Reputation: 8018
i am working on a multilabel classification problem as
import pandas as pd
import pickle
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
tdf = pd.read_csv("data.csv", index_col="DocID",error_bad_lines=False)[:8]
print tdf
gives me
DocID Content Tags
1 some text here... [70]
2 some text here... [59]
3 some text here... [183]
4 some text here... [173]
5 some text here... [71]
6 some text here... [98]
7 some text here... [211]
8 some text here... [188]
then i identify and transform the columns as needed
X=tdf["Content"]
y=tdf["Tags"]
t=TfidfVectorizer()
print t.fit_transform(X).toarray()
print MultiLabelBinarizer().fit_transform(y)
gives me
[[ 0. 0.01058315 0. ..., 0.00529157 0. 0. ]
[ 0. 0.00947091 0. ..., 0.00473545 0. 0. ]
[ 0.01190602 0.00950931 0. ..., 0.00475465 0. 0. ]
...,
[ 0. 0.01314373 0. ..., 0.00657187 0. 0. ]
[ 0. 0.01200425 0.37574455 ..., 0.00600212 0.01502978 0. ]
[ 0. 0.02206688 0. ..., 0.01103344 0. 0. ]]
[[1 0 0 0 0 1 0 0 1 1]
[0 0 0 0 1 0 0 1 1 1]
[0 1 0 1 0 0 1 0 1 1]
[0 1 0 1 0 1 0 0 1 1]
[0 1 0 0 0 1 0 0 1 1]
[0 0 0 0 0 0 1 1 1 1]
[0 1 1 0 0 0 0 0 1 1]
[0 1 0 0 0 0 1 0 1 1]]
Looking at my data, shouldn't there be only 8 columns here for y? why are there 10 columns?
then i split,transform,fit and score
Xtrain, Xvalidate, ytrain, yvalidate = train_test_split(X, y, test_size=.5)
Xtrain=t.fit_transform(Xtrain).toarray()
Xvalidate=t.fit_transform(Xvalidate).toarray()
ytrain=MultiLabelBinarizer().fit_transform(ytrain)
yvalidate=MultiLabelBinarizer().fit_transform(yvalidate)
clf = OneVsRestClassifier(LogisticRegression(penalty='l2', C=0.01)).fit(Xtrain, ytrain)
print "One vs rest accuracy: %.3f" % clf.score(Xvalidate,yvalidate)
but i get the error
print "One vs rest accuracy: %.3f" % clf.score(Xvalidate,yvalidate)
File "X:\Anaconda2\lib\site-packages\sklearn\base.py", line 310, in score
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
File "X:\Anaconda2\lib\site-packages\sklearn\multiclass.py", line 325, in predict
indices.extend(np.where(_predict_binary(e, X) > thresh)[0])
File "X:\Anaconda2\lib\site-packages\sklearn\multiclass.py", line 83, in _predict_binary
score = np.ravel(estimator.decision_function(X))
File "X:\Anaconda2\lib\site-packages\sklearn\linear_model\base.py", line 249, in decision_function
% (X.shape[1], n_features))
ValueError: X has 1546 features per sample; expecting 1354
what does this error mean? could it be the data? i have worked with the exact same algorithm with similar (same column format and data format) data and did not have a problem. Also, why does the fit function work?
What am i doing wrong here?
EDIT
so in my Tags column, the data is being read as string. hence the two extra columns in y. i tried
X=tdf["Content"]
y=tdf["Tags"]
y = [map(int, list(_y.replace(',','').replace('[','').replace(']',''))) for _y in y]
to accommodate for multiple values, but i still the same error. at least i get the correct number of columns for y.
Upvotes: 0
Views: 1493
Reputation: 2386
When you call fit_transform()
you are first adjusting the feature extractor to the data (fit part) and then transforming the data (transform part). By calling fit_transform()
multiple times on the same feature extractor (with different data) you are performing different fits, e.g. your TFIDF Vectorizer might learn one vocabulary for your training set and a completely different one for the validation set, which results in a different number of columns (different number of unique words). You have to call fit_transform()
on X
and y
first and split to training and validation set afterwards (one fit, one transform). Alternatively you can call fit_transform()
to generate the training set and then just transform()
to generate the validation set (one fit, multiple transforms).
Upvotes: 1