[train] fix training mistakes and eval

diff --git a/complexity_regularizer/train.py b/complexity_regularizer/train.py
index 54b6fe98f4d2b90a8f38ec22aa864d7e884c3c16..fe79d6c2a83dc71dc21cc825bd15f9a75ce45700 100644 (file)
--- a/complexity_regularizer/train.py
+++ b/complexity_regularizer/train.py
@@ -5,6 +5,9 @@ import argparse
 from dataclasses import dataclass
 from complexity_regularizer.dataset_builder import create_regression_data
 from torch.utils.data import random_split, DataLoader
+from complexity_regularizer.utils import seed_everything
+import matplotlib.pyplot as plt
+import numpy as np
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser("Simple regression problem with an exponential regularization of the parameters")
@@ -13,9 +16,11 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--model_degree", type=int, default=2)
     parser.add_argument("--noise_factor", type=float, default=0.05)
     parser.add_argument("--lr", type=float, default=1e-3)
+    parser.add_argument("--gamma", type=float, default=1.5)
     parser.add_argument("--train_batch_size", type=int, default=8)
     parser.add_argument("--val_batch_size", type=int, default=4)
-    parser.add_argument("--epochs", type=int, default=1)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--trials", type=int, default=4)
     return parser.parse_args()
 
 @dataclass
@@ -27,7 +32,9 @@ class TrainConfig:
     lr: float
     train_batch_size: int
     val_batch_size: int
-    epochs: int
+    gamma: float
+    seed: int
+    trials: int
 
 
 class Trainer():
@@ -40,45 +47,76 @@ class Trainer():
                 model_degree=config.model_degree
         )
         self.config = config
-        train_dataset, val_dataset = random_split(data, lengths=[0.8, 0.2])
+        train_dataset, val_dataset = random_split(data, lengths=[0.5, 0.5])
         self.train_loader = DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True)
         self.val_loader = DataLoader(val_dataset, batch_size=config.val_batch_size, shuffle=False)
         self.model = LinearRegressionModel(n_features=config.model_degree + 1)
         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=config.lr)
         self.criterion = torch.nn.MSELoss()
 
-    def train(self) -> None:
-        self.model.train()
+    @staticmethod
+    def RMSELoss(y_hat: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        return torch.sqrt(torch.mean((y_hat - y).pow(2)))
+
+    def train(self) -> list[float]:
         running_loss: float = 0.0
+        test_losses: list[float] = []
         for (X, y) in self.train_loader:
+            self.model.train()
             self.optimizer.zero_grad()
             y_hat: torch.Tensor = self.model(X)
-            loss: torch.Tensor = self.criterion(y_hat.view(-1), y)
+            mse_loss: torch.Tensor = self.criterion(y_hat.view(-1), y)
+            """
+            weights: torch.Tensor = self.model.weights.squeeze()
+            complexity_term: torch.Tensor = torch.zeros(1)
+            for degree in range(len(weights)):
+                complexity_term += ((self.config.gamma ** degree) * (weights[degree].pow(2)))
+            #loss = mse_loss + complexity_term
+            """
+            loss = mse_loss
             running_loss += loss.item()
             loss.backward()
             self.optimizer.step()
+            # Evaluate
+            self.model.eval()
+            test_losses.append(self.val())
 
-        print(f"Train loss: {(running_loss / len(self.train_loader)):.3f}")
+        return test_losses
 
     @torch.no_grad()
-    def val(self) -> None:
-        self.model.eval()
-        running_loss: float = 0.0
+    def val(self) -> float:
+        preds = []
+        targets = []
         for (X, y) in self.val_loader:
             y_hat: torch.Tensor = self.model(X)
-            loss: torch.Tensor = self.criterion(y_hat.view(-1), y)
-            running_loss += loss.item()
+            preds.append(y_hat.view(-1))
+            targets.append(y)
 
-        print(f"Validation loss: {(running_loss / len(self.val_loader)):.3f}")
+        all_preds = torch.cat(preds)
+        all_targets = torch.cat(targets)
+        val_loss = torch.sqrt(self.criterion(all_preds, all_targets)).item()
+        return val_loss
 
 def main() -> None:
     args: argparse.Namespace = parse_args()
     config: TrainConfig = TrainConfig(**vars(args))
-    trainer: Trainer = Trainer(config)
-    for epoch in range(config.epochs):
-        print(f"Starting training for epoch: {epoch + 1}")
-        trainer.train()
-        trainer.val()
+    seed_everything(config.seed)
+    test_losses: list[list[float]] = []
+    for trial in range(config.trials):
+        print(f"Trial: {trial}")
+        trainer: Trainer = Trainer(config)
+        test_losses.append(trainer.train())
+
+    mean_val_loss = np.mean(test_losses, axis=0)
+    std_val_loss = np.std(test_losses, axis=0)
+
+    plt.plot(mean_val_loss, label='Mean Loss')
+    plt.fill_between(range(len(mean_val_loss)),
+                                      mean_val_loss - std_val_loss,
+                                      mean_val_loss + std_val_loss,
+                                      alpha=0.1, label='±1 Std Dev')
+    plt.legend()
+    plt.show()
     
 if __name__ == "__main__":
     main()

diff --git a/complexity_regularizer/utils.py b/complexity_regularizer/utils.py
new file mode 100644 (file)
index 0000000..bb0b735
--- /dev/null
+++ b/complexity_regularizer/utils.py
@@ -0,0 +1,16 @@
+import random
+import os
+import numpy as np
+import torch
+
+
+def seed_everything(seed: int):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = True
+
+

diff --git a/pyproject.toml b/pyproject.toml
index 33e4bd7e48c9d768b90f5545839c019e28cbfdfd..9598438568ede3421c5956738ce5528fb735112f 100644 (file)
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "matplotlib>=3.10.7",
     "numpy>=2.3.4",
     "torch>=2.9.0",
     "tqdm>=4.67.1",

diff --git a/uv.lock b/uv.lock
index 9d4d2950081d2a10f1d841f9a3b3333f480547e4..e15d3bb67b2a34ffb0ff463610f74ae43faeb081 100644 (file)
--- a/uv.lock
+++ b/uv.lock
@@ -16,6 +16,7 @@ name = "complexity-regularizer"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "matplotlib" },
     { name = "numpy" },
     { name = "torch" },
     { name = "tqdm" },
@@ -30,6 +31,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "matplotlib", specifier = ">=3.10.7" },
     { name = "numpy", specifier = ">=2.3.4" },
     { name = "torch", specifier = ">=2.9.0" },
     { name = "tqdm", specifier = ">=4.67.1" },
@@ -42,6 +44,70 @@ dev = [
     { name = "ruff", specifier = ">=0.14.0" },
 ]
 
+[[package]]
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+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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+dependencies = [
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+wheels = [
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+
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