Create bagging_classifier_viz.py

bagging_classifier_viz.py

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+import matplotlib.pyplot as plt
+import streamlit as st
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import make_moons
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.ensemble import BaggingClassifier
+from sklearn.metrics import accuracy_score
+
+# Generate data
+X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+# Function to draw meshgrid for decision boundary visualization
+def draw_meshgrid():
+    a = np.arange(start=X[:, 0].min() - 1, stop=X[:, 0].max() + 1, step=0.01)
+    b = np.arange(start=X[:, 1].min() - 1, stop=X[:, 1].max() + 1, step=0.01)
+    XX, YY = np.meshgrid(a, b)
+    input_array = np.array([XX.ravel(), YY.ravel()]).T
+    return XX, YY, input_array
+
+plt.style.use('seaborn-v0_8-bright')
+
+st.sidebar.markdown("# Bagging Classifier")
+
+# Sidebar inputs
+estimators = st.sidebar.selectbox(
+    'Select base estimator',
+    ('Decision Tree', 'KNN', 'SVM')
+)
+
+n_estimators = int(st.sidebar.number_input('Enter number of estimators', min_value=1, value=10))
+max_samples = st.sidebar.slider('Max Samples', 1, 375, 375, step=25)
+bootstrap_samples = st.sidebar.radio("Bootstrap Samples", ('True', 'False')) == 'True'
+max_features = st.sidebar.slider('Max Features', 1, 2, 2, key=1234)
+bootstrap_features = st.sidebar.radio("Bootstrap Features", ('False', 'True'), key=2345) == 'True'
+
+# Load initial graph
+fig, ax = plt.subplots()
+ax.scatter(X.T[0], X.T[1], c=y, cmap='rainbow')
+orig = st.pyplot(fig)
+
+if st.sidebar.button('Run Algorithm'):
+    if estimators == "Decision Tree":
+        estimator = DecisionTreeClassifier()
+    elif estimators == "KNN":
+        estimator = KNeighborsClassifier()
+    else:
+        estimator = SVC()
+
+    clf = estimator.fit(X_train, y_train)
+    y_pred_tree = clf.predict(X_test)
+
+    bag_clf = BaggingClassifier(
+        estimator=estimator,
+        n_estimators=n_estimators,
+        max_samples=max_samples,
+        bootstrap=bootstrap_samples,
+        max_features=max_features,
+        bootstrap_features=bootstrap_features,
+        random_state=42
+    )
+    bag_clf.fit(X_train, y_train)
+    y_pred = bag_clf.predict(X_test)
+
+    orig.empty()
+
+    fig, ax = plt.subplots()
+    fig1, ax1 = plt.subplots()
+
+    XX, YY, input_array = draw_meshgrid()
+    labels = clf.predict(input_array)
+    labels1 = bag_clf.predict(input_array)
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header(estimators)
+        ax.scatter(X.T[0], X.T[1], c=y, cmap='rainbow')
+        ax.contourf(XX, YY, labels.reshape(XX.shape), alpha=0.5, cmap='rainbow')
+        orig = st.pyplot(fig)
+        st.subheader(f"Accuracy for {estimators}: {round(accuracy_score(y_test, y_pred_tree), 2)}")
+    with col2:
+        st.header("Bagging Classifier")
+        ax1.scatter(X.T[0], X.T[1], c=y, cmap='rainbow')
+        ax1.contourf(XX, YY, labels1.reshape(XX.shape), alpha=0.5, cmap='rainbow')
+        orig1 = st.pyplot(fig1)
+        st.subheader(f"Accuracy for Bagging: {round(accuracy_score(y_test, y_pred), 2)}")