def test_print_help_net(self, print_help, net, capsys):
print_help(net)
out = capsys.readouterr()[0]
expected_snippets = [
'-- --help', '<NeuralNetClassifier> options',
'--module : torch module (class or instance)',
'--batch_size : int (default=128)', '<MLPModule> options',
'--module__hidden_units : int (default=10)'
]
for snippet in expected_snippets:
assert snippet in out
def test_print_help_net_custom_defaults(self, print_help, net, capsys):
defaults = {'batch_size': 256, 'module__hidden_units': 55}
print_help(net, defaults)
out = capsys.readouterr()[0]
expected_snippets = [
'-- --help', '<NeuralNetClassifier> options',
'--module : torch module (class or instance)',
'--batch_size : int (default=256)', '<MLPModule> options',
'--module__hidden_units : int (default=55)'
]
for snippet in expected_snippets:
assert snippet in out
def test_print_help_pipeline(self, print_help, pipe, capsys):
print_help(pipe)
out = capsys.readouterr()[0]
expected_snippets = [
'-- --help', '<MinMaxScaler> options',
'--features__scale__feature_range',
'<NeuralNetClassifier> options',
'--net__module : torch module (class or instance)',
'--net__batch_size : int (default=128)', '<MLPModule> options',
'--net__module__hidden_units : int (default=10)'
]
for snippet in expected_snippets:
assert snippet in out
def test_print_help_sklearn_estimator(self, print_help, clf_sklearn, capsys):
# Should also work with non-skorch sklearn estimator;
# need to assert that count==1 since there was a bug in my
# first implementation that resulted in the help for the final
# estimator appearing twice.
print_help(clf_sklearn)
out = capsys.readouterr()[0]
expected_snippets = [
'-- --help',
'--fit_intercept',
'--copy_X',
'--normalize',
]
for snippet in expected_snippets:
assert snippet in out
assert out.count(snippet) == 1
def test_print_help_sklearn_pipeline(self, print_help, pipe_sklearn, capsys):
# Should also work with non-skorch sklearn pipelines;
# need to assert that count==1 since there was a bug in my
# first implementation that resulted in the help for the final
# estimator appearing twice.
print_help(pipe_sklearn)
out = capsys.readouterr()[0]
expected_snippets = [
'-- --help',
'<MinMaxScaler> options',
'--features__scale__feature_range',
'--clf__fit_intercept',
'--clf__normalize',
]
for snippet in expected_snippets:
assert snippet in out
assert out.count(snippet) == 1