"""Module containing meta data information about private data."""
import collections
import logging
import sys
import numpy as np
import pandas as pd
from dice_ml.data_interfaces.base_data_interface import _BaseData
[docs]class PrivateData(_BaseData):
"""A data interface for private data with meta information."""
def __init__(self, params):
"""Init method
:param features: Dictionary or OrderedDict with feature names as keys and range in int/float
(for continuous features) or categories in string (for categorical features)
as values. For python version <=3.6, should provide only an OrderedDict.
:param outcome_name: Outcome feature name.
:param type_and_precision (optional): Dictionary with continuous feature names as keys.
If the feature is of type int, just string 'int' should be provided,
if the feature is of type float, a list of type and precision should be
provided. For instance, type_and_precision: {cont_f1: 'int',
cont_f2: ['float', 2]} for continuous features cont_f1 and cont_f2 of
type int and float (and precision up to 2 decimal places) respectively.
Default value is None and all features are treated as int.
:param mad (optional): Dictionary with feature names as keys and corresponding Median Absolute Deviations (MAD)
as values.
Default MAD value is 1 for all features.
:param data_name (optional): Dataset name
"""
if sys.version_info > (3, 6, 0) and type(params['features']) in [dict, collections.OrderedDict]:
features_dict = params['features']
elif sys.version_info <= (3, 6, 0) and type(params['features']) is collections.OrderedDict:
features_dict = params['features']
else:
raise ValueError(
"should provide dictionary with feature names as keys and range"
"(for continuous features) or categories (for categorical features) as values. "
"For python version <3.6, should provide an OrderedDict")
self._validate_and_set_outcome_name(params=params)
self._validate_and_set_type_and_precision(params=params)
self.continuous_feature_names = []
self.permitted_range = {}
self.categorical_feature_names = []
self.categorical_levels = {}
for feature in features_dict:
if type(features_dict[feature][0]) is int: # continuous feature
self.continuous_feature_names.append(feature)
self.permitted_range[feature] = features_dict[feature]
else:
self.categorical_feature_names.append(feature)
self.categorical_levels[feature] = features_dict[feature]
self._validate_and_set_mad(params=params)
# self.continuous_feature_names + self.categorical_feature_names
self.feature_names = list(features_dict.keys())
self.continuous_feature_indexes = [list(features_dict.keys()).index(
name) for name in self.continuous_feature_names if name in features_dict]
self.categorical_feature_indexes = [list(features_dict.keys()).index(
name) for name in self.categorical_feature_names if name in features_dict]
for feature_name in self.continuous_feature_names:
if feature_name not in self.type_and_precision:
self.type_and_precision[feature_name] = 'int'
# # Initializing a label encoder to obtain label-encoded values for categorical variables
# self.labelencoder = {}
#
# self.label_encoded_data = {}
#
# for column in self.categorical_feature_names:
# self.labelencoder[column] = LabelEncoder()
# self.label_encoded_data[column] = \
# self.labelencoder[column].fit_transform(self.categorical_levels[column])
# self.max_range = -np.inf
# for feature in self.continuous_feature_names:
# self.max_range = max(self.max_range, self.permitted_range[feature][1])
self._validate_and_set_data_name(params=params)
def _validate_and_set_type_and_precision(self, params):
"""Validate and set the type and precision."""
if 'type_and_precision' in params:
self.type_and_precision = params['type_and_precision']
else:
self.type_and_precision = {}
def _validate_and_set_mad(self, params):
"""Validate and set the MAD."""
if 'mad' in params:
self.mad = params['mad']
else:
self.mad = {}
[docs] def one_hot_encode_data(self, data):
"""One-hot-encodes the data."""
return pd.get_dummies(data, drop_first=False, columns=self.categorical_feature_names)
[docs] def normalize_data(self, df, encoding='one-hot'):
"""Normalizes continuous features to make them fall in the range [0,1]."""
result = df.copy()
for feature_name in self.continuous_feature_names:
max_value = self.permitted_range[feature_name][1]
min_value = self.permitted_range[feature_name][0]
result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
# if encoding == 'label': # need not do this if not required
# for ix in self.categorical_feature_indexes:
# feature_name = self.feature_names[ix]
# max_value = len(self.categorical_levels[feature_name])-1
# min_value = 0
# result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
return result
[docs] def de_normalize_data(self, df):
"""De-normalizes continuous features from [0,1] range to original range."""
if len(df) == 0:
return df
result = df.copy()
for feature_name in self.continuous_feature_names:
max_value = self.permitted_range[feature_name][1]
min_value = self.permitted_range[feature_name][0]
result[feature_name] = (
df[feature_name]*(max_value - min_value)) + min_value
return result
[docs] def get_minx_maxx(self, normalized=True):
"""Gets the min/max value of features in normalized or de-normalized form."""
