voting_classifier_viz.py

import streamlit as st
import matplotlib.pyplot as plt
from sthelper import StHelper
import data_helper

# Import all datasets
concentric, linear, outlier, spiral, ushape, xor = data_helper.load_dataset()

# Configure matplotlib styling
plt.style.use('seaborn-v0_8-bright')

# Dataset selection dropdown
st.sidebar.markdown("# Voting Classifier")
dataset = st.sidebar.selectbox(
    "Dataset",
    ("U-Shaped", "Linearly Separable", "Outlier", "Two Spirals", "Concentric Circles", "XOR")
)

# Estimator multi-select
estimators = st.sidebar.multiselect(
    'Estimators',
    [
        'KNN',
        'Logistic Regression',
        'Gaussian Naive Bayes',
        'SVM',
        'Random Forest'
    ]
)

# Voting type radio button
voting_type = st.sidebar.radio(
    "Voting Type",
    (
        'hard',
        'soft',
    )
)

st.header(dataset)
fig, ax = plt.subplots()

# Plot initial graph
df = data_helper.load_initial_graph(dataset, ax)
orig = st.pyplot(fig)

# Extract X and Y
X = df.iloc[:, :2].values
y = df.iloc[:, -1].values

# Create sthelper object
sthelper = StHelper(X, y)

# On button click
if st.sidebar.button("Run Algorithm"):
    algos = sthelper.create_base_estimators(estimators, voting_type)
    voting_clf, voting_clf_accuracy = sthelper.train_voting_classifier(algos, voting_type)
    sthelper.draw_main_graph(voting_clf, ax)
    orig.pyplot(fig)
    figs = sthelper.plot_other_graphs(algos)
    
    # Plot accuracies
    st.sidebar.header("Classification Metrics")
    st.sidebar.text("Voting Classifier accuracy: " + str(voting_clf_accuracy))
    accuracies = sthelper.calculate_base_model_accuracy(algos)
    for i in range(len(accuracies)):
        st.sidebar.text("Accuracy for Model " + str(i + 1) + " - " + str(accuracies[i]))

    counter = 0
    for i in st.columns(len(figs)):
        with i:
            st.pyplot(figs[counter])
            st.text(counter)
        counter += 1