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Downstream Evaluation
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ImputeGAP includes a dedicated module for systematically evaluating the impact of data imputation on downstream tasks. Currently, forecasting is the primary supported task, with plans to expand to additional applications in the future. The example below demonstrates how to define the forecasting task and specify Prophet as the predictive model


Below is an example of how to call the downstream process for the model Prophet by defining a dictionary for the evaluator and selecting the model:

.. code-block:: python

    from imputegap.recovery.imputation import Imputation
    from imputegap.recovery.manager import TimeSeries
    from imputegap.tools import utils

    # initialize the TimeSeries() object
    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.normalize(normalizer="min_max")

    # contaminate the time series
    ts_m = ts.Contamination.missing_percentage(ts.data, rate_series=0.8)

    # define and impute the contaminated series
    imputer = Imputation.MatrixCompletion.CDRec(ts_m)
    imputer.impute()

    # compute print the downstream results
    downstream_config = {"task": "forecast", "model": "prophet"}
    imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
    ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)