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import logging
import math
from typing import List
import numpy as np
import torch
import torch.nn as nn
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import DataLoader, TensorDataset
from core.data_bundle import DataBundle
from models.base import BaseModel
logger = logging.getLogger(__name__)
ACTIVATION_MAP: dict = {
"relu": nn.ReLU,
"tanh": nn.Tanh,
"sigmoid": nn.Sigmoid,
"leaky_relu": nn.LeakyReLU,
"elu": nn.ELU,
}
def _build_fc_block(
input_size: int,
output_size: int,
activation_name: str,
dropout_rate: float,
) -> List[nn.Module]:
"""Returns [Linear, Activation, Dropout] layers for one FC block."""
activation_class = ACTIVATION_MAP.get(activation_name, nn.ReLU)
return [
nn.Linear(input_size, output_size),
activation_class(),
nn.Dropout(p=dropout_rate),
]
def _build_fcnn(
input_size: int,
hidden_layers: List[int],
num_outputs: int,
activation_name: str,
dropout_rate: float,
) -> nn.Sequential:
"""Build a fully-connected feedforward network from config."""
layer_sizes = [input_size] + hidden_layers
layers: List[nn.Module] = []
for layer_index in range(len(layer_sizes) - 1):
layers.extend(
_build_fc_block(
layer_sizes[layer_index],
layer_sizes[layer_index + 1],
activation_name,
dropout_rate,
)
)
layers.append(nn.Linear(layer_sizes[-1], num_outputs))
return nn.Sequential(*layers)
class PyTorchFCNNClassifierWrapper(BaseModel):
"""Fully-connected neural network classifier built from config.yaml architecture."""
def __init__(self, config: dict) -> None:
super().__init__(config)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.label_encoder: LabelEncoder = LabelEncoder()
def train(self, data: DataBundle) -> None:
merged_config = self._merge_hyperparameter_overrides(data)
X_train = data.common.X_train.astype(np.float32)
# Remap arbitrary class labels to consecutive integers 0..N-1.
# CrossEntropyLoss requires target indices in [0, num_classes); without this
# remapping a dataset with labels {0,1,3,...,14} causes "Target out of bounds".
y_train = self.label_encoder.fit_transform(data.common.y_train).astype(np.int64)
num_features = X_train.shape[1]
# Derive num_classes from actual data so the model output layer matches the
# dataset even when the config default (e.g. 10) doesn't match class count.
num_classes = len(self.label_encoder.classes_)
hidden_layers: List[int] = list(merged_config["hidden_layers"])
activation_name: str = merged_config["activation"]
dropout_rate: float = float(merged_config["dropout"])
learning_rate: float = float(merged_config["learning_rate"])
num_epochs: int = int(merged_config["epochs"])
batch_size: int = int(merged_config["batch_size"])
weight_decay: float = float(merged_config.get("weight_decay", 1e-4))
self.model = _build_fcnn(
num_features, hidden_layers, num_classes, activation_name, dropout_rate
).to(self.device)
optimizer = torch.optim.Adam(
self.model.parameters(), lr=learning_rate, weight_decay=weight_decay
)
loss_fn = nn.CrossEntropyLoss()
X_tensor = torch.tensor(X_train)
y_tensor = torch.tensor(y_train)
dataset = TensorDataset(X_tensor, y_tensor)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
self.model.train()
for epoch_index in range(num_epochs):
epoch_loss = 0.0
for X_batch, y_batch in loader:
X_batch = X_batch.to(self.device)
y_batch = y_batch.to(self.device)
optimizer.zero_grad()
logits = self.model(X_batch)
loss = loss_fn(logits, y_batch)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
logger.debug(
"=> PyTorchFCNNClassifier epoch %d/%d loss=%.4f",
epoch_index + 1,
num_epochs,
epoch_loss / len(loader),
)
def predict(self, X: np.ndarray) -> np.ndarray:
X_tensor = torch.tensor(X.astype(np.float32)).to(self.device)
self.model.eval()
with torch.no_grad():
logits = self.model(X_tensor)
encoded_predictions = logits.argmax(dim=1).cpu().numpy()
# Decode back to original class labels.
return self.label_encoder.inverse_transform(encoded_predictions)
def _prepare_for_serialization(self) -> nn.Module:
return self.model.cpu()
class _CNNClassifierNet(nn.Module):
"""Internal CNN network for classification. Supports Conv1d and Conv2d."""
