Create voting_classifier_viz.py

voting_classifier_viz.py

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+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