/
test_models.py
46 lines (38 loc) · 1.23 KB
/
test_models.py
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import numpy as np
from skorch import NeuralNet
import torch
from torch import nn
from spacecutter.callbacks import AscensionCallback
from spacecutter.losses import CumulativeLinkLoss
from spacecutter.models import OrdinalLogisticModel
SEED = 666
def test_loss_lowers_on_each_epoch():
torch.manual_seed(SEED)
num_classes = 5
num_features = 5
size = 200
y = torch.randint(0, num_classes, (size, 1), dtype=torch.long)
X = torch.rand((size, num_features))
predictor = nn.Sequential(
nn.Linear(num_features, num_features),
nn.ReLU(),
nn.Linear(num_features, 1)
)
skorch_model = NeuralNet(
module=OrdinalLogisticModel,
module__predictor=predictor,
module__num_classes=num_classes,
criterion=CumulativeLinkLoss,
max_epochs=10,
optimizer=torch.optim.Adam,
lr=0.01,
train_split=None,
callbacks=[
('ascension', AscensionCallback()),
],
)
skorch_model.fit(X, y)
losses = [epoch['train_loss'] for epoch in skorch_model.history]
for idx, loss in enumerate(losses[:-1]):
# Next epoch's loss is less than this epoch's loss.
assert losses[idx + 1] < loss, 'Loss lowers on each epoch'