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estimation.py
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estimation.py
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import shap
import copy
import time
import numpy as np
import pandas as pd
from utils import flip, accuracy, violation
from sklearn.utils import resample
from sklearn.preprocessing import LabelEncoder,StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from fairlearn.reductions import ExponentiatedGradient, GridSearch
from fairlearn.reductions import DemographicParity, ErrorRate, EqualizedOdds
from ProxyConstraint import ProxyEqualizedOdds2
error_rate = [[0.35, 0.45], [0.10, 0.06]]
X_raw, Y = shap.datasets.adult()
# X_raw['label'] = Y
# df_majority = X_raw[X_raw.label == False]
# df_minority = X_raw[X_raw.label == True]
# df_minority_upsampled = resample(df_majority, replace=True, n_samples=7841, random_state=123)
# df = pd.concat([df_minority, df_minority_upsampled])
# print(df.label.value_counts())
# Y = df['label'].values
# X_raw = df.drop('label', axis=1)
# print(X_raw.head())
A = X_raw["Sex"]
X = X_raw.drop(labels=['Sex'],axis = 1)
X = pd.get_dummies(X)
sc = StandardScaler()
X_scaled = sc.fit_transform(X)
X_scaled = pd.DataFrame(X_scaled, columns=X.columns)
le = LabelEncoder()
Y = le.fit_transform(Y)
X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled,
Y,
A,
test_size = 0.2,
random_state=0,
stratify=Y)
X_train = X_train.values
A_train = A_train.values
X_test = X_test.reset_index(drop=True)
A_test = A_test.reset_index(drop=True)
# print(X_train)
Y_noised = flip(Y_train, A_train, error_rate=error_rate)
print(np.mean(Y_train), np.mean(Y_noised))
def NearestNeighbor(i):
# print(X_train.shape)
distance = max(np.linalg.norm(X_train[i,] - X_train[0,]), np.linalg.norm(X_train[i,] - X_train[1,]))
nn = 0
for j in range(len(X_train)):
if i == j:
continue
if A_train[i]== A_train[j] and np.linalg.norm(X_train[i] - X_train[j]) < distance:
distance = np.linalg.norm(X_train[i] - X_train[j])
nn = j
# print(nn, distance)
return nn
def estimate_delta(X, A, Y):
c1 = np.array([0., 0.])
t = np.array([0., 0.])
num = np.array([0., 0.])
for i in range(len(X)):
num[int(A[i])] += 1.
if Y[i] == 1:
j = NearestNeighbor(i)
# print(i, j)
t[int(A[i])] += Y[i] == Y[j]
c1[int(A[i])] += 1
c1 = 2 * c1 / num
c2 = 2 * t / num
print(f"c1: {c1}, c2: {c2}")
return np.sqrt(2 * c2 - c1 * c1)
delta = estimate_delta(X_train, A_train, Y_noised)
print(f"True Error Rate: {error_rate}, Estimated Delta: {delta}.")
def run_clean(fairness_constraints):
print(f"Start running experiment with clean data.")
unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)
# unmitigated_predictor.fit(X_train, Y_train)
unmitigated_predictor.fit(X_train, Y_train)
sweep = GridSearch(LogisticRegression(solver='liblinear', fit_intercept=True),
constraints=EqualizedOdds(),
grid_size=71)
sweep.fit(X_train, Y_train, sensitive_features=A_train)
predictors = [ unmitigated_predictor ] + [ z.predictor for z in sweep.all_results]
all_results_train, all_results_test = [], []
for predictor in predictors:
prediction_train = predictor.predict(X_train)
prediction_test = predictor.predict(X_test)
all_results_train.append({'accuracy': accuracy(prediction_train, Y_train), 'violation': violation(prediction_train, Y_train, A_train)})
all_results_test.append({'accuracy': accuracy(prediction_test, Y_test), 'violation': violation(prediction_test, Y_test, A_test)})
# print(all_results_train)
# print(all_results_test)
best_train, best_test = [], []
for constraint in fairness_constraints:
best = 0.0
for result in all_results_train:
if result['violation'] <= constraint and result['accuracy'] > best:
best = result['accuracy']
best_train.append(best)
best = 0.0
for result in all_results_test:
if result['violation'] <= constraint and result['accuracy'] > best:
best = result['accuracy']
best_test.append(best)
return best_train, best_test
def run_estimation(fairness_constraints, proxy=False, lnl=False):
print(f"Start running experiment with Proxy: {proxy}, Learning with Noisy Labels: {lnl}.")
all_results_train, all_results_test = [], []
for eps in fairness_constraints:
begin = time.time()
if proxy and lnl:
clf = ExponentiatedGradient(LogisticRegression(solver='liblinear', fit_intercept=True),
constraints=ProxyEqualizedOdds2(delta=delta),
eps=eps)
sweep = LearningWithNoisyLabels(clf=clf)
elif proxy:
sweep = ExponentiatedGradient(LogisticRegression(solver='liblinear', fit_intercept=True),
constraints=ProxyEqualizedOdds2(delta=delta),
eps=eps)
elif lnl:
clf = ExponentiatedGradient(LogisticRegression(solver='liblinear', fit_intercept=True),
constraints=EqualizedOdds(),
eps=eps)
sweep = LearningWithNoisyLabels(clf=clf)
else:
sweep = ExponentiatedGradient(LogisticRegression(solver='liblinear', fit_intercept=True),
constraints=EqualizedOdds(),
eps=eps)
sweep.fit(X_train, Y_noised, sensitive_features=A_train)
prediction_train = sweep.predict(X_train)
prediction_test = sweep.predict(X_test)
accuracy_train = accuracy(prediction_train, Y_train)
accuracy_test = accuracy(prediction_test, Y_test)
all_results_train.append(accuracy_train)
all_results_test.append(accuracy_test)
print(f"Running fairness constraint: {eps}, Training Accuracy: {accuracy_train}, Test Accuracy: {accuracy_test}, Training Violation: {violation(prediction_train, Y_train, A_train)}, Test Violation: {violation(prediction_test, Y_test, A_test)}, Time cost: {time.time() - begin}")
return all_results_train, all_results_test
fairness_constraints = [0.008 * i for i in range(1, 11)]
train_result1, test_result1 = run(fairness_constraints, proxy=True, lnl=False)
# train_result2, test_result2 = run(fairness_constraints, proxy=False, lnl=True)
# train_result3, test_result3 = run(fairness_constraints, proxy=False, lnl=False)
train_result2, test_result2 = run_clean(fairness_constraints)
# train_result4, test_result4 = run(fairness_constraints, proxy=True, lnl=True)
with open('logs/est_result2.txt', 'w') as f:
for i in range(len(fairness_constraints)):
# f.write(f"{fairness_constraints[i]}\t{train_result1[i]}\t{test_result1[i]}\t{train_result2[i]}\t{test_result2[i]}\t{train_result3[i]}\t{test_result3[i]}\t{train_result4[i]}\t{test_result4[i]}\n")
f.write(f"{fairness_constraints[i]}\t{train_result2[i]}\t{test_result2[i]}\t{train_result1[i]}\t{test_result1[i]}\n")