From e1378f9978bdd78c85d449ac3d7470be157a31ea Mon Sep 17 00:00:00 2001
From: rink <rink@ssec.wisc.edu>
Date: Wed, 2 Apr 2025 16:24:44 -0500
Subject: [PATCH] add option to pass in absolute tolerance
---
modules/deeplearning/quantile_regression.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/modules/deeplearning/quantile_regression.py b/modules/deeplearning/quantile_regression.py
index c7b25850..863f2f89 100644
--- a/modules/deeplearning/quantile_regression.py
+++ b/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')
--
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