Merge branch 'RidgeRMSEwithLamda'

Author: SiriwatHuntra

Regularization/scratching/Ridge_ass2.py

@@ -24,7 +24,7 @@ def polyRidge(degree):
         y_train, y_test = y[train_index], y[test_index]
 
         # Create and train the Ridge regression model
-        ridge = Ridge(alpha=10000)  # Adjust alpha for regularization strength
+        ridge = Ridge(alpha=100000)  # Adjust alpha for regularization strength
         ridge.fit(X_train, y_train)
 
         # Make predictions on the test and training sets
@@ -50,7 +50,6 @@ def polyRidge(degree):
     # Print results and return errors
     return E_train, E_out
 
-
 def generate_sin():
     np.random.seed(42)
     X = np.random.rand(100, 1) * 10

Regularization/scratching/lamda.py

@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import Ridge
+from sklearn.metrics import mean_squared_error
+
+def generate_sin():
+    np.random.seed(42)
+    X = np.random.rand(100, 1) * 10
+    Y = np.sin(X) + np.random.randn(100) / 10
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
+    return X_train, Y_train, X_test, Y_test
+
+def import_csv(path):
+    df = pd.read_csv(path)
+    X = df['Height'].values.reshape(-1, 1)  # Reshape for sklearn
+    Y = df['Weight'].values
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
+    return X_train, Y_train, X_test, Y_test
+
+def ridge_regression(X_train, Y_train, X_test, Y_test, alpha):
+    model = Ridge(alpha=alpha)
+    model.fit(X_train, Y_train)
+
+    train_rmse = mean_squared_error(Y_train, model.predict(X_train))
+    test_rmse = mean_squared_error( Y_test, model.predict(X_test))
+
+    return train_rmse, test_rmse
+
+def plot_rmse_vs_alpha(alphas, train_rmse, test_rmse):
+
+    plt.figure(figsize=(8, 6))
+    plt.plot(alphas, train_rmse, label="Train", marker='o', linestyle='-')
+    plt.plot(alphas, test_rmse, label="Test", marker='s', linestyle='-')
+    plt.xlabel("Model Complexity (Log scale)")
+    plt.xscale('log')
+    plt.ylabel("RMSE")
+    plt.title("Model Complexity vs. E_train and E_test")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+X_train, Y_train, X_test, Y_test = import_csv("Regularization/dataset/HeightWeight.csv")
+#X_train, Y_train, X_test, Y_test = generate_sin()
+
+alphas = np.arange(1, 100000, 100)
+E_train = []
+E_test = []
+
+for alpha_ in alphas:
+    train_rmse, test_rmse = ridge_regression(X_train, Y_train, X_test, Y_test, alpha_)
+    E_train.append(np.sqrt(train_rmse))
+    E_test.append(np.sqrt(test_rmse))
+
+plot_rmse_vs_alpha(alphas, E_train, E_test)
+
+