Downstream Evaluation¶

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:

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)