def test_batches(session):
session.execute(
'INSERT INTO "batches" VALUES ("batch1", "sku1", 100, null)')
session.execute(
'INSERT INTO "batches" VALUES ("batch2", "sku2", 200, "2011-04-11")')
expected = [
model.Batch("batch1", "sku1", 100, eta=None),
model.Batch("batch2", "sku2", 200, eta=date(2011, 4, 11)),
]
assert session.query(model.Batch).all() == expected
def test_retrieving_batches(session):
session.execute(
'INSERT INTO batches (reference, sku, _purchased_quantity, eta)'
' VALUES ("batch1", "sku1", 100, null)')
session.execute(
'INSERT INTO batches (reference, sku, _purchased_quantity, eta)'
' VALUES ("batch2", "sku2", 200, "2011-04-11")')
expected = [
model.Batch("batch1", "sku1", 100, eta=None),
model.Batch("batch2", "sku2", 200, eta=date(2011, 4, 11)),
]
assert session.query(model.Batch).all() == expected
session.close()
session = None
def test_returns_allocation():
line = model.OrderLine("o1", "COMPLICATED-LAMP", 10)
batch = model.Batch("b1", "COMPLICATED-LAMP", 100, eta=None)
repo = FakeRepository([batch])
result = services.allocate(line, repo, FakeSession())
assert result == "b1"
def test_commits():
line = model.OrderLine('o1', 'OMINOUS-MIRROR', 10)
batch = model.Batch('b1', 'OMINOUS-MIRROR', 100, eta=None)
repo = FakeRepository([batch])
session = FakeSession()
services.allocate(line, repo, session)
assert session.committed is True
def test_error_for_invalid_sku():
line = model.OrderLine("o1", "NONEXISTENTSKU", 10)
batch = model.Batch("b1", "AREALSKU", 100, eta=None)
repo = FakeRepository([batch])
with pytest.raises(services.InvalidSku,
match="Invalid sku NONEXISTENTSKU"):
services.allocate(line, repo, FakeSession())
def test_saving_allocations(session):
batch = model.Batch("batch1", "sku1", 100, eta=None)
line = model.OrderLine("order1", "sku1", 10)
batch.allocate(line)
session.add(batch)
session.commit()
rows = list(session.execute('SELECT orderline_id, batch_id FROM "allocations"'))
assert rows == [(batch.id, line.id)]
def test_saving_batches(session):
batch = model.Batch("batch1", "sku1", 100, eta=None)
session.add(batch)
session.commit()
rows = session.execute(
'SELECT reference, sku, _purchased_quantity, eta FROM "batches"'
)
assert list(rows) == [("batch1", "sku1", 100, None)]
def test_retrieving_batches(session):
session.execute(
"INSERT INTO batches (reference, sku, _purchased_quantity, manufacture_date)"
'VALUES("batch1", "sku1",100,null)'
)
session.execute(
"INSERT INTO batches(reference, sku,_purchased_quantity, manufacture_date)"
'VALUES ("batch2", "sku2", 200, "2021-04-11")'
)
expected = [
model.Batch("batch1", "sku1", 100, manufacture_date=None),
model.Batch("batch2", "sku2", 200, manufacture_date=date(2021, 4, 4)),
]
assert session.query(model.Batch).all() == expected
def test_repository_can_save_a_batch(session: Session):
batch = model.Batch("batch1", "RUSTY-SOAPDISH", 100, eta=None)
repo = repository.SqlAlchemyRepository(session)
repo.add(batch)
session.commit()
rows = session.execute(
'SELECT reference, sku, _purchased_quantity, eta FROM "batches"')
assert list(rows) == [("batch1", "RUSTY-SOAPDISH", 100, None)]
def test_saving_batches(session):
batch = model.Batch('batch1', 'sku1', 100, eta=None)
session.add(batch)
session.commit()
rows = list(
session.execute(
'SELECT reference, sku, _purchased_quantity, eta FROM "batches"'))
assert rows == [('batch1', 'sku1', 100, None)]
session.close()
session = None
def test_repository_can_save_a_batch(session):
batch = model.Batch("batch1", "RUSTY-SOAPDISH", 100, eta=None)
repo = repository.SqlAlchemyRepository(session)
repo.add(batch) # repo.add() is the method under test here.
session.commit(
) # keep the .commit() outside of the repository and make it the responsibility of the caller.
rows = list(
session.execute(
'SELECT reference, sku, _purchased_quantity, eta FROM "batches"' # use the raw SQL to verify that the right data has been saved.
