# 验证集看一下得分效果
print("auc: {}".format(get_scores(y_valid_catb, y_valid_predict_catb)))
predict = model.predict(test_catb)
test_catb[label_column] = predict
test_catb[label_column] = test_catb[label_column].map(lambda x: 1
if x >= 0.5 else 0)
test_catb[label_column].to_csv('submit_catb.csv')
print("total costs {} seconds".format(time.time() - start_time))
# ngBoost
import ngboost as ng
model = ng.NGBClassifier(n_estimators=1000,
learning_rate=0.01,
verbose=True,
verbose_eval=200)
model.fit(x_train_ngb, y_train_ngb)
y_valid_predict_ngb = model.predict_proba(x_valid_ngb)[:, 1]
y_valid_predict_ngb = pd.Series(y_valid_predict_ngb)
y_valid_predict_ngb = y_valid_predict_ngb.map(lambda x: 1 if x >= 0.5 else 0)
# 验证集看一下得分效果
print("auc: {}".format(get_scores(y_valid_ngb, y_valid_predict_ngb)))
# predict = model.predict(test_ngb)
predict = model.predict_proba(test_ngb)[:, 1] # 这里可以直接获取概率值，我们取其为1对应的概率
test_ngb[label_column] = predict
test_ngb[label_column] = test_ngb[label_column].map(lambda x: 1
def main():
train_0 = pd.read_csv('train.csv')
test_0 = pd.read_csv('test.csv')
#print(train_0.head(10))
header = [
'BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole',
'MaritalStatus', 'OverTime'
]
# 删除无用特征
user_id = test_0['user_id']
train_0 = train_0.drop(['user_id', 'EmployeeCount', 'Over18'], axis=1)
test_0 = test_0.drop(['user_id', 'EmployeeCount', 'Over18'], axis=1)
#特征编码
for index in header:
LE = LabelEncoder()
train_0[index] = LE.fit_transform(train_0[index])
test_0[index] = LE.transform(test_0[index])
LE = LabelEncoder()
label_0 = LE.fit_transform(train_0['Attrition'])
train_0 = train_0.drop(['Attrition'], axis=1)
train_x, train_y, label_x, label_y = train_test_split(train_0,
label_0,
test_size=0.3,
random_state=1)
# 标准化
# LGBM 调参
parameters = {
'max_depth': [15, 20, 25],
'learning_rate': [0.01, 0.05],
'feature_fraction': [0.6, 0.7, 0.8, 0.9, 0.95],
'bagging_fraction': [0.6, 0.7, 0.8, 0.9, 0.95],
'bagging_freq': [2, 4, 5, 6, 8],
'lambda_l1': [0.6, 0.7, 0.8],
'lambda_l2': [0, 15, 35],
'cat_smooth': [1, 10, 15]
}
LGB = lgb.LGBMClassifier(
boosting_type='gbdt',
objective='binary',
metric='auc',
verbose=0,
learning_rate=0.01,
num_leaves=35,
feature_fraction=0.8,
bagging_fraction=0.7,
bagging_freq=2,
lambda_l1=0.8,
lambda_l2=0,
max_depth=15,
#silent = False
cat_smooth=1)
# gsearch = GridSearchCV(LGB, param_grid=parameters, scoring='roc_auc', cv = 3)
# gsearch.fit(train_0, label_0)
#
# print("Best score: %0.3f" % gsearch.best_score_)
# print("Best parameters set:")
# best_parameters = gsearch.best_estimator_.get_params()
# for param_name in sorted(parameters.keys()):
# print("\t%s: %r" % (param_name, best_parameters[param_name]))
# LGB.fit(train_0, label_0)
# predict = LGB.predict_proba(test_0)[:,1]
#
# test_0['Attrition'] = predict
# test_0['user_id'] = user_id
# test_0[['user_id','Attrition']].to_csv('submit_lgb.csv', index = False)
LGB.fit(train_x, label_x)
predict = LGB.predict_proba(train_y)[:, 1]
print("LGB auc：%0.6lf" % metrics.roc_auc_score(label_y, predict))
SVM = SVC(kernel='rbf', probability=True, C=0.2)
SVM.fit(train_x, label_x)
predict_svm = SVM.predict_proba(train_y)[:, 1]
print("SVM auc：%0.6lf" % metrics.roc_auc_score(label_y, predict_svm))
CAT = cat.CatBoostClassifier()
CAT.fit(train_x, label_x)
predict_svm = CAT.predict_proba(train_y)[:, 1]
print("cat auc：%0.6lf" % metrics.roc_auc_score(label_y, predict_svm))
NG = ng.NGBClassifier()
NG.fit(train_x, label_x)
predict_ng = NG.pred_dist(train_y)
predict_ng = predict_ng.probs[1, :]
print("NG auc：%0.6lf" % metrics.roc_auc_score(label_y, predict_ng))