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from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

from sklearn.metrics import log_loss

from sklearn.metrics import roc_auc_score

from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import  DecisionTreeClassifier

import lightgbm as lgb

import sklearn
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(criterion='entropy', max_depth=5, random_state=0)
model.fit(X_train, y_train)

from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score
model = lgb.LGBMClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_val)
y_pred_prob = model.predict_proba(X_val)
print(model.score(X_train, y_train))
print(model.score(X_val, y_val))
acc = accuracy_score(y_val, y_pred)
print('Acc :', acc)
logloss = log_loss(y_val, y_pred_prob)
print('logloss :', logloss)
auc = roc_auc_score(y_val, y_pred_prob[:, 1])
print('AUC :', auc)