Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from itertools import combinations
+from functools import partial
+
+plt.rcParams['figure.dpi'] = 100
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import (
+    RandomForestClassifier,
+    ExtraTreesClassifier,
+    AdaBoostClassifier,
+)
+from sklearn.tree import DecisionTreeClassifier
+
+import gradio as gr
+
+# ========================================
+
+C1, C2, C3 = '#ff0000', '#ffff00', '#0000ff'
+CMAP = ListedColormap([C1, C2, C3])
+GRANULARITY = 0.01
+SEED = 1
+N_ESTIMATORS = 30
+
+FEATURES = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
+LABELS = ["Setosa", "Versicolour", "Virginica"]
+MODEL_NAMES = ['DecisionTreeClassifier', 'RandomForestClassifier', 'ExtraTreesClassifier', 'AdaBoostClassifier']
+
+iris = load_iris()
+
+MODELS = [
+        DecisionTreeClassifier(max_depth=None),
+        RandomForestClassifier(n_estimators=N_ESTIMATORS, n_jobs=-1),
+        ExtraTreesClassifier(n_estimators=N_ESTIMATORS, n_jobs=-1),
+        AdaBoostClassifier(DecisionTreeClassifier(max_depth=3), n_estimators=N_ESTIMATORS)
+        ]
+
+# ========================================
+
+def create_plot(feature_string, n_estimators, model_idx):
+    np.random.seed(SEED)
+
+    feature_list = feature_string.split(',')
+    feature_list = [s.strip() for s in feature_list]
+    idx_x = FEATURES.index(feature_list[0])
+    idx_y = FEATURES.index(feature_list[1])
+
+    X = iris.data[:, [idx_x, idx_y]]
+    y = iris.target
+
+    rnd_idx = np.random.permutation(X.shape[0])
+    X = X[rnd_idx]
+    y = y[rnd_idx]
+
+    X = (X - X.mean(0)) / X.std(0)
+
+    model_name = MODEL_NAMES[model_idx]
+    model = MODELS[model_idx]
+    
+    model.fit(X, y)
+    score = round(model.score(X, y), 3)
+
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xrange = np.arange(x_min, x_max, 0.1)
+    yrange = np.arange(y_min, y_max, 0.1)
+    xx, yy = np.meshgrid(xrange, yrange)
+
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+
+    ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.65)
+
+    for i, label in enumerate(LABELS):
+        X_label = X[y==i,:]
+        y_label = y[y==i]
+        ax.scatter(X_label[:, 0], X_label[:, 1], c=[[C1], [C2], [C3]][i]*len(y_label), edgecolor='k', s=40, label=label)
+
+    ax.set_xlabel(feature_list[0]); ax.set_ylabel(feature_list[1])
+    ax.legend()
+    ax.set_title(f'{model_name} | Score: {score}')
+
+    return fig
+
+def iter_grid(n_rows, n_cols):
+    for _ in range(n_rows):
+        with gr.Row():
+            for _ in range(n_cols):
+                with gr.Column():
+                    yield
+
+with gr.Blocks() as demo:
+    selections = combinations(FEATURES, 2)
+    selections = [f'{s[0]}, {s[1]}' for s in selections] 
+    dd = gr.Dropdown(selections, value=selections[0], interactive=True, label="Input features")
+    slider = gr.Slider(1, 100, value=30, step=1, label='n_estimators')
+
+    counter = 0
+    for _ in iter_grid(2, 2):
+        if counter >= len(MODELS):
+            break
+        
+        plot = gr.Plot(label=f'{MODEL_NAMES[counter]}')
+        fn = partial(create_plot, model_idx=counter)
+
+        dd.change(fn, inputs=[dd, slider], outputs=[plot])
+        slider.change(fn, inputs=[dd, slider], outputs=[plot])
+        demo.load(fn, inputs=[dd, slider], outputs=[plot])
+
+        counter += 1
+
+demo.launch(share=True, debug=True)