def classify():
global model, verbose
print("-" * 30)
print("Sleep Ensemble Classification")
print("Current model:", model.name)
print("-" * 30)
print("Select files to classify")
print("This model is configured to accept", model.reader.standard)
filepaths = ask_filenames(filetypes=model.reader.filetypes)
if len(filepaths) == 0:
print("You didn't select any files! Returning you to the main menu")
return
print("Select where classifications should go:")
destination = ask_directory()
if destination is None or not os.path.isdir(destination):
print(
"You didn't select a destination! Returning you to the main menu")
return
save_input = yes_no_loop(
"Do you also want to export processed data alongside classifications?")
print("Identified", len(filepaths), "files to classify")
if verbose == 1: jobs = trange(len(filepaths))
else: jobs = range(len(filepaths))
for i in jobs:
if verbose > 1: print("Classifying", filepaths[i])
data, _ = model.read(filepaths[i], labels=False)
name = Path(filepaths[i]).stem
ds = model.process(data, None, name)
p = model.predict(ds.data)
result = Dataset(label_names=['PREDICTION'], labels=p.reshape(-1, 1))
if save_input:
result = ds.concatenate(result)
result.name = name + "-predictions"
if verbose > 1: print("Writing results")
result.write(destination)
print("Completed classification jobs!")
def validate():
global model, verbose
print("-" * 30)
print("Sleep Ensemble Validation")
print("Current model:", model.name)
print("-" * 30)
print("Select files to use for validation:")
print("This model is configured to accept", model.reader.standard)
filepaths = ask_filenames(filetypes=model.reader.filetypes)
if len(filepaths) == 0:
print("You didn't select any files! Returning you to the main menu")
return
save_input = yes_no_loop("Do you want to export the results?")
if save_input:
print("Select where results should go:")
destination = ask_directory()
if destination is None or not os.path.isdir(destination):
print(
"You didn't select a destination! Returning you to the main menu"
)
return
print("Identified", len(filepaths), "files for validation")
req_train = yes_no_loop(
"Do you wish to train and validate via cross-validation (y) or just validate (n)?"
)
ds, data, labels = [], [], []
if verbose == 1: jobs = trange(len(filepaths))
else: jobs = range(len(filepaths))
for i in jobs:
if verbose > 1: print("Reading", filepaths[i])
d, l = model.read(filepaths[i], labels=True)
name = Path(filepaths[i]).stem
ds_ = model.process(d, l, name)
ds.append(ds_)
data.append(ds_.data)
labels.append(ds_.labels)
if req_train:
p, Y_hat = model.cross_validate(data, labels)
else:
p, Y_hat = model.predict(data)
if p[0].ndim != 1:
p_ = []
start = 0
for i in range(len(data)):
p_.append(p[start:start + len(data[i])])
start = start + len(data[i])
p = p_
score = model.score(p, labels)
print("Overall Score:", score)
p_overall = np.concatenate(p)
labels_overall = np.concatenate(labels)
print_report(p_overall.reshape(-1), labels_overall.reshape(-1))
if save_input:
if verbose > 1: print("Writing results")
for i in range(len(p)):
r = np.concatenate((Y_hat[i], p[i].reshape(-1, 1)), axis=1)
result = Dataset(label_names=['AW', 'QW', 'NR', 'R', 'P'],
labels=r)
result = ds[i].concatenate(result)
result.name = ds[i].name + "-validated-" + "%.4f" % score[i]
result.write(destination)
print("Completed validation")