使用Titanic 数据集，通过特征筛选的方法一步步提升决策树的预测性能

运行python机器学习及实践代码59会出错，本人经过调试对其进行改进，主要原因是因为python 的版本不同，我的是python3

源代码：

import pandas as pdtitanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')y = titanic['survived']#将survived属性列抽取出来赋给y作为标签值x = titanic.drop(['row.names', 'name', 'survived'], axis=1)#将row.names,names,survived三列去掉，不作为属性特征x.info()x['age'].fillna(x['age'].mean(), inplace=True)#age属性列有些值缺少了，采用这一属性的其他值的平均值来进行填充x.fillna('UNKNOWN', inplace=True)#对于其他属性值的缺少，全部用unknown来填充from sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)from sklearn.feature_extraction import DictVectorizervec = DictVectorizer()x_train = vec.fit_transform(x_train.to_dict(orient='record'))x_test = vec.transform(x_test.to_dict(orient='record'))print(len(vec.feature_names_))from sklearn.tree import DecisionTreeClassifierdt = DecisionTreeClassifier(criterion='entropy')dt.fit(x_train, y_train)print(dt.score(x_test, y_test))#从sklearn导入特征筛选器from sklearn import feature_selectionfs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=20)x_train_fs = fs.fit_transform(x_train, y_train)dt.fit(x_train_fs, y_train)x_test_fs = fs.transform(x_test)print(dt.score(x_test_fs, y_test))from sklearn.model_selection import cross_val_scoreimport numpy as nppercentiles = range(1, 100, 2)results = []for i in percentiles:    fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=i)    x_train_fs = fs.fit_transform(x_train, y_train)    scores = cross_val_score(dt, x_train_fs, y_train, cv=5)    results = np.append(results, scores.mean())print(results)opt = np.where(results == results.max())[0]print('Optimal number of features %d'%percentiles[opt])import pylab as plpl.plot(percentiles, results)pl.xlabel('percentiles of features')pl.ylabel('accuracy')pl.show()from sklearn import feature_selectionfs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=7)x_train_fs = fs.fit_transform(x_train, y_train)dt.fit(x_train_fs, y_train)x_test_fs = fs.transform(x_test)dt.score(x_test_fs, y_test)

运行源代码会出现如下错误：

Traceback (most recent call last):  File "D:/Python362/a_机器学习及实战/features_choose.py", line 51, in <module>    print('Optimal number of features %d'%percentiles[opt])TypeError: only integer scalar arrays can be converted to a scalar index

经过多次调试错误代码锁定在opt = np.where(results == results.max())[0]上，一下代码是本人对其进行修改并注释的代码：

import pandas as pdtitanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')y = titanic['survived']#将survived属性列抽取出来赋给y作为标签值x = titanic.drop(['row.names', 'name', 'survived'], axis=1)#将row.names,names,survived三列去掉，不作为属性特征x.info()x['age'].fillna(x['age'].mean(), inplace=True)#age属性列有些值缺少了，采用这一属性的其他值的平均值来进行填充x.fillna('UNKNOWN', inplace=True)#对于其他属性值的缺少，全部用unknown来填充from sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)from sklearn.feature_extraction import DictVectorizervec = DictVectorizer()x_train = vec.fit_transform(x_train.to_dict(orient='record'))x_test = vec.transform(x_test.to_dict(orient='record'))print(len(vec.feature_names_))from sklearn.tree import DecisionTreeClassifierdt = DecisionTreeClassifier(criterion='entropy')dt.fit(x_train, y_train)print(dt.score(x_test, y_test))#从sklearn导入特征筛选器from sklearn import feature_selectionfs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=20)x_train_fs = fs.fit_transform(x_train, y_train)dt.fit(x_train_fs, y_train)x_test_fs = fs.transform(x_test)print(dt.score(x_test_fs, y_test))from sklearn.model_selection import cross_val_scoreimport numpy as nppercentiles = range(1, 100, 2)results = []for i in percentiles:    fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=i)    x_train_fs = fs.fit_transform(x_train, y_train)    scores = cross_val_score(dt, x_train_fs, y_train, cv=5)    results = np.append(results, scores.mean())print(results)#print(results.max())#print(results == results.max())调试所用print(np.where(results == results.max()))#返回的是一个(array([3], dtype=int64),)元组形式的数据，我们需要的是这个results.max的索引，3正是索引，#我们就要想办法把3提取出来，可以看出[3]是一个array也就是矩阵形式的，那么3所在的位置是一行一列，所以在下一步骤做相应的提取opt = np.where(results == results.max())[0][0]#这一句跟源代码有出入，查看文档np.where返回的是 ndarray or tuple of ndarrays类型数据print('Optimal number of features %d'%percentiles[opt])import pylab as plpl.plot(percentiles, results)pl.xlabel('percentiles of features')pl.ylabel('accuracy')pl.show()from sklearn import feature_selectionfs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=7)x_train_fs = fs.fit_transform(x_train, y_train)dt.fit(x_train_fs, y_train)x_test_fs = fs.transform(x_test)dt.score(x_test_fs, y_test)

最终定位结果如下：

<class 'pandas.core.frame.DataFrame'>RangeIndex: 1313 entries, 0 to 1312Data columns (total 8 columns):pclass       1313 non-null objectage          633 non-null float64embarked     821 non-null objecthome.dest    754 non-null objectroom         77 non-null objectticket       69 non-null objectboat         347 non-null objectsex          1313 non-null objectdtypes: float64(1), object(7)memory usage: 82.1+ KB4740.8145896656530.817629179331[ 0.85063904  0.85673057  0.87602556  0.88622964  0.86691404  0.86896516  0.86691404  0.87404659  0.86692435  0.86997526  0.86694496  0.86795506  0.86692435  0.86791383  0.85981241  0.86284271  0.86791383  0.86487322  0.86690373  0.858792    0.86588332  0.86386312  0.87302618  0.86691404  0.86489384  0.86791383  0.87098536  0.87199546  0.86690373  0.87202639  0.87300557  0.87201608  0.86791383  0.87198516  0.86996496  0.87402597  0.86996496  0.86894455  0.86691404  0.86688312  0.86692435  0.87198516  0.86184292  0.86284271  0.8598021   0.8597918   0.86491445  0.85981241  0.86285302  0.85876108]Optimal number of features 7

效果图如下：