def _generator(self):
B = self.batch_size
while True:
if self.shuffle:
self.reader.random_shuffle()
remaining = self.n_examples
while remaining > 0:
current_size = min(self.chunk_size, remaining)
remaining -= current_size
ret = common_utils.read_chunk(self.reader, current_size)
Xs = ret["X"]
ts = ret["t"]
ys = ret["y"]
names = ret["name"]
Xs = preprocess_chunk(Xs, ts, self.discretizer, self.normalizer)
(Xs, ys, ts, names) = common_utils.sort_and_shuffle([Xs, ys, ts, names], B)
for i in range(0, current_size, B):
X = nn_utils.pad_zeros(Xs[i:i + B])
y = np.array(ys[i:i + B])
batch_names = names[i:i+B]
batch_ts = ts[i:i+B]
batch_data = (X, y)
if not self.return_names:
yield batch_data
else:
yield {"data": batch_data, "names": batch_names, "ts": batch_ts}
def _generator(self):
B = self.batch_size
while True:
if self.shuffle:
N = len(self.data[1])
order = range(N)
random.shuffle(order)
tmp_data = [[None] * N, [None] * N]
tmp_names = [None] * N
tmp_ts = [None] * N
for i in range(N):
tmp_data[0][i] = self.data[0][order[i]]
tmp_data[1][i] = self.data[1][order[i]]
tmp_names[i] = self.names[order[i]]
tmp_ts[i] = self.ts[order[i]]
self.data = tmp_data
self.names = tmp_names
self.ts = tmp_ts
else:
# sort entirely
X = self.data[0]
y = self.data[1]
(X, y, self.names, self.ts) = common_utils.sort_and_shuffle([X, y, self.names, self.ts], B)
self.data = [X, y]
self.data[1] = np.array(self.data[1]) # this is important for Keras
for i in range(0, len(self.data[0]), B):
x = self.data[0][i:i+B]
y = self.data[1][i:i+B]
names = self.names[i:i + B]
ts = self.ts[i:i + B]
x = nn_utils.pad_zeros(x)
y = np.array(y) # (B, 25)
if self.target_repl:
y_rep = np.expand_dims(y, axis=1).repeat(x.shape[1], axis=1) # (B, T, 25)
batch_data = (x, [y, y_rep])
else:
batch_data = (x, y)
if not self.return_names:
yield batch_data
else:
yield {"data": batch_data, "names": names, "ts": ts}
def _generator(self):
B = self.batch_size
while True:
if self.shuffle:
N = len(self.data[1])
order = range(N)
random.shuffle(order)
tmp_data = [[[None]*N, [None]*N], [None]*N]
tmp_names = [None] * N
tmp_ts = [None] * N
for i in range(N):
tmp_data[0][0][i] = self.data[0][0][order[i]]
tmp_data[0][1][i] = self.data[0][1][order[i]]
tmp_data[1][i] = self.data[1][order[i]]
tmp_names[i] = self.names[order[i]]
tmp_ts[i] = self.ts[order[i]]
self.data = tmp_data
self.names = tmp_names
self.ts = tmp_ts
else:
# sort entirely
Xs = self.data[0][0]
masks = self.data[0][1]
ys = self.data[1]
(Xs, masks, ys, self.names, self.ts) = common_utils.sort_and_shuffle([Xs, masks, ys,
self.names, self.ts], B)
self.data = [[Xs, masks], ys]
for i in range(0, len(self.data[1]), B):
X = self.data[0][0][i:i + B]
mask = self.data[0][1][i:i + B]
y = self.data[1][i:i + B]
names = self.names[i:i + B]
ts = self.ts[i:i + B]
X = nn_utils.pad_zeros(X) # (B, T, D)
mask = nn_utils.pad_zeros(mask) # (B, T)
y = nn_utils.pad_zeros(y)
y = np.expand_dims(y, axis=-1) # (B, T, 1)
batch_data = ([X, mask], y)
if not self.return_names:
yield batch_data
else:
yield {"data": batch_data, "names": names, "ts": ts}
def _generator(self):
B = self.batch_size
while True:
# convert to right format for sort_and_shuffle
kvpairs = self.data.items()
mas = [kv[1] for kv in kvpairs]
mas = common_utils.sort_and_shuffle(mas, B)
for i in range(len(kvpairs)):
self.data[kvpairs[i][0]] = mas[i]
