Question

我一直在使用以下代码进行多类分类，该分类使用scikit-learn中的GradientBoostingClassifier。我面临一个已知问题，稀疏矩阵转换为密集矩阵。

我已应用以下解决方案stackoverflow，但它对我的情况不起作用。虽然我使用的解决方案适用于RandomForestClassifier，但AFAIK适用于GradientBoostingClassifier！

如果我用RandomForestClassifier替换GradientBoostingClassifier，那么添加此代码也是完美的。

本例中的数据是具有8个目标类的数字93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))

回溯：

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

Answer 1

感谢@imaluengo。

以防万一有需要。问题出在这些方面。

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

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

使用pandas和scikit的Gradient Boosting Classifier稀疏矩阵问题

1 个答案: