def train(CONFIG, CONFIG_TRAIN, spike_train, data_folder):
"""
Train neural network
Parameters
----------
CONFIG
YASS configuration file
CONFIG_TRAIN
YASS Neural Network configuration file
spike_train: numpy.ndarray
Spike train, first column is spike index and second is main channel
"""
logger = logging.getLogger(__name__)
chosen_templates = CONFIG_TRAIN['templates']['ids']
min_amp = CONFIG_TRAIN['templates']['minimum_amplitude']
nspikes = CONFIG_TRAIN['training']['n_spikes']
n_filters_detect = CONFIG_TRAIN['network_detector']['n_filters']
n_iter = CONFIG_TRAIN['training']['n_iterations']
n_batch = CONFIG_TRAIN['training']['n_batch']
l2_reg_scale = CONFIG_TRAIN['training']['l2_regularization_scale']
train_step_size = CONFIG_TRAIN['training']['step_size']
detectnet_name = './' + CONFIG_TRAIN['network_detector']['name'] + '.ckpt'
n_filters_triage = CONFIG_TRAIN['network_triage']['n_filters']
triagenet_name = './' + CONFIG_TRAIN['network_triage']['name'] + '.ckpt'
n_features = CONFIG_TRAIN['network_autoencoder']['n_features']
ae_name = './' + CONFIG_TRAIN['network_autoencoder']['name'] + '.ckpt'
# generate training data for detection, triage and autoencoder
logger.info('Generating training data...')
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG,
spike_train,
chosen_templates,
min_amp,
nspikes,
data_folder=data_folder)
# train detector
NeuralNetDetector.train(x_detect, y_detect, n_filters_detect, n_iter,
n_batch, l2_reg_scale, train_step_size,
detectnet_name)
# train triage
NeuralNetTriage.train(x_triage, y_triage, n_filters_triage, n_iter,
n_batch, l2_reg_scale, train_step_size,
triagenet_name)
# train autoencoder
AutoEncoder.train(x_ae, y_ae, n_features, n_iter, n_batch, train_step_size,
ae_name)
def test_can_use_neural_network_detector(path_to_tests,
path_to_standarized_data):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
data = RecordingsReader(path_to_standarized_data, loader='array').data
channel_index = make_channel_index(CONFIG.neigh_channels, CONFIG.geom)
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th, channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
with tf.Session() as sess:
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
neuralnetwork.run_detect_triage_featurize(data, sess, NND.x_tf,
output_tf, neighbors, rot)
def test_can_train_triage(path_to_tests, path_to_sample_pipeline_folder,
tmp_folder):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG, spike_train, chosen_templates,
min_amplitude, n_spikes,
path_to_sample_pipeline_folder)
_, waveform_length, n_neighbors = x_triage.shape
path_to_model = path.join(tmp_folder, 'triage-net.ckpt')
triage = NeuralNetTriage(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
n_iter=10)
triage.fit(x_detect, y_detect)
def test_can_use_detect_and_triage_after_reload(path_to_tests,
path_to_sample_pipeline_folder,
tmp_folder,
path_to_standarized_data):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
(x_detect, y_detect, x_triage, y_triage, x_ae,
y_ae) = make_training_data(CONFIG, spike_train, chosen_templates,
min_amplitude, n_spikes,
path_to_sample_pipeline_folder)
_, waveform_length, n_neighbors = x_detect.shape
path_to_model = path.join(tmp_folder, 'detect-net.ckpt')
detector = NeuralNetDetector(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
channel_index=CONFIG.channel_index,
n_iter=10)
detector.fit(x_detect, y_detect)
detector = NeuralNetDetector.load(path_to_model,
threshold=0.5,
channel_index=CONFIG.channel_index)
triage = NeuralNetTriage(path_to_model,
filters,
waveform_length,
n_neighbors,
threshold=0.5,
n_iter=10)
triage.fit(x_detect, y_detect)
triage = NeuralNetTriage.load(path_to_model, threshold=0.5)
data = RecordingExplorer(path_to_standarized_data).reader.data
output_names = ('spike_index', 'waveform', 'probability')
(spike_index, waveform,
proba) = detector.predict(data, output_names=output_names)
triage.predict(waveform[:, :, :n_neighbors])
def run_neural_network(standarized_path, standarized_params, whiten_filter,
output_directory, if_file_exists, save_results):
"""Run neural network detection and autoencoder dimensionality reduction
Returns
-------
scores
Scores for all spikes
spike_index_clear
Spike indexes for clear spikes
spike_index_all
Spike indexes for all spikes
"""
logger = logging.getLogger(__name__)
CONFIG = read_config()
folder = Path(CONFIG.data.root_folder, output_directory, 'detect')
folder.mkdir(exist_ok=True)
TMP_FOLDER = str(folder)