for i in range(0, len(self.data['X']), B):
outputs = []
# X
X = self.data['X'][i:i + B]
X = nn_utils.pad_zeros(X, min_length=self.ihm_pos + 1)
T = X.shape[1]
## ihm
ihm_M = np.array(self.data['ihm_M'][i:i + B])
ihm_M = np.expand_dims(ihm_M, axis=-1) # (B, 1)
ihm_y = np.array(self.data['ihm_y'][i:i + B])
ihm_y = np.expand_dims(ihm_y, axis=-1) # (B, 1)
outputs.append(ihm_y)
if self.target_repl:
ihm_seq = np.expand_dims(ihm_y, axis=-1).repeat(
T, axis=1) # (B, T, 1)
outputs.append(ihm_seq)
## decomp
decomp_M = self.data['decomp_M'][i:i + B]
decomp_M = nn_utils.pad_zeros(decomp_M,
min_length=self.ihm_pos + 1)
decomp_y = self.data['decomp_y'][i:i + B]
decomp_y = nn_utils.pad_zeros(decomp_y,
min_length=self.ihm_pos + 1)
decomp_y = np.expand_dims(decomp_y, axis=-1) # (B, T, 1)
outputs.append(decomp_y)
## los
los_M = self.data['los_M'][i:i + B]
los_M = nn_utils.pad_zeros(los_M, min_length=self.ihm_pos + 1)
los_y = self.data['los_y'][i:i + B]
los_y_true = nn_utils.pad_zeros(los_y,
min_length=self.ihm_pos + 1)
if self.partition == 'log':
los_y = [
np.array([metrics.get_bin_log(x, 10) for x in z])
for z in los_y
]
if self.partition == 'custom':
los_y = [
np.array([metrics.get_bin_custom(x, 10) for x in z])
for z in los_y
]
los_y = nn_utils.pad_zeros(los_y, min_length=self.ihm_pos + 1)
los_y = np.expand_dims(los_y, axis=-1) # (B, T, 1)
outputs.append(los_y)
## pheno
pheno_y = np.array(self.data['pheno_y'][i:i + B])
outputs.append(pheno_y)
if self.target_repl:
pheno_seq = np.expand_dims(pheno_y, axis=1).repeat(
T, axis=1) # (B, T, 25)
outputs.append(pheno_seq)
inputs = [X, ihm_M, decomp_M, los_M]
if self.return_y_true:
yield (inputs, outputs, los_y_true)
else:
yield (inputs, outputs)
def _generator(self):
B = self.batch_size
while True:
if self.shuffle:
N = len(self.data[1])
order = list(range(N))
random.shuffle(order)
tmp_data = [[[None] * N, [None] * N], [None] * N]
tmp_names = [None] * N
tmp_ts = [None] * N
for i in range(N):
tmp_data[0][0][i] = self.data[0][0][order[i]]
tmp_data[0][1][i] = self.data[0][1][order[i]]
tmp_data[1][i] = self.data[1][order[i]]
tmp_names[i] = self.names[order[i]]
tmp_ts[i] = self.ts[order[i]]
self.data = tmp_data
self.names = tmp_names
self.ts = tmp_ts
else:
# sort entirely
Xs = self.data[0][0]
masks = self.data[0][1]
ys = self.data[1]
(Xs, masks, ys, self.names,
self.ts) = common_utils.sort_and_shuffle(
[Xs, masks, ys, self.names, self.ts], B)
self.data = [[Xs, masks], ys]
for i in range(0, len(self.data[1]), B):
X = self.data[0][0][i:i + B]
mask = self.data[0][1][i:i + B]
y = self.data[1][i:i + B]
names = self.names[i:i + B]
ts = self.ts[i:i + B]
y_true = [np.array(x) for x in y]
y_true = common_utils.pad_zeros(y_true)
y_true = np.expand_dims(y_true, axis=-1)
if self.partition == 'log':
y = [
np.array([metrics.get_bin_log(x, 10) for x in z])
for z in y
]
if self.partition == 'custom':
y = [
np.array([metrics.get_bin_custom(x, 10) for x in z])
for z in y
]
X = common_utils.pad_zeros(X) # (B, T, D)
mask = common_utils.pad_zeros(mask) # (B, T)
y = common_utils.pad_zeros(y)
y = np.expand_dims(y, axis=-1)
if self.return_y_true:
batch_data = ([X, mask], y, y_true)
else:
batch_data = ([X, mask], y)
if not self.return_names:
yield batch_data
else:
yield {"data": batch_data, "names": names, "ts": ts}
def _generator(self):
B = self.batch_size
while True:
# convert to right format for sort_and_shuffle
kv_pairs = self.data.items()
mas = [kv[1] for kv in kv_pairs]
if self.shuffle:
N = len(self.data['X'])
order = range(N)
random.shuffle(order)
tmp = [None] * len(mas)
for mas_idx in range(len(mas)):
tmp[mas_idx] = [None] * len(mas[mas_idx])
for i in range(N):
tmp[mas_idx][i] = mas[mas_idx][order[i]]
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = tmp[i]
else:
# sort entirely
mas = common_utils.sort_and_shuffle(mas, B)
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = mas[i]
for i in range(0, len(self.data['X']), B):
outputs = []
# X
X = self.data['X'][i:i+B]
X = nn_utils.pad_zeros(X, min_length=self.ihm_pos+1)
T = X.shape[1]
# ihm
ihm_M = np.array(self.data['ihm_M'][i:i+B])
ihm_M = np.expand_dims(ihm_M, axis=-1) # (B, 1)
ihm_y = np.array(self.data['ihm_y'][i:i+B])
ihm_y = np.expand_dims(ihm_y, axis=-1) # (B, 1)
outputs.append(ihm_y)
if self.target_repl:
ihm_seq = np.expand_dims(ihm_y, axis=-1).repeat(T, axis=1) # (B, T, 1)
outputs.append(ihm_seq)
# decomp
decomp_M = self.data['decomp_M'][i:i+B]
decomp_M = nn_utils.pad_zeros(decomp_M, min_length=self.ihm_pos+1)
decomp_y = self.data['decomp_y'][i:i+B]
decomp_y = nn_utils.pad_zeros(decomp_y, min_length=self.ihm_pos+1)
decomp_y = np.expand_dims(decomp_y, axis=-1) # (B, T, 1)
outputs.append(decomp_y)
# los
los_M = self.data['los_M'][i:i+B]
los_M = nn_utils.pad_zeros(los_M, min_length=self.ihm_pos+1)
los_y = self.data['los_y'][i:i+B]
los_y_true = nn_utils.pad_zeros(los_y, min_length=self.ihm_pos+1)
if self.partition == 'log':
los_y = [np.array([metrics.get_bin_log(x, 10) for x in z]) for z in los_y]
if self.partition == 'custom':
los_y = [np.array([metrics.get_bin_custom(x, 10) for x in z]) for z in los_y]
los_y = nn_utils.pad_zeros(los_y, min_length=self.ihm_pos+1)
los_y = np.expand_dims(los_y, axis=-1) # (B, T, 1)
outputs.append(los_y)
# pheno
pheno_y = np.array(self.data['pheno_y'][i:i+B])
outputs.append(pheno_y)
if self.target_repl:
pheno_seq = np.expand_dims(pheno_y, axis=1).repeat(T, axis=1) # (B, T, 25)
outputs.append(pheno_seq)
inputs = [X, ihm_M, decomp_M, los_M]
if self.return_y_true:
batch_data = (inputs, outputs, los_y_true)
else:
batch_data = (inputs, outputs)
if not self.return_names:
yield batch_data
else:
yield {'data': batch_data,
'names': self.data['names'][i:i+B],
'decomp_ts': self.data['decomp_ts'][i:i+B],
'los_ts': self.data['los_ts'][i:i+B],
'pheno_ts': self.data['pheno_ts'][i:i + B]}
def _generator(self):
B = self.batch_size
while True:
# convert to right format for sort_and_shuffle
kv_pairs = list(self.data.items())
data_index = [pair[0] for pair in kv_pairs].index('X')
if data_index > 0:
kv_pairs[0], kv_pairs[data_index] = kv_pairs[
data_index], kv_pairs[0]
mas = [kv[1] for kv in kv_pairs]
if self.shuffle:
N = len(self.data['X'])
order = list(range(N))
random.shuffle(order)
tmp = [None] * len(mas)
for mas_idx in range(len(mas)):
tmp[mas_idx] = [None] * len(mas[mas_idx])
for i in range(N):
tmp[mas_idx][i] = mas[mas_idx][order[i]]
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = tmp[i]
else:
# sort entirely
mas = common_utils.sort_and_shuffle(mas, B)
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = mas[i]
for i in range(0, len(self.data['X']), B):
outputs = []
# X
X = self.data['X'][i:i + B]
