def func_main(fe):
df = pd.read_csv('Datasets/GermanCredit.csv')
model_job = load('adf_baseline/TestCases/NBCredit.joblib')
#Calling the random testing approach to test strong group monotonicity
fair_score = symbolic_generation.sg_main(model_job, fe)
return fair_score
def func_main(sensitive_param):
#Reading the dataset
X, Y, shape, nb_classes = credit.credit_data()
model = DecisionTreeClassifier(class_weight=None,
criterion='gini',
max_depth=None,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
random_state=None,
splitter='best')
#Fitting the model with the dataset
model = model.fit(X, Y)
#Computing time
start_time = time.time()
#Calling the random testing approach to test strong group monotonicity
model_job = load('adf_baseline/TestCases/DecTreeCredit.joblib')
fair_score = symbolic_generation.sg_main(model_job, sensitive_param)
return fair_score
def func_main(sensitive_param):
#Reading the dataset
X, Y, shape, nb_classes = census.census_data()
model = LogisticRegression(penalty='l2',
dual=False,
tol=0.0001,
C=1.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=10,
solver='lbfgs',
max_iter=5000,
multi_class='auto',
verbose=0,
warm_start=False,
n_jobs=None,
l1_ratio=None)
#Fitting the model with the dataset
model = model.fit(X, Y)
#Computing time
start_time = time.time()
#Calling the random testing approach to test strong group monotonicity
model_job = load('adf_baseline/TestCases/LogRegAdult.joblib')
fair_score = symbolic_generation.sg_main(model_job, sensitive_param)
return fair_score
def func_main(sensitive_param):
#Reading the dataset
X, Y, shape, nb_classes = credit.credit_data()
model = MultinomialNB()
#Fitting the model with the dataset
model = model.fit(X, Y)
#Computing time
start_time = time.time()
#Calling the random testing approach to test strong group monotonicity
model_job = load('adf_baseline/TestCases/NBAdult.joblib')
fair_score = symbolic_generation.sg_main(model_job, sensitive_param)
return fair_score