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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 = 0.05 | |
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, max_depth, 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] | |
if model_idx != 0: model.n_estimators = n_estimators | |
if model_idx != 3: model.max_depth = max_depth | |
if model_idx == 3: model.estimator.max_depth = max_depth | |
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, GRANULARITY) | |
yrange = np.arange(y_min, y_max, GRANULARITY) | |
xx, yy = np.meshgrid(xrange, yrange) | |
Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) | |
Z = Z.reshape(xx.shape) | |
fig = plt.figure(figsize=(4, 3.5)) | |
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}') | |
fig.set_tight_layout(True) | |
fig.set_constrained_layout(True) | |
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 | |
info = ''' | |
# Plot the decision surfaces of ensembles of trees on the Iris dataset | |
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. | |
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. | |
Play around with the **number of estimators** in the ensembles and the **max depth** of the trees using the sliders. | |
Created by [@hubadul](https://huggingface.co/huabdul) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html). | |
''' | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(scale=1): | |
gr.Markdown(info) | |
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_estimators = gr.Slider(1, 100, value=30, step=1, label='n_estimators') | |
slider_max_depth = gr.Slider(1, 50, value=10, step=1, label='max_depth') | |
with gr.Column(scale=2): | |
counter = 0 | |
for _ in iter_grid(2, 2): | |
if counter >= len(MODELS): | |
break | |
plot = gr.Plot(show_label=False) | |
fn = partial(create_plot, model_idx=counter) | |
dd.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) | |
slider_estimators.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) | |
slider_max_depth.change(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) | |
demo.load(fn, inputs=[dd, slider_estimators, slider_max_depth], outputs=[plot]) | |
counter += 1 | |
demo.launch() | |