def on_session_selected(self, sid):
self.sid = sid
self.ui.processButton.setEnabled(True)
try:
f = filestruct.find_feature_file(
self.cfg.data_path, self.pid, self.sid)
data = processing.read_feature_file_list([f])
self.plot_data(*data)
except:
# couldn't find feature file
self.clear_plot()
return
def build_pipeline(self):
"""
Builds the processing pipeline. Most of the pipeline is specified by
the config, but we need to gather training data, build a classifier
with that data, and insert the classifier into the pipeline.
"""
train_list = []
for i in range(self.ui.trainingList.count()):
item = self.ui.trainingList.item(i)
if item.checkState():
train_list.append(str(item.text()))
self.training_sessions = train_list
if not train_list:
QtWidgets.QMessageBox().critical(
self, "Error",
"No sessions selected for training.",
QtWidgets.QMessageBox.Ok)
return
# get only the labels for the selected TAC session
# need to loop over available gestures to catch those with no dof
labels = []
mapping = {}
for gesture in self.cfg.gestures:
if gesture.dof is None:
labels.append(gesture.label)
mapping[gesture.label] = gesture.action
else:
if gesture in self.tac_session.gestures:
labels.append(gesture.label)
mapping[gesture.label] = gesture.action
file_list = filestruct.get_feature_file_list(
self.cfg.data_path, self.pid, train_list)
training_data = processing.read_feature_file_list(
file_list, labels=labels)
# get average MAV for each gesture label to auto-set boosts
# warning: super hacky
j = 0
start = 0
for i, feature in enumerate(self.cfg.feature_extractor.features):
if 'MAV' in str(feature):
start = j
break
else:
j += feature.dim_per_channel*len(self.cfg.channels)
X, y = training_data
X = X[:, start:len(self.cfg.channels)]
boosts = dict()
for label in labels:
mav_avg = np.mean(X[y == label, :], axis=1)
# -np.partition(-data, N) gets N largest elements of data
boosts[label] = 1 / np.mean(-np.partition(-mav_avg, 10)[:10])
self.boosts = boosts
self.controller = control.DBVRController(
mapping=mapping,
ramp_length=self.cfg.controller.ramp_length,
boosts=1 if self.test else boosts)
clf_type = self.ui.classifierComboBox.currentText()
if clf_type == 'LDA':
clf = LDA()
elif clf_type == 'SVM':
clf = SVC(C=50, kernel='linear')
else:
clf = LDA()
preproc = StandardScaler()
skpipeline = Pipeline([('preproc', preproc), ('clf', clf)])
classifier = pipeline.Classifier(skpipeline)
classifier.fit(*training_data)
pl = pipeline.Pipeline(
[
self.cfg.conditioner,
self.cfg.windower,
(
features.FeatureExtractor(
[features.MAV()],
len(self.cfg.channels)),
[
self.cfg.feature_extractor,
classifier
],
)
]
)
self.record_thread.set_pipeline(pl)
