fdl.WeightingParams

Holds weighting information for class imbalanced models which can then be passed into a fdl.ModelInfo object. Please note that the use of weighting params requires the presence of model outputs in the baseline dataset.

Input Parameters	Type	Default	Description
class_weight	List[float]	None	List of floats representing weights for each of the classes. The length must equal the no. of classes.
weighted_reference_histograms	bool	True	Flag indicating if baseline histograms must be weighted or not when calculating drift metrics.
weighted_surrogate_training	bool	True	Flag indicating if weighting scheme should be used when training the surrogate model.

import pandas as pd
import sklearn.utils
import fiddler as fdl

df = pd.read_csv('example_dataset.csv')
computed_weight = sklearn.utils.class_weight.compute_class_weight(
        class_weight='balanced',
        classes=np.unique(df[TARGET_COLUMN]),
        y=df[TARGET_COLUMN]
    ).tolist()
weighting_params =  fdl.WeightingParams(class_weight=computed_weight)
dataset_info = fdl.DatasetInfo.from_dataframe(df=df)

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    features=[
        'feature_1',
        'feature_2',
        'feature_3'
    ],
    outputs=['output_column'],
    target='target_column',
    weighting_params=weighting_params,
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION
)