传递了一个稀疏矩阵，但需要密集数据.使用 X.toarray() 转换为密集的 numpy 数组 [英] A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array

问题描述

代码如下，我正在尝试将训练数据用于 GBRT 回归树，相同的数据适用于其他分类器，但给出了 GBRT 的上述错误.请帮忙:

Code goes as follows, I am trying to use training data for GBRT regression trees, same data works good for other classifiers but gives above error for GBRT. please help :

dataset = load_files('train')
vectorizer = TfidfVectorizer(encoding='latin1')
X_train = vectorizer.fit_transform((open(f).read() for f in dataset.filenames)) 
assert sp.issparse(X_train)     
print("n_samples: %d, n_features: %d" % X_train.shape)
y_train = dataset.target
def benchmark(clf_class, params, name):
    clf = clf_class(**params).fit(X_train, y_train)

推荐答案

我在尝试使用 load_svmlight_files 加载的数据训练 GradientBoostingClassifier 时遇到了同样的问题.通过将稀疏矩阵转换为 numpy 数组来解决.

I came accross the same problem trying to train a GradientBoostingClassifier using the data loaded by load_svmlight_files. Solved by transforming a sparse matrix to a numpy array.

X_train.todense()

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