Create app.py and requirements

app.py

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+
+import matplotlib.pyplot as plt
+
+from collections import OrderedDict
+from sklearn.datasets import make_classification
+from sklearn.ensemble import RandomForestClassifier
+
+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 (<n_estimators>, <error rate>) 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('''
+            <div>
+            <h1 style='text-align: center'>Out-of-Bag(OOB) Errors for Random Forests</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py\">scikit-learn</a>")
+    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=min_estimators, maximum=300, step=5, value=150, label="Maximum Number of trees")
+
+    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.launch()

requirements.txt

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+scikit-learn
+matplotlib