Intital 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.ensemble import RandomForestClassifier, VotingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.naive_bayes import GaussianNB
+
+
+def choose_model(model):
+    if model == "Logistic Regression":
+        return LogisticRegression(max_iter=1000, random_state=123)
+    elif model == "Random Forest":
+        return RandomForestClassifier(n_estimators=100, random_state=123)
+    elif model == "Gaussian Naive Bayes":
+        return GaussianNB()
+    else:
+        raise ValueError("Model is not supported.")
+
+
+def get_proba_plots(
+    model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight
+):
+    clf1 = choose_model(model_1)
+    clf2 = choose_model(model_2)
+    clf3 = choose_model(model_3)
+    X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
+    y = np.array([1, 1, 2, 2])
+
+    eclf = VotingClassifier(
+        estimators=[("clf1", clf1), ("clf2", clf2), ("clf3", clf3)],
+        voting="soft",
+        weights=[model_1_weight, model_2_weight, model_3_weight],
+    )
+
+    # predict class probabilities for all classifiers
+    probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]
+
+    # get class probabilities for the first sample in the dataset
+    class1_1 = [pr[0, 0] for pr in probas]
+    class2_1 = [pr[0, 1] for pr in probas]
+
+    # plotting
+
+    N = 4  # number of groups
+    ind = np.arange(N)  # group positions
+    width = 0.35  # bar width
+
+    fig, ax = plt.subplots()
+
+    # bars for classifier 1-3
+    p1 = ax.bar(
+        ind, np.hstack(([class1_1[:-1], [0]])), width, color="green", edgecolor="k"
+    )
+    p2 = ax.bar(
+        ind + width,
+        np.hstack(([class2_1[:-1], [0]])),
+        width,
+        color="lightgreen",
+        edgecolor="k",
+    )
+
+    # bars for VotingClassifier
+    ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color="blue", edgecolor="k")
+    ax.bar(
+        ind + width, [0, 0, 0, class2_1[-1]], width, color="steelblue", edgecolor="k"
+    )
+
+    # plot annotations
+    plt.axvline(2.8, color="k", linestyle="dashed")
+    ax.set_xticks(ind + width)
+    ax.set_xticklabels(
+        [
+            f"{model_2}\nweight {model_1_weight}",
+            f"{model_1}\nweight {model_2_weight}",
+            f"{model_3}\nweight {model_3_weight}",
+            "VotingClassifier\n(average probabilities)",
+        ],
+        rotation=40,
+        ha="right",
+    )
+    plt.ylim([0, 1])
+    plt.title("Class probabilities for sample 1 by different classifiers")
+    plt.legend([p1[0], p2[0]], ["class 1", "class 2"], loc="upper left")
+    plt.tight_layout()
+    plt.show()
+    return fig
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        model_1 = gr.Dropdown(
+            [
+                "Logistic Regression",
+                "Random Forest",
+                "Gaussian Naive Bayes",
+            ],
+            label="Model 1",
+            value="Logistic Regression",
+        )
+        model_2 = gr.Dropdown(
+            [
+                "Logistic Regression",
+                "Random Forest",
+                "Gaussian Naive Bayes",
+            ],
+            label="Model 2",
+            value="Random Forest",
+        )
+        model_3 = gr.Dropdown(
+            [
+                "Logistic Regression",
+                "Random Forest",
+                "Gaussian Naive Bayes",
+            ],
+            label="Model 3",
+            value="Gaussian Naive Bayes",
+        )
+
+    with gr.Row():
+        model_1_weight = gr.Slider(
+            minimum=1, maximum=10, value=1, label="Model 1 Weight", step=1
+        )
+        model_2_weight = gr.Slider(
+            minimum=1, maximum=10, value=1, label="Model 2 Weight", step=1
+        )
+        model_3_weight = gr.Slider(
+            minimum=1, maximum=10, value=5, label="Model 3 Weight", step=1
+        )
+
+    proba_plots = gr.Plot()
+
+    model_1.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+    model_2.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+    model_3.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+    model_1_weight.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+    model_2_weight.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+    model_3_weight.change(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+
+    demo.load(
+        get_proba_plots,
+        [model_1, model_2, model_3, model_1_weight, model_2_weight, model_3_weight],
+        proba_plots,
+        queue=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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