def __init__(
self,
conv_dim: int,
in_channels: int,
conv_channels: List[int],
kernel_size: int,
fc_input_size: int,
fc_layers: List[int],
num_classes: int,
dropout_rate: float,
) -> None:
super().__init__()
conv_layer_class = nn.Conv2d if conv_dim == 2 else nn.Conv1d
pool_layer_class = nn.MaxPool2d if conv_dim == 2 else nn.MaxPool1d
conv_blocks: List[nn.Module] = []
current_channels = in_channels
for out_channels in conv_channels:
conv_blocks += [
conv_layer_class(current_channels, out_channels, kernel_size, padding=1),
nn.ReLU(),
pool_layer_class(2),
]
current_channels = out_channels
self.conv_layers = nn.Sequential(*conv_blocks)
fc_sizes = [fc_input_size] + fc_layers
fc_blocks: List[nn.Module] = [nn.Flatten()]
for layer_index in range(len(fc_sizes) - 1):
fc_blocks += [
nn.Linear(fc_sizes[layer_index], fc_sizes[layer_index + 1]),
nn.ReLU(),
nn.Dropout(p=dropout_rate),
]
fc_blocks.append(nn.Linear(fc_sizes[-1], num_classes))
self.fc_layers = nn.Sequential(*fc_blocks)
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
features = self.conv_layers(input_tensor)
return self.fc_layers(features)
def _compute_cnn_fc_input_size(
conv_dim: int,
input_spatial_size: int,
conv_channels: List[int],
kernel_size: int,
num_conv_layers: int,
) -> int:
"""Compute the flattened size after all conv+pool layers."""
spatial_size = input_spatial_size
for _ in range(num_conv_layers):
# conv with padding=1 preserves size; pool with kernel=2 halves it
spatial_size = spatial_size // 2
last_channels = conv_channels[-1]
if conv_dim == 2:
return last_channels * spatial_size * spatial_size
return last_channels * spatial_size
class PyTorchCNNClassifierWrapper(BaseModel):
"""CNN classifier. conv_dim=2 for image input (MNIST), conv_dim=1 for 1D signals."""
def __init__(self, config: dict) -> None:
super().__init__(config)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def _reshape_for_cnn(
self, X: np.ndarray, conv_dim: int
) -> np.ndarray:
"""Reshape flat (N, features) input into CNN-compatible shape."""
num_samples = X.shape[0]
num_features = X.shape[1]
if conv_dim == 2:
# Infer spatial dimension from sqrt of feature count
spatial_side = int(math.isqrt(num_features))
if spatial_side * spatial_side != num_features:
# Pad to next perfect square
next_square = (spatial_side + 1) ** 2
X = np.pad(X, ((0, 0), (0, next_square - num_features)))
spatial_side = spatial_side + 1
logger.debug(
"=> PyTorchCNNClassifier: padded features to %d for 2D reshape.",
next_square,
)
return X.reshape(num_samples, 1, spatial_side, spatial_side)
else:
# Conv1d: treat each feature as one time step
return X.reshape(num_samples, 1, num_features)
def train(self, data: DataBundle) -> None:
merged_config = self._merge_hyperparameter_overrides(data)
X_train = data.common.X_train.astype(np.float32)
y_train = data.common.y_train.astype(np.int64)
conv_dim: int = int(merged_config["conv_dim"])
conv_channels: List[int] = list(merged_config["conv_channels"])
kernel_size: int = int(merged_config["kernel_size"])
fc_layers: List[int] = list(merged_config["fc_layers"])
num_classes: int = int(merged_config["num_classes"])
dropout_rate: float = float(merged_config["dropout"])
learning_rate: float = float(merged_config["learning_rate"])
num_epochs: int = int(merged_config["epochs"])
batch_size: int = int(merged_config["batch_size"])
weight_decay: float = float(merged_config.get("weight_decay", 1e-4))
X_reshaped = self._reshape_for_cnn(X_train, conv_dim)
spatial_size = X_reshaped.shape[2] # height (or length for 1D)
fc_input_size = _compute_cnn_fc_input_size(
conv_dim, spatial_size, conv_channels, kernel_size, len(conv_channels)
)
self.model = _CNNClassifierNet(
conv_dim=conv_dim,
in_channels=1,
conv_channels=conv_channels,
kernel_size=kernel_size,
fc_input_size=fc_input_size,
fc_layers=fc_layers,
num_classes=num_classes,
dropout_rate=dropout_rate,
).to(self.device)
optimizer = torch.optim.Adam(
self.model.parameters(), lr=learning_rate, weight_decay=weight_decay
)
loss_fn = nn.CrossEntropyLoss()
X_tensor = torch.tensor(X_reshaped)
y_tensor = torch.tensor(y_train)
dataset = TensorDataset(X_tensor, y_tensor)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
self.model.train()
for epoch_index in range(num_epochs):
epoch_loss = 0.0
for X_batch, y_batch in loader:
X_batch = X_batch.to(self.device)
y_batch = y_batch.to(self.device)
optimizer.zero_grad()
logits = self.model(X_batch)
loss = loss_fn(logits, y_batch)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
logger.debug(
"=> PyTorchCNNClassifier epoch %d/%d loss=%.4f",
epoch_index + 1,
num_epochs,
epoch_loss / len(loader),
)
# Store conv_dim so predict() can apply the same reshape
self.trained_conv_dim = conv_dim
def predict(self, X: np.ndarray) -> np.ndarray:
X_reshaped = self._reshape_for_cnn(X.astype(np.float32), self.trained_conv_dim)
X_tensor = torch.tensor(X_reshaped).to(self.device)
self.model.eval()
with torch.no_grad():
logits = self.model(X_tensor)
predicted_classes = logits.argmax(dim=1).cpu().numpy()
return predicted_classes
def _prepare_for_serialization(self) -> nn.Module:
return self.model.cpu()
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