of the plot in inches. Default is (16, 8). + algorithm : str, optional + Name of the algorithm used for imputation. save_path : str, optional Path to save the plot locally. display : bool, optional @@ -366,6 +386,9 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n >>> ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") # imputation """ number_of_series = 0 + if algorithm is None: + algorithm = "imputegap" + algorithm.lower() if nbr_series is None or nbr_series == -1: nbr_series = input_data.shape[0] @@ -400,6 +423,7 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n y_size = y_size_screen fig, axes = plt.subplots(n_rows, n_cols, figsize=(x_size, y_size), squeeze=False) + fig.canvas.manager.set_window_title(algorithm) axes = axes.flatten() else: plt.figure(figsize=size) @@ -426,28 +450,28 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n if incomp_data is None and recov_data is None: # plot only raw matrix ax.plot(timestamps, input_data[i, :nbr_val], linewidth=2.5, - color=color, linestyle='-', label=f'TS {i + 1}') + color=color, linestyle='-', label=f'Series {i + 1}') if incomp_data is not None and recov_data is None: # plot infected matrix if np.isnan(incomp_data[i, :]).any(): ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5, - color=color, linestyle='--', label=f'TS-INCOMP {i + 1}') + color=color, linestyle='--', label=f'Missing Data {i + 1}') if np.isnan(incomp_data[i, :]).any() or not subplot: ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val], - color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}') + color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}') if recov_data is not None: # plot imputed matrix if np.isnan(incomp_data[i, :]).any(): ax.plot(np.arange(min(recov_data.shape[1], nbr_val)), recov_data[i, :nbr_val], - linestyle='-', color="r", label=f'TS-RECOV {i + 1}') + linestyle='-', color="r", label=f'Imputed Data {i + 1}') ax.plot(timestamps, input_data[i, :nbr_val], linewidth=1.5, - linestyle='--', color=color, label=f'TS-INCOM {i + 1}') + linestyle='--', color=color, label=f'Missing Data {i + 1}') if np.isnan(incomp_data[i, :]).any() or not subplot: ax.plot(np.arange(min(incomp_data.shape[1], nbr_val)), incomp_data[i, :nbr_val], - color=color, linewidth=2.5, linestyle='-', label=f'TS-INPUT {i + 1}') + color=color, linewidth=2.5, linestyle='-', label=f'Series {i + 1}') # Label and legend for subplot if subplot: @@ -485,7 +509,7 @@ def plot(self, input_data, incomp_data=None, recov_data=None, nbr_series=None, n now = datetime.datetime.now() current_time = now.strftime("%y_%m_%d_%H_%M_%S") - file_path = os.path.join(save_path + "/" + current_time + "_plot.jpg") + file_path = os.path.join(save_path + "/" + current_time + "_" + algorithm + "_plot.jpg") plt.savefig(file_path, bbox_inches='tight') print("plots saved in ", file_path) @@ -582,16 +606,16 @@ def mcar(input_data, rate_dataset=0.2, rate_series=0.2, block_size=10, offset=0. values_nbr = int(NS * rate_series) if not explainer: - print(f"\n\n\tMCAR contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\ta block size of {block_size}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tthis selection of series {series_selected}\n\n") + print(f"\n\nMCAR contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\ta block size of {block_size}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tthis selection of series {series_selected}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -672,13 +696,13 @@ def aligned(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1): values_nbr = int(NS * rate_series) - print("\n\n\tALIGNED (missing percentage) contamination has been called with :" - "\n\t\ta number of series impacted ", rate_dataset * 100, "%", - "\n\t\ta missing rate of ", rate_series * 100, "%", - "\n\t\ta starting position at ", offset, - "\n\t\tshape of the set ", ts_contaminated.shape, - "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted, - "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") + print("\n\nALIGNED (missing percentage) contamination has been called with :" + "\n\ta number of series impacted ", rate_dataset * 100, "%", + "\n\ta missing rate of ", rate_series * 100, "%", + "\n\ta starting position at ", offset, + "\n\tshape of the set ", ts_contaminated.shape, + "\n\tthis selection of series : ", 1, "->", nbr_series_impacted, + "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") if offset_nbr + values_nbr > NS: @@ -746,13 +770,13 @@ def scattered(input_data, rate_dataset=0.2, rate_series=0.2, offset=0.1, seed=Tr values_nbr = int(NS * rate_series) - print("\n\n\tSCATTER (missing percentage AT RANDOM) contamination has been called with :" - "\n\t\ta number of series impacted ", rate_dataset * 100, "%", - "\n\t\ta missing rate of ", rate_series * 100, "%", - "\n\t\ta starting position at ", offset, - "\n\t\tshape of the set ", ts_contaminated.shape, - "\n\t\tthis selection of series : ", 1, "->", nbr_series_impacted, - "\n\t\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") + print("\n\nSCATTER (missing percentage AT RANDOM) contamination has been called with :" + "\n\ta number of series impacted ", rate_dataset * 100, "%", + "\n\ta missing rate of ", rate_series * 100, "%", + "\n\ta starting position at ", offset, + "\n\tshape of the set ", ts_contaminated.shape, + "\n\tthis selection of series : ", 1, "->", nbr_series_impacted, + "\n\tvalues : ", offset_nbr, "->", offset_nbr + values_nbr, "\n\n") if offset_nbr + values_nbr > NS: @@ -836,6 +860,7 @@ def gaussian(input_data, rate_dataset=0.2, rate_series=0.2, std_dev=0.2, offset= ---------- https://imputegap.readthedocs.io/en/latest/patterns.html """ + from scipy.stats import norm ts_contaminated = input_data.copy() M, NS = ts_contaminated.shape @@ -854,16 +879,16 @@ def gaussian(input_data, rate_dataset=0.2, rate_series=0.2, std_dev=0.2, offset= offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tGAUSSIAN contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tGaussian std_dev {std_dev}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tthis selection of series {nbr_series_impacted}\n\n") + print(f"\n\nGAUSSIAN contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tGaussian std_dev {std_dev}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tthis selection of series {nbr_series_impacted}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -945,16 +970,16 @@ def distribution(input_data, rate_dataset=0.2, rate_series=0.2, probabilities=No offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tDISTRIBUTION contamination has been called with :" - f"\n\t\ta number of series impacted {rate_dataset * 100}%" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\twith a seed option set to {seed}" - f"\n\t\twith a seed value set to {seed_value}" - f"\n\t\tshape of the set {ts_contaminated.shape}" - f"\n\t\tprobabilities list {np.array(probabilities).shape}" - f"\n\t\tthis selection of series {nbr_series_impacted}\n\n") + print(f"\n\nDISTRIBUTION contamination has been called with :" + f"\n\ta number of series impacted {rate_dataset * 100}%" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\twith a seed option set to {seed}" + f"\n\twith a seed value set to {seed_value}" + f"\n\tshape of the set {ts_contaminated.shape}" + f"\n\tprobabilities list {np.array(probabilities).shape}" + f"\n\tthis selection of series {nbr_series_impacted}\n\n") if offset_nbr + values_nbr > NS: raise ValueError( @@ -1018,12 +1043,12 @@ def disjoint(input_data, rate_series=0.1, limit=1, offset=0.1): values_nbr = int(NS * rate_series) - print(f"\n\n\tDISJOINT contamination has been called with :" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\tlimit to stop {limit}" - f"\n\t\tshape of the set {ts_contaminated.shape}") + print(f"\n\nDISJOINT contamination has been called with :" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\tlimit to stop {limit}" + f"\n\tshape of the set {ts_contaminated.shape}") if offset_nbr + values_nbr > NS: @@ -1091,15 +1116,15 @@ def overlap(input_data, rate_series=0.2, limit=1, shift=0.05, offset=0.1): offset_nbr = int(offset * NS) values_nbr = int(NS * rate_series) - print(f"\n\n\tOVERLAP contamination has been called with :" - f"\n\t\ta missing rate of {rate_series * 100}%" - f"\n\t\ta offset of {offset*100}%" - f"\n\t\ta starting position at {offset_nbr}" - f"\n\t\tvalues to remove by series {values_nbr}" - f"\n\t\ta shift overlap of {shift * 100} %" - f"\n\t\ta shift in number {int(shift * NS)}" - f"\n\t\tlimit to stop {limit}" - f"\n\t\tshape of the set {ts_contaminated.shape}") + print(f"\n\nOVERLAP contamination has been called with :" + f"\n\ta missing rate of {rate_series * 100}%" + f"\n\ta offset of {offset*100}%" + f"\n\ta starting position at {offset_nbr}" + f"\n\tvalues to remove by series {values_nbr}" + f"\n\ta shift overlap of {shift * 100} %" + f"\n\ta shift in number {int(shift * NS)}" + f"\n\tlimit to stop {limit}" + f"\n\tshape of the set {ts_contaminated.shape}") if offset_nbr + values_nbr > NS: raise ValueError( @@ -1133,3 +1158,8 @@ def overlap(input_data, rate_series=0.2, limit=1, shift=0.05, offset=0.1): return ts_contaminated + missing_completely_at_random = mcar + mp = aligned + missing_percentage = aligned + + diff --git a/imputegap/recovery/optimization.py b/imputegap/recovery/optimization.py index 77496a31..8e11bcb9 100644 --- a/imputegap/recovery/optimization.py +++ b/imputegap/recovery/optimization.py @@ -1,25 +1,11 @@ -import os import time from itertools import product import numpy as np - from imputegap.recovery.imputation import Imputation from imputegap.tools import utils from imputegap.tools.algorithm_parameters import SEARCH_SPACES, ALL_ALGO_PARAMS, PARAM_NAMES, SEARCH_SPACES_PSO, RAYTUNE_PARAMS import imputegap.tools.algorithm_parameters as sh_params -# RAY TUNE IMPORT -from ray import tune -import ray - -# PSO IMPORT -from functools import partial -import pyswarms as ps - -# BAYESIAN IMPORT -import skopt -from skopt.space import Integer - from pyswarms.utils.reporter import Reporter reporter = Reporter() @@ -289,6 +275,10 @@ def optimize(self, input_data, incomp_data, metrics=["RMSE"], algorithm="cdrec", tuple A tuple containing the best parameters and their corresponding score. """ + # BAYESIAN IMPORT + import skopt + from skopt.space import Integer + start_time = time.time() # Record start time search_spaces = SEARCH_SPACES @@ -416,6 +406,9 @@ def optimize(self, input_data, incomp_data, metrics, algorithm, n_particles, c1, tuple A tuple containing the best parameters and their corresponding score. """ + from functools import partial + import pyswarms as ps + start_time = time.time() # Record start time if not isinstance(metrics, list): @@ -620,6 +613,9 @@ def optimize(self, input_data, incomp_data, metrics=["RMSE"], algorithm="cdrec", tuple A tuple containing the best parameters and their corresponding score. """ + from ray import tune + import ray + if not ray.is_initialized(): ray.init() used_metric = metrics[0] diff --git a/imputegap/runner_benchmark.py b/imputegap/runner_benchmark.py index 99ab2745..9171c2e6 100644 --- a/imputegap/runner_benchmark.py +++ b/imputegap/runner_benchmark.py @@ -1,45 +1,17 @@ -import numpy as np - from imputegap.recovery.benchmark import Benchmark -# define analysis global variables -reconstruction = False -save_dir = "./analysis" -nbr_run = 1 +save_dir = "./imputegap_assets/benchmark" +nbr_runs = 1 -# define the datasets to evaluate -datasets_full = ["eeg-alcohol", "eeg-reading", "fmri-objectviewing", "fmri-stoptask", "chlorine", "drift"] -datasets = ["chlorine", "eeg-reading"] +datasets = ["eeg-alcohol"] -# define the optimizer to fine-tine the algorithms -optimiser_bayesian = {"optimizer": "bayesian", "options": {"n_calls": 2, "n_random_starts": 50, "acq_func": "gp_hedge", "metrics": "RMSE"}} -optimiser_greedy = {"optimizer": "greedy", "options": {"n_calls": 250, "metrics": "RMSE"}} -optimiser_pso = {"optimizer": "pso", "options": {"n_particles": 50, "iterations": 10, "metrics": "RMSE"}} -optimiser_sh = {"optimizer": "sh", "options": {"num_configs": 10, "num_iterations": 5, "metrics": "RMSE"}} -optimiser_ray = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}} -optimizers = [optimiser_ray] +optimizers = ["default_params"] -# define the algorithms for the imputation -algorithms = ["MeanImpute", "CDRec", "STMVL", "IIM", "MRNN"] +algorithms = ["SoftImpute", "KNNImpute"] -# define the missing pattern to contaminate the time series -patterns_full = ["mcar", "mp", "blackout", "disjoint", "overlap", "gaussian"] patterns = ["mcar"] -# define missing values percentages to see the evolution of the imputation range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] - -if not reconstruction: - # launch the evaluation - list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_run) -else: - test_plots = {'eegreading': {'mp': {'mean': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.9585345039764159, 'MAE': 0.71318962961796, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0001308917999267578, 'optimization': 0.0, 'imputation': 0.0005505084991455078, 'log_imputation': -3.25923597169041}}, '0.1': {'scores': {'RMSE': 1.158802114694506, 'MAE': 0.9654264194724749, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.00028133392333984375, 'optimization': 0.0, 'imputation': 0.0002911090850830078, 'log_imputation': -3.5359442406627037}}, '0.2': {'scores': {'RMSE': 0.9366041090001302, 'MAE': 0.713304743455646, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.00025653839111328125, 'optimization': 0.0, 'imputation': 0.0002605915069580078, 'log_imputation': -3.5840397426578834}}, '0.4': {'scores': {'RMSE': 1.0599636170074507, 'MAE': 0.8381968262587581, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0006632804870605469, 'optimization': 0.0, 'imputation': 0.00026726722717285156, 'log_imputation': -3.573054292012613}}, '0.6': {'scores': {'RMSE': 1.0562417941470534, 'MAE': 0.8508551190781458, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0013227462768554688, 'optimization': 0.0, 'imputation': 0.0002796649932861328, 'log_imputation': -3.553361892492057}}, '0.7': {'scores': {'RMSE': 1.040204405772873, 'MAE': 0.8335213108852025, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.0017957687377929688, 'optimization': 0.0, 'imputation': 0.00025272369384765625, 'log_imputation': -3.597354039342816}}}}, 'cdrec': {'ray_tune': {'0.05': {'scores': {'RMSE': 1.1697500835978993, 'MAE': 0.9591122423282666, 'MI': 0.874795673555792, 'CORRELATION': -0.6435768999074719}, 'times': {'contamination': 0.00013375282287597656, 'optimization': 7.705925226211548, 'imputation': 0.03410458564758301, 'log_imputation': -1.4671872225457172}}, '0.1': {'scores': {'RMSE': 1.160748195036071, 'MAE': 0.9801100319738002, 'MI': 0.24843228556023436, 'CORRELATION': -0.1457168801420723}, 'times': {'contamination': 0.0010766983032226562, 'optimization': 7.705925226211548, 'imputation': 0.0187838077545166, 'log_imputation': -1.7262163652567906}}, '0.2': {'scores': {'RMSE': 0.9258702230012604, 'MAE': 0.7412912028091162, 'MI': 0.09990051537425793, 'CORRELATION': 0.018312853013798043}, 'times': {'contamination': 0.0012714862823486328, 'optimization': 7.705925226211548, 'imputation': 0.06531453132629395, 'log_imputation': -1.184990185145398}}, '0.4': {'scores': {'RMSE': 1.0158290954098952, 'MAE': 0.7699069240021237, 'MI': 0.043509996942918995, 'CORRELATION': 0.026854101443570477}, 'times': {'contamination': 0.002095460891723633, 'optimization': 7.705925226211548, 'imputation': 0.06960487365722656, 'log_imputation': -1.1573603504995333}}, '0.6': {'scores': {'RMSE': 1.074841191421588, 'MAE': 0.8305870491121395, 'MI': 0.04052775735888883, 'CORRELATION': 0.02240964134518767}, 'times': {'contamination': 0.003614664077758789, 'optimization': 7.705925226211548, 'imputation': 0.11719846725463867, 'log_imputation': -0.9310780680722405}}, '0.7': {'scores': {'RMSE': 1.028461165376999, 'MAE': 0.8037932945848046, 'MI': 0.04862782947355959, 'CORRELATION': 0.28310309461657}, 'times': {'contamination': 0.004042148590087891, 'optimization': 7.705925226211548, 'imputation': 0.14756202697753906, 'log_imputation': -0.8310253877423743}}}}, 'stmvl': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.7277001471585042, 'MAE': 0.6187129093081218, 'MI': 0.849293361425945, 'CORRELATION': 0.6050898026864381}, 'times': {'contamination': 0.0003566741943359375, 'optimization': 401.3516755104065, 'imputation': 13.703130722045898, 'log_imputation': 1.1368198007314763}}, '0.1': {'scores': {'RMSE': 0.9169700690065775, 'MAE': 0.7874056676843385, 'MI': 0.3666479681879978, 'CORRELATION': 0.5010483466574436}, 'times': {'contamination': 0.00020051002502441406, 'optimization': 401.3516755104065, 'imputation': 13.661174535751343, 'log_imputation': 1.1354880399360694}}, '0.2': {'scores': {'RMSE': 0.7183875117312635, 'MAE': 0.5896797263562054, 'MI': 0.24774404354441104, 'CORRELATION': 0.46263398990236104}, 'times': {'contamination': 0.0002193450927734375, 'optimization': 401.3516755104065, 'imputation': 13.761161088943481, 'log_imputation': 1.1386550787575944}}, '0.4': {'scores': {'RMSE': 0.8742119003541577, 'MAE': 0.6297161236774441, 'MI': 0.12171661757737935, 'CORRELATION': 0.2830622046394087}, 'times': {'contamination': 0.0006110668182373047, 'optimization': 401.3516755104065, 'imputation': 14.14597225189209, 'log_imputation': 1.1506328018545884}}, '0.6': {'scores': {'RMSE': 20.12875114987835, 'MAE': 3.972851513364007, 'MI': 0.005760162876990282, 'CORRELATION': -0.0020763310368411936}, 'times': {'contamination': 0.001154184341430664, 'optimization': 401.3516755104065, 'imputation': 8.714907884597778, 'log_imputation': 0.9402628010361087}}, '0.7': {'scores': {'RMSE': 0.9089996638242581, 'MAE': 0.7155219794334919, 'MI': 0.0, 'CORRELATION': 0.0}, 'times': {'contamination': 0.001634359359741211, 'optimization': 401.3516755104065, 'imputation': 8.389863967895508, 'log_imputation': 0.9237549192945785}}}}, 'iim': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.9917499844047692, 'MAE': 0.6835294570359077, 'MI': 1.0383833294554503, 'CORRELATION': 0.39342144548050073}, 'times': {'contamination': 0.00010991096496582031, 'optimization': 87.12388730049133, 'imputation': 0.1563720703125, 'log_imputation': -0.8058408138870692}}, '0.1': {'scores': {'RMSE': 0.5640999644452854, 'MAE': 0.436875650594336, 'MI': 0.9382015646212765, 'CORRELATION': 0.8446597946056835}, 'times': {'contamination': 0.000209808349609375, 'optimization': 87.12388730049133, 'imputation': 1.0088622570037842, 'log_imputation': 