ogboost 0.6.1

Ordinal Gradient Boosting

Ordinal Gradient Boosting (OGBoost)

Overview

OGBoost is a scikit-learn-compatible, Python package for gradient boosting tailored to ordinal regression problems. It does so by alternating between:

1. Fitting a Machine Learning (ML) regression model - such as a decision tree - to predict a latent score that specifies the mean of a probability density function (PDF), and
2. Fitting a set of thresholds that generate discrete outcomes from the PDF.

In other words, OGBoost implements coordinate-descent optimization that combines functional gradient descent - for updating the regression function - with ordinary gradient descent - for updating the threshold vector.

The main class of the package, GradientBoostingOrdinal, is designed to have the same look and feel as scikit-learn's GradientBoostingClassifier. It includes many of the same features such as custom link functions, sample weighting, early stopping using a validation set, and staged predictions.

There are, however, important differences as well.

Unique Features of OGBoost

Latent-Score Prediction

The decision_function method of the GradientBoostingOrdinal behaves differently from scikit-learn's classifiers. Assuming the target variable has K distinct classes, a nominal classifier's decision function would return K values for each sample. On the other hand, decision_function in ogboost would return the latent score for each sample, which is a single value. This latent score can be considered a high-resolution alternative to class labels, and thus may have superior ranking performance.

Early Stopping using Cross-Validation (CV)

In addition to using a single validation set for early stopping, similar to GradientBoostingClassifier, ogboost implements early stopping using CV, which means the entire data is used for calculating out-of-sample performance. This can improve the robustness of early-stopping, especially for small and/or imbalanced datasets.

Heterogeneous Ensemble

While most gradient-boosting software packages exclusively use decision trees with a predetermined set of hyperparameters as the base learner in all boosting iterations, ogboost offers significantly more flexibility.

1. Users can pass in a base_learner parameter to the class initializer to override the default choice of a DecisionTreeRegressor. This can be any scikit-learn regression algorithm such as a feed-forward neural network (MLPRegressor), or a K-nearest-neighbor regressor (KNeighborsRegressor), etc.
2. Rather than a single base learner, users can specify a list (or a generator) of base learners, which will be drawn from in that order in each boosting iteration. This amounts to creating a heterogeneous ensemble as opposed to a homogeneous ensemble.

Installation

pip install ogboost

Quick Start

Load the Wine Quality Dataset

The package includes a utility to load the wine quality dataset (red and white) from the UCI repository. Note that load_wine_quality shifts the target variable (quality) to start from 0. (This is required by the GradientBoostingOrdinal class.)

from ogboost import load_wine_quality
X, y, _, _ = load_wine_quality(return_X_y=True)

Training, Prediction and Evaluation

Latent scores perform better on discrminative tasks vs. class labels as they contain more information due to higher resolution:

from ogboost import GradientBoostingOrdinal

## training ##
model = GradientBoostingOrdinal(n_estimators=100, link_function='logit', verbose=1)
model.fit(X, y)

## prediction ##
# class labels
predicted_labels = model.predict(X)
# class probabilities
predicted_probabilities = model.predict_proba(X)
# latent score
predicted_latent = model.decision_function(X)

# evaluation
concordance_latent = model.score(X, y) # concordance using latent scores
concordance_label = model.score(X, y, pred_type = 'labels') # concordance using class labels
print(f"Concordance - class labels: {concordance_label:.3f}")
print(f"Concordance - latent scores: {concordance_latent:.3f}")

Early-Stopping using Cross-Validation

Using cross-validation for early stopping can produce more robust results compared to a single holdout set, especially for small and/or imbalanced datasets:

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
import time

n_splits = 10
n_repeats = 10
kf = RepeatedKFold(n_splits=n_splits, n_repeats=n_repeats)

# early-stopping using a simple holdout set
model_earlystop_simple = GradientBoostingOrdinal(n_iter_no_change=10, validation_fraction=0.2)
start = time.time()
c_index_simple = cross_val_score(model_earlystop_simple, X, y, cv=kf, n_jobs=-1)
end = time.time()
print(f'Simple early stopping: {c_index_simple.mean():.3f} ({end - start:.1f} seconds)')

# early-stopping using cross-validation
model_earlystop_cv = GradientBoostingOrdinal(n_iter_no_change=10, cv_early_stopping_splits=5)
start = time.time()
c_index_cv = cross_val_score(model_earlystop_cv, X, y, cv=kf, n_jobs=-1)
end = time.time()
print(f'CV early stopping: {c_index_cv.mean():.3f} ({end - start:.1f} seconds)')

Heterogeneous Ensemble

Rather than a single base learner, users can supply a heterogeneous list of base learners to GradientBoostingOrdinal. The utility function generate_heterogeneous_learners can be used to easily generate random samples from hyperparameter spaces of one or more base learners:

import numpy as np
from sklearn.tree import DecisionTreeRegressor
from ogboost import generate_heterogeneous_learners

# Number of samples to generate
n_samples = 100

max_depth_choices = [3, 6, 9, None]
max_leaf_nodes_choices = [10, 20, 30, None]

dt_overrides = {
    "max_depth": lambda rng: rng.choice(max_depth_choices),
    "max_leaf_nodes": lambda rng: rng.choice(max_leaf_nodes_choices)
}

# Create list of DecisionTreeRegressor models
random_learners = generate_heterogeneous_learners(
    [DecisionTreeRegressor()], 
    [dt_overrides], 
    total_samples=n_samples
)

Such heterogenous boosting ensembles can be a more efficient alternative to hyperparameter tuning (e.g., via grid search):

model_heter = GradientBoostingOrdinal(
    base_learner=random_learners,
    n_estimators=n_samples
)
cv_heter = cross_val_score(model_heter, X, y, cv=kf, n_jobs=-1)
print(f'average cv score of heteogeneous ensemble: {np.mean(cv_heter):.3f}')

Release Notes

0.6.1

• Debugged _fit_cv and plot_loss methods of GradientBoostingOrdinal to produce correct plots of training/validation loss, and loss improvement after each g and theta update when using cross-validation for early stopping.
• Enhanced docstrings for plot_loss.

0.6.0

• Improved the logic for detecting random_state as a parameter in the base learners (switching from hasattr to get_params), as the old method was tricked by sklearn's inheritance mechanics into thinking estimators such as SVM included random_state as a modifiable parameter.
• Added a utility function, generate_heterogeneous_learners, to stochastically generate a list of base learners to supply to GradientBoostingOrdinal (heterogenous boosting ensemble).
• Edited code examples in README.md to reflect the enhancements to the package.
• Enhanced load_wine_quality to add option for returning X and y - instead of a single dataframe - for red and white datasets.

0.5.6

• Tweaked the default hyperparameters of DecisionTreeRegressor (itself the default base_learner for GradientBoostingOrdinal) to match those in scikit-learn's GradientBoostingClassifier.
• Small improvements to the plot_loss method of GradientBoostingOrdinal.
• Added the Release Notes section to the README.md file.
• Small edits to the text and code in README.md.

0.5.5

• First public release.

License

This package is licensed under the MIT License.