Feature Transformations With Ensembles of Trees

Introduction

In this lab, we will learn how to transform features into a higher dimensional, sparse space using an ensemble of trees. We will then train a linear model on these features. We will use different types of ensemble methods including Random Forest and Gradient Boosting and compare their performance.

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Skills Graph

Data Preparation

First, we will create a large dataset of 80,000 samples 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.

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
)

Training Ensemble Methods

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

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

n_estimators = 10
max_depth = 3

random_forest = RandomForestClassifier(
    n_estimators=n_estimators, max_depth=max_depth, random_state=10
)
random_forest.fit(X_train_ensemble, y_train_ensemble)

gradient_boosting = GradientBoostingClassifier(
    n_estimators=n_estimators, max_depth=max_depth, random_state=10
)
_ = gradient_boosting.fit(X_train_ensemble, y_train_ensemble)

Training Embedding Transformer

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

from sklearn.ensemble import RandomTreesEmbedding

random_tree_embedding = RandomTreesEmbedding(
    n_estimators=n_estimators, max_depth=max_depth, random_state=0
)

rt_model = make_pipeline(random_tree_embedding, LogisticRegression(max_iter=1000))
rt_model.fit(X_train_linear, y_train_linear)

Transforming Features with Random Forest and Gradient Boosting

Now, we will create two pipelines that will use the above embedding as a preprocessing stage. The feature transformation will happen by calling the method apply. We will then pipeline random forest or gradient boosting with a logistic regression. However, the pipeline in scikit-learn expects a call to transform. Therefore, we wrapped the call to apply within a FunctionTransformer.

from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder

def rf_apply(X, model):
    return model.apply(X)

rf_leaves_yielder = FunctionTransformer(rf_apply, kw_args={"model": random_forest})

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

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)

Evaluating Models

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

import matplotlib.pyplot as plt
from sklearn.metrics import RocCurveDisplay

fig, 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")

Summary

In this lab, we learned how to transform features into a higher dimensional, sparse space using an ensemble of trees. We used different types of ensemble methods including Random Forest and Gradient Boosting and compared their performance. The RandomTreesEmbedding is an unsupervised method and does not require to be trained independently. We also learned how to pipeline random forest or gradient boosting with a logistic regression using the FunctionTransformer. Finally, we evaluated the performance of all the models using ROC curves.