即使整个管道都安装了,管道中的 Sklearn 组件也没有安装? [英] Sklearn components in pipeline is not fitted even if the whole pipeline is?
问题描述
我试图从安装好的管道中挑出一个组件/变压器来检查它的行为.但是,当我检索组件时,该组件显示为未安装,但是将管道作为一个整体使用是没有问题的.这表明管道已安装,组件也已安装.
I'm trying to single out a component/transformer from a fitted pipeline to inspect it's behavior. However, when I retrieved the component, the component is showed as unfitted, but using the pipeline as a whole works without problem. This suggest the pipeline is fitted and the components are fitted as well.
有人可以解释原因,并建议如何检查装配好的管道中的组件吗?
Can someone explain why, and also suggest how to inspect a component in a fitted pipeline?
这是一个可重现的例子:
Here's a reproducible example:
import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
np.random.seed(0)
# Read data from Titanic dataset.
titanic_url = ('https://raw.githubusercontent.com/amueller/'
'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')
data = pd.read_csv(titanic_url)
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', LogisticRegression(solver='lbfgs'))])
X = data.drop('survived', axis=1)
y = data['survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))
调用:
clf.get_params()['preprocessor__cat__imputer'].transform(X)
或
clf.named_steps['preprocessor'].transformers[0][1].named_steps['imputer'].transform(X)
会导致这样的错误:
NotFittedError: This SimpleImputer instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.
推荐答案
ColumnTransformer
属性 transformers
是输入 unfitted 转换器.要访问拟合的转换器,请使用属性 transformers_
或 named_transformers_
.我想 get_params()['preprocessor__cat__imputer']
也得到了未安装的输入转换器.
The ColumnTransformer
attribute transformers
is the input unfitted transformers. To access the fitted transformers, use the attribute transformers_
or named_transformers_
. I suppose get_params()['preprocessor__cat__imputer']
is also getting the unfitted input transformer.
(您仍然会收到错误消息,因为输入器也会尝试处理字符串数据,而 strategy='median'
将失败.)
(You'll still get an error, because the imputer will try to work on the string data as well, and strategy='median'
will fail.)
这篇关于即使整个管道都安装了,管道中的 Sklearn 组件也没有安装?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!