def test_checkpoint_minibatch_generator():
n_samples = 48
data = np.arange(n_samples)
for checkpoints in ([0, 20, 30, 63, 100], [20, 30, 63, 100]):
for slice_when_possible in (True, False):
iterator = checkpoint_minibatch_index_generator(n_samples=n_samples, checkpoints=checkpoints, slice_when_possible=slice_when_possible)
assert np.array_equal(data[next(iterator)], np.arange(20))
assert np.array_equal(data[next(iterator)], np.arange(20, 30))
assert np.array_equal(data[next(iterator)], np.arange(30, 63) % 48)
assert np.array_equal(data[next(iterator)], np.arange(63, 100) % 48)
try:
next(iterator)
except StopIteration:
pass
except:
raise Exception("Failed to stop iteration")
def test_checkpoint_minibatch_generator():
n_samples = 48
data = np.arange(n_samples)
for checkpoints in ([0, 20, 30, 63, 100], [20, 30, 63, 100]):
for slice_when_possible in (True, False):
iterator = checkpoint_minibatch_index_generator(n_samples=n_samples, checkpoints=checkpoints, slice_when_possible=slice_when_possible)
assert np.array_equal(data[next(iterator)], np.arange(20))
assert np.array_equal(data[next(iterator)], np.arange(20, 30))
assert np.array_equal(data[next(iterator)], np.arange(30, 63) % 48)
assert np.array_equal(data[next(iterator)], np.arange(63, 100) % 48)
try:
next(iterator)
except StopIteration:
pass
except:
raise Exception("Failed to stop iteration")
def assess_online_predictor(predictor, dataset, evaluation_function, test_epochs, minibatch_size, test_on = 'training+test',
accumulator = None, report_test_scores=True, test_batch_size = None, test_callback = None):
"""
DEPRECATED: use assess_prediction_functions_on_generator in train_and_test_old.py
Train an online predictor and return the LearningCurveData.
:param predictor: An IPredictor object
:param dataset: A DataSet object
:param evaluation_function: A function of the form: score=fcn(actual_values, target_values)
:param test_epochs: List of epochs to test at. Eg. [0.5, 1, 2, 4]
:param minibatch_size: Number of samples per minibatch, or:
'full' to do full-batch.
'stretch': to stretch the size of each batch so that we make just one call to "train" between each test. Use
this, for instance, if your predictor trains on one sample at a time in sequence anyway.
:param report_test_scores: Print out the test scores as they're computed (T/F)
:param test_callback: A callback which takes the predictor, and is called every time a test
is done. This can be useful for plotting/debugging the state.
:return: LearningCurveData containing the score on the test sets
"""
# TODO: Remove this class, as it is deprecated
record = LearningCurveData()
testing_sets = dataset_to_testing_sets(dataset, test_on)
if accumulator is None:
prediction_functions = {k: predictor.predict for k in testing_sets}
else:
accum_constructor = {'avg': RunningAverage}[accumulator] \
if isinstance(accumulator, str) else accumulator
accumulators = {k: accum_constructor() for k in testing_sets}
prediction_functions = {k: lambda inp, kp=k: accumulators[kp](predictor.predict(inp)) for k in testing_sets}
