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
(data, ts, labels,
header) = read_chunk(self.reader, current_size)
data = preprocess_chunk(data, ts, self.discretizer,
self.normalizer)
data = (data, labels)
data = common_utils.sort_and_shuffle(data, B)
for i in range(0, current_size, B):
X = nn_utils.pad_zeros(data[0][i:i + B])
y = data[1][i:i + B]
y_true = np.array(y)
if self.partition == 'log':
y = [metrics.get_bin_log(x, 10) for x in y]
if self.partition == 'custom':
y = [metrics.get_bin_custom(x, 10) for x in y]
y = np.array(y)
if self.return_y_true:
yield (X, y, y_true)
else:
yield (X, y)
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, Ts = preprocess_chunk_time(Xs,
ts,
self.discretizer,
self.normalizer,
max_seq_len=1200,
mask_value=0.)
(Xs, Ts, ys, ts, names) = common_utils.sort_and_shuffle(
[Xs, Ts, ys, ts, names], B)
for i in range(0, current_size, B):
X = common_utils.pad_zeros(Xs[i:i + B])
T = common_utils.pad_zeros(Ts[i:i + B])
y = ys[i:i + B]
y_true = np.array(y)
batch_names = names[i:i + B]
batch_ts = ts[i:i + B]
if self.partition == 'log':
y = [metrics.get_bin_log(x, 10) for x in y]
if self.partition == 'custom':
y = [metrics.get_bin_custom(x, 10) for x in y]
y = np.array(y)
if self.use_time:
if self.return_y_true:
batch_data = ([X, T], y, y_true)
else:
batch_data = ([X, T], y)
else:
if self.return_y_true:
batch_data = (X, y, y_true)
else:
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 = 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):
print(f"examples: {self.n_examples} steps: {self.steps}")
B = self.batch_size
while True:
if self.shuffle:
self.reader.random_shuffle()
remaining = int(self.n_examples * 1.15)
while remaining > 0:
current_size = min(self.chunk_size, remaining)
remaining -= current_size
print(f"Reading chunk size: {current_size} with {remaining} remaining")
ret = common_utils.read_chunk(self.reader, current_size)
Xs = ret["X"]
ts = ret["t"]
ys = ret["y"]
names = ret["name"]
print(f"len(Xs): {len(Xs)}")
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 = common_utils.pad_zeros(Xs[i:i + B])
y = ys[i:i+B]
y_true = np.array(y)
batch_names = names[i:i+B]
batch_ts = ts[i:i+B]
if self.partition == 'log':
y = [metrics.get_bin_log(x, 10) for x in y]
if self.partition == 'custom':
y = [metrics.get_bin_custom(x, 10) for x in y]
y = np.array(y)
#aflanders: debug-Convert to tensors
# X = tf.convert_to_tensor(X)
# y = tf.convert_to_tensor(y)
# y_true = tf.convert_to_tensor(y_true)
#aflanders: debug-Convert to tensors
if self.return_y_true:
batch_data = (X, y, y_true)
else:
batch_data = (X, y)
if not self.return_names:
yield batch_data
else:
yield {"data": batch_data, "names": batch_names, "ts": batch_ts}
def process_input(self, data_raw):
Xs = nn_utils.pad_zeros(data_raw[0]).astype(np.float32)
lens = map(len, data_raw[0])
ys = np.array(data_raw[1]).astype(np.float32)
bin_ids = [metrics.get_bin_custom(x, self.nbins) for x in ys]
for x in bin_ids:
assert x >= 0 and x < self.nbins
return (Xs, lens, np.array(bin_ids, dtype=np.int32), ys)
def _generator(self):
B = self.batch_size
while True:
if self.shuffle:
N = len(self.data[1])
order = range(N)
random.shuffle(order)
tmp = [[[None] * N, [None] * N], [None] * N]
for i in range(N):
tmp[0][0][i] = self.data[0][0][order[i]]
tmp[0][1][i] = self.data[0][1][order[i]]
tmp[1][i] = self.data[1][order[i]]
self.data = tmp
else:
# sort entirely
Xs = self.data[0][0]
masks = self.data[0][1]
ys = self.data[1]
(Xs, masks,
ys) = common_utils.sort_and_shuffle([Xs, masks, ys], 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]
y_true = [np.array(x) for x in y]
y_true = nn_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 = 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)
