dice_ml.data_interfaces package

Submodules

dice_ml.data_interfaces.base_data_interface module

Module containing base class for data interfaces for dice-ml.

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: dice_ml.data_interfaces.base_data_interface._BaseData

A data interface for private data with meta information.

create_ohe_params()

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.

from_label(data)

Transforms label encoded data back to categorical values

get_data_params_for_gradient_dice()

Gets all data related params for DiCE.

get_decimal_precisions()

“Gets the precision of continuous features in the data.

get_decoded_data(data, encoding='one-hot')

Gets the original data from encoded data.

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_inverse_ohe_min_max_normalized_data(transformed_data)

Transforms one-hot-encoded and min-max normalized data into raw user-fed data format. transformed_data should be a dataframe or an array

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_ohe_min_max_normalized_data(query_instance)

Transforms query_instance into one-hot-encoded and min-max normalized data. query_instance should be a dict, a dataframe, a list, or a list of dicts

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, encoding='one-hot')

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_ohe_encoding()

Create base dataframe to do OHE for a single instance or a set of instances

prepare_query_instance(query_instance)

Prepares user defined test input(s) for DiCE.

dice_ml.data_interfaces.public_data_interface module

Module containing all required information about the interface between raw (or transformed) public data and DiCE explainers.

class dice_ml.data_interfaces.public_data_interface.PublicData(params)

Bases: dice_ml.data_interfaces.base_data_interface._BaseData

A data interface for public data. This class is an interface to DiCE explainers and contains methods to transform user-fed raw data into the format a DiCE explainer requires, and vice versa.

check_features_to_vary(features_to_vary)

check_mad_validity(feature_weights)

checks feature MAD validity and throw warnings. TODO: add comments as to where this is used if this function is necessary, else remove.

check_permitted_range(permitted_range)

create_ohe_params()

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.

from_label(data)

Transforms label encoded data back to categorical values

get_data_params_for_gradient_dice()

Gets all data related params for DiCE.

get_data_type(col)

Infers data type of a continuous feature from the training data.

get_decimal_precisions(output_type='list')

“Gets the precision of continuous features in the data.

get_decoded_data(data, encoding='one-hot')

Gets the original data from encoded data.

get_encoded_categorical_feature_indexes()

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

get_features_range(permitted_range_input=None)

get_indexes_of_features_to_vary(features_to_vary='all')

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

get_inverse_ohe_min_max_normalized_data(transformed_data)

Transforms one-hot-encoded and min-max normalized data into raw user-fed data format. transformed_data should be a dataframe or an array

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_ohe_min_max_normalized_data(query_instance)

Transforms query_instance into one-hot-encoded and min-max normalized data. query_instance should be a dict, a dataframe, a list, or a list of dicts

get_quantiles_from_training_data(quantile=0.05, normalized=False)

Computes required quantile of Absolute Deviations of features.

get_valid_feature_range(feature_range_input, normalized=True)

Gets the min/max value of features in normalized or de-normalized form. Assumes that all features are already encoded to numerical form such that the number of features remains the same.

# TODO needs work adhere to label encoded max and to support permitted_range for both continuous and discrete when provided in _generate_counterfactuals.

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_ohe_encoding()

Create base dataframe to do OHE for a single instance or a set of instances

prepare_query_instance(query_instance)

Prepares user defined test input(s) for DiCE.

Module contents