app.py

"""
Binary Classification Web App for Mushroom Dataset.

This Streamlit application allows users to classify mushrooms as edible
or poisonous using different machine learning algorithms.
"""
import streamlit as st
import pandas as pd
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
    ConfusionMatrixDisplay,
    RocCurveDisplay,
    PrecisionRecallDisplay,
    precision_score,
    recall_score
)
import matplotlib.pyplot as plt


def main():
    """Main function to run the Streamlit classification app."""
    st.title("Binary Classification Web App")
    st.sidebar.title("Binary Classification Web App")
    st.markdown("Are your mushrooms edible or poisonous? 🍄")
    st.sidebar.markdown("Are your mushrooms edible or poisonous? 🍄")

    @st.cache_data
    def load_data():
        data = pd.read_csv("mushrooms.csv")
        labelencoder = LabelEncoder()
        for col in data.columns:
            data[col] = labelencoder.fit_transform(data[col])
        return data

    @st.cache_data
    def split(df):
        y = df.type
        x = df.drop(columns=['type'])
        x_train, x_test, y_train, y_test = train_test_split(
            x, y, test_size=0.3, random_state=0
        )
        return x_train, x_test, y_train, y_test

    def plot_metrics(metrics_list, model, x_test, y_test, class_names):
        if 'Confusion Matrix' in metrics_list:
            st.subheader("Confusion Matrix")
            fig, ax = plt.subplots()
            ConfusionMatrixDisplay.from_estimator(
                model, x_test, y_test, display_labels=class_names, ax=ax
            )
            st.pyplot(fig)

        if 'ROC Curve' in metrics_list:
            st.subheader("ROC Curve")
            fig, ax = plt.subplots()
            RocCurveDisplay.from_estimator(model, x_test, y_test, ax=ax)
            st.pyplot(fig)

        if 'Precision-Recall Curve' in metrics_list:
            st.subheader('Precision-Recall Curve')
            fig, ax = plt.subplots()
            PrecisionRecallDisplay.from_estimator(model, x_test, y_test, ax=ax)
            st.pyplot(fig)

    df = load_data()
    class_names = ['edible', 'poisonous']

    x_train, x_test, y_train, y_test = split(df)

    st.sidebar.subheader("Choose Classifier")
    classifier = st.sidebar.selectbox(
        "Classifier",
        ("Support Vector Machine (SVM)", "Logistic Regression", "Random Forest")
    )

    if classifier == 'Support Vector Machine (SVM)':
        st.sidebar.subheader("Model Hyperparameters")
        c_value = st.sidebar.number_input(
            "C (Regularization parameter)",
            0.01,
            10.0,
            step=0.01,
            key='C_SVM'
        )
        kernel = st.sidebar.radio("Kernel", ("rbf", "linear"), key='kernel')
        gamma = st.sidebar.radio(
            "Gamma (Kernel Coefficient)",
            ("scale", "auto"),
            key='gamma'
        )

        metrics = st.sidebar.multiselect(
            "What metrics to plot?",
            ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve')
        )

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Support Vector Machine (SVM) Results")
            model = SVC(C=c_value, kernel=kernel, gamma=gamma)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write(f"Accuracy: {accuracy:.2f}")
            st.write(
                f"Precision: {precision_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            st.write(
                f"Recall: {recall_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            plot_metrics(metrics, model, x_test, y_test, class_names)

    if classifier == 'Logistic Regression':
        st.sidebar.subheader("Model Hyperparameters")
        c_value = st.sidebar.number_input(
            "C (Regularization parameter)",
            0.01,
            10.0,
            step=0.01,
            key='C_LR'
        )
        max_iter = st.sidebar.slider(
            "Maximum number of iterations",
            100,
            500,
            key='max_iter'
        )

        metrics = st.sidebar.multiselect(
            "What metrics to plot?",
            ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve')
        )

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Logistic Regression Results")
            model = LogisticRegression(C=c_value, penalty='l2', max_iter=max_iter)
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write(f"Accuracy: {accuracy:.2f}")
            st.write(
                f"Precision: {precision_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            st.write(
                f"Recall: {recall_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            plot_metrics(metrics, model, x_test, y_test, class_names)

    if classifier == 'Random Forest':
        st.sidebar.subheader("Model Hyperparameters")
        n_estimators = st.sidebar.number_input(
            "The number of trees in the forest",
            100,
            5000,
            step=10,
            key='n_estimators'
        )
        max_depth = st.sidebar.number_input(
            "The maximum depth of the tree",
            1,
            20,
            step=1,
            key='max_depth'
        )
        bootstrap = st.sidebar.radio(
            "Bootstrap samples when building trees",
            (True, False),
            key='bootstrap'
        )
        metrics = st.sidebar.multiselect(
            "What metrics to plot?",
            ('Confusion Matrix', 'ROC Curve', 'Precision-Recall Curve')
        )

        if st.sidebar.button("Classify", key='classify'):
            st.subheader("Random Forest Results")
            model = RandomForestClassifier(
                n_estimators=n_estimators,
                max_depth=max_depth,
                bootstrap=bootstrap,
                n_jobs=-1
            )
            model.fit(x_train, y_train)
            accuracy = model.score(x_test, y_test)
            y_pred = model.predict(x_test)
            st.write(f"Accuracy: {accuracy:.2f}")
            st.write(
                f"Precision: {precision_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            st.write(
                f"Recall: {recall_score(y_test, y_pred, labels=[0, 1]):.2f}"
            )
            plot_metrics(metrics, model, x_test, y_test, class_names)

    if st.sidebar.checkbox("Show raw data", False):
        st.subheader("Mushroom Data Set (Classification)")
        st.write(df)
        st.markdown(
            "This [data set](https://archive.ics.uci.edu/ml/datasets/Mushroom) "
            "includes descriptions of hypothetical samples corresponding to 23 "
            "species of gilled mushrooms in the Agaricus and Lepiota Family "
            "(pp. 500-525). Each species is identified as definitely edible, "
            "definitely poisonous, or of unknown edibility and not recommended. "
            "This latter class was combined with the poisonous one."
        )


if __name__ == '__main__':
    main()