if self.return_y_true:
yield ([X, mask], y, y_true)
else:
yield ([X, mask], y)
def read_and_extract_features(reader, count):
read_chunk_size = 1000
#assert (count % read_chunk_size == 0)
Xs = []
ys = []
for i in range(count // read_chunk_size):
(chunk, ts, y, header) = utils.read_chunk(reader, read_chunk_size)
X = common_utils.extract_features_from_rawdata(chunk, header,
args.period,
args.features)
Xs.append(X)
ys += y
Xs = np.concatenate(Xs, axis=0)
bins = np.array([one_hot(metrics.get_bin_custom(x, nbins)) for x in ys])
return (Xs, bins, ys)
def read_and_extract_features(reader, count, period, features):
read_chunk_size = 1000
Xs = []
ys = []
names = []
ts = []
for i in range(0, count, read_chunk_size):
j = min(count, i + read_chunk_size)
ret = common_utils.read_chunk(reader, j - i)
X = common_utils.extract_features_from_rawdata(ret['X'], ret['header'], period, features)
Xs.append(X)
ys += ret['y']
names += ret['name']
ts += ret['t']
Xs = np.concatenate(Xs, axis=0)
bins = np.array([one_hot(metrics.get_bin_custom(x, n_bins)) for x in ys])
return (Xs, bins, ys, names, ts)
def read_and_extract_features(reader, count, period, features):
read_chunk_size = 1000
Xs = []
ys = []
names = []
ts = []
for i in range(0, count, read_chunk_size):
j = min(count, i + read_chunk_size)
ret = common_utils.read_chunk(reader, j - i)
X = common_utils.extract_features_from_rawdata(ret['X'], ret['header'], period, features)
Xs.append(X)
ys += ret['y']
names += ret['name']
ts += ret['t']
Xs = np.concatenate(Xs, axis=0)
bins = np.array([one_hot(metrics.get_bin_custom(x, n_bins)) for x in ys])
return (Xs, bins, ys, names, ts)
def getitem(self, index, return_y_true=False):
print(f"Start: {index} from reader:{self.reader.listfile}")
B = self.batch_size
ret = common_utils.read_chunk_index(self.reader, index*B, B)
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)
i=0
X = common_utils.pad_zeros(Xs[i:i + B])
y = ys[i:i+B]
y_true = np.array(y)
batch_names = names[i:i+B]
batch_ts = ts[i:i+B]
if self.partition == 'log':
y = [metrics.get_bin_log(x, 10) for x in y]
if self.partition == 'custom':
y = [metrics.get_bin_custom(x, 10) for x in y]
y = np.array(y)
#aflanders: debug-Convert to tensors
# X = tf.convert_to_tensor(X)
# y = tf.convert_to_tensor(y)
# y_true = tf.convert_to_tensor(y_true)
#aflanders: debug-Convert to tensors
if return_y_true:
batch_data = (X, y, y_true)
else:
batch_data = (X, y)
print(f"End: {index} from reader:{self.reader.listfile}")
if not self.return_names:
return batch_data
else:
return {"data": batch_data, "names": batch_names, "ts": batch_ts}
def _generator(self):
B = self.batch_size
while True:
# convert to right format for sort_and_shuffle
Xs = self.data[0][0]
masks = self.data[0][1]
ys = self.data[1]
(Xs, masks, ys) = common_utils.sort_and_shuffle([Xs, masks, ys], 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]
y_true = [np.array(x) for x in y]
y_true = nn_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 = 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)
if self.return_y_true:
yield ([X, mask], y, y_true)
else:
yield ([X, mask], y)
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:
# convert to right format for sort_and_shuffle
kvpairs = self.data.items()
mas = [kv[1] for kv in kvpairs]
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(kvpairs)):
self.data[kvpairs[i][0]] = tmp[i]
else:
# sort entirely
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:
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],
"ts": self.data["ts"][i:i + B],
"decomp_ts": self.data["decomp_ts"][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 = 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]
}
