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MRG, ENH: Better error messages for ICA #7879

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Jun 8, 2020
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MRG, ENH: Better error messages for ICA #7879

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2 changes: 2 additions & 0 deletions doc/changes/latest.inc
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,8 @@ Changelog

- Add "on_missing='raise'" to :meth:`mne.io.Raw.set_montage` and related functions to allow ignoring of missing electrode coordinates by `Adam Li`_

- Add better sanity checking of ``max_pca_components`` and ``n_components`` to provide more informative error messages for :class:`mne.preprocessing.ICA` by `Eric Larson`_

- Add ``plot`` option to :meth:`mne.viz.plot_filter` allowing selection of which filter properties are plotted and added option for user to supply ``axes`` by `Robert Luke`_

- Add estimation method legend to :func:`mne.viz.plot_snr_estimate` by `Eric Larson`_
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82 changes: 36 additions & 46 deletions mne/preprocessing/ica.py
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Expand Up @@ -483,11 +483,39 @@ def fit(self, inst, picks=None, start=None, stop=None, decim=None,
_check_for_unsupported_ica_channels(
picks, inst.info, allow_ref_meg=self.allow_ref_meg)

# Actually start fitting
t_start = time()
if self.current_fit != 'unfitted':
self._reset()

logger.info('Fitting ICA to data using %i channels '
'(please be patient, this may take a while)' % len(picks))

if self.max_pca_components is None:
self.max_pca_components = len(picks)
logger.info('Inferring max_pca_components from picks')
elif self.max_pca_components > len(picks):
raise ValueError(
f'ica.max_pca_components ({self.max_pca_components}) cannot '
f'be greater than len(picks) ({len(picks)})')
# n_components could be float 0 < x <= 1, but that's okay here
if self.n_components is not None and self.n_components > len(picks):
raise ValueError(
f'ica.n_components ({self.n_components}) cannot '
f'be greater than len(picks) ({len(picks)})')

# filter out all the channels the raw wouldn't have initialized
self.info = pick_info(inst.info, picks)

if self.info['comps']:
self.info['comps'] = []
self.ch_names = self.info['ch_names']

if isinstance(inst, BaseRaw):
self._fit_raw(inst, picks, start, stop, decim, reject, flat,
tstep, reject_by_annotation, verbose)
elif isinstance(inst, BaseEpochs):
else:
assert isinstance(inst, BaseEpochs)
self._fit_epochs(inst, picks, decim, verbose)

# sort ICA components by explained variance
Expand All @@ -500,37 +528,16 @@ def fit(self, inst, picks=None, start=None, stop=None, decim=None,

def _reset(self):
"""Aux method."""
del self.pre_whitener_
del self.unmixing_matrix_
del self.mixing_matrix_
del self.n_components_
del self.n_samples_
del self.pca_components_
del self.pca_explained_variance_
del self.pca_mean_
del self.n_iter_
if hasattr(self, 'drop_inds_'):
del self.drop_inds_
if hasattr(self, 'reject_'):
del self.reject_
for key in ('pre_whitener_', 'unmixing_matrix_', 'mixing_matrix_',
'n_components_', 'n_samples_', 'pca_components_',
'pca_explained_variance_', 'pca_mean_', 'n_iter_',
'drop_inds_', 'reject_'):
if hasattr(self, key):
delattr(self, key)

def _fit_raw(self, raw, picks, start, stop, decim, reject, flat, tstep,
reject_by_annotation, verbose):
"""Aux method."""
if self.current_fit != 'unfitted':
self._reset()

logger.info('Fitting ICA to data using %i channels '
'(please be patient, this may take a while)' % len(picks))

if self.max_pca_components is None:
self.max_pca_components = len(picks)
logger.info('Inferring max_pca_components from picks')

self.info = pick_info(raw.info, picks)
if self.info['comps']:
self.info['comps'] = []
self.ch_names = self.info['ch_names']
start, stop = _check_start_stop(raw, start, stop)

reject_by_annotation = 'omit' if reject_by_annotation else None
Expand Down Expand Up @@ -558,28 +565,11 @@ def _fit_raw(self, raw, picks, start, stop, decim, reject, flat, tstep,

def _fit_epochs(self, epochs, picks, decim, verbose):
"""Aux method."""
if self.current_fit != 'unfitted':
self._reset()

if epochs.events.size == 0:
raise RuntimeError('Tried to fit ICA with epochs, but none were '
'found: epochs.events is "{}".'
.format(epochs.events))

logger.info('Fitting ICA to data using %i channels '
'(please be patient, this may take a while)' % len(picks))

