dice_ml.data_interfaces package¶
Submodules¶
dice_ml.data_interfaces.private_data_interface module¶
Module containing meta data information about private data.
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class
dice_ml.data_interfaces.private_data_interface.
PrivateData
(params)[source]¶ Bases:
object
A data interface for private data with meta information.
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de_normalize_data
(df)[source]¶ De-normalizes continuous features from [0,1] range to original range.
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from_dummies
(data, prefix_sep='_')[source]¶ Gets the original data from dummy encoded data with k levels.
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get_dev_data
(model_interface, desired_class, filter_threshold=0.5)[source]¶ Constructs dev data by extracting part of the test data for which finding counterfactuals make sense.
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get_encoded_categorical_feature_indexes
()[source]¶ Gets the column indexes categorical features after one-hot-encoding.
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get_indexes_of_features_to_vary
(features_to_vary='all')[source]¶ Gets indexes from feature names of one-hot-encoded data.
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get_minx_maxx
(normalized=True)[source]¶ Gets the min/max value of features in normalized or de-normalized form.
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dice_ml.data_interfaces.public_data_interface module¶
Module containing all required information about the raw or transformed public data.
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class
dice_ml.data_interfaces.public_data_interface.
PublicData
(params)[source]¶ Bases:
object
A data interface for public data.
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de_normalize_data
(df)[source]¶ De-normalizes continuous features from [0,1] range to original range.
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from_dummies
(data, prefix_sep='_')[source]¶ Gets the original data from dummy encoded data with k levels.
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get_dev_data
(model_interface, desired_class, filter_threshold=0.5)[source]¶ Constructs dev data by extracting part of the test data for which finding counterfactuals make sense.
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get_encoded_categorical_feature_indexes
()[source]¶ Gets the column indexes categorical features after one-hot-encoding.
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get_indexes_of_features_to_vary
(features_to_vary='all')[source]¶ Gets indexes from feature names of one-hot-encoded data.
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get_minx_maxx
(normalized=True)[source]¶ Gets the min/max value of features in normalized or de-normalized form.
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get_quantiles_from_training_data
(quantile=0.05, normalized=False)[source]¶ Computes required quantile of Absolute Deviations of features.
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