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) to reveal how different time series features influence the model’s predictions.

Let’s illustrate the explainer using the CDRec Algorithm and MCAR missingness pattern:

from imputegap.recovery.manager import TimeSeries
from imputegap.recovery.explainer import Explainer
from imputegap.tools import utils

# initialize the TimeSeries() object
ts = TimeSeries()

# load and normalize the timeseries
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", file_name=ts.name, algorithm="CDRec")

# print the impact of each feature
Explainer.print(shap_values, shap_details)