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fairgnn_utils.py
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fairgnn_utils.py
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#%%
import ipdb
import numpy as np
import scipy.sparse as sp
import torch
import os
import random
import pandas as pd
import dgl
from scipy.spatial import distance_matrix
def encode_onehot(labels):
classes = set(labels)
classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
enumerate(classes)}
labels_onehot = np.array(list(map(classes_dict.get, labels)),
dtype=np.int32)
return labels_onehot
def build_relationship(x, thresh=0.25):
df_euclid = pd.DataFrame(1 / (1 + distance_matrix(x.T.T, x.T.T)), columns=x.T.columns, index=x.T.columns)
df_euclid = df_euclid.to_numpy()
idx_map = []
for ind in range(df_euclid.shape[0]):
max_sim = np.sort(df_euclid[ind, :])[-2]
neig_id = np.where(df_euclid[ind, :] > thresh*max_sim)[0]
import random
random.seed(912)
random.shuffle(neig_id)
for neig in neig_id[:200]:
if neig != ind:
idx_map.append([ind, neig])
# print('building edge relationship complete')
idx_map = np.array(idx_map)
return idx_map
def load_credit(dataset, sens_attr="Age", predict_attr="NoDefaultNextMonth", path="./dataset/credit/", label_number=1000, sens_number=100):
# print('Loading {} dataset from {}'.format(dataset, path))
idx_features_labels = pd.read_csv(os.path.join(path,"{}.csv".format(dataset)))
header = list(idx_features_labels.columns)
header.remove(predict_attr)
header.remove('Single')
# # Normalize MaxBillAmountOverLast6Months
# idx_features_labels['MaxBillAmountOverLast6Months'] = (idx_features_labels['MaxBillAmountOverLast6Months']-idx_features_labels['MaxBillAmountOverLast6Months'].mean())/idx_features_labels['MaxBillAmountOverLast6Months'].std()
#
# # Normalize MaxPaymentAmountOverLast6Months
# idx_features_labels['MaxPaymentAmountOverLast6Months'] = (idx_features_labels['MaxPaymentAmountOverLast6Months'] - idx_features_labels['MaxPaymentAmountOverLast6Months'].mean())/idx_features_labels['MaxPaymentAmountOverLast6Months'].std()
#
# # Normalize MostRecentBillAmount
# idx_features_labels['MostRecentBillAmount'] = (idx_features_labels['MostRecentBillAmount']-idx_features_labels['MostRecentBillAmount'].mean())/idx_features_labels['MostRecentBillAmount'].std()
#
# # Normalize MostRecentPaymentAmount
# idx_features_labels['MostRecentPaymentAmount'] = (idx_features_labels['MostRecentPaymentAmount']-idx_features_labels['MostRecentPaymentAmount'].mean())/idx_features_labels['MostRecentPaymentAmount'].std()
#
# # Normalize TotalMonthsOverdue
# idx_features_labels['TotalMonthsOverdue'] = (idx_features_labels['TotalMonthsOverdue']-idx_features_labels['TotalMonthsOverdue'].mean())/idx_features_labels['TotalMonthsOverdue'].std()
# build relationship
if os.path.exists(f'{path}/{dataset}_edges.txt'):
edges_unordered = np.genfromtxt(f'{path}/{dataset}_edges.txt').astype('int')
else:
edges_unordered = build_relationship(idx_features_labels[header], thresh=0.7)
np.savetxt(f'{path}/{dataset}_edges.txt', edges_unordered)
features = sp.csr_matrix(idx_features_labels[header], dtype=np.float32)
labels = idx_features_labels[predict_attr].values
idx = np.arange(features.shape[0])
idx_map = {j: i for i, j in enumerate(idx)}
edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
dtype=int).reshape(edges_unordered.shape)
adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
shape=(labels.shape[0], labels.shape[0]),
dtype=np.float32)
# build symmetric adjacency matrix
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
# features = normalize(features)
adj = adj + sp.eye(adj.shape[0])
features = torch.FloatTensor(np.array(features.todense()))
labels = torch.LongTensor(labels)
import random
random.seed(20)
label_idx_0 = np.where(labels==0)[0]
label_idx_1 = np.where(labels==1)[0]
random.shuffle(label_idx_0)
random.shuffle(label_idx_1)
idx_train = np.append(label_idx_0[:min(int(0.5 * len(label_idx_0)), label_number//2)], label_idx_1[:min(int(0.5 * len(label_idx_1)), label_number//2)])
idx_val = np.append(label_idx_0[int(0.5 * len(label_idx_0)):int(0.75 * len(label_idx_0))], label_idx_1[int(0.5 * len(label_idx_1)):int(0.75 * len(label_idx_1))])