0.003831874753497683}}, '0.2': {'scores': {'RMSE': 0.5524062619653164, 'MAE': 0.4527739222276905, 'MI': 0.4682825796414235, 'CORRELATION': 0.734130718955202}, 'times': {'contamination': 0.00021195411682128906, 'optimization': 87.12388730049133, 'imputation': 6.273438930511475, 'log_imputation': 0.7975056746615543}}, '0.4': {'scores': {'RMSE': 0.6703666990440234, 'MAE': 0.5167364139824571, 'MI': 0.25478017143468495, 'CORRELATION': 0.6160285049199379}, 'times': {'contamination': 0.0006020069122314453, 'optimization': 87.12388730049133, 'imputation': 48.24830389022827, 'log_imputation': 1.683482050859879}}, '0.6': {'scores': {'RMSE': 0.846371619650148, 'MAE': 0.6521374554849746, 'MI': 0.10898611984678795, 'CORRELATION': 0.37877848480193976}, 'times': {'contamination': 0.0012598037719726562, 'optimization': 87.12388730049133, 'imputation': 148.6065971851349, 'log_imputation': 2.1720380897576836}}, '0.7': {'scores': {'RMSE': 0.939708165479016, 'MAE': 0.7342180002582285, 'MI': 0.0747388622783703, 'CORRELATION': 0.3115372974410071}, 'times': {'contamination': 0.0016543865203857422, 'optimization': 87.12388730049133, 'imputation': 240.74761581420898, 'log_imputation': 2.3815619948874964}}}}, 'mrnn': {'ray_tune': {'0.05': {'scores': {'RMSE': 0.5647614331747706, 'MAE': 0.4956801381604213, 'MI': 0.602588078427742, 'CORRELATION': 0.7892828777307545}, 'times': {'contamination': 0.00017881393432617188, 'optimization': 23.75439763069153, 'imputation': 9.659311532974243, 'log_imputation': 0.9849461731975306}}, '0.1': {'scores': {'RMSE': 1.4942930218243802, 'MAE': 1.2039603558089516, 'MI': 0.47511351289726134, 'CORRELATION': -0.24282448220213368}, 'times': {'contamination': 0.00033783912658691406, 'optimization': 23.75439763069153, 'imputation': 9.660546064376831, 'log_imputation': 0.985001675695301}}, '0.2': {'scores': {'RMSE': 1.493030946284299, 'MAE': 1.2759205639542883, 'MI': 0.14247255829853323, 'CORRELATION': 0.17585501858419272}, 'times': {'contamination': 0.0004456043243408203, 'optimization': 23.75439763069153, 'imputation': 9.614870071411133, 'log_imputation': 0.982943419864104}}, '0.4': {'scores': {'RMSE': 1.6610979347034713, 'MAE': 1.32902912214158, 'MI': 0.07259583818478979, 'CORRELATION': -0.04241780698399349}, 'times': {'contamination': 0.0007512569427490234, 'optimization': 23.75439763069153, 'imputation': 9.778327465057373, 'log_imputation': 0.9902645771992502}}, '0.6': {'scores': {'RMSE': 1.6248154262874046, 'MAE': 1.3587893211292972, 'MI': 0.04243232622955761, 'CORRELATION': -0.05349772421574814}, 'times': {'contamination': 0.0013408660888671875, 'optimization': 23.75439763069153, 'imputation': 9.961318492889404, 'log_imputation': 0.9983168260028805}}, '0.7': {'scores': {'RMSE': 1.3781088381679387, 'MAE': 1.102321907630172, 'MI': 0.013439658692967605, 'CORRELATION': -0.01874660352385742}, 'times': {'contamination': 0.0019338130950927734, 'optimization': 23.75439763069153, 'imputation': 9.934946298599243, 'log_imputation': 0.9971655239586622}}}}, 'knn': {'ray_tune': {'0.05': {'scores': {'RMSE': 1.0891820888848962, 'MAE': 0.8741423177191895, 'MI': 1.0726621001615337, 'CORRELATION': 0.23766946742758965}, 'times': {'contamination': 0.0002646446228027344, 'optimization': 5.975560188293457, 'imputation': 0.004753589630126953, 'log_imputation': -2.3229783129452315}}, '0.1': {'scores': {'RMSE': 0.6835893631062424, 'MAE': 0.5234507579018903, 'MI': 0.7407074666713884, 'CORRELATION': 0.7645134370284421}, 'times': {'contamination': 0.00014519691467285156, 'optimization': 5.975560188293457, 'imputation': 0.006691694259643555, 'log_imputation': -2.174463909966519}}, '0.2': {'scores': {'RMSE': 0.5992380715293445, 'MAE': 0.4859085244932665, 'MI': 0.4086948138880703, 'CORRELATION': 0.6965923314128772}, 'times': {'contamination': 0.0002143383026123047, 'optimization': 5.975560188293457, 'imputation': 0.013432025909423828, 'log_imputation': -1.8718584791459945}}, '0.4': {'scores': {'RMSE': 0.6868836297408376, 'MAE': 0.5093608359137556, 'MI': 0.2868485602164113, 'CORRELATION': 0.6240763887663399}, 'times': {'contamination': 0.0005633831024169922, 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'log_imputation': -2.289656219851888}}, '0.1': {'scores': {'RMSE': 1.0026771443357805, 'MAE': 0.8585128588487265, 'MI': 0.7175551546841298, 'CORRELATION': 0.2953791171260645}, 'times': {'contamination': 0.00017213821411132812, 'optimization': 5.141337633132935, 'imputation': 0.005180835723876953, 'log_imputation': -2.2856001782870616}}, '0.2': {'scores': {'RMSE': 0.8233555140431951, 'MAE': 0.6703672843514312, 'MI': 0.13551988197466275, 'CORRELATION': 0.015002239969896275}, 'times': {'contamination': 0.00030040740966796875, 'optimization': 5.141337633132935, 'imputation': 0.005160808563232422, 'log_imputation': -2.287282250496757}}, '0.4': {'scores': {'RMSE': 0.9363419996773169, 'MAE': 0.7074318048367104, 'MI': 0.08116508553549509, 'CORRELATION': -0.08485974486524711}, 'times': {'contamination': 0.0009136199951171875, 'optimization': 5.141337633132935, 'imputation': 0.004884481430053711, 'log_imputation': -2.3111815371695186}}, '0.6': {'scores': {'RMSE': 0.8950363345080617, 'MAE': 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76.60794115066528, 'imputation': 28.5592999458313, 'log_imputation': 1.4557475576773615}}, '0.6': {'scores': {'RMSE': 61.98612564576895, 'MAE': 49.755647879167334, 'MI': 0.004322458488836388, 'CORRELATION': 0.01233963956767782}, 'times': {'contamination': 0.0013575553894042969, 'optimization': 76.60794115066528, 'imputation': 32.24260687828064, 'log_imputation': 1.5084301481170062}}, '0.7': {'scores': {'RMSE': 62.379540899173236, 'MAE': 50.156074239740796, 'MI': 0.0031404425022982423, 'CORRELATION': 0.009111022593762223}, 'times': {'contamination': 0.0018019676208496094, 'optimization': 76.60794115066528, 'imputation': 35.09843707084656, 'log_imputation': 1.545287777815695}}}}}}} - Benchmark().generate_plots(runs_plots_scores=test_plots, ticks=range, subplot=False, y_size=max(4, int(len(algorithms)*0.28)), save_dir=save_dir) - Benchmark().generate_plots(runs_plots_scores=test_plots, ticks=range, subplot=True, y_size=max(4, int(len(algorithms)*0.28)),save_dir=save_dir) - Benchmark().generate_reports_txt(runs_plots_scores=test_plots, save_dir=save_dir, dataset="chlorine", run=0) - Benchmark().generate_reports_excel(runs_plots_scores=test_plots, save_dir=save_dir, dataset="chlorine", run=0) - - Benchmark().generate_heatmap(np.array([[1.12395851, 0.6470281, 0.67607129, 0.52176976, 0.52304288, 0.36444636, 0.89733918, 0.35040938, 0.35273418, 0.96177331, 0.53492822, 1.20178291, 54.63150047, 0.89519623, 0.36984999, 100., 0.91834629, 0.4267107, 100.], [1.10116065, 1.06258333, 0.95263409, 0.67823842, 2.30571521, 0.76078378, 0.87041159, 1.77119164, 0.78415296, 1.03505842, 1.00102834, 1.36935127, 60.07340763, 0.88553276, 1.73914404, 100., 4.04583674, 0.67136028, 100.]]), - np.array(['brits', 'cdrec', 'deep_mvi', 'dynammo', 'grouse', 'iim', 'interpolation', 'iter_svd', 'knn', 'mean', 'mpin', 'mrnn', 'pristi', 'rosl', 'soft_imp', 'spirit', 'stmvl', 'svt', 'tkcm']), - np.array(['eegalcohol', 'eegreading']), save_dir=save_dir, display=False) +# launch the evaluation +list_results, sum_scores = Benchmark().eval(algorithms=algorithms, datasets=datasets, patterns=patterns, x_axis=range, optimizers=optimizers, save_dir=save_dir, runs=nbr_runs) \ No newline at end of file diff --git a/imputegap/runner_downstream.py b/imputegap/runner_downstream.py index 1d90cf86..e7cdecfb 100644 --- a/imputegap/runner_downstream.py +++ b/imputegap/runner_downstream.py @@ -18,6 +18,6 @@ imputer.impute() # compute and print the downstream results -downstream_config = {"task": "forecast", "model": "hw-add"} +downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"} imputer.score(ts.data, imputer.recov_data, downstream=downstream_config) -ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm) \ No newline at end of file +ts.print_results(imputer.downstream_metrics, algorithm="hw-add") \ No newline at end of file diff --git a/imputegap/runner_imputation.py b/imputegap/runner_imputation.py index 0ceb8bf5..db2ad428 100644 --- a/imputegap/runner_imputation.py +++ b/imputegap/runner_imputation.py @@ -15,11 +15,11 @@ # impute the contaminated series imputer = Imputation.MatrixCompletion.CDRec(ts_m) -imputer.impute() # could also use a dictionary for params: params={"rank": 5, "epsilon": 0.01, "iterations": 100} +imputer.impute() # compute and print the imputation metrics imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics, imputer.algorithm) +ts.print_results(imputer.metrics) # plot the recovered time series -ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets") \ No newline at end of file +ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets") \ No newline at end of file diff --git a/imputegap/runner_loading.py b/imputegap/runner_loading.py index 673d6494..894aad36 100644 --- a/imputegap/runner_loading.py +++ b/imputegap/runner_loading.py @@ -11,4 +11,4 @@ # plot and print a subset of time series ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets") -ts.print(nbr_series=9, nbr_val=100) \ No newline at end of file +ts.print(nbr_series=9, nbr_val=20) \ No newline at end of file diff --git a/imputegap/runner_optimization.py b/imputegap/runner_optimization.py index ca405f6b..47c16066 100644 --- a/imputegap/runner_optimization.py +++ b/imputegap/runner_optimization.py @@ -4,7 +4,6 @@ # initialize the time series object ts = TimeSeries() -print(f"AutoML Optimizers : {ts.optimizers}") # load and normalize the dataset ts.load_series(utils.search_path("eeg-alcohol")) @@ -17,12 +16,19 @@ # use Ray Tune to fine tune the imputation algorithm imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"}) -# compute and print the imputation metrics +# compute the imputation metrics with optimized parameter values imputer.score(ts.data, imputer.recov_data) -ts.print_results(imputer.metrics) + +# compute the imputation metrics with default parameter values +imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute() +imputer_def.score(ts.data, imputer_def.recov_data) + +# print the imputation metrics with default and optimized parameter values +ts.print_results(imputer_def.metrics, text="Imputation metrics with default parameter values") +ts.print_results(imputer.metrics, text="Imputation metrics with optimized parameter values") # plot the recovered time series ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, save_path="./imputegap_assets", display=True) # save hyperparameters -utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune") \ No newline at end of file +utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune") diff --git a/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc b/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc index b924e14f..182a2a66 100644 Binary files a/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc and b/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc differ diff --git a/imputegap/tools/__pycache__/utils.cpython-312.pyc b/imputegap/tools/__pycache__/utils.cpython-312.pyc index 13cc6480..3516ee29 100644 Binary files a/imputegap/tools/__pycache__/utils.cpython-312.pyc and b/imputegap/tools/__pycache__/utils.cpython-312.pyc differ diff --git a/imputegap/tools/algorithm_parameters.py b/imputegap/tools/algorithm_parameters.py index 5d8f3b1a..296d451a 100644 --- a/imputegap/tools/algorithm_parameters.py +++ b/imputegap/tools/algorithm_parameters.py @@ -61,7 +61,7 @@ 'cdrec': { "rank": tune.grid_search([i for i in range(2, 16, 1)]), "eps": tune.loguniform(1e-6, 1), - "iters": tune.grid_search([i * 100 for i in range(1, 3)]) + "iters": tune.grid_search([i * 50 for i in range(1, 4)]) }, "iim": { "learning_neighbors": tune.grid_search([i for i in range(1, 20)]) # Up to 100 learning neighbors @@ -157,6 +157,12 @@ "device": tune.choice(["cpu"]) # Allow switching between CPU and GPU }, + "knn_impute": { + "k": tune.grid_search([1, 12, 1]), + "weights": tune.choice(["uniform", "distance"]) + }, + + "knn": { "k": tune.grid_search([1, 12, 1]), "weights": tune.choice(["uniform", "distance"]) diff --git a/imputegap/tools/utils.py b/imputegap/tools/utils.py index 9ec1b96b..13bfab9b 100644 --- a/imputegap/tools/utils.py +++ b/imputegap/tools/utils.py @@ -2,9 +2,8 @@ import os import toml import importlib.resources -import numpy as __numpy_import; - - +import numpy as __numpy_import +import platform def config_impute_algorithm(incomp_data, algorithm): """ @@ -62,8 +61,8 @@ def config_impute_algorithm(incomp_data, algorithm): imputer = Imputation.DeepLearning.PRISTI(incomp_data) # 3rd generation - elif algorithm == "knn" or algorithm == "KNN": - imputer = Imputation.Statistics.KNN(incomp_data) + elif algorithm == "knn" or algorithm == "KNN" or algorithm == "knn_impute" or algorithm == "KNNImpute": + imputer = Imputation.Statistics.KNNImpute(incomp_data) elif algorithm == "interpolation" or algorithm == "Interpolation": imputer = Imputation.Statistics.Interpolation(incomp_data) elif algorithm == "mean_series" or algorithm == "MeanImputeBySeries": @@ -364,7 +363,7 @@ def load_parameters(query: str = "default", algorithm: str = "cdrec", dataset: s with open(filepath, "r") as _: config = toml.load(filepath) - print("\n\t\t\t\t(SYS) Inner files loaded : ", filepath, "\n") + print("\n(SYS) Inner files loaded : ", filepath, "\n") if algorithm == "cdrec": truncation_rank = int(config[algorithm]['rank']) @@ -450,7 +449,7 @@ def load_parameters(query: str = "default", algorithm: str = "cdrec", dataset: s seed = int(config[algorithm]['seed']) device = str(config[algorithm]['device']) return (target_strategy, unconditional, seed, device) - elif algorithm == "knn": + elif algorithm == "knn" or algorithm == "knn_impute": k = int(config[algorithm]['k']) weights = str(config[algorithm]['weights']) return (k, weights) @@ -755,18 +754,25 @@ def load_share_lib(name="lib_cdrec", lib=True): ctypes.CDLL The loaded shared library object. """ + system = platform.system() + if system == "Windows": + ext = ".so" + elif system == "Darwin": + ext = ".dylib" # macOS uses .dylib for dynamic libraries + else: + ext = ".so" if lib: - lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name)) + lib_path = importlib.resources.files('imputegap.algorithms.lib').joinpath("./" + str(name) + ext) else: - local_path_lin = './algorithms/lib/' + name + '.so' + local_path_lin = './algorithms/lib/' + name + ext if not os.path.exists(local_path_lin): - local_path_lin = './imputegap/algorithms/lib/' + name + '.so' + local_path_lin = './imputegap/algorithms/lib/' + name + ext lib_path = os.path.join(local_path_lin) - print("\t\t(SYS) lib loaded from:", lib_path) + print("\n(SYS) Wrapper files loaded for C++ : ", lib_path, "\n") return ctypes.CDLL(lib_path) @@ -895,7 +901,7 @@ def save_optimization(optimal_params, algorithm="cdrec", dataset="", optimizer=" "seed": 42, # Default seed "device": "cpu" # Default device } - elif algorithm == "knn": + elif algorithm == "knn" or algorithm == "knn_impute": params_to_save = { "k": int(optimal_params[0]), "weights": str(optimal_params[1]) @@ -988,9 +994,9 @@ def save_optimization(optimal_params, algorithm="cdrec", dataset="", optimizer=" try: with open(file_name, 'w') as file: toml.dump(params_to_save, file) - print(f"\n\t\t(SYS) Optimization parameters successfully saved to {file_name}") + print(f"\n(SYS) Optimization parameters successfully saved to {file_name}") except Exception as e: - print(f"\n\t\t(SYS) An error occurred while saving the file: {e}") + print(f"\n(SYS) An error occurred while saving the file: {e}") def list_of_algorithms(): @@ -1010,7 +1016,7 @@ def list_of_algorithms(): "XGBOOST", "MICE", "MissForest", - "KNN", + "KNNImpute", "Interpolation", "MinImpute", "MeanImpute", diff --git a/imputegap/wrapper/AlgoCollection/Makefile b/imputegap/wrapper/AlgoCollection/Makefile index 10938eea..3dfccf58 100644 --- a/imputegap/wrapper/AlgoCollection/Makefile +++ b/imputegap/wrapper/AlgoCollection/Makefile @@ -13,6 +13,7 @@ all: libAlgoCollection.so AlgoCollection.dll +# ================================================================================================================================== libAlgoCollection.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o libAlgoCollection.so -Wall -Werror -Wextra -pedantic \ @@ -23,6 +24,7 @@ libAlgoCollection.so: Algebra/CentroidDecomposition.cpp Algebra/RSVD.cpp Stats/Correlation.cpp shared/SharedLibFunctions.cpp \ -lopenblas -larpack -lmlpack +# ================================================================================================================================== libSTMVL.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_stmvl.so -Wall -Werror -Wextra -pedantic \ @@ -30,18 +32,44 @@ libSTMVL.so: Algorithms/ST_MVL.cpp shared/SharedSTMVL.cpp \ -lopenblas -larpack -lmlpack +libSTMVL.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_stmvl.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/ST_MVL.cpp shared/SharedSTMVL.cpp \ + -larmadillo -lopenblas -larpack +# ================================================================================================================================== + libIterativeSVD.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_iterative_svd.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/RSVD.cpp Algorithms/IterativeSVD.cpp shared/SharedLibIterativeSVD.cpp \ -lopenblas -larpack +libIterativeSVD.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_iterative_svd.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/RSVD.cpp Algorithms/IterativeSVD.cpp shared/SharedLibIterativeSVD.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libGROUSE.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_grouse.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/GROUSE.cpp shared/SharedLibGROUSE.cpp \ -lopenblas -larpack +libGROUSE.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_grouse.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/GROUSE.cpp shared/SharedLibGROUSE.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libDynaMMo.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_dynammo.so -Wall -Werror -Wextra -pedantic \ @@ -49,48 +77,122 @@ libDynaMMo.so: Algebra/Auxiliary.cpp Algorithms/DynaMMo.cpp shared/SharedLibDynaMMo.cpp \ -lopenblas -larpack +libDynaMMo.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_dynammo.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/Auxiliary.cpp Algorithms/DynaMMo.cpp shared/SharedLibDynaMMo.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libNMF.