def extract_reliable_negatives_fuselier(model,
data_stuff,
negative_idx,
th=0.5,
yshape=2,
N_inputs=1):
negative_stuff = slice_data(data_stuff, negative_idx, N_inputs)
drugability = model.predict(negative_stuff[0])
if yshape == 2:
drugability = drugability[:, 1]
new_reliable_negatives_internal = np.where(drugability < th)[0]
new_reliable_negatives = negative_idx[new_reliable_negatives_internal]
return new_reliable_negatives, make_stats_from_vector(
drugability), np.where(drugability > 0.5, 1, 0).sum()
def masked_balanced_score(y_pred, y_true, weights, balance=True, auc=False):
w = np.where(weights>0)[0]
if balance:
function = balanced_accuracy_score
else:
function = accuracy_score
if auc:
function = roc_auc_score
to_use_pred = y_pred[w]
to_use_true = y_true[w]
return function(to_use_true, to_use_pred)
def get_new_weights(y_all, reliable_negatives):
binary_elements_condition = set(y_all.reshape(-1)) in [{0, 1}, {0.0, 1.0}]
two_class_condition = len(y_all.shape) <= 2
message = 'Y should be binary, y.shape=%s' % str(y_all.shape)
assert binary_elements_condition and two_class_condition, message
if len(y_all.shape) == 2:
y_to_use = y.argmax(axis=1)
else:
y_to_use = y_all
N_positive = y_to_use.sum()
N_negative = len(reliable_negatives)
N_tot = float(N_positive + N_negative)
w_pos = N_negative / N_tot
w_neg = N_positive / N_tot
weights = np.where(y_to_use == 1, w_pos, w_neg)
return weights
#======= loading stuff ==========
logger.info('Start')
config = load_yaml(args.config)
if args.data_checkpoint is None:
args.data_checkpoint = args.output_core + '_data_chk.npz'
config['loader_config']['data_checkpoint'] = args.data_checkpoint
config['training_cfg'] = dict(batch_size=100,
epochs=args.epochs,
shuffle=True,
verbose=0)
x, y, weights = load_from_config(config, args.reload)
#!!!! QUICK FIX!!!!
x = np.nan_to_num(x)
x = np.where(abs(x) < 10.0, x, 0)
data_stuff = [x, y, weights]
if len(y.shape) == 2:
assert y.shape[1] == 2, "Y should be binary, y.shape=%s" % str(y.shape)
positive_idx = np.where(y.argmax(axis=1) == 1)[0]
unlabelled_idx = np.where(y.argmax(axis=1) == 0)[0]
negative_idx = np.where(y.argmax(axis=1) == 0)[0]
elif len(y.shape) == 1:
positive_idx = np.where(y == 1)[0]
unlabelled_idx = np.where(y == 0)[0]
negative_idx = np.where(y == 0)[0]
else:
raise ValueError('Incorrect shape, y.shape=' % str(y.shape))