Python的决策树和随机森林

决策树模型是一种简单易用的非参数分类器。它不需要对数据有任何的先验假设，计算速度较快，结果容易解释，而且稳健性强，对噪声数据和缺失数据不敏感。下面示范用titanic中的数据集为做决策树分类，目标变量为survive。

第一步：读取数据

%pylab inline
import pandas as pd
df = pd.read_csv('titanic.csv')
df.head()
#df.info()

Populating the interactive namespace from numpy and matplotlib

第二步：数据整理

只取出三个自变量
将将age缺失值进行补全
将pclass变量转为三个哑变量
将sex转为0-1变量

subdf = df[['pclass','sex','age']]
y = df.survived
# sklearn中的Imputer也可以
age = subdf['age'].fillna(value=subdf.age.mean())
# sklearn OneHotEncoder也可以
pclass = pd.get_dummies(subdf['pclass'],prefix='pclass')
sex = (subdf['sex']=='male').astype('int')
X = pd.concat([pclass,age,sex],axis=1)
X.head()

第三步：建模

数据切分为train和test

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)

使用决策树观察在检验集表现

from sklearn import tree
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3,min_samples_leaf=5)
clf = clf.fit(X_train,y_train)
print "准确率为：{:.2f}".format(clf.score(X_test,y_test))

准确率为：0.83

观察各变量的重要性

clf.feature_importances_

array([ 0.08398076,  0.        ,  0.23320717,  0.10534824,  0.57746383])

使用更多指标来评估模型

from sklearn import metrics
def measure_performance(X,y,clf, show_accuracy=True, 
                        show_classification_report=True, 
                        show_confusion_matrix=True):
    y_pred=clf.predict(X)   
    if show_accuracy:
        print "Accuracy:{0:.3f}".format(metrics.accuracy_score(y,y_pred)),"\n"

    if show_classification_report:
        print "Classification report"
        print metrics.classification_report(y,y_pred),"\n"
        
    if show_confusion_matrix:
        print "Confusion matrix"
        print metrics.confusion_matrix(y,y_pred),"\n"
        
measure_performance(X_test,y_test,clf, show_classification_report=True, show_confusion_matrix=True)

Accuracy:0.834 

Classification report
             precision    recall  f1-score   support

          0       0.85      0.88      0.86       134
          1       0.81      0.76      0.79        89

avg / total       0.83      0.83      0.83       223


Confusion matrix
[[118  16]
 [ 21  68]]

使用交叉验证来评估模型

from sklearn import cross_validation
scores1 = cross_validation.cross_val_score(clf, X, y, cv=10)
scores1

array([ 0.82222222,  0.82222222,  0.7752809 ,  0.87640449,  0.82022472,
        0.76404494,  0.7752809 ,  0.76404494,  0.83146067,  0.78409091])

第三步：决策树画图

需要安装GraphViz'

import pydot,StringIO
dot_data = StringIO.StringIO()

tree.export_graphviz(clf, out_file=dot_data, feature_names=['age','sex','1st_class','2nd_class','3rd_class']) 
dot_data.getvalue()
pydot.graph_from_dot_data(dot_data.getvalue())
graph = pydot.graph_from_dot_data(dot_data.getvalue()) 
#graph.write_png('titanic.png') 
#from IPython.core.display import Image 
#Image(filename='titanic.png')

第四步：使用随机森林进行比较

from sklearn.ensemble import RandomForestClassifier
clf2 = RandomForestClassifier(n_estimators=1000,random_state=33)
clf2 = clf2.fit(X_train,y_train)
scores2 = cross_validation.cross_val_score(clf2,X, y, cv=10)
clf2.feature_importances_

array([ 0.05526809,  0.02266161,  0.08156048,  0.46552672,  0.37498309])

scores2.mean(), scores1.mean()

(0.81262938372488946, 0.80352769265690616)

Python贝叶斯文本分类识别垃圾短信

1、读取数据，type表示短信类别，text是短信内容

%pylab inline
import pandas as pd
import numpy as np
df = pd.read_csv('sms_spam.csv')
df.head()

Populating the interactive namespace from numpy and matplotlib

2、使用sklearn包转换文本为结构化数据，将矩阵分切为训练集和检验集

CountVectorizer负责将文档转为文档词频矩阵，重要的参数有如下几个：

ngram_range：ngrame频率范围，如果需要识别词组的话需要设置
stop_words：停词列表
token_pattern：分词的字符模式，默认空格
max_df：词频上限，超过该值的词项不作为特征，即过滤常用词
min_df：词频下限，低于该值的词项不作为特征
max_features：只选择词频较高的几个作为特征

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(ngram_range=(1,1),stop_words='english',lowercase=True,min_df=1)
X = vectorizer.fit_transform(df.text) 
y = (df.type == 'spam').values.astype(int)

TfidfVectorizer则可以计算tfidf值，而非仅仅文档词频矩阵

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(ngram_range=(1,1),stop_words='english',lowercase=True,min_df=1)
X = vectorizer.fit_transform(df.text)

3、将数据切分为train和test

from sklearn.cross_validation import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X, y)

4、使用贝叶斯分类器进行训练

重要的参数alpha用于设置平滑系数

from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB(alpha =1).fit(xtrain, ytrain)

