def raw_validation_load():
dirpath1 = '/Users/Jonathan/PhD/Seizure_related/batchSept_UC_20'
testdataobj20 = SeizureData(dirpath1, amount_to_downsample=40)
testdataobj20.load_data()
datasettest20 = testdataobj20.data_array
dirpath2 = '/Users/Jonathan/PhD/Seizure_related/batchSept_UC_40'
testdataobj40 = SeizureData(dirpath2, amount_to_downsample=40)
testdataobj40.load_data()
datasettest40 = testdataobj40.data_array
print datasettest40.shape, 'is correct data shape'
datasettest = np.vstack([datasettest20, datasettest40])
return datasettest
def raw_training_load():
################# 'NEW data' ###################
dirpath = '/Users/Jonathan/PhD/Seizure_related/20150616'
_20150616dataobj = SeizureData(dirpath, amount_to_downsample=40)
_20150616dataobj.load_data()
_20150616data = _20150616dataobj.data_array
_20150616labels = _20150616dataobj.label_colarray
_20150616data_norm = normalise(_20150616data)
print _20150616dataobj.filename_list.shape
_20150616dataobj.filenames_list = [
_20150616dataobj.filename_list[i]
for i in range(_20150616dataobj.filename_list.shape[0])
]
for name in _20150616dataobj.filenames_list[0:20]:
print name[-34:]
# select out the stuff we want
#inds = np.loadtxt('0901_400newdata.csv', delimiter=',')
notebook_dir = '/Users/jonathan/PhD/Seizure_related/2015_08_PyRanalysis/'
inds = np.loadtxt(notebook_dir + '0616correctedintervals.csv',
delimiter=',')
data0616_unnorm = _20150616data[list(inds[:, 0])]
data0616 = _20150616data_norm[list(inds[:, 0])]
labels0616 = _20150616labels[list(inds[:, 0])]
for i in range(data0616.shape[0]):
labels0616[i] = inds[i, 1]
################## Original Data ####################
dirpath = '/Users/Jonathan/PhD/Seizure_related/Classified'
dataobj = SeizureData(dirpath, amount_to_downsample=20)
dataobj.load_data()
dataobj = relabel(dataobj)
dataobj = reorder(dataobj)
dataset301 = dataobj.data_array
labels301 = dataobj.label_colarray
new_labels = np.loadtxt(notebook_dir + 'new_event_labels_28082015.csv',
delimiter=',')
for x in new_labels:
labels301[x[0]] = x[1]
selection = np.loadtxt(notebook_dir + 'perfect_event_labels_28082015.csv',
delimiter=',')
indexes = list(selection[:, 0])
dataset129_unnorm = dataset301[indexes, :]
dataset129_norm = normalise(dataset129_unnorm)
dataset301_norm = normalise(dataset301)
labels129 = labels301[indexes]
return np.vstack((data0616_unnorm, dataset301))
def assess_states(self,
raw_path=None,
downsample_rate=None,
savestring='example',
threshold=65,
raw_load=True,
saved_path=None,
make_pdfs=True):
self.threshold = '65' # 'sureity' threshold
self.savestring = savestring
if raw_load:
self.dataobj = SeizureData(raw_path, fs_dict=self.fs_dict)
self.dataobj.load_data()
f = open('../' + savestring + '_saved', 'wb')
pickle.dump(self.dataobj, f)
else:
assert saved_path != None
self.dataobj = pickle.load(open(saved_path, 'rb'))
#print 'printing filename_list'
#print self.dataobj.filename_list
self.norm_data = utils.normalise(self.dataobj.data_array)
feature_obj = FeatureExtractor(self.norm_data)
i_features = self.classifier.imputer.transform(
feature_obj.feature_array)
iss_features = self.classifier.std_scaler.transform(i_features)
lda_iss_features = self.lda.transform(iss_features)
np.set_printoptions(precision=3, suppress=True)
#self.pred_table = self.r_forest.predict_proba(iss_features)*100
#self.preds = self.r_forest.predict(iss_features)
self.pred_table = self.r_forest_lda.predict_proba(
lda_iss_features) * 100
self.preds = self.r_forest_lda.predict(lda_iss_features)
self.predslist = list(self.preds) # why need this?
self.predslist[self.predslist == 4] = 'Baseline'
self.max_preds = np.max(self.pred_table, axis=1)
#print pred_table
self.threshold_for_mixed = np.where(
self.max_preds < int(self.threshold), 1, 0) # 1 when below
self._string_fun2()
self._write_to_excel()
if make_pdfs:
self.plot_pdfs()
