import gradio as gr import matplotlib.pyplot as plt from collections import OrderedDict from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier def compare(number1, number2): if number1 > number2: number2 = number1 return number2 def do_train(random_state, n_samples, min_estimators, max_estimators): RANDOM_STATE = random_state # Generate a binary classification dataset. X, y = make_classification( n_samples=n_samples, n_features=25, n_clusters_per_class=1, n_informative=15, random_state=RANDOM_STATE, ) # NOTE: Setting the `warm_start` construction parameter to `True` disables # support for parallelized ensembles but is necessary for tracking the OOB # error trajectory during training. ensemble_clfs = [ ( "RandomForestClassifier, max_features='sqrt'", RandomForestClassifier( warm_start=True, oob_score=True, max_features="sqrt", random_state=RANDOM_STATE, ), ), ( "RandomForestClassifier, max_features='log2'", RandomForestClassifier( warm_start=True, max_features="log2", oob_score=True, random_state=RANDOM_STATE, ), ), ( "RandomForestClassifier, max_features=None", RandomForestClassifier( warm_start=True, max_features=None, oob_score=True, random_state=RANDOM_STATE, ), ), ] # Map a classifier name to a list of (, ) pairs. error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs) # Range of `n_estimators` values to explore. min_estimators = 15 max_estimators = 150 for label, clf in ensemble_clfs: for i in range(min_estimators, max_estimators + 1, 5): clf.set_params(n_estimators=i) clf.fit(X, y) # Record the OOB error for each `n_estimators=i` setting. oob_error = 1 - clf.oob_score_ error_rate[label].append((i, oob_error)) # Generate the "OOB error rate" vs. "n_estimators" plot. fig, ax = plt.subplots() for label, clf_err in error_rate.items(): xs, ys = zip(*clf_err) ax.plot(xs, ys, label=label) ax.set_xlim(min_estimators, max_estimators) ax.set_xlabel("n_estimators") ax.set_ylabel("OOB error rate") ax.legend(loc="upper right") return fig model_card = f""" ## Description The ``RandomForestClassifier`` is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations $z_i = (x_i, y_i)$. The out-of-bag (OOB) error is the average error for each $z_i$ calculated using predictions from the trees that do not contain $z_i$ in their respective bootstrap sample. This allows the ``RandomForestClassifier`` to be fit and validated whilst being trained. You can play around with ``number of samples``, ``random seed``, ``min estimators`` and ``max estimators`` controlling the number of trees. The example demonstrates how the OOB error can be measured at the addition of each new tree during training. The resulting plot allows a practitioner to approximate a suitable value of ``n_estimators`` at which the error stabilizes. ## Dataset Simulation data """ with gr.Blocks() as demo: gr.Markdown('''

Out-of-Bag(OOB) Errors for Random Forests

''') gr.Markdown(model_card) gr.Markdown("Author: Bharat Raghunathan. Based on the example from scikit-learn") n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples") random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random seed") min_estimators = gr.Slider(minimum=5, maximum=300, step=5, value=15, label="Minimum number of trees") max_estimators = gr.Slider(minimum=5, maximum=300, step=5, value=150, label="Maximum number of trees") min_estimators.change(compare, [min_estimators, max_estimators], max_estimators) with gr.Row(): with gr.Column(): plot = gr.Plot() n_samples.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot]) random_state.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot]) min_estimators.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot]) max_estimators.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot]) demo.queue().launch()