# check if all scores, clear and collision spikes exist..
path_to_score = os.path.join(TMP_FOLDER, 'scores_clear.npy')
path_to_spike_index_clear = os.path.join(TMP_FOLDER,
'spike_index_clear.npy')
path_to_spike_index_all = os.path.join(TMP_FOLDER, 'spike_index_all.npy')
path_to_rotation = os.path.join(TMP_FOLDER, 'rotation.npy')
paths = [path_to_score, path_to_spike_index_clear, path_to_spike_index_all]
exists = [os.path.exists(p) for p in paths]
if (if_file_exists == 'overwrite'
or if_file_exists == 'abort' and not any(exists)
or if_file_exists == 'skip' and not all(exists)):
max_memory = (CONFIG.resources.max_memory_gpu
if GPU_ENABLED else CONFIG.resources.max_memory)
# instantiate batch processor
bp = BatchProcessor(standarized_path,
standarized_params['dtype'],
standarized_params['n_channels'],
standarized_params['data_order'],
max_memory,
buffer_size=CONFIG.spike_size)
# load parameters
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th,
CONFIG.channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
rotation = NNAE.load_rotation()
# gather all output tensors
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
# run detection
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
mc = bp.multi_channel_apply
res = mc(neuralnetwork.run_detect_triage_featurize,
mode='memory',
cleanup_function=neuralnetwork.fix_indexes,
sess=sess,
x_tf=NND.x_tf,
output_tf=output_tf,
rot=rotation,
neighbors=neighbors)
# get clear spikes
clear = np.concatenate([element[1] for element in res], axis=0)
logger.info('Removing clear indexes outside the allowed range to '
'draw a complete waveform...')
clear, idx = detect.remove_incomplete_waveforms(
clear, CONFIG.spike_size + CONFIG.templates.max_shift,
bp.reader._n_observations)
# get all spikes
spikes_all = np.concatenate([element[2] for element in res], axis=0)
logger.info('Removing indexes outside the allowed range to '
'draw a complete waveform...')
spikes_all, _ = detect.remove_incomplete_waveforms(
spikes_all, CONFIG.spike_size + CONFIG.templates.max_shift,
bp.reader._n_observations)
# get scores
scores = np.concatenate([element[0] for element in res], axis=0)
logger.info('Removing scores for indexes outside the allowed range to '
'draw a complete waveform...')
scores = scores[idx]
# transform scores to location + shape feature space
# TODO: move this to another place
if CONFIG.cluster.method == 'location':
threshold = 2
scores = get_locations_features(scores, rotation, clear[:, 1],
CONFIG.channel_index, CONFIG.geom,
threshold)
idx_nan = np.where(np.isnan(np.sum(scores, axis=(1, 2))))[0]
scores = np.delete(scores, idx_nan, 0)
clear = np.delete(clear, idx_nan, 0)
# save partial results if required
if save_results:
# save clear spikes
np.save(path_to_spike_index_clear, clear)
logger.info('Saved spike index clear in {}...'.format(
path_to_spike_index_clear))
# save all ppikes
np.save(path_to_spike_index_all, spikes_all)
logger.info('Saved spike index all in {}...'.format(
path_to_spike_index_all))
# save rotation
np.save(path_to_rotation, rotation)
logger.info(
'Saved rotation matrix in {}...'.format(path_to_rotation))
# saves scores
np.save(path_to_score, scores)
logger.info('Saved spike scores in {}...'.format(path_to_score))
elif if_file_exists == 'abort' and any(exists):
conflict = [p for p, e in zip(paths, exists) if e]
message = reduce(lambda x, y: str(x) + ', ' + str(y), conflict)
raise ValueError('if_file_exists was set to abort, the '
'program halted since the following files '
'already exist: {}'.format(message))
elif if_file_exists == 'skip' and all(exists):
logger.warning('Skipped execution. All output files exist'
', loading them...')
scores = np.load(path_to_score)
clear = np.load(path_to_spike_index_clear)
spikes_all = np.load(path_to_spike_index_all)
else:
raise ValueError(
'Invalid value for if_file_exists {}'
'must be one of overwrite, abort or skip'.format(if_file_exists))
return scores, clear, spikes_all
def prepare_nn(channel_index, whiten_filter, threshold_detect,
threshold_triage, detector_filename, autoencoder_filename,
triage_filename):
"""Prepare neural net tensors in advance. This is to effciently run
neural net with batch processor as we don't have to recreate tf
tensors in every batch
Parameters
----------
channel_index: np.array (n_channels, n_neigh)
Each row indexes its neighboring channels.
