/
test_model.py
529 lines (468 loc) · 21.6 KB
/
test_model.py
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"""Unit tests for scalarstop.model."""
import doctest
import logging
import os
import tempfile
import unittest
import unittest.mock
import warnings
import tensorflow as tf
import scalarstop as sp
from tests.assertions import (
assert_directory,
assert_keras_saved_model_directory,
assert_model_after_fit,
assert_spkeras_models_are_equal,
)
from tests.fixtures import MyDataBlob, MyModelTemplate, requires_sqlite_json
def load_tests(loader, tests, ignore): # pylint: disable=unused-argument
"""Have the unittest loader also run doctests."""
tests.addTests(doctest.DocTestSuite(sp.model))
return tests
class TestModel(unittest.TestCase):
"""Test Model."""
def test_not_implemented(self):
"""Assert that various methods need to be implemented in a subclass."""
datablob = MyDataBlob(hyperparams=dict(rows=10, cols=5)).batch(2)
model_template = MyModelTemplate(hyperparams=dict(layer_1_units=2))
with self.assertRaises(sp.exceptions.IsNotImplemented):
sp.Model.from_filesystem(
datablob=datablob, model_template=model_template, models_directory="/"
)
with self.assertRaises(sp.exceptions.IsNotImplemented):
sp.Model.load("/")
model = sp.Model(datablob=datablob, model_template=model_template)
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.history # pylint: disable=pointless-statement
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.current_epoch # pylint: disable=pointless-statement
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.save("a")
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.fit(final_epoch=1)
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.predict(dataset=tf.data.Dataset.from_tensor_slices([1, 2]))
with self.assertRaises(sp.exceptions.IsNotImplemented):
model.evaluate(dataset=tf.data.Dataset.from_tensor_slices([1, 2]))
class TestKerasModel(unittest.TestCase): # pylint: disable=too-many-public-methods
"""Test KerasModel."""
def setUp(self):
self.temp_dir_context = (
tempfile.TemporaryDirectory() # pylint: disable=consider-using-with
)
self.models_directory = self.temp_dir_context.name
self.datablob = MyDataBlob(hyperparams=dict(rows=10, cols=5)).batch(2)
self.model_template = MyModelTemplate(hyperparams=dict(layer_1_units=2))
self.keras_model = sp.KerasModel(
datablob=self.datablob,
model_template=self.model_template,
)
def tearDown(self):
self.temp_dir_context.cleanup()
def test_model_not_found(self):
"""Test what happens when we try to load a nonexistent model."""
with self.assertRaises(sp.exceptions.ModelNotFoundError):
sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory="",
)
with self.assertRaises(sp.exceptions.ModelNotFoundError):
sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
def test_model_not_found_missing_history(self):
"""Test what happens when we load a KerasModel that has no history."""
# Fit a model.
retval = self.keras_model.fit(final_epoch=2, verbose=0)
assert_model_after_fit(
return_value=retval, model=self.keras_model, expected_epochs=2
)
# Use Keras's save function, which will not save a history.json.
self.keras_model.model.save(
filepath=os.path.join(self.models_directory, self.keras_model.name),
overwrite=True,
include_optimizer=True,
save_format="tf",
)
# Try to load it back.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
loaded_model = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_model_after_fit(return_value={}, model=loaded_model, expected_epochs=0)
def test_model_already_trained(self):
"""
Test what happens when we train to a final epoch less
than the number of epochs already trained.
"""
# Assert that the model has not been trained.
assert_model_after_fit(
return_value={}, model=self.keras_model, expected_epochs=0
)
# Train for 3 epochs.
retval = self.keras_model.fit(final_epoch=3, verbose=0)
assert_model_after_fit(
return_value=retval, model=self.keras_model, expected_epochs=3
)
# Calling fit() again should have no effect.
retval = self.keras_model.fit(final_epoch=3, verbose=0)
assert_model_after_fit(
return_value=retval, model=self.keras_model, expected_epochs=3
)
# Also should have no effect.
retval = self.keras_model.fit(final_epoch=2, verbose=0)
assert_model_after_fit(
return_value=retval, model=self.keras_model, expected_epochs=3
)
