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import numpy as np |
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import matplotlib.pyplot as plt |
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from matplotlib.colors import ListedColormap |
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from itertools import combinations |
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from functools import partial |
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plt.rcParams['figure.dpi'] = 100 |
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from sklearn.datasets import load_iris |
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from sklearn.ensemble import ( |
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RandomForestClassifier, |
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ExtraTreesClassifier, |
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AdaBoostClassifier, |
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) |
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from sklearn.tree import DecisionTreeClassifier |
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import gradio as gr |
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C1, C2, C3 = '#ff0000', '#ffff00', '#0000ff' |
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CMAP = ListedColormap([C1, C2, C3]) |
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GRANULARITY = 0.05 |
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SEED = 1 |
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N_ESTIMATORS = 30 |
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FEATURES = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"] |
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LABELS = ["Setosa", "Versicolour", "Virginica"] |
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MODEL_NAMES = ['DecisionTreeClassifier', 'RandomForestClassifier', 'ExtraTreesClassifier', 'AdaBoostClassifier'] |
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iris = load_iris() |
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MODELS = [ |
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DecisionTreeClassifier(max_depth=None), |
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RandomForestClassifier(n_estimators=N_ESTIMATORS, n_jobs=-1), |
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ExtraTreesClassifier(n_estimators=N_ESTIMATORS, n_jobs=-1), |
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AdaBoostClassifier(DecisionTreeClassifier(max_depth=3), n_estimators=N_ESTIMATORS) |
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] |
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def create_plot(feature_string, n_estimators, max_depth, model_idx): |
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np.random.seed(SEED) |
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feature_list = feature_string.split(',') |
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feature_list = [s.strip() for s in feature_list] |
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idx_x = FEATURES.index(feature_list[0]) |
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idx_y = FEATURES.index(feature_list[1]) |
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X = iris.data[:, [idx_x, idx_y]] |
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y = iris.target |
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rnd_idx = np.random.permutation(X.shape[0]) |
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X = X[rnd_idx] |
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y = y[rnd_idx] |
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X = (X - X.mean(0)) / X.std(0) |
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model_name = MODEL_NAMES[model_idx] |
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model = MODELS[model_idx] |
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if model_idx != 0: model.n_estimators = n_estimators |
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if model_idx != 3: model.max_depth = max_depth |
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if model_idx == 3: model.estimator.max_depth = max_depth |
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model.fit(X, y) |
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score = round(model.score(X, y), 3) |
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 |
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 |
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xrange = np.arange(x_min, x_max, GRANULARITY) |
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yrange = np.arange(y_min, y_max, GRANULARITY) |
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xx, yy = np.meshgrid(xrange, yrange) |
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Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) |
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Z = Z.reshape(xx.shape) |
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fig = plt.figure(figsize=(4, 3.5)) |
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ax = fig.add_subplot(111) |
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ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.65) |
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for i, label in enumerate(LABELS): |
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X_label = X[y==i,:] |
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y_label = y[y==i] |
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ax.scatter(X_label[:, 0], X_label[:, 1], c=[[C1], [C2], [C3]][i]*len(y_label), edgecolor='k', s=40, label=label) |
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ax.set_xlabel(feature_list[0]); ax.set_ylabel(feature_list[1]) |
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ax.legend() |
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ax.set_title(f'{model_name} | Score: {score}') |
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fig.set_tight_layout(True) |
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fig.set_constrained_layout(True) |
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return fig |
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def iter_grid(n_rows, n_cols): |
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for _ in range(n_rows): |
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with gr.Row(): |
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for _ in range(n_cols): |
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with gr.Column(): |
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yield |
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info = ''' |
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# Plot the decision surfaces of ensembles of trees on the Iris dataset |
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This plot compares the **decision surfaces** learned by a decision tree classifier, a random forest classifier, an extra-trees classifier, and by an AdaBoost classifier. |
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There are in total **four features** in the Iris dataset. In this example you can select **two features at a time** for visualization purposes using the dropdown box below. All features are normalized to zero mean and unit standard deviation. |
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Play around with the **number of estimators** in the ensembles and the **max depth** of the trees using the sliders. |
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Created by [@hubadul](https://huggingface.co/huabdul) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html). |
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''' |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(scale=1): |
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gr.Markdown(info) |
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selections = combinations(FEATURES, 2) |
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selections = [f'{s[0]}, {s[1]}' for s in selections] |
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dd = gr.Dropdown(selections, value=selections[0], interactive=True, label="Input features") |
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slider_estimators = gr.Slider(1, 100, value=30, step=1, label='n_estimators') |
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slider_max_depth = gr.Slider(1, 50, value=10, step=1, label='max_depth') |
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with gr.Column(scale=2): |
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counter = 0 |
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for _ in iter_grid(2, 2): |
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if counter >= len(MODELS): |
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break |
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plot = gr.Plot(show_label=False) |
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fn = partial(create_plot, model_idx=counter) |
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dd.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) |
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slider_estimators.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) |
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slider_max_depth.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) |
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demo.load(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) |
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counter += 1 |
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demo.launch() |
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