如何使用管道和 FeatureUnion 添加功能
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【中文标题】如何使用管道和 FeatureUnion 添加功能【英文标题】：How to add a feature using a pipeline and FeatureUnion 【发布时间】：2019-11-08 12:30:04 【问题描述】：在下面的代码中，我使用了一个高音扬声器数据集来执行情绪分析。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) 添加一个额外的功能（文本长度）
4) 分类
我想再添加一项功能，即按比例缩放的关注者数量。我编写了一个函数，该函数将整个数据帧（df）作为输入，并返回一个具有缩放数量的追随者的新数据帧。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中。
任何有关此问题的帮助或建议将不胜感激。
下面的问题和代码灵感来自 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)
【问题讨论】：
【参考方案1】：您可以使用FeatureUnion 组合从数据框的不同列中提取的特征。您应该将数据框提供给管道并使用 FunctionTransformer 提取特定列。它可能看起来像这样（我没有运行它，可能有一些错误）
sentiment_pipeline = Pipeline([
FeatureUnion([
# your added feature (maybe you'll need to reshape it so ndim == 2)
('scaled_followers', FunctionTransformer(lambda df: get_scalled_followers(df).values,
validate=False)),
# previous features
('text_features', Pipeline([
('extractor', FunctionTransformer(lambda df: df.text.values, validate=False))
('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, df_train, y_train, df_test, y_test)
另一种解决方案可能是不使用Pipeline，而只是将功能与np.hstack 堆叠在一起。
【讨论】：
【参考方案2】：到目前为止我找到的最好的解释是在以下帖子中：pipelines
我的数据包含异构特征，以下分步方法运行良好且易于理解：
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))
【讨论】：
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