Question

我有大约100个样本，有35个特征。我需要从一系列特征中找到最佳特征。这些特征是患有特定类型癌症的患者的临床和基因组细节。我需要找到什么是最好的组合给出高ACC。

这是我的代码：

import sys

import csv

import pandas as pd

from mlxtend.feature_selection import SequentialFeatureSelector as SFS

from sklearn.neighbors import KNeighborsClassifier

from sklearn.cross_validation import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.pipeline import make_pipeline

with open("feature matrix", "r") as f:

for line in f:
        entries = line.split(',')
    for i in entries:
    if i.decode('utf-8')[0]==u'-':
        print 0-float(i.decode('utf-8')[1:])
            X = i.decode('utf-8')
y='survival'

X.shape 

y.shape

X_train, X_test, y_train, y_test= train_test_split(X, y, 
                                               stratify=y,
                                               test_size=0.3,
                                               random_state=1)


knn = KNeighborsClassifier(n_neighbors=2)

sfs1 = SFS(estimator=knn,

       k_features=(3,35),

       forward=True, 

       floating=False, 

       scoring='accuracy',

       print_progress=False,

       cv=5)

 pipe = make_pipeline(StandardScaler(), sfs1)

 pipe.fit(x_train,y_train)

 print('best combination (ACC: %.3f): %s\n' % 
 (sfs1.k_score_,sfs1.k_feature_idx_))

当我运行代码时，我收到以下错误：回溯（最近一次调用最后一次）：

File "sample_test.py", line 21, in <module>

X.shape 

AttributeError: 'unicode' object has no attribute 'shape'

我还没有足够的经验来了解故障排除的下一步是什么。有更好的方法来编写代码吗？

AttributeError：＆＃39; unicode＆＃39;对象没有属性＆＃39; shape＆＃39;

0 个答案: