ValueError: Input contains NaN, infinity or a value too large for dtype('float32')

Thu Apr 27 13:39:41 EDT 2017

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.