[Example of Sklearn] - 分类对比

refrence ：http://cloga.info/python/2014/02/07/classify_use_Sklearn/

 

加载数据集

这里我使用pandas来加载数据集，数据集采用kaggle的titanic的数据集，下载train.csv。

import pandas as pd

df = pd.read_csv('train.csv')

df = df.fillna(0) #将缺失值都替换为0

df.head()

 

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22	1	0	A/5 21171	7.2500	0	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26	0	0	STON/O2. 3101282	7.9250	0	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35	0	0	373450	8.0500	0	S

5 rows × 12 columns

len(df)

891

 

可以看到训练集中共有891条记录，有12个列（其中一列Survived是目标分类）。将数据集分为特征集和目标分类集，两个DataFrame。

exc_cols = [u'PassengerId', u'Survived', u'Name']

cols = [c for c in df.columns if c not in exc_cols]

x = df.ix[:,cols]

y = df['Survived'].values

 

由于Sklearn为了效率，接受的特征数据类型是dtype=np.float32以便获得最佳的算法效率。因此，对于类别类型的特征就需要转化为向量。Sklearn 提供了DictVectorizer类将类别的特征转化为向量。DictVectorizer接受记录的形式为字典的列表。因此需要用pandas的to_dict方法转 换DataFrame。

from sklearn.feature_extraction import DictVectorizer

v = DictVectorizer()

x = v.fit_transform(x.to_dict(outtype='records')).toarray()

 

让我们比较一下同一个实例的原始信息及向量化后的结果。

print 'Vectorized:', x[10]

print 'Unvectorized:', v.inverse_transform(x[10])

Vectorized: [ 4.  0.  0. ...,  0.  0.  0.]

Unvectorized: [{'Fare': 16.699999999999999, 'Name=Sandstrom, Miss. Marguerite Rut': 1.0, 'Embarked=S': 1.0, 'Age': 4.0, 'Sex=female': 1.0, 'Parch': 1.0, 'Pclass': 3.0, 'Ticket=PP 9549': 1.0, 'Cabin=G6': 1.0, 'SibSp': 1.0, 'PassengerId': 11.0}]

 

如果分类的标签也是字符的，那么就还需要用LabelEncoder方法进行转化。

将数据集分成训练集和测试集。

from sklearn.cross_validation import train_test_split

data_train, data_test, target_train, target_test = train_test_split(x, y)

len(data_train)

668

len(data_test)

223

 

默认是以数据集的25%作为测试集。到这里为止，用于训练和测试的数据集都已经准备好了。

用Sklearn做判别分析

Sklearn训练模型的基本流程

Model = EstimatorObject()

Model.fit(dataset.data, dataset.target)

dataset.data = dataset

dataset.target = labels

Model.predict(dataset.data)

 

这里选择朴素贝叶斯、决策树、随机森林和SVM来做一个对比。

from sklearn import cross_validation

from sklearn.naive_bayes import GaussianNB

from sklearn import tree

from sklearn.ensemble import RandomForestClassifier

from sklearn import svm

import datetime

estimators = {}

estimators['bayes'] = GaussianNB()

estimators['tree'] = tree.DecisionTreeClassifier()

estimators['forest_100'] = RandomForestClassifier(n_estimators = 100)

estimators['forest_10'] = RandomForestClassifier(n_estimators = 10)

estimators['svm_c_rbf'] = svm.SVC()

estimators['svm_c_linear'] = svm.SVC(kernel='linear')

estimators['svm_linear'] = svm.LinearSVC()

estimators['svm_nusvc'] = svm.NuSVC()

 

首先是定义各个model所用的算法。

for k in estimators.keys():

    start_time = datetime.datetime.now()

    print '----%s----' % k

    estimators[k] = estimators[k].fit(data_train, target_train)

    pred = estimators[k].predict(data_test)

    print("%s Score: %0.2f" % (k, estimators[k].score(data_test, target_test)))

    scores = cross_validation.cross_val_score(estimators[k], data_test, target_test, cv=5)

    print("%s Cross Avg. Score: %0.2f (+/- %0.2f)" % (k, scores.mean(), scores.std() * 2))

    end_time = datetime.datetime.now()

    time_spend = end_time - start_time

    print("%s Time: %0.2f" % (k, time_spend.total_seconds()))

 

----svm_c_rbf----

svm_c_rbf Score: 0.63

svm_c_rbf Cross Avg. Score: 0.54 (+/- 0.18)

svm_c_rbf Time: 1.67

----tree----

tree Score: 0.81

tree Cross Avg. Score: 0.75 (+/- 0.09)

tree Time: 0.90

----forest_10----

forest_10 Score: 0.83

forest_10 Cross Avg. Score: 0.80 (+/- 0.10)

forest_10 Time: 0.56

----forest_100----

forest_100 Score: 0.84

forest_100 Cross Avg. Score: 0.80 (+/- 0.14)

forest_100 Time: 5.38

----svm_linear----

svm_linear Score: 0.74

svm_linear Cross Avg. Score: 0.65 (+/- 0.18)

svm_linear Time: 0.15

----svm_nusvc----

svm_nusvc Score: 0.63

svm_nusvc Cross Avg. Score: 0.55 (+/- 0.21)

svm_nusvc Time: 1.62

----bayes----

bayes Score: 0.44

bayes Cross Avg. Score: 0.47 (+/- 0.07)

bayes Time: 0.16

----svm_c_linear----

svm_c_linear Score: 0.83

svm_c_linear Cross Avg. Score: 0.79 (+/- 0.14)

svm_c_linear Time: 465.57

这里通过算法的score方法及cross_validation来计算预测的准确性。

可以看到准确性比较高的算法需要的时间也会增加。性价比较高的算法是随机森林。 让我们用kaggle给出的test.csv的数据集测试一下。

test = pd.read_csv('test.csv')

test = test.fillna(0) 

test_d = test.to_dict(outtype='records')

test_vec = v.transform(test_d).toarray()

 

这里需要注意的是test的数据也需要经过同样的DictVectorizer转换。

for k in estimators.keys():

    estimators[k] = estimators[k].fit(x, y)

    pred = estimators[k].predict(test_vec)

    test['Survived'] = pred

    test.to_csv(k + '.csv', cols=['Survived', 'PassengerId'], index=False)

 

好了，向Kaggle提交你的结果吧~