dice_ml.data_interfaces package

Submodules

dice_ml.data_interfaces.private_data_interface module

Module containing meta data information about private data.

class dice_ml.data_interfaces.private_data_interface.PrivateData(params)

Bases: object

A data interface for private data with meta information.

de_normalize_data(df)

De-normalizes continuous features from [0,1] range to original range.

from_dummies(data, prefix_sep='_')

Gets the original data from dummy encoded data with k levels.

get_data_params()

Gets all data related params for DiCE.

get_decimal_precisions()

“Gets the precision of continuous features in the data.

get_decoded_data(data)

Gets the original data from dummy encoded data.

get_dev_data(model_interface, desired_class, filter_threshold=0.5)

Constructs dev data by extracting part of the test data for which finding counterfactuals make sense.

get_encoded_categorical_feature_indexes()

Gets the column indexes categorical features after one-hot-encoding.

get_indexes_of_features_to_vary(features_to_vary='all')

Gets indexes from feature names of one-hot-encoded data.

get_mads(normalized=True)

Computes Median Absolute Deviation of features.

get_minx_maxx(normalized=True)

Gets the min/max value of features in normalized or de-normalized form.

get_valid_mads(normalized=False, display_warnings=False, return_mads=True)

Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead

normalize_data(df)

Normalizes continuous features to make them fall in the range [0,1].

one_hot_encode_data(data)

One-hot-encodes the data.

prepare_df_for_encoding()

Facilitates get_test_inputs() function.

prepare_query_instance(query_instance, encode)

Prepares user defined test input for DiCE.

dice_ml.data_interfaces.public_data_interface module

Module containing all required information about the raw or transformed public data.

class dice_ml.data_interfaces.public_data_interface.PublicData(params)

Bases: object

A data interface for public data.

check_features_range()

de_normalize_data(df)

De-normalizes continuous features from [0,1] range to original range.

from_dummies(data, prefix_sep='_')

Gets the original data from dummy encoded data with k levels.

get_data_params()

Gets all data related params for DiCE.

get_data_type(col)

Infers data type of a feature from the training data.

get_decimal_precisions()

“Gets the precision of continuous features in the data.

get_decoded_data(data)

Gets the original data from dummy encoded data.

get_dev_data(model_interface, desired_class, filter_threshold=0.5)

Constructs dev data by extracting part of the test data for which finding counterfactuals make sense.

get_encoded_categorical_feature_indexes()

Gets the column indexes categorical features after one-hot-encoding.

get_features_range()

get_indexes_of_features_to_vary(features_to_vary='all')

Gets indexes from feature names of one-hot-encoded data.

get_mads(normalized=False)

Computes Median Absolute Deviation of features.

get_minx_maxx(normalized=True)

Gets the min/max value of features in normalized or de-normalized form.

get_quantiles_from_training_data(quantile=0.05, normalized=False)

Computes required quantile of Absolute Deviations of features.

get_valid_mads(normalized=False, display_warnings=False, return_mads=True)

Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead

normalize_data(df)

Normalizes continuous features to make them fall in the range [0,1].

one_hot_encode_data(data)

One-hot-encodes the data.

prepare_df_for_encoding()

Facilitates prepare_query_instance() function.

prepare_query_instance(query_instance, encode)

Prepares user defined test input for DiCE.

split_data(data)

Module contents