python中使用超參數估計法結合特徵篩選的方法提升決策樹的預測性能

import pandas as pd
titanic = pd.read_csv('../Datasets/Breast-Cancer/titanic.txt')


y=titanic['survived']

X = titanic.drop(['row.names','name','survived'],axis=1)

X['age'].fillna(X['age'].mean(),inplace=True)
X.fillna('UNKNOWN',inplace=True)


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)


from sklearn.feature_extraction import DictVectorizer
vec  = 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_))

#使用決策樹模型依靠所有特徵進行預測，並作性能評估
from sklearn.tree import  DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy')
dt.fit(X_train,y_train)
print(dt.score(X_test,y_test))

#從sklearn導入特徵篩選器
from sklearn import feature_selection

fs = 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.cross_validation import cross_val_score
import numpy as np

percentiles = 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 feature %d' %percentiles[opt])
import pylab as pl
pl.plot(percentiles,results)
pl.xlabel('percentiles of features')
pl.ylabel('accuracy')
pl.show()
#使用最佳篩選後的特徵，利用相同配置的模型在測試集上進行性能評估

from sklearn import feature_selection
fs = 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)
print(dt.score(X_test_fs,y_test))

運行結果如下：

Traceback (most recent call last):
  File "D:\Python35\demo\titanic_select.py", line 2, in <module>
    titanic = pd.read_csv('../Datasets/Breast-Cancer/titanic.txt')
  File "D:\Python35\lib\site-packages\pandas\io\parsers.py", line 709, in parser_f
    return _read(filepath_or_buffer, kwds)
  File "D:\Python35\lib\site-packages\pandas\io\parsers.py", line 449, in _read
    parser = TextFileReader(filepath_or_buffer, **kwds)
  File "D:\Python35\lib\site-packages\pandas\io\parsers.py", line 818, in __init__
    self._make_engine(self.engine)
  File "D:\Python35\lib\site-packages\pandas\io\parsers.py", line 1049, in _make_engine
    self._engine = CParserWrapper(self.f, **self.options)
  File "D:\Python35\lib\site-packages\pandas\io\parsers.py", line 1695, in __init__
    self._reader = parsers.TextReader(src, **kwds)
  File "pandas\_libs\parsers.pyx", line 402, in pandas._libs.parsers.TextReader.__cinit__
  File "pandas\_libs\parsers.pyx", line 718, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: File b'../Datasets/Breast-Cancer/titanic.txt' does not exist
>>> 

提示找不到相應的數據集，導入數據集，運行結果如下：

474
0.8267477203647416
0.8206686930091185
[0.85063904 0.85673057 0.87501546 0.88622964 0.86692435 0.87403628
 0.87302618 0.86998557 0.86691404 0.87200577 0.86894455 0.86795506
 0.86692435 0.86691404 0.86285302 0.86689342 0.86083282 0.86387343
 0.86694496 0.8608122  0.86284271 0.86284271 0.86487322 0.86388374
 0.86997526 0.86896516 0.86894455 0.86589363 0.87200577 0.86690373
 0.86588332 0.86996496 0.86996496 0.86588332 0.87404659 0.86994434
 0.86488353 0.87100598 0.87201608 0.86993403 0.87199546 0.86994434
 0.8608122  0.86591424 0.85878169 0.86589363 0.86388374 0.8618223
 0.8598021  0.85981241]

超參數預估結果曲線圖如下：

從圖示中可以看出，當超參數調整爲7時，預估精度達到最高，相應的預估精度結果如下：

Optimal accuarcy of feature is
0.8571428571428571