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_rosl.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -I/usr/include/ensmallen -std=gnu++14 \ Algorithms/NMFMissingValueRecovery.cpp shared/SharedLibNMF.cpp \ -lopenblas -larpack +libNMF.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_nmf.dylib \ + -I/opt/homebrew/include -I/opt/homebrew/include/ensmallen \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/NMFMissingValueRecovery.cpp shared/SharedLibNMF.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libROSL.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_rosl.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/Auxiliary.cpp Algorithms/ROSL.cpp shared/SharedLibROSL.cpp \ -lopenblas -larpack +libROSL.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_rosl.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/Auxiliary.cpp Algorithms/ROSL.cpp shared/SharedLibROSL.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSoftImpute.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_soft_impute.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algebra/RSVD.cpp Algorithms/SoftImpute.cpp shared/SharedLibSoftImpute.cpp \ -lopenblas -larpack +libSoftImpute.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_soft_impute.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algebra/RSVD.cpp Algorithms/SoftImpute.cpp shared/SharedLibSoftImpute.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSPIRIT.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_spirit.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/SPIRIT.cpp shared/SharedLibSPIRIT.cpp \ -lopenblas -larpack +libSPIRIT.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_spirit.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/SPIRIT.cpp shared/SharedLibSPIRIT.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libSVT.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_svt.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/SVT.cpp shared/SharedLibSVT.cpp \ -lopenblas -larpack +libSVT.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_svt.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/SVT.cpp shared/SharedLibSVT.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== + libTKCM.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_tkcm.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Algorithms/TKCM.cpp shared/SharedLibTKCM.cpp \ -lopenblas -larpack +libTKCM.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_tkcm.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib -L/opt/homebrew/opt/openblas/lib \ + Algorithms/TKCM.cpp shared/SharedLibTKCM.cpp \ + -larmadillo -lopenblas -larpack + +# ================================================================================================================================== libCDREC.so: g++ -O3 -D ARMA_DONT_USE_WRAPPER -fPIC -rdynamic -shared -o lib_cdrec.so -Wall -Werror -Wextra -pedantic \ -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 \ Stats/Correlation.cpp Algorithms/CDMissingValueRecovery.cpp Algebra/Auxiliary.cpp \ Algebra/CentroidDecomposition.cpp shared/SharedLibCDREC.cpp \ -lopenblas -larpack -lmlpack + +libCDREC.dylib: + clang++ -dynamiclib -O3 -fPIC -std=c++17 -o lib_cdrec.dylib \ + -I/opt/homebrew/include \ + -L/opt/homebrew/lib \ + -L/opt/homebrew/opt/openblas/lib \ + Stats/Correlation.cpp Algorithms/CDMissingValueRecovery.cpp Algebra/Auxiliary.cpp \ + Algebra/CentroidDecomposition.cpp shared/SharedLibCDREC.cpp \ + -larmadillo -lopenblas -larpack +# ================================================================================================================================== + + libAlgoCollection.dll: g++ -O3 -D ARMA_DONT_USE_WRAPPER -shared -o libAlgoCollection.dll -Wall -Werror -Wextra -pedantic -Wconversion -Wsign-conversion -msse2 -msse3 -msse4 -msse4.1 -msse4.2 -fopenmp -std=gnu++14 -fPIC \ diff --git a/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt new file mode 100644 index 00000000..51ad7318 Binary files /dev/null and b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/epoch=19-step=20.ckpt differ diff --git a/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547910.diufpc048844.345185.0 b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547910.diufpc048844.345185.0 new file mode 100644 index 00000000..71d928c8 Binary files /dev/null and b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547910.diufpc048844.345185.0 differ diff --git a/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547938.diufpc048844.345185.1 b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547938.diufpc048844.345185.1 new file mode 100644 index 00000000..bbe769d3 Binary files /dev/null and b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/events.out.tfevents.1742547938.diufpc048844.345185.1 differ diff --git a/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/hparams.yaml b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/hparams.yaml new file mode 100644 index 00000000..15c3a699 --- /dev/null +++ b/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-21_10-04-56_42/model/version_0/hparams.yaml @@ -0,0 +1,9231 @@ +adj: !!python/object/apply:numpy.core.multiarray._reconstruct + args: + - !!python/name:numpy.ndarray '' + - 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b/imputegap/wrapper/AlgoPython/MissNet/temp/model.pkl differ diff --git a/requirements.txt b/requirements.txt index cec68d2b..106bf717 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,7 +5,6 @@ toml==0.10.2 scikit-learn>=1.5.0 #scikit-learn==1.3.2 scipy==1.14.1 -setuptools==75.1.0 tensorflow==2.17.0 # Additional Libraries diff --git a/requirements_dev.txt b/requirements_dev.txt index 2938aecf..6607b09a 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,5 +1,4 @@ torch-geometric==2.6.1 -torch-cluster==1.6.3 pytorch-lightning==2.5.0 torchmetrics==1.6.1 lightning==2.5.0 diff --git a/setup.py b/setup.py index 618c9b84..e2a7675f 100644 --- a/setup.py +++ b/setup.py @@ -1,10 +1,9 @@ import pathlib - import setuptools setuptools.setup( name="imputegap", - version="1.0.6", + version="1.0.7", description="A Library of Imputation Techniques for Time Series Data", long_description=open('README.md').read(), long_description_content_type="text/markdown", @@ -13,7 +12,7 @@ author_email="quentin.nater@unifr.ch", license="MIT License", project_urls = { - "Documentation": "https://imputegap.readthedocs.io/", + "Documentation": "https://exascaleinfolab.github.io/ImputeGAP/", "Source" : "https://github.com/eXascaleInfolab/ImputeGAP" }, classifiers=[ @@ -37,6 +36,7 @@ 'dataset/*.txt', # Include TXT files from dataset 'algorithms/lib/*.dll', # Include DLL files from algorithms/lib (for Windows) 'algorithms/lib/*.so' # Include SO files from algorithms/lib (for Linux/Unix) + 'algorithms/lib/*.dylib' # Include dylib files from algorithms/lib (for MACOS) ], }, entry_points={"console_scripts": ["imputegap = imputegap.runner_display:display_title"]} diff --git a/tests/incomp_data.tmp b/tests/incomp_data.tmp deleted file mode 100644 index 74543839..00000000 --- a/tests/incomp_data.tmp +++ /dev/null @@ -1,64 +0,0 @@ -{"label": 0, "forward": {"values": [[1.0], [0.9428596506626875], [0.8857193013253751], [0.8571344977910412], [0.8143011790865736], [0.7714386026507505], 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Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:58,643 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:58,677 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:58,727 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:58,743 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:58,775 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:58,819 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:58,836 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:58,886 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:58,931 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:58,946 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,009 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,057 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,071 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,140 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,189 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,206 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,258 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,303 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,319 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,362 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,409 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,428 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,477 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,522 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,537 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,567 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,614 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,629 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,657 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,708 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,729 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,755 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,812 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,832 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,855 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:19:59,909 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:19:59,928 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:19:59,955 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,012 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,032 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,087 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,140 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,159 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,202 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,257 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,272 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,318 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,377 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,396 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,427 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,484 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,501 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,530 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,586 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,603 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,661 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,717 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,736 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,781 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,838 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,859 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:00,903 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:00,957 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:00,977 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,021 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,080 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,099 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,145 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,201 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,220 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,245 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,306 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,324 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,411 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,464 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,482 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,525 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,581 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,597 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,642 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,701 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,721 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,786 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:01,844 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:01,865 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:01,956 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,015 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,033 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,072 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,126 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,144 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,247 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,303 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,322 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,402 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,454 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,470 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,544 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,595 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,617 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,701 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,761 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,778 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,816 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:02,870 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:02,888 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:02,954 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,008 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,027 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,111 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,166 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,184 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,311 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,369 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,385 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,411 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,460 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,476 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,537 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,602 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,624 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,668 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,723 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,744 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,771 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,823 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,841 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,883 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:03,937 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:03,956 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:03,998 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,052 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,073 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,104 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,160 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,179 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,230 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,287 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,304 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,366 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,421 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,440 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,503 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,554 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,570 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,636 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,696 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,715 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,812 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,866 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:04,884 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:04,922 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:04,980 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,000 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,041 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,099 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,115 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,169 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,215 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,232 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,302 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,353 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,370 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,438 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,487 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,506 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,568 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,626 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,645 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,695 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:05,749 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:05,769 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:05,832 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:06,494 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:06,511 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:06,545 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:06,602 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:06,621 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:06,651 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:06,710 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:06,729 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:06,756 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:06,811 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:06,830 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:06,853 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:06,910 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:06,930 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:06,959 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,018 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,038 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,065 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,118 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,138 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,185 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,246 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,267 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,316 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,378 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,397 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,427 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,482 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,505 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,542 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,602 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,624 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,682 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,737 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,758 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,780 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,838 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,858 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:07,908 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:07,962 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:07,981 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,024 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,082 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,098 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,144 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,199 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,219 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,244 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,300 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,316 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,401 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,458 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,475 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,509 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,570 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,591 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,657 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,705 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,721 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,776 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,822 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,838 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:08,914 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:08,968 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:08,986 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,023 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,079 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,097 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,140 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,200 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,222 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,295 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,356 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,375 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,432 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,487 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,508 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,595 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,651 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,669 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,709 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,766 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,784 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:09,854 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:09,910 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:09,929 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,017 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,077 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,093 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,223 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,280 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,299 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,332 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,390 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,408 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,464 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,523 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,541 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,586 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,644 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,665 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,692 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,748 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,769 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,809 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,867 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:10,886 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:10,936 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:10,994 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,015 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,050 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,112 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,133 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,280 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,340 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,357 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,422 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,477 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,496 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,561 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,617 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,636 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,712 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,766 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,782 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,871 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:11,919 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:11,935 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:11,967 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,026 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,044 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,083 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,143 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,163 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,230 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,287 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,305 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,384 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,437 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,456 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,535 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,588 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,607 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,673 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,726 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,743 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,795 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,850 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,868 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:12,925 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:12,975 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:12,990 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,019 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,083 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,099 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,128 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,185 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,206 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,235 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,288 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,308 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,328 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,383 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,403 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,432 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,486 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,505 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,530 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,586 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,603 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,646 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,701 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,719 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,765 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,823 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,840 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,870 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:13,929 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:13,950 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:13,978 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:14,033 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:14,055 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:14,121 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:14,214 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:14,249 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:14,284 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:14,386 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:14,429 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:14,513 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:14,611 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:14,646 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:14,729 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:14,834 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:14,875 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:14,956 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:15,053 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:15,091 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:15,129 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:15,238 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:15,275 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:15,419 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:15,506 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:15,539 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:15,600 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:15,699 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:15,735 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:15,851 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:15,951 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:15,987 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,099 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,181 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,203 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,315 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,379 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,398 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,440 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,501 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,520 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,565 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,632 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,652 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,727 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,791 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,811 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:16,833 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:16,898 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:16,919 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,008 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,068 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,089 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,133 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,194 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,215 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,287 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,347 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,367 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,465 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,523 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,542 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,665 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,725 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,747 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,778 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,833 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,851 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:17,880 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:17,944 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:17,965 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,007 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,064 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,083 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,110 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,172 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,193 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,236 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,295 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,315 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,364 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,412 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,431 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,460 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,513 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,528 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,548 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,599 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,616 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,670 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,730 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,750 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,816 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,876 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,895 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:18,913 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:18,970 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-02-28 12:20:18,992 - cmdstanpy - INFO - Chain [1] start processing -2025-02-28 12:20:19,099 - cmdstanpy - INFO - Chain [1] done processing -2025-02-28 12:20:55,917 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:55,920 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:55,922 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:55,935 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:55,958 - shap - INFO - np.sum(w_aug) = 15.999999999999998 -2025-02-28 12:20:55,958 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-02-28 12:20:56,016 - shap - INFO - phi = array([ 0.00000000e+00, -8.88019875e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, -2.11160641e-04, 0.00000000e+00]) -2025-02-28 12:20:56,044 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,051 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:56,052 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:56,091 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:56,119 - shap - INFO - np.sum(w_aug) = 16.0 -2025-02-28 12:20:56,121 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:56,164 - shap - INFO - phi = array([ 0.00000000e+00, 3.55240624e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-02-28 12:20:56,207 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,208 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:56,208 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:56,237 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:56,265 - shap - INFO - np.sum(w_aug) = 15.999999999999998 -2025-02-28 12:20:56,266 - shap - INFO - np.sum(self.kernelWeights) = 1.0 -2025-02-28 12:20:56,372 - shap - INFO - phi = array([ 0.00000000e+00, 4.73599708e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-02-28 12:20:56,386 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,388 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:56,392 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:56,424 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:56,463 - shap - INFO - np.sum(w_aug) = 16.0 -2025-02-28 12:20:56,464 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:56,501 - shap - INFO - phi = array([0.00000000e+00, 4.73599708e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 1.37321572e-04, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-02-28 12:20:56,512 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,513 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:56,516 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:56,545 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:56,584 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:56,585 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-02-28 12:20:56,682 - shap - INFO - phi = array([ 0. , 0.004736 , 0. , 0. , 0. , - 0.00013732, -0.00031293, 0. , 0. , 0.00014505, - 0. , 0. , 0. , 0. , 0.00010558, - 0. ]) -2025-02-28 12:20:56,717 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,719 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:56,720 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:56,772 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:56,797 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:56,797 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:56,833 - shap - INFO - phi = array([ 0.00000000e+00, -8.88019875e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, -7.25230953e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, -2.11160641e-04, 0.00000000e+00]) -2025-02-28 12:20:56,847 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:56,854 