def model_stack2():
_, test_df, train_label = data_process.get_person_data()
train_data, test_data = data_process.get_scale_data()
X_train, X_val, y_train, y_val = train_test_split(train_data,
train_label,
test_size=0.2,
random_state=66)
id_list = list(test_df.pop('ID'))
model1 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:linear')
model2 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:gamma')
model3 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:tweedie')
model4 = svm.SVR()
stack_model = StackingClassifier(
classifiers=[model1, model2, model3, model4], meta_classifier=model3)
stack_model.fit(train_data, train_label)
yHat = stack_model.predict(test_data)
result = pd.DataFrame({'id': id_list, 'yhat': yHat})
result.to_csv('result/result6.csv',
index=False,
header=None,
encoding='utf-8')
def train_svr():
train_data, test_data, train_label = data_process.get_scale_data()
_, test_df, _ = data_process.get_person_data()
id_list = list(test_df.pop('ID'))
X_train, X_val, y_train, y_val = train_test_split(train_data,
train_label,
test_size=0.2,
random_state=66)
model2 = svm.SVR()
# model2 = gbt.XGBRegressor(n_estimators=1000, subsample=0.8, learning_rate=0.25, objective='reg:tweedie')
model2.fit(train_data, train_label)
yHat = model2.predict(test_data)
result = pd.DataFrame({'id': id_list, 'yhat': yHat})
result.to_csv('result/result11.csv',
index=False,
header=None,
encoding='utf-8')
def model_stack():
kf = KFold(n_splits=10, random_state=5)
train_df, test_df, train_label = data_process.get_person_data()
train_df.drop('ID', axis=1, inplace=True)
train_data = train_df.values
X_train, X_val, y_train, y_val = train_test_split(train_data,
train_label,
test_size=0.2,
random_state=66)
id_list = list(test_df.pop('ID'))
test_data = test_df.values
model1 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:linear')
model2 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:gamma')
model3 = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:tweedie')
model4 = RandomForestRegressor()
model5 = GradientBoostingRegressor()
# model6 = AdaBoostRegressor(n_estimators=200)
stack_model = StackingClassifier(
classifiers=[model1, model2, model4, model5], meta_classifier=model3)
train_data = np.array(train_data)
yHat_list = []
for i, (train_index, test_index) in enumerate(kf.split(train_data)):
new_train_data = train_data[train_index]
new_train_label = [train_label[i] for i in train_index]
stack_model.fit(new_train_data, new_train_label)
yHat = stack_model.predict(test_data)
yHat_list.append(yHat)
yHat_list = np.array(yHat_list)
yHat_list = yHat_list.mean(axis=0)
result = pd.DataFrame({'id': id_list, 'yhat': yHat_list})
result.to_csv('result/result17.csv',
index=False,
header=None,
encoding='utf-8')
def train_model():
train_df, _, train_label = data_process.get_person_data()
train_df.drop('ID', axis=1, inplace=True)
train_data = train_df.values
X_train, X_val, y_train, y_val = train_test_split(train_data,
train_label,
test_size=0.2,
random_state=66)
model = gbt.XGBRegressor(n_estimators=1000,
subsample=0.8,
learning_rate=0.25,
objective='reg:tweedie')
# model = gbt.XGBRegressor(n_estimators=1000, subsample=0.8, learning_rate=0.25, objective='reg:linear')
# model = gbt.XGBRegressor(n_estimators=1000, subsample=0.8, learning_rate=0.25, objective='reg:gamma')
# model=AdaBoostRegressor(n_estimators=200)
model.fit(X_train, y_train)
new_X_train = model.apply(X_train)
new_X_val = model.apply(X_val)
model.fit(new_X_train, y_train)
yHat = model.predict(new_X_val)
print(mean_squared_error(y_val, yHat))
print(explained_variance_score(y_val, yHat))
#encoding=utf-8
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error,explained_variance_score
import numpy as np
import xgboost as gbt
from guangfu_dianzhan import data_process
train_df, test_df, train_label = data_process.get_person_data()
train_df.drop('ID', axis=1, inplace=True)
train_data = train_df.values
id_list=list(test_df.pop('ID'))
test_data=test_df.values
X_train,X_test,y_train,y_test=train_test_split(train_data,train_label,test_size=0.25,random_state=3)
N_Folds=5
ntrain=X_train.shape[0]
ntest=X_test.shape[0]
kf=KFold(n_splits=N_Folds,random_state=5)
def get_off(clf,X_train,y_train,X_test):
X_train=np.array(X_train)
y_train=np.array(y_train)
X_test=np.array(X_test)
off_train=np.zeros((ntrain,))
off_test=np.zeros((ntest,))
off_test_skf=np.zeros((N_Folds,ntest))
for i,(train_index,test_index) in enumerate(kf.split(X_train)):
print(train_index)
kf_X_train=X_train[train_index]
kf_y_train=y_train[train_index]
kf_X_test=X_train[test_index]
clf.fit(kf_X_train,kf_y_train)