From b73ee9be1b873dd79443e9174ff1a6dd2acb317e Mon Sep 17 00:00:00 2001 From: Quentin Nater Date: Mon, 17 Mar 2025 18:18:02 +0100 Subject: [PATCH 1/3] alpha 01a - documentation and whole refunt --- build/lib/imputegap/runner_explainer.py | 2 +- imputegap/dataset/README.md | 137 ++- .../features_forecast-economy.txt | 22 + .../forecast-economy/forecast-economy_1.jpg | Bin 0 -> 125414 bytes .../forecast-economy/forecast-economy_M.jpg | Bin 0 -> 133004 bytes .../dataset/docs/soccer/features_soccer.txt | 22 + .../docs/temperature/features_temperature.txt | 22 + imputegap/dataset/forecast-economy.txt | 931 ++++++++++++++++++ imputegap/env/default_values.toml | 93 ++ .../__pycache__/downstream.cpython-312.pyc | Bin 11210 -> 11924 bytes .../__pycache__/explainer.cpython-312.pyc | Bin 43252 -> 43284 bytes imputegap/recovery/benchmark.py | 7 +- imputegap/recovery/downstream.py | 86 +- imputegap/recovery/explainer.py | 22 +- imputegap/report.log | 113 --- imputegap/runner_datasets.py | 53 - imputegap/runner_downstream.py | 4 +- imputegap/runner_features.py | 22 + imputegap/runner_loading.py | 2 +- .../algorithm_parameters.cpython-312.pyc | Bin 11791 -> 11791 bytes .../tools/__pycache__/utils.cpython-312.pyc | Bin 36530 -> 45106 bytes imputegap/tools/algorithm_parameters.py | 4 +- imputegap/tools/utils.py | 214 +++- requirements.txt | 1 + tests/test_downstream.py | 5 +- tests/test_explainer.py | 2 +- tests/test_pipeline.py | 2 +- 27 files changed, 1529 insertions(+), 237 deletions(-) create mode 100644 imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt create mode 100644 imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg create mode 100644 imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg create mode 100644 imputegap/dataset/docs/soccer/features_soccer.txt create mode 100644 imputegap/dataset/docs/temperature/features_temperature.txt create mode 100644 imputegap/dataset/forecast-economy.txt delete mode 100644 imputegap/runner_datasets.py create mode 100644 imputegap/runner_features.py diff --git a/build/lib/imputegap/runner_explainer.py b/build/lib/imputegap/runner_explainer.py index 07c7709d..c92f2784 100644 --- a/build/lib/imputegap/runner_explainer.py +++ b/build/lib/imputegap/runner_explainer.py @@ -9,7 +9,7 @@ ts_1.load_series(utils.search_path("eeg-alcohol")) # 3. call the explanation of your dataset with a specific algorithm to gain insight on the Imputation results -shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, extractor="pycatch22", pattern="mcar", missing_rate=0.25, limit_ratio=1, split_ratio=0.7, file_name="eeg-alcohol", algorithm="cdrec") +shap_values, shap_details = Explainer.shap_explainer(input_data=ts_1.data, extractor="pycatch22", pattern="mcar", missing_rate=0.25, rate_dataset=1, training_ratio=0.7, file_name="eeg-alcohol", algorithm="cdrec") # [OPTIONAL] print the results with the impact of each feature. Explainer.print(shap_values, shap_details) \ No newline at end of file diff --git a/imputegap/dataset/README.md b/imputegap/dataset/README.md index f34e75c9..bae717fd 100644 --- a/imputegap/dataset/README.md +++ b/imputegap/dataset/README.md @@ -9,8 +9,8 @@ ImputeGap uses several complete datasets containing different characteristics to This dataset, which has been sampled, defines the air quality for 10 series and 1000 values. -![AIR-QUALITY dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_airq_m.jpg) -![AIR-QUALITY dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_airq_1.jpg) +![AIR-QUALITY dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/airq/01_airq_m.jpg) +![AIR-QUALITY dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/airq/03_airq_1.jpg) ### Features | Category | Feature | Value | @@ -136,7 +136,6 @@ Chlorine dataset - raw data 20x400 provides a subset of the data, limited to 20 Finally, Chlorine - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset. ![Chlorine dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/01_chlorine-rawdata-NxM_graph.jpg) -![Chlorine dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/02_chlorine-rawdata20x400_graph.jpg) ![Chlorine dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/chlorine/03_chlorine-rawdata01x400_graph.jpg) ### Features @@ -204,7 +203,6 @@ Climate dataset - raw data 20x400 provides a subset of the data, limited to 20 t Finally, Climate - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset. ![Climate dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/01_climate-rawdata-NxM_graph.jpg) -![Climate dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/02_climate-rawdata20x400_graph.jpg) ![Climate dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/climate/03_climate-rawdata01x400_graph.jpg) @@ -269,7 +267,6 @@ Drift dataset - raw data 20x400 provides a subset of the data, limited to 20 tim Finally, Drift - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset. ![Drift dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/01_drift-rawdata-NxM_graph.jpg) -![Drift dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/02_drift-rawdata20x400_graph.jpg) ![Drift dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/drift/03_drift-rawdata01x400_graph.jpg) ### Features @@ -347,7 +344,6 @@ EEG-ALCOHOL dataset - raw data 20x400 provides a subset of the data, limited to Finally, EEG-ALCOHOL - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset. ![EEG-ALCOHOL dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/01_eeg-alcohol-rawdata-NxM_graph.jpg) -![EEG-ALCOHOL dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/02_eeg-alcohol-rawdata20x400_graph.jpg) ![EEG-ALCOHOL dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-alcohol/03_eeg-alcohol-rawdata01x400_graph.jpg) @@ -492,8 +488,8 @@ Finally, EEG-READING - normalized 20x400 demonstrates the impact of "MIN-MAX" no This dataset records the electricity consumption of 370 individual points or clients. The data has already been normalized and reduced to a certain size. -![ELECTRICITY dataset - raw data 20x5000](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-reading/01_electricity_M.jpg) -![ELECTRICITY dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/eeg-reading/03_electricity_1.jpg) +![ELECTRICITY dataset - raw data 20x5000](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/electricity/01_electricity_M.jpg) +![ELECTRICITY dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/electricity/03_electricity_1.jpg) @@ -630,7 +626,6 @@ fMRI-STOPTASK dataset - raw data 360x182 shows the full raw dataset, consisting fMRI-STOPTASK dataset - raw data 20x182 provides a subset of the data, limited to 20 time series over 182 time steps, while fMRI-STOPTASK dataset - raw data 01x182 focuses on a single time series extracted from the dataset. Finally, fMRI-STOPTASK - normalized 20x182 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x182 subset. -![fMRI-STOPTASK dataset - raw data 360x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/01_fmri-stoptask-rawdata-NxM_plot.jpg) ![fMRI-STOPTASK dataset - raw data 20x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/02_fmri-stoptask-rawdata20x182_plot.jpg) ![fMRI-STOPTASK dataset - raw data 01x182](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/fmri-stoptask/03_fmri-stoptask-rawdata01x182_plot.jpg) @@ -684,6 +679,62 @@ Finally, fMRI-STOPTASK - normalized 20x182 demonstrates the impact of "MIN-MAX"


+## FORECAST-ECONOMY + +This economic dataset is used for testing downstream forecasting. It exhibits a seasonality of 7 and consists of 16 time series, each containing 931 values. + + +![FORECAST-ECONOMY dataset - raw data M](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/forecast-economy/forecast-economy_M.jpg) +![FORECAST-ECONOMY dataset - raw data 1](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg) + + + +### Features +| Category | Feature | Value | +|---------------|-------------------------------------------------------------------|-----------------------| +| Geometry | 5-bin histogram mode | -0.5710874806115164 | +| Geometry | 10-bin histogram mode | -0.9082987200476134 | +| Geometry | Proportion of high incremental changes in the series | 0.7816717019133937 | +| Geometry | Longest stretch of above-mean values | 357.0 | +| Geometry | Transition matrix column variance | 0.011316872427983538 | +| Geometry | Goodness of exponential fit to embedding distance distribution | 0.12664898312226522 | +| Geometry | Positive outlier timing | 0.18958109559613323 | +| Geometry | Negative outlier timing | -0.2299274973147154 | +| Geometry | Longest stretch of decreasing values | 7.0 | +| Geometry | Rescaled range fluctuation analysis (low-scale scaling) | 0.3 | +| Geometry | Detrended fluctuation analysis (low-scale scaling) | 0.22 | +| Correlation | First 1/e crossing of the ACF | 124.60446764082629 | +| Correlation | First minimum of the ACF | 1 | +| Correlation | Histogram-based automutual information (lag 2, 5 bins) | 0.22074051585149523 | +| Correlation | Time reversibility | 0.28049126008447584 | +| Correlation | First minimum of the AMI function | 5.0 | +| Correlation | Change in autocorrelation timescale after incremental differencing| 0.0012224938875305623 | +| Trend | Wangs periodicity metric | 6 | +| Trend | Entropy of successive pairs in symbolized series | 1.8906454432766748 | +| Trend | Error of 3-point rolling mean forecast | 0.7191953107910503 | +| Transformation| Power in the lowest 20% of frequencies | 0.6678786769493903 | +| Transformation| Centroid frequency | 0.009203884727314454 | + + + + +### Summary + +| Data info | | +|--------------------|------------------------------------------------------------------------------------------| +| Dataset codename | forecast-economy | +| Dataset name | ECONOMY | +| Dataset source | https://zenodo.org/records/14023107 | +| Dataset dimensions | M=16 N=931 | + + + + + +


+ + + @@ -699,9 +750,7 @@ Meteo dataset - raw data 20x400 provides a subset of the data, limited to 20 tim Finally, Meteo - normalized 20x400 demonstrates the impact of "MIN-MAX" normalization on the raw data, applied to the same 20x400 subset. ![Meteo dataset - raw data 64x256](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_meteo-rawdata-NxM_graph.jpg) -![Meteo dataset - raw data 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/02_meteo-rawdata20x400_graph.jpg) ![Meteo dataset - raw data 01x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_meteo-rawdata01x400_graph.jpg) -![Meteo - normalized 20x400](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/04_meteo-normmin_maxdata01x400_graph.jpg) ### Features @@ -774,8 +823,8 @@ Example: 13 = observation period 12:41 to 13:40 This dataset consists of time series data collected from accelerometer and gyroscope sensors, capturing attributes such as attitude, gravity, user acceleration, and rotation rate [[4]](#ref4). Recorded at a high sampling rate of 50Hz using an iPhone 6s placed in users' front pockets, the data reflects various human activities. While the motion time series are non-periodic, they display partial trend similarities. -![Motion dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_motion_M.jpg) -![Motion dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_motion_1.jpg) +![Motion dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/motion/01_motion_M.jpg) +![Motion dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/motion/03_motion_1.jpg) ### Features | Category | Feature | Value | @@ -824,8 +873,35 @@ This dataset consists of time series data collected from accelerometer and gyros This dataset, initially presented in the DEBS Challenge 2013 [[3]](#ref3), captures player positions during a football match. The data is collected from sensors placed near players' shoes and the goalkeeper's hands. With a high tracking frequency of 200Hz, it generates 15,000 position events per second. Soccer time series exhibit bursty behavior and contain numerous outliers. -![Soccer dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_soccer_M.jpg) -![Soccer dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_soccer_1.jpg) +![Soccer dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/soccer/01_soccer_M.jpg) +![Soccer dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/soccer/03_soccer_1.jpg) + +### Features +| Category | Feature | Value | +|---------------|-------------------------------------------------------------------|-----------------------| +| Geometry | 5-bin histogram mode | 0.09084722786947164 | +| Geometry | 10-bin histogram mode | -0.2583118928950434 | +| Geometry | Proportion of high incremental changes in the series | 0.0011092863312203406 | +| Geometry | Longest stretch of above-mean values | 71757.0 | +| Geometry | Transition matrix column variance | 0.006802721088435373 | +| Geometry | Goodness of exponential fit to embedding distance distribution | 0.3417367925024475 | +| Geometry | Positive outlier timing | 0.1377957597962023 | +| Geometry | Negative outlier timing | 0.05898850648030396 | +| Geometry | Longest stretch of decreasing values | 1096.0 | +| Geometry | Rescaled range fluctuation analysis (low-scale scaling) | 0.48 | +| Geometry | Detrended fluctuation analysis (low-scale scaling) | 0.46 | +| Correlation | First 1/e crossing of the ACF | 17792.5919437391 | +| Correlation | First minimum of the ACF | 5221 | +| Correlation | Histogram-based automutual information (lag 2, 5 bins) | 1.1086843892176654 | +| Correlation | Time reversibility | 6.552378315312122e-06 | +| Correlation | First minimum of the AMI function | 40.0 | +| Correlation | Change in autocorrelation timescale after incremental differencing| 0.0006084989404959624 | +| Trend | Wangs periodicity metric | 11198 | +| Trend | Entropy of successive pairs in symbolized series | 1.1035630861406998 | +| Trend | Error of 3-point rolling mean forecast | 0.01179174759304637 | +| Transformation| Power in the lowest 20% of frequencies | 0.9999572395164824 | +| Transformation| Centroid frequency | 0.0001370695723550248 | + ### Summary @@ -849,8 +925,35 @@ This dataset, initially presented in the DEBS Challenge 2013 [[3]](#ref3), captu ## Temperature -![Temperature dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/01_temperature_20.jpg) -![Temperature dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/meteo/03_temperature_1.jpg) +![Temperature dataset - raw data](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/temperature/01_temperature_20.jpg) +![Temperature dataset - one series](https://github.com/eXascaleInfolab/ImputeGAP/raw/main/imputegap/dataset/docs/temperature/03_temperature_1.jpg) + +### Features +| Category | Feature | Value | +|---------------|-------------------------------------------------------------------|-----------------------| +| Geometry | 5-bin histogram mode | 22.045650551725167 | +| Geometry | 10-bin histogram mode | 8.743492676833597 | +| Geometry | Proportion of high incremental changes in the series | 0.7958665687091343 | +| Geometry | Longest stretch of above-mean values | 21931.0 | +| Geometry | Transition matrix column variance | 0.0008670367268468369 | +| Geometry | Goodness of exponential fit to embedding distance distribution | 0.0037844314057919114 | +| Geometry | Positive outlier timing | 0.4030755233654515 | +| Geometry | Negative outlier timing | -0.572629720089644 | +| Geometry | Longest stretch of decreasing values | 15.0 | +| Geometry | Rescaled range fluctuation analysis (low-scale scaling) | 0.4 | +| Geometry | Detrended fluctuation analysis (low-scale scaling) | 0.38 | +| Correlation | First 1/e crossing of the ACF | 81.7405995576158 | +| Correlation | First minimum of the ACF | 183 | +| Correlation | Histogram-based automutual information (lag 2, 5 bins) | 1.439425719697347e-06 | +| Correlation | Time reversibility | 0.005588686797775345 | +| Correlation | First minimum of the AMI function | 40.0 | +| Correlation | Change in autocorrelation timescale after incremental differencing| 0.00847457627118644 | +| Trend | Wangs periodicity metric | 365 | +| Trend | Entropy of successive pairs in symbolized series | 1.4530196005684877 | +| Trend | Error of 3-point rolling mean forecast | 0.3145083996285715 | +| Transformation| Power in the lowest 20% of frequencies | 0.9542560901714987 | +| Transformation| Centroid frequency | 0.017202605837583908 | + ### Summary diff --git a/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt b/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt new file mode 100644 index 00000000..6e5b1a70 --- /dev/null +++ b/imputegap/dataset/docs/forecast-economy/features_forecast-economy.txt @@ -0,0 +1,22 @@ +|Geometry|5-bin histogram mode|-0.5710874806115164| +|Geometry|10-bin histogram mode|-0.9082987200476134| +|Correlation|First 1/e crossing of the ACF|124.60446764082629| +|Correlation|First minimum of the ACF|1| +|Correlation|Histogram-based automutual information (lag 2, 5 bins)|0.22074051585149523| +|Correlation|Time reversibility|0.28049126008447584| +|Geometry|Proportion of high incremental changes in the series|0.7816717019133937| +|Geometry|Longest stretch of above-mean values|357.0| +|Geometry|Transition matrix column variance|0.011316872427983538| +|Trend|Wangs periodicity