注意
转到结尾 下载完整的示例代码。或通过JupyterLite或Binder在浏览器中运行此示例。
显示管道#
在Jupyter Notebook中显示管道的默认配置是'diagram'，其中set_config(display='diagram')。要停用HTML表示，请使用set_config(display='text')。
要在管道可视化中查看更详细的步骤，请点击管道中的步骤。
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
显示包含预处理步骤和分类器的管道#
本节构建一个具有预处理步骤StandardScaler和分类器LogisticRegression的Pipeline，并显示其可视化表示。
from sklearn import set_config
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
steps = [
("preprocessing", StandardScaler()),
("classifier", LogisticRegression()),
]
pipe = Pipeline(steps)
要可视化图表，默认值为display='diagram'。
set_config(display="diagram")
pipe # click on the diagram below to see the details of each step
要查看文本管道，请更改为display='text'。
set_config(display="text")
pipe
Pipeline(steps=[('preprocessing', StandardScaler()),
('classifier', LogisticRegression())])
恢复默认显示
set_config(display="diagram")
显示链接多个预处理步骤和分类器的管道#
本节构建一个具有多个预处理步骤PolynomialFeatures和StandardScaler以及分类器步骤LogisticRegression的Pipeline，并显示其可视化表示。
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
steps = [
("standard_scaler", StandardScaler()),
("polynomial", PolynomialFeatures(degree=3)),
("classifier", LogisticRegression(C=2.0)),
]
pipe = Pipeline(steps)
pipe # click on the diagram below to see the details of each step
显示具有降维和分类器的管道#
本节构建一个具有降维步骤PCA、分类器SVC的Pipeline，并显示其可视化表示。
显示链接列转换器的复杂管道#
本节构建一个具有ColumnTransformer和分类器LogisticRegression的复杂Pipeline，并显示其可视化表示。
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
numeric_preprocessor = Pipeline(
steps=[
("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
("scaler", StandardScaler()),
]
)
categorical_preprocessor = Pipeline(
steps=[
(
"imputation_constant",
SimpleImputer(fill_value="missing", strategy="constant"),
),
("onehot", OneHotEncoder(handle_unknown="ignore")),
]
)
preprocessor = ColumnTransformer(
[
("categorical", categorical_preprocessor, ["state", "gender"]),
("numerical", numeric_preprocessor, ["age", "weight"]),
]
)
pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))
pipe # click on the diagram below to see the details of each step
显示对具有分类器的管道的网格搜索#
本节构建一个对具有RandomForestClassifier的Pipeline进行GridSearchCV，并显示其可视化表示。
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
numeric_preprocessor = Pipeline(
steps=[
("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")),
("scaler", StandardScaler()),
]
)
categorical_preprocessor = Pipeline(
steps=[
(
"imputation_constant",
SimpleImputer(fill_value="missing", strategy="constant"),
),
("onehot", OneHotEncoder(handle_unknown="ignore")),
]
)
preprocessor = ColumnTransformer(
[
("categorical", categorical_preprocessor, ["state", "gender"]),
("numerical", numeric_preprocessor, ["age", "weight"]),
]
)
pipe = Pipeline(
steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())]
)
param_grid = {
"classifier__n_estimators": [200, 500],
"classifier__max_features": ["auto", "sqrt", "log2"],
"classifier__max_depth": [4, 5, 6, 7, 8],
"classifier__criterion": ["gini", "entropy"],
}
grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)
grid_search # click on the diagram below to see the details of each step
脚本总运行时间：（0分钟0.096秒）
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