def start(self, current, selections):
current.progress.begin(self.get_name())
param_grid = self.get_param_grid()
current.progress.set_ticks(int(
self.n_folds * len(param_grid[self.tau_key]) *
len(param_grid[self.c_key])))
print "Align spike trains"
# FIXME: This needs to be done completely different as we need the
# spike trains per unit which is a dict and not a list. Then the lists
# in the dict have to be split into the individual sets for training,
# test, cross-validation and so on. But because the lists are values in
# a dict it cannot be done automatically by scikit learn.
trains = []
targets = []
for i, s in enumerate(selections):
events = s.labeled_events(self.align)
for seg in events: # Align on first event in each segment
events[seg] = events[seg][0]
aligned_trains = rate_estimation.aligned_spike_trains(
s.spike_trains(), events)
trains.extend(aligned_trains)
targets.extend([i] * len(s.spike_trains()))
parent_conn, child_conn = multiprocessing.Pipe()
p = multiprocessing.Process(
target=self._run, args=(child_conn, trains, targets))
print "Starting grid search"
p.start()
while True:
data = parent_conn.recv()
if isinstance(data, str):
self._process_gridsearch_msg(current.progress, data)
else:
grid_scores = data
break
best_score = parent_conn.recv()
best_params = parent_conn.recv()
report = parent_conn.recv()
confmat = parent_conn.recv()
parent_conn.close()
p.join()
print "Best score %.3f (tau=%s, C=%f)." % \
(best_score, str(best_params[self.tau_key]),
best_params[self.c_key])
print report
print "Confusion matrix:"
print confmat
self.plot_gridsearch_scores(grid_scores)
current.progress.done()
plt.savefig(self.output_file)
def start(self, current, selections):
current.progress.begin(self.get_name())
param_grid = self.get_param_grid()
current.progress.set_ticks(int(
self.n_folds * len(param_grid[self.metric_key]) *
len(param_grid[self.tau_key]) * len(param_grid[self.c_key])))
print "Align spike trains"
trains = []
targets = []
for i, s in enumerate(selections):
events = s.labeled_events(self.align)
for seg in events: # Align on first event in each segment
events[seg] = events[seg][0]
aligned_trains = rate_estimation.aligned_spike_trains(
s.spike_trains(), events)
trains.extend(aligned_trains)
targets.extend([i] * len(s.spike_trains()))
parent_conn, child_conn = multiprocessing.Pipe()
p = multiprocessing.Process(
target=self._run, args=(child_conn, trains, targets))
print "Starting grid search"
p.start()
while True:
data = parent_conn.recv()
if isinstance(data, str):
self._process_gridsearch_msg(current.progress, data)
else:
grid_scores = data
break
best_score = parent_conn.recv()
best_params = parent_conn.recv()
report = parent_conn.recv()
confmat = parent_conn.recv()
parent_conn.close()
p.join()
print "Best score %.3f with %s (tau=%s, C=%f)." % \
(best_score, metric_defs[best_params[self.metric_key]][0],
str(best_params[self.tau_key]), best_params[self.c_key])
print report
print "Confusion matrix:"
print confmat
self.plot_gridsearch_scores_per_metric(grid_scores)
current.progress.done()
plt.savefig(self.output_file)
