1

我一直在使用以下代码进行多类分类，它使用来自 scikit-learn 的 GradientBoostingClassifier。我面临一个将稀疏矩阵转换为密集矩阵的已知问题。

我已经应用了以下解决方案stackoverflow，但它不适用于我的情况。虽然我使用的解决方案适用于 RandomForestClassifier，但 AFAIK 它应该适用于 GradientBoostingClassifier！

如果我用 RandomForestClassifier 替换 GradientBoostingClassifier，也可以完美地添加此代码。

本例中的数据是具有 8 个目标类的 93 个数字特征。数据可以从Kaggle获取 

# load data
train = pd.read_csv('data/train.csv')
test = pd.read_csv('data/test.csv')
sample = pd.read_csv('submissions/sampleSubmission.csv')
labels = train.target.values
ids = train.id.values
train = train.drop('id', axis=1)
train = train.drop('target', axis=1)
train_orig = train
test = test.drop('id', axis=1)

# transform counts to TFIDF features
tfidf = feature_extraction.text.TfidfTransformer()
train = tfidf.fit_transform(train)
test = tfidf.transform(test).toarray() # Update line

# encode labels 
lbl_enc = preprocessing.LabelEncoder()
labels = lbl_enc.fit_transform(labels)

# train a random forest classifier
print('starting training ... ')
clf = ensemble.GradientBoostingClassifier( n_estimators=config.estimators)
clf.fit(train, labels)

# predict on test set
print('starting prediction ... ')
preds = clf.predict_proba(test) # Error on this line even when test is dense
train_pred = clf.predict(tfidf.transform(train_orig))

追溯：

python boosted_trees.py 
starting training ... 
Traceback (most recent call last):
  File "boosted_trees.py", line 57, in <module>
    clf.fit(train, labels)
  File "/usr/local/lib/python2.7/site-        packages/sklearn/ensemble/gradient_boosting.py", line 941, in fit
    X, y = check_X_y(X, y, dtype=DTYPE)
  File "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py", line 439, in check_X_y
    ensure_min_features)
  File "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py",     line 331, in check_array
    copy, force_all_finite)
  File "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py",     line 239, in _ensure_sparse_format
    raise TypeError('A sparse matrix was passed, but dense '
TypeError: A sparse matrix was passed, but dense data is required. Use         X.toarray() to convert to a dense numpy array.ere
4

1 回答 1

1

感谢@imaluengo。

以防万一有人需要。问题在于这些方面。

train = tfidf.fit_transform(train)
test = tfidf.transform(test).toarray() # Update line

这两行都应该有一个 toarray() 来解决这个问题。

train = tfidf.fit_transform(train).toarray()
test = tfidf.transform(test).toarray() # Update line
于 2015-04-20T16:14:49.893 回答