# 逻辑回归

## 逻辑回归处理二元分类

%matplotlib inlineimport matplotlib.pyplot as plt#显示中文from matplotlib.font_manager import FontPropertiesfont=FontProperties(fname=r"c:\windows\fonts\msyh.ttc", size=10)

import numpy as npplt.figure()plt.axis([-6,6,0,1])plt.grid(True)X=np.arange(-6,6,0.1)y=1/(1+np.e**(-X))plt.plot(X,y,'b-')

## 垃圾邮件分类

import pandas as pddf=pd.read_csv('SMSSpamCollection',delimiter='\t',header=None)df.head()

from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.cross_validation import train_test_split#用pandas加载数据.csv文件，然后用train_test_split分成训练集（75%）和测试集（25%）：X_train_raw, X_test_raw, y_train, y_test = train_test_split(df[1],df[0])#我们建一个TfidfVectorizer实例来计算TF-IDF权重：vectorizer=TfidfVectorizer()X_train=vectorizer.fit_transform(X_train_raw)X_test=vectorizer.transform(X_test_raw)#LogisticRegression同样实现了fit()和predict()方法classifier=LogisticRegression()classifier.fit(X_train,y_train)predictions=classifier.predict(X_test)for i ,prediction in enumerate(predictions[-5:]):    print '预测类型：%s.信息：%s' %(prediction,X_test_raw.iloc[i])

输出结果：

预测类型：ham.信息：Waiting in e car 4 my mum lor. U leh? Reach home already?

预测类型：ham.信息：Dear got train and seat mine lower seat

预测类型：spam.信息：I just really need shit before tomorrow and I know you won't be awake before like 6

预测类型：ham.信息：What should i eat fo lunch senor

预测类型：ham.信息：645

## 二元分类效果评估方法

#混淆矩阵from sklearn.metrics import confusion_matriximport matplotlib.pyplot as plty_test = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]y_pred = [0, 1, 0, 0, 0, 0, 0, 1, 1, 1]confusion_matrix=confusion_matrix(y_test,y_pred)print confusion_matrix

plt.matshow(confusion_matrix)plt.title(u'混淆矩阵')plt.colorbar()plt.ylabel(u'实际类型')plt.xlabel(u'预测类型')plt.show()

## 准确率

import pandas as pdimport numpy as npfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.cross_validation import train_test_split,cross_val_scoredf=pd.read_csv('SMSSpamCollection',delimiter='\t',names=["label","message"])X_train_raw, X_test_raw, y_train, y_test = train_test_split(df['message'], df['label'])vectorizer=TfidfVectorizer()X_train=vectorizer.fit_transform(X_train_raw)X_test=vectorizer.transform(X_test_raw)classifier=LogisticRegression()classifier.fit(X_train,y_train)scores=cross_val_score(classifier,X_train,y_train,cv=5)print '准确率',np.mean(scores),scores

输出结果：

准确率 0.954292731612 [ 0.96057348  0.96052632  0.94617225  0.95808383  0.94610778]

 ## 精确率和召回率

scikit-learn结合真实类型数据，提供了一个函数来计算一组预测值的精确率和召回率。

%matplotlib inlineimport numpy as npimport pandas as pdfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.cross_validation import train_test_split, cross_val_scoredf['label']=pd.factorize(df['label'])[0]X_train_raw, X_test_raw, y_train, y_test = train_test_split(df['message'],df['label'])vectorizer = TfidfVectorizer()X_train = vectorizer.fit_transform(X_train_raw)X_test = vectorizer.transform(X_test_raw)classifier = LogisticRegression()classifier.fit(X_train, y_train)precisions = cross_val_score(classifier, X_train, y_train, cv=5, scoring='precision')print u'精确率：', np.mean(precisions), precisionsrecalls = cross_val_score(classifier, X_train, y_train, cv=5, scoring='recall')print u'召回率：', np.mean(recalls), recallsplt.scatter(recalls, precisions)

输出结果：

精确率： 0.990243902439 [ 1.          0.95121951  1.          1.          1.        ]

召回率： 0.691498103666 [ 0.65486726  0.69026549  0.69911504  0.71681416  0.69642857]

 

## 计算综合评价指标

fls=cross_val_score(classifier,X_train,y_train,cv=5,scoring='f1')print '综合指标评价',np.mean(fls),fls　　

输出结果：

综合指标评价 0.791683999687 [ 0.76243094  0.79781421  0.8         0.77094972  0.82722513] ## ROC AUC

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.cross_validation import train_test_split,cross_val_scorefrom sklearn.metrics import roc_curve,aucdf['label']=pd.factorize(df['label'])[0]X_train_raw, X_test_raw, y_train, y_test = train_test_split(df['message'],df['label'])vectorizer = TfidfVectorizer()X_train = vectorizer.fit_transform(X_train_raw)X_test = vectorizer.transform(X_test_raw)classifier = LogisticRegression()classifier.fit(X_train, y_train)predictions=classifier.predict_proba(X_test)#每一类的概率false_positive_rate, recall, thresholds = roc_curve(y_test, predictions[:, 1])roc_auc=auc(false_positive_rate,recall)plt.title('Receiver Operating Characteristic')plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' % roc_auc)plt.legend(loc='lower right')plt.plot([0,1],[0,1],'r--')plt.xlim([0.0,1.0])plt.ylim([0.0,1.0])plt.ylabel('Recall')plt.xlabel('Fall-out')plt.show()

 

## 网格搜索

import pandas as pdfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.grid_search import GridSearchCVfrom sklearn.pipeline import Pipelinefrom sklearn.cross_validation import train_test_splitfrom sklearn.metrics import precision_score, recall_score, accuracy_scorepipeline = Pipeline([('vect', TfidfVectorizer(stop_words='english')),('clf', LogisticRegression())])parameters = {'vect__max_df': (0.25, 0.5, 0.75),'vect__stop_words': ('english', None),'vect__max_features': (2500, 5000, 10000, None),'vect__ngram_range': ((1, 1), (1, 2)),'vect__use_idf': (True, False),'vect__norm': ('l1', 'l2'),'clf__penalty': ('l1', 'l2'),'clf__C': (0.01, 0.1, 1, 10),}grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='accuracy', cv=3)df=pd.read_csv('SMSSpamCollection',delimiter='\t',names=["label","message"])df['label']=pd.factorize(df['label'])[0]X_train, X_test, y_train, y_test = train_test_split(df['message'],df['label'])grid_search.fit(X_train, y_train)print('最佳效果：%0.3f' % grid_search.best_score_)

输出结果;

最佳效果：0.986

print '最优参数组合'best_parameters=grid_search.best_estimator_.get_params()

for param_name in sorted(parameters.keys()):    print '\t%s:%r' %(param_name,best_parameters[param_name])    predictions=grid_search.predict(X_test)print '准确率：',accuracy_score(y_test,predictions)print '精确率：',precision_score(y_test,predictions)print '召回率：',recall_score(y_test,predictions)

输出结果:

clf__C:10

clf__penalty:'l2'

vect__max_df:0.25

vect__max_features:2500

vect__ngram_range:(1, 2)

vect__norm:'l2'

vect__stop_words:None

vect__use_idf:True

准确率： 0.979899497487

精确率： 0.974683544304

召回率： 0.865168539326

# logistics 多分类

import pandas as pddf=pd.read_csv("logistic_data/train.tsv",header=0,delimiter='\t')print df.count()print df.head()df.Phrase.head(10)df.Sentiment.describe()df.Sentiment.value_counts()df.Sentiment.value_counts()/df.Sentiment.count()

import pandas as pdfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.linear_model.logistic import LogisticRegressionfrom sklearn.cross_validation import train_test_splitfrom sklearn.metrics import classification_report,accuracy_score,confusion_matrixfrom sklearn.pipeline import Pipelinefrom sklearn.grid_search import GridSearchCVpipeline=Pipeline([        ('vect',TfidfVectorizer(stop_words='english')),        ('clf',LogisticRegression())])parameters={            'vect__max_df':(0.25,0.5),            'vect__ngram_range':((1,1),(1,2)),            'vect__use_idf':(True,False),            'clf__C':(0.1,1,10),           }df=pd.read_csv("logistic_data/train.tsv",header=0,delimiter='\t')X,y=df.Phrase,df.Sentiment.as_matrix()X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.5)grid_search=GridSearchCV(pipeline,parameters,n_jobs=-1,verbose=1,scoring="accuracy")grid_search.fit(X_train,y_train)print u'最佳效果：%0.3f'%grid_search.best_score_print u'最优参数组合：'best_parameters=grid_search.best_estimator_.get_params()for param_name in sorted(parameters.keys()):    print '\t%s:%r'%(param_name,best_parameters[param_name])

数据结果：

Fitting 3 folds for each of 24 candidates, totalling 72 fits

 [Parallel(n_jobs=-1)]: Done 46 tasks | elapsed: 2.0min [Parallel(n_jobs=-1)]: Done 72 out of 72 | elapsed: 4.5min finished

 最佳效果：0.619 最优参数组合： clf__C:10 vect__max_df:0.25 vect__ngram_range:(1, 2) vect__use_idf:False

 ## 多类分类效果评估

predictions=grid_search.predict(X_test)print u'准确率',accuracy_score(y_test,predictions)print u'混淆矩阵',confusion_matrix(y_test,predictions)print u'分类报告',classification_report(y_test,predictions)

数据结果：

准确率 0.636614122773

混淆矩阵 [[ 1133  1712   595    67     1]

[  919  6136  6006   553    35]

[  213  3212 32637  3634   138]

[   22   420  6548  8155  1274]

[    4    45   546  2411  1614]]

分类报告              precision    recall  f1-score   support

          0       0.49      0.32      0.39      3508

          1       0.53      0.45      0.49     13649

          2       0.70      0.82      0.76     39834

          3       0.55      0.50      0.52     16419

          4       0.53      0.35      0.42      4620

avg / total       0.62      0.64      0.62     78030