Feature transformations with ensembles of trees (original) (raw)

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Transform your features into a higher dimensional, sparse space. Then train a linear model on these features.

First fit an ensemble of trees (totally random trees, a random forest, or gradient boosted trees) on the training set. Then each leaf of each tree in the ensemble is assigned a fixed arbitrary feature index in a new feature space. These leaf indices are then encoded in a one-hot fashion.

Each sample goes through the decisions of each tree of the ensemble and ends up in one leaf per tree. The sample is encoded by setting feature values for these leaves to 1 and the other feature values to 0.

The resulting transformer has then learned a supervised, sparse, high-dimensional categorical embedding of the data.

Authors: The scikit-learn developers

SPDX-License-Identifier: BSD-3-Clause

First, we will create a large dataset and split it into three sets:

• a set to train the ensemble methods which are later used to as a feature engineering transformer;
• a set to train the linear model;
• a set to test the linear model.

It is important to split the data in such way to avoid overfitting by leaking data.

from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=80_000, random_state=10)

X_full_train, X_test, y_full_train, y_test = train_test_split( X, y, test_size=0.5, random_state=10 ) X_train_ensemble, X_train_linear, y_train_ensemble, y_train_linear = train_test_split( X_full_train, y_full_train, test_size=0.5, random_state=10 )

For each of the ensemble methods, we will use 10 estimators and a maximum depth of 3 levels.

n_estimators = 10 max_depth = 3

First, we will start by training the random forest and gradient boosting on the separated training set

Notice that HistGradientBoostingClassifier is much faster than GradientBoostingClassifier starting with intermediate datasets (n_samples >= 10_000), which is not the case of the present example.

The RandomTreesEmbedding is an unsupervised method and thus does not required to be trained independently.

Now, we will create three pipelines that will use the above embedding as a preprocessing stage.

The random trees embedding can be directly pipelined with the logistic regression because it is a standard scikit-learn transformer.

Pipeline(steps=[('randomtreesembedding', RandomTreesEmbedding(max_depth=3, n_estimators=10, random_state=0)), ('logisticregression', LogisticRegression(max_iter=1000))])

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Then, we can pipeline random forest or gradient boosting with a logistic regression. However, the feature transformation will happen by calling the method apply. The pipeline in scikit-learn expects a call to transform. Therefore, we wrapped the call to apply within a FunctionTransformer.

Pipeline(steps=[('functiontransformer', FunctionTransformer(func=<function rf_apply at 0x7faf219321f0>, kw_args={'model': RandomForestClassifier(max_depth=3, n_estimators=10, random_state=10)})), ('onehotencoder', OneHotEncoder(handle_unknown='ignore')), ('logisticregression', LogisticRegression(max_iter=1000))])

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def gbdt_apply(X, model): return model.apply(X)[:, :, 0]

gbdt_leaves_yielder = FunctionTransformer( gbdt_apply, kw_args={"model": gradient_boosting} )

gbdt_model = make_pipeline( gbdt_leaves_yielder, OneHotEncoder(handle_unknown="ignore"), LogisticRegression(max_iter=1000), ) gbdt_model.fit(X_train_linear, y_train_linear)

Pipeline(steps=[('functiontransformer', FunctionTransformer(func=<function gbdt_apply at 0x7faeaf7ef5e0>, kw_args={'model': GradientBoostingClassifier(n_estimators=10, random_state=10)})), ('onehotencoder', OneHotEncoder(handle_unknown='ignore')), ('logisticregression', LogisticRegression(max_iter=1000))])

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We can finally show the different ROC curves for all the models.

import matplotlib.pyplot as plt

from sklearn.metrics import RocCurveDisplay

_, ax = plt.subplots()

models = [ ("RT embedding -> LR", rt_model), ("RF", random_forest), ("RF embedding -> LR", rf_model), ("GBDT", gradient_boosting), ("GBDT embedding -> LR", gbdt_model), ]

model_displays = {} for name, pipeline in models: model_displays[name] = RocCurveDisplay.from_estimator( pipeline, X_test, y_test, ax=ax, name=name ) _ = ax.set_title("ROC curve")

_, ax = plt.subplots() for name, pipeline in models: model_displays[name].plot(ax=ax)

ax.set_xlim(0, 0.2) ax.set_ylim(0.8, 1) _ = ax.set_title("ROC curve (zoomed in at top left)")

Total running time of the script: (0 minutes 2.807 seconds)

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