Source code for dice_ml.utils.helpers

"""
This module containts helper functions to load data and get meta deta.
"""
import numpy as np
import pandas as pd
import shutil
import os

import dice_ml

# for data transformations
from sklearn.preprocessing import FunctionTransformer
from sklearn.model_selection import train_test_split


[docs]def load_adult_income_dataset(only_train=True): """Loads adult income dataset from https://archive.ics.uci.edu/ml/datasets/Adult and prepares the data for data analysis based on https://rpubs.com/H_Zhu/235617 :return adult_data: returns preprocessed adult income dataset. """ raw_data = np.genfromtxt('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', delimiter=', ', dtype=str, invalid_raise=False) # column names from "https://archive.ics.uci.edu/ml/datasets/Adult" column_names = ['age', 'workclass', 'fnlwgt', 'education', 'educational-num', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'] adult_data = pd.DataFrame(raw_data, columns=column_names) # For more details on how the below transformations are made, please refer to https://rpubs.com/H_Zhu/235617 adult_data = adult_data.astype({"age": np.int64, "educational-num": np.int64, "hours-per-week": np.int64}) adult_data = adult_data.replace({'workclass': {'Without-pay': 'Other/Unknown', 'Never-worked': 'Other/Unknown'}}) adult_data = adult_data.replace({'workclass': {'Federal-gov': 'Government', 'State-gov': 'Government', 'Local-gov': 'Government'}}) adult_data = adult_data.replace({'workclass': {'Self-emp-not-inc': 'Self-Employed', 'Self-emp-inc': 'Self-Employed'}}) adult_data = adult_data.replace({'workclass': {'Never-worked': 'Self-Employed', 'Without-pay': 'Self-Employed'}}) adult_data = adult_data.replace({'workclass': {'?': 'Other/Unknown'}}) adult_data = adult_data.replace( { 'occupation': { 'Adm-clerical': 'White-Collar', 'Craft-repair': 'Blue-Collar', 'Exec-managerial': 'White-Collar', 'Farming-fishing': 'Blue-Collar', 'Handlers-cleaners': 'Blue-Collar', 'Machine-op-inspct': 'Blue-Collar', 'Other-service': 'Service', 'Priv-house-serv': 'Service', 'Prof-specialty': 'Professional', 'Protective-serv': 'Service', 'Tech-support': 'Service', 'Transport-moving': 'Blue-Collar', 'Unknown': 'Other/Unknown', 'Armed-Forces': 'Other/Unknown', '?': 'Other/Unknown' } } ) adult_data = adult_data.replace({'marital-status': {'Married-civ-spouse': 'Married', 'Married-AF-spouse': 'Married', 'Married-spouse-absent': 'Married', 'Never-married': 'Single'}}) adult_data = adult_data.replace({'race': {'Black': 'Other', 'Asian-Pac-Islander': 'Other', 'Amer-Indian-Eskimo': 'Other'}}) adult_data = adult_data[['age', 'workclass', 'education', 'marital-status', 'occupation', 'race', 'gender', 'hours-per-week', 'income']] adult_data = adult_data.replace({'income': {'<=50K': 0, '>50K': 1}}) adult_data = adult_data.replace({'education': {'Assoc-voc': 'Assoc', 'Assoc-acdm': 'Assoc', '11th': 'School', '10th': 'School', '7th-8th': 'School', '9th': 'School', '12th': 'School', '5th-6th': 'School', '1st-4th': 'School', 'Preschool': 'School'}}) adult_data = adult_data.rename(columns={'marital-status': 'marital_status', 'hours-per-week': 'hours_per_week'}) if only_train: train, _ = train_test_split(adult_data, test_size=0.2, random_state=17) adult_data = train.reset_index(drop=True) # Remove the downloaded dataset if os.path.isdir('archive.ics.uci.edu'): entire_path = os.path.abspath('archive.ics.uci.edu') shutil.rmtree(entire_path) return adult_data
[docs]def load_custom_testing_dataset(): data = [['a', 10, 0], ['b', 10000, 0], ['c', 14, 0], ['a', 88, 0], ['c', 14, 0]] return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
[docs]def load_min_max_equal_dataset(): data = [['a', 10, 0], ['b', 10, 0], ['c', 10, 0], ['a', 10, 0], ['c', 10, 0]] return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
[docs]def load_outcome_not_last_column_dataset(): data = [['a', 0, 10], ['a', 0, 10000], ['a', 0, 14], ['a', 0, 10], ['a', 0, 10]] return pd.DataFrame(data, columns=['Categorical', 'Outcome', 'Numerical'])
[docs]def load_custom_testing_dataset_binary(): data = [['a', 1, 0], ['b', 5, 1], ['c', 2, 0], ['a', 3, 0], ['c', 4, 1]] return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
[docs]def load_custom_testing_dataset_multiclass(): data = [['a', 10, 1], ['b', 20, 2], ['c', 14, 1], ['a', 23, 2], ['c', 7, 0]] return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
[docs]def load_custom_testing_dataset_regression(): data = [['a', 10, 1], ['b', 21, 2.1], ['c', 14, 1.4], ['a', 23, 2.3], ['c', 7, 0.7]] return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
[docs]def get_adult_income_modelpath(backend='TF1'): pkg_path = dice_ml.__path__[0] model_ext = '.h5' if 'TF' in backend else '.pth' modelpath = os.path.join(pkg_path, 'utils', 'sample_trained_models', 'adult'+model_ext) return modelpath
