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import numpy as np
import gradio as gr
from sklearn.svm import LinearSVC
from sklearn.datasets import load_iris
from sklearn.pipeline import make_pipeline
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize, StandardScaler
import utils
def app_fn(n_random_features: int, test_size: float, random_state_val: int):
X, y = load_iris(return_X_y=True)
# Add noisy features
random_state = np.random.RandomState(random_state_val)
n_samples, n_features = X.shape
X = np.concatenate([X, random_state.randn(n_samples, n_random_features)], axis=1)
# Solving Binary Problem
X_train, X_test, y_train, y_test = train_test_split(
X[y < 2], y[y < 2], test_size=test_size, random_state=random_state
)
clf_bin = make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
clf_bin.fit(X_train, y_train)
fig_bin = utils.plot_binary_pr_curve(clf_bin, X_test, y_test)
# Solving Multi-Label Problem
Y = label_binarize(y, classes=[0, 1, 2])
X_train_multi, X_test_multi, Y_train, Y_test = train_test_split(
X, Y, test_size=test_size, random_state=random_state
)
clf = OneVsRestClassifier(
make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
)
clf.fit(X_train_multi, Y_train)
fig_multi = utils.plot_multi_label_pr_curve(clf, X_test_multi, Y_test)
return fig_bin, fig_multi
title = "Precision-Recall Curves"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(
"""
This demo shows the precision-recall curves on the Iris dataset \
using a Linear SVM classifier + StandardScaler. \
Noise is added to the dataset to make the problem more challenging. \
The dataset is split into train and test sets. \
The model is trained on the train set and evaluated on the test set. \
Two separate problems are solved:
- Binary classification: class 0 vs class 1
- Multi-label classification: class 0 vs class 1 vs class 2
See the scikit-learn example [here](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py).
"""
)
with gr.Row():
n_random_features = gr.inputs.Slider(0, 1000, 50, 800,label="Number of Random Features")
test_size = gr.inputs.Slider(0.1, 0.9, 0.01, 0.5, label="Test Size")
random_state_val = gr.inputs.Slider(0, 100, 5, 0,label="Random State")
with gr.Row():
fig_bin = gr.Plot(label="Binary PR Curve")
fig_multi = gr.Plot(label="Multi-Label PR Curve")
n_random_features.change(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
test_size.change(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
random_state_val.change(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
demo.load(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
demo.launch()
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