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import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
def plot_penalties():
# Plot the results
plt.clf()
l1_color = 'r' # hard coded as color picker not working
l2_color = 'g' # hard coded as color picker not working
elastic_net_color = 'b' # hard coded as color picker not working
line = np.linspace(-1.5, 1.5, 1001)
xx, yy = np.meshgrid(line, line)
l2 = xx**2 + yy**2
l1 = np.abs(xx) + np.abs(yy)
rho = 0.5
elastic_net = rho * l1 + (1 - rho) * l2
fig = plt.figure(figsize=(10, 10), dpi=100)
ax = plt.gca()
elastic_net_contour = plt.contour(
xx, yy, elastic_net, levels=[1], colors=elastic_net_color
)
l2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)
l1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)
ax.set_aspect("equal")
ax.spines["left"].set_position("center")
ax.spines["right"].set_color("none")
ax.spines["bottom"].set_position("center")
ax.spines["top"].set_color("none")
plt.clabel(
elastic_net_contour,
inline=1,
fontsize=18,
fmt={1.0: "elastic-net"},
manual=[(-1, -1)],)
plt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: "L2"}, manual=[(-1, -1)])
plt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: "L1"}, manual=[(-1, -1)])
plt.tight_layout()
# plt.show()
return fig
title = "SGD Penalties"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(
"""
### The plot shows the contours of L1, L2 and Elastic Net regularizers.
### The value of penalties is equal to 1 in all of them.
### L2 regularizer is used for linear SVM models, L1 and elastic net brings sparsity in the models
### SGDClassifier and SGDRegressor support all of the above.
""")
gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py)**")
btn = gr.Button(value="Visualize SGD penalties")
btn.click(plot_penalties, outputs= gr.Plot() ) #
demo.launch() |