idx_test = np.append(label_idx_0[int(0.75 * len(label_idx_0)):], label_idx_1[int(0.75 * len(label_idx_1)):])
sens = idx_features_labels[sens_attr].values.astype(int)
sens_idx = set(np.where(sens >= 0)[0])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
sens = torch.FloatTensor(sens)
idx_sens_train = list(sens_idx - set(idx_val) - set(idx_test))
random.seed(20)
random.shuffle(idx_sens_train)
idx_sens_train = torch.LongTensor(idx_sens_train[:sens_number])
return adj, features, labels, idx_train, idx_val, idx_test, sens, idx_sens_train
def load_bail(dataset, sens_attr="WHITE", predict_attr="RECID", path="../dataset/bail/", label_number=1000, sens_number=100):
# print('Loading {} dataset from {}'.format(dataset, path))
idx_features_labels = pd.read_csv(os.path.join(path,"{}.csv".format(dataset)))
header = list(idx_features_labels.columns)
header.remove(predict_attr)
# # Normalize School
# idx_features_labels['SCHOOL'] = 2*(idx_features_labels['SCHOOL']-idx_features_labels['SCHOOL'].min()).div(idx_features_labels['SCHOOL'].max() - idx_features_labels['SCHOOL'].min()) - 1
# # Normalize RULE
# idx_features_labels['RULE'] = 2*(idx_features_labels['RULE']-idx_features_labels['RULE'].min()).div(idx_features_labels['RULE'].max() - idx_features_labels['RULE'].min()) - 1
# # Normalize AGE
# idx_features_labels['AGE'] = 2*(idx_features_labels['AGE']-idx_features_labels['AGE'].min()).div(idx_features_labels['AGE'].max() - idx_features_labels['AGE'].min()) - 1
# # Normalize TSERVD
# idx_features_labels['TSERVD'] = 2*(idx_features_labels['TSERVD']-idx_features_labels['TSERVD'].min()).div(idx_features_labels['TSERVD'].max() - idx_features_labels['TSERVD'].min()) - 1
# # Normalize FOLLOW
# idx_features_labels['FOLLOW'] = 2*(idx_features_labels['FOLLOW']-idx_features_labels['FOLLOW'].min()).div(idx_features_labels['FOLLOW'].max() - idx_features_labels['FOLLOW'].min()) - 1
# # Normalize TIME
# idx_features_labels['TIME'] = 2*(idx_features_labels['TIME']-idx_features_labels['TIME'].min()).div(idx_features_labels['TIME'].max() - idx_features_labels['TIME'].min()) - 1
# build relationship
if os.path.exists(f'{path}/{dataset}_edges.txt'):
edges_unordered = np.genfromtxt(f'{path}/{dataset}_edges.txt').astype('int')
else:
edges_unordered = build_relationship(idx_features_labels[header], thresh=0.6)
np.savetxt(f'{path}/{dataset}_edges.txt', edges_unordered)
features = sp.csr_matrix(idx_features_labels[header], dtype=np.float32)
labels = idx_features_labels[predict_attr].values
idx = np.arange(features.shape[0])
idx_map = {j: i for i, j in enumerate(idx)}
edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
dtype=int).reshape(edges_unordered.shape)
adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
shape=(labels.shape[0], labels.shape[0]),
dtype=np.float32)
# build symmetric adjacency matrix
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
# features = normalize(features)
adj = adj + sp.eye(adj.shape[0])
features = torch.FloatTensor(np.array(features.todense()))
labels = torch.LongTensor(labels)
import random
random.seed(20)
label_idx_0 = np.where(labels==0)[0]
label_idx_1 = np.where(labels==1)[0]
random.shuffle(label_idx_0)
random.shuffle(label_idx_1)
idx_train = np.append(label_idx_0[:min(int(0.5 * len(label_idx_0)), label_number//2)], label_idx_1[:min(int(0.5 * len(label_idx_1)), label_number//2)])
idx_val = np.append(label_idx_0[int(0.5 * len(label_idx_0)):int(0.75 * len(label_idx_0))], label_idx_1[int(0.5 * len(label_idx_1)):int(0.75 * len(label_idx_1))])
idx_test = np.append(label_idx_0[int(0.75 * len(label_idx_0)):], label_idx_1[int(0.75 * len(label_idx_1)):])
sens = idx_features_labels[sens_attr].values.astype(int)
sens_idx = set(np.where(sens >= 0)[0])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
sens = torch.FloatTensor(sens)
idx_sens_train = list(sens_idx - set(idx_val) - set(idx_test))
random.seed(20)
random.shuffle(idx_sens_train)
idx_sens_train = torch.LongTensor(idx_sens_train[:sens_number])
return adj, features, labels, idx_train, idx_val, idx_test, sens, idx_sens_train
def load_german(dataset, sens_attr="Gender", predict_attr="GoodCustomer", path="../dataset/german/", label_number=1000, sens_number=100):
