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

import pandas as pd

import numpy as np

# from sklearn.ensemble import GradientBoostingClassifier

import matplotlib.pyplot as plt

from sklearn.datasets import make_classification

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,

GradientBoostingClassifier)

from sklearn.preprocessing import OneHotEncoder

from sklearn.model_selection import train_test_split

from sklearn.metrics import roc_curve

from sklearn.pipeline import make_pipeline

df = pd.read_excel(r'../../excell/450_3.xlsx')

# print (df)

# gbdt=GradientBoostingClassifier(n_estimators=200)

y_train = np.arange(400)

y_test = np.arange(50)

list1 = list(df.ix[0:400, 7])

list2 = list(df.ix[400:450, 7])

for i in range(400):

y_train[i] = list1[i]

for i in range(50):

y_test[i] = list2[i]

X_train = np.zeros((400, 5))

X_test = np.zeros((50, 5))

list3 = []

for j in range(5):

# print(df.ix[:, j])

# print(j)

list3.append(list(df.ix[:, j + 8]))

# print(list3)

# list3.append(list(df.ix[: , 26]))

# list3.append(list(df.ix[: , 27]))

for j in range(5):

for i in range(400):

X_train[i][j] = list3[j][i]

for j in range(5):

for i in range(50):

X_test[i][j] = list3[j][400 + i]

x = X_train[:, :]

print(x.shape)

# print (X_train.shape, y_train.shape, X_test.shape, y_test.shape)

'''

X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,

y_train,

test_size=0.5)

n_estimator = 10

grd = GradientBoostingClassifier(n_estimators=n_estimator)

grd_enc = OneHotEncoder()

grd_lm = LogisticRegression()

grd.fit(X_train, y_train)

grd_enc.fit(grd.apply(X_train)[:, :, 0])

grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)[:, :, 0]), y_train_lr)

y_pred_grd_lm = grd_lm.predict_proba(

grd_enc.transform(grd.apply(X_test)[:, :, 0]))[:, 1]

fpr_grd_lm, tpr_grd_lm, _ = roc_curve(y_test, y_pred_grd_lm)

# The gradient boosted model by itself

y_pred_grd = grd.predict_proba(X_test)[:, 1]

fpr_grd, tpr_grd, _ = roc_curve(y_test, y_pred_grd)

plt.figure(1)

plt.plot([0, 1], [0, 1], 'k--')

# plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')

#plt.plot(fpr_rf, tpr_rf, label='RF')

#plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')

plt.plot(fpr_grd, tpr_grd, label='GBT')

# plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')

plt.xlabel('False positive rate')

plt.ylabel('True positive rate')

plt.title('ROC curve')

plt.legend(loc='best')

plt.show()

plt.figure(2)

plt.xlim(0, 1)

plt.ylim(0.8, 1)

plt.plot([0, 1], [0, 1], 'k--')

#plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')

#plt.plot(fpr_rf, tpr_rf, label='RF')

#plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')

plt.plot(fpr_grd, tpr_grd, label='GBT')

#plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')

plt.xlabel('False positive rate')

plt.ylabel('True positive rate')

plt.title('ROC curve (zoomed in at top left)')

plt.legend(loc='best')

plt.show()

'''

params = {'n_estimators': 2000, 'max_depth': 3, 'subsample': 0.5,

'learning_rate': 0.015, 'min_samples_leaf': 1, 'random_state': 3}

clf = GradientBoostingClassifier(**params)

print(X_train.shape)

clf.fit(X_train, y_train)

acc = clf.score(X_test, y_test)

pred = clf.predict(X_test)

# print(pred,y_test)

acc1 = 0

tp = 0

fn = 0

fp = 0

for i in range(50):

if pred[i] == y_test[i]:

acc1 = acc1 + 1

if pred[i] == 1:

tp = tp + 1

elif pred[i] == 0 and y_test[i] == 1:

fn = fn + 1

else:

fp = fp + 1

re = tp / (tp + fn)

pre = tp / (tp + fp)

fscore = 2 * re * pre / (re + pre)

print(re, pre, fscore)

print("Accuracy: {:.4f}".format(acc))