Question

我正在从文本语料库中提取特征，我正在使用td-fidf矢量化器并从scikit-learn中截断奇异值分解以实现这一点。但是，由于我想要尝试的算法需要密集矩阵并且向量化器返回稀疏矩阵，我需要将这些矩阵转换为密集数组。但是，每当我尝试转换这些数组时，我都会收到一个错误，告诉我我的numpy数组对象没有属性＆＃34; toarray＆＃34;。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:\Users\Anonymous\workspace\final_submission\src\linearSVM.py", line 24, in <module>
    x_train,x_test,test_set = feature_extraction(x_train,x_test,test_set)
  File "C:\Users\Anonymous\workspace\final_submission\src\Preprocessing.py", line 57, in feature_extraction
    train = train.toarray()
AttributeError: 'numpy.ndarray' object has no attribute 'toarray'

更新威利指出，我对矩阵稀疏的假设可能是错误的。所以我尝试将我的数据输入到我的算法中，降低了维数，它实际上没有任何转换，但是当我排除维数减少时，这给了我大约53k的功能，我得到以下错误：

    Traceback (most recent call last):
  File "C:\Users\Anonymous\workspace\final_submission\src\linearSVM.py", line 28, in <module>
    result = bayesian_ridge(x_train,x_test,y_train,y_test,test_set)
  File "C:\Users\Anonymous\workspace\final_submission\src\Algorithms.py", line 84, in bayesian_ridge
    algo = algo.fit(x_train,y_train[:,i])
  File "C:\Python27\lib\site-packages\sklearn\linear_model\bayes.py", line 136, in fit
    dtype=np.float)
  File "C:\Python27\lib\site-packages\sklearn\utils\validation.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.

有人可以解释一下吗？

UPDATE2

根据要求，我将提供所涉及的所有代码。由于它分散在不同的文件上，我只是按步骤发布它。为清楚起见，我将保留所有模块导入。

这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]))))

将train_set拆分为x_train和x_test进行交叉验证后，我使用上面的feature_extraction函数转换数据。

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

最后我将它们输入我的算法

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，在目前的scikit-learn版本中，只有矢量化器返回稀疏矩阵。

AttributeError：'numpy.ndarray'对象没有属性'toarray'

1 个答案: