def plot_features(subject, data_path, model_path, test_labels, dataset='test'):
with open(model_path + '/' + subject + '.pickle', 'rb') as f:
state_dict = cPickle.load(f)
cnn = ConvNet(state_dict['params'])
cnn.set_weights(state_dict['weights'])
scalers = state_dict['scalers']
if dataset == 'test':
d = load_test_data(data_path, subject)
x = d['x']
y = test_labels['preictal']
elif dataset == 'train':
d = load_train_data(data_path, subject)
x, y = d['x'], d['y']
else:
raise ValueError('dataset')
x, _ = scale_across_time(x, x_test=None, scalers=scalers) if state_dict['params']['scale_time'] \
else scale_across_features(x, x_test=None, scalers=scalers)
cnn.batch_size.set_value(x.shape[0])
get_features = theano.function([cnn.x, Param(cnn.training_mode, default=0)], cnn.feature_extractor.output,
allow_input_downcast=True)
logits_test = get_features(x)
model = TSNE(n_components=2, random_state=0)
z = model.fit_transform(np.float64(logits_test))
plt.scatter(z[:, 0], z[:, 1], s=60, c=y)
plt.show()
def predict(subject, data_path, model_path, submission_path):
patient_filenames = [filename for filename in os.listdir(model_path) if
subject in filename and filename.endswith('.pickle')]
for filename in patient_filenames:
print filename
d = load_test_data(data_path, subject)
x, id = d['x'], d['id']
with open(model_path + '/' + filename, 'rb') as f:
state_dict = cPickle.load(f)
scalers = state_dict['scalers']
x, _ = scale_across_time(x, x_test=None, scalers=scalers) if state_dict['params']['scale_time'] \
else scale_across_features(x, x_test=None, scalers=scalers)
cnn = ConvNet(state_dict['params'])
cnn.set_weights(state_dict['weights'])
test_proba = cnn.get_test_proba(x)
ans = zip(id, test_proba)
df = DataFrame(data=ans, columns=['clip', 'preictal'])
csv_name = '.'.join(filename.split('.')[:-1]) if '.' in filename else filename
df.to_csv(submission_path + '/' + csv_name + '.csv', index=False, header=True)
def predict(subject, data_path, model_path, submission_path):
patient_filenames = [
filename for filename in os.listdir(model_path)
if subject in filename and filename.endswith('.pickle')
]
for filename in patient_filenames:
print(filename)
d = load_test_data(data_path, subject)
x, id = d['x'], d['id']
with open(model_path + '/' + filename, 'rb') as f:
state_dict = pickle.load(f)
scalers = state_dict['scalers']
x, _ = scale_across_time(x, x_test=None, scalers=scalers) if state_dict['params']['scale_time'] \
else scale_across_features(x, x_test=None, scalers=scalers)
cnn = ConvNet(state_dict['params'])
cnn.set_weights(state_dict['weights'])
test_proba = cnn.get_test_proba(x)
ans = list(zip(id, test_proba))
df = DataFrame(data=ans, columns=['clip', 'preictal'])
csv_name = '.'.join(
filename.split('.')[:-1]) if '.' in filename else filename
df.to_csv(submission_path + '/' + csv_name + '.csv',
index=False,
header=True)
def curve_per_subject(subject, data_path, test_labels):
d = load_train_data(data_path, subject)
x, y_10m = d['x'], d['y']
n_train_examples = x.shape[0]
n_timesteps = x.shape[-1]
print('n_preictal', np.sum(y_10m))
print('n_inetrictal', np.sum(y_10m - 1))
x, y = reshape_data(x, y_10m)
data_scaler = StandardScaler()
x = data_scaler.fit_transform(x)
lda = LDA()
lda.fit(x, y)
pred_1m = lda.predict_proba(x)[:, 1]
pred_10m = np.reshape(pred_1m, (n_train_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
fpr, tpr, threshold = roc_curve(y_10m, pred_10m)
c = np.sqrt((1 - tpr) ** 2 + fpr ** 2)
opt_threshold = threshold[np.where(c == np.min(c))[0]][-1]
print(opt_threshold)
# ------- TEST ---------------
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
n_test_examples = x_test.shape[0]
n_timesteps = x_test.shape[3]
x_test = reshape_data(x_test)
x_test = data_scaler.transform(x_test)
pred_1m = lda.predict_proba(x_test)[:, 1]
pred_10m = np.reshape(pred_1m, (n_test_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
