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11. naterq integration #31

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Nov 13, 2024
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11. naterq integration #31

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41 changes: 21 additions & 20 deletions .idea/workspace.xml
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14 changes: 7 additions & 7 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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")
Expand Down Expand Up @@ -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)
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8 changes: 1 addition & 7 deletions imputegap/recovery/manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

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