Init commit

.gitignore

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+
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# VS Code
+.vscode/
+
+# pycache
+__pycache__/

app.py

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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()

requirements.txt

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+scikit-learn==1.2.2
+matplotlib==3.7.1