Python Pandas 中的稀疏矩阵和数据框

Question

我尝试在 Python Binary Classification: Twitter sentiment analysis

上复制这个项目

步骤是：

Step 1: Get data
Step 2: Text preprocessing using R
Step 3: Feature engineering
Step 4: Split the data into train and test
Step 5: Train prediction model
Step 6: Evaluate model performance
Step 7: Publish prediction web service

我现在在 Step 4，但我想我不能继续了。

import pandas
import re
from sklearn.feature_extraction import FeatureHasher

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

from sklearn import cross_validation

#read the dataset of tweets

header_row=['sentiment','tweetid','date','query', 'user', 'text']
train = pandas.read_csv("training.1600000.processed.noemoticon.csv",names=header_row)

#keep only the right columns

train = train[["sentiment","text"]]

#remove puctuation, special characters, numbers and lower case the text

def remove_spch(text):
    
    return re.sub("[^a-z]", ' ', text.lower())

train['text'] = train['text'].apply(remove_spch)


#Feature Hashing

def tokens(doc):
    """Extract tokens from doc.

    This uses a simple regex to break strings into tokens.
    """
    return (tok.lower() for tok in re.findall(r"\w+", doc))

n_features = 2**18
hasher = FeatureHasher(n_features=n_features, input_type="string", non_negative=True)
X = hasher.transform(tokens(d) for d in train['text'])

#Feature Selection and choose the best 20.000 features using Chi-Square

X_new = SelectKBest(chi2, k=20000).fit_transform(X, train['sentiment'])

#Using Stratified KFold, split my data to train and test

skf = cross_validation.StratifiedKFold(X_new, n_folds=2)

我确定最后一行是错误的，因为它只包含 20.000 个特征，而不是 Pandas 中的 Sentiment 列。如何将稀疏矩阵 X_new 与 Dataframe train“连接”起来，将其包含在 cross_validation 中，然后将其用于分类器？

Answer 1

你应该将你的类标签传递给StratifiedKFold，然后使用skf作为迭代器，在每次迭代时它会产生测试集和训练集的索引，你可以用它们来分离数据集。

查看官方 scikit-learn 文档中的代码示例： StratifiedKFold

Python Pandas 中的稀疏矩阵和数据框

Sparse Matrix and Dataframe in Python Pandas

python

classification

machine-learning

scikit-learn

cross-validation