# This should train for 2 more epochs.
retval = self.keras_model.fit(final_epoch=5, verbose=0)
assert_model_after_fit(
return_value=retval, model=self.keras_model, expected_epochs=5
)
# Save and load back. Assert the model is the same.
self.keras_model.save(self.models_directory)
loaded_model_1 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_spkeras_models_are_equal(self.keras_model, loaded_model_1)
assert_model_after_fit(
return_value=retval, model=loaded_model_1, expected_epochs=5
)
# Calling fit() should have no effect.
retval = loaded_model_1.fit(final_epoch=5, verbose=0)
assert_model_after_fit(
return_value=retval, model=loaded_model_1, expected_epochs=5
)
def test_fit_works(self):
"""Test that fitting a model works."""
history = self.keras_model.fit(final_epoch=2, verbose=0)
self.assertEqual(
sorted(history.keys()),
[
"binary_accuracy",
"loss",
"precision",
"recall",
"val_binary_accuracy",
"val_loss",
"val_precision",
"val_recall",
],
)
assert_model_after_fit(
return_value=history, model=self.keras_model, expected_epochs=2
)
def test_from_filesystem_or_new(self):
"""
Test that we can load models from the filesystem when it exists,
and that we fallback to creating a new model.
"""
# Create two brand new models and assert that they've been untrained.
model1 = sp.KerasModel.from_filesystem_or_new(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
model2 = sp.KerasModel.from_filesystem_or_new(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_model_after_fit(return_value={}, model=model1, expected_epochs=0)
assert_model_after_fit(return_value={}, model=model2, expected_epochs=0)
assert_spkeras_models_are_equal(model1, model2)
# Assert that we have no saved models.
assert_directory(self.models_directory, [])
# Fit the first model and verify that it has been saved.
model1_history = model1.fit(
final_epoch=2, verbose=0, models_directory=self.models_directory
)
assert_directory(self.models_directory, [model1.name])
# Assert that our other model has been unchanged.
assert_model_after_fit(return_value={}, model=model2, expected_epochs=0)
# Create a new model. This time it should be loaded from the filesystem.
model3 = sp.KerasModel.from_filesystem_or_new(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_model_after_fit(
return_value=model1_history, model=model3, expected_epochs=2
)
assert_spkeras_models_are_equal(model1, model3)
def test_save_and_load(self):
"""Test that we can save and load Keras models."""
# Test that we can save a model before doing any training.
self.keras_model.save(self.models_directory)
assert_directory(self.models_directory, [self.keras_model.name])
model_path = os.path.join(self.models_directory, self.keras_model.name)
assert_keras_saved_model_directory(model_path)
# Test loading the model we saved.
loaded_model_1 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
self.assertEqual(self.keras_model.name, loaded_model_1.name)
assert_spkeras_models_are_equal(self.keras_model, loaded_model_1)
# Test fitting the model and saving again.
loaded_model_1.fit(final_epoch=3, verbose=0)
self.assertEqual(loaded_model_1.current_epoch, 3)
loaded_model_1.save(self.models_directory)
loaded_model_1.fit(final_epoch=3, verbose=0)
# Load the model from the filesystem again.
loaded_model_2 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_directory(self.models_directory, [loaded_model_2.name])
assert_keras_saved_model_directory(model_path)
assert_spkeras_models_are_equal(loaded_model_1, loaded_model_2)
# Fit the model again and save.
loaded_model_2.fit(final_epoch=5, verbose=0)
self.assertEqual(loaded_model_2.current_epoch, 5)
loaded_model_2.save(self.models_directory)
# And load it again.
loaded_model_3 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=self.models_directory,
)
assert_directory(self.models_directory, [loaded_model_3.name])
assert_keras_saved_model_directory(model_path)
assert_spkeras_models_are_equal(loaded_model_2, loaded_model_3)
def test_save_callback_with_different_directories(self):
"""Test that the save callback works."""
