def quantile_transform():
a, b = _randint(100, 1000), _randint(1000, 1e5)
c = _randchoice(['normal', 'uniform'])
scaler = QuantileTransformer(n_quantiles=a,
output_distribution=c,
subsample=b)
tmp = str(a) + "_" + str(b) + "_" + c + "_" + QuantileTransformer.__name__
return scaler, tmp
def mutate_classifier(self, best_classifier, worst_classifier):
best_params = best_classifier.get_params()
worst_params = worst_classifier.get_params()
mutated_params = {}
for k, v in best_params.items():
if isinstance(v, float):
mutated_params[k] = _randuniform(
best_params[k], (best_params[k] + worst_params[k]) / 2)
elif isinstance(v, int):
mutated_params[k] = _randint(
best_params[k], int(
(best_params[k] + worst_params[k]) / 2))
else:
mutated_params[k] = best_params[k]
# for k,v in best_params.items():
# print('best param = ', k,v)
# for k,v in mutated_params.items():
# print('mutated param = ', k,v)
mutated_classifier = new_classifier(best_classifier)
mutated_classifier.set_params(**mutated_params)
return mutated_classifier
def polynomial():
a = _randint(2,10)
b = _randchoice([True, False])
c = _randchoice([True, False])
scaler=PolynomialFeatures(degree=a, interaction_only=b, include_bias=c)
tmp = str(a) + "_" + str(b) + "_" + str(c)+ "_"+PolynomialFeatures.__name__
return scaler, tmp
def KNN():
a = _randint(2, 25)
b = _randchoice(['uniform', 'distance'])
c = _randchoice(['minkowski', 'chebyshev'])
if c == 'minkowski':
d = _randint(1, 15)
else:
d = 2
model = KNeighborsClassifier(n_neighbors=a,
weights=b,
algorithm='auto',
p=d,
metric=c,
n_jobs=-1)
tmp = str(a) + "_" + b + "_" + c + "_" + str(
d) + "_" + KNeighborsClassifier.__name__
return model, tmp
def LR():
a = _randchoice(['l2'])
b = _randuniform(0.0, 0.1)
c = _randint(1, 500)
# model = LogisticRegression(penalty=a, tol=b, C=float(c), solver='liblinear', multi_class='warn')
model = LogisticRegression(penalty=a, tol=b, C=float(c))
tmp = a + "_" + str(round(
b, 5)) + "_" + str(c) + "_" + LogisticRegression.__name__
return model, tmp
def SVM():
# from sklearn.preprocessing import MinMaxScaler
# scaling = MinMaxScaler(feature_range=(-1, 1)).fit(train_data)
# train_data = scaling.transform(train_data)
# test_data = scaling.transform(test_data)
a = _randint(1, 500)
b = _randchoice(['linear', 'poly', 'rbf', 'sigmoid'])
c = _randint(2, 10)
d = _randuniform(0.0, 1.0)
e = _randuniform(0.0, 0.1)
f = _randuniform(0.0, 0.1)
model = SVC(C=float(a),
kernel=b,
degree=c,
gamma=d,
coef0=e,
tol=f,
cache_size=20000)
tmp = str(a) + "_" + b + "_" + str(c) + "_" + str(round(d, 5)) + "_" + str(
round(e, 5)) + "_" + str(round(f, 5)) + "_" + SVC.__name__
return model, tmp
def RF():
a = _randint(50, 150)
b = _randchoice(['gini', 'entropy'])
c = _randuniform(0.0, 1.0)
model = RandomForestClassifier(n_estimators=a,
criterion=b,
min_samples_split=c,
max_features=None,
min_impurity_decrease=0.0,
n_jobs=-1)
tmp = str(a) + "_" + b + "_" + str(round(
c, 5)) + "_" + RandomForestClassifier.__name__
return model, tmp
def mutate_preprocessor(self, best_preprocessor, worst_preprocessor):
best_params = best_preprocessor.get_params()
worst_params = worst_preprocessor.get_params()
mutated_params = {}
for k, v in best_params.items():
if isinstance(v, float):
mutated_params[k] = _randuniform(
best_params[k], (best_params[k] + worst_params[k]) / 2)
elif isinstance(v, int):
mutated_params[k] = _randint(
best_params[k], (best_params[k] + worst_params[k]) / 2)
else:
mutated_params[k] = best_params[k]
mutated_preprocessor = new_preprocessor(best_preprocessor)
mutated_preprocessor.set_params(**best_params)
return mutated_preprocessor
def robust_scaler():
a,b=_randint(0,50),_randint(51,100)
scaler = RobustScaler(quantile_range=(a, b))
tmp=str(a)+"_"+str(b)+"_"+RobustScaler.__name__
return scaler, tmp
