在流水线 sklearn 中包含特征提取

Question

对于一个文本分类项目，我为特征选择和分类器制作了一个管道。现在我的问题是是否可以在管道中包含特征提取模块以及如何。我查了一些关于它的东西，但它似乎不适合我当前的代码。

这是我现在拥有的：

# feature_extraction module.  
from sklearn.preprocessing import LabelEncoder, StandardScaler 
from sklearn.feature_extraction import DictVectorizer  
import numpy as np

vec = DictVectorizer() 
X = vec.fit_transform(instances)
scaler = StandardScaler(with_mean=False) # we use cross validation, no train/test set 
X_scaled = scaler.fit_transform(X) # To make sure everything is on the same scale

enc = LabelEncoder()
y = enc.fit_transform(labels)

# Feature selection and classification pipeline
from sklearn.feature_selection import SelectKBest, mutual_info_classif
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn import linear_model
from sklearn.pipeline import Pipeline

feat_sel = SelectKBest(mutual_info_classif, k=200)  
clf = linear_model.LogisticRegression() 
pipe = Pipeline([('mutual_info', feat_sel), ('logistregress', clf)])) 
y_pred = model_selection.cross_val_predict(pipe, X_scaled, y, cv=10)

如何将 dictvectorizer 放入管道中的标签编码器？

Answer 1

以下是您的操作方法。假设 instances 是一个类似字典的对象，如 API 中所指定，那么只需像这样构建您的管道：

pipe = Pipeline([('vectorizer', DictVectorizer()),
                 ('scaler', StandardScaler(with_mean=False)),
                 ('mutual_info', feat_sel),
                 ('logistregress', clf)])

进行预测，然后调用cross_val_predict，传递instances作为X:

y_pred = model_selection.cross_val_predict(pipe, instances, y, cv=10)

在流水线 sklearn 中包含特征提取

Include feature extraction in pipeline sklearn

python

pipeline

machine-learning

feature-extraction

scikit-learn