python - 如何使用管道和 FeatureUnion 添加功能

Question

在下面的代码中，我使用高音扬声器数据集来执行情感分析。我使用执行以下过程的管道:

1) 执行一些基本的文本预处理

2) 向量化推文文本

3) 添加一个额外的特征(文本长度)

4)分类

我想再添加一项功能，即缩放的关注者数量。我写了一个函数，它将整个数据框 (df) 作为输入，并返回一个新的数据框，其中包含按比例缩放的关注者数量。但是，我发现在管道中添加此过程具有挑战性，例如使用 sklearn 管道将此功能添加到其他功能。

任何有关此问题的帮助或建议将不胜感激。

下面的问题和代码的灵感来自于 Ryan 的帖子:pipelines

import nltk
import re
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

def import_data(filename,sep,eng,header = None,skiprows=1):
    #read csv
    dataset = pd.read_csv(filename,sep=sep,engine=eng,header = header,skiprows=skiprows)
    #rename columns
    dataset.columns = ['text','followers','sentiment']
    return dataset

df = import_data('apple_v3.txt','\t','python')
X, y = df.text, df.sentiment
X_train, X_test, y_train, y_test = train_test_split(X, y)

tokenizer = nltk.casual.TweetTokenizer(preserve_case=False, reduce_len=True)
count_vect = CountVectorizer(tokenizer=tokenizer.tokenize) 
classifier = LogisticRegression()

def get_scalled_followers(df):
    scaler = MinMaxScaler()
    df[['followers']] = df[['followers']].astype(float)
    df[['followers']] = scaler.fit_transform(df[['followers']])
    followers = df['followers'].values
    followers_reshaped = followers.reshape((len(followers),1))
    return df

def get_tweet_length(text):
    return len(text)
import numpy as np

def genericize_mentions(text):
    return re.sub(r'@[\w_-]+', 'thisisanatmention', text)

def reshape_a_feature_column(series):
    return np.reshape(np.asarray(series), (len(series), 1))

def pipelinize_feature(function, active=True):
    def list_comprehend_a_function(list_or_series, active=True):
        if active:
            processed = [function(i) for i in list_or_series]
            processed = reshape_a_feature_column(processed)
            return processed

        else:
            return reshape_a_feature_column(np.zeros(len(list_or_series)))

from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn_helpers import pipelinize, genericize_mentions, train_test_and_evaluate


sentiment_pipeline = Pipeline([
        ('genericize_mentions', pipelinize(genericize_mentions, active=True)),
        ('features', FeatureUnion([
                    ('vectorizer', count_vect),
                    ('post_length', pipelinize_feature(get_tweet_length, active=True))
                ])),
        ('classifier', classifier)
    ])

sentiment_pipeline, confusion_matrix = train_test_and_evaluate(sentiment_pipeline, X_train, y_train, X_test, y_test)

Answer 1

到目前为止，我找到的最佳解释是在以下帖子中:pipelines

我的数据包含异构特征，以下分步方法效果很好且易于理解:

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline, FeatureUnion

#step1 - select data from dataframe and split the dataset in train and test sets

features= [c for c in df.columns.values if c  not in ['sentiment']]
numeric_features= [c for c in df.columns.values if c  not in ['text','sentiment']]
target = 'sentiment'

X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.33, random_state=42)

#step2 - create a number selector class and text selector class. These classes allow to select specific columns from the dataframe

class NumberSelector(BaseEstimator, TransformerMixin):

    def __init__(self, key):
        self.key = key

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X[[self.key]]

class TextSelector(BaseEstimator, TransformerMixin):

    def __init__(self, key):
        self.key = key

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X[self.key]

#step 3 create one pipeline for the text data and one for the numerical data


text = Pipeline([
                ('selector', TextSelector(key='content')),
                ('tfidf', TfidfVectorizer( stop_words='english'))
            ])

text.fit_transform(X_train)

followers =  Pipeline([
                ('selector', NumberSelector(key='followers')),
                ('standard', MinMaxScaler())
            ])

followers.fit_transform(X_train)

#step 4 - features union

feats = FeatureUnion([('text', text), 
                      ('length', followers)])

feature_processing = Pipeline([('feats', feats)])
feature_processing.fit_transform(X_train)

# step 5 - add the classifier and predict 

pipeline = Pipeline([
    ('features',feats),
    ('classifier', SVC(kernel = 'linear', probability=True, C=1, class_weight = 'balanced'))
])

pipeline.fit(X_train, y_train)

preds = pipeline.predict(X_test)
np.mean(preds == y_test)

# step 6 use the model to predict new data not included in the test set
# in my example the pipeline expects a dataframe as an input which should have a column called 'text' and a column called 'followers'

array = [["@apple is amazing",25000]]
dfObj = pd.DataFrame(array,columns = ['text' , 'followers']) 

#prints the expected class e.g. positive or negative sentiment
print(pipeline.predict(dfObj))

#print the probability for each class
print(pipeline.predict_proba(dfObj))