问题描述
我正在尝试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范数)并捕获了奇异值矩阵错误(提高LinAlgError('Singular matrix'),尽管排名[我使用np.linalg.matrix_rank(data [ train_cols] .values)]
I was trying to classify the wine data set here -http://archive.ics.uci.edu/ml/datasets/Wine+Qualityusing logistic regression (with method ='bfgs' and l1 norm) and caught a singular value matrix error(raise LinAlgError('Singular matrix'), in-spite of full rank [which I tested using np.linalg.matrix_rank(data[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Grid search/LinearSVC-并得到以下错误以及我的代码&数据集.
This is how I came to the conclusion that some features might be linear combinations of others. Towards this, I experimented of using Grid search/LinearSVC - and I get the error below, along with my code & data-set .
我看到只有6/7个功能实际上是独立的"-我在比较x_train_new [0]和x_train的行时会解释这些功能(这样我就可以知道哪些列是多余的)
I can see that only 6/7 features are actually "independent" - which I interpret when comparing the rows of x_train_new[0] and x_train (so I can get which columns are redundant)
# 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-我做错了吗?在所有模型都使用此模型进行训练之后,这应该不会发生,对吗?
根据该线程,我还检查了我的csv文件中是否没有空值(尽管我将质量"重新标记为仅5和7个标签(从range(3,10)如何修复"NaN或无穷大" ;稀疏矩阵在python中出现问题?
According to this thread, I also checked that there are no null's in my csv file (though I relabeled the "quality' to 5 and 7 labels only (from range(3,10)How to fix "NaN or infinity" issue for sparse matrix in python?
也-这是x_test& 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:\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:也请提及,鉴于我的用例,关于使用LinearSVC的思考过程是否正确,还是应该使用Grid-search?
Tip : Please also mention if my thought process on using LinearSVC is correct, given my use case, or should I use Grid-search ?
免责声明:这段代码的一部分是基于StackOverflow和其他来源的类似上下文中的建议而构建的-如果此方法非常适合我的情况,我的实际用例就是尝试访问.就是这样.
Disclaimer : Parts of this code have been built on suggestions in similar contexts from StackOverflow and miscellaneous sources - My real use case is just trying to access if this method is a good fit for my scenario. That's all.
推荐答案
此方法有效.我唯一真正需要更改的是使用x_test * .values *以及其余的pandas Dataframes(x_train,y_train,y_test).正如指出的那样,唯一的原因是熊猫df和scikit-learn(使用numpy数组)之间不兼容
This worked. The only I had to really change was use x_test*.values* along with the rest of pandas Dataframes(x_train, y_train, y_test) . As pointed out the only reason was incompatibility between pandas df and scikit-learn(which uses numpy arrays)
#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'>
此黑客来自此帖子 http://python.dzone. com/articles/python-making-scikit-learn-and 和@AndreasMueller-指出了不一致之处.
This hack comes from this post http://python.dzone.com/articles/python-making-scikit-learn-and and @AndreasMueller - who pointed out the inconsistency.
这篇关于ValueError:在LinearSVC期间,数组在_assert_all_finite中包含NaN或无穷大的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!