initial commit

README.md

@@ -1,10 +1,10 @@
 ---
-title: Rf Multi Output Meta Estimator Comparison
+title: Comparing random forests and the multi-output meta estimator
 emoji: 📈
 colorFrom: green
 colorTo: gray
 sdk: gradio
-sdk_version: 3.24.1
+sdk_version: 3.27.0
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,86 @@
+import gradio as gr
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.multioutput import MultiOutputRegressor
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+def compare(max_depth,n_estimators):
+    rng = np.random.RandomState(1)
+    X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
+    y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
+    y += 0.5 - rng.rand(*y.shape)
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, train_size=400, test_size=200, random_state=4
+    )
+
+    regr_multirf = MultiOutputRegressor(
+        RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=0)
+    )
+    regr_multirf.fit(X_train, y_train)
+
+    regr_rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=2)
+    regr_rf.fit(X_train, y_train)
+
+    # Predict on new data
+    y_multirf = regr_multirf.predict(X_test)
+    y_rf = regr_rf.predict(X_test)
+
+    # Plot the results
+    fig, ax = plt.subplots()
+    s = 50
+    a = 0.4
+    ax.scatter(
+        y_test[:, 0],
+        y_test[:, 1],
+        edgecolor="k",
+        c="navy",
+        s=s,
+        marker="s",
+        alpha=a,
+        label="Data",
+    )
+    ax.scatter(
+        y_multirf[:, 0],
+        y_multirf[:, 1],
+        edgecolor="k",
+        c="cornflowerblue",
+        s=s,
+        alpha=a,
+        label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test),
+    )
+    ax.scatter(
+        y_rf[:, 0],
+        y_rf[:, 1],
+        edgecolor="k",
+        c="c",
+        s=s,
+        marker="^",
+        alpha=a,
+        label="RF score=%.2f" % regr_rf.score(X_test, y_test),
+    )
+    ax.set_xlim([-6, 6])
+    ax.set_ylim([-6, 6])
+    ax.set_xlabel("target 1")
+    ax.set_ylabel("target 2")
+    ax.set_title("Comparing random forests and the multi-output meta estimator")
+    ax.legend()
+    return fig
+
+title = "Comparing random forests and the multi-output meta estimator"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown("This app demonstrates random forests and the multi-output meta estimator comparison")
+
+    max_depth = gr.Slider(minimum=10, maximum=50, step=1, label = "Maximum Depth")
+    n_estimators = gr.Slider(minimum=50, maximum=300, step=1, label = "Number of Estimators")
+
+    plot = gr.Plot(label=title)
+    n_estimators.change(fn=compare, inputs=[max_depth,n_estimators], outputs=[plot])
+    max_depth.change(fn=compare, inputs=[max_depth,n_estimators], outputs=[plot])
+
+demo.launch()
+

requirements.txt

@@ -0,0 +1,2 @@
+scikit-learn==1.2.2
+matplotlib==3.7.1