DeepTraCDR/data_sampler.py

import torch
import numpy as np
import scipy.sparse as sp
from typing import Tuple, Optional
from utils import to_coo_matrix, to_tensor, mask


class RandomSampler:
    """
    Samples edges from an adjacency matrix to create train/test sets.
    Converts the training set into torch.Tensor format.
    """
    def __init__(
        self,
        adj_mat_original: np.ndarray,
        train_index: np.ndarray,
        test_index: np.ndarray,
        null_mask: np.ndarray
    ) -> None:
        self.adj_mat = to_coo_matrix(adj_mat_original)
        self.train_index = train_index
        self.test_index = test_index
        self.null_mask = null_mask

        # Sample positive edges
        self.train_pos = self._sample_edges(train_index)
        self.test_pos = self._sample_edges(test_index)

        # Sample negative edges
        self.train_neg, self.test_neg = self._sample_negative_edges()

        # Create masks
        self.train_mask = mask(self.train_pos, self.train_neg, dtype=int)
        self.test_mask = mask(self.test_pos, self.test_neg, dtype=bool)

        # Convert to tensors
        self.train_data = to_tensor(self.train_pos)
        self.test_data = to_tensor(self.test_pos)

    def _sample_edges(self, index: np.ndarray) -> sp.coo_matrix:
        """Samples edges from the adjacency matrix based on provided indices."""
        row = self.adj_mat.row[index]
        col = self.adj_mat.col[index]
        data = self.adj_mat.data[index]
        return sp.coo_matrix(
            (data, (row, col)),
            shape=self.adj_mat.shape
        )

    def _sample_negative_edges(self) -> Tuple[sp.coo_matrix, sp.coo_matrix]:
        """
        Samples negative edges for training and testing.
        Negative edges are those not present in the adjacency matrix.
        """
        pos_adj_mat = self.null_mask + self.adj_mat.toarray()
        neg_adj_mat = sp.coo_matrix(np.abs(pos_adj_mat - 1))
        all_row, all_col, all_data = neg_adj_mat.row, neg_adj_mat.col, neg_adj_mat.data
        indices = np.arange(all_data.shape[0])

        # Sample negative test edges
        test_n = self.test_index.shape[0]
        test_neg_indices = np.random.choice(indices, test_n, replace=False)
        test_row, test_col, test_data = (
            all_row[test_neg_indices],
            all_col[test_neg_indices],
            all_data[test_neg_indices]
        )
        test_neg = sp.coo_matrix(
            (test_data, (test_row, test_col)),
            shape=self.adj_mat.shape
        )

        # Sample negative train edges
        train_neg_indices = np.delete(indices, test_neg_indices)
        train_row, train_col, train_data = (
            all_row[train_neg_indices],
            all_col[train_neg_indices],
            all_data[train_neg_indices]
        )
        train_neg = sp.coo_matrix(
            (train_data, (train_row, train_col)),
            shape=self.adj_mat.shape
        )

        return train_neg, test_neg


class NewSampler:
    """
    Samples train/test data and masks for a specific target dimension/index.
    """
    def __init__(
        self,
        original_adj_mat: np.ndarray,
        null_mask: np.ndarray,
        target_dim: Optional[int],
        target_index: int
    ) -> None:
        self.adj_mat = original_adj_mat
        self.null_mask = null_mask
        self.dim = target_dim
        self.target_index = target_index
        self.train_data, self.test_data = self._sample_train_test_data()
        self.train_mask, self.test_mask = self._sample_train_test_mask()

    def _sample_target_test_index(self) -> np.ndarray:
        """Samples indices for positive test edges based on target dimension."""
        if self.dim:
            return np.where(self.adj_mat[:, self.target_index] == 1)[0]
        return np.where(self.adj_mat[self.target_index, :] == 1)[0]

    def _sample_train_test_data(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Samples train and test data based on target indices."""
        test_data = np.zeros(self.adj_mat.shape, dtype=np.float32)
        test_index = self._sample_target_test_index()

        if self.dim:
            test_data[test_index, self.target_index] = 1
        else:
            test_data[self.target_index, test_index] = 1

        train_data = self.adj_mat - test_data
        return torch.from_numpy(train_data), torch.from_numpy(test_data)

    def _sample_train_test_mask(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Creates train and test masks, including negative sampling."""
        test_index = self._sample_target_test_index()
        neg_value = np.ones(self.adj_mat.shape, dtype=np.float32) - self.adj_mat - self.null_mask
        neg_test_mask = np.zeros(self.adj_mat.shape, dtype=np.float32)

