Create bagging_regressor_viz.py

bagging_regressor_viz.py

@@ -0,0 +1,96 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import streamlit as st
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import BaggingRegressor
+from sklearn.svm import SVR
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import r2_score
+
+plt.style.use('seaborn-v0_8-bright')
+
+n_train = 150
+n_test = 100
+noise = 0.1
+
+np.random.seed(0)
+
+# Generate data
+def f(x):
+    x = x.ravel()
+    return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)
+
+def generate(n_samples, noise):
+    X = np.random.rand(n_samples) * 10 - 5
+    X = np.sort(X).ravel()
+    y = np.exp(-X ** 2) + 1.5 * np.exp(-(X - 2) ** 2) + np.random.normal(0.0, noise, n_samples)
+    X = X.reshape((n_samples, 1))
+    return X, y
+
+X_train, y_train = generate(n_samples=n_train, noise=noise)
+X_test, y_test = generate(n_samples=n_test, noise=noise)
+
+st.sidebar.markdown("# Bagging Regressor")
+
+estimator = st.sidebar.selectbox(
+    'Select base estimator',
+    ('Decision Tree', 'SVM', 'KNN')
+)
+
+n_estimators = int(st.sidebar.number_input('Enter number of estimators', min_value=1, value=10))
+
+max_samples = st.sidebar.slider('Max Samples', 1, n_train, n_train, step=25)
+
+bootstrap_samples = st.sidebar.radio(
+    "Bootstrap Samples",
+    ('True', 'False')
+) == 'True'  # Convert string to boolean
+
+# Load initial graph
+fig, ax = plt.subplots()
+
+# Plot initial graph
+ax.scatter(X_train, y_train, color="yellow", edgecolor="black")
+orig = st.pyplot(fig)
+
+if st.sidebar.button('Run Algorithm'):
+
+    if estimator == 'Decision Tree':
+        algo = DecisionTreeRegressor()
+    elif estimator == 'SVM':
+        algo = SVR()
+    else:
+        algo = KNeighborsRegressor()
+
+    reg = algo.fit(X_train, y_train)
+
+    bag_reg = BaggingRegressor(
+        estimator=algo,  # Updated parameter name
+        n_estimators=n_estimators,
+        max_samples=max_samples,
+        bootstrap=bootstrap_samples
+    ).fit(X_train, y_train)
+    
+    bag_reg_predict = bag_reg.predict(X_test)
+    reg_predict = reg.predict(X_test)
+
+    # R2 scores
+    bag_r2 = r2_score(y_test, bag_reg_predict)
+    reg_r2 = r2_score(y_test, reg_predict)
+
+    orig.empty()
+
+    fig, ax = plt.subplots()
+    fig1, ax1 = plt.subplots()
+
+    st.subheader(f"Bagging - {estimator} (R2 score - {bag_r2:.2f})")
+    ax1.scatter(X_train, y_train, color="yellow", edgecolor="black")
+    ax1.plot(X_test, bag_reg_predict, linewidth=1, label="Bagging")
+    ax1.legend()
+    st.pyplot(fig1)
+
+    st.subheader(f"{estimator} (R2 score - {reg_r2:.2f})")
+    ax.scatter(X_train, y_train, color="yellow", edgecolor="black")
+    ax.plot(X_test, reg_predict, linewidth=1, color='red', label=estimator)
+    ax.legend()
+    st.pyplot(fig)