# filter out all the channels the raw wouldn't have initialized
self.info = pick_info(epochs.info, picks)

if self.info['comps']:
self.info['comps'] = []
self.ch_names = self.info['ch_names']

if self.max_pca_components is None:
self.max_pca_components = len(picks)
logger.info('Inferring max_pca_components from picks')

# this should be a copy (picks a list of int)
data = epochs.get_data()[:, picks]
# this will be a view
Expand Down Expand Up @@ -1018,7 +1008,7 @@ def score_sources(self, inst, target=None, score_func='pearsonr',
reject_by_annotation)

if sources.shape[-1] != target.shape[-1]:
raise ValueError('Sources and target do not have the same'
raise ValueError('Sources and target do not have the same '
'number of time slices.')
# auto target selection
if isinstance(inst, BaseRaw):
Expand Down
48 changes: 34 additions & 14 deletions mne/preprocessing/tests/test_ica.py
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Expand Up @@ -122,7 +122,8 @@ def test_ica_full_data_recovery(method):
else:
diff = np.abs(evoked.data[:n_channels] - data2)
assert (np.max(diff) > 1e-14)
pytest.raises(ValueError, ICA, method='pizza-decomposision')
with pytest.raises(ValueError, match='Invalid value'):
ICA(method='pizza-decomposision')


@pytest.mark.parametrize("method", ["fastica", "picard"])
Expand Down Expand Up @@ -264,22 +265,27 @@ def test_ica_core(method):
picks_, methods)

# # test init catchers
pytest.raises(ValueError, ICA, n_components=3, max_pca_components=2)
pytest.raises(ValueError, ICA, n_components=2.3, max_pca_components=2)
with pytest.raises(ValueError, match='must be smaller than max_pca'):
ICA(n_components=3, max_pca_components=2)
with pytest.raises(ValueError, match='explained variance needs values'):
ICA(n_components=2.3, max_pca_components=3)

# test essential core functionality
for n_cov, n_comp, max_n, pcks, method in iter_ica_params:
# Test ICA raw
ica = ICA(noise_cov=n_cov, n_components=n_comp,
max_pca_components=max_n, n_pca_components=max_n,
random_state=0, method=method, max_iter=1)
pytest.raises(ValueError, ica.__contains__, 'mag')
with pytest.raises(ValueError, match='Cannot check for channels of t'):
'meg' in ica

print(ica) # to test repr

# test fit checker
pytest.raises(RuntimeError, ica.get_sources, raw)
pytest.raises(RuntimeError, ica.get_sources, epochs)
with pytest.raises(RuntimeError, match='No fit available'):
ica.get_sources(raw)
with pytest.raises(RuntimeError, match='No fit available'):
ica.get_sources(epochs)

# Test error upon empty epochs fitting
with pytest.raises(RuntimeError, match='none were found'):
Expand Down Expand Up @@ -336,14 +342,14 @@ def test_ica_core(method):
sources = ica.get_sources(epochs).get_data()
assert (sources.shape[1] == ica.n_components_)

pytest.raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
with pytest.raises(ValueError, match='target do not have the same nu'):
ica.score_sources(epochs, target=np.arange(1))

# test preload filter
epochs3 = epochs.copy()
epochs3.preload = False
pytest.raises(RuntimeError, ica.apply, epochs3,
include=[1, 2])
with pytest.raises(RuntimeError, match='requires epochs data to be l'):
ica.apply(epochs3, include=[1, 2])

# test for bug with whitener updating
_pre_whitener = ica.pre_whitener_.copy()
Expand All @@ -353,12 +359,26 @@ def test_ica_core(method):

# test expl. var threshold leading to empty sel
ica.n_components = 0.1
pytest.raises(RuntimeError, ica.fit, epochs)
with pytest.raises(RuntimeError, match='One PCA component captures most'):
ica.fit(epochs)

offender = 1, 2, 3,
pytest.raises(ValueError, ica.get_sources, offender)
pytest.raises(TypeError, ica.fit, offender)
pytest.raises(TypeError, ica.apply, offender)
with pytest.raises(ValueError, match='Data input must be of Raw'):
ica.get_sources(offender)
with pytest.raises(TypeError, match='must be an instance of'):
ica.fit(offender)
with pytest.raises(TypeError, match='must be an instance of'):
ica.apply(offender)

# gh-7868
ica.max_pca_components = 3
ica.n_components = 0.99
with pytest.raises(ValueError, match='pca_components.*cannot be greater'):
ica.fit(epochs, picks=[0, 1])
ica.max_pca_components = None
ica.n_components = 3
with pytest.raises(ValueError, match='n_components.*cannot be greater'):
ica.fit(epochs, picks=[0, 1])


@requires_sklearn
Expand Down