def Kernelcv(alpha, gamma):
return -(
(-cv_s(KernelRidge(alpha=10**alpha, gamma=10**gamma, kernel='rbf'),
traindata,
target,
"mean_squared_error",
cv=10).mean())**0.5)
def xgbcv(
max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
subsample,
colsample_bytree,
reg_alpha,
reg_lambda,
silent=True):
return cv_s(xgb.XGBRegressor(
max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
silent=silent,
gamma=gamma,
min_child_weight=min_child_weight,
subsample=subsample,
colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha,
reg_lambda=reg_lambda),
train,
target,
"mean_squared_error",
cv=4).mean()
def xgbcv(
max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
subsample,
colsample_bytree,
reg_alpha,
silent=True,
nthread=8):
return cv_s(XGBClassifier(
max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
silent=silent,
nthread=nthread,
gamma=gamma,
reg_alpha=reg_alpha,
min_child_weight=min_child_weight,
subsample=subsample,
colsample_bytree=colsample_bytree,
objective='multi:softprob'),
train,
outcome,
"accuracy",
cv=4).mean()
def xgbcv(
max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
subsample,
colsample_bytree,
silent=True,
nthread=8):
return cv_s(xgb.XGBClassifier(
max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
silent=silent,
nthread=nthread,
gamma=gamma,
min_child_weight=min_child_weight,
subsample=subsample,
colsample_bytree=colsample_bytree,
objective='multi:softprob'),
xd,
yd,
"log_loss",
cv=4).mean()
def xgbcv(max_depth, max_features, n_estimators, min_samples_leaf, n_jobs=-1):
return cv_s(ExtraTreesRegressor(max_depth=int(max_depth),
max_features=max_features,
n_estimators=int(n_estimators),
min_samples_leaf=int(min_samples_leaf)),
train,
target,
"mean_squared_error",
cv=4).mean()
def xgbcv(max_depth, max_features, n_estimators, min_samples_leaf, n_jobs=-1):
return cv_s(ExtraTreesClassifier(max_depth=int(max_depth),
max_features=max_features,
n_estimators=int(n_estimators),
min_samples_leaf=int(min_samples_leaf)),
train,
target['status_group'],
"log_loss",
cv=4).mean()
def xgbcv(max_depth, max_features, n_estimators, min_samples_leaf, n_jobs=-1):
return cv_s(RandomForestClassifier(max_depth=int(max_depth),
max_features=max_features,
n_estimators=int(n_estimators),
min_samples_leaf=int(min_samples_leaf)),
train,
target['status_group'],
"accuracy",
cv=4).mean()
def rfcv(n_estimators, min_samples_split, max_features, min_samples_leaf):
return cv_s(ExtraTreesClassifier(n_estimators=int(n_estimators),
min_samples_split=int(min_samples_split),
max_features=min(max_features, 0.999),
min_samples_leaf=int(min_samples_leaf),
criterion="gini"),
cx,
cy,
"accuracy",
cv=4).mean()
def KNNcv(n_neighbors):
return cv_s(KNeighborsRegressor(
n_neighbors=int(n_neighbors),
weights='distance',
algorithm='brute',
metric='cosine'),
train,
target,
"mean_squared_error",
cv=15).mean()
def rfcv(n_estimators, min_samples_split, max_features, min_samples_leaf):
return cv_s(RandomForestClassifier(
n_estimators=int(n_estimators),
min_samples_split=int(min_samples_split),
max_features=min(max_features, 0.999),
min_samples_leaf=int(min_samples_leaf),
criterion="gini"),
xc,
yc,
"log_loss",
cv=4).mean()
def Kernelcv(
alpha_1,
alpha_2,
lambda_1,
lambda_2):
return cv_s(BayesianRidge(n_iter=800,
alpha_1=alpha_1,
alpha_2=alpha_2,
lambda_1=lambda_1,
lambda_2=lambda_2
),
train,
target,
"mean_squared_error",
cv=4).mean()
def xgbcv(max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
reg_alpha,
subsample=1,
colsample_bytree=1,
silent=True):
return cv_s(xgb.XGBRegressor(max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
gamma=gamma,
reg_alpha=reg_alpha,
min_child_weight=min_child_weight,
objective='reg:linear'),
gpstrain,
height,
"mean_squared_error",
cv=4).mean()
def xgbcv(max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
subsample,
colsample_bytree,
reg_alpha,
reg_lambda,
silent=True):
return cv_s(xgb.XGBClassifier(max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
gamma=gamma,
reg_alpha=reg_alpha,
min_child_weight=min_child_weight,
objective='multi:softmax'),
train,
target['status_group'],
"accuracy",
cv=4).mean()
def xgbcv(max_depth,
learning_rate,
n_estimators,
min_child_weight,
gamma,
subsample,
colsample_bytree,
silent=True,
nthread=8):
return cv_s(xgb.XGBRegressor(max_depth=int(max_depth),
learning_rate=learning_rate,
n_estimators=int(n_estimators),
silent=silent,
gamma=gamma,
min_child_weight=min_child_weight,
subsample=subsample,
colsample_bytree=colsample_bytree,
objective='reg:linear'),
train,
target,
"mean_squared_error",
cv=4).mean()
def Kernelcv(alpha):
return cv_s(Lasso(alpha=10**alpha),
train,
target,
"mean_squared_error",
cv=15).mean()
def lrcv(C):
return cv_s(LogisticRegression(C=10**C),
train,
target['status_group'],
"log_loss",
cv=4).mean()
def Kernelcv(alpha, gamma):
return cv_s(KernelRidge(alpha=10**alpha, gamma=10**gamma, kernel='rbf'),
train,
target,
"mean_squared_error",
cv=4).mean()