minx = np.array([[0.0]*len(self.ohe_encoded_feature_names)])
maxx = np.array([[1.0]*len(self.ohe_encoded_feature_names)])
if normalized:
return minx, maxx
else:
for idx, feature_name in enumerate(self.continuous_feature_names):
minx[0][idx] = self.permitted_range[feature_name][0]
maxx[0][idx] = self.permitted_range[feature_name][1]
return minx, maxx
[docs] def get_mads(self, normalized=True):
"""Computes Median Absolute Deviation of features."""
if normalized is False:
return self.mad.copy()
else:
mads = {}
for feature in self.continuous_feature_names:
if feature in self.mad:
mads[feature] = (self.mad[feature])/(self.permitted_range[feature][1] - self.permitted_range[feature][0])
return mads
[docs] def get_valid_mads(self, normalized=False, display_warnings=False, return_mads=True):
"""Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead"""
mads = self.get_mads(normalized=normalized)
for feature in self.continuous_feature_names:
if feature in mads:
if mads[feature] <= 0:
mads[feature] = 1.0
if display_warnings:
logging.warning(" MAD for feature %s is 0, so replacing it with 1.0 to avoid error.", feature)
else:
mads[feature] = 1.0
if display_warnings:
logging.info(" MAD is not given for feature %s, so using 1.0 as MAD instead.", feature)
if return_mads:
return mads
[docs] def create_ohe_params(self):
if len(self.categorical_feature_names) > 0:
# simulating sklearn's one-hot-encoding
# continuous features on the left
self.ohe_encoded_feature_names = [
feature for feature in self.continuous_feature_names]
for feature_name in self.categorical_feature_names:
for category in sorted(self.categorical_levels[feature_name]):
self.ohe_encoded_feature_names.append(
feature_name+'_'+category)
else:
# one-hot-encoded data is same as original data if there is no categorical features.
self.ohe_encoded_feature_names = [feat for feat in self.feature_names]
# base dataframe for doing one-hot-encoding
# ohe_encoded_feature_names and ohe_base_df are created (and stored as data class's parameters)
# when get_data_params_for_gradient_dice() is called from gradient-based DiCE explainers
self.ohe_base_df = self.prepare_df_for_ohe_encoding()
[docs] def get_data_params_for_gradient_dice(self):
"""Gets all data related params for DiCE."""
self.create_ohe_params()
minx, maxx = self.get_minx_maxx(normalized=True)
# get the column indexes of categorical and continuous features after one-hot-encoding
encoded_categorical_feature_indexes = self.get_encoded_categorical_feature_indexes()
flattened_indexes = [item for sublist in encoded_categorical_feature_indexes for item in sublist]
encoded_continuous_feature_indexes = [ix for ix in range(len(minx[0])) if ix not in flattened_indexes]
# min and max for continuous features in original scale
org_minx, org_maxx = self.get_minx_maxx(normalized=False)
cont_minx = list(org_minx[0][encoded_continuous_feature_indexes])
cont_maxx = list(org_maxx[0][encoded_continuous_feature_indexes])
# decimal precisions for continuous features
cont_precisions = [self.get_decimal_precisions()[ix] for ix in range(len(self.continuous_feature_names))]
return minx, maxx, encoded_categorical_feature_indexes, encoded_continuous_feature_indexes, \
cont_minx, cont_maxx, cont_precisions
[docs] def get_encoded_categorical_feature_indexes(self):
"""Gets the column indexes categorical features after one-hot-encoding."""
cols = []
for col_parent in self.categorical_feature_names:
temp = [self.ohe_encoded_feature_names.index(
col) for col in self.ohe_encoded_feature_names if col.startswith(col_parent) and
col not in self.continuous_feature_names]
cols.append(temp)
return cols
[docs] def get_indexes_of_features_to_vary(self, features_to_vary='all'):
"""Gets indexes from feature names of one-hot-encoded data."""
if features_to_vary == "all":
return [i for i in range(len(self.ohe_encoded_feature_names))]
else:
ixs = []
encoded_cats_ixs = self.get_encoded_categorical_feature_indexes()
encoded_cats_ixs = [item for sublist in encoded_cats_ixs for item in sublist]
for colidx, col in enumerate(self.encoded_feature_names):
if colidx in encoded_cats_ixs and col.startswith(tuple(features_to_vary)):
ixs.append(colidx)
elif colidx not in encoded_cats_ixs and col in features_to_vary:
ixs.append(colidx)
return ixs
[docs] def from_label(self, data):
"""Transforms label encoded data back to categorical values"""
out = data.copy()
if isinstance(data, pd.DataFrame) or isinstance(data, dict):
for column in self.categorical_feature_names:
out[column] = self.labelencoder[column].inverse_transform(out[column].round().astype(int).tolist())
return out
elif isinstance(data, list):
for column in self.categorical_feature_indexes:
out[column] = self.labelencoder[self.feature_names[column]].inverse_transform([round(out[column])])[0]
return out
[docs] def from_dummies(self, data, prefix_sep='_'):
"""Gets the original data from dummy encoded data with k levels."""
out = data.copy()
for feature_name in self.categorical_feature_names:
cols, labs = [[c.replace(
x, "") for c in data.columns if feature_name+prefix_sep in c] for x in ["", feature_name+prefix_sep]]
out[feature_name] = pd.Categorical(
np.array(labs)[np.argmax(data[cols].values, axis=1)])
out.drop(cols, axis=1, inplace=True)
return out
[docs] def get_decimal_precisions(self):
""""Gets the precision of continuous features in the data."""
precisions = [0]*len(self.continuous_feature_names)
for ix, feature_name in enumerate(self.continuous_feature_names):
type_prec = self.type_and_precision[feature_name]
if type_prec == 'int':
precisions[ix] = 0
else:
precisions[ix] = self.type_and_precision[feature_name][1]
return precisions
[docs] def get_decoded_data(self, data, encoding='one-hot'):
"""Gets the original data from encoded data."""
if len(data) == 0:
return data
index = [i for i in range(0, len(data))]
if encoding == 'one-hot':
if isinstance(data, pd.DataFrame):
return self.from_dummies(data)
elif isinstance(data, np.ndarray):
data = pd.DataFrame(data=data, index=index,
columns=self.ohe_encoded_feature_names)
return self.from_dummies(data)
else:
raise ValueError("data should be a pandas dataframe or a numpy array")
elif encoding == 'label':
data = pd.DataFrame(data=data, index=index,
columns=self.feature_names)
return data
[docs] def prepare_df_for_ohe_encoding(self):
"""Create base dataframe to do OHE for a single instance or a set of instances"""
levels = []
colnames = [feat for feat in self.categorical_feature_names]
for cat_feature in colnames:
levels.append(self.categorical_levels[cat_feature])
if len(colnames) > 0:
df = pd.DataFrame({colnames[0]: levels[0]})
else:
df = pd.DataFrame()
for col in range(1, len(colnames)):
temp_df = pd.DataFrame({colnames[col]: levels[col]})
df = pd.concat([df, temp_df], axis=1, sort=False)
colnames = [feat for feat in self.continuous_feature_names]
for col in range(0, len(colnames)):
temp_df = pd.DataFrame({colnames[col]: []})
df = pd.concat([df, temp_df], axis=1, sort=False)
return df
[docs] def prepare_query_instance(self, query_instance):
"""Prepares user defined test input(s) for DiCE."""
if isinstance(query_instance, list):
if isinstance(query_instance[0], dict): # prepare a list of query instances
test = pd.DataFrame(query_instance, columns=self.feature_names)
else: # prepare a single query instance in list
query_instance = {'row1': query_instance}
test = pd.DataFrame.from_dict(
query_instance, orient='index', columns=self.feature_names)
elif isinstance(query_instance, dict):
test = pd.DataFrame({k: [v] for k, v in query_instance.items()}, columns=self.feature_names)
elif isinstance(query_instance, pd.DataFrame):
test = query_instance.copy()
else:
raise ValueError("Query instance should be a dict, a pandas dataframe, a list, or a list of dicts")
test = test.reset_index(drop=True)
return test
[docs] def get_ohe_min_max_normalized_data(self, 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"""
query_instance = self.prepare_query_instance(query_instance)
temp = self.ohe_base_df.append(query_instance, ignore_index=True, sort=False)
temp = self.one_hot_encode_data(temp)
temp = temp.tail(query_instance.shape[0]).reset_index(drop=True)
# returns a pandas dataframe
return self.normalize_data(temp)
[docs] def get_inverse_ohe_min_max_normalized_data(self, 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"""
raw_data = self.get_decoded_data(transformed_data, encoding='one-hot')
raw_data = self.de_normalize_data(raw_data)
precisions = self.get_decimal_precisions()
for ix, feature in enumerate(self.continuous_feature_names):
raw_data[feature] = raw_data[feature].astype(float).round(precisions[ix])
raw_data = raw_data[self.feature_names]
# returns a pandas dataframe
return raw_data