# Train the first model and save it.
model1 = self.keras_model
model_name = model1.name
with tempfile.TemporaryDirectory() as dir1:
model1.fit(final_epoch=3, verbose=0, models_directory=dir1)
self.assertEqual(model1.current_epoch, 3)
assert_directory(dir1, [model_name])
assert_keras_saved_model_directory(os.path.join(dir1, model_name))
# Load the model from the filesystem, train it, and save it.
model2 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=dir1,
)
self.assertEqual(model2.current_epoch, 3)
assert_spkeras_models_are_equal(model1, model2)
with tempfile.TemporaryDirectory() as dir2:
model2.fit(final_epoch=6, verbose=0, models_directory=dir2)
# Load the model from the filesystem again... train it... and save it.
model3 = sp.KerasModel.from_filesystem(
datablob=self.datablob,
model_template=self.model_template,
models_directory=dir2,
)
self.assertEqual(model3.current_epoch, 6)
assert_spkeras_models_are_equal(model2, model3)
def test_predict(self):
"""Test that KerasModel.predict() works."""
something = self.keras_model.predict(dataset=self.datablob.training, verbose=0)
self.assertEqual(len(something), len(list(self.datablob.training.unbatch())))
def test_evaluate(self):
"""Test that KerasModel.evaluate() works."""
# Demonstrate the ability for the model to evaluate its own test set.
on_test_set = self.keras_model.evaluate(verbose=0)
# It is 4 because the model has 3 metrics in addition to loss.
self.assertEqual(len(on_test_set), 4)
# Assert that the loss is somewhat greater than 0.
self.assertTrue(on_test_set[0] > 0.01)
# Now demonstrate the ability to evaluate a custom dataset.
on_provided_data = self.keras_model.evaluate(
dataset=tf.data.Dataset.from_tensor_slices(tf.zeros(shape=(3, 5))).batch(2),
verbose=0,
)
self.assertEqual(len(on_provided_data), 4)
self.assertAlmostEqual(on_provided_data[0], 0.0)
def test_fit_invalid_profile_batch(self):
"""Test that KerasModel.fit() needs tensorboard_directory to specify profile_batch."""
model = sp.KerasModel(
datablob=self.datablob,
model_template=self.model_template,
)
with self.assertRaises(ValueError):
model.fit(final_epoch=3, verbose=0, profile_batch=(1, 2))
def test_fit_with_logging_args_default(self):
"""Test training an epoch with the default logging arguments."""
# Make sure that no exceptions are thrown.
self.keras_model.fit(verbose=0, final_epoch=1)
def test_fit_with_log_batches(self):
"""Test training an epoch while logging batches to the default logger."""
# Make sure that no exceptions are thrown.
self.keras_model.fit(verbose=0, final_epoch=1, log_batches=True)
def test_fit_with_log_epochs(self):
"""Test training an epoch while logging epochs to the default logger."""
# Make sure that no exceptions are thrown.
self.keras_model.fit(verbose=0, final_epoch=1, log_epochs=True)
def test_fit_with_log_batches_and_log_epochs(self):
"""Test training an epoch while logging batches and epochs to the default logger."""
# Make sure that no exceptions are thrown.
self.keras_model.fit(
verbose=0, final_epoch=1, log_batches=True, log_epochs=True
)
def test_fit_with_logging_args_default_custom_logger(self):
"""Test that we do NOT log to a custom logger if log_batches = log_epochs = False."""
custom_logger = logging.Logger("testlogger")
with unittest.mock.patch.object(custom_logger, "info") as mock_logger_info:
self.keras_model.fit(verbose=0, final_epoch=1, logger=custom_logger)
mock_logger_info.assert_not_called()
def test_fit_with_log_batches_custom_logger(self):
"""Test training while logging batches to a custom logger."""
custom_logger = logging.Logger("testlogger")
with unittest.mock.patch.object(custom_logger, "info") as mock_logger_info:
self.keras_model.fit(
verbose=0, final_epoch=1, logger=custom_logger, log_batches=True
)
mock_logger_info.assert_called()
def test_fit_with_log_epochs_custom_logger(self):
"""Test training while logging epoches to a custom logger."""
custom_logger = logging.Logger("testlogger")
with unittest.mock.patch.object(custom_logger, "info") as mock_logger_info:
self.keras_model.fit(
verbose=0, final_epoch=1, logger=custom_logger, log_epochs=True
)
mock_logger_info.assert_called()
def test_fit_with_log_batches_and_log_epochs_custom_logger(self):
"""Test training while logging both batches and epochs to a custom logger."""
