属性错误： 'numpy.ndarray'对象没有属性'toarray'

Question

我正在从文本语料库中提取特征，并使用td fidf矢量器和scikit learn中的截断奇异值分解来实现这一点。然而，由于我想要尝试的算法需要密集矩阵，并且矢量器返回稀疏矩阵，所以我需要将这些矩阵转换为密集阵列。但是，每当我试图转换这些数组时，我都会收到一个错误，告诉我我的numpy数组对象没有数组"toarray"。我做错了什么？

功能：

def feature_extraction(train,train_test,test_set):
    vectorizer = TfidfVectorizer(min_df = 3,strip_accents = "unicode",analyzer = "word",token_pattern = r'w{1,}',ngram_range = (1,2))        
    print("fitting Vectorizer")
    vectorizer.fit(train)
    print("transforming text")
    train = vectorizer.transform(train)
    train_test = vectorizer.transform(train_test)
    test_set = vectorizer.transform(test_set)
    print("Dimensionality reduction")
    svd = TruncatedSVD(n_components = 100)
    svd.fit(train)
    train = svd.transform(train)
    train_test = svd.transform(train_test)
    test_set = svd.transform(test_set)
    print("convert to dense array")
    train = train.toarray()
    test_set = test_set.toarray()
    train_test = train_test.toarray()
    print(train.shape)
    return train,train_test,test_set

回溯：

Traceback (most recent call last):
  File "C:UsersAnonymousworkspacefinal_submissionsrclinearSVM.py", line 24, in <module>
    x_train,x_test,test_set = feature_extraction(x_train,x_test,test_set)
  File "C:UsersAnonymousworkspacefinal_submissionsrcPreprocessing.py", line 57, in feature_extraction
    train = train.toarray()
AttributeError: 'numpy.ndarray' object has no attribute 'toarray'

更新：Willy指出，我关于矩阵稀疏的假设可能是错误的。因此，我尝试将我的数据输入到具有降维功能的算法中，它实际上在没有任何转换的情况下工作，然而，当我排除降维功能时，我得到了以下错误：

    Traceback (most recent call last):
  File "C:UsersAnonymousworkspacefinal_submissionsrclinearSVM.py", line 28, in <module>
    result = bayesian_ridge(x_train,x_test,y_train,y_test,test_set)
  File "C:UsersAnonymousworkspacefinal_submissionsrcAlgorithms.py", line 84, in bayesian_ridge
    algo = algo.fit(x_train,y_train[:,i])
  File "C:Python27libsite-packagessklearnlinear_modelbayes.py", line 136, in fit
    dtype=np.float)
  File "C:Python27libsite-packagessklearnutilsvalidation.py", line 220, in check_arrays
    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.

有人能解释一下吗？

更新2

根据要求，我将提供所有涉及的代码。由于它分散在不同的文件中，我将分步骤发布它。为了清楚起见，我将省略所有模块导入。

这就是我预处理代码的方式：

def regexp(data):
    for row in range(len(data)):
        data[row] = re.sub(r'[W_]+'," ",data[row])
        return data
def clean_the_text(data):
    alist = []
    data = nltk.word_tokenize(data)
    for j in data:
        j = j.lower()
        alist.append(j.rstrip('n'))
    alist = " ".join(alist)
    return alist
def loop_data(data):
    for i in range(len(data)):
        data[i] = clean_the_text(data[i])
    return data  

if __name__ == "__main__":
    print("loading train")
    train_text = porter_stemmer(loop_data(regexp(list(np.array(p.read_csv(os.path.join(dir,"train.csv")))[:,1]))))
    print("loading test_set")
    test_set = porter_stemmer(loop_data(regexp(list(np.array(p.read_csv(os.path.join(dir,"test.csv")))[:,1]))))

在将我的train_set分解为交叉验证的x_train和x_test之后，我使用上面的feature_extraction函数转换我的数据。

x_train,x_test,test_set = feature_extraction(x_train,x_test,test_set)

最后我把它们输入到我的算法中

def bayesian_ridge(x_train,x_test,y_train,y_test,test_set):
    algo = linear_model.BayesianRidge()
    algo = algo.fit(x_train,y_train)
    pred = algo.predict(x_test)
    error = pred - y_test
    result.append(algo.predict(test_set))
    print("Bayes_error: ",cross_val(error))
    return result

Answer 1

TruncatedSVD.transform返回一个数组，而不是稀疏矩阵。事实上，在当前版本的scikit-learn中，只有矢量器返回稀疏矩阵。