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-02-28 12:20:56,856 - shap - INFO - num_paired_subset_sizes = 8 -2025-02-28 12:20:56,895 - shap - INFO - weight_left = 0.5181019626448611 -2025-02-28 12:20:56,921 - shap - INFO - np.sum(w_aug) = 17.000000000000007 -2025-02-28 12:20:56,922 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:57,024 - shap - INFO - phi = array([ 0.00000000e+00, 4.78175895e-03, 0.00000000e+00, -8.23870107e-04, - 3.52707678e-04, 5.84070762e-02, -6.58176070e-05, 4.16744541e-02, - 1.63386594e-02, 1.27366766e-05, -5.19480170e-04, 2.77921160e-03, - -2.78906921e-03, 2.18486196e-04, 4.19067576e-05, -1.61020629e-04, - -2.80003094e-04]) -2025-02-28 12:20:57,063 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,072 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-02-28 12:20:57,073 - shap - INFO - num_paired_subset_sizes = 8 -2025-02-28 12:20:57,114 - shap - INFO - weight_left = 0.528623703595323 -2025-02-28 12:20:57,141 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-02-28 12:20:57,142 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 -2025-02-28 12:20:57,251 - shap - INFO - phi = array([-4.23740400e-03, 4.86594850e-03, 2.03953900e-04, -8.84065259e-04, - 1.38516632e-03, 7.92636066e-04, 1.59348446e-03, -3.82106791e-04, - 4.19611966e-04, 1.24311529e-04, 3.39022229e-03, 2.30180720e-04, - 2.53010519e-03, -4.21856301e-03, -2.20044941e-04, 9.26701278e-04, - 7.40477018e-04, 4.16044238e-05]) -2025-02-28 12:20:57,278 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,282 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-02-28 12:20:57,283 - shap - INFO - num_paired_subset_sizes = 8 -2025-02-28 12:20:57,312 - shap - INFO - weight_left = 0.5181019626448611 -2025-02-28 12:20:57,352 - shap - INFO - np.sum(w_aug) = 17.000000000000004 -2025-02-28 12:20:57,353 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:57,382 - shap - INFO - phi = array([-1.46251498e-02, -9.09576982e-03, 4.25612431e-04, -1.11308778e-03, - 7.60190988e-04, 3.16631560e-04, 7.77586437e-04, 0.00000000e+00, - 1.41757456e-04, -4.68811641e-04, 3.00263634e-03, -4.64003818e-03, - 5.11925711e-04, -4.53961415e-03, -2.28118751e-04, 7.57353258e-04, - 2.57639654e-05]) -2025-02-28 12:20:57,404 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,405 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-02-28 12:20:57,406 - shap - INFO - num_paired_subset_sizes = 8 -2025-02-28 12:20:57,460 - shap - INFO - weight_left = 0.5181019626448611 -2025-02-28 12:20:57,488 - shap - INFO - np.sum(w_aug) = 17.000000000000007 -2025-02-28 12:20:57,489 - shap - INFO - np.sum(self.kernelWeights) = 1.0 -2025-02-28 12:20:57,592 - shap - INFO - phi = array([-1.48965200e-02, -1.01127722e-02, 2.03585575e-04, -1.17224161e-03, - 7.81832942e-04, 2.82597834e-04, 8.71948585e-04, 0.00000000e+00, - 5.77036931e-05, -4.76796221e-04, -2.33829883e-04, -4.76516715e-03, - 4.76501332e-04, -4.53316051e-03, -2.38378566e-04, 7.49106138e-04, - -6.34185477e-05]) -2025-02-28 12:20:57,604 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,605 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-02-28 12:20:57,608 - shap - INFO - num_paired_subset_sizes = 8 -2025-02-28 12:20:57,638 - shap - INFO - weight_left = 0.5181019626448611 -2025-02-28 12:20:57,685 - shap - INFO - np.sum(w_aug) = 17.000000000000004 -2025-02-28 12:20:57,687 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:57,731 - shap - INFO - phi = array([-1.74825582e-02, -1.27453727e-02, 2.71099426e-04, -5.03192628e-04, - 6.14453598e-04, -7.77677359e-05, 2.21968340e-05, -8.10889482e-04, - -7.61363376e-04, -3.65571534e-04, -2.36984952e-03, -8.16528928e-03, - 2.76638520e-04, -4.66492905e-03, -3.06599353e-04, 8.52268525e-04, - -3.45073031e-04]) -2025-02-28 12:20:57,747 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,761 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:57,762 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:57,796 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:57,829 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:57,830 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:57,930 - shap - INFO - phi = array([-0.01821529, -0.0130146 , 0.00022738, -0.00050559, 0.00051753, - -0.00023178, -0.00010514, -0.00100915, -0.00078202, -0.00044573, - -0.00278685, -0.00925884, 0.00015105, -0.00466223, -0.0002263 , - -0.00069266]) -2025-02-28 12:20:57,953 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:57,958 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:57,959 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:58,011 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:58,040 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:58,041 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:58,076 - shap - INFO - phi = array([-1.96253651e-02, -1.39657904e-02, -6.51680781e-05, -4.80932783e-04, - 0.00000000e+00, -2.83003112e-04, -3.39480117e-04, -1.20421516e-03, - -9.47237644e-04, -4.21915560e-04, -3.20497836e-03, -9.94263712e-03, - -1.98131879e-05, -4.71469473e-03, -2.74155541e-04, -8.29883198e-04]) -2025-02-28 12:20:58,095 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:58,100 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:58,102 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:58,133 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:58,170 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:58,171 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:58,213 - shap - INFO - phi = array([-1.80974611e-02, -1.33876191e-02, 0.00000000e+00, 0.00000000e+00, - -1.78947332e-04, -2.79410456e-04, -1.55006965e-04, 1.68559334e-02, - -8.76923381e-04, -5.56393706e-04, -3.14028031e-03, -9.47646213e-03, - 0.00000000e+00, 3.57457310e-05, -2.60980264e-04, -5.54136155e-04]) -2025-02-28 12:20:58,232 - shap - INFO - num_full_subsets = 2 -2025-02-28 12:20:58,232 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-02-28 12:20:58,233 - shap - INFO - num_paired_subset_sizes = 7 -2025-02-28 12:20:58,277 - shap - INFO - weight_left = 0.5063344810024111 -2025-02-28 12:20:58,306 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-02-28 12:20:58,308 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-02-28 12:20:58,341 - shap - INFO - phi = array([ 0.00000000e+00, -8.88018310e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -7.40321285e-05, 6.26351585e-05, -6.26276553e-04, - 0.00000000e+00, -7.41192462e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05602980e-04, 0.00000000e+00]) -2025-02-28 12:21:05,876 - tsfresh.feature_extraction.settings - WARNING - Dependency not available for matrix_profile, this feature will be disabled! -2025-02-28 12:32:51,033 - lightning_fabric.utilities.seed - INFO - Seed set to 42 -2025-02-28 12:32:51,200 - lightning.pytorch.utilities.rank_zero - INFO - GPU available: True (cuda), used: True -2025-02-28 12:32:51,202 - lightning.pytorch.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores -2025-02-28 12:32:51,202 - lightning.pytorch.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs -2025-02-28 12:32:51,219 - lightning.pytorch.utilities.rank_zero - INFO - You are using a CUDA device ('NVIDIA GeForce RTX 4070 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision -2025-02-28 12:32:51,327 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-02-28 12:32:51,354 - pytorch_lightning.callbacks.model_summary - INFO - - | Name | Type | Params | Mode ------------------------------------------------------------ -0 | loss_fn | MaskedMetric | 0 | train -1 | train_metrics | MetricCollection | 0 | train -2 | val_metrics | MetricCollection | 0 | train -3 | test_metrics | MetricCollection | 0 | train -4 | model | GRINet | 62.1 K | train ------------------------------------------------------------ -62.1 K Trainable params -0 Non-trainable params -62.1 K Total params -0.248 Total estimated model params size (MB) -63 Modules in train mode -0 Modules in eval mode -2025-02-28 12:33:04,170 - lightning.pytorch.utilities.rank_zero - INFO - `Trainer.fit` stopped: `max_epochs=20` reached. -2025-02-28 12:33:04,344 - lightning.pytorch.utilities.rank_zero - INFO - Restoring states from the checkpoint path at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-02-28_12-32-51_42/epoch=19-step=20.ckpt -2025-02-28 12:33:04,405 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-02-28 12:33:04,441 - lightning.pytorch.utilities.rank_zero - INFO - Loaded model weights from the checkpoint at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-02-28_12-32-51_42/epoch=19-step=20.ckpt -2025-02-28 12:33:05,004 - lightning_fabric.utilities.seed - INFO - Seed set to 42 -2025-02-28 12:33:05,063 - lightning.pytorch.utilities.rank_zero - INFO - GPU available: True (cuda), used: True -2025-02-28 12:33:05,064 - lightning.pytorch.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores -2025-02-28 12:33:05,064 - lightning.pytorch.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs -2025-02-28 12:33:05,153 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-02-28 12:33:05,182 - pytorch_lightning.callbacks.model_summary - INFO - - | Name | Type | Params | Mode ------------------------------------------------------------ -0 | loss_fn | MaskedMetric | 0 | train -1 | train_metrics | MetricCollection | 0 | train -2 | val_metrics | MetricCollection | 0 | train -3 | test_metrics | MetricCollection | 0 | train -4 | model | GRINet | 62.1 K | train ------------------------------------------------------------ -62.1 K Trainable params -0 Non-trainable params -62.1 K Total params -0.248 Total estimated model params size (MB) -63 Modules in train mode -0 Modules in eval mode -2025-02-28 12:33:16,309 - lightning.pytorch.utilities.rank_zero - INFO - `Trainer.fit` stopped: `max_epochs=20` reached. -2025-02-28 12:33:16,404 - lightning.pytorch.utilities.rank_zero - INFO - Restoring states from the checkpoint path at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-02-28_12-33-05_42/epoch=19-step=20.ckpt -2025-02-28 12:33:16,428 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-02-28 12:33:16,447 - lightning.pytorch.utilities.rank_zero - INFO - Loaded model weights from the checkpoint at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-02-28_12-33-05_42/epoch=19-step=20.ckpt -2025-03-13 19:53:52,910 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:52,929 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:52,961 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,008 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,023 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,055 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,096 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,111 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,167 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,212 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,230 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,294 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,338 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,353 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,418 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,459 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,478 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,531 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,573 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,588 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,629 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,674 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,688 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,739 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,785 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,803 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,837 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,889 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,908 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:53,933 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:53,977 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:53,993 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,016 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,061 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,077 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,099 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,150 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,172 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,211 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,283 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,304 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,368 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,433 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,453 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,503 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,570 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,595 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,647 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,702 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,719 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,754 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,814 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,834 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,863 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:54,910 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:54,926 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:54,975 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,026 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,044 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,084 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,148 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,170 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,229 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,305 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,330 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,386 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,453 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,478 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,537 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,604 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,626 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,659 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,729 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,757 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,854 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:55,922 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:55,944 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:55,996 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,058 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,076 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,117 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,169 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,187 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,239 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,291 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,307 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,387 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,440 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,462 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,504 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,564 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,582 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,680 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,737 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,756 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,826 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:56,883 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:56,908 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:56,999 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,052 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,071 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,140 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,185 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,202 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,234 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,283 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,299 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,355 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,401 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,416 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,492 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,540 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,556 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,659 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,707 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,727 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,755 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,807 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,823 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,868 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:57,917 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:57,936 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:57,974 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,027 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,044 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,067 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,118 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,136 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,176 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,229 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,246 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,295 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,346 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,362 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,390 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,438 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,454 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,500 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,547 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,563 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,619 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,667 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,684 