y_pred = np.zeros_like(test_labels)
y_pred[np.where(pred_10m >= opt_threshold)] = 1
cm = confusion_matrix(test_labels, y_pred)
print(print_cm(cm, labels=['interictal', 'preictal']))
sn = 1.0 * cm[1, 1] / (cm[1, 1] + cm[1, 0])
sp = 1.0 * cm[0, 0] / (cm[0, 0] + cm[0, 1])
print(sn, sp)
sn, sp = [], []
t_list = np.arange(0.0, 1.0, 0.01)
for t in t_list:
y_pred = np.zeros_like(test_labels)
y_pred[np.where(pred_10m >= t)] = 1
cm = confusion_matrix(test_labels, y_pred)
sn_t = 1.0 * cm[1, 1] / (cm[1, 1] + cm[1, 0])
sp_t = 1.0 * cm[0, 0] / (cm[0, 0] + cm[0, 1])
sn.append(sn_t)
sp.append(sp_t)
return t_list, sn, sp
def curve_per_subject(subject, data_path, test_labels):
d = load_train_data(data_path, subject)
x, y_10m = d['x'], d['y']
n_train_examples = x.shape[0]
n_timesteps = x.shape[-1]
print 'n_preictal', np.sum(y_10m)
print 'n_inetrictal', np.sum(y_10m - 1)
x, y = reshape_data(x, y_10m)
data_scaler = StandardScaler()
x = data_scaler.fit_transform(x)
lda = LDA()
lda.fit(x, y)
pred_1m = lda.predict_proba(x)[:, 1]
pred_10m = np.reshape(pred_1m, (n_train_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
fpr, tpr, threshold = roc_curve(y_10m, pred_10m)
c = np.sqrt((1 - tpr) ** 2 + fpr ** 2)
opt_threshold = threshold[np.where(c == np.min(c))[0]][-1]
print opt_threshold
# ------- TEST ---------------
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
n_test_examples = x_test.shape[0]
n_timesteps = x_test.shape[3]
x_test = reshape_data(x_test)
x_test = data_scaler.transform(x_test)
pred_1m = lda.predict_proba(x_test)[:, 1]
pred_10m = np.reshape(pred_1m, (n_test_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
y_pred = np.zeros_like(test_labels)
y_pred[np.where(pred_10m >= opt_threshold)] = 1
cm = confusion_matrix(test_labels, y_pred)
print print_cm(cm, labels=['interictal', 'preictal'])
sn = 1.0 * cm[1, 1] / (cm[1, 1] + cm[1, 0])
sp = 1.0 * cm[0, 0] / (cm[0, 0] + cm[0, 1])
print sn, sp
sn, sp = [], []
t_list = np.arange(0.0, 1.0, 0.01)
for t in t_list:
y_pred = np.zeros_like(test_labels)
y_pred[np.where(pred_10m >= t)] = 1
cm = confusion_matrix(test_labels, y_pred)
sn_t = 1.0 * cm[1, 1] / (cm[1, 1] + cm[1, 0])
sp_t = 1.0 * cm[0, 0] / (cm[0, 0] + cm[0, 1])
sn.append(sn_t)
sp.append(sp_t)
return t_list, sn, sp
def predict(subject, model, data_scaler, data_path, submission_path, test_labels, opt_threshold_train):
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
n_test_examples = x_test.shape[0]
n_timesteps = x_test.shape[3]
x_test = reshape_data(x_test)
x_test = data_scaler.transform(x_test)
pred_1m = model.predict_proba(x_test)[:, 1]
pred_10m = np.reshape(pred_1m, (n_test_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
ans = zip(id, pred_10m)
df = DataFrame(data=ans, columns=['clip', 'preictal'])
df.to_csv(submission_path + '/' + subject + '.csv', index=False, header=True)
def predict(subject, model, data_scaler, data_path, submission_path):
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
n_test_examples = x_test.shape[0]
n_timesteps = x_test.shape[3]
x_test = reshape_data(x_test)
x_test = data_scaler.transform(x_test)
pred_1m = model.predict_proba(x_test)[:, 1]
pred_10m = np.reshape(pred_1m, (n_test_examples, n_timesteps))
pred_10m = np.mean(pred_10m, axis=1)
ans = zip(id, pred_10m)
df = DataFrame(data=ans, columns=['clip', 'preictal'])
df.to_csv(submission_path + '/' + subject + '.csv', index=False, header=True)
return pred_10m
def plot_train_test(subject, data_path):
d = load_train_data(data_path, subject)
x_train = d['x']
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1] * x_train.shape[2] * x_train.shape[3])
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1] * x_test.shape[2] * x_test.shape[3]))
x_all = np.vstack((np.float64(x_train), np.float64(x_test)))
scaler = StandardScaler()
x_all = scaler.fit_transform(x_all)
