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generate_data.py
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generate_data.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
from prepareData import get_sample_indices
def generate_graph_seq2seq_io_data(
df, x_offsets, y_offsets, add_time_in_day=True, add_day_in_week=False, scaler=None
):
"""
Generate samples from
:param df:
:param x_offsets:
:param y_offsets:
:param add_time_in_day:
:param add_day_in_week:
:param scaler:
:return:
# x: (epoch_size, input_length, num_nodes, input_dim)
# y: (epoch_size, output_length, num_nodes, output_dim)
"""
num_samples, num_nodes = df.shape
data = np.expand_dims(df.values, axis=-1)
feature_list = [data]
if add_time_in_day:
time_ind = (df.index.values - df.index.values.astype("datetime64[D]")) / np.timedelta64(1, "D")
time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))
feature_list.append(time_in_day)
if add_day_in_week:
dow = df.index.dayofweek
dow_tiled = np.tile(dow, [1, num_nodes, 1]).transpose((2, 1, 0))
feature_list.append(dow_tiled)
data = np.concatenate(feature_list, axis=-1)
x, y = [], []
min_t = abs(min(x_offsets))
max_t = abs(num_samples - abs(max(y_offsets))) # Exclusive
for t in range(min_t, max_t): # t is the index of the last observation.
x.append(data[t + x_offsets, ...])
y.append(data[t + y_offsets, ...])
x = np.stack(x, axis=0)
y = np.stack(y, axis=0)
return x, y
def generate_train_val_test(args):
seq_len = str(args.seq_length_x)
seq_length_x, seq_length_y = args.seq_length_x, args.seq_length_y
df = pd.read_hdf(args.traffic_df_filename)
# 0 is the latest observed sample.
x_offsets = np.sort(np.concatenate((np.arange(-(seq_length_x - 1), 1, 1),)))
# Predict the next one hour
y_offsets = np.sort(np.arange(args.y_start, (seq_length_y + 1), 1))
# x: (num_samples, input_length, num_nodes, input_dim)
# y: (num_samples, output_length, num_nodes, output_dim)
x, y = generate_graph_seq2seq_io_data(
df,
x_offsets=x_offsets,
y_offsets=y_offsets,
add_time_in_day=True,
add_day_in_week=True,
)
print("x shape: ", x.shape, ", y shape: ", y.shape)
# Write the data into npz file.
num_samples = x.shape[0]
num_train = round(num_samples * 0.7)
num_test = round(num_samples * 0.2)
num_val = num_samples - num_test - num_train
x_train, y_train = x[:num_train], y[:num_train]
# x_test, y_test = (
# x[num_train: num_train + num_test],
# y[num_train: num_train + num_test],
# )
# x_val, y_val = x[-num_val:], y[-num_val:]
x_val, y_val = (
x[num_train: num_train + num_val],
y[num_train: num_train + num_val],
)
x_test, y_test = x[-num_test:], y[-num_test:]
for cat in ["train", "val", "test"]:
_x, _y = locals()["x_" + cat], locals()["y_" + cat]
print(cat, "x: ", _x.shape, "y:", _y.shape)
np.savez_compressed(
os.path.join(args.output_dir, f"{cat}.npz"),
x=_x,
y=_y,
x_offsets=x_offsets.reshape(list(x_offsets.shape) + [1]),
y_offsets=y_offsets.reshape(list(y_offsets.shape) + [1]),
)
def read_and_generate_dataset(args, add_time_in_day=False, add_day_in_week=False):
df = pd.read_hdf(args.traffic_df_filename)
num_of_weeks = args.num_of_weeks
num_of_days = args.num_of_days
num_of_hours = args.num_of_hours
num_for_predict = args.seq_length_x
num_predict = args.seq_length_y
points_per_hour = 12
num_samples, num_nodes = df.shape
data = np.expand_dims(df.values, axis=-1)
feature_list = [data]
if add_time_in_day:
time_ind = (df.index.values - df.index.values.astype("datetime64[D]")) / np.timedelta64(1, "D")
time_in_day = np.tile(time_ind, [1, num_nodes, 1]).transpose((2, 1, 0))