        if self.dim:
            target_neg_index = np.where(neg_value[:, self.target_index] == 1)[0]
        else:
            target_neg_index = np.where(neg_value[self.target_index, :] == 1)[0]

        target_neg_test_index = (
            np.random.choice(target_neg_index, len(test_index), replace=False)
            if len(test_index) < len(target_neg_index)
            else target_neg_index
        )

        if self.dim:
            neg_test_mask[target_neg_test_index, self.target_index] = 1
            neg_value[:, self.target_index] = 0
        else:
            neg_test_mask[self.target_index, target_neg_test_index] = 1
            neg_value[self.target_index, :] = 0

        train_mask = (self.train_data.numpy() + neg_value).astype(bool)
        test_mask = (self.test_data.numpy() + neg_test_mask).astype(bool)
        return torch.from_numpy(train_mask), torch.from_numpy(test_mask)


class SingleSampler:
    """
    Samples train/test data and masks for a specific target index.
    Returns results as torch.Tensor.
    """
    def __init__(
        self,
        origin_adj_mat: np.ndarray,
        null_mask: np.ndarray,
        target_index: int,
        train_index: np.ndarray,
        test_index: np.ndarray
    ) -> None:
        self.adj_mat = origin_adj_mat
        self.null_mask = null_mask
        self.target_index = target_index
        self.train_index = train_index
        self.test_index = test_index
        self.train_data, self.test_data = self._sample_train_test_data()
        self.train_mask, self.test_mask = self._sample_train_test_mask()

    def _sample_train_test_data(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Samples train and test data for the target index."""
        test_data = np.zeros(self.adj_mat.shape, dtype=np.float32)
        test_data[self.test_index, self.target_index] = 1
        train_data = self.adj_mat - test_data
        return torch.from_numpy(train_data), torch.from_numpy(test_data)

    def _sample_train_test_mask(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Creates train and test masks with negative sampling."""
        neg_value = np.ones(self.adj_mat.shape, dtype=np.float32) - self.adj_mat - self.null_mask
        neg_test_mask = np.zeros(self.adj_mat.shape, dtype=np.float32)

        target_neg_index = np.where(neg_value[:, self.target_index] == 1)[0]
        target_neg_test_index = np.random.choice(target_neg_index, len(self.test_index), replace=False)
        neg_test_mask[target_neg_test_index, self.target_index] = 1
        neg_value[target_neg_test_index, self.target_index] = 0

        train_mask = (self.train_data.numpy() + neg_value).astype(bool)
        test_mask = (self.test_data.numpy() + neg_test_mask).astype(bool)
        return torch.from_numpy(train_mask), torch.from_numpy(test_mask)



class TargetSampler(object):
    """
    Samples train/test data and masks for multiple target indices.
    """
    def __init__(self, response_mat: np.ndarray, null_mask: np.ndarray, target_indexes: np.ndarray,
                 pos_train_index: np.ndarray, pos_test_index: np.ndarray):
        self.response_mat = response_mat
        self.null_mask = null_mask
        self.target_indexes = target_indexes
        self.pos_train_index = pos_train_index
        self.pos_test_index = pos_test_index
        self.train_data, self.test_data = self.sample_train_test_data()
        self.train_mask, self.test_mask = self.sample_train_test_mask()

    def sample_train_test_data(self):
        n_target = self.target_indexes.shape[0]
        target_response = self.response_mat[:, self.target_indexes].reshape((-1, n_target))
        train_data = self.response_mat.copy()
        train_data[:, self.target_indexes] = 0
        target_pos_value = sp.coo_matrix(target_response)
        target_train_data = sp.coo_matrix((target_pos_value.data[self.pos_train_index],
                                           (target_pos_value.row[self.pos_train_index],
                                            target_pos_value.col[self.pos_train_index])),
                                          shape=target_response.shape).toarray()
        target_test_data = sp.coo_matrix((target_pos_value.data[self.pos_test_index],
                                          (target_pos_value.row[self.pos_test_index],
                                           target_pos_value.col[self.pos_test_index])),
                                         shape=target_response.shape).toarray()
        test_data = np.zeros(self.response_mat.shape, dtype=np.float32)
        for i, value in enumerate(self.target_indexes):
            train_data[:, value] = target_train_data[:, i]
            test_data[:, value] = target_test_data[:, i]
        train_data = torch.from_numpy(train_data)
        test_data = torch.from_numpy(test_data)
        return train_data, test_data

