Explainer¶

The library provides insights into the algorithm’s behavior by identifying the features that impact 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 time series and explainer object
ts = TimeSeries()
exp = Explainer()

# load and normalize the dataset
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")

# configure the explanation
exp.shap_explainer(input_data=ts.data, extractor="pycatch", pattern="mcar", file_name=ts.name, algorithm="CDRec")

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

All feature extractors developed in ImputeGAP are available in the ts.extractors module, which can be listed as follows:

from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"ImputeGAP features extractors : {ts.extractors}")