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import gradio as gr
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold, permutation_test_score
import numpy as np
import tempfile
import os
def run_permutation_test(display_option, kernel, random_state, n_permutations):
iris = load_iris()
X = iris.data
y = iris.target
n_uncorrelated_features = 20
rng = np.random.RandomState(seed=0)
X_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))
clf = SVC(kernel=kernel, random_state=random_state)
cv = StratifiedKFold(2, shuffle=True, random_state=0)
score_iris, perm_scores_iris, pvalue_iris = permutation_test_score(
clf, X, y, scoring="accuracy", cv=cv, n_permutations=n_permutations
)
score_rand, perm_scores_rand, pvalue_rand = permutation_test_score(
clf, X_rand, y, scoring="accuracy", cv=cv, n_permutations=n_permutations
)
original_plot_path = None
random_plot_path = None
if display_option in ['original', 'both']:
# Original data
fig, ax = plt.subplots()
ax.hist(perm_scores_iris, bins=20, density=True)
ax.axvline(score_iris, ls="--", color="r")
score_label = f"Score on original\ndata: {score_iris:.2f}\n(p-value: {pvalue_iris:.3f})"
ax.text(0.7, 10, score_label, fontsize=12)
ax.set_xlabel("Accuracy score")
ax.set_ylabel("Probability")
original_plot_path = os.path.join(tempfile.mkdtemp(), "original_plot.png")
plt.savefig(original_plot_path)
plt.close()
if display_option in ['random', 'both']:
# Random data
fig, ax = plt.subplots()
ax.hist(perm_scores_rand, bins=20, density=True)
ax.set_xlim(0.13)
ax.axvline(score_rand, ls="--", color="r")
score_label = f"Score on original\ndata: {score_rand:.2f}\n(p-value: {pvalue_rand:.3f})"
ax.text(0.14, 7.5, score_label, fontsize=12)
ax.set_xlabel("Accuracy score")
ax.set_ylabel("Probability")
random_plot_path = os.path.join(tempfile.mkdtemp(), "random_plot.png")
plt.savefig(random_plot_path)
plt.close()
return original_plot_path, random_plot_path
iface = gr.Interface(
fn=run_permutation_test,
inputs=[
gr.inputs.Dropdown(
choices=["original", "random", "both"],
label="Display Option",
default="both"
),
gr.inputs.Dropdown(
choices=["linear", "rbf", "poly"],
label="Kernel",
default="linear"
),
gr.inputs.Slider(
minimum=0, maximum=10, step=1,
label="Random State",
default=7
),
gr.inputs.Slider(
minimum=100, maximum=2000, step=100,
label="Number of Permutations",
default=1000
)
],
outputs=["image", "image"],
title="Test with permutations the significance of a classification score",
description="This example demonstrates the use of permutation_test_score to evaluate the significance of a cross-validated score using permutations. This operation is being performed on the Iris Dataset. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_permutation_tests_for_classification.html",
examples=[
["both", "linear", 7, 1000],
["original", "rbf", 3, 500],
["random", "poly", 5, 1500]
],
allow_flagging=False
)
iface.launch()
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