gaitsetpy.classification.models.cnn API documentation

# gaitsetpy.classification.models.cnn

View Source

      1import torch
      2import torch.nn as nn
      3import torch.optim as optim
      4import numpy as np
      5from typing import List, Dict, Any, Optional, Union
      6from ...core.base_classes import BaseClassificationModel
      7from ..utils.preprocess import preprocess_features
      8from sklearn.model_selection import train_test_split
      9from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
     10
     11class SimpleCNN(nn.Module):
     12    def __init__(self, input_channels, num_classes, seq_len=1):
     13        super(SimpleCNN, self).__init__()
     14        self.conv1 = nn.Conv1d(input_channels, 32, kernel_size=3, padding=1)
     15        self.relu = nn.ReLU()
     16        self.pool = nn.AdaptiveMaxPool1d(1)
     17        self.fc = nn.Linear(32, num_classes)
     18    def forward(self, x):
     19        # x: (batch, channels, seq_len)
     20        x = self.conv1(x)
     21        x = self.relu(x)
     22        x = self.pool(x)
     23        x = x.view(x.size(0), -1)
     24        x = self.fc(x)
     25        return x
     26
     27class CNNModel(BaseClassificationModel):
     28    """
     29    Simple 1D CNN classification model using PyTorch.
     30    Implements the BaseClassificationModel interface.
     31    """
     32    def __init__(self, input_channels=10, num_classes=2, lr=0.001, epochs=20, batch_size=32, device=None):
     33        super().__init__(
     34            name="cnn",
     35            description="1D CNN classifier for gait data classification"
     36        )
     37        self.config = {
     38            'input_channels': input_channels,
     39            'num_classes': num_classes,
     40            'lr': lr,
     41            'epochs': epochs,
     42            'batch_size': batch_size
     43        }
     44        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
     45        self.model = SimpleCNN(input_channels, num_classes).to(self.device)
     46        self.epochs = epochs
     47        self.batch_size = batch_size
     48        self.trained = False
     49        self.feature_names = []
     50        self.class_names = []
     51
     52    def train(self, features: List[Dict], **kwargs):
     53        X, y = preprocess_features(features)
     54        # Reshape X for CNN: (samples, channels, seq_len)
     55        # Here, treat each feature vector as a channel with seq_len=1
     56        X = X.reshape((X.shape[0], X.shape[1], 1))
     57        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
     58        self.class_names = list(set(y))
     59        test_size = kwargs.get('test_size', 0.2)
     60        validation_split = kwargs.get('validation_split', True)
     61        if validation_split:
     62            X_train, X_test, y_train, y_test = train_test_split(
     63                X, y, test_size=test_size, random_state=42
     64            )
     65            self.X_test = X_test
     66            self.y_test = y_test
     67        else:
     68            X_train, y_train = X, y
     69        X_train = torch.tensor(X_train, dtype=torch.float32).to(self.device)
     70        y_train = torch.tensor(y_train, dtype=torch.long).to(self.device)
     71        criterion = nn.CrossEntropyLoss()
     72        optimizer = optim.Adam(self.model.parameters(), lr=self.config['lr'])
     73        for epoch in range(self.epochs):
     74            self.model.train()
     75            optimizer.zero_grad()
     76            outputs = self.model(X_train)
     77            loss = criterion(outputs, y_train)
     78            loss.backward()
     79            optimizer.step()
     80            if (epoch+1) % 5 == 0 or epoch == 0:
     81                print(f"Epoch [{epoch+1}/{self.epochs}], Loss: {loss.item():.4f}")
     82        self.trained = True
     83        print("CNN model trained successfully.")
     84
     85    def predict(self, features: List[Dict], **kwargs) -> np.ndarray:
     86        if not self.trained:
     87            raise ValueError("Model must be trained before making predictions")
     88        X, _ = preprocess_features(features)
     89        X = X.reshape((X.shape[0], X.shape[1], 1))
     90        X = torch.tensor(X, dtype=torch.float32).to(self.device)
     91        self.model.eval()
     92        with torch.no_grad():
     93            outputs = self.model(X)
     94            _, predicted = torch.max(outputs.data, 1)
     95        return predicted.cpu().numpy()
     96
     97    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
     98        if not self.trained:
     99            raise ValueError("Model must be trained before evaluation")
    100        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    101            X_test, y_test = self.X_test, self.y_test
    102        else:
    103            X_test, y_test = preprocess_features(features)
    104            X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
    105        X_test = torch.tensor(X_test, dtype=torch.float32).to(self.device)
    106        y_test = np.array(y_test)
    107        self.model.eval()
    108        with torch.no_grad():
    109            outputs = self.model(X_test)
    110            _, y_pred = torch.max(outputs.data, 1)
    111        y_pred = y_pred.cpu().numpy()
    112        accuracy = accuracy_score(y_test, y_pred)
    113        conf_matrix = confusion_matrix(y_test, y_pred)
    114        metrics = {
    115            'accuracy': accuracy,
    116            'confusion_matrix': conf_matrix.tolist()
    117        }
    118        detailed_report = kwargs.get('detailed_report', False)
    119        if detailed_report:
    120            class_report = classification_report(y_test, y_pred, output_dict=True)
    121            metrics['classification_report'] = class_report
    122        return metrics
    123
    124    def save_model(self, filepath: str):
    125        if not self.trained:
    126            raise ValueError("Model must be trained before saving")
    127        torch.save({
    128            'model_state_dict': self.model.state_dict(),
    129            'config': self.config,
    130            'feature_names': self.feature_names,
    131            'class_names': self.class_names,
    132            'trained': self.trained
    133        }, filepath)
    134        print(f"CNN model saved to {filepath}")
    135
    136    def load_model(self, filepath: str):
    137        checkpoint = torch.load(filepath, map_location=self.device)
    138        self.model = SimpleCNN(
    139            self.config['input_channels'],
    140            self.config['num_classes']
    141        ).to(self.device)
    142        self.model.load_state_dict(checkpoint['model_state_dict'])
    143        self.config = checkpoint.get('config', self.config)
    144        self.feature_names = checkpoint.get('feature_names', [])
    145        self.class_names = checkpoint.get('class_names', [])
    146        self.trained = checkpoint.get('trained', True)
    147        print(f"CNN model loaded from {filepath}")

