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()