| import numpy as np | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| from sklearn import datasets | |
| from sklearn import linear_model | |
| from sklearn.svm import l1_min_c | |
| def train_it(solver, intersect_scaling, tol, max_iter): | |
| iris = datasets.load_iris() | |
| X = iris.data | |
| y = iris.target | |
| X = X[y != 2] | |
| y = y[y != 2] | |
| X /= X.max() | |
| cs = l1_min_c(X, y, loss="log") * np.logspace(0, 7, 16) | |
| clf = linear_model.LogisticRegression( | |
| penalty="l1", | |
| solver=solver, | |
| tol=tol, | |
| max_iter=int(max_iter), | |
| warm_start=True, | |
| intercept_scaling=intersect_scaling, | |
| ) | |
| coefs_ = [] | |
| for c in cs: | |
| clf.set_params(C=c) | |
| clf.fit(X, y) | |
| coefs_.append(clf.coef_.ravel().copy()) | |
| coefs_ = np.array(coefs_) | |
| plt.plot(np.log10(cs), coefs_, marker="o") | |
| ymin, ymax = plt.ylim() | |
| plt.xlabel("log(C)") | |
| plt.ylabel("Coefficients") | |
| plt.title("Logistic Regression Path") | |
| plt.axis("tight") | |
| plt.show() | |
| return plt | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Regularization path of L1- Logistic Regression") | |
| gr.Markdown( | |
| """ | |
| This interactive demo is based on the [Regularization path of L1- Logistic Regression] (https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html).This demonstrates how to perform l1-penalized logistic regression on a binary classification problem derived from the Iris dataset. The regularization path plots the progression of the coefficients from exactly 0 to non-zero values as the regularization becomes progressively looser. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| solver = gr.Dropdown( | |
| ["liblinear", "saga"], label="Solver", value="liblinear" | |
| ) | |
| warm_start = gr.Dropdown( | |
| ["True", "False"], label="Warm Start", value="True" | |
| ) | |
| with gr.Column(align="center"): | |
| intersect_scaling = gr.Slider( | |
| value=10000.0, | |
| minimum=0, | |
| maximum=100000, | |
| step=0.1, | |
| label="Intersect Scaling", | |
| ) | |
| tol = gr.Slider( | |
| value=1e-6, minimum=0, maximum=1, step=0.1, label="Tolerance" | |
| ) | |
| max_iter = gr.Slider( | |
| value=1e6, | |
| minimum=0, | |
| maximum=1000000, | |
| step=0.1, | |
| label="Maximum Iterations", | |
| ) | |
| train_buttion = gr.Button(label="Train") | |
| train_buttion.click( | |
| train_it, inputs=[solver, intersect_scaling, tol, max_iter], outputs=gr.Plot() | |
| ) | |
| demo.launch() | |