    def sample_train_test_mask(self):
        target_response = self.response_mat[:, self.target_indexes]
        target_ones = np.ones(target_response.shape, dtype=np.float32)
        target_neg_value = target_ones - target_response - self.null_mask[:, self.target_indexes]
        target_neg_value = sp.coo_matrix(target_neg_value)
        ids = np.arange(target_neg_value.data.shape[0])
        target_neg_test_index = np.random.choice(ids, self.pos_test_index.shape[0], replace=False)
        target_neg_test_mask = sp.coo_matrix((target_neg_value.data[target_neg_test_index],
                                              (target_neg_value.row[target_neg_test_index],
                                               target_neg_value.col[target_neg_test_index])),
                                             shape=target_response.shape).toarray()
        neg_test_mask = np.zeros(self.response_mat.shape, dtype=np.float32)
        for i, value in enumerate(self.target_indexes):
            neg_test_mask[:, value] = target_neg_test_mask[:, i]
        other_neg_value = np.ones(self.response_mat.shape,
                                  dtype=np.float32) - neg_test_mask - self.response_mat - self.null_mask
        test_mask = (self.test_data.numpy() + neg_test_mask).astype(bool)
        train_mask = (self.train_data.numpy() + other_neg_value).astype(bool)
        test_mask = torch.from_numpy(test_mask)
        train_mask = torch.from_numpy(train_mask)
        return train_mask, test_mask


class ExterSampler:
    """
    Samples train/test data and masks based on row indices.
    """
    def __init__(
        self,
        original_adj_mat: np.ndarray,
        null_mask: np.ndarray,
        train_index: np.ndarray,
        test_index: np.ndarray
    ) -> None:
        self.adj_mat = original_adj_mat
        self.null_mask = null_mask
        self.train_index = train_index
        self.test_index = test_index
        self.train_data, self.test_data = self._sample_train_test_data()
        self.train_mask, self.test_mask = self._sample_train_test_mask()

    def _sample_train_test_data(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Samples train and test data based on row indices."""
        test_data = self.adj_mat.copy()
        test_data[self.train_index, :] = 0
        train_data = self.adj_mat - test_data
        return torch.from_numpy(train_data), torch.from_numpy(test_data)

    def _sample_train_test_mask(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """Creates train and test masks with negative sampling."""
        neg_value = np.ones(self.adj_mat.shape, dtype=np.float32) - self.adj_mat - self.null_mask
        neg_train = neg_value.copy()
        neg_train[self.test_index, :] = 0
        neg_test = neg_value.copy()
        neg_test[self.train_index, :] = 0

        train_mask = (self.train_data.numpy() + neg_train).astype(bool)
        test_mask = (self.test_data.numpy() + neg_test).astype(bool)
        return torch.from_numpy(train_mask), torch.from_numpy(test_mask)
    
    
class RegressionSampler(object):
    def __init__(self, adj_mat_original, train_index, test_index, null_mask):
        super(RegressionSampler, self).__init__()
        
        if isinstance(adj_mat_original, torch.Tensor):
            adj_mat_np = adj_mat_original.cpu().numpy()
        else:
            adj_mat_np = adj_mat_original.copy()
        
        self.full_data = torch.FloatTensor(adj_mat_np)

        rows, cols = adj_mat_np.shape
        train_mask = np.zeros((rows, cols), dtype=bool)
        test_mask = np.zeros((rows, cols), dtype=bool)
        
        for idx in train_index:
            row = idx // cols
            col = idx % cols
            if not null_mask[row, col]:  
                train_mask[row, col] = True
        
        for idx in test_index:
            row = idx // cols
            col = idx % cols
            if not null_mask[row, col]:  
                test_mask[row, col] = True
        
        self.train_mask = torch.BoolTensor(train_mask)
        self.test_mask = torch.BoolTensor(test_mask)
        
        self.train_data = self.full_data.clone()
        self.test_data = self.full_data.clone()

        assert not torch.any(self.train_mask & self.test_mask), "Train and test masks have overlap!"
        
    def get_train_indices(self):
        indices = torch.nonzero(self.train_mask)
        return indices
        
    def get_test_indices(self):
        indices = torch.nonzero(self.test_mask)
        return indices