# print('Loading {} dataset from {}'.format(dataset, path))
idx_features_labels = pd.read_csv(os.path.join(path,"{}.csv".format(dataset)))
header = list(idx_features_labels.columns)
header.remove(predict_attr)
header.remove('OtherLoansAtStore')
header.remove('PurposeOfLoan')
# Sensitive Attribute
idx_features_labels['Gender'][idx_features_labels['Gender'] == 'Female'] = 1
idx_features_labels['Gender'][idx_features_labels['Gender'] == 'Male'] = 0
# for i in range(idx_features_labels['PurposeOfLoan'].unique().shape[0]):
# val = idx_features_labels['PurposeOfLoan'].unique()[i]
# idx_features_labels['PurposeOfLoan'][idx_features_labels['PurposeOfLoan'] == val] = i
# # Normalize LoanAmount
# idx_features_labels['LoanAmount'] = 2*(idx_features_labels['LoanAmount']-idx_features_labels['LoanAmount'].min()).div(idx_features_labels['LoanAmount'].max() - idx_features_labels['LoanAmount'].min()) - 1
#
# # Normalize Age
# idx_features_labels['Age'] = 2*(idx_features_labels['Age']-idx_features_labels['Age'].min()).div(idx_features_labels['Age'].max() - idx_features_labels['Age'].min()) - 1
#
# # Normalize LoanDuration
# idx_features_labels['LoanDuration'] = 2*(idx_features_labels['LoanDuration']-idx_features_labels['LoanDuration'].min()).div(idx_features_labels['LoanDuration'].max() - idx_features_labels['LoanDuration'].min()) - 1
#
# build relationship
if os.path.exists(f'{path}/{dataset}_edges.txt'):
edges_unordered = np.genfromtxt(f'{path}/{dataset}_edges.txt').astype('int')
else:
edges_unordered = build_relationship(idx_features_labels[header], thresh=0.8)
np.savetxt(f'{path}/{dataset}_edges.txt', edges_unordered)
features = sp.csr_matrix(idx_features_labels[header], dtype=np.float32)
labels = idx_features_labels[predict_attr].values
labels[labels == -1] = 0
idx = np.arange(features.shape[0])
idx_map = {j: i for i, j in enumerate(idx)}
edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
dtype=int).reshape(edges_unordered.shape)
adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
shape=(labels.shape[0], labels.shape[0]),
dtype=np.float32)
# build symmetric adjacency matrix
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
# features = normalize(features)
adj = adj + sp.eye(adj.shape[0])
features = torch.FloatTensor(np.array(features.todense()))
labels = torch.LongTensor(labels)
import random
random.seed(20)
label_idx_0 = np.where(labels==0)[0]
label_idx_1 = np.where(labels==1)[0]
random.shuffle(label_idx_0)
random.shuffle(label_idx_1)
idx_train = np.append(label_idx_0[:min(int(0.5 * len(label_idx_0)), label_number//2)], label_idx_1[:min(int(0.5 * len(label_idx_1)), label_number//2)])
idx_val = np.append(label_idx_0[int(0.5 * len(label_idx_0)):int(0.75 * len(label_idx_0))], label_idx_1[int(0.5 * len(label_idx_1)):int(0.75 * len(label_idx_1))])
idx_test = np.append(label_idx_0[int(0.75 * len(label_idx_0)):], label_idx_1[int(0.75 * len(label_idx_1)):])
sens = idx_features_labels[sens_attr].values.astype(int)
sens_idx = set(np.where(sens >= 0)[0])
idx_test = np.asarray(list(sens_idx & set(idx_test)))
sens = torch.FloatTensor(sens)
idx_sens_train = list(sens_idx - set(idx_val) - set(idx_test))
random.seed(20)
random.shuffle(idx_sens_train)
idx_sens_train = torch.LongTensor(idx_sens_train[:sens_number])
idx_train = torch.LongTensor(idx_train)
idx_val = torch.LongTensor(idx_val)
idx_test = torch.LongTensor(idx_test)
return adj, features, labels, idx_train, idx_val, idx_test, sens, idx_sens_train
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
mx = r_mat_inv.dot(mx)
return mx
def feature_norm(features):
min_values = features.min(axis=0)[0]
max_values = features.max(axis=0)[0]
return 2*(features - min_values).div(max_values-min_values) - 1
def accuracy(output, labels):
output = output.squeeze()
preds = (output>0).type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def accuracy_softmax(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def sparse_mx_to_torch_sparse_tensor(sparse_mx):
"""Convert a scipy sparse matrix to a torch sparse tensor."""
sparse_mx = sparse_mx.tocoo().astype(np.float32)
indices = torch.from_numpy(
np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
values = torch.from_numpy(sparse_mx.data)
shape = torch.Size(sparse_mx.shape)
return torch.sparse.FloatTensor(indices, values, shape)