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import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from sklearn.linear_model import Lasso, ElasticNet
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
)
model_card = f"""
## Description
This demo estimates **Lasso** and **Elastic-Net** regression models on a manually generated sparse signal corrupted with an additive noise.
You can play around with different ``regularization strength``, ``mixing ratio between L1 and L2``, ``number of samples``, ``number of features`` to see the effect
## Dataset
Simulation dataset
"""
def do_train(alpha, l1_ratio, n_samples, n_features):
np.random.seed(42)
X = np.random.randn(n_samples, n_features)
# Decreasing coef w. alternated signs for visualization
idx = np.arange(n_features)
coef = (-1) ** idx * np.exp(-idx / 10)
coef[10:] = 0 # sparsify coef
y = np.dot(X, coef)
# Add noise
y += 0.01 * np.random.normal(size=n_samples)
# Split data in train set and test set
n_samples = X.shape[0]
X_train, y_train = X[: n_samples // 2], y[: n_samples // 2]
X_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]
lasso = Lasso(alpha=alpha)
y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
r2_score_lasso = r2_score(y_test, y_pred_lasso)
enet = ElasticNet(alpha=alpha, l1_ratio=l1_ratio)
y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
r2_score_enet = r2_score(y_test, y_pred_enet)
fig, axes = plt.subplots()
m, s, _ = axes.stem(
np.where(enet.coef_)[0],
enet.coef_[enet.coef_ != 0],
markerfmt="x",
label="Elastic net coefficients",
)
plt.setp([m, s], color="#2ca02c")
m, s, _ = plt.stem(
np.where(lasso.coef_)[0],
lasso.coef_[lasso.coef_ != 0],
markerfmt="x",
label="Lasso coefficients",
)
plt.setp([m, s], color="#ff7f0e")
axes.stem(
np.where(coef)[0],
coef[coef != 0],
label="True coefficients",
markerfmt="bx",
)
axes.legend(loc="best")
axes.set_title("Elastic net and Lasso coefficients")
text = f"Lasso R^2: {r2_score_lasso:.3f}, Elastic Net R^2: {r2_score_enet:.3f}"
return fig, text
with gr.Blocks(theme=theme) as demo:
gr.Markdown('''
<div>
<h1 style='text-align: center'>Lasso and Elastic Net for Sparse Signals</h1>
</div>
''')
gr.Markdown(model_card)
gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py\">scikit-learn</a>")
alpha = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Controlling regularization strength: alpha. Using alpha = 0 with the Lasso object is not advised")
l1_ratio = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label="The ElasticNet mixing parameter: l1_ratio. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.")
n_samples = gr.Slider(minimum=50, maximum=500, step=50, value=50, label="Number of samples")
n_features = gr.Slider(minimum=50, maximum=200, step=50, value=50, label="Number of features")
with gr.Row():
with gr.Column():
plot = gr.Plot(label="Coefficients plot")
with gr.Column():
results = gr.Textbox(label="Results")
alpha.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
l1_ratio.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
n_samples.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
n_features.change(fn=do_train, inputs=[alpha, l1_ratio, n_samples, n_features], outputs=[plot, results])
demo.launch() |