))
assert rows == [("batch1", "RUSTY-SOAPDISH", 100, None)]
def test_repository_can_retrieve_a_batch_with_allocations(session):
orderline_id = insert_order_line(session)
batch1_id = insert_batch(session, "batch1")
insert_batch(session, "batch2")
insert_allocation(session, orderline_id, batch1_id)
repo = repository.SqlAlchemyRepository(session)
retrieved = repo.get("batch1")
expected = model.Batch("batch1", "GENERIC-SOFA", 100, eta=None)
assert retrieved == expected
assert retrieved.sku == expected.sku
assert retrieved._purchased_quantity == expected._purchased_quantity
assert retrieved._allocations == {
model.OrderLine("order1", "GENERIC-SOFA", 12),
}
def test_repository_can_retrieve_a_batch_with_allocations(session):
orderline_id = insert_order_line(session)
batch1_id = insert_batch(session, "batch1")
insert_batch(session, "batch2")
insert_allocation(session, orderline_id, batch1_id)
repo = repository.SqlAlchemyRepository(session)
retrieved = repo.get("batch1")
expected = model.Batch("batch1", "GENERIC-SOFA", 100, eta=None)
# checks that the types match, and that the reference is the same
assert retrieved == expected # Batch.__eq__ only compares reference
# explicitly check on its major attributes, including ._allocations, which is a Python set of OrderLine value objects.
assert retrieved.sku == expected.sku
assert retrieved._purchased_quantity == expected._purchased_quantity
assert retrieved._allocations == {
model.OrderLine("order1", "GENERIC-SOFA", 12),
}
def main(_):
# total_x, total_y, x_dim, y_dim
ckpt_path = os.path.join(FLAGS.ckpt_dir, FLAGS.name)
(train_x, train_y), (test_x, test_y) = preprocess.create_dataset()
batch = model.Batch(train_x, train_y, FLAGS.epoch)
print('start session')
with tf.Session() as sess:
predicator = model.Predicator(matrix_shape=[9, 8],
num_time=7,
out_time=7,
kernels=[[5, 5], [5, 5], [5, 5], [5, 5],
[5, 5]],
depths=[256, 128, 128, 64, 32],
learning_rate=FLAGS.learning_rate,
beta1=FLAGS.beta1)
train_path = os.path.join(FLAGS.summary_dir, FLAGS.name, 'train')
test_path = os.path.join(FLAGS.summary_dir, FLAGS.name, 'test')
train_writer = tf.summary.FileWriter(train_path, sess.graph)
test_writer = tf.summary.FileWriter(test_path, sess.graph)
print('start training')
sess.run(tf.global_variables_initializer())
for i in range(FLAGS.epoch):
for n in range(batch.iter_per_epoch):
batch_x, batch_y = batch()
predicator.train(sess, batch_x, batch_y)
print(i, 'th epoch')
summary = predicator.inference(sess, predicator.summary, batch_x,
batch_y)
train_writer.add_summary(summary, global_step=i)
summary = predicator.inference(sess, predicator.summary, test_x,
test_y)
test_writer.add_summary(summary, global_step=i)
if (i + 1) % FLAGS.ckpt_interval == 0:
predicator.dump(sess, ckpt_path, i)
def main(_):
# total_x, total_y, x_dim, y_dim
ckpt_path = os.path.join(FLAGS.ckpt_dir, FLAGS.name)
batch = model.Batch(total_x, total_y, 128)
with tf.Session() as sess:
basic_model = model.BasicModel(x_dim, y_dim, FLAGS.learning_rate, FLAGS.beta1)
writer = tf.summary.FileWriter(os.path.join(FLAGS.summary_dir, FLAGS.name), sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(FLAGS.epoch):
for n in range(batch.iter_per_epoch):
batch_x, batch_y = batch()
basic_model.train(sess, batch_x, batch_y)
summary = basic_model.inference(sess, basic_model.summary, batch_x, batch_y)
writer.add_summary(summary)
if (i + 1) % FLAGS.ckpt_interval == 0:
basic_model.dump(sess, ckpt_path)
