diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 77d58de4..56b43817 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,8 +3,8 @@ - - + + - { - "keyToString": { - "RunOnceActivity.OpenProjectViewOnStart": "true", - "RunOnceActivity.ShowReadmeOnStart": "true", - "WebServerToolWindowFactoryState": "false", - "last_opened_file_path": "C:/Users/nquen/switchdrive/MST_MasterThesis/imputegap/imputegap/algorithms", - "nodejs_package_manager_path": "npm", - "settings.editor.selected.configurable": "preferences.pluginManager", - "vue.rearranger.settings.migration": "true" + +}]]> + - @@ -70,7 +70,7 @@ - + - + + - + - @@ -273,7 +273,7 @@ - + @@ -301,7 +301,7 @@ - + @@ -315,6 +315,7 @@ + diff --git a/README.md b/README.md index 9a4826ef..06831733 100644 --- a/README.md +++ b/README.md @@ -147,7 +147,7 @@ from imputegap.tools import utils ts_1 = TimeSeries() # 2. load the timeseries from file or from the code -ts_1.load_timeseries(utils.search_path("eeg-test")) +ts_1.load_timeseries(utils.search_path("eeg-alcohol")) ts_1.normalize(normalizer="z_score") # [OPTIONAL] you can plot your raw data / print the information @@ -178,7 +178,7 @@ from imputegap.tools import utils ts_1 = TimeSeries() # 2. load the timeseries from file or from the code -ts_1.load_timeseries(utils.search_path("eeg-test")) +ts_1.load_timeseries(utils.search_path("eeg-alcohol")) ts_1.normalize(normalizer="min_max") # 3. contamination of the data with MCAR scenario @@ -213,7 +213,7 @@ from imputegap.tools import utils ts_1 = TimeSeries() # 2. load the timeseries from file or from the code -ts_1.load_timeseries(utils.search_path("eeg-test")) +ts_1.load_timeseries(utils.search_path("eeg-alcohol")) ts_1.normalize(normalizer="min_max") # 3. contamination of the data @@ -262,7 +262,7 @@ from imputegap.tools import utils ts_1 = TimeSeries() # 2. load the timeseries from file or from the code -ts_1.load_timeseries(utils.search_path("eeg-test")) +ts_1.load_timeseries(utils.search_path("eeg-alcohol")) ts_1.normalize(normalizer="min_max") # 3. contamination of the data @@ -280,7 +280,7 @@ cdrec.score(ts_1.data, cdrec.imputed_matrix) # 6. [OPTIONAL] display the results ts_1.print_results(cdrec.metrics) ts_1.plot(raw_data=ts_1.data, infected_data=infected_data, imputed_data=cdrec.imputed_matrix, title="imputation", - max_series=1, save_path="./assets", display=True) + max_series=1, save_path="./imputegap/assets", display=True) # 7. [OPTIONAL] save hyperparameters utils.save_optimization(optimal_params=cdrec.parameters, algorithm="cdrec", dataset="eeg", optimizer="b") @@ -312,10 +312,10 @@ from imputegap.tools import utils ts_1 = TimeSeries() # 2. load the timeseries from file or from the code -ts_1.load_timeseries(utils.search_path("eeg-test")) +ts_1.load_timeseries(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(raw_data=ts_1.data, file_name="eeg-test", algorithm="cdrec") +shap_values, shap_details = Explainer.shap_explainer(raw_data=ts_1.data, file_name="eeg-alcohol", algorithm="cdrec") # [OPTIONAL] print the results with the impact of each feature. Explainer.print(shap_values, shap_details) diff --git a/imputegap/recovery/__pycache__/manager.cpython-312.pyc b/imputegap/recovery/__pycache__/manager.cpython-312.pyc index fa177707..44a25bd3 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/manager.py b/imputegap/recovery/manager.py index fa191c81..0a6d36e6 100644 --- a/imputegap/recovery/manager.py +++ b/imputegap/recovery/manager.py @@ -15,17 +15,11 @@ print("Running in a headless environment or CI. Using Agg backend.") else: try: - # Attempt to use TkAgg if a display is available and we're not in CI matplotlib.use("TkAgg") - if importlib.util.find_spec("tkinter") is not None: - print("tkinter is available.") - else: + if importlib.util.find_spec("tkinter") is None: print("tkinter is not available.") - print("Using TkAgg backend with tkinter support.") except (ImportError, RuntimeError): - # Fallback to Agg if TkAgg is unavailable matplotlib.use("Agg") - print("TkAgg is unavailable. Using Agg backend.") from matplotlib import pyplot as plt # type: ignore