class SimpleCNN(torch.nn.modules.module.Module): View Source

    12class SimpleCNN(nn.Module):
    13    def __init__(self, input_channels, num_classes, seq_len=1):
    14        super(SimpleCNN, self).__init__()
    15        self.conv1 = nn.Conv1d(input_channels, 32, kernel_size=3, padding=1)
    16        self.relu = nn.ReLU()
    17        self.pool = nn.AdaptiveMaxPool1d(1)
    18        self.fc = nn.Linear(32, num_classes)
    19    def forward(self, x):
    20        # x: (batch, channels, seq_len)
    21        x = self.conv1(x)
    22        x = self.relu(x)
    23        x = self.pool(x)
    24        x = x.view(x.size(0), -1)
    25        x = self.fc(x)
    26        return x

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

    import torch.nn as nn
    import torch.nn.functional as F

    class Model(nn.Module):
        def __init__(self) -> None:
            super().__init__()
            self.conv1 = nn.Conv2d(1, 20, 5)
            self.conv2 = nn.Conv2d(20, 20, 5)

        def forward(self, x):
            x = F.relu(self.conv1(x))
            return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call `to()`, etc.

As per the example above, an `__init__()` call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

SimpleCNN(input_channels, num_classes, seq_len=1) View Source

    13    def __init__(self, input_channels, num_classes, seq_len=1):
    14        super(SimpleCNN, self).__init__()
    15        self.conv1 = nn.Conv1d(input_channels, 32, kernel_size=3, padding=1)
    16        self.relu = nn.ReLU()
    17        self.pool = nn.AdaptiveMaxPool1d(1)
    18        self.fc = nn.Linear(32, num_classes)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

conv1
relu
pool
fc
def forward(self, x): View Source

    19    def forward(self, x):
    20        # x: (batch, channels, seq_len)
    21        x = self.conv1(x)
    22        x = self.relu(x)
    23        x = self.pool(x)
    24        x = x.view(x.size(0), -1)
    25        x = self.fc(x)
    26        return x

Define the computation performed at every call.

