Source Code:
clean_train_reviews=[]
for review in train["review"]:
clean_train_reviews.append(review_to_wordlist(review,
remove_stopwords=True))
trainDataVecs=getAvgFeatureVecs(clean_train_reviews, model, num_features)
print "Creating average feature vecs for test reviews"
clean_test_reviews=[]
for review in test["review"]:
clean_test_reviews.append(review_to_wordlist(review,remove_stopwords=True))
testDataVecs=getAvgFeatureVecs(clean_test_reviews, model, num_features)
forest = RandomForestClassifier(n_estimators = 100)
forest = forest.fit(trainDataVecs, train["sentiment"])
result = forest.predict(testDataVecs)
output = pd.DataFrame(data={"id":test["id"], "sentiment":result})
output.to_csv("Word2Vec_AverageVectors.csv", index=False, quoting=3)
Error Message:
Traceback (most recent call last):
File "/test_IMDB_W2V_RF.py", line 224, in <module>
result = forest.predict(testDataVecs)
File "/.local/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line
534, in predict
proba = self.predict_proba(X)
File "/.local/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line
573, in predict_proba
X = self._validate_X_predict(X)
File "/.local/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line
355, in _validate_X_predict
return self.estimators_[0]._validate_X_predict(X, check_input=True)
File "/.local/lib/python2.7/site-packages/sklearn/tree/tree.py", line 365, in
_validate_X_predict
X = check_array(X, dtype=DTYPE, accept_sparse="csr")
File "/.local/lib/python2.7/site-packages/sklearn/utils/validation.py", line
407, in check_array
_assert_all_finite(array)
File "/.local/lib/python2.7/site-packages/sklearn/utils/validation.py", line
58, in _assert_all_finite
" or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for
dtype('float32').
Process finished with exit code 1
Description :
Can any one help with the error message.
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