X = common_utils.pad_zeros(X, min_length=self.ihm_pos + 1)
T = X.shape[1]
# ihm
ihm_M = np.array(self.data['ihm_M'][i:i + B])
ihm_M = np.expand_dims(ihm_M, axis=-1) # (B, 1)
ihm_y = np.array(self.data['ihm_y'][i:i + B])
ihm_y = np.expand_dims(ihm_y, axis=-1) # (B, 1)
outputs.append(ihm_y)
if self.target_repl:
ihm_seq = np.expand_dims(ihm_y, axis=-1).repeat(
T, axis=1) # (B, T, 1)
outputs.append(ihm_seq)
# los
los_M = self.data['los_M'][i:i + B]
los_M = common_utils.pad_zeros(los_M,
min_length=self.ihm_pos + 1)
los_y = self.data['los_y'][i:i + B]
los_y_true = common_utils.pad_zeros(los_y,
min_length=self.ihm_pos +
1)
if self.partition == 'log':
los_y = [
np.array([metrics.get_bin_log(x, 10) for x in z])
for z in los_y
]
if self.partition == 'custom':
los_y = [
np.array([metrics.get_bin_custom(x, 10) for x in z])
for z in los_y
]
los_y = common_utils.pad_zeros(los_y,
min_length=self.ihm_pos + 1)
los_y = np.expand_dims(los_y, axis=-1) # (B, T, 1)
outputs.append(los_y)
inputs = [X, ihm_M, los_M]
if self.return_y_true:
batch_data = (inputs, outputs, los_y_true)
else:
batch_data = (inputs, outputs)
if not self.return_names:
yield batch_data
else:
yield {
'data': batch_data,
'names': self.data['names'][i:i + B],
'los_ts': self.data['los_ts'][i:i + B]
}
def _generator(self):
B = self.batch_size
while True:
# convert to right format for sort_and_shuffle
kv_pairs = list(self.data.items())
data_index = [pair[0] for pair in kv_pairs].index('X')
if data_index > 0:
kv_pairs[0], kv_pairs[data_index] = kv_pairs[
data_index], kv_pairs[0]
mas = [kv[1] for kv in kv_pairs]
if self.shuffle:
N = len(self.data['X'])
order = list(range(N))
random.shuffle(order)
tmp = [None] * len(mas)
for mas_idx in range(len(mas)):
tmp[mas_idx] = [None] * len(mas[mas_idx])
for i in range(N):
tmp[mas_idx][i] = mas[mas_idx][order[i]]
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = tmp[i]
else:
# sort entirely
mas = common_utils.sort_and_shuffle(mas, B)
for i in range(len(kv_pairs)):
self.data[kv_pairs[i][0]] = mas[i]
for i in range(0, len(self.data['X']), B):
outputs = []
# X
X = self.data['X'][i:i + B]
T = self.data['T'][i:i + B]
X = common_utils.pad_zeros(X, min_length=self.ihm_pos + 1)
T = common_utils.pad_zeros(T, min_length=self.ihm_pos + 1)
t = X.shape[1]
# ihm
ihm_M = np.array(self.data['ihm_M'][i:i + B])
ihm_M = np.expand_dims(ihm_M, axis=-1) # (B, 1)
ihm_y = np.array(self.data['ihm_y'][i:i + B])
ihm_y = np.expand_dims(ihm_y, axis=-1) # (B, 1)
outputs.append(ihm_y)
if self.target_repl:
ihm_seq = np.expand_dims(ihm_y, axis=-1).repeat(
t, axis=1) # (B, t, 1)
outputs.append(ihm_seq)
# decomp
decomp_y = self.data['decomp_y'][i:i + B]
decomp_y = np.expand_dims(decomp_y, axis=-1) # (B, 1)
outputs.append(decomp_y)
# los
los_y = self.data['los_y'][i:i + B]
los_y = np.expand_dims(los_y, axis=-1) # (B, 1)
outputs.append(los_y)
# pheno
pheno_y = np.array(self.data['pheno_y'][i:i + B])
outputs.append(pheno_y)
if self.target_repl:
pheno_seq = np.expand_dims(pheno_y, axis=1).repeat(
t, axis=1) # (B, t, 25)
outputs.append(pheno_seq)
inputs = [X, T, ihm_M]
batch_data = (inputs, outputs)
if not self.return_names:
yield batch_data
else:
yield {
'data': batch_data,
'names': self.data['names'][i:i + B],
'decomp_ts': self.data['decomp_ts'][i:i + B],
'los_ts': self.data['los_ts'][i:i + B],
'pheno_ts': self.data['pheno_ts'][i:i + B]
}
