PINA/pina/data/dataset.py

"""
This module provide basic data management functionalities
"""

from abc import abstractmethod
from torch.utils.data import Dataset
from torch_geometric.data import Data
from ..graph import Graph, LabelBatch


class PinaDatasetFactory:
    """
    Factory class for the PINA dataset. Depending on the type inside the
    conditions it creates a different dataset object:
    - PinaTensorDataset for torch.Tensor
    - PinaGraphDataset for list of torch_geometric.data.Data objects
    """

    def __new__(cls, conditions_dict, **kwargs):
        # Check if conditions_dict is empty
        if len(conditions_dict) == 0:
            raise ValueError("No conditions provided")

        # Check is a Graph is present in the conditions
        is_graph = cls._is_graph_dataset(conditions_dict)
        if is_graph:
            # If a Graph is present, return a PinaGraphDataset
            return PinaGraphDataset(conditions_dict, **kwargs)
        # If no Graph is present, return a PinaTensorDataset
        return PinaTensorDataset(conditions_dict, **kwargs)

    @staticmethod
    def _is_graph_dataset(conditions_dict):
        for v in conditions_dict.values():
            for cond in v.values():
                if isinstance(cond, (Data, Graph, list)):
                    return True
        return False


class PinaDataset(Dataset):
    """
    Abstract class for the PINA dataset
    """

    def __init__(
        self, conditions_dict, max_conditions_lengths, automatic_batching
    ):
        # Store the conditions dictionary
        self.conditions_dict = conditions_dict
        # Store the maximum number of conditions to consider
        self.max_conditions_lengths = max_conditions_lengths
        # Store length of each condition
        self.conditions_length = {
            k: len(v["input"]) for k, v in self.conditions_dict.items()
        }
        # Store the maximum length of the dataset
        self.length = max(self.conditions_length.values())
        # Dynamically set the getitem function based on automatic batching
        if automatic_batching:
            self._getitem_func = self._getitem_int
        else:
            self._getitem_func = self._getitem_dummy

    def _get_max_len(self):
        """"""
        max_len = 0
        for condition in self.conditions_dict.values():
            max_len = max(max_len, len(condition["input"]))
        return max_len

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        return self._getitem_func(idx)

    def _getitem_dummy(self, idx):
        # If automatic batching is disabled, return the data at the given index
        return idx

    def _getitem_int(self, idx):
        # If automatic batching is enabled, return the data at the given index
        return {
            k: {k_data: v[k_data][idx % len(v["input"])] for k_data in v.keys()}
            for k, v in self.conditions_dict.items()
        }

    def get_all_data(self):
        """
        Return all data in the dataset

        :return: All data in the dataset
        :rtype: dict
        """
        index = list(range(len(self)))
        return self.fetch_from_idx_list(index)

    def fetch_from_idx_list(self, idx):
        """
        Return data from the dataset given a list of indices

        :param idx: List of indices
        :type idx: list
        :return: Data from the dataset
        :rtype: dict
        """
        to_return_dict = {}
        for condition, data in self.conditions_dict.items():
            # Get the indices for the current condition
            cond_idx = idx[: self.max_conditions_lengths[condition]]
            # Get the length of the current condition
            condition_len = self.conditions_length[condition]
            # If the length of the dataset is greater than the length of the
            # current condition, repeat the indices
            if self.length > condition_len:
                cond_idx = [idx % condition_len for idx in cond_idx]
            # Retrieve the data from the current condition
            to_return_dict[condition] = self._retrive_data(data, cond_idx)
        return to_return_dict

    @abstractmethod
    def _retrive_data(self, data, idx_list):
        pass


class PinaTensorDataset(PinaDataset):
    """
    Class for the PINA dataset with torch.Tensor data
    """

    # Override _retrive_data method for torch.Tensor data
    def _retrive_data(self, data, idx_list):
        return {k: v[idx_list] for k, v in data.items()}

    @property
    def input(self):
        """
        Method to return input points for training.
        """
        return {k: v["input"] for k, v in self.conditions_dict.items()}


class PinaGraphDataset(PinaDataset):
    """
    Class for the PINA dataset with torch_geometric.data.Data data
    """

    def _create_graph_batch_from_list(self, data):
        batch = LabelBatch.from_data_list(data)
        return batch

    def _create_output_batch(self, data):
        out = data.reshape(-1, *data.shape[2:])
        return out

    def create_graph_batch(self, data):
        """
        Create a Batch object from a list of Data objects.

        :param data: List of Data objects
        :type data: list
        :return: Batch object
        :rtype: Batch or PinaBatch
        """
        if isinstance(data[0], Data):
            return self._create_graph_batch_from_list(data)
        return self._create_output_batch(data)

    # Override _retrive_data method for graph handling
    def _retrive_data(self, data, idx_list):
        # Return the data from the current condition
        # If the data is a list of Data objects, create a Batch object
        # If the data is a list of torch.Tensor objects, create a torch.Tensor
        return {
            k: (
                self._create_graph_batch_from_list([v[i] for i in idx_list])
                if isinstance(v, list)
                else self._create_output_batch(v[idx_list])
            )
            for k, v in data.items()
        }