numpy sum 给出一个错误

Question

如何解决以下错误：dist = np.sum（train_data_features， axis=0） 文件"/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/fromnumeric.py"，第 1711 行，总计 返回总和（轴=轴，dtype=dtype，out=out）类型错误：sum（） 得到一个意外的关键字参数 'dtype'

这是我的代码：

import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from KaggleWord2VecUtility import KaggleWord2VecUtility
import pandas as pd
import numpy as np
if __name__ == '__main__':
    train = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'NYTimesBlogTrain.csv'), header=0)
    test = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data', 'NYTimesBlogTest.csv'), header=0)
    train["Abstract"].fillna(0)
    print 'A sample Abstract is:'
    print train["Abstract"][0]
    #raw_input("Press Enter to continue...")

    #print 'Download text data sets. If you already have NLTK datasets downloaded, just close the Python download window...'
    #nltk.download()  # Download text data sets, including stop words
    # Initialize an empty list to hold the clean reviews
    clean_train_reviews = []
    # Loop over each review; create an index i that goes from 0 to the length
    # of the movie review list
    print "Cleaning and parsing the training set abstracts...n"
    #for i in xrange( 0, len(train["Abstract"])):
    for i in xrange( 0, 10):
        if pd.isnull(train["Abstract"][i])==False:
            clean_train_reviews.append(" ".join(KaggleWord2VecUtility.review_to_wordlist(train["Abstract"][i], True)))
        else:
            clean_train_reviews.append(" ")
    print clean_train_reviews  
    # ****** Create a bag of words from the training set
    #
    print "Creating the bag of words...n"

    # Initialize the "CountVectorizer" object, which is scikit-learn's
    # bag of words tool.
    vectorizer = CountVectorizer(analyzer = "word",   
                             tokenizer = None,    
                             preprocessor = None, 
                             stop_words = None,   
                             max_features = 5000)
    # fit_transform() does two functions: First, it fits the model
    # and learns the vocabulary; second, it transforms our training data
    # into feature vectors. The input to fit_transform should be a list of
    # strings.
    print clean_train_reviews
    train_data_features = vectorizer.fit_transform(clean_train_reviews)
    print 'train_data_features'
    print train_data_features
    print train_data_features.shape
    # Take a look at the words in the vocabulary
    vocab = vectorizer.get_feature_names()
    print vocab
    # Sum up the counts of each vocabulary word
    dist = np.sum(train_data_features, axis=0)

Answer 1

看起来你不能对矢量化器给你的东西求和。您将需要一种不同的方法来求和，您应该能够在scipy的稀疏库中找到，很可能只需调用

dist = train_data_features.sum (axis=0)

这是我从coo_sparse矩阵总和文档中得到的。请参阅下面的详细信息

来自 sklearn 文档：

此实现使用 scipy.sparse.coo_matrix生成计数的稀疏表示形式。

从谷歌搜索这种类型的错误：

这在以前从未奏效，因为numpy对scipy.sparse一无所知。