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,746 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,801 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,819 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:58,874 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:58,921 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:58,938 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,019 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,067 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,083 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,114 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,160 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,177 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,211 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,261 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,277 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,334 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,381 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,398 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,471 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,518 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,538 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,610 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,656 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,672 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,727 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,772 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,788 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,836 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:53:59,890 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:53:59,906 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:53:59,960 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,008 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,025 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,056 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,105 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,125 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,150 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,201 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,217 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,241 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,292 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,313 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,334 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,380 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,396 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,420 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,470 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,486 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,509 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,555 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,572 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,607 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,649 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,666 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,704 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,750 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,765 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,789 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,830 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,845 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,874 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:00,923 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:00,939 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:00,982 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,026 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,042 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,059 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,105 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,124 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,162 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,207 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,223 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,259 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,305 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,324 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,358 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,401 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,422 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,440 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,484 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,501 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,572 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,620 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,637 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,670 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,719 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,736 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,802 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,853 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,873 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:01,928 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:01,977 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:01,990 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,062 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,106 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,122 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,151 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,196 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,213 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,247 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,292 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,308 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,361 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,409 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,426 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,471 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,516 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,533 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,602 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,649 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,669 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,699 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,743 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,757 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,813 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,856 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:02,872 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:02,945 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:02,989 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,007 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,139 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,189 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,204 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,227 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,272 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,289 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,333 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,376 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,396 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,431 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,475 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,491 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,513 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,557 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,574 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,613 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,664 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,684 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,732 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,782 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,797 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:03,832 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:03,885 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:03,902 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,025 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,076 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,094 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,148 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,199 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,215 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,271 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,322 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,339 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,406 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,456 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,473 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,567 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,619 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,636 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,667 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,712 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,728 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,763 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,811 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,829 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:04,880 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:04,923 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:04,942 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,004 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,048 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,063 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,130 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,176 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,191 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,242 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,287 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,304 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,343 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,389 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,407 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,454 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,499 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,514 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,543 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,590 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,608 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,629 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,680 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,696 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,721 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,772 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,789 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,810 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,857 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,873 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,895 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:05,941 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:05,956 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:05,979 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,025 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,040 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,074 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,117 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,132 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,171 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,217 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,235 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,259 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,301 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,316 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,342 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,384 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,402 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,447 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,490 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,505 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,522 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,564 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,581 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,620 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,665 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,683 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,718 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,766 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,781 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,827 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,876 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,892 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:06,912 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:06,961 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:06,977 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,050 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,115 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,130 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,162 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,219 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,236 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,295 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,349 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,365 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,428 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,482 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,498 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,583 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,636 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,656 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,692 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,748 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,766 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,808 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:07,864 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:07,882 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:07,954 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,008 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,026 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,044 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,096 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,116 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,198 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,254 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,274 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,312 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,365 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,386 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,450 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,506 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,527 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,624 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,679 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,699 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,833 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,888 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:08,908 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:08,935 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:08,988 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,008 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,038 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,095 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,112 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,153 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,207 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,226 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,252 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,306 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,327 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,372 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,432 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,454 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,505 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,560 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,580 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,611 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,664 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,684 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,704 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,763 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,783 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,843 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:09,893 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:09,911 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:09,962 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:10,007 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:10,026 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:10,047 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:10,101 - prophet - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. -2025-03-13 19:54:10,118 - cmdstanpy - INFO - Chain [1] start processing -2025-03-13 19:54:10,217 - cmdstanpy - INFO - Chain [1] done processing -2025-03-13 19:54:46,528 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:46,530 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:46,532 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:46,549 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:46,577 - shap - INFO - np.sum(w_aug) = 15.999999999999998 -2025-03-13 19:54:46,580 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-13 19:54:46,643 - shap - INFO - phi = array([ 0.00000000e+00, -8.88019875e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, -2.11160641e-04, 