Few Shot Learning

FewShotLearner

Source code in engines/contentFilterEngine/learning_paradigms/few_shot.py

class FewShotLearner:
    def __init__(self, model, data_loader, criterion, optimizer, num_epochs):
        """
        Initializes the FewShotLearner with the given model, data loader, criterion, optimizer, and number of epochs.

        Parameters:
            model (torch.nn.Module): The model to be trained and used for predictions.
            data_loader (torch.utils.data.DataLoader): DataLoader providing the training data.
            criterion (torch.nn.Module): Loss function used to evaluate the model's performance.
            optimizer (torch.optim.Optimizer): Optimization algorithm used to update model weights.
            num_epochs (int): Number of epochs to train the model.
        """
        self.model = model
        self.data_loader = data_loader
        self.criterion = criterion
        self.optimizer = optimizer
        self.num_epochs = num_epochs

    def train(self):
        """
        Trains the model using the few-shot learning approach.

        This method iterates over the data provided by the data_loader for a specified number of epochs,
        updating the model's weights using the optimizer and evaluating its performance using the criterion.
        """
        print("Training with Few-Shot Learning...")
        train_model(self.model, self.data_loader, self.criterion, self.optimizer, self.num_epochs)

    def predict(self, graph, node_index, top_k=5, threshold=0.5):
        """
        Predicts the top-k items for a given node in a graph using the trained model.

        Parameters:
            graph (torch.Tensor): The graph data structure containing nodes and edges.
            node_index (int): The index of the node for which predictions are to be made.
            top_k (int, optional): The number of top items to return. Defaults to 5.
            threshold (float, optional): The threshold for prediction confidence. Defaults to 0.5.

        Returns:
            List[int]: A list of indices representing the top-k predicted items.
        """
        print("Predicting with Few-Shot Learning...")
        return predict(self.model, graph, node_index, top_k, threshold)

__init__(model, data_loader, criterion, optimizer, num_epochs)

Initializes the FewShotLearner with the given model, data loader, criterion, optimizer, and number of epochs.

Parameters:

Name	Type	Description	Default
model	Module	The model to be trained and used for predictions.	required
data_loader	DataLoader	DataLoader providing the training data.	required
criterion	Module	Loss function used to evaluate the model's performance.	required
optimizer	Optimizer	Optimization algorithm used to update model weights.	required
num_epochs	int	Number of epochs to train the model.	required

Source code in engines/contentFilterEngine/learning_paradigms/few_shot.py

def __init__(self, model, data_loader, criterion, optimizer, num_epochs):
    """
    Initializes the FewShotLearner with the given model, data loader, criterion, optimizer, and number of epochs.

    Parameters:
        model (torch.nn.Module): The model to be trained and used for predictions.
        data_loader (torch.utils.data.DataLoader): DataLoader providing the training data.
        criterion (torch.nn.Module): Loss function used to evaluate the model's performance.
        optimizer (torch.optim.Optimizer): Optimization algorithm used to update model weights.
        num_epochs (int): Number of epochs to train the model.
    """
    self.model = model
    self.data_loader = data_loader
    self.criterion = criterion
    self.optimizer = optimizer
    self.num_epochs = num_epochs

predict(graph, node_index, top_k=5, threshold=0.5)

Predicts the top-k items for a given node in a graph using the trained model.

Parameters:

Name	Type	Description	Default
graph	Tensor	The graph data structure containing nodes and edges.	required
node_index	int	The index of the node for which predictions are to be made.	required
top_k	int	The number of top items to return. Defaults to 5.	5
threshold	float	The threshold for prediction confidence. Defaults to 0.5.	0.5

Returns:

Type	Description
	List[int]: A list of indices representing the top-k predicted items.

Source code in engines/contentFilterEngine/learning_paradigms/few_shot.py

def predict(self, graph, node_index, top_k=5, threshold=0.5):
    """
    Predicts the top-k items for a given node in a graph using the trained model.

    Parameters:
        graph (torch.Tensor): The graph data structure containing nodes and edges.
        node_index (int): The index of the node for which predictions are to be made.
        top_k (int, optional): The number of top items to return. Defaults to 5.
        threshold (float, optional): The threshold for prediction confidence. Defaults to 0.5.

    Returns:
        List[int]: A list of indices representing the top-k predicted items.
    """
    print("Predicting with Few-Shot Learning...")
    return predict(self.model, graph, node_index, top_k, threshold)

train()

Trains the model using the few-shot learning approach.

This method iterates over the data provided by the data_loader for a specified number of epochs, updating the model's weights using the optimizer and evaluating its performance using the criterion.

Source code in engines/contentFilterEngine/learning_paradigms/few_shot.py

def train(self):
    """
    Trains the model using the few-shot learning approach.

    This method iterates over the data provided by the data_loader for a specified number of epochs,
    updating the model's weights using the optimizer and evaluating its performance using the criterion.
    """
    print("Training with Few-Shot Learning...")
    train_model(self.model, self.data_loader, self.criterion, self.optimizer, self.num_epochs)