colors = ['r'] * len(x_train) + ['b'] * len(x_test)
markers = ['o'] * len(x_train) + ['^'] * len(x_test)
pca = PCA(50)
pca.fit(x_all)
x_all = pca.fit_transform(x_all)
model = TSNE(n_components=2, perplexity=40, learning_rate=100, random_state=42)
z = model.fit_transform(x_all)
for a, b, c, d in zip(z[:, 0], z[:, 1], colors, markers):
plt.scatter(a, b, c=c, s=60, marker=d)
plt.scatter(z[0, 0], z[0, 1], c=colors[0], marker=markers[0], s=60, label='train')
plt.scatter(z[-1, 0], z[-1, 1], c=colors[-1], marker=markers[-1], s=60, label='test')
zz = z[np.where(np.array(markers) != u' ')[0], :]
ax = plt.subplot(111)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
ncol=2, fancybox=True, shadow=True)
plt.xlim([min(zz[:, 0]) - 0.5, max(zz[:, 0] + 0.5)])
plt.ylim([min(zz[:, 1]) - 0.5, max(zz[:, 1] + 0.5)])
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(20)
plt.ylabel('Z_2', fontsize=20)
plt.xlabel('Z_1', fontsize=20)
plt.show()
def train(subject, data_path, model_path, model_params, validation_params):
d = load_train_data(data_path, subject)
x, y, filename_to_idx = d['x'], d['y'], d['filename_to_idx']
x_test = load_test_data(data_path,
subject)['x'] if model_params['use_test'] else None
# --------- add params
model_params['n_channels'] = x.shape[1]
model_params['n_fbins'] = x.shape[2]
model_params['n_timesteps'] = x.shape[3]
print '============ parameters'
for key, value in model_params.items():
print key, ':', value
print '========================'
x_train, y_train = None, None
x_valid, y_valid = None, None
if model_params['overlap']:
# no validation if overlap
filenames_grouped_by_hour = cPickle.load(open('filenames.pickle'))
data_grouped_by_hour = load_grouped_train_data(
data_path, subject, filenames_grouped_by_hour)
x, y = generate_overlapped_data(data_grouped_by_hour,
overlap_size=model_params['overlap'],
window_size=x.shape[-1],
overlap_interictal=True,
overlap_preictal=True)
print x.shape
x, scalers = scale_across_time(x, x_test=None) if model_params['scale_time'] \
else scale_across_features(x, x_test=None)
cnn = ConvNet(model_params)
cnn.train(train_set=(x, y), max_iter=175000)
state_dict = cnn.get_state()
state_dict['scalers'] = scalers
with open(model_path + '/' + subject + '.pickle', 'wb') as f:
cPickle.dump(state_dict, f, protocol=cPickle.HIGHEST_PROTOCOL)
return
else:
if validation_params['random_split']:
skf = StratifiedShuffleSplit(y,
n_iter=1,
test_size=0.25,
random_state=0)
for train_idx, valid_idx in skf:
x_train, y_train = x[train_idx], y[train_idx]
x_valid, y_valid = x[valid_idx], y[valid_idx]
else:
filenames_grouped_by_hour = cPickle.load(open('filenames.pickle'))
d = split_train_valid_filenames(subject, filenames_grouped_by_hour)
train_filenames, valid_filenames = d['train_filenames'], d[
'valid_filnames']
train_idx = [filename_to_idx[i] for i in train_filenames]
valid_idx = [filename_to_idx[i] for i in valid_filenames]
x_train, y_train = x[train_idx], y[train_idx]
x_valid, y_valid = x[valid_idx], y[valid_idx]
if model_params['scale_time']:
x_train, scalers_train = scale_across_time(x=x_train, x_test=x_test)
x_valid, _ = scale_across_time(x=x_valid,
x_test=x_test,
scalers=scalers_train)
else:
x_train, scalers_train = scale_across_features(x=x_train,
x_test=x_test)
x_valid, _ = scale_across_features(x=x_valid,
x_test=x_test,
scalers=scalers_train)
del x, x_test
print '============ dataset'
print 'train:', x_train.shape
print 'n_pos:', np.sum(y_train), 'n_neg:', len(y_train) - np.sum(y_train)
print 'valid:', x_valid.shape
print 'n_pos:', np.sum(y_valid), 'n_neg:', len(y_valid) - np.sum(y_valid)
# -------------- validate
cnn = ConvNet(model_params)
best_iter = cnn.validate(train_set=(x_train, y_train),
valid_set=(x_valid, y_valid),
valid_freq=validation_params['valid_freq'],
max_iter=validation_params['max_iter'],
fname_out=model_path + '/' + subject + '.txt')
# ---------------- scale
d = load_train_data(data_path, subject)
x, y, filename_to_idx = d['x'], d['y'], d['filename_to_idx']
x_test = load_test_data(data_path,
subject)['x'] if model_params['use_test'] else None
x, scalers = scale_across_time(x=x, x_test=x_test) if model_params['scale_time'] \
else scale_across_features(x=x, x_test=x_test)
del x_test
cnn = ConvNet(model_params)
cnn.train(train_set=(x, y), max_iter=best_iter)
state_dict = cnn.get_state()
state_dict['scalers'] = scalers
with open(model_path + '/' + subject + '.pickle', 'wb') as f:
cPickle.dump(state_dict, f, protocol=cPickle.HIGHEST_PROTOCOL)
def plot_sequences(subject, data_path, test_labels):
# data train
filenames_grouped_by_hour = cPickle.load(open('filenames.pickle'))
data_grouped_by_hour = load_grouped_train_data(data_path, subject, filenames_grouped_by_hour)
interictal_hours = data_grouped_by_hour['interictal']
preictal_hours = data_grouped_by_hour['preictal']
marker_type = {u'D': u'diamond', u's': u'square', u'^': u'triangle_up', u'd': u'thin_diamond', u'h': u'hexagon1',
u'*': u'star', u'o': u'circle', u'.': u'point', u'p': u'pentagon', u'H': u'hexagon2',
u'v': u'triangle_down', u'8': u'octagon', u'<': u'triangle_left', u'>': u'triangle_right'}
marker_list = marker_type.keys() * 50
x_train, colors, markers = [], [], []
cmap = get_cmap(len(preictal_hours))
print len(preictal_hours)
for i, hour in enumerate(preictal_hours):
for clip in hour:
x_train.append(np.reshape(clip, (1, clip.shape[0] * clip.shape[1] * clip.shape[2])))
colors.extend([cmap(i)] * len(hour))
markers.extend([marker_list[i]] * len(hour))
for i, hour in enumerate(interictal_hours):
for clip in hour:
x_train.append(np.reshape(clip, (1, clip.shape[0] * clip.shape[1] * clip.shape[2])))
colors.extend(['r'] * len(hour))
markers.extend([u' '] * len(hour))
x_train = np.vstack(x_train)
print x_train.shape
d = load_test_data(data_path, subject)
x_test, id = d['x'], d['id']
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1] * x_test.shape[2] * x_test.shape[3]))
color_test = test_labels['preictal']
print np.sum(test_labels['preictal'])
color_test[np.where(color_test == 0)[0]] = 'b'
color_test[np.where(color_test == 1)[0]] = 'b'
colors.extend(list(color_test))
markers.extend([u' '] * len(x_test))
x_all = np.vstack((np.float64(x_train), np.float64(x_test)))
scaler = StandardScaler()
x_all = scaler.fit_transform(x_all)
pca = PCA(50)
pca.fit(x_all)
x_all = pca.fit_transform(x_all)
model = TSNE(n_components=2, perplexity=40, learning_rate=100, random_state=42)
z = model.fit_transform(x_all)
prev_c, i = 0, 0
for a, b, c, d in zip(z[:, 0], z[:, 1], colors, markers):
if c != prev_c and d != u' ':
plt.scatter(a, b, c=c, s=70, marker=d, label=str(i))
i += 1
else:
plt.scatter(a, b, c=c, s=70, marker=d)
prev_c = c
zz = z[np.where(np.array(markers) != u' ')[0], :]
ax = plt.subplot(111)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
ncol=2, fancybox=True, shadow=True)
plt.xlim([min(zz[:, 0]) - 0.5, max(zz[:, 0] + 0.5)])
plt.ylim([min(zz[:, 1]) - 0.5, max(zz[:, 1] + 0.5)])
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(20)
plt.ylabel('Z_2', fontsize=20)
plt.xlabel('Z_1', fontsize=20)
plt.show()
make probabilistic predictions for each method within a given patient
combine the probalities by either:
1) just summing them up
2) subtracting the mean prediction probability from each class
for each method and then summing (Avoids all 0 predictions) """
all_predictions = []
all_predictions_ns = []
validations_true = []
validations_preds = []
for patient in all_patients:
#LOAD DATA
d = load_train_data('preprocessed/cnn/', patient)
x, y, filename_to_idx = d['x'], d['y'], d['filename_to_idx']
x_test = load_test_data('preprocessed/cnn/', patient)['x']
test_preds,test_preds_ns,val_preds, val_true,train_loss,valid_loss= train_predict_test_cnn(
patient,CNN(patient),x,x_test,enhance_size = 1000)
