def grid_search(parameters,
X_train_res,
y_train_res,
X_test,
y_test,
useTrainCV=False):
xgbmodel = XGBRegressor()
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=10)
grid_search_xg = GridSearchCV(xgbmodel,
parameters,
scoring='roc_auc',
n_jobs=1,
cv=kfold,
verbose=1)
result_gcv_xgb = grid_search_xg.fit(X_train_res, y_train_res)
best_params = result_gcv_xgb.best_params_
print("Best params: %s" % (best_params))
# rebuild using best params
xg_reg = XGBRegressor(objective=best_params['objective'],
learning_rate=best_params['learning_rate'],
max_depth=best_params['max_depth'],
n_estimators=best_params['n_estimators'],
min_child_weight=best_params['min_child_weight'],
gamma=best_params['gamma'],
colsample_bytree=best_params['colsample_bytree'],
subsample=best_params['subsample'],
reg_alpha=best_params['reg_alpha'])
if useTrainCV:
xgb_param = xg_reg.get_xgb_params()
xgtrain = DMatrix(X_train_res, label=y_train_res)
cvresult = cv(xgb_param,
xgtrain,
num_boost_round=xg_reg.get_params()['n_estimators'],
folds=kfold,
metrics='auc',
early_stopping_rounds=20)
xg_reg.set_params(n_estimators=cvresult.shape[0])
print("Best number of estimators: %i" % (cvresult.shape[0]))
eval_set = [(X_test, y_test)]
xg_reg.fit(X_train_res,
y_train_res,
eval_metric="error",
eval_set=eval_set,
verbose=False)
y_pred_train = xg_reg.predict(X_train_res)
#print("Accuracy train: %f" % (accuracy_score(y_train_res, y_pred_train)))
#print("Recall train: %f" % (recall_score(y_train_res, y_pred_train)))
#print("Precision train: %f" % (precision_score(y_train_res, y_pred_train)))
print("AUC train: %f" % (roc_auc_score(y_train_res, y_pred_train)))
y_pred = xg_reg.predict(X_test)
#print("Accuracy test: %f" % (accuracy_score(y_test, y_pred)))
#print("Recall test: %f" % (recall_score(y_test, y_pred)))
#print("Precision test: %f" % (precision_score(y_test, y_pred)))
print("AUC test: %f" % (roc_auc_score(y_test, y_pred)))
warnings.filterwarnings('ignore')
xgtrain = xgb.DMatrix(train_df, label=target.values)
#finding numof boosting rounds and learning rate
alg = XGBRegressor(
learning_rate =0.1,
n_estimators=1000,
max_depth=5,
min_child_weight=1,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective= 'reg:squarederror',
seed=27)
xgb_param = alg.get_xgb_params()
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=5,metrics='rmse', early_stopping_rounds=50)
n_estimators = cvresult.shape[0]
param_test1 = {
'max_depth':range(3,10,2),
'min_child_weight':range(1,6,2)
}
gsearch1 = GridSearchCV(estimator = XGBRegressor( learning_rate =0.1, n_estimators=n_estimators, max_depth=5,
min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
objective= 'reg:squarederror', nthread=4, seed=27),
param_grid = param_test1, scoring=make_scorer(mean_squared_error),n_jobs=4,iid=False, cv=5)
gsearch1.fit(train_df,target)
x_train = df[list(features)].values
y_train = df["SPEED_AVG"].values
gb = XGBRegressor(learning_rate=0.1,
n_estimators=750,
max_depth=5,
min_child_weight=1,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective='reg:gamma',
nthread=4,
scale_pos_weight=1,
seed=27)
xgb_param = gb.get_xgb_params()
xgtrain = xgb.DMatrix(df[features].values, label=df['SPEED_AVG'].values)
cvresult = xgb.cv(xgb_param,
xgtrain,
num_boost_round=gb.get_params()['n_estimators'],
nfold=10,
metrics='mae',
early_stopping_rounds=50)
gb.set_params(n_estimators=cvresult.shape[0])
gb.fit(x_train, y_train, eval_metric='mae')
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