metric|6| +|Geometry|Goodness of exponential fit to embedding distance distribution|0.12664898312226522| +|Correlation|First minimum of the AMI function|5.0| +|Correlation|Change in autocorrelation timescale after incremental differencing|0.0012224938875305623| +|Geometry|Positive outlier timing|0.18958109559613323| +|Geometry|Negative outlier timing|-0.2299274973147154| +|Transformation|Power in the lowest 20% of frequencies|0.6678786769493903| +|Geometry|Longest stretch of decreasing values|7.0| +|Trend|Entropy of successive pairs in symbolized series|1.8906454432766748| +|Geometry|Rescaled range fluctuation analysis (low-scale scaling)|0.3| +|Geometry|Detrended fluctuation analysis (low-scale scaling)|0.22| +|Transformation|Centroid frequency|0.009203884727314454| +|Trend|Error of 3-point rolling mean forecast|0.7191953107910503| diff --git a/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg b/imputegap/dataset/docs/forecast-economy/forecast-economy_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..496e58f8172d0a357edd654a235ccac3d83c5505 GIT binary patch literal 125414 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z5Dz_3i**aCd__a$zwce z(A*sxjY#RGTw8WE-F#fXWeUeM1w<+1YtAyewhfndTtckvi_@LFyx6lTG;BE=@z?SO fZam*kKYKCHvHf^rCCXjvNxxiiuxu3Hu3r2D@iq$k diff --git a/imputegap/recovery/benchmark.py b/imputegap/recovery/benchmark.py index 71b47e64..e25cc722 100644 --- a/imputegap/recovery/benchmark.py +++ b/imputegap/recovery/benchmark.py @@ -121,10 +121,13 @@ def average_runs_by_names(self, data): # Add scores and times for score_key, v in level_value["scores"].items(): + if v is None : + v = 0 merger["scores"][score_key] = (merger["scores"].get(score_key, 0) + v / count) for time_key, time_value in level_value["times"].items(): - merger["times"][time_key] = ( - merger["times"].get(time_key, 0) + time_value / count) + if time_value is None : + time_value = 0 + merger["times"][time_key] = (merger["times"].get(time_key, 0) + time_value / count) results_avg.append(merged_dict) diff --git a/imputegap/recovery/downstream.py b/imputegap/recovery/downstream.py index 2bb55136..23e4da7d 100644 --- a/imputegap/recovery/downstream.py +++ b/imputegap/recovery/downstream.py @@ -6,10 +6,13 @@ from imputegap.tools import utils -from sktime.forecasting.exp_smoothing import ExponentialSmoothing -from sktime.forecasting.fbprophet import Prophet -from sktime.forecasting.naive import NaiveForecaster -from sktime.performance_metrics.forecasting import mean_absolute_error, mean_squared_error +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 + + + + class Downstream: """ @@ -64,6 +67,7 @@ def __init__(self, input_data, recov_data, incomp_data, downstream): self.incomp_data = incomp_data self.downstream = downstream self.split = 0.8 + self.sktime_models = utils.list_of_downstreams_sktime() def downstream_analysis(self): """ @@ -85,9 +89,9 @@ def downstream_analysis(self): params = utils.load_parameters(query="default", algorithm=loader) print("\n\t\t\t\tDownstream analysis launched for <", evaluator, "> on the model <", model, - "> with parameters :\n\t\t\t\t\t", params) + "> with parameters :\n\t\t\t\t\t", params, " \n\n") - if evaluator == "forecast" or evaluator == "forecaster"or evaluator == "forecasting": + if evaluator in ["forecast", "forecaster", "forecasting"]: y_train_all, y_test_all, y_pred_all = [], [], [] mae, mse = [], [] @@ -106,35 +110,57 @@ def downstream_analysis(self): y_train = data[:, :train_len] y_test = data[:, train_len:] - y_pred = np.zeros_like(y_test) - # Forecast for each series - for series_idx in range(data.shape[0]): - series_train = y_train[series_idx, :] + forecaster = utils.config_forecaster(model, params) + + if model in self.sktime_models: + # --- SKTIME APPROACH --- + y_pred = np.zeros_like(y_test) + + for series_idx in range(data.shape[0]): + series_train = y_train[series_idx, :] + forecaster.fit(series_train) + fh = np.arange(1, y_test.shape[1] + 1) # Forecast horizon + series_pred = forecaster.predict(fh=fh) + y_pred[series_idx, :] = series_pred.ravel() + + # Compute metrics using sktime + mae.append(mean_absolute_error(y_test, y_pred)) + mse.append(mean_squared_error(y_test, y_pred)) + + else: + # --- DARTS APPROACH --- + # Convert entire matrix to a Darts multivariate TimeSeries object + 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) + + # Fit the model + forecaster.fit(y_train_ts) + + # Predict for the entire series at once + forecast_horizon = y_test.shape[1] + y_pred_ts = forecaster.predict(n=forecast_horizon) + + # Convert predictions back to NumPy + y_pred = y_pred_ts.values().T # Shape: (n_series, time_steps) + + - # Initialize and fit the forecasting model - if model == "prophet": - forecaster = Prophet(**params) - elif model == "exp-smoothing": - forecaster = ExponentialSmoothing(**params) - else: - forecaster = NaiveForecaster(**params) + # Ensure y_pred_ts has the same components as y_test_ts + y_pred_ts = y_pred_ts.with_columns_renamed(y_pred_ts.components, y_test_ts.components) - forecaster.fit(series_train) - fh = np.arange(1, y_test.shape[1] + 1) # Forecast horizon - series_pred = forecaster.predict(fh=fh) - series_pred = series_pred.ravel() + # Shift time index to match + if y_pred_ts.start_time() != y_test_ts.start_time(): + y_pred_ts = y_pred_ts.shift(y_test_ts.start_time() - y_pred_ts.start_time()) - # Store predictions - y_pred[series_idx, :] = series_pred + # Compute metrics safely + mae_score = darts_mae(y_test_ts, y_pred_ts) + mse_score = darts_mse(y_test_ts, y_pred_ts) - # Validate shapes - if y_pred.shape != y_test.shape: - raise ValueError(f"Shape mismatch: y_pred={y_pred.shape}, y_test={y_test.shape}, y_train={y_train.shape}") - # Calculate metrics - mae.append(mean_absolute_error(y_test, y_pred)) - mse.append(mean_squared_error(y_test, y_pred)) + # Compute metrics using Darts + mae.append(mae_score) + mse.append(mse_score) # Store for plotting y_train_all.append(y_train) @@ -150,7 +176,7 @@ def downstream_analysis(self): "DOWNSTREAM-MEANI-MAE": mae[2], "DOWNSTREAM-RECOV-MSE": mse[0], "DOWNSTREAM-INPUT-MSE": mse[1], "DOWNSTREAM-MEANI-MSE": mse[2]} - print("\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n") + print("\n\t\t\t\tDownstream analysis complete. " + "*" * 58 + "\n") return metrics else: diff --git a/imputegap/recovery/explainer.py b/imputegap/recovery/explainer.py index 01714076..1934f819 100644 --- a/imputegap/recovery/explainer.py +++ b/imputegap/recovery/explainer.py @@ -635,7 +635,7 @@ def execute_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spl return results_shap def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatch", pattern="mcar", missing_rate=0.4, - block_size=10, offset=0.1, seed=True, limit_ratio=1, split_ratio=0.6, + block_size=10, offset=0.1, seed=True, rate_dataset=1, training_ratio=0.6, file_name="ts", display=False, verbose=False): """ Handle parameters and set variables to launch the SHAP model. @@ -660,9 +660,9 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc Size of the uncontaminated section at the beginning of the time series (default is 0.1). seed : bool, optional Whether to use a seed for reproducibility (default is True). - limit_ratio : flaot, optional + rate_dataset : flaot, optional Limitation on the number of series for the model (default is 1). - split_ratio : flaot, optional + training_ratio : flaot, optional Limitation on the training series for the model (default is 0.6). file_name : str, optional Name of the dataset file (default is 'ts'). @@ -688,18 +688,18 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc """ start_time = time.time() # Record start time - if limit_ratio < 0.05 or limit_ratio > 1: + if rate_dataset < 0.05 or rate_dataset > 1: print("\nlimit percentage higher than 100%, reduce to 100% of the dataset") - limit_ratio = 1 + rate_dataset = 1 M = input_data.shape[0] - limit = math.ceil(M * limit_ratio) + limit = math.ceil(M * rate_dataset) - if split_ratio < 0.05 or split_ratio > 0.95: + if training_ratio < 0.05 or training_ratio > 0.95: print("\nsplit ratio to small or to high, reduce to 60% of the dataset") - split_ratio = 0.6 + training_ratio = 0.6 - training_ratio = int(limit * split_ratio) + training_ratio = int(limit * training_ratio) if limit > M: limit = M @@ -718,7 +718,7 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc input_data_matrices, obfuscated_matrices = [], [] output_metrics, output_rmse, input_params, input_params_full = [], [], [], [] - if extractor == "pycatch": + if extractor == "pycatch" or extractor == "pycatch22": categories, features, _ = Explainer.load_configuration() for current_series in range(0, limit): @@ -733,7 +733,7 @@ def shap_explainer(input_data, algorithm="cdrec", params=None, extractor="pycatc input_data_matrices.append(input_data) obfuscated_matrices.append(incomp_data) - if extractor == "pycatch": + if extractor == "pycatch" or extractor == "pycatch22": catch_fct, descriptions = Explainer.extractor_pycatch(incomp_data, categories, features, False) extracted_features = np.array(list(catch_fct.values())) elif extractor == "tsfel": diff --git a/imputegap/report.log b/imputegap/report.log index 0606aa98..e69de29b 100644 --- a/imputegap/report.log +++ b/imputegap/report.log @@ -1,113 +0,0 @@ -2025-03-06 18:22:32,474 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:32,481 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:32,557 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:32,606 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:49,647 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:49,654 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:49,756 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:22:49,802 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:23:05,697 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 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input tensor. -2025-03-06 18:24:36,680 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:19,196 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:19,203 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:19,247 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:33,245 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:33,249 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:34,843 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:47,569 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:47,598 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:28:49,541 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:31:45,503 - tensorboardX.x2num - WARNING - NaN or 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input tensor. -2025-03-06 18:36:35,494 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:36:46,120 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:36:46,133 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:36:47,513 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:36:47,518 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:40:51,117 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:40:51,123 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:40:51,128 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:40:51,202 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:40,100 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:40,106 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:40,426 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:40,996 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:52,207 