roc_area = roc_auc_score(val_true,val_preds)
print patient, roc_area
plot = plt.figure()
plot_train_val_loss(train_loss,valid_loss,patient)
plot.savefig('./figs/CNN'+patient+'train_val.png')
all_predictions.append(test_preds)
all_predictions_ns.append(test_preds_ns)
validations_preds.append(val_preds)
def train(subject, data_path, model_path, model_params, validation_params):
d = load_train_data(data_path, subject)
x, y, filename_to_idx = d['x'], d['y'], d['filename_to_idx']
x_test = load_test_data(data_path, subject)['x'] if model_params['use_test'] else None
# --------- add params
model_params['n_channels'] = x.shape[1]
model_params['n_fbins'] = x.shape[2]
model_params['n_timesteps'] = x.shape[3]
print '============ parameters'
for key, value in model_params.items():
print key, ':', value
print '========================'
x_train, y_train = None, None
x_valid, y_valid = None, None
if model_params['overlap']:
# no validation if overlap
filenames_grouped_by_hour = cPickle.load(open('filenames.pickle'))
data_grouped_by_hour = load_grouped_train_data(data_path, subject, filenames_grouped_by_hour)
x, y = generate_overlapped_data(data_grouped_by_hour, overlap_size=model_params['overlap'],
window_size=x.shape[-1],
overlap_interictal=True,
overlap_preictal=True)
print x.shape
x, scalers = scale_across_time(x, x_test=None) if model_params['scale_time'] \
else scale_across_features(x, x_test=None)
cnn = ConvNet(model_params)
cnn.train(train_set=(x, y), max_iter=175000)
state_dict = cnn.get_state()
state_dict['scalers'] = scalers
with open(model_path + '/' + subject + '.pickle', 'wb') as f:
cPickle.dump(state_dict, f, protocol=cPickle.HIGHEST_PROTOCOL)
return
else:
if validation_params['random_split']:
skf = StratifiedShuffleSplit(y, n_iter=1, test_size=0.25, random_state=0)
for train_idx, valid_idx in skf:
x_train, y_train = x[train_idx], y[train_idx]
x_valid, y_valid = x[valid_idx], y[valid_idx]
else:
filenames_grouped_by_hour = cPickle.load(open('filenames.pickle'))
d = split_train_valid_filenames(subject, filenames_grouped_by_hour)
train_filenames, valid_filenames = d['train_filenames'], d['valid_filnames']
train_idx = [filename_to_idx[i] for i in train_filenames]
valid_idx = [filename_to_idx[i] for i in valid_filenames]
x_train, y_train = x[train_idx], y[train_idx]
x_valid, y_valid = x[valid_idx], y[valid_idx]
if model_params['scale_time']:
x_train, scalers_train = scale_across_time(x=x_train, x_test=x_test)
x_valid, _ = scale_across_time(x=x_valid, x_test=x_test, scalers=scalers_train)
else:
x_train, scalers_train = scale_across_features(x=x_train, x_test=x_test)
x_valid, _ = scale_across_features(x=x_valid, x_test=x_test, scalers=scalers_train)
del x, x_test
print '============ dataset'
print 'train:', x_train.shape
print 'n_pos:', np.sum(y_train), 'n_neg:', len(y_train) - np.sum(y_train)
print 'valid:', x_valid.shape
print 'n_pos:', np.sum(y_valid), 'n_neg:', len(y_valid) - np.sum(y_valid)
# -------------- validate
cnn = ConvNet(model_params)
best_iter = cnn.validate(train_set=(x_train, y_train),
valid_set=(x_valid, y_valid),
valid_freq=validation_params['valid_freq'],
max_iter=validation_params['max_iter'],
fname_out=model_path + '/' + subject + '.txt')
# ---------------- scale
d = load_train_data(data_path, subject)
x, y, filename_to_idx = d['x'], d['y'], d['filename_to_idx']
x_test = load_test_data(data_path, subject)['x'] if model_params['use_test'] else None
x, scalers = scale_across_time(x=x, x_test=x_test) if model_params['scale_time'] \
else scale_across_features(x=x, x_test=x_test)
del x_test
cnn = ConvNet(model_params)
cnn.train(train_set=(x, y), max_iter=best_iter)
state_dict = cnn.get_state()
state_dict['scalers'] = scalers
with open(model_path + '/' + subject + '.pickle', 'wb') as f:
cPickle.dump(state_dict, f, protocol=cPickle.HIGHEST_PROTOCOL)