custom_logger = logging.Logger("testlogger")
with unittest.mock.patch.object(custom_logger, "info") as mock_logger_info:
self.keras_model.fit(
verbose=0,
final_epoch=1,
logger=custom_logger,
log_batches=True,
log_epochs=True,
)
mock_logger_info.assert_called()
def test_fit_with_tensorboard(self):
"""Test that we can enable the TensorBoard callback."""
with tempfile.TemporaryDirectory() as tensorboard_directory:
model = sp.KerasModel(
datablob=self.datablob,
model_template=self.model_template,
)
assert_directory(tensorboard_directory, [])
model.fit(
final_epoch=2,
verbose=0,
tensorboard_directory=tensorboard_directory,
)
assert_directory(tensorboard_directory, [model.name])
model_tb_dir = os.path.join(tensorboard_directory, model.name)
assert_directory(model_tb_dir, ["train", "validation"])
assert not os.path.exists(
os.path.join(model_tb_dir, "train", "plugins", "profile")
)
model.fit(
final_epoch=3,
verbose=0,
tensorboard_directory=tensorboard_directory,
profile_batch=(1, 2),
)
assert os.path.exists(
os.path.join(model_tb_dir, "train", "plugins", "profile")
)
@requires_sqlite_json
def test_fit_with_train_store(self):
"""Test that KerasModel.fit() can log to the TrainStore."""
with tempfile.TemporaryDirectory() as temp_dir:
sqlite_filename = os.path.join(temp_dir, "train_store.sqlite3")
with sp.TrainStore.from_filesystem(filename=sqlite_filename) as train_store:
# Create and fit the model.
# When we pass the train store, it will save the datablob,
# model, and model template.
model = sp.KerasModel(
datablob=self.datablob,
model_template=self.model_template,
)
model.fit(final_epoch=3, verbose=0, train_store=train_store)
# Check that the models table contains the 1 model we saved.
models_df = train_store.list_models()
self.assertEqual(
sorted(models_df.columns),
[
"datablob_group_name",
"datablob_name",
"dbh__cols",
"dbh__rows",
"model_class_name",
"model_last_modified",
"model_name",
"model_template_group_name",
"model_template_name",
"mth__layer_1_units",
"mth__loss",
"mth__optimizer",
],
)
self.assertEqual(
models_df["datablob_group_name"].tolist(),
[self.datablob.group_name],
)
self.assertEqual(
models_df["datablob_name"].tolist(), [self.datablob.name]
)
self.assertEqual(
models_df["dbh__cols"].tolist(), [self.datablob.hyperparams.cols]
)
self.assertEqual(
models_df["dbh__rows"].tolist(), [self.datablob.hyperparams.rows]
)
self.assertEqual(models_df["model_class_name"].tolist(), ["KerasModel"])
self.assertEqual(models_df["model_name"].tolist(), [model.name])
self.assertEqual(
models_df["model_template_group_name"].tolist(),
[self.model_template.group_name],
)
self.assertEqual(
models_df["model_template_name"].tolist(),
[self.model_template.name],
)
self.assertEqual(
models_df["mth__layer_1_units"].tolist(),
[self.model_template.hyperparams.layer_1_units],
)
self.assertEqual(
models_df["mth__loss"].tolist(),
[self.model_template.hyperparams.loss],
)
self.assertEqual(
models_df["mth__optimizer"].tolist(),
[self.model_template.hyperparams.optimizer],
)
# Check that the model_epochs table contains the 3 epochs we saved.
model_epochs_df = train_store.list_model_epochs()
self.assertEqual(
sorted(model_epochs_df.columns),
[
"epoch_num",
"last_modified",
"metric__binary_accuracy",
"metric__loss",
"metric__precision",
"metric__recall",
"metric__val_binary_accuracy",
"metric__val_loss",
"metric__val_precision",
"metric__val_recall",
"model_name",
],
)
self.assertEqual(model_epochs_df["epoch_num"].tolist(), [1, 2, 3])
self.assertEqual(
model_epochs_df["model_name"].tolist(),
[model.name, model.name, model.name],
)