Should be overridden by all subclasses.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class CNNModel(gaitsetpy.core.base_classes.BaseClassificationModel): View Source

     28class CNNModel(BaseClassificationModel):
     29    """
     30    Simple 1D CNN classification model using PyTorch.
     31    Implements the BaseClassificationModel interface.
     32    """
     33    def __init__(self, input_channels=10, num_classes=2, lr=0.001, epochs=20, batch_size=32, device=None):
     34        super().__init__(
     35            name="cnn",
     36            description="1D CNN classifier for gait data classification"
     37        )
     38        self.config = {
     39            'input_channels': input_channels,
     40            'num_classes': num_classes,
     41            'lr': lr,
     42            'epochs': epochs,
     43            'batch_size': batch_size
     44        }
     45        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
     46        self.model = SimpleCNN(input_channels, num_classes).to(self.device)
     47        self.epochs = epochs
     48        self.batch_size = batch_size
     49        self.trained = False
     50        self.feature_names = []
     51        self.class_names = []
     52
     53    def train(self, features: List[Dict], **kwargs):
     54        X, y = preprocess_features(features)
     55        # Reshape X for CNN: (samples, channels, seq_len)
     56        # Here, treat each feature vector as a channel with seq_len=1
     57        X = X.reshape((X.shape[0], X.shape[1], 1))
     58        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
     59        self.class_names = list(set(y))
     60        test_size = kwargs.get('test_size', 0.2)
     61        validation_split = kwargs.get('validation_split', True)
     62        if validation_split:
     63            X_train, X_test, y_train, y_test = train_test_split(
     64                X, y, test_size=test_size, random_state=42
     65            )
     66            self.X_test = X_test
     67            self.y_test = y_test
     68        else:
     69            X_train, y_train = X, y
     70        X_train = torch.tensor(X_train, dtype=torch.float32).to(self.device)
     71        y_train = torch.tensor(y_train, dtype=torch.long).to(self.device)
     72        criterion = nn.CrossEntropyLoss()
     73        optimizer = optim.Adam(self.model.parameters(), lr=self.config['lr'])
     74        for epoch in range(self.epochs):
     75            self.model.train()
     76            optimizer.zero_grad()
     77            outputs = self.model(X_train)
     78            loss = criterion(outputs, y_train)
     79            loss.backward()
     80            optimizer.step()
     81            if (epoch+1) % 5 == 0 or epoch == 0:
     82                print(f"Epoch [{epoch+1}/{self.epochs}], Loss: {loss.item():.4f}")
     83        self.trained = True
     84        print("CNN model trained successfully.")
     85
     86    def predict(self, features: List[Dict], **kwargs) -> np.ndarray:
     87        if not self.trained:
     88            raise ValueError("Model must be trained before making predictions")
     89        X, _ = preprocess_features(features)
     90        X = X.reshape((X.shape[0], X.shape[1], 1))
     91        X = torch.tensor(X, dtype=torch.float32).to(self.device)
     92        self.model.eval()
     93        with torch.no_grad():
     94            outputs = self.model(X)
     95            _, predicted = torch.max(outputs.data, 1)
     96        return predicted.cpu().numpy()
     97
     98    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
     99        if not self.trained:
    100            raise ValueError("Model must be trained before evaluation")
    101        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    102            X_test, y_test = self.X_test, self.y_test
    103        else:
    104            X_test, y_test = preprocess_features(features)
    105            X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
    106        X_test = torch.tensor(X_test, dtype=torch.float32).to(self.device)
    107        y_test = np.array(y_test)
    108        self.model.eval()
    109        with torch.no_grad():
    110            outputs = self.model(X_test)
    111            _, y_pred = torch.max(outputs.data, 1)
    112        y_pred = y_pred.cpu().numpy()
    113        accuracy = accuracy_score(y_test, y_pred)
    114        conf_matrix = confusion_matrix(y_test, y_pred)
    115        metrics = {
    116            'accuracy': accuracy,
    117            'confusion_matrix': conf_matrix.tolist()
    118        }
    119        detailed_report = kwargs.get('detailed_report', False)
    120        if detailed_report:
    121            class_report = classification_report(y_test, y_pred, output_dict=True)
    122            metrics['classification_report'] = class_report
    123        return metrics
    124
    125    def save_model(self, filepath: str):
    126        if not self.trained:
    127            raise ValueError("Model must be trained before saving")
    128        torch.save({
    129            'model_state_dict': self.model.state_dict(),
    130            'config': self.config,
    131            'feature_names': self.feature_names,
    132            'class_names': self.class_names,
    133            'trained': self.trained
    134        }, filepath)
    135        print(f"CNN model saved to {filepath}")
    136
    137    def load_model(self, filepath: str):
    138        checkpoint = torch.load(filepath, map_location=self.device)
    139        self.model = SimpleCNN(
    140            self.config['input_channels'],
    141            self.config['num_classes']
    142        ).to(self.device)
    143        self.model.load_state_dict(checkpoint['model_state_dict'])
    144        self.config = checkpoint.get('config', self.config)
    145        self.feature_names = checkpoint.get('feature_names', [])
    146        self.class_names = checkpoint.get('class_names', [])
    147        self.trained = checkpoint.get('trained', True)
    148        print(f"CNN model loaded from {filepath}")