0.00000000e+00]) -2025-03-13 19:54:46,653 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:46,672 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:46,673 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:46,697 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:46,729 - shap - INFO - np.sum(w_aug) = 16.0 -2025-03-13 19:54:46,731 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:46,792 - shap - INFO - phi = array([ 0.00000000e+00, 3.55240624e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-03-13 19:54:46,826 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:46,828 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:46,828 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:46,871 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:46,905 - shap - INFO - np.sum(w_aug) = 15.999999999999998 -2025-03-13 19:54:46,906 - shap - INFO - np.sum(self.kernelWeights) = 1.0 -2025-03-13 19:54:47,010 - shap - INFO - phi = array([ 0.00000000e+00, 4.73599708e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-03-13 19:54:47,023 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,026 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:47,030 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:47,056 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:47,102 - shap - INFO - np.sum(w_aug) = 16.0 -2025-03-13 19:54:47,103 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:47,135 - shap - INFO - phi = array([0.00000000e+00, 4.73599708e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 1.37321572e-04, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, 1.45046191e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05580321e-04, 0.00000000e+00]) -2025-03-13 19:54:47,153 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,155 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:47,163 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:47,208 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:47,234 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:47,235 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-13 19:54:47,297 - shap - INFO - phi = array([ 0. , 0.004736 , 0. , 0. , 0. , - 0.00013732, -0.00031293, 0. , 0. , 0.00014505, - 0. , 0. , 0. , 0. , 0.00010558, - 0. ]) -2025-03-13 19:54:47,315 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,316 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:47,317 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:47,352 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:47,382 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:47,383 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:47,427 - shap - INFO - phi = array([ 0.00000000e+00, -8.88019875e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -6.86607862e-05, 6.25857095e-05, 0.00000000e+00, - 0.00000000e+00, -7.25230953e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, -2.11160641e-04, 0.00000000e+00]) -2025-03-13 19:54:47,448 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,464 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-03-13 19:54:47,465 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-13 19:54:47,491 - shap - INFO - weight_left = 0.5181019626448611 -2025-03-13 19:54:47,527 - shap - INFO - np.sum(w_aug) = 17.000000000000007 -2025-03-13 19:54:47,528 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:47,646 - shap - INFO - phi = array([ 0.00000000e+00, 4.78175895e-03, 0.00000000e+00, -8.23870107e-04, - 3.52707678e-04, 5.84070762e-02, -6.58176070e-05, 4.16744541e-02, - 1.63386594e-02, 1.27366766e-05, -5.19480170e-04, 2.77921160e-03, - -2.78906921e-03, 2.18486196e-04, 4.19067576e-05, -1.61020629e-04, - -2.80003094e-04]) -2025-03-13 19:54:47,659 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,679 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-03-13 19:54:47,682 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-13 19:54:47,714 - shap - INFO - weight_left = 0.528623703595323 -2025-03-13 19:54:47,748 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-03-13 19:54:47,749 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 -2025-03-13 19:54:47,834 - shap - INFO - phi = array([-4.23740400e-03, 4.86594850e-03, 2.03953900e-04, -8.84065259e-04, - 1.38516632e-03, 7.92636066e-04, 1.59348446e-03, -3.82106791e-04, - 4.19611966e-04, 1.24311529e-04, 3.39022229e-03, 2.30180720e-04, - 2.53010519e-03, -4.21856301e-03, -2.20044941e-04, 9.26701278e-04, - 7.40477018e-04, 4.16044238e-05]) -2025-03-13 19:54:47,850 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:47,863 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-03-13 19:54:47,864 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-13 19:54:47,893 - shap - INFO - weight_left = 0.5181019626448611 -2025-03-13 19:54:47,926 - shap - INFO - np.sum(w_aug) = 17.000000000000004 -2025-03-13 19:54:47,928 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:47,964 - shap - INFO - phi = array([-1.46251498e-02, -9.09576982e-03, 4.25612431e-04, -1.11308778e-03, - 7.60190988e-04, 3.16631560e-04, 7.77586437e-04, 0.00000000e+00, - 1.41757456e-04, -4.68811641e-04, 3.00263634e-03, -4.64003818e-03, - 5.11925711e-04, -4.53961415e-03, -2.28118751e-04, 7.57353258e-04, - 2.57639654e-05]) -2025-03-13 19:54:47,980 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:48,003 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-03-13 19:54:48,005 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-13 19:54:48,037 - shap - INFO - weight_left = 0.5181019626448611 -2025-03-13 19:54:48,071 - shap - INFO - np.sum(w_aug) = 17.000000000000007 -2025-03-13 19:54:48,072 - shap - INFO - np.sum(self.kernelWeights) = 1.0 -2025-03-13 19:54:48,186 - shap - INFO - phi = array([-1.48965200e-02, -1.01127722e-02, 2.03585575e-04, -1.17224161e-03, - 7.81832942e-04, 2.82597834e-04, 8.71948585e-04, 0.00000000e+00, - 5.77036931e-05, -4.76796221e-04, -2.33829883e-04, -4.76516715e-03, - 4.76501332e-04, -4.53316051e-03, -2.38378566e-04, 7.49106138e-04, - -6.34185477e-05]) -2025-03-13 19:54:48,203 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:48,205 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-03-13 19:54:48,206 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-13 19:54:48,258 - shap - INFO - weight_left = 0.5181019626448611 -2025-03-13 19:54:48,291 - shap - INFO - np.sum(w_aug) = 17.000000000000004 -2025-03-13 19:54:48,293 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:48,397 - shap - INFO - phi = array([-1.74825582e-02, -1.27453727e-02, 2.71099426e-04, -5.03192628e-04, - 6.14453598e-04, -7.77677359e-05, 2.21968340e-05, -8.10889482e-04, - -7.61363376e-04, -3.65571534e-04, -2.36984952e-03, -8.16528928e-03, - 2.76638520e-04, -4.66492905e-03, -3.06599353e-04, 8.52268525e-04, - -3.45073031e-04]) -2025-03-13 19:54:48,416 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:48,426 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:48,426 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:48,465 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:48,511 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:48,512 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:48,563 - shap - INFO - phi = array([-0.01821529, -0.0130146 , 0.00022738, -0.00050559, 0.00051753, - -0.00023178, -0.00010514, -0.00100915, -0.00078202, -0.00044573, - -0.00278685, -0.00925884, 0.00015105, -0.00466223, -0.0002263 , - -0.00069266]) -2025-03-13 19:54:48,595 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:48,596 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:48,596 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:48,639 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:48,671 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:48,672 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:48,766 - shap - INFO - phi = array([-1.96253651e-02, -1.39657904e-02, -6.51680781e-05, -4.80932783e-04, - 0.00000000e+00, -2.83003112e-04, -3.39480117e-04, -1.20421516e-03, - -9.47237644e-04, -4.21915560e-04, -3.20497836e-03, -9.94263712e-03, - -1.98131879e-05, -4.71469473e-03, -2.74155541e-04, -8.29883198e-04]) -2025-03-13 19:54:48,784 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:48,810 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:48,814 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:48,841 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:48,876 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:48,877 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:48,993 - shap - INFO - phi = array([-1.80974611e-02, -1.33876191e-02, 0.00000000e+00, 0.00000000e+00, - -1.78947332e-04, -2.79410456e-04, -1.55006965e-04, 1.68559334e-02, - -8.76923381e-04, -5.56393706e-04, -3.14028031e-03, -9.47646213e-03, - 0.00000000e+00, 3.57457310e-05, -2.60980264e-04, -5.54136155e-04]) -2025-03-13 19:54:49,011 - shap - INFO - num_full_subsets = 2 -2025-03-13 19:54:49,015 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-13 19:54:49,016 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-13 19:54:49,053 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-13 19:54:49,085 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-13 19:54:49,086 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-13 19:54:49,128 - shap - INFO - phi = array([ 0.00000000e+00, -8.88018310e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, -7.40321285e-05, 6.26351585e-05, -6.26276553e-04, - 0.00000000e+00, -7.41192462e-04, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 1.05602980e-04, 0.00000000e+00]) -2025-03-13 19:54:57,638 - tsfresh.feature_extraction.settings - WARNING - Dependency not available for matrix_profile, this feature will be disabled! -2025-03-13 20:07:51,647 - lightning_fabric.utilities.seed - INFO - Seed set to 42 -2025-03-13 20:07:51,822 - lightning.pytorch.utilities.rank_zero - INFO - GPU available: True (cuda), used: True -2025-03-13 20:07:51,825 - lightning.pytorch.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores -2025-03-13 20:07:51,826 - lightning.pytorch.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs -2025-03-13 20:07:51,844 - lightning.pytorch.utilities.rank_zero - INFO - You are using a CUDA device ('NVIDIA GeForce RTX 4070 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision -2025-03-13 20:07:51,938 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-03-13 20:07:51,964 - pytorch_lightning.callbacks.model_summary - INFO - - | Name | Type | Params | Mode ------------------------------------------------------------ -0 | loss_fn | MaskedMetric | 0 | train -1 | train_metrics | MetricCollection | 0 | train -2 | val_metrics | MetricCollection | 0 | train -3 | test_metrics | MetricCollection | 0 | train -4 | model | GRINet | 62.1 K | train ------------------------------------------------------------ -62.1 K Trainable params -0 Non-trainable params -62.1 K Total params -0.248 Total estimated model params size (MB) -63 Modules in train mode -0 Modules in eval mode -2025-03-13 20:08:00,765 - lightning.pytorch.utilities.rank_zero - INFO - `Trainer.fit` stopped: `max_epochs=20` reached. -2025-03-13 20:08:00,900 - lightning.pytorch.utilities.rank_zero - INFO - Restoring states from the checkpoint path at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-07-51_42/epoch=19-step=20.ckpt -2025-03-13 20:08:00,933 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-03-13 20:08:01,091 - lightning.pytorch.utilities.rank_zero - INFO - Loaded model weights from the checkpoint at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-07-51_42/epoch=19-step=20.ckpt -2025-03-13 20:08:01,581 - lightning_fabric.utilities.seed - INFO - Seed set to 42 -2025-03-13 20:08:01,666 - lightning.pytorch.utilities.rank_zero - INFO - GPU available: True (cuda), used: True -2025-03-13 20:08:01,667 - lightning.pytorch.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores -2025-03-13 20:08:01,668 - lightning.pytorch.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs -2025-03-13 20:08:01,714 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-03-13 20:08:01,728 - pytorch_lightning.callbacks.model_summary - INFO - - | Name | Type | Params | Mode ------------------------------------------------------------ -0 | loss_fn | MaskedMetric | 0 | train -1 | train_metrics | MetricCollection | 0 | train -2 | val_metrics | MetricCollection | 0 | train -3 | test_metrics | MetricCollection | 0 | train -4 | model | GRINet | 62.1 K | train ------------------------------------------------------------ -62.1 K Trainable params -0 Non-trainable params -62.1 K Total params -0.248 Total estimated model params size (MB) -63 Modules in train mode -0 Modules in eval mode -2025-03-13 20:08:10,772 - lightning.pytorch.utilities.rank_zero - INFO - `Trainer.fit` stopped: `max_epochs=20` reached. -2025-03-13 20:08:10,910 - lightning.pytorch.utilities.rank_zero - INFO - Restoring states from the checkpoint path at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/epoch=19-step=20.ckpt -2025-03-13 20:08:10,947 - pytorch_lightning.accelerators.cuda - INFO - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] -2025-03-13 20:08:11,112 - lightning.pytorch.utilities.rank_zero - INFO - Loaded model weights from the checkpoint at /mnt/c/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/wrapper/AlgoPython/GRIN/logs/grin/2025-03-13_20-08-01_42/epoch=19-step=20.ckpt -2025-03-13 20:08:36,533 - root - INFO - epoch 1: 0.03266041607969961 -2025-03-13 20:08:58,859 - root - INFO - epoch 2: 0.02425540741909426 -2025-03-13 20:08:58,862 - root - INFO - Early stop in EpochConvergeCallback.on_epoch(). -2025-03-13 20:09:20,039 - root - INFO - epoch 1: 0.03349533186827601 -2025-03-13 20:09:44,943 - root - INFO - epoch 2: 0.02404131812087564 -2025-03-13 20:09:44,947 - root - INFO - Early stop in EpochConvergeCallback.on_epoch(). -2025-03-14 00:05:46,041 - root - INFO - avg_epoch_loss:0.0, epoch:0 -2025-03-14 00:05:47,241 - root - INFO - avg_epoch_loss:0.0, epoch:1 -2025-03-14 00:05:48,476 - root - INFO - avg_epoch_loss:1.331366777420044, epoch:2 -2025-03-14 00:05:49,595 - root - INFO - avg_epoch_loss:0.0, epoch:3 -2025-03-14 00:05:50,769 - root - INFO - avg_epoch_loss:0.0, epoch:4 -2025-03-14 00:05:52,136 - root - INFO - avg_epoch_loss:1.3258730173110962, epoch:5 -2025-03-14 00:05:53,303 - root - INFO - avg_epoch_loss:0.0, epoch:6 -2025-03-14 00:05:54,495 - root - INFO - avg_epoch_loss:0.0, epoch:7 -2025-03-14 00:05:55,737 - root - INFO - avg_epoch_loss:0.0, epoch:8 -2025-03-14 00:05:56,948 - root - INFO - avg_epoch_loss:1.2452423572540283, epoch:9 -2025-03-14 00:05:58,132 - root - INFO - avg_epoch_loss:0.0, epoch:10 -2025-03-14 00:05:59,330 - root - INFO - avg_epoch_loss:0.0, epoch:11 -2025-03-14 00:06:00,486 - root - INFO - avg_epoch_loss:1.0952250957489014, epoch:12 -2025-03-14 00:06:01,858 - root - INFO - avg_epoch_loss:1.1661832332611084, epoch:13 -2025-03-14 00:06:03,019 - root - INFO - avg_epoch_loss:0.0, epoch:14 -2025-03-14 00:06:16,549 - root - INFO - valid_avg_epoch_loss0.0, epoch:14 -2025-03-14 00:06:16,551 - root - INFO - best loss is updated to 0.0 at 14 -2025-03-14 00:06:17,793 - root - INFO - avg_epoch_loss:0.0, epoch:15 -2025-03-14 00:06:18,983 - root - INFO - avg_epoch_loss:0.0, epoch:16 -2025-03-14 00:06:20,220 - root - INFO - avg_epoch_loss:0.0, epoch:17 -2025-03-14 00:06:21,542 - root - INFO - avg_epoch_loss:1.0875152349472046, epoch:18 -2025-03-14 00:06:22,709 - root - INFO - avg_epoch_loss:0.0, epoch:19 -2025-03-14 00:06:35,344 - root - INFO - valid_avg_epoch_loss1.0857791900634766, epoch:19 -2025-03-14 00:06:50,874 - root - INFO - avg_epoch_loss:0.0, epoch:0 -2025-03-14 00:06:52,021 - root - INFO - avg_epoch_loss:0.0, epoch:1 -2025-03-14 00:06:53,303 - root - INFO - avg_epoch_loss:1.331366777420044, epoch:2 -2025-03-14 00:06:54,443 - root - INFO - avg_epoch_loss:0.0, epoch:3 -2025-03-14 00:06:55,659 - root - INFO - avg_epoch_loss:0.0, epoch:4 -2025-03-14 00:06:57,206 - root - INFO - avg_epoch_loss:1.3258730173110962, epoch:5 -2025-03-14 00:06:58,433 - root - INFO - avg_epoch_loss:0.0, epoch:6 -2025-03-14 00:06:59,554 - root - INFO - avg_epoch_loss:0.0, epoch:7 -2025-03-14 00:07:00,775 - root - INFO - avg_epoch_loss:0.0, epoch:8 -2025-03-14 00:07:01,966 - root - INFO - avg_epoch_loss:1.2452423572540283, epoch:9 -2025-03-14 00:07:03,183 - root - INFO - avg_epoch_loss:0.0, epoch:10 -2025-03-14 00:07:04,354 - root - INFO - avg_epoch_loss:0.0, epoch:11 -2025-03-14 00:07:05,650 - root - INFO - avg_epoch_loss:1.0952249765396118, epoch:12 -2025-03-14 00:07:07,094 - root - INFO - avg_epoch_loss:1.166185975074768, epoch:13 -2025-03-14 00:07:08,336 - root - INFO - avg_epoch_loss:0.0, epoch:14 -2025-03-14 00:07:23,005 - root - INFO - valid_avg_epoch_loss0.0, epoch:14 -2025-03-14 00:07:23,006 - root - INFO - best loss is updated to 0.0 at 14 -2025-03-14 00:07:24,205 - root - INFO - avg_epoch_loss:0.0, epoch:15 -2025-03-14 00:07:25,422 - root - INFO - avg_epoch_loss:0.0, epoch:16 -2025-03-14 00:07:26,682 - root - INFO - avg_epoch_loss:0.0, epoch:17 -2025-03-14 00:07:28,126 - root - INFO - avg_epoch_loss:1.0875149965286255, epoch:18 -2025-03-14 00:07:29,360 - root - INFO - avg_epoch_loss:0.0, epoch:19 -2025-03-14 00:07:42,542 - root - INFO - valid_avg_epoch_loss1.0857793092727661, epoch:19 -2025-03-14 00:18:40,003 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} -2025-03-14 00:18:40,276 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.919337618172575, best pos: [3.00104154e+00 2.62311705e-01 5.20573580e+02] -2025-03-14 03:13:55,589 - root - INFO - avg_epoch_loss:0.0, epoch:0 -2025-03-14 03:13:55,847 - root - INFO - avg_epoch_loss:0.0, epoch:1 -2025-03-14 03:13:56,140 - root - INFO - avg_epoch_loss:1.4083999395370483, epoch:2 -2025-03-14 03:13:56,417 - root - INFO - avg_epoch_loss:0.0, epoch:3 -2025-03-14 03:13:56,646 - root - INFO - avg_epoch_loss:0.0, epoch:4 -2025-03-14 03:13:57,231 - root - INFO - avg_epoch_loss:1.3338640928268433, epoch:5 -2025-03-14 03:13:57,513 - root - INFO - avg_epoch_loss:0.0, epoch:6 -2025-03-14 03:13:57,787 - root - INFO - avg_epoch_loss:0.0, epoch:7 -2025-03-14 03:13:58,054 - root - INFO - avg_epoch_loss:0.0, epoch:8 -2025-03-14 03:13:58,396 - root - INFO - avg_epoch_loss:1.2289820909500122, epoch:9 -2025-03-14 03:13:58,652 - root - INFO - avg_epoch_loss:0.0, epoch:10 -2025-03-14 03:13:58,910 - root - INFO - avg_epoch_loss:0.0, epoch:11 -2025-03-14 03:13:59,222 - root - INFO - avg_epoch_loss:1.0910987854003906, epoch:12 -2025-03-14 03:13:59,735 - root - INFO - avg_epoch_loss:1.164913535118103, epoch:13 -2025-03-14 03:13:59,999 - root - INFO - avg_epoch_loss:0.0, epoch:14 -2025-03-14 03:14:03,411 - root - INFO - valid_avg_epoch_loss0.0, epoch:14 -2025-03-14 03:14:03,413 - root - INFO - best loss is updated to 0.0 at 14 -2025-03-14 03:14:03,713 - root - INFO - avg_epoch_loss:0.0, epoch:15 -2025-03-14 03:14:03,944 - root - INFO - avg_epoch_loss:0.0, epoch:16 -2025-03-14 03:14:04,179 - root - INFO - avg_epoch_loss:0.0, epoch:17 -2025-03-14 03:14:04,548 - root - INFO - avg_epoch_loss:1.0830341577529907, epoch:18 -2025-03-14 03:14:04,827 - root - INFO - avg_epoch_loss:0.0, epoch:19 -2025-03-14 03:14:08,407 - root - INFO - valid_avg_epoch_loss1.0864672660827637, epoch:19 -2025-03-14 03:14:16,122 - root - INFO - avg_epoch_loss:0.0, epoch:0 -2025-03-14 03:14:17,319 - root - INFO - avg_epoch_loss:0.0, epoch:1 -2025-03-14 03:14:18,532 - root - INFO - avg_epoch_loss:1.4083999395370483, epoch:2 -2025-03-14 03:14:19,700 - root - INFO - avg_epoch_loss:0.0, epoch:3 -2025-03-14 03:14:20,927 - root - INFO - avg_epoch_loss:0.0, epoch:4 -2025-03-14 03:14:22,473 - root - INFO - avg_epoch_loss:1.3338640928268433, epoch:5 -2025-03-14 03:14:23,627 - root - INFO - avg_epoch_loss:0.0, epoch:6 -2025-03-14 03:14:24,833 - root - INFO - avg_epoch_loss:0.0, epoch:7 -2025-03-14 03:14:25,896 - root - INFO - avg_epoch_loss:0.0, epoch:8 -2025-03-14 03:14:27,076 - root - INFO - avg_epoch_loss:1.2289817333221436, epoch:9 -2025-03-14 03:14:28,270 - root - INFO - avg_epoch_loss:0.0, epoch:10 -2025-03-14 03:14:29,418 - root - INFO - avg_epoch_loss:0.0, epoch:11 -2025-03-14 03:14:30,604 - root - INFO - avg_epoch_loss:1.0911017656326294, epoch:12 -2025-03-14 03:14:31,982 - root - INFO - avg_epoch_loss:1.1649134159088135, epoch:13 -2025-03-14 03:14:33,117 - root - INFO - avg_epoch_loss:0.0, epoch:14 -2025-03-14 03:14:46,271 - root - INFO - valid_avg_epoch_loss0.0, epoch:14 -2025-03-14 03:14:46,273 - root - INFO - best loss is updated to 0.0 at 14 -2025-03-14 03:14:47,462 - root - INFO - avg_epoch_loss:0.0, epoch:15 -2025-03-14 03:14:50,234 - root - INFO - avg_epoch_loss:0.0, epoch:16 -2025-03-14 03:14:51,396 - root - INFO - avg_epoch_loss:0.0, epoch:17 -2025-03-14 03:14:52,720 - root - INFO - avg_epoch_loss:1.0830390453338623, epoch:18 -2025-03-14 03:14:53,833 - root - INFO - avg_epoch_loss:0.0, epoch:19 -2025-03-14 03:15:06,402 - root - INFO - valid_avg_epoch_loss1.0864744186401367, epoch:19 -2025-03-14 03:15:31,160 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} -2025-03-14 03:15:33,187 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.08817197043863872, best pos: [5.07813132e+00 5.72547880e-01 9.89081876e+02] -2025-03-14 03:15:54,085 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,087 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,089 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,102 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:54,134 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:54,136 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:54,255 - shap - INFO - phi = array([-1.77279742e-04, 3.12102296e-03, 7.52091639e-05, 4.65556716e-04, - -2.61863150e-04, -5.28207776e-05, -1.15176580e-05, -2.66818839e-05, - -8.78281949e-05, 2.23159426e-04, -2.77053757e-04, -1.26703208e-04, - -8.29004594e-05, -2.40644051e-04, -1.04998145e-04]) -2025-03-14 03:15:54,277 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,278 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,278 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,329 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:54,367 - shap - INFO - np.sum(w_aug) = 14.999999999999995 -2025-03-14 03:15:54,369 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-14 03:15:54,403 - shap - INFO - phi = array([-1.77434914e-04, 3.12148102e-03, -3.66718471e-05, 4.65663991e-04, - -2.61952212e-04, 1.18926004e-04, -1.14940296e-05, -2.74295429e-05, - -8.76147475e-05, 2.23241951e-04, -2.76977149e-04, -1.26778136e-04, - -8.52848169e-05, -2.48309840e-04, -1.05107925e-04]) -2025-03-14 03:15:54,449 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,450 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,451 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,486 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:54,520 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:54,522 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:54,562 - shap - INFO - phi = array([ 3.19535484e-04, -1.57631468e-03, 4.42741600e-05, -1.36420641e-03, - 4.70214520e-04, -1.79937917e-04, 3.98858153e-05, 1.51215727e-05, - 3.12005109e-04, 2.91012310e-04, 6.80023964e-04, 7.83868310e-05, - 4.30106808e-04, 1.90892158e-04, 5.14749072e-04]) -2025-03-14 03:15:54,591 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,592 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,593 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,632 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:54,673 - shap - INFO - np.sum(w_aug) = 15.0 -2025-03-14 03:15:54,675 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-14 03:15:54,725 - shap - INFO - phi = array([ 3.19411228e-04, -1.38891540e-03, -3.15875780e-05, -1.39710457e-03, - 4.72743682e-04, 0.00000000e+00, 3.00483087e-05, 9.60751770e-06, - 2.16230371e-04, 2.91523045e-04, 1.83628223e-04, 8.60858562e-05, - 2.04423965e-04, 4.26558700e-04, 3.04113382e-04]) -2025-03-14 03:15:54,768 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,769 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,770 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,793 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:54,833 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:54,836 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:54,922 - shap - INFO - phi = array([ 1.53119254e-04, -1.37966837e-03, 5.93613180e-05, 4.60252880e-04, - 2.51417870e-04, -7.00328318e-05, 2.96510264e-05, 0.00000000e+00, - -9.01419673e-05, 2.67833998e-04, -1.51995959e-04, 8.04815598e-05, - -1.52212359e-04, 1.91831644e-04, -1.45777831e-04]) -2025-03-14 03:15:54,932 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:54,933 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:54,934 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:54,978 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:55,024 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:55,026 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:55,125 - shap - INFO - phi = array([ 3.43708246e-05, -1.25049057e-03, -3.68917632e-05, 4.60743853e-04, - -1.15160374e-04, -5.77661943e-05, 3.03086720e-05, -1.67970043e-05, - -8.44574447e-05, 2.52645039e-04, -2.61001552e-04, 8.68600531e-05, - -1.46155097e-04, -6.89299529e-05, -2.72482196e-04]) -2025-03-14 03:15:55,147 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:55,148 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:55,148 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:55,204 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:55,243 - shap - INFO - np.sum(w_aug) = 14.999999999999998 -2025-03-14 03:15:55,244 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:55,277 - shap - INFO - phi = array([-2.12173903e-04, -1.32121533e-03, -3.69126161e-05, 4.56524285e-04, - -2.69638596e-04, 1.20716390e-04, -4.79971927e-05, 3.38160646e-05, - -8.41976709e-05, -7.65667133e-04, -2.10514410e-04, 8.17323070e-05, - -6.97974402e-05, -8.00358062e-05, -9.26165400e-05]) -2025-03-14 03:15:55,301 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:55,306 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:55,308 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:55,351 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:55,391 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:55,394 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:55,433 - shap - INFO - phi = array([-2.60242209e-04, 6.73466443e-04, -3.69713775e-05, 4.51709947e-04, - -2.86406999e-04, 1.19293009e-04, -5.91088157e-05, 1.84558825e-05, - -9.46333098e-05, -7.83216325e-04, 3.13835323e-04, -1.60852907e-04, - -9.82480689e-05, -1.71722972e-04, -9.77271274e-05]) -2025-03-14 03:15:55,475 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:55,477 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-03-14 03:15:55,477 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-14 03:15:55,519 - shap - INFO - weight_left = 0.528623703595323 -2025-03-14 03:15:57,301 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-03-14 03:15:57,303 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999999 -2025-03-14 03:15:57,408 - shap - INFO - phi = array([ 1.31176750e-04, 3.02401050e-03, 2.63198414e-04, 4.61650168e-04, - 4.49270317e-04, 8.56341634e-02, 2.44202924e-04, 2.78534479e-02, - -1.52608683e-05, -1.28370005e-04, 8.53351298e-03, -7.59589587e-04, - 4.52443265e-03, 6.55310415e-05, 1.83363655e-03, 2.46509145e-04, - 9.28437474e-05, 2.15939519e-04]) -2025-03-14 03:15:57,438 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:57,439 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-03-14 03:15:57,440 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-14 03:15:57,483 - shap - INFO - weight_left = 0.528623703595323 -2025-03-14 03:15:57,521 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-03-14 03:15:57,522 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 -2025-03-14 03:15:57,624 - shap - INFO - phi = array([ 2.60191813e-04, 3.52178867e-03, 6.67041558e-04, -1.29808808e-03, - 1.38997634e-03, 1.59067115e-04, 6.05080436e-04, 1.00581661e-04, - 5.02090345e-05, 9.58829158e-03, -9.78547784e-04, 5.02930062e-03, - -1.88548905e-03, 2.92792312e-03, 4.19345857e-04, 8.38696676e-05, - 2.85158923e-04, 6.44906051e-04]) -2025-03-14 03:15:57,650 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:57,651 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-03-14 03:15:57,652 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-14 03:15:57,701 - shap - INFO - weight_left = 0.528623703595323 -2025-03-14 03:15:57,738 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-03-14 03:15:57,739 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 -2025-03-14 03:15:57,779 - shap - INFO - phi = array([-5.53616914e-03, -1.72892268e-03, 8.14175972e-04, 4.69056671e-04, - 9.24074106e-04, -1.20060899e-03, 2.61272608e-05, 1.84695145e-05, - -2.69199987e-04, 2.29397622e-03, -3.17760485e-03, 4.51315642e-03, - -3.75445816e-03, 1.76229845e-03, 2.38779520e-04, 3.05587313e-05, - 4.60214847e-06, 2.73111639e-04]) -2025-03-14 03:15:57,838 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:57,839 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, - 0.11662019, 0.11518043]) -2025-03-14 03:15:57,841 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-14 03:15:57,857 - shap - INFO - weight_left = 0.528623703595323 -2025-03-14 03:15:57,892 - shap - INFO - np.sum(w_aug) = 17.999999999999996 -2025-03-14 03:15:57,895 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 -2025-03-14 03:15:57,940 - shap - INFO - phi = array([-5.72905946e-03, -2.35452302e-03, 5.06416850e-04, -1.39955022e-03, - 5.85286308e-04, -3.10397204e-03, -2.56083815e-05, -2.33464915e-04, - -2.19061273e-04, 1.74244225e-04, -3.24205577e-03, 1.69560466e-05, - -3.85174339e-03, -3.64419930e-04, 9.46185490e-05, 5.43795689e-07, - 1.55842346e-04, 2.06832365e-04]) -2025-03-14 03:15:57,967 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:57,968 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, - 0.13480029]) -2025-03-14 03:15:57,968 - shap - INFO - num_paired_subset_sizes = 8 -2025-03-14 03:15:58,006 - shap - INFO - weight_left = 0.5181019626448611 -2025-03-14 03:15:58,046 - shap - INFO - np.sum(w_aug) = 17.000000000000007 -2025-03-14 03:15:58,048 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:58,149 - shap - INFO - phi = array([-1.00921625e-02, -1.05908490e-02, 4.00118671e-04, 4.97959792e-04, - 5.94218360e-04, -2.75155124e-03, 2.50324081e-04, -1.45635248e-05, - 8.06945283e-05, -2.71814799e-03, -7.19796782e-03, -2.79371604e-03, - -1.89105403e-05, 2.45756049e-04, 2.65884734e-05, 5.39583185e-04, - 2.11705020e-04]) -2025-03-14 03:15:58,190 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:58,191 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-14 03:15:58,192 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:58,229 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-14 03:15:58,266 - shap - INFO - np.sum(w_aug) = 15.999999999999998 -2025-03-14 03:15:58,268 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-14 03:15:58,370 - shap - INFO - phi = array([-1.10590110e-02, -1.10525358e-02, 2.38023547e-04, -1.52327916e-03, - 4.56545659e-04, -3.25511077e-03, -1.23889189e-04, 4.22636591e-05, - 1.73787932e-04, -2.83807610e-03, -7.77755940e-03, -3.06934845e-03, - -9.14990866e-05, 1.56199973e-04, 4.42089905e-05, 4.49342065e-05]) -2025-03-14 03:15:58,385 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:58,387 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-14 03:15:58,393 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:58,452 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-14 03:15:58,489 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-14 03:15:58,490 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 -2025-03-14 03:15:58,528 - shap - INFO - phi = array([-1.21396233e-02, -1.24316313e-02, 1.60153732e-04, 5.17321975e-04, - 2.69343256e-04, -3.26566588e-03, -2.09891347e-04, -5.66610254e-05, - -3.94879121e-05, -3.11298097e-03, -9.30768895e-03, -2.14630586e-03, - 1.79251005e-04, 1.71877236e-04, 5.62768919e-05, -1.68346442e-04]) -2025-03-14 03:15:58,551 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:58,557 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, - 0.13849389]) -2025-03-14 03:15:58,560 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:58,607 - shap - INFO - weight_left = 0.5063344810024111 -2025-03-14 03:15:58,646 - shap - INFO - np.sum(w_aug) = 15.999999999999996 -2025-03-14 03:15:58,649 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:58,688 - shap - INFO - phi = array([-1.10418948e-02, -1.12854877e-02, 8.01052660e-05, -1.56957848e-03, - 6.47607841e-05, -3.14173468e-04, -2.21064311e-04, -8.40092860e-05, - 4.17579715e-05, -2.99651036e-03, -8.74012174e-03, -2.07426776e-03, - 2.32825759e-04, 1.25948182e-04, 3.84384884e-05, -1.40148712e-04]) -2025-03-14 03:15:58,712 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:58,715 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:58,719 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:58,760 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:58,802 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:58,804 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:58,838 - shap - INFO - phi = array([ 3.00932794e-04, -1.41618149e-03, 1.92562321e-04, -1.38164057e-03, - 4.18912079e-04, -9.05397295e-04, -6.44387102e-05, 1.53900635e-05, - 3.74262728e-04, 8.38883005e-04, 7.05331946e-04, 7.82227662e-05, - 4.09925869e-04, 4.31468967e-04, 5.20597314e-04]) -2025-03-14 03:15:58,864 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:58,869 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:58,874 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:58,920 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:58,956 - shap - INFO - np.sum(w_aug) = 14.999999999999996 -2025-03-14 03:15:58,957 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:59,001 - shap - INFO - phi = array([ 1.75009660e-04, -1.16203544e-03, 5.86787231e-05, -1.39591883e-03, - 2.70277565e-04, -1.27245812e-04, 2.90846859e-05, -1.18361180e-05, - 3.43111710e-04, 8.44121511e-04, 1.82890545e-04, 9.20340660e-05, - 1.92582385e-04, -6.61140803e-05, 3.01660394e-04]) -2025-03-14 03:15:59,022 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:59,026 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:59,034 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:59,084 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:59,121 - shap - INFO - np.sum(w_aug) = 14.999999999999998 -2025-03-14 03:15:59,122 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:59,160 - shap - INFO - phi = array([-1.43531431e-04, -1.33772072e-03, -3.65557253e-05, -1.38348687e-03, - -1.32808140e-04, -6.48305635e-05, -1.21716249e-04, 0.00000000e+00, - -1.17345948e-04, -7.74327503e-04, -1.52775261e-04, -1.60602704e-04, - -1.46009927e-04, -1.68654118e-04, -2.62943869e-04]) -2025-03-14 03:15:59,203 - shap - INFO - num_full_subsets = 2 -2025-03-14 03:15:59,205 - shap - INFO - remaining_weight_vector = array([0.25989514, 0.21264148, 0.1871245 , 0.17326343, 0.16707545]) -2025-03-14 03:15:59,208 - shap - INFO - num_paired_subset_sizes = 7 -2025-03-14 03:15:59,248 - shap - INFO - weight_left = 0.4930585722173909 -2025-03-14 03:15:59,283 - shap - INFO - np.sum(w_aug) = 14.999999999999998 -2025-03-14 03:15:59,285 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 -2025-03-14 03:15:59,319 - shap - INFO - phi = array([-1.65582004e-04, 2.97169024e-03, 7.50082413e-05, 4.65659052e-04, - -2.56436885e-04, -6.29711925e-05, -2.68315367e-05, 4.90845060e-05, - -8.76947195e-05, 2.23158556e-04, -2.64207990e-04, -1.11078044e-04, - -8.27635906e-05, -2.40413492e-04, -8.49945429e-05]) diff --git a/tests/test_imputation_knn.py b/tests/test_imputation_knn.py index d0e13d36..a4aee2b7 100644 --- a/tests/test_imputation_knn.py +++ b/tests/test_imputation_knn.py @@ -25,7 +25,7 @@ def test_imputation_knn(self): miss_ts = ts_0.Contamination.aligned(input_data=ts_0.data, rate_series=0.18, offset=0.1) - imputer = Imputation.Statistics.KNN(miss_ts) + imputer = Imputation.Statistics.KNNImpute(miss_ts) imputer.impute(user_def=True, params={"k":k, "weights":weight}) imputer.score(ts_0.data, imputer.recov_data) @@ -36,7 +36,7 @@ def test_imputation_knn(self): knn = KNNImputer(n_neighbors=k, weights=weight) recov = knn.fit_transform(miss_ts) - imputer2 = Imputation.Statistics.KNN(miss_ts) + imputer2 = Imputation.Statistics.KNNImpute(miss_ts) imputer2.recov_data = None imputer2.metrics = None diff --git a/tests/test_opti_ray_whole.py b/tests/test_opti_ray_whole.py index 8d517a93..d6135b4b 100644 --- a/tests/test_opti_ray_whole.py +++ b/tests/test_opti_ray_whole.py @@ -26,8 +26,7 @@ def test_optimization_ray(self): # 4. imputation of the contaminated data # imputation with AutoML which will discover the optimal hyperparameters for your dataset and your algorithm - algorithms_all = ["cdrec", "stmvl", "iim", "mrnn", "iter_svd", "grouse", "dynammo", "rosl", "soft_imp", - "spirit", "svt", "tkcm", "brits", "deep_mvi", "mpin", "pristi"] + algorithms_all = ["cdrec", "stmvl", "xgboost", "deep_mvi", "knn"] for alg in algorithms_all: imputer = utils.config_impute_algorithm(incomp_data=ts_mask, algorithm=alg)