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import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
def get_plots(min_alpha, max_alpha):
clf = Ridge()
X, y, w = make_regression(
n_samples=10, n_features=10, coef=True, random_state=1, bias=3.5
)
coefs = []
errors = []
alphas = np.logspace(min_alpha, max_alpha, 200)
# Train the model with different regularisation strengths
for a in alphas:
clf.set_params(alpha=a)
clf.fit(X, y)
coefs.append(clf.coef_)
errors.append(mean_squared_error(clf.coef_, w))
# Display results
fig, ax = plt.subplots(1, 2, figsize=(20, 6))
ax[0].plot(alphas, coefs)
ax[0].set_xscale("log")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("weights")
ax[0].set_title("Ridge coefficients as a function of the regularization")
ax[1].plot(alphas, errors)
ax[1].set_xscale("log")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("error")
ax[1].set_title("Coefficient error as a function of the regularization")
fig.tight_layout()
return fig
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown(
"Choose the range of alpha values to plot."
+ " The models you input for alpha are for the exponents of 10,"
+ " so a value of -6 means 10^-6."
)
min_alpha = gr.Slider(
minimum=-10,
maximum=10,
step=0.5,
value=-6,
label="Minimum Alpha Exponent",
)
max_alpha = gr.Slider(
minimum=-10,
maximum=10,
step=0.5,
value=6,
label="Maximum Alpha Exponent",
)
with gr.Column(scale=4):
plots = gr.Plot()
min_alpha.change(
get_plots,
[min_alpha, max_alpha],
plots,
queue=False,
)
max_alpha.change(
get_plots,
[min_alpha, max_alpha],
plots,
queue=False,
)
demo.load(
get_plots,
[min_alpha, max_alpha],
plots,
queue=False,
)
if __name__ == "__main__":
demo.launch()
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