decagon-pytorch/tests/icosagon/test_batch.py

from icosagon.batch import PredictionsBatch, \
    FlatPredictions, \
    flatten_predictions, \
    BatchIndices, \
    gather_batch_indices
from icosagon.declayer import Predictions, \
    RelationPredictions, \
    RelationFamilyPredictions
from icosagon.trainprep import prepare_training, \
    TrainValTest
from icosagon.data import Data
import torch
import pytest


def test_flat_predictions_01():
    pred = FlatPredictions(torch.tensor([0, 1, 0, 1]),
        torch.tensor([1, 0, 1, 0]), 'train')

    assert torch.all(pred.predictions == torch.tensor([0, 1, 0, 1]))
    assert torch.all(pred.truth == torch.tensor([1, 0, 1, 0]))
    assert pred.part_type == 'train'


def test_flatten_predictions_01():
    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    pred_flat = flatten_predictions(pred, part_type='train')

    assert torch.all(pred_flat.predictions == \
        torch.tensor([1, 0, 1, 0, 1, 1, 0, 1, 0, 1], dtype=torch.float32))
    assert torch.all(pred_flat.truth == \
        torch.tensor([1, 1, 1, 1, 1, 0, 0, 0, 0, 0], dtype=torch.float32))
    assert pred_flat.part_type == 'train'


def test_flatten_predictions_02():
    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    pred_flat = flatten_predictions(pred, part_type='val')

    assert len(pred_flat.predictions) == 0
    assert len(pred_flat.truth) == 0
    assert pred_flat.part_type == 'val'


def test_flatten_predictions_03():
    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    pred_flat = flatten_predictions(pred, part_type='test')

    assert len(pred_flat.predictions) == 0
    assert len(pred_flat.truth) == 0
    assert pred_flat.part_type == 'test'


def test_flatten_predictions_04():
    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    with pytest.raises(TypeError):
        pred_flat = flatten_predictions(1, part_type='test')

    with pytest.raises(ValueError):
        pred_flat = flatten_predictions(pred, part_type='x')


def test_flatten_predictions_05():
    x = torch.rand(5000)
    y = torch.cat([ x, x ])
    z = torch.cat([ torch.ones(5000), torch.zeros(5000) ])

    rel_pred = RelationPredictions(
        TrainValTest(x, torch.zeros(0), torch.zeros(0)),
        TrainValTest(x, torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    for _ in range(10):
        pred_flat = flatten_predictions(pred, part_type='train')
        assert torch.all(pred_flat.predictions == y)
        assert torch.all(pred_flat.truth == z)
        assert pred_flat.part_type == 'train'


def test_batch_indices_01():
    indices = BatchIndices(torch.tensor([0, 1, 2, 3, 4]), 'train')
    assert torch.all(indices.indices == torch.tensor([0, 1, 2, 3, 4]))
    assert indices.part_type == 'train'


def test_gather_batch_indices_01():
    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    pred_flat = flatten_predictions(pred, part_type='train')

    indices = BatchIndices(torch.tensor([0, 2, 4, 5, 7, 9]), 'train')

    (input, target) = gather_batch_indices(pred_flat, indices)
    assert torch.all(input == \
        torch.tensor([1, 1, 1, 1, 1, 1], dtype=torch.float32))
    assert torch.all(target == \
        torch.tensor([1, 1, 1, 0, 0, 0], dtype=torch.float32))


def test_predictions_batch_01():
    d = Data()
    d.add_node_type('Dummy', 5)
    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
    fam.add_relation_type('Dummy Rel', torch.tensor([
        [0, 1, 0, 0, 0],
        [1, 0, 0, 0, 0],
        [0, 0, 0, 1, 0],
        [0, 0, 0, 0, 1],
        [0, 1, 0, 0, 0]
    ], dtype=torch.float32))

    prep_d = prepare_training(d, TrainValTest(1., 0., 0.))

    assert len(prep_d.relation_families) == 1
    assert len(prep_d.relation_families[0].relation_types) == 1
    assert len(prep_d.relation_families[0].relation_types[0].edges_pos.train) == 5
    assert len(prep_d.relation_families[0].relation_types[0].edges_neg.train) == 5
    assert len(prep_d.relation_families[0].relation_types[0].edges_pos.val) == 0
    assert len(prep_d.relation_families[0].relation_types[0].edges_pos.test) == 0

    rel_pred = RelationPredictions(
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.tensor([1, 0, 1, 0, 1], dtype=torch.float32), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0)),
        TrainValTest(torch.zeros(0), torch.zeros(0), torch.zeros(0))
    )
    fam_pred = RelationFamilyPredictions([ rel_pred ])
    pred = Predictions([ fam_pred ])

    pred_flat = flatten_predictions(pred, part_type='train')

    batch = PredictionsBatch(prep_d, part_type='train', batch_size=1)
    count = 0
    lst = []
    for indices in batch:
        (input, target) = gather_batch_indices(pred_flat, indices)
        assert len(input) == 1
        assert len(target) == 1
        lst.append((input[0], target[0]))
        count += 1
    assert lst == [ (1, 1), (0, 1), (1, 1), (0, 1), (1, 1),
        (1, 0), (0, 0), (1, 0), (0, 0), (1, 0) ]

    assert count == 10