# Bewate the in-loop lambda - but I think we're ok here.
if isinstance(evaluation_function, str):
evaluation_function = get_evaluation_function(evaluation_function)
def do_test(current_epoch):
scores = [(k, evaluation_function(process_in_batches(prediction_functions[k], x, test_batch_size), y)) for k, (x, y) in testing_sets.items()]
if report_test_scores:
print('Scores at Epoch %s: %s, after %.2fs' % (current_epoch, ', '.join('%s: %.3f' % (set_name, score) for set_name, score in scores), time.time()-start_time))
record.add(current_epoch, scores)
if test_callback is not None:
record.add(current_epoch, ('callback', test_callback(predictor)))
start_time = time.time()
if minibatch_size == 'stretch':
test_samples = (np.array(test_epochs) * dataset.training_set.n_samples).astype(int)
i=0
if test_samples[0] == 0:
do_test(i)
i += 1
for indices in checkpoint_minibatch_index_generator(n_samples=dataset.training_set.n_samples, checkpoints=test_samples, slice_when_possible=True):
predictor.train(dataset.training_set.input[indices], dataset.training_set.target[indices])
do_test(test_epochs[i])
i += 1
else:
checker = CheckPointCounter(test_epochs)
last_n_samples_seen = 0
for (n_samples_seen, input_minibatch, target_minibatch) in \
dataset.training_set.minibatch_iterator(minibatch_size = minibatch_size, epochs = float('inf'), single_channel = True):
current_epoch = (float(last_n_samples_seen))/dataset.training_set.n_samples
last_n_samples_seen = n_samples_seen
time_for_a_test, done = checker.check(current_epoch)
if time_for_a_test:
do_test(current_epoch)
if done:
break
predictor.train(input_minibatch, target_minibatch)
return record
def assess_online_predictor(predictor,
dataset,
evaluation_function,
test_epochs,
minibatch_size,
test_on='training+test',
accumulator=None,
report_test_scores=True,
test_batch_size=None,
test_callback=None):
"""
DEPRECATED: use assess_prediction_functions_on_generator in train_and_test_old.py
Train an online predictor and return the LearningCurveData.
:param predictor: An IPredictor object
:param dataset: A DataSet object
:param evaluation_function: A function of the form: score=fcn(actual_values, target_values)
:param test_epochs: List of epochs to test at. Eg. [0.5, 1, 2, 4]
:param minibatch_size: Number of samples per minibatch, or:
'full' to do full-batch.
'stretch': to stretch the size of each batch so that we make just one call to "train" between each test. Use
this, for instance, if your predictor trains on one sample at a time in sequence anyway.
:param report_test_scores: Print out the test scores as they're computed (T/F)
:param test_callback: A callback which takes the predictor, and is called every time a test
is done. This can be useful for plotting/debugging the state.
:return: LearningCurveData containing the score on the test sets
"""
# TODO: Remove this class, as it is deprecated
record = LearningCurveData()
testing_sets = dataset_to_testing_sets(dataset, test_on)
if accumulator is None:
prediction_functions = {k: predictor.predict for k in testing_sets}
else:
accum_constructor = {'avg': RunningAverage}[accumulator] \
if isinstance(accumulator, str) else accumulator
accumulators = {k: accum_constructor() for k in testing_sets}
prediction_functions = {
k: lambda inp, kp=k: accumulators[kp](predictor.predict(inp))
for k in testing_sets
}
# Bewate the in-loop lambda - but I think we're ok here.
if isinstance(evaluation_function, str):
evaluation_function = get_evaluation_function(evaluation_function)
def do_test(current_epoch):
scores = [(k,
evaluation_function(
process_in_batches(prediction_functions[k], x,
test_batch_size), y))
for k, (x, y) in testing_sets.items()]
if report_test_scores:
print('Scores at Epoch %s: %s, after %.2fs' %
(current_epoch, ', '.join('%s: %.3f' % (set_name, score)
for set_name, score in scores),
time.time() - start_time))
record.add(current_epoch, scores)
if test_callback is not None:
record.add(current_epoch, ('callback', test_callback(predictor)))
start_time = time.time()
if minibatch_size == 'stretch':
test_samples = (np.array(test_epochs) *
dataset.training_set.n_samples).astype(int)
i = 0
if test_samples[0] == 0:
do_test(i)
i += 1
for indices in checkpoint_minibatch_index_generator(
n_samples=dataset.training_set.n_samples,
checkpoints=test_samples,
slice_when_possible=True):
predictor.train(dataset.training_set.input[indices],
dataset.training_set.target[indices])
do_test(test_epochs[i])
i += 1
else:
checker = CheckPointCounter(test_epochs)
last_n_samples_seen = 0
for (n_samples_seen, input_minibatch, target_minibatch) in \
dataset.training_set.minibatch_iterator(minibatch_size = minibatch_size, epochs = float('inf'), single_channel = True):
current_epoch = (
float(last_n_samples_seen)) / dataset.training_set.n_samples
last_n_samples_seen = n_samples_seen
time_for_a_test, done = checker.check(current_epoch)
if time_for_a_test:
do_test(current_epoch)
if done:
break
predictor.train(input_minibatch, target_minibatch)
return record
