如何添加一列并保存在文本文件中的稀疏矩阵？

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TfidfVectorizer在输出上返回稀疏矩阵，可以很容易地将其转换为SparseDataFrame（不是常规的）。但我无法弄清楚如何添加一列（s）并保存在csv文件中。如何添加一列并保存在文本文件中的稀疏矩阵？

import pandas as pd 
import numpy as np 
from sklearn.feature_extraction.text import TfidfVectorizer 

def get_features(data, train=False): 
    tfv = TfidfVectorizer() 
    if train: 
     features = tfv.fit_transform(data["text"]) 
    else: 
     features = tfv.transform(data["text"]) 

    features_pd = pd.SparseDataFrame([ pd.SparseSeries(features[i].toarray().ravel()) 
           for i in np.arange(features.shape[0]) ], columns = tfv.get_feature_names()) 
# the next 2 lines in replacement of the previous result in empty (commas only) output 
# features_pd = pd.DataFrame([ pd.Series(features[i].toarray().ravel()) 
#         for i in np.arange(features.shape[0]) ], columns = tfv.get_feature_names()) 
# the next line results in TypeError: ufunc 'isnan' not supported for the input types ... 
    # features_pd['_class_'] = pd.SparseSeries(data["class"]) 

    print "F:",features_pd.iloc[[0]] 
    return features_pd 

if __name__ == '__main__': 

    train = pd.read_csv('train.csv', header=None, names = ["class", "text"]).fillna("") 
    features = get_features(train, train=True) 
    features.to_csv('out.csv', index=False)

来源

2016-03-09 cur4so

稀疏矩阵可以被转换为阵列，并且可以使用常规的数据帧变化的核心来执行其后的所有操作，以进行：features.toarray()

features_pd = pd.DataFrame(data=features.toarray(), 
          columns = tfv.get_feature_names()) 

features_pd['_class_'] = pd.Series(data["class"], index = features_pd.index)

来源

2016-03-09 21:04:14 cur4so

如何添加一列并保存在文本文件中的稀疏矩阵？

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