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:52,211 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:53,818 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. -2025-03-06 18:41:53,838 - tensorboardX.x2num - WARNING - NaN or Inf found in input tensor. diff --git a/imputegap/runner_datasets.py b/imputegap/runner_datasets.py deleted file mode 100644 index 1662d1d8..00000000 --- a/imputegap/runner_datasets.py +++ /dev/null @@ -1,53 +0,0 @@ -from imputegap.recovery.explainer import Explainer -from imputegap.recovery.manager import TimeSeries -from imputegap.tools import utils - -datasets = ["electricity", "soccer", "temperature", "motion"] - -for dataset in datasets: - # small one - data_n = TimeSeries() - data_n.load_series(data=utils.search_path(dataset), nbr_series=20, nbr_val=400, header=False) - data_n.plot(input_data=data_n.data, nbr_series=20, save_path="./dataset/docs/" + dataset + "", display=False) - data_n.plot(input_data=data_n.data, nbr_series=1, save_path="./dataset/docs/" + dataset + "", display=False) - data_n.normalize(normalizer="min_max") - data_n.plot(input_data=data_n.data, nbr_series=20, save_path="./dataset/docs/" + dataset + "", display=False) - - # 5x one - data_n = TimeSeries() - max_series = 3 - max_value = 500 - if dataset == "bafu": - max_value = 10000 - elif dataset == "chlorine": - max_value = 1000 - elif dataset == "eeg-alcohol": - max_value = 256 - elif dataset == "eeg-reading": - max_value = 1201 - elif dataset == "drift": - max_value = 400 - - data_n.load_series(data=utils.search_path(dataset), nbr_series=max_series, nbr_val=max_value, header=False) - data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False) - data_n.normalize(normalizer="min_max") - data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False) - - # full one - data_n = TimeSeries() - data_n.load_series(data=utils.search_path(dataset), header=False) - data_n.plot(input_data=data_n.data, save_path="./dataset/docs/" + dataset + "", display=False) - - categories, features, _ = Explainer.load_configuration() - characteristics, descriptions = Explainer.extractor_pycatch(data=data_n.data, features_categories=categories, features_list=features, do_catch24=False) - - p = "./dataset/docs/"+dataset+"/features_"+dataset+".txt" - with open(p, 'w') as f: - for desc in descriptions: - key, category, description = desc - if key in characteristics: - value = characteristics[key] - f.write(f"|{category}|{description}|{value}|\n") - else: - f.write(f"Warning: Key '{key}' not found in characteristics!\n") - print(f"Table exported to {p}") diff --git a/imputegap/runner_downstream.py b/imputegap/runner_downstream.py index 5c152944..043ae208 100644 --- a/imputegap/runner_downstream.py +++ b/imputegap/runner_downstream.py @@ -7,7 +7,7 @@ print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}") # load and normalize the timeseries -ts.load_series(utils.search_path("chlorine")) +ts.load_series(utils.search_path("forecast-economy")) ts.normalize(normalizer="min_max") # contaminate the time series @@ -18,6 +18,6 @@ imputer.impute() # compute print the downstream results -downstream_config = {"task": "forecast", "model": "prophet"} +downstream_config = {"task": "forecast", "model": "arima"} 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 diff --git a/imputegap/runner_features.py b/imputegap/runner_features.py new file mode 100644 index 00000000..f80f750a --- /dev/null +++ b/imputegap/runner_features.py @@ -0,0 +1,22 @@ +from imputegap.recovery.explainer import Explainer +from imputegap.recovery.manager import TimeSeries +from imputegap.tools import utils + +dataset = "temperature" + +ts = TimeSeries() +ts.load_series(data=utils.search_path(dataset), header=False) + +categories, features, _ = Explainer.load_configuration() +characteristics, descriptions = Explainer.extractor_pycatch(data=ts.data, features_categories=categories, features_list=features, do_catch24=False) + +p = "./dataset/docs/"+dataset+"/features_"+dataset+".txt" +with open(p, 'w') as f: + for desc in descriptions: + key, category, description = desc + if key in characteristics: + value = characteristics[key] + f.write(f"|{category}|{description}|{value}|\n") + else: + f.write(f"Warning: Key '{key}' not found in characteristics!\n") +print(f"Table exported to {p}") diff --git a/imputegap/runner_loading.py b/imputegap/runner_loading.py index 32a195b8..544933b6 100644 --- a/imputegap/runner_loading.py +++ b/imputegap/runner_loading.py @@ -7,7 +7,7 @@ # load the timeseries from file or from the code -ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=1) +ts.load_series(utils.search_path("eeg-alcohol")) # plot a subset of time series ts.plot(input_data=ts.data, save_path="./imputegap/assets") diff --git a/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc b/imputegap/tools/__pycache__/algorithm_parameters.cpython-312.pyc index 52e5fec7b86004e0ba591055e6bd228ce9f1b158..b924e14f15c3731aa470c56aecd69648b648edee 100644 GIT binary patch delta 77 zcmeB=>5t((&CAQh00hrHZlqUk0$UgZ4quOKvc85t((&CAQh00iL=PNuhRtKeyC+~8D>omdWOi>|_wi<+Ew?RE-iYR z*V3VpcQ10jFuS!}(x4#u)5BE@|5ttt(kGZG!?8Wf7>49>8crKzLTpUKG8!hT;T8p$ zDEln?7#q_@wR@4NOEdLBO-LKlQKl{n(~xEwgE}dskLf8@pM`2lQ_Vp`$QUzFrXdT{ zl4e?irjR*iq)cNLrY+612Q4AV8Z%L0^G zdvXU0Le7|twzp-rr(rIpOb7I|FjyFJ#q5-6Kal(-o^go-~fvU ze)2n(VyQDi_zu4y^28I0aD31M0uLAI4+|pU{UMJ(><DnAZ~ 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zI^gq@MQl#pR32^&dPBNWGGWRTm!d0N7fWc@vSPJ>4K0ZFF1wm!+K6p$q8B&iMsA5f zxA^PpN_}B3A*e@UW^l;^HH3oVLL#aWheCAO8w}|x#)gOu8C*AZSX@;O(T&AE}=GUQ8g z>Hn`Fhp;&=-_M}S<-vNFa#w{8#X^$t1YE5y!*N@*MQ|wm2_leZzdGHp#uzcKR4!jG zR~{ONkYtFMJ06C^Y!vbULI11#erZ2y%*x*empL!