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import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.covariance import OAS
def generate_data(n_samples, n_features):
X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
if n_features > 1:
X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
return X, y
def classify(n_train, n_test, n_averages, n_features_max, step):
acc_clf1, acc_clf2, acc_clf3 = [], [], []
n_features_range = range(1, n_features_max + 1, step)
for n_features in n_features_range:
score_clf1, score_clf2, score_clf3 = 0, 0, 0
for _ in range(n_averages):
X, y = generate_data(n_train, n_features)
clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
oa = OAS(store_precision=False, assume_centered=False)
clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(X, y)
X, y = generate_data(n_test, n_features)
score_clf1 += clf1.score(X, y)
score_clf2 += clf2.score(X, y)
score_clf3 += clf3.score(X, y)
acc_clf1.append(score_clf1 / n_averages)
acc_clf2.append(score_clf2 / n_averages)
acc_clf3.append(score_clf3 / n_averages)
features_samples_ratio = np.array(n_features_range) / n_train
plt.plot(
features_samples_ratio,
acc_clf1,
linewidth=2,
label="LDA",
color="gold",
linestyle="solid",
)
plt.plot(
features_samples_ratio,
acc_clf2,
linewidth=2,
label="LDA with Ledoit Wolf",
color="navy",
linestyle="dashed",
)
plt.plot(
features_samples_ratio,
acc_clf3,
linewidth=2,
label="LDA with OAS",
color="red",
linestyle="dotted",
)
plt.xlabel("n_features / n_samples")
plt.ylabel("Classification accuracy")
plt.legend(loc="lower left")
plt.ylim((0.65, 1.0))
plt.suptitle(
"LDA (Linear Discriminant Analysis) vs. "
+ "\n"
+ "LDA with Ledoit Wolf vs. "
+ "\n"
+ "LDA with OAS (1 discriminative feature)"
)
# Convert the plot to Gradio compatible format
plt.tight_layout()
plt.savefig("plot.png")
return "plot.png"
# Define the input and output interfaces
inputs = [
gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_train", default=20),
gr.inputs.Slider(minimum=1, maximum=500, step=1, label="n_test", default=200),
gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_averages", default=50),
gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_features_max", default=75),
gr.inputs.Slider(minimum=1, maximum=20, step=1, label="step", default=4),
]
output = gr.outputs.Image(type="pil")
examples = [
[20, 200, 50, 75, 4],
[30, 250, 60, 80, 5],
[40, 300, 70, 90, 6],
]
# Create the Gradio app
title = "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification"
description = "This example illustrates how the Ledoit-Wolf and Oracle Shrinkage Approximating (OAS) estimators of covariance can improve classification. See the original example: https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html"
gr.Interface(classify, inputs, output, examples=examples, title=title, description=description).launch()
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