# -*- coding: utf-8 -*-

"""

Created on Wed Nov 8 23:46:40 2017

@author: BRYAN

"""

import time

from sklearn import linear_model

from sklearn.model_selection import train_test_split

import pandas as pd

from sklearn import metrics

from sklearn.preprocessing import MinMaxScaler,StandardScaler

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.neural_network import MLPClassifier

from sklearn.svm import SVC

from xgboost.sklearn import XGBClassifier

from lightgbm import LGBMClassifier

from sklearn.ensemble.forest import ExtraTreesClassifier

from sklearn.neighbors.classification import KNeighborsClassifier

data=pd.read_csv('d:/trainCG.csv').fillna(0,axis=1)

x=data.drop(['label'],axis=1)

# min_max_scaler =MinMaxScaler()

# x=min_max_scaler.fit_transform(x)

y=data['label']

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)

start=time.time()

reg = linear_model.LogisticRegression(penalty='l1',max_iter=100,C=1.5)

reg.fit (X_train,y_train)

end=time.time()

y_pred=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred))

print(end-start)

start=time.time()

reg = DecisionTreeClassifier(max_depth=10,min_samples_split=10,min_samples_leaf=5,max_features=0.75)

reg.fit (X_train,y_train)

end=time.time()

y_pred=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred))

print(end-start)

start=time.time()

reg = RandomForestClassifier(n_jobs=8)

reg.fit (X_train,y_train)

end=time.time()

y_pred=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred))

print(end-start)

start=time.time()

reg = RandomForestClassifier(n_estimators=200,n_jobs=8,random_state=0)

reg.fit (X_train,y_train)

end=time.time()

y_pred_rf=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_rf))

print(end-start)

start=time.time()

reg = MLPClassifier(batch_size=50,hidden_layer_sizes=(20),learning_rate_init=0.1,max_iter=300,random_state=0,early_stopping=True)

reg.fit (X_train,y_train)

end=time.time()

y_pred_mlp=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_mlp))

print(end-start)

start=time.time()

reg = SVC(kernel='rbf',C=0.1, max_iter=100,probability=True)

reg.fit (X_train,y_train)

end=time.time()

y_pred_svc=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_svc))

print(end-start)

start=time.time()

reg = XGBClassifier(max_depth=4,n_estimators=500,min_child_weight=10,subsample=0.7, colsample_bytree=0.7,reg_alpha=0, reg_lambda=0.5)

reg.fit (X_train,y_train)

end=time.time()

y_pred=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred))

print(end-start)

start=time.time()

reg = LGBMClassifier(num_leaves=40,max_depth=7,n_estimators=200,min_child_weight=10,subsample=0.7, colsample_bytree=0.7,reg_alpha=0, reg_lambda=0.5)

reg.fit (X_train,y_train)

end=time.time()

y_pred_lgb=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_lgb))

print(end-start)

start=time.time()

reg = ExtraTreesClassifier(n_estimators=100,max_depth=7,min_samples_leaf=10,n_jobs=8,random_state=4)

reg.fit (X_train,y_train)

end=time.time()

y_pred_et=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_et))

print(end-start)

start=time.time()

reg = KNeighborsClassifier(n_neighbors=4,algorithm='kd_tree')

reg.fit (X_train,y_train)

end=time.time()

y_pred_knn=reg.predict_proba(X_test)[:,1]

print(metrics.roc_auc_score(y_test,y_pred_knn))

print(end-start)

y_pred=0.5*y_pred_et+0.5*y_pred_knn

y_pred=0.5*pd.DataFrame(y_pred_et).rank()+0.5*pd.DataFrame(y_pred_knn).rank()

print(metrics.roc_auc_score(y_test,y_pred))