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) + np.sin((2*np.pi/5)*x)
+    return 1 + np.exp(x / 4) + 3*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/10, size=(num_points, 1))
+    epsilon = np.random.normal(0, 0.25 + X/5, size=(num_points, 1))
     Y = true_func(X)
     Y_eps = Y + epsilon
 
@@ -42,7 +42,7 @@ def make_data(num_points=1000):
 def build_quantile_model(q):
     model = tf.keras.models.Sequential([
         tf.keras.layers.InputLayer(shape=(1,)),
-        tf.keras.layers.Dense(64, activation='relu'),  # Hidden layer
+        tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(1)  # Output layer
@@ -53,7 +53,7 @@ def build_quantile_model(q):
 def build_bulk_quantile_model():
     model = tf.keras.models.Sequential([
         tf.keras.layers.InputLayer(shape=(1,)),
-        tf.keras.layers.Dense(64, activation='relu'),  # Hidden layer
+        tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(1)  # Output layer
@@ -64,7 +64,7 @@ def build_bulk_quantile_model():
 def build_mae_model():
     model = tf.keras.models.Sequential([
         tf.keras.layers.InputLayer(shape=(1,)),
-        tf.keras.layers.Dense(64, activation='relu'),  # Hidden layer
+        tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(1)  # Output layer
@@ -76,7 +76,7 @@ def build_mae_model():
 def build_mse_model():
     model = tf.keras.models.Sequential([
         tf.keras.layers.InputLayer(shape=(1,)),
-        tf.keras.layers.Dense(64, activation='relu'),  # Hidden layer
+        tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(64, activation='relu'),
         tf.keras.layers.Dense(1)  # Output layer
@@ -119,7 +119,7 @@ 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.scatter(X_test[:, 0], Y_test[:, 0], alpha=0.3, label="Test Data")
     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[0.95], label="Quantile 0.95", color='blue')