chapter11(sklearn)

第十一 講 使用神經網絡進行客戶流失預警

from sklearn.neural_network import MLPClassifier
%matplotlib inline
import os
import numpy as np
from scipy import stats
import pandas as pd
import sklearn.cross_validation as cross_validation
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt

#os.chdir('Q:/data')
pd.set_option('display.max_columns', None)

/home/quant/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

data = pd.read_csv('telecom_churn.csv')
data.head()

	subscriberID	churn	gender	AGE	edu_class	incomeCode	duration	feton	peakMinAv	peakMinDiff	posTrend	negTrend	nrProm	prom	curPlan	avgplan	planChange	posPlanChange	negPlanChange	call_10000
0	19164958	1	0	20	2	12	16	0	113.666667	-8.0	0	1	0	0	1	1	0	0	0	0
1	39244924	1	1	20	0	21	5	0	274.000000	-371.0	0	1	2	1	3	2	2	1	0	1
2	39578413	1	0	11	1	47	3	0	392.000000	-784.0	0	1	0	0	3	3	0	0	0	1
3	40992265	1	0	43	0	4	12	0	31.000000	-76.0	0	1	2	1	3	3	0	0	0	1
4	43061957	1	1	60	0	9	14	0	129.333333	-334.0	0	1	0	0	3	3	0	0	0	0

#隨機抽樣，建立訓練集與測試集
train, test = cross_validation.train_test_split(data, test_size=1000)

from sklearn import preprocessing
#進行極差標準化
train_X = train.ix[:, 3:-1]
test_X = test.ix[:, 3:-1]
scaler = preprocessing.MinMaxScaler().fit(train_X)
train_X = scaler.transform(train_X)
test_X = scaler.transform(test_X)
train_Y = train['churn'].get_values()  # 爲滿足後續(pybrain)建模需要做相應變換
test_Y = test['churn'].get_values()

http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier

clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
                     hidden_layer_sizes=(100), random_state=1)

clf.fit(train_X, train_Y) 

MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
       beta_2=0.999, early_stopping=False, epsilon=1e-08,
       hidden_layer_sizes=100, learning_rate='constant',
       learning_rate_init=0.001, max_iter=200, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
       warm_start=False)

test_Y_pred=clf.predict(test_X)

Index

pd.crosstab(test_Y, test_Y_pred)

col_0	0	1
row_0
0	450	94
1	85	371

from pybrain.tools.validation import Validator

Validator.classificationPerformance( test_Y_pred, test_Y )

0.821

import sklearn.metrics as metrics
print metrics.classification_report(test_Y, test_Y_pred)

             precision    recall  f1-score   support

          0       0.84      0.83      0.83       544
          1       0.80      0.81      0.81       456

avg / total       0.82      0.82      0.82      1000

ROC Curve

test_est_p=clf.predict_proba(test_X)[:,1]
train_est_p=clf.predict_proba(train_X)[:,1]

fpr_test, tpr_test, th_test = metrics.roc_curve(test_Y, test_est_p)
fpr_train, tpr_train, th_train = metrics.roc_curve(train_Y, train_est_p)
plt.figure(figsize=[6,6])
plt.plot(fpr_test, tpr_test,color='red')
plt.plot(fpr_train, tpr_train,color='black')
plt.title('ROC curve')

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[外鏈圖片轉存(img-08vVdNo8-1562750899428)(output_14_1.png)]