用Sklearn做判别分析(分类)

来自:http://cloga.info/python/2014/02/07/classify_use_Sklearn/#wat_e_12612920-6fe4-464e-a2b0-3b1f13c1a4f6_zss_

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~

你可以查看本文的ipython notebook版本

  • python 15
  • Sklearn 1
  • Python 13

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