Simple 1D CNN classification model using PyTorch. Implements the BaseClassificationModel interface.

CNNModel( input_channels=10, num_classes=2, lr=0.001, epochs=20, batch_size=32, device=None) View Source

    33    def __init__(self, input_channels=10, num_classes=2, lr=0.001, epochs=20, batch_size=32, device=None):
    34        super().__init__(
    35            name="cnn",
    36            description="1D CNN classifier for gait data classification"
    37        )
    38        self.config = {
    39            'input_channels': input_channels,
    40            'num_classes': num_classes,
    41            'lr': lr,
    42            'epochs': epochs,
    43            'batch_size': batch_size
    44        }
    45        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
    46        self.model = SimpleCNN(input_channels, num_classes).to(self.device)
    47        self.epochs = epochs
    48        self.batch_size = batch_size
    49        self.trained = False
    50        self.feature_names = []
    51        self.class_names = []

Initialize the classification model.

Args: name: Name of the classification model description: Description of the classification model

config
device
model
epochs
batch_size
trained
feature_names
class_names
def train(self, features: List[Dict], **kwargs): View Source

    53    def train(self, features: List[Dict], **kwargs):
    54        X, y = preprocess_features(features)
    55        # Reshape X for CNN: (samples, channels, seq_len)
    56        # Here, treat each feature vector as a channel with seq_len=1
    57        X = X.reshape((X.shape[0], X.shape[1], 1))
    58        self.feature_names = [f"feature_{i}" for i in range(X.shape[1])]
    59        self.class_names = list(set(y))
    60        test_size = kwargs.get('test_size', 0.2)
    61        validation_split = kwargs.get('validation_split', True)
    62        if validation_split:
    63            X_train, X_test, y_train, y_test = train_test_split(
    64                X, y, test_size=test_size, random_state=42
    65            )
    66            self.X_test = X_test
    67            self.y_test = y_test
    68        else:
    69            X_train, y_train = X, y
    70        X_train = torch.tensor(X_train, dtype=torch.float32).to(self.device)
    71        y_train = torch.tensor(y_train, dtype=torch.long).to(self.device)
    72        criterion = nn.CrossEntropyLoss()
    73        optimizer = optim.Adam(self.model.parameters(), lr=self.config['lr'])
    74        for epoch in range(self.epochs):
    75            self.model.train()
    76            optimizer.zero_grad()
    77            outputs = self.model(X_train)
    78            loss = criterion(outputs, y_train)
    79            loss.backward()
    80            optimizer.step()
    81            if (epoch+1) % 5 == 0 or epoch == 0:
    82                print(f"Epoch [{epoch+1}/{self.epochs}], Loss: {loss.item():.4f}")
    83        self.trained = True
    84        print("CNN model trained successfully.")

