Using bagging for text classification, trying to work with document-term-matrix,
in ()
7 tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
8 bag = Bagging(base_classifier=tree, n_classifiers=10)
----> 9 bag.fit(X_train_dtm, y_train)
10
11 div = Diversity(metric='q')
C:\Anaconda3\lib\site-packages\brew-0.1.4-py3.5.egg\brew\generation\bagging.py in fit(self, X, y)
27 # bootstrap
28 idx = np.random.choice(X.shape[0], X.shape[0], replace=True)
---> 29 data, target = X[idx, :], y[idx]
30
31 classifier = sklearn.base.clone(self.base_classifier)
TypeError: only integer scalar arrays can be converted to a scalar index
In this, X_train_dtm is a document-term-matrix, which is a sparse matrix, Is there any way to solve this problem.
Using bagging for text classification, trying to work with document-term-matrix,
in ()
7 tree = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)
8 bag = Bagging(base_classifier=tree, n_classifiers=10)
----> 9 bag.fit(X_train_dtm, y_train)
10
11 div = Diversity(metric='q')
C:\Anaconda3\lib\site-packages\brew-0.1.4-py3.5.egg\brew\generation\bagging.py in fit(self, X, y)
27 # bootstrap
28 idx = np.random.choice(X.shape[0], X.shape[0], replace=True)
---> 29 data, target = X[idx, :], y[idx]
30
31 classifier = sklearn.base.clone(self.base_classifier)
TypeError: only integer scalar arrays can be converted to a scalar index
In this, X_train_dtm is a document-term-matrix, which is a sparse matrix, Is there any way to solve this problem.