Introduction
This lab is a step-by-step guide on how to construct and display pipelines in Scikit-Learn.
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Constructing a Simple Pipeline with a Preprocessing Step and Classifier
In this step, we will construct a simple pipeline with a preprocessing step and a classifier, and display its visual representation.
First, we import the necessary modules:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn import set_config
Next, we define the steps of the pipeline:
steps = [
("preprocessing", StandardScaler()),
("classifier", LogisticRegression()),
]
Then, we create the pipeline:
pipe = Pipeline(steps)
Finally, we display the visual representation of the pipeline:
set_config(display="diagram")
pipe
Constructing a Pipeline Chaining Multiple Preprocessing Steps & Classifier
In this step, we will construct a pipeline with multiple preprocessing steps and a classifier, and display its visual representation.
First, we import the necessary modules:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.linear_model import LogisticRegression
Next, we define the steps of the pipeline:
steps = [
("standard_scaler", StandardScaler()),
("polynomial", PolynomialFeatures(degree=3)),
("classifier", LogisticRegression(C=2.0)),
]
Then, we create the pipeline:
pipe = Pipeline(steps)
Finally, we display the visual representation of the pipeline:
pipe
Constructing a Pipeline with Dimensionality Reduction and Classifier
In this step, we will construct a pipeline with a dimensionality reduction step and a classifier, and display its visual representation.
First, we import the necessary modules:
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
Next, we define the steps of the pipeline:
steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))]
Then, we create the pipeline:
pipe = Pipeline(steps)
Finally, we display the visual representation of the pipeline:
pipe
Constructing a Complex Pipeline Chaining a Column Transformer
In this step, we will construct a complex pipeline with a column transformer and a classifier, and display its visual representation.
First, we import the necessary modules:
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
Next, we define the preprocessing steps for the numerical and categorical features:
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")),
]
)
Then, we create the column transformer:
preprocessor = ColumnTransformer(
[
("categorical", categorical_preprocessor, ["state", "gender"]),
("numerical", numeric_preprocessor, ["age", "weight"]),
]
)
Next, we create the pipeline:
pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))
Finally, we display the visual representation of the pipeline:
pipe
Constructing a Grid Search over a Pipeline with a Classifier
In this step, we will construct a grid search over a pipeline with a classifier, and display its visual representation.
First, we import the necessary modules:
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
Next, we define the preprocessing steps for the numerical and categorical features:
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")),
]
)
Then, we create the column transformer:
preprocessor = ColumnTransformer(
[
("categorical", categorical_preprocessor, ["state", "gender"]),
("numerical", numeric_preprocessor, ["age", "weight"]),
]
)
Next, we create the pipeline:
pipe = Pipeline(
steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())]
)
Then, we define the parameter grid for the grid search:
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"],
}
Finally, we create the grid search:
grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)
And display the visual representation of the grid search:
grid_search
Summary
This lab provided a step-by-step guide on how to construct and display pipelines in Scikit-Learn. We covered simple pipelines with a preprocessing step and classifier, pipelines chaining multiple preprocessing steps and a classifier, pipelines with dimensionality reduction and a classifier, complex pipelines chaining a column transformer and a classifier, and grid searches over pipelines with a classifier.