For example, channel_index[c] is the index of
neighboring channels (including itself)
If any value is equal to n_channels, it is nothing but
a space holder in a case that a channel has less than
n_neigh neighboring channels
whiten_filter: numpy.ndarray (n_channels, n_neigh, n_neigh)
whitening matrix such that whiten_filter[c] is the whitening
filter of channel c and its neighboring channel determined from
channel_index.
threshold_detect: int
threshold for neural net detection
threshold_triage: int
threshold for neural net triage
detector_filename: str
location of trained neural net detectior
autoencoder_filename: str
location of trained neural net autoencoder
triage_filename: str
location of trained neural net triage
Returns
-------
x_tf: tf.tensors (n_observations, n_channels)
placeholder of recording for running tensorflow
output_tf: tuple of tf.tensors
a tuple of tensorflow tensors that produce score, spike_index_clear,
and spike_index_collision
NND: class
an instance of class, NeuralNetDetector
NNT: class
an instance of class, NeuralNetTriage
"""
# placeholder for input recording
x_tf = tf.placeholder("float", [None, None])
# load Neural Net's
NND = NeuralNetDetector(detector_filename)
NNAE = AutoEncoder(autoencoder_filename)
NNT = NeuralNetTriage(triage_filename)
# make spike_index tensorflow tensor
spike_index_tf_all = NND.make_detection_tf_tensors(x_tf, channel_index,
threshold_detect)
# remove edge spike time
spike_index_tf = remove_edge_spikes(x_tf, spike_index_tf_all,
NND.filters_dict['size'])
# make waveform tensorflow tensor
waveform_tf = make_waveform_tf_tensor(x_tf, spike_index_tf, channel_index,
NND.filters_dict['size'])
# make score tensorflow tensor from waveform
score_tf = NNAE.make_score_tf_tensor(waveform_tf)
# run neural net triage
nneigh = NND.filters_dict['n_neighbors']
idx_clean = NNT.triage_wf(waveform_tf[:, :, :nneigh], threshold_triage)
# gather all output tensors
output_tf = (score_tf, spike_index_tf, idx_clean)
return x_tf, output_tf, NND, NNAE, NNT
def nn_detection(recordings, neighbors, geom, temporal_features,
temporal_window, th_detect, th_triage, detector_filename,
autoencoder_filename, triage_filename):
"""Detect spikes using a neural network
Parameters
----------
recordings: numpy.ndarray (n_observations, n_channels)
Neural recordings
neighbors: numpy.ndarray (n_channels, n_channels)
Channels neighbors matric
geom: numpy.ndarray (n_channels, 2)
Cartesian coordinates for the channels
temporal_features: int
?
temporal_window: int
?
th_detect: float?
Spike threshold [improve this explanation]
th_triage: float?
Triage threshold [improve this explanation]
detector_filename: str
Path to neural network detector
autoencoder_filename: str
Path to neural network autoencoder
triage_filename: str
Path to triage neural network
Returns
-------
clear_scores: numpy.ndarray (n_spikes, n_features, n_channels)
3D array with the scores for the clear spikes, first simension is
the number of spikes, second is the nymber of features and third the
number of channels
spike_index_clear: numpy.ndarray (n_clear_spikes, 2)
2D array with indexes for clear spikes, first column contains the
spike location in the recording and the second the main channel
(channel whose amplitude is maximum)
spike_index_collision: numpy.ndarray (n_collided_spikes, 2)
2D array with indexes for collided spikes, first column contains the
spike location in the recording and the second the main channel
(channel whose amplitude is maximum)
"""
nnd = NeuralNetDetector(detector_filename, autoencoder_filename)
nnt = NeuralNetTriage(triage_filename)
T, C = recordings.shape
a, b = neighbors.shape
if a != b:
raise ValueError('neighbors is not a square matrix, verify')
if a != C:
raise ValueError(
'Number of channels in recording are {} but the '
'neighbors matrix has {} elements, they must match'.format(C, a))
# neighboring channel info
nneigh = np.max(np.sum(neighbors, 0))
c_idx = np.ones((C, nneigh), 'int32') * C
for c in range(C):
ch_idx, temp = order_channels_by_distance(c,
np.where(neighbors[c])[0],
geom)
c_idx[c, :ch_idx.shape[0]] = ch_idx
# input
x_tf = tf.placeholder("float", [T, C])