Train the classification model.

Args: features: List of feature dictionaries **kwargs: Additional arguments for training

def predict(self, features: List[Dict], **kwargs) -> numpy.ndarray: View Source

    86    def predict(self, features: List[Dict], **kwargs) -> np.ndarray:
    87        if not self.trained:
    88            raise ValueError("Model must be trained before making predictions")
    89        X, _ = preprocess_features(features)
    90        X = X.reshape((X.shape[0], X.shape[1], 1))
    91        X = torch.tensor(X, dtype=torch.float32).to(self.device)
    92        self.model.eval()
    93        with torch.no_grad():
    94            outputs = self.model(X)
    95            _, predicted = torch.max(outputs.data, 1)
    96        return predicted.cpu().numpy()

Make predictions using the trained model.

Args: features: List of feature dictionaries **kwargs: Additional arguments for prediction

Returns: Array of predictions

def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]: View Source

     98    def evaluate(self, features: List[Dict], **kwargs) -> Dict[str, float]:
     99        if not self.trained:
    100            raise ValueError("Model must be trained before evaluation")
    101        if hasattr(self, 'X_test') and hasattr(self, 'y_test'):
    102            X_test, y_test = self.X_test, self.y_test
    103        else:
    104            X_test, y_test = preprocess_features(features)
    105            X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
    106        X_test = torch.tensor(X_test, dtype=torch.float32).to(self.device)
    107        y_test = np.array(y_test)
    108        self.model.eval()
    109        with torch.no_grad():
    110            outputs = self.model(X_test)
    111            _, y_pred = torch.max(outputs.data, 1)
    112        y_pred = y_pred.cpu().numpy()
    113        accuracy = accuracy_score(y_test, y_pred)
    114        conf_matrix = confusion_matrix(y_test, y_pred)
    115        metrics = {
    116            'accuracy': accuracy,
    117            'confusion_matrix': conf_matrix.tolist()
    118        }
    119        detailed_report = kwargs.get('detailed_report', False)
    120        if detailed_report:
    121            class_report = classification_report(y_test, y_pred, output_dict=True)
    122            metrics['classification_report'] = class_report
    123        return metrics

Evaluate the model performance.

Args: features: List of feature dictionaries **kwargs: Additional arguments for evaluation

Returns: Dictionary containing evaluation metrics

def save_model(self, filepath: str): View Source

    125    def save_model(self, filepath: str):
    126        if not self.trained:
    127            raise ValueError("Model must be trained before saving")
    128        torch.save({
    129            'model_state_dict': self.model.state_dict(),
    130            'config': self.config,
    131            'feature_names': self.feature_names,
    132            'class_names': self.class_names,
    133            'trained': self.trained
    134        }, filepath)
    135        print(f"CNN model saved to {filepath}")

Save the trained model to a file.

Args: filepath: Path to save the model

def load_model(self, filepath: str): View Source

    137    def load_model(self, filepath: str):
    138        checkpoint = torch.load(filepath, map_location=self.device)
    139        self.model = SimpleCNN(
    140            self.config['input_channels'],
    141            self.config['num_classes']
    142        ).to(self.device)
    143        self.model.load_state_dict(checkpoint['model_state_dict'])
    144        self.config = checkpoint.get('config', self.config)
    145        self.feature_names = checkpoint.get('feature_names', [])
    146        self.class_names = checkpoint.get('class_names', [])
    147        self.trained = checkpoint.get('trained', True)
    148        print(f"CNN model loaded from {filepath}")

Load a trained model from a file.

Args: filepath: Path to the saved model

##### Inherited Members

gaitsetpy.core.base_classes.BaseClassificationModel
  name
  description
  configure
  get_info