Hi,
I'm completely new to sklearn, so it's entirely likely that I'm just
misunderstanding something very fundamental here. I thought that the
inputs for the GradientBoostingClassifier would be the same as for other
classifiers (LinearSVC, MultinomialNB, etc.), but when trying to run the
code below, the GradientBoostingClassifier gives me the following error:
Traceback (most recent call last):
File "classify.py", line 27, in <module>
c = dict[label].fit(training_set,votes)
File
"../lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.py",
line 633, in fit
return super(GradientBoostingClassifier, self).fit(X, y)
File
"../lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.py",
line 417, in fit
X = np.asfortranarray(X, dtype=DTYPE)
File "../lib/python2.7/site-packages/numpy/core/numeric.py", line
359, in asfortranarray
return array(a, dtype, copy=False, order='F', ndmin=1)
ValueError: setting an array element with a sequence.
This leads me to assume that I'm failing to prepare my inputs to the
GBClassifier correctly, but have so far been unable to find information
that will correct my course.
Can anyone shed some light on where I've gone astray? Thanks.
Brian
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
dict = { 'N Bayes': MultinomialNB(),
'SVC': LinearSVC(),
# 'DTC': DecisionTreeClassifier(),
'GBC': GradientBoostingClassifier() }
def load_training_set():
myset = fetch_20newsgroups(subset='train')
training_documents = myset.data[:200]
votes_for_training_set = myset.target[:200]
return training_documents, votes_for_training_set
documents, votes = load_training_set()
vect = TfidfVectorizer(min_n=1, max_n=3)
training_set = vect.fit_transform(documents)
for label in dict.keys():
print "Trying " + label + " .... "
c = dict[label].fit(training_set,votes)
print " DONE."
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