# detect spike index
local_max_idx_tf = nnd.get_spikes(x_tf, T, nneigh, c_idx, temporal_window,
th_detect)
# get score train
score_train_tf = nnd.get_score_train(x_tf)
# get energy for detected index
energy_tf = tf.reduce_sum(tf.square(score_train_tf), axis=2)
energy_val_tf = tf.gather_nd(energy_tf, local_max_idx_tf)
# get triage probability
triage_prob_tf = nnt.triage_prob(x_tf, T, nneigh, c_idx)
# gather all results above
result = (local_max_idx_tf, score_train_tf, energy_val_tf, triage_prob_tf)
# remove duplicates
energy_train_tf = tf.placeholder("float", [T, C])
spike_index_tf = remove_duplicate_spikes_by_energy(energy_train_tf, T,
c_idx, temporal_window)
# get score
score_train_placeholder = tf.placeholder("float",
[T, C, temporal_features])
spike_index_clear_tf = tf.placeholder("int64", [None, 2])
score_tf = get_score(score_train_placeholder, spike_index_clear_tf, T,
temporal_features, c_idx)
###############################
# get values of above tensors #
###############################
with tf.Session() as sess:
nnd.saver.restore(sess, nnd.path_to_detector_model)
nnd.saver_ae.restore(sess, nnd.path_to_ae_model)
nnt.saver.restore(sess, nnt.path_to_triage_model)
local_max_idx, score_train, energy_val, triage_prob = sess.run(
result, feed_dict={x_tf: recordings})
energy_train = np.zeros((T, C))
energy_train[local_max_idx[:, 0], local_max_idx[:, 1]] = energy_val
spike_index = sess.run(spike_index_tf,
feed_dict={energy_train_tf: energy_train})
idx_clean = triage_prob[spike_index[:, 0], spike_index[:,
1]] > th_triage
spike_index_clear = spike_index[idx_clean]
spike_index_collision = spike_index[~idx_clean]
score = sess.run(score_tf,
feed_dict={
score_train_placeholder: score_train,
spike_index_clear_tf: spike_index_clear
})
return score, spike_index_clear, spike_index_collision
def test_splitting_in_batches_does_not_affect(path_to_tests,
path_to_standarized_data,
path_to_sample_pipeline_folder):
yass.set_config(path.join(path_to_tests, 'config_nnet.yaml'))
CONFIG = yass.read_config()
PATH_TO_DATA = path_to_standarized_data
data = RecordingsReader(PATH_TO_DATA, loader='array').data
with open(
path.join(path_to_sample_pipeline_folder, 'preprocess',
'standarized.yaml')) as f:
PARAMS = yaml.load(f)
channel_index = make_channel_index(CONFIG.neigh_channels, CONFIG.geom)
detection_th = CONFIG.detect.neural_network_detector.threshold_spike
triage_th = CONFIG.detect.neural_network_triage.threshold_collision
detection_fname = CONFIG.detect.neural_network_detector.filename
ae_fname = CONFIG.detect.neural_network_autoencoder.filename
triage_fname = CONFIG.detect.neural_network_triage.filename
# instantiate neural networks
NND = NeuralNetDetector.load(detection_fname, detection_th, channel_index)
NNT = NeuralNetTriage.load(triage_fname,
triage_th,
input_tensor=NND.waveform_tf)
NNAE = AutoEncoder(ae_fname, input_tensor=NND.waveform_tf)
output_tf = (NNAE.score_tf, NND.spike_index_tf, NNT.idx_clean)
# run all at once
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
(scores, clear, collision) = neuralnetwork.run_detect_triage_featurize(
data, sess, NND.x_tf, output_tf, neighbors, rot)
# run in batches - buffer size makes sure we can detect spikes if they
# appear at the end of any batch
bp = BatchProcessor(PATH_TO_DATA,
PARAMS['dtype'],
PARAMS['n_channels'],
PARAMS['data_order'],
'100KB',
buffer_size=CONFIG.spike_size)
with tf.Session() as sess:
# get values of above tensors
NND.restore(sess)
NNAE.restore(sess)
NNT.restore(sess)
rot = NNAE.load_rotation()
neighbors = n_steps_neigh_channels(CONFIG.neigh_channels, 2)
res = bp.multi_channel_apply(
neuralnetwork.run_detect_triage_featurize,
mode='memory',
cleanup_function=neuralnetwork.fix_indexes,
sess=sess,
x_tf=NND.x_tf,
output_tf=output_tf,
rot=rot,
neighbors=neighbors)
scores_batch = np.concatenate([element[0] for element in res], axis=0)
clear_batch = np.concatenate([element[1] for element in res], axis=0)
collision_batch = np.concatenate([element[2] for element in res], axis=0)
np.testing.assert_array_equal(clear_batch, clear)
np.testing.assert_array_equal(collision_batch, collision)
np.testing.assert_array_equal(scores_batch, scores)