decagon-pytorch/src/triacontagon/sampling.py

#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#


import numpy as np
import torch
import torch.utils.data
from typing import List, \
    Union, \
    Tuple
from .data import Data, \
    EdgeType
from .cumcount import cumcount
import time


def fixed_unigram_candidate_sampler(
    true_classes: Union[np.array, torch.Tensor],
    unigrams: List[Union[int, float]],
    distortion: float = 1.):

    if isinstance(true_classes, torch.Tensor):
        true_classes = true_classes.detach().cpu().numpy()

    if isinstance(unigrams, torch.Tensor):
        unigrams = unigrams.detach().cpu().numpy()

    if len(true_classes.shape) != 2:
        raise ValueError('true_classes must be a 2D matrix with shape (num_samples, num_true)')

    num_samples = true_classes.shape[0]
    unigrams = np.array(unigrams)
    if distortion != 1.:
        unigrams = unigrams.astype(np.float64) ** distortion
    # print('unigrams:', unigrams)
    indices = np.arange(num_samples)
    result = np.zeros(num_samples, dtype=np.int64)
    while len(indices) > 0:
        # print('len(indices):', len(indices))
        sampler = torch.utils.data.WeightedRandomSampler(unigrams, len(indices))
        candidates = np.array(list(sampler))
        candidates = np.reshape(candidates, (len(indices), 1))
        # print('candidates:', candidates)
        # print('true_classes:', true_classes[indices, :])
        result[indices] = candidates.T
        # print('result:', result)
        mask = (candidates == true_classes[indices, :])
        mask = mask.sum(1).astype(np.bool)
        # print('mask:', mask)
        indices = indices[mask]
        # result[indices] = 0
    return torch.tensor(result)


def get_edges_and_degrees(adj_mat: torch.Tensor) -> \
    Tuple[torch.Tensor, torch.Tensor]:

    if adj_mat.is_sparse:
        adj_mat = adj_mat.coalesce()
        degrees = torch.zeros(adj_mat.shape[1], dtype=torch.int64,
            device=adj_mat.device)
        degrees = degrees.index_add(0, adj_mat.indices()[1],
            torch.ones(adj_mat.indices().shape[1], dtype=torch.int64,
                device=adj_mat.device))
        edges_pos = adj_mat.indices().transpose(0, 1)
    else:
        degrees = adj_mat.sum(0)
        edges_pos = torch.nonzero(adj_mat, as_tuple=False)
    return edges_pos, degrees


def get_true_classes(adj_mat: torch.Tensor) -> torch.Tensor:
    indices = adj_mat.indices()
    row_count = torch.zeros(adj_mat.shape[0], dtype=torch.long)
    #print('indices[0]:', indices[0], count[indices[0]])
    row_count = row_count.index_add(0, indices[0],
        torch.ones(indices.shape[1], dtype=torch.long))
    #print('count:', count)
    max_true_classes = torch.max(row_count).item()
    #print('max_true_classes:', max_true_classes)
    true_classes = torch.full((adj_mat.shape[0], max_true_classes),
        -1, dtype=torch.long)


    # inv = torch.unique(indices[0], return_inverse=True)

    # indices = indices.copy()
    # true_classes[indices[0], 0] = indices[1]
    t = time.time()
    cc = cumcount(indices[0].cpu().numpy())
    print('cumcount() took:', time.time() - t)
    cc = torch.tensor(cc)
    t = time.time()
    true_classes[indices[0], cc] = indices[1]
    print('assignment took:', time.time() - t)

    ''' count = torch.zeros(adj_mat.shape[0], dtype=torch.long)
    for i in range(indices.shape[1]):
        # print('looping...')
        row = indices[0, i]
        col = indices[1, i]
        #print('row:', row, 'col:', col, 'count[row]:', count[row])
        true_classes[row, count[row]] = col
        count[row] += 1 '''

    t = time.time()
    true_classes = torch.repeat_interleave(true_classes, row_count, dim=0)
    print('repeat_interleave() took:', time.time() - t)

    return true_classes


def negative_sample_adj_mat(adj_mat: torch.Tensor) -> torch.Tensor:
    if not isinstance(adj_mat, torch.Tensor):
        raise ValueError('adj_mat must be a torch.Tensor, got: %s' % adj_mat.__class__.__name__)

    edges_pos, degrees = get_edges_and_degrees(adj_mat)

    true_classes = get_true_classes(adj_mat)
    # true_classes = edges_pos[:, 1].view(-1, 1)
    # print('true_classes:', true_classes)

    neg_neighbors = fixed_unigram_candidate_sampler(
        true_classes, degrees, 0.75).to(adj_mat.device)

    print('neg_neighbors:', neg_neighbors)

    edges_neg = torch.cat([ edges_pos[:, 0].view(-1, 1),
        neg_neighbors.view(-1, 1) ], 1)

    adj_mat_neg = torch.sparse_coo_tensor(indices = edges_neg.transpose(0, 1),
        values=torch.ones(len(edges_neg)), size=adj_mat.shape,
        dtype=adj_mat.dtype, device=adj_mat.device)

    adj_mat_neg = adj_mat_neg.coalesce()
    indices = adj_mat_neg.indices()
    adj_mat_neg = torch.sparse_coo_tensor(indices,
        torch.ones(indices.shape[1]), adj_mat.shape,
        dtype=adj_mat.dtype, device=adj_mat.device)

    adj_mat_neg = adj_mat_neg.coalesce()

    return adj_mat_neg


def negative_sample_data(data: Data) -> Data:
    new_edge_types = {}
    res = Data()
    for vt in data.vertex_types:
        res.add_vertex_type(vt.name, vt.count)
    for key, et in data.edge_types.items():
        adjacency_matrices_neg = []
        for adj_mat in et.adjacency_matrices:
            adj_mat_neg = negative_sample_adj_mat(adj_mat)
            adjacency_matrices_neg.append(adj_mat_neg)
        res.add_edge_type(et.name,
            et.vertex_type_row, et.vertex_type_column,
            adjacency_matrices_neg, et.decoder_factory)
        #new_et = EdgeType(et.name, et.vertex_type_row,
        #    et.vertex_type_column, adjacency_matrices_neg,
        #    et.decoder_factory, et.total_connectivity)
        #new_edge_types[key] = new_et
    #res = Data(data.vertex_types, new_edge_types)
    return res