feature_list.append(time_in_day)
if add_day_in_week:
dow = df.index.dayofweek
dow_tiled = np.tile(dow, [1, num_nodes, 1]).transpose((2, 1, 0))
feature_list.append(dow_tiled)
data = np.concatenate(feature_list, axis=-1)
print(num_samples, num_nodes, data.shape)
all_samples = []
for idx in range(data.shape[0]):
sample = get_sample_indices(data, num_of_weeks, num_of_days,
num_of_hours, idx, num_for_predict,
num_predict, points_per_hour)
if ((sample[0] is None) and (sample[1] is None) and (sample[2] is None)):
continue
week_sample, day_sample, hour_sample, target = sample
sample = [] # [(week_sample),(day_sample),(hour_sample),target]
if num_of_weeks > 0:
week_sample = np.expand_dims(week_sample, axis=0).transpose((0, 2, 3, 1)) # (1,N,F,T)
sample.append(week_sample)
if num_of_days > 0:
day_sample = np.expand_dims(day_sample, axis=0).transpose((0, 2, 3, 1)) # (1,N,F,T)
sample.append(day_sample)
if num_of_hours > 0:
hour_sample = np.expand_dims(hour_sample, axis=0).transpose((0, 2, 3, 1)) # (1,N,F,T)
sample.append(hour_sample)
target = np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 0, :] # (1,N,T)
sample.append(target)
all_samples.append(
sample) # sampe：[(week_sample),(day_sample),(hour_sample),target] = [(1,N,F,Tw),(1,N,F,Td),(1,N,F,Th),(1,N,Tpre)]
# print(all_samples[0])
print(all_samples[0][0].shape, all_samples[0][1].shape)
split_line1 = int(len(all_samples) * 0.7)
split_line2 = int(len(all_samples) * 0.8)
training_set = [np.concatenate(i, axis=0)
for i in zip(*all_samples[:split_line1])] # [(B,N,F,Tw),(B,N,F,Td),(B,N,F,Th),(B,N,Tpre)]
validation_set = [np.concatenate(i, axis=0)
for i in zip(*all_samples[split_line1: split_line2])]
testing_set = [np.concatenate(i, axis=0)
for i in zip(*all_samples[split_line2:])]
train_x = np.concatenate(training_set[:-1], axis=-1).transpose((0, 3, 1, 2)) # (B,N,F,T') -> (B,T,N,F)
val_x = np.concatenate(validation_set[:-1], axis=-1).transpose((0, 3, 1, 2))
test_x = np.concatenate(testing_set[:-1], axis=-1).transpose((0, 3, 1, 2))
train_target = training_set[-1].transpose((0, 2, 1)) # (B,N,T) -> (B,T,N)
val_target = validation_set[-1].transpose((0, 2, 1))
test_target = testing_set[-1].transpose((0, 2, 1))
for cat in ["train", "val", "test"]:
_x, _y = locals()[cat + "_x"], locals()[cat + "_target"]
print(cat, "x: ", _x.shape, "y:", _y.shape)
filename = os.path.join(args.output_dir, cat)
np.savez_compressed(
filename,
x=_x,
y=_y
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir", type=str, default="data/PEMS-BAY-6", help="Output directory.")
# parser.add_argument("--traffic_df_filename", type=str, default="data/metr-la.h5", help="Raw traffic readings.", )
parser.add_argument("--traffic_df_filename", type=str, default="data/pems-bay.h5", help="Raw traffic readings.",)
parser.add_argument("--seq_length_x", type=int, default=12, help="Sequence Length.", )
parser.add_argument("--seq_length_y", type=int, default=6, help="Sequence Length.", )
parser.add_argument("--y_start", type=int, default=1, help="Y pred start", )
parser.add_argument("--num_of_weeks", type=int, default=1)
parser.add_argument("--num_of_days", type=int, default=1)
parser.add_argument("--num_of_hours", type=int, default=1)
args = parser.parse_args()
if os.path.exists(args.output_dir):
reply = str(input(f'{args.output_dir} exists. Do you want to overwrite it? (y/n)')).lower().strip()
if reply[0] != 'y': exit
else:
os.makedirs(args.output_dir)
# generate_train_val_test(args)
read_and_generate_dataset(args, False, False)