~gMttd$YY3E4B_*dpoJM<7UnuQD2Kj5VLEXC} Date: Tue, 18 Mar 2025 15:05:10 +0100 Subject: [PATCH 2/3] 01b - documentation and whole refunt --- .idea/imputegap.iml | 2 +- .idea/misc.xml | 2 +- .../__pycache__/downstream.cpython-312.pyc | Bin 11924 -> 12435 bytes imputegap/recovery/downstream.py | 38 +++++++++++++++--- imputegap/runner_downstream.py | 2 +- .../tools/__pycache__/utils.cpython-312.pyc | Bin 45106 -> 45081 bytes imputegap/tools/utils.py | 4 -- requirements.txt | 5 ++- 8 files changed, 39 insertions(+), 14 deletions(-) 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 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It computes metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE) and visualizes the results for better interpretability. + ImputeGAP downstream models for forcasting : ['arima', 'bats', 'croston', 'deepar', 'ets', 'exp-smoothing', + 'hw-add', 'lightgbm', 'lstm', 'naive', 'nbeats', 'prophet', 'sf-arima', 'theta', + 'transformer', 'unobs', 'xgboost'] + Attributes ---------- input_data : numpy.ndarray @@ -73,6 +79,10 @@ def downstream_analysis(self): """ Compute a set of evaluation metrics with a downstream analysis + ImputeGAP downstream models for forcasting : ['arima', 'bats', 'croston', 'deepar', 'ets', 'exp-smoothing', + 'hw-add', 'lightgbm', 'lstm', 'naive', 'nbeats', 'prophet', 'sf-arima', 'theta', + 'transformer', 'unobs', 'xgboost'] + Returns ------- dict or None @@ -119,9 +129,15 @@ def downstream_analysis(self): for series_idx in range(data.shape[0]): series_train = y_train[series_idx, :] - forecaster.fit(series_train) fh = np.arange(1, y_test.shape[1] + 1) # Forecast horizon - series_pred = forecaster.predict(fh=fh) + + if model == "ltsf" or model == "rnn": + forecaster.fit(series_train, fh=ForecastingHorizon(fh)) + series_pred = forecaster.predict() + else: + forecaster.fit(series_train) + series_pred = forecaster.predict(fh=fh) + y_pred[series_idx, :] = series_pred.ravel() # Compute metrics using sktime @@ -169,7 +185,7 @@ 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) + self._plot_downstream(y_train_all, y_test_all, y_pred_all, self.incomp_data, model, evaluator) # Save metrics in a dictionary metrics = {"DOWNSTREAM-RECOV-MAE": mae[0], "DOWNSTREAM-INPUT-MAE": mae[1], @@ -185,7 +201,7 @@ def downstream_analysis(self): return None @staticmethod - def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth vs Predictions", max_series=4, save_path="./imputegap/assets"): + 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"): """ Plot ground truth vs. predictions for contaminated series (series with NaN values). @@ -199,6 +215,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v Forecasted data array of shape (n_series, test_len). incomp_data : np.ndarray Incomplete data array of shape (n_series, total_len), used to identify contaminated series. + model : str + Name of the current model used + type : str + Name of the current type used title : str Title of the plot. max_series : int @@ -208,6 +228,9 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v x_size = max_series * 5 + if max_series == 1: + x_size = 24 + fig, axs = plt.subplots(3, max_series, figsize=(x_size, 15)) fig.suptitle(title, fontsize=16) @@ -218,7 +241,10 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v for col_idx, series_idx in enumerate(valid_indices): # Access the correct subplot - ax = axs[row_idx, col_idx] + if max_series > 1: + ax = axs[row_idx, col_idx] + else: + ax = axs[row_idx] # Extract the corresponding data for this data type and series s_y_train = y_train[row_idx] @@ -273,7 +299,7 @@ def _plot_downstream(y_train, y_test, y_pred, incomp_data, title="Ground Truth v now = datetime.datetime.now() current_time = now.strftime("%y_%m_%d_%H_%M_%S") - file_path = os.path.join(save_path + "/" + current_time + "_downstream.jpg") + file_path = os.path.join(save_path + "/" + current_time + "_" + type + "_" + model + "_downstream.jpg") plt.savefig(file_path, bbox_inches='tight') print("plots saved in ", file_path) diff --git a/imputegap/runner_downstream.py b/imputegap/runner_downstream.py index 043ae208..84376518 100644 --- a/imputegap/runner_downstream.py +++ b/imputegap/runner_downstream.py @@ -18,6 +18,6 @@ imputer.impute() # compute print the downstream results -downstream_config = {"task": "forecast", "model": "arima"} +downstream_config = {"task": "forecast", "model": "hw-add"} 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 diff --git a/imputegap/tools/__pycache__/utils.cpython-312.pyc b/imputegap/tools/__pycache__/utils.cpython-312.pyc index cfddcd463e8ca3c0e99dec80cf9156fa83f17424..73d6230525242d528047f11b82c9071af6cfc9ef 100644 GIT binary patch delta 383 zcmdn=fNACfCf?J$yj%=GP*`y@y>cUOrIMgP3R8-7j%cn}EiWSj6GIK}Chu0d9t3 zj&!a}tpHd~0A|K!Y2`ai0*<96`K}?sx7d@5@{3FI^Co{*b=e%K*2O3(R-9dunVYJY zmS2>boLF3vnU}7YT2frpHTk-_0%OnQAL??9eUrsClqV-?9AWg{tgd;4(ZuYgq{2l> ztp#R4qQmQh0)v3m1rEs%@(g^!6EZLH%HQR&0;-rT36zt9%1KWCt0gHV0_6#VRHk3z zmA%X3byHIJvZVgzKYqi+Fh%*+0v1FtQf~0s{;H$N_Qb delta 432 zcmbRFfN9eMCf?J$yj%=GP#AI}eaS}NN+nDF6s8pE9MN2{T3$v5CWac`)gXBwsAaBU z4rZufNRi3bU|=W`0IHMC5zm#V1EcmLDv~k70%a+ziDW z>0Fsw0kArO6y;2Y8pb7zeTmG3Z3=2ml%y2V_Sm#4{iOVBU1v?wvhEx#x=IkC7T z$j@)Hw^|qD_ve<*G*o#+-maFwYH2NlP|B;W!x~Caoq;SEt5B`+s?Ria@P7qynKx8 KpA|S5A*umxWqvLI diff --git a/imputegap/tools/utils.py b/imputegap/tools/utils.py index 8c98c8d9..3488760f 100644 --- a/imputegap/tools/utils.py +++ b/imputegap/tools/utils.py @@ -204,9 +204,6 @@ def config_forecaster(model, params): elif model == "unobs": from sktime.forecasting.structural import UnobservedComponents forecaster = UnobservedComponents() - elif model == "rnn": - from sktime.forecasting.neuralforecast import NeuralForecastRNN - forecaster = NeuralForecastRNN(**params) else: @@ -1092,7 +1089,6 @@ def list_of_downstreams_sktime(): "croston", "theta", "unobs", - "rnn", "naive" ]) diff --git a/requirements.txt b/requirements.txt index 313af840..658d9b7f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,12 +24,15 @@ sktime==0.35.0 statsmodels==0.14.4 prophet==1.1.6 plotly==5.24.1 -darts==0.34.0 +darts<0.34.0 + # PyTorch and Related Libraries torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 +neuralforecast==1.6.4 + # PyTorch Geometric and Extensions (use prebuilt binaries) #-f https://data.pyg.org/whl/torch-2.5.1.html From e8c0f3c8b8ddd76a98ebc968b2e7ef02c88e6ae3 Mon Sep 17 00:00:00 2001 From: Quentin Nater Date: Tue, 18 Mar 2025 18:32:19 +0100 Subject: [PATCH 3/3] alpha 02a - documentation and whole refunt --- .idea/imputegap.iml | 2 +- .idea/misc.xml | 2 +- README.md | 12 +- .../build/doctrees/downstream.doctree | Bin 5578 -> 5592 bytes .../build/doctrees/environment.pickle | Bin 657206 -> 670208 bytes .../doctrees/imputegap.explainer.doctree | Bin 96698 -> 96730 bytes .../doctrees/imputegap.imputation.doctree | Bin 1024466 -> 1024290 bytes .../build/doctrees/imputegap.manager.doctree | Bin 136844 -> 136844 bytes .../build/doctrees/imputegap.utils.doctree | Bin 76209 -> 84071 bytes .../build/doctrees/tutorials.doctree | Bin 19220 -> 19220 bytes docs/generation/build/html/downstream.html | 4 +- docs/generation/build/html/genindex.html | 10 +- 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Bin 54178 -> 0 bytes imputegap/env/default_values.toml | 35 +- .../shap => imputegap_assets}/.gitkeep | 0 .../25_03_18_18_16_42_plot.jpg | Bin 0 -> 177276 bytes imputegap/imputegap_assets/shap/.gitkeep | 0 .../__pycache__/benchmark.cpython-312.pyc | Bin 41319 -> 41359 bytes .../__pycache__/downstream.cpython-312.pyc | Bin 12435 -> 13018 bytes .../__pycache__/explainer.cpython-312.pyc | Bin 43284 -> 43294 bytes .../__pycache__/imputation.cpython-312.pyc | Bin 118145 -> 118624 bytes .../__pycache__/manager.cpython-312.pyc | Bin 43718 -> 43768 bytes imputegap/recovery/downstream.py | 5 +- imputegap/recovery/explainer.py | 2 +- imputegap/recovery/imputation.py | 300 +++++++++--------- imputegap/recovery/manager.py | 4 +- imputegap/runner_contamination.py | 2 +- imputegap/runner_imputation.py | 2 +- imputegap/runner_loading.py | 2 +- imputegap/runner_optimization.py | 2 +- .../tools/__pycache__/utils.cpython-312.pyc | Bin 45081 -> 45233 bytes imputegap/tools/utils.py | 17 +- requirements.txt | 2 +- tests/test_downstream.py | 2 +- 68 files changed, 695 insertions(+), 444 deletions(-) delete mode 100644 imputegap/assets/25_01_08_17_34_43_plot.jpg delete mode 100644 imputegap/assets/25_01_08_17_35_48_plot.jpg delete mode 100644 imputegap/assets/25_01_24_16_04_19_plot.jpg delete mode 100644 imputegap/assets/25_02_07_13_59_51_plot.jpg delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Beeswarm.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_DTL_Waterfall.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_results.txt delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_aggregate_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_aggregate_reverse_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_correlation_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_geometry_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_reverse_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_transformation_plot.png delete mode 100644 imputegap/assets/shap/eeg-alcohol_cdrec_pycatch_shap_trend_plot.png rename imputegap/{assets/shap => imputegap_assets}/.gitkeep (100%) create mode 100644 imputegap/imputegap_assets/25_03_18_18_16_42_plot.jpg create mode 100644 imputegap/imputegap_assets/shap/.gitkeep diff --git a/.idea/imputegap.iml b/.idea/imputegap.iml index a5ca65ce..5fe0bf86 100644 --- a/.idea/imputegap.iml +++ b/.idea/imputegap.iml @@ -2,7 +2,7 @@ - + diff --git a/.idea/misc.xml b/.idea/misc.xml index 8c3a99c1..3b46d595 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 23c8abd0..2fd1f0b6 100644 --- a/README.md +++ b/README.md @@ -152,7 +152,7 @@ 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, nbr_series=9, nbr_val=100, save_path="./imputegap/assets") +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) @@ -190,7 +190,7 @@ ts.normalize(normalizer="z_score") ts_m = ts.Contamination.missing_completely_at_random(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") +ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets") ``` --- @@ -236,7 +236,7 @@ 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, save_path="./imputegap_assets") ``` --- @@ -275,7 +275,7 @@ 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", 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") @@ -343,7 +343,7 @@ ts = TimeSeries() print(f"ImputeGAP downstream models for forcasting : {ts.downstream_models}") # load and normalize the timeseries -ts.load_series(utils.search_path("chlorine")) +ts.load_series(utils.search_path("forecast-economy")) ts.normalize(normalizer="min_max") # contaminate the time series @@ -354,7 +354,7 @@ imputer = Imputation.MatrixCompletion.CDRec(ts_m) imputer.impute() # compute print the downstream results -downstream_config = {"task": "forecast", "model": "prophet"} +downstream_config = {"task": "forecast", "model": "hw-add"} 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/doctrees/downstream.doctree b/docs/generation/build/doctrees/downstream.doctree index 71d6b8bb5ff52023924bacca8c14100e998fd9a5..d13e9132c02317c36e854b98256725b87a62c5ca 100644 GIT binary patch delta 112 zcmX@5eM6h2fpzNHjVu*xEaA)y43h=9BsR}rvttxU%P&ezPAo3bO-;_v%g?Rce2+bh Zku9TKH!&q;vNh*i(v(lztjjx>0|4%WBw_#n delta 87 zcmcbieM+09fpzNsjVu*xi~*Y;a7Hq6BxmI07iH$9ZuaC3V`MKV$}h-BEt$NOXBtNa Sd+d}9(FlC%HZSEd<^%v@lOEFm diff --git 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