add option to pass in absolute tolerance

modules/deeplearning/quantile_regression.py

@@ -22,14 +22,14 @@ def true_func(x):
     # Y = 2 + 1.5 * X + epsilon  # Linear relationship with variance increasing
     # Y = 1 + np.exp(X / 4) + epsilon
     # Y = 1 + np.exp(X / 4) + np.sin((2*np.pi/5)*X)
-    return 1 + np.exp(x / 4) + 0.5*np.sin((2*np.pi/5)*x)
+    return 1 + np.exp(x / 4) + np.sin((2*np.pi/5)*x)
 
 # Generate synthetic dataset
 def make_data(num_points=1000):
     np.random.seed(42)
     X = np.random.rand(num_points, 1) * 10
     # epsilon = np.random.normal(0, X/2, size=(num_points, 1))  # Noise increasing with X
-    epsilon = np.random.normal(0, 0.5 + X/6, size=(num_points, 1))
+    epsilon = np.random.normal(0, 0.5 + X/10, size=(num_points, 1))
     Y = true_func(X)
     Y_eps = Y + epsilon
 
@@ -86,7 +86,7 @@ def build_mse_model():
 
 def run(num_points=1000, num_plot_pts=200):
     # Define quantiles
-    quantiles = [0.1, 0.5, 0.9]
+    quantiles = [0.05, 0.5, 0.95]
     models = {}
 
     X_train, X_test, Y_train, Y_test, X, Y = make_data(num_points=num_points)
@@ -120,9 +120,9 @@ def run(num_points=1000, num_plot_pts=200):
     # Plot the results
     plt.figure(figsize=(8, 6))
     plt.scatter(X_test, Y_test, alpha=0.3, label="Test Data")
-    plt.plot(X_range, predictions[0.1], label="Quantile 0.1", color='red')
+    plt.plot(X_range, predictions[0.05], label="Quantile 0.05", color='red')
     plt.plot(X_range, predictions[0.5], label="Quantile 0.5 (Median)", color='green')
-    plt.plot(X_range, predictions[0.9], label="Quantile 0.9", color='blue')
+    plt.plot(X_range, predictions[0.95], label="Quantile 0.95", color='blue')
     plt.plot(X_range, mae_predictions, label="MAE", color='magenta')
     plt.plot(X_range, mse_predictions, label="MSE", color='cyan')
     plt.plot(X_range, bulk_predictions, label="Bulk Quantile Model (Wimmers)", color='orange')