initial commit

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Mohammad Haizad
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import numpy as np
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.tree import DecisionTreeRegressor
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def train_estimators(max_depth,n_estimators):
+    rng = np.random.RandomState(1)
+    X = np.linspace(0, 6, 100)[:, np.newaxis]
+    y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
+
+    regr_1 = DecisionTreeRegressor(max_depth=4)
+    regr_2 = AdaBoostRegressor(
+        DecisionTreeRegressor(max_depth=max_depth), n_estimators=n_estimators, random_state=rng
+    )
+    regr_1.fit(X, y)
+    regr_2.fit(X, y)
+    y_1 = regr_1.predict(X)
+    y_2 = regr_2.predict(X)
+    colors = sns.color_palette("colorblind")
+
+    fig, ax = plt.subplots()
+    ax.scatter(X, y, color=colors[0], label="training samples")
+    ax.plot(X, y_1, color=colors[1], label="Decision tree (max_depth=4)", linewidth=2)
+    ax.plot(X, y_2, color=colors[2], label=f"Adaboost (max_depth={max_depth}, estimators={n_estimators})", linewidth=2)
+    ax.set_xlabel("data")
+    ax.set_ylabel("target")
+    ax.legend()
+    return fig
+
+title = "Decision Tree Regression with AdaBoost"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown("This app demonstrates bosting of decision tree regressor using Adaboost")
+
+    max_depth = gr.Slider(minimum=1, maximum=50, step=1, label = "Maximum Depth")
+    n_estimators = gr.Slider(minimum=1, maximum=300, step=1, label = "Number of Estimators")
+
+    plot = gr.Plot(label=title)
+    n_estimators.change(fn=train_estimators, inputs=[max_depth,n_estimators], outputs=[plot])
+    max_depth.change(fn=train_estimators, inputs=[max_depth,n_estimators], outputs=[plot])
+
+demo.launch()
+

requirements.txt

@@ -0,0 +1,2 @@
+scikit-learn==1.2.2
+seaborn==0.12.2