5、观察分类效果

training_accuracy = clf.score(xtrain, ytrain)
test_accuracy = clf.score(xtest, ytest)
print "训练集准确率:  {:.2f}".format(training_accuracy)
print "检验集准确率:  {:.2f}".format(test_accuracy)

训练集准确率:  0.98
检验集准确率:  0.97

6、使用CV选择最优参数，参数为0.2

from sklearn import svm, grid_search
nb = MultinomialNB()
parameters = {'alpha':np.linspace(0,10,101)}
clf = grid_search.GridSearchCV(nb, parameters)
clf.fit(X, y)

GridSearchCV(cv=None,
       estimator=MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True),
       fit_params={}, iid=True, loss_func=None, n_jobs=1,
       param_grid={'alpha': array([  0. ,   0.1, ...,   9.9,  10. ])},
       pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,
       verbose=0)

print "最佳参数:  {:.3f}".format(clf.best_params_['alpha'] )
print "最佳准确率:  {:.3f}".format(clf.best_score_)

最佳参数:  0.200
最佳准确率:  0.984

accuracy = [t[1] for t in clf.grid_scores_]
para = [t[0]['alpha'] for t in clf.grid_scores_]

import matplotlib.pylab as plt
accuracy = [t[1] for t in clf.grid_scores_]
para = [t[0]['alpha'] for t in clf.grid_scores_]
plt.plot(para,accuracy,lw=3)

[]

python中的线性回归

对于统计模型来说，最简单也最经典的模型要数线性回归模型，它可以满足统计建模的所有标准流程，并且适用范围也非常广。R里面是使用lm函数来做回归，而在python里面有几个包都提供了这一功能，首先介绍sklearn包中的回归函数，然后介绍statsmodels包中的回归函数。前者适合于机器学习中的预测，不需要太多中间结果的观察。后者适合于分析，需要对中间结果，例如系数，残差以及效果做判断的时候使用。

第一步：加载各种包

%pylab inline
import pandas as pd
import matplotlib.pylab as ply
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import statsmodels.formula.api as sm

Populating the interactive namespace from numpy and matplotlib

第二步：读取数据并画图

df = pd.read_csv('iris.csv')
lmdf = df[['Petal_Length','Petal_Width']]
lmdf.plot(kind='scatter',x=0,y=1)

<matplotlib.axes.AxesSubplot at 0x119b1f110>

第三步：使用sklearn包中的函数进行回归

# 建立回归对象
linear_sk = LinearRegression()
X=lmdf[['Petal_Length']]
y=lmdf['Petal_Width']
linear_sk.fit(X,y)
linear_fit.intercept_,linear_fit.coef_  # coef

(-0.36651404521672837, array([ 0.41641913]))

linear_fit.score(X,y) # R2

0.92690122792200369

yhat = linear_fit.predict(X=lmdf[['Petal_Length']])
mean_squared_error(lmdf['Petal_Width'],yhat)  #MSE

0.04228994631948424

第四步：观察回归效果

plt.scatter(lmdf['Petal_Length'],lmdf['Petal_Width'])
plt.plot(lmdf['Petal_Length'],yhat)

[<matplotlib.lines.Line2D at 0x11991a790>]

使用statmodels包的过程和结果，可以使用和R类似的公式实施

linear_model = sm.ols(formula='Petal_Width ~ Petal_Length', data=lmdf)
results = linear_model.fit()
results.summary()

	survived	pclass	name	sex	age	sibsp	ticket	fare	cabin	embarked
0	0	3	Braund, Mr. Owen Harris	male	22	1	A/5 21171	7.2500	NaN	S
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38	1	PC 17599	71.2833	C85	C
2	1	3	Heikkinen, Miss. Laina	female	26	0	STON/O2. 3101282	7.9250	NaN	S
3	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	113803	53.1000	C123	S
4	0	3	Allen, Mr. William Henry	male	35	0	373450	8.0500	NaN	S

	type	text
0	ham	Hope you are having a good week. Just checking in
1	ham	K..give back my thanks.
2	ham	Am also doing in cbe only. But have to pay.
3	spam	complimentary 4 STAR Ibiza Holiday or £10,000 ...
4	spam	okmail: Dear Dave this is your final notice to...

Dep. Variable:	Petal_Width	R-squared:	0.927
Model:	OLS	Adj. R-squared:	0.926
Method:	Least Squares	F-statistic:	1877.
Date:	Sat, 18 Oct 2014	Prob (F-statistic):	5.78e-86
Time:	18:00:43	Log-Likelihood:	24.400
No. Observations:	150	AIC:	-44.80
Df Residuals:	148	BIC:	-38.78
Df Model:	1

	coef	std err	t	P>\|t\|	[95.0% Conf. Int.]
Intercept	-0.3665	0.040	-9.188	0.000	-0.445 -0.288
Petal_Length	0.4164	0.010	43.320	0.000	0.397 0.435

Omnibus:	5.498	Durbin-Watson:	1.461
Prob(Omnibus):	0.064	Jarque-Bera (JB):	5.217
Skew:	0.353	Prob(JB):	0.0736
Kurtosis:	3.579	Cond. No.	10.3

	pclass_1	pclass_3	age	sex
0	0	1	22	1
1	1	0	38	0
2	0	1	26	0
3	1	0	35	0
4	0	1	35	1

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