我试图对此处的葡萄酒数据集进行分类 - http://archive.ics.uci.edu/ml/datasets/Wine+Quality 使用逻辑回归（使用method ='bfgs'和l1 norm）并捕获奇异值矩阵错误（提升LinAlgError（'奇异矩阵'），尽管满级[我使用np.linalg.matrix_rank进行测试（数据[ train_cols] .values）]。
这就是我得出的结论，即某些功能可能是其他功能的线性组合。为此，我尝试使用网格搜索/ LinearSVC - 我得到下面的错误，以及我的代码＆amp;数据集。
我可以看到只有6/7个功能实际上是“独立的” - 我在比较x_train_new [0]和x_train的行时会解释（所以我可以得到哪些列是多余的）
# Train & test DATA CREATION
from sklearn.svm import LinearSVC
import numpy, random
import pandas as pd
df = pd.read_csv("https://github.com/ekta1007/Predicting_wine_quality/blob/master/wine_red_dataset.csv")
#,skiprows=0, sep=',')
df=df.dropna(axis=1,how='any') # also tried how='all' - still get NaN errors as below
header=list(df.columns.values) # or df.columns
X = df[df.columns - [header[-1]]] # header[-1] = ['quality'] - this is to make the code genric enough
Y = df[header[-1]] # df['quality']
rows = random.sample(df.index, int(len(df)*0.7)) # indexing the rows that will be picked in the train set
x_train, y_train = X.ix[rows],Y.ix[rows] # Fetching the data frame using indexes
x_test,y_test = X.drop(rows),Y.drop(rows)
# Training the classifier using C-Support Vector Classification.
clf = LinearSVC(C=0.01, penalty="l1", dual=False) #,tol=0.0001,fit_intercept=True, intercept_scaling=1)
clf.fit(x_train, y_train)
x_train_new = clf.fit_transform(x_train, y_train)
#print x_train_new #works
clf.predict(x_test) # does NOT work and gives NaN errors for some x_tests
clf.score(x_test, y_test) # Does NOT work
clf.coef_ # Works, but I am not sure, if this is OK, given huge NaN's - or does the coef's get impacted ?
clf.predict(x_train)
552 NaN
209 NaN
427 NaN
288 NaN
175 NaN
427 NaN
748 7
552 NaN
429 NaN
[... and MORE]
Name: quality, Length: 1119
clf.predict(x_test)
76 NaN
287 NaN
420 7
812 NaN
443 7
420 7
430 NaN
373 5
624 5
[..and More]
Name: quality, Length: 480
奇怪的是，当我运行clf.predict（x_train）时，我仍然看到一些NaN - 我做错了什么？毕竟模型是用这个训练的，这不应该发生，对吧？ /强>
根据这个帖子，我还检查了我的csv文件中没有空白（虽然我将“质量”重新标记为5和7标签（从范围（3,10）） How to fix "NaN or infinity" issue for sparse matrix in python?
此外 - 这是x_test＆amp;的数据类型y_test /火车...
x_test
<class 'pandas.core.frame.DataFrame'>
Int64Index: 480 entries, 1 to 1596
Data columns:
alcohol 480 non-null values
chlorides 480 non-null values
citric acid 480 non-null values
density 480 non-null values
fixed acidity 480 non-null values
free sulfur dioxide 480 non-null values
pH 480 non-null values
residual sugar 480 non-null values
sulphates 480 non-null values
total sulfur dioxide 480 non-null values
volatile acidity 480 non-null values
dtypes: float64(11)
y_test
1 5
10 5
18 5
21 5
30 5
31 7
36 7
40 5
50 5
52 7
53 5
55 5
57 5
60 5
61 5
[..And MORE]
Name: quality, Length: 480
最后..
clf.score(x_test, y_test)
Traceback (most recent call last):
File "<pyshell#31>", line 1, in <module>
clf.score(x_test, y_test)
File "C:\Python27\lib\site-packages\sklearn\base.py", line 279, in score
return accuracy_score(y, self.predict(X))
File "C:\Python27\lib\site-packages\sklearn\metrics\metrics.py", line 742, in accuracy_score
y_true, y_pred = check_arrays(y_true, y_pred)
File "C:\Python27\Lib\site-packages\sklearn\utils\validation.py", line 215, in check_arrays
File "C:\Python27\Lib\site-packages\sklearn\utils\validation.py", line 18, in _assert_all_finite
ValueError: Array contains NaN or infinity.
#I also explicitly checked for NaN's as here -:
for i in df.columns:
df[i].isnull()
提示：还请提一下，根据我的使用案例，我对使用LinearSVC的思考过程是否正确，还是应该使用网格搜索？
免责声明：此代码的部分内容是基于StackOverflow和其他来源的类似上下文中的建议构建的 - 我的实际用例只是尝试访问此方法是否适合我的方案。就是这样。
答案 0 :(得分：2)
这很有用。我必须真正改变的是使用x_test * .values *以及其余的pandas Dataframes（x_train，y_train，y_test）。正如所指出的，唯一的原因是pandas df和scikit-learn（使用numpy数组）之间的不兼容性
#changing your Pandas Dataframe elegantly to work with scikit-learn by transformation to numpy arrays
>>> type(x_test)
<class 'pandas.core.frame.DataFrame'>
>>> type(x_test.values)
<type 'numpy.ndarray'>
这个黑客来自这篇帖子http://python.dzone.com/articles/python-making-scikit-learn-and和@AndreasMueller--他指出了这种不一致。