ML_app.py

import streamlit as st
import pandas as pd
import numpy as np
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 plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve
from sklearn.metrics import precision_score, recall_score
st.set_option('deprecation.showPyplotGlobalUse', False)

def main():
    st.title("Binary Classification Web App")
    st.sidebar.title("Binary Classisfier Hyperparameter Tuning")
    st.markdown(
    """
<style>
.sidebar .sidebar-content {
    background-image: linear-gradient(#2e7bcf,#2e7bcf);
    color: yellow;
},
</style>

""",
    unsafe_allow_html=True,
)
    st.sidebar.markdown("Are your mushrooms 🍄 edible or poisonous?")
    st.markdown("Are your mushrooms 🍄 edible or poisonous?")
    
    @st.cache(persist=True) #Using the st.cache function decorator to cache the output of a function to disk,
    #so that if the function or its inputs remain unchanged, we do not unneccesarily call it again.
    def load_data():
        data = pd.read_csv('mushrooms.csv')
        label = LabelEncoder()
        for col in data.columns:
            data[col] = label.fit_transform(data[col])
        return data

    @st.cache(persist=True)
    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=50)
        return x_train, x_test, y_train, y_test

    def plot_metrics(metrics_list):
        if 'Confusion Matrix' in metrics_list:
            st.subheader("Confusion Matrix")
            plot_confusion_matrix(model, x_test, y_test, display_labels = class_names)
            st.pyplot()

        if 'ROC Curve' in metrics_list:
            st.subheader("ROC Curve")
            plot_roc_curve(model, x_test, y_test)
            st.pyplot()

        if 'Precision-Recall Curve' in metrics_list:
            st.subheader("Precision-Recall Curve")
            plot_precision_recall_curve(model,x_test,y_test)
            st.pyplot()

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

    if st.sidebar.checkbox("Show raw data", False):
        st.subheader("Mushroom Data Set (Classification)")
        st.write(df)
    
    class_names = ['edible', 'poisonous']
    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 = st.sidebar.number_input("C (Inverse regularization parameter)", 0.01,10.0,step=0.01,key="C")
        kernel = st.sidebar.radio("Kernel", ("rbf","linear"), key="kernel")
        gamma = st.sidebar.radio("Gamm (Kernel Coefficient", ("scale","auto"), key='gamma')

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

        if st.sidebar.button("Classify", key="classify"):
            st.subheader("Support Vector Machine (SVM) Results")
            model = SVC(C=C, 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("Accuracy: ",accuracy.round(2))
            st.write("Precision: ",precision_score(y_test,y_pred, labels=class_names).round(2))
            st.write("Recall: ",recall_score(y_test,y_pred, labels=class_names).round(2))
            plot_metrics(metrics)

    if classifier == "Logistic Regression":
        st.sidebar.subheader("Model Hyperparameters")
        C = st.sidebar.number_input("C (Inverse regularization parameter)", 0.01,10.0,step=0.01,key="C_LR")
        max_iter = st.sidebar.slider("Maximum Iterations", 100, 500, key="max_iter")

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

        if st.sidebar.button("Classify", key="classify"):
            st.subheader("Logistic Regression Results")
            model = LogisticRegression(C=C, 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("Accuracy: ",accuracy.round(2))
            st.write("Precision: ",precision_score(y_test,y_pred, labels=class_names).round(2))
            st.write("Recall: ",recall_score(y_test,y_pred, labels=class_names).round(2))
            plot_metrics(metrics)

    if classifier == 'Random Forest':
        st.sidebar.subheader("Model Hyperparameters")
        n_estimators = st.sidebar.number_input("The number of decision trees",100,500,step=10,key="n_estimators")
        max_depth = st.sidebar.number_input("The maximum depth of the trees",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 would you like to plot?", ('Confusion Matrix', 'Precision-Recall Curve', 'ROC 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("Accuracy: ",accuracy.round(2))
            st.write("Precision: ",precision_score(y_test,y_pred, labels=class_names).round(2))
            st.write("Recall: ",recall_score(y_test,y_pred, labels=class_names).round(2))
            plot_metrics(metrics)
if __name__ == '__main__':
    main()