ValueError:阵列在线性期间_ASSERT_ALL_FINITE中包含Nan或Infinity

Question

我试图在此处对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（使用方法='bfgs'和l1 norm）并捕获了一个单数值矩阵误差（提高linalgerror（'singular matrix'），并具有完整的等级[我使用np.linalg.matrix_rank进行了测试[data [data [data [data [data [data [data [data [data [data [data [data]train_cols]。值）]。

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，我尝试了使用网格搜索/线性SVC的实验 - 以及下面的错误以及我的代码＆amp;数据集。

我可以看到只有6/7的功能实际上是"独立的" - 我在比较x_train_new [0]和x_train的行时解释了这一点（因此我可以获得哪些列是冗余的）

    # 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，我仍然会看到一些nan的 - 我在做什么错？在使用此模型训练之后，这不应该发生，对吗？/strong>

根据此线程，我还检查了我的CSV文件中没有null （尽管我将"质量"重新标记为5和7标签（从范围（3,10）中如何修复" nan或infinity"Python中稀疏矩阵的问题？

也 - 这是x_test＆amp;的数据类型y_test/train ...

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:Python27libsite-packagessklearnbase.py", line 279, in score
    return accuracy_score(y, self.predict(X))
  File "C:Python27libsite-packagessklearnmetricsmetrics.py", line 742, in accuracy_score
    y_true, y_pred = check_arrays(y_true, y_pred)
  File "C:Python27Libsite-packagessklearnutilsvalidation.py", line 215, in check_arrays
  File "C:Python27Libsite-packagessklearnutilsvalidation.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：还请提及如果我使用LinearSVC的思考过程正确，给定我的用例，或者我应该使用Grid-search？

免责声明：该代码的一部分是基于stackoverflow和其他货物的类似上下文中的建议构建的 - 如果此方法非常适合我的场景，我的真正用例就是尝试访问。仅此而已。

Answer 1

这有效。我唯一需要更改的是使用x_test* .values *以及其他pandas dataframes（x_train，y__train，y_test）。正如指出的那样，唯一的原因是Pandas DF和Scikit-Learn（使用Numpy Arrays）

之间不兼容

 #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hack来自这篇文章http://python.dzone.com/articles/python-making-making-scikit-learn------ @andreasmueller，他指出了不一致。