[docs]def get_custom_dataset_modelpath_pipeline(): pkg_path = dice_ml.__path__[0] model_ext = '.sav' modelpath = os.path.join(pkg_path, 'utils', 'sample_trained_models', 'custom'+model_ext) return modelpath
[docs]def get_custom_dataset_modelpath_pipeline_binary(): pkg_path = dice_ml.__path__[0] model_ext = '.sav' modelpath = os.path.join(pkg_path, 'utils', 'sample_trained_models', 'custom_binary'+model_ext) return modelpath
[docs]def get_custom_dataset_modelpath_pipeline_multiclass(): pkg_path = dice_ml.__path__[0] model_ext = '.sav' modelpath = os.path.join(pkg_path, 'utils', 'sample_trained_models', 'custom_multiclass'+model_ext) return modelpath
[docs]def get_custom_dataset_modelpath_pipeline_regression(): pkg_path = dice_ml.__path__[0] model_ext = '.sav' modelpath = os.path.join(pkg_path, 'utils', 'sample_trained_models', 'custom_regression'+model_ext) return modelpath
[docs]def get_adult_data_info(): feature_description = { 'age': 'age', 'workclass': 'type of industry (Government, Other/Unknown, Private, Self-Employed)', 'education': 'education level (Assoc, Bachelors, Doctorate, HS-grad, Masters, Prof-school, School, Some-college)', 'marital_status': 'marital status (Divorced, Married, Separated, Single, Widowed)', 'occupation': 'occupation (Blue-Collar, Other/Unknown, Professional, Sales, Service, White-Collar)', 'race': 'white or other race?', 'gender': 'male or female?', 'hours_per_week': 'total work hours per week', 'income': '0 (<=50K) vs 1 (>50K)'} return feature_description
[docs]def get_base_gen_cf_initialization(data_interface, encoded_size, cont_minx, cont_maxx, margin, validity_reg, epochs, wm1, wm2, wm3, learning_rate): # Dice Imports - TODO: keep this method for VAE as a spearate module or move it to feasible_base_vae.py. # Check dependencies. from dice_ml.utils.sample_architecture.vae_model import CF_VAE # Pytorch from torch import optim # Dataset for training Variational Encoder Decoder model for CF Generation df = data_interface.normalize_data(data_interface.one_hot_encoded_data) encoded_data = df[data_interface.ohe_encoded_feature_names + [data_interface.outcome_name]] dataset = encoded_data.to_numpy() print('Dataset Shape:', encoded_data.shape) print('Datasets Columns:', encoded_data.columns) # Normalise_Weights normalise_weights = {} for idx in range(len(cont_minx)): _max = cont_maxx[idx] _min = cont_minx[idx] normalise_weights[idx] = [_min, _max] # Train, Val, Test Splits np.random.shuffle(dataset) test_fraction = 0.2 # TODO: create an input parameter for data interface test_size = int(test_fraction*len(data_interface.data_df)) vae_test_dataset = dataset[:test_size] dataset = dataset[test_size:] vae_val_dataset = dataset[:test_size] vae_train_dataset = dataset[test_size:] # BaseGenCF Model cf_vae = CF_VAE(data_interface, encoded_size) # Optimizer cf_vae_optimizer = optim.Adam([ {'params': filter(lambda p: p.requires_grad, cf_vae.encoder_mean.parameters()), 'weight_decay': wm1}, {'params': filter(lambda p: p.requires_grad, cf_vae.encoder_var.parameters()), 'weight_decay': wm2}, {'params': filter(lambda p: p.requires_grad, cf_vae.decoder_mean.parameters()), 'weight_decay': wm3}, ], lr=learning_rate ) # Check: If base_obj was passsed via reference and it mutable; might not need to have a return value at all return vae_train_dataset, vae_val_dataset, vae_test_dataset, normalise_weights, cf_vae, cf_vae_optimizer
[docs]def ohe_min_max_transformation(data, data_interface): """the data is one-hot-encoded and min-max normalized and fed to the ML model""" return data_interface.get_ohe_min_max_normalized_data(data).values
[docs]class DataTransfomer: """A class to transform data based on user-defined function to get predicted outcomes. This class calls FunctionTransformer of scikit-learn internally (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html).""" def __init__(self, func=None, kw_args=None): self.func = func self.kw_args = kw_args
[docs] def feed_data_params(self, data_interface): if self.kw_args is not None: self.kw_args['data_interface'] = data_interface else: self.kw_args = {'data_interface': data_interface}
[docs] def initialize_transform_func(self): if self.func == 'ohe-min-max': self.data_transformer = FunctionTransformer(func=ohe_min_max_transformation, kw_args=self.kw_args, validate=False) elif self.func is None: # identity transformation # add more ready-to-use transformers (such as label-encoding) in elif loops. self.data_transformer = FunctionTransformer(func=self.func, kw_args=None, validate=False) else: # add more ready-to-use transformers (such as label-encoding) in elif loops. self.data_transformer = FunctionTransformer(func=self.func, kw_args=self.kw_args, validate=False)
[docs] def transform(self, data): return self.data_transformer.transform(data) # should return a numpy array
[docs] def inverse_transform(self, data): return self.data_transformer.inverse_transform(data) # should return a numpy array