Python_sklearn机器学习库学习笔记（三）logistic regression（逻辑回归） - 金秀

Python_sklearn机器学习库学习笔记（三）logistic regression（逻辑回归）

# 逻辑回归

## 逻辑回归处理二元分类

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

import numpy as np
plt.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 pd
df=pd.read_csv(\'SMSSpamCollection\',delimiter=\'\t\',header=None)
df.head()

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from 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_matrix
import matplotlib.pyplot as plt
y_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 pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.cross_validation import train_test_split,cross_val_score

df=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 inline
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.cross_validation import train_test_split, cross_val_score

df[\'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), precisions
recalls = cross_val_score(classifier, X_train, y_train, cv=5, scoring=\'recall\')
print u\'召回率：\', np.mean(recalls), recalls
plt.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 np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.cross_validation import train_test_split,cross_val_score
from sklearn.metrics import roc_curve,auc


df[\'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 pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score, recall_score, accuracy_score

pipeline = 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 pd
df=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 pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report,accuracy_score,confusion_matrix
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV

pipeline=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