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f24ba2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """Medium-length EQL experiment (10+10 epochs) to verify full pipeline."""
import os, sys, time
import numpy as np
from tqdm import tqdm
from scipy.io import loadmat
import tensorflow as tf
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from reproduce_eql import (
SymbolicNet, Constant, Identity, Square, Sin, Sigmoid, Product,
count_double, l12_smooth, get_folders_started, record_base_info,
append_text_to_summary, plot_train_vs_validation, plot_histogram,
plot_descaled_real_vs_prediction, generate_all_data, generate_variable_list,
save_weights, network
)
if __name__ == "__main__":
target_city = "Roskilde"
steps_ahead = 6
feature = "wind_speed"
target_cities = ["Esbjerg", "Odense", "Roskilde"]
city_index = target_cities.index(target_city)
data = loadmat('Denmark_data/wind_speed/step1.mat')
y_min = data["y_min_tr"][0][city_index]
y_max = data["y_max_tr"][0][city_index]
config = {
"use_rescaled_MSE": True,
"a_L_0.5": 5e-3,
"threshold_value": 7.5e-3,
"lambda_reg": 3.0,
"steps_ahead": 6,
"feature": feature,
"target_city": target_city,
"epochs1": 10,
"epochs2": 10,
"use_phase2": True,
"batch_size": 200,
"phase1_lr": 1e-4,
"phase2_lr": 1e-5,
"eql_number_layers": 2,
"optimizer": "rmsprop",
}
x_dim = 80
activation_funcs = [
*[Constant()] * 2, *[Identity()] * 4, *[Square()] * 4,
*[Sin()] * 2, *[Sigmoid()] * 2, *[Product()] * 2
]
n_layers = 2
n_double = count_double(activation_funcs)
width = len(activation_funcs)
init_weights = [
tf.random.truncated_normal([x_dim, width + n_double], stddev=0.1),
tf.random.truncated_normal([width, width + n_double], stddev=0.5),
tf.random.truncated_normal([width, 1], stddev=1.0)
]
model = SymbolicNet(n_layers, funcs=activation_funcs, initial_weights=init_weights)
phase1_optimizer = tf.keras.optimizers.RMSprop(learning_rate=1e-4)
experiment_number = get_folders_started()
print("\n" + "*" * 10 + " Experiment {} ".format(experiment_number) + "*" * 10 + "\n")
record_base_info(experiment_number, **config)
x_train, y_train = generate_all_data("train", steps_ahead, feature, city_index)
x_test, y_test = generate_all_data("test", steps_ahead, feature, city_index)
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(200)
val_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(200)
# Phase 1
best_val_loss = float('inf')
best_val_weights = None
train_loss_results = []
valid_loss_results = []
for epoch in range(config["epochs1"]):
epoch_loss_avg = tf.keras.metrics.MeanSquaredError()
for xb, yb in tqdm(train_dataset, desc=f"P1 Epoch {epoch+1}", leave=False):
with tf.GradientTape() as tape:
yp = model(xb)
err = tf.keras.losses.MeanSquaredError()(yb * (y_max - y_min) + y_min, yp * (y_max - y_min) + y_min)
reg = l12_smooth(model.get_weights(), 5e-3)
loss = err + 3.0 * reg
grads = tape.gradient(loss, model.get_weights())
phase1_optimizer.apply_gradients(zip(grads, model.get_weights()))
epoch_loss_avg.update_state(yb, yp)
train_mse = epoch_loss_avg.result().numpy()
val_mse = tf.keras.metrics.MeanSquaredError()
for xv, yv in val_dataset:
val_mse.update_state(yv, model(xv))
val_mse = val_mse.result().numpy()
train_loss_results.append(train_mse)
valid_loss_results.append(val_mse)
if val_mse < best_val_loss:
best_val_loss = val_mse
best_val_weights = [w.numpy() for w in model.get_weights()]
print(f"P1 Epoch {epoch+1}: val MSE improved to {val_mse:.4e} (train {train_mse:.4e})")
else:
print(f"P1 Epoch {epoch+1}: val MSE {val_mse:.4e} (train {train_mse:.4e})")
save_weights(best_val_weights, experiment_number, "phase1", best_val_loss)
append_text_to_summary(experiment_number, f"phase 1 best MSE validation: {best_val_loss}\n")
plot_train_vs_validation(experiment_number, config["epochs1"], train_loss_results, valid_loss_results, "phase1")
plot_histogram(experiment_number, best_val_weights[0], "phase1", "weights1", 5e-3)
plot_histogram(experiment_number, best_val_weights[1], "phase1", "weights2", 5e-3)
plot_histogram(experiment_number, best_val_weights[2], "phase1", "weights3", 5e-3)
# Phase 2
masked_weights = []
for w_i in best_val_weights:
mask = tf.cast(tf.constant(tf.abs(w_i) > 7.5e-3), tf.float32)
masked_weights.append(tf.multiply(w_i, mask))
model2 = SymbolicNet(n_layers, funcs=activation_funcs, initial_weights=masked_weights)
opt2 = tf.keras.optimizers.RMSprop(learning_rate=1e-5)
train_loss_results2 = []
valid_loss_results2 = []
best_val_loss2 = float('inf')
best_val_weights2 = None
for epoch in range(config["epochs2"]):
epoch_loss_avg = tf.keras.metrics.MeanSquaredError()
for xb, yb in tqdm(train_dataset, desc=f"P2 Epoch {epoch+1}", leave=False):
with tf.GradientTape() as tape:
yp = model2(xb)
err = tf.keras.losses.MeanSquaredError()(yb * (y_max - y_min) + y_min, yp * (y_max - y_min) + y_min)
grads = tape.gradient(err, model2.get_weights())
opt2.apply_gradients(zip(grads, model2.get_weights()))
epoch_loss_avg.update_state(yb, yp)
train_mse = epoch_loss_avg.result().numpy()
val_mse = tf.keras.metrics.MeanSquaredError()
for xv, yv in val_dataset:
val_mse.update_state(yv, model2(xv))
val_mse = val_mse.result().numpy()
train_loss_results2.append(train_mse)
valid_loss_results2.append(val_mse)
if val_mse < best_val_loss2:
best_val_loss2 = val_mse
best_val_weights2 = [w.numpy() for w in model2.get_weights()]
print(f"P2 Epoch {epoch+1}: val MSE improved to {val_mse:.4e} (train {train_mse:.4e})")
else:
print(f"P2 Epoch {epoch+1}: val MSE {val_mse:.4e} (train {train_mse:.4e})")
save_weights(best_val_weights2, experiment_number, "phase2", best_val_loss2)
plot_train_vs_validation(experiment_number, config["epochs2"], train_loss_results2, valid_loss_results2, "phase2")
expr = network(best_val_weights2, activation_funcs, generate_variable_list(26, 80)[:80], threshold=0)
append_text_to_summary(experiment_number, f"Formula after phase2: {expr}\n")
print(f"\nExtracted formula: {expr}")
best_model = SymbolicNet(n_layers, funcs=activation_funcs,
initial_weights=[tf.constant(w) for w in best_val_weights2])
yp = best_model(x_test)
mae = plot_descaled_real_vs_prediction(experiment_number, y_test, yp, y_min, y_max)
append_text_to_summary(experiment_number, f"MAE: {mae}\n")
print(f"\nMAE: {mae}")
print("\nMedium experiment complete!")
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