File size: 3,924 Bytes
919d971 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | 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
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)
data_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))
data_list.append(time_in_day)
if add_day_in_week:
day_in_week = np.zeros(shape=(num_samples, num_nodes, 7))
day_in_week[np.arange(num_samples), :, df.index.dayofweek] = 1
data_list.append(day_in_week)
data = np.concatenate(data_list, axis=-1)
# epoch_len = num_samples + min(x_offsets) - max(y_offsets)
x, y = [], []
# t is the index of the last observation.
min_t = abs(min(x_offsets))
max_t = abs(num_samples - abs(max(y_offsets))) # Exclusive
for t in range(min_t, max_t):
x_t = data[t + x_offsets, ...]
y_t = data[t + y_offsets, ...]
x.append(x_t)
y.append(y_t)
x = np.stack(x, axis=0)
y = np.stack(y, axis=0)
return x, y
def generate_train_val_test(args):
df = pd.read_hdf(args.traffic_df_filename)
# 0 is the latest observed sample.
x_offsets = np.sort(
# np.concatenate(([-week_size + 1, -day_size + 1], np.arange(-11, 1, 1)))
np.concatenate((np.arange(-11, 1, 1),))
)
# Predict the next one hour
y_offsets = np.sort(np.arange(1, 13, 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=False,
)
print("x shape: ", x.shape, ", y shape: ", y.shape)
# Write the data into npz file.
# num_test = 6831, using the last 6831 examples as testing.
# for the rest: 7/8 is used for training, and 1/8 is used for validation.
num_samples = x.shape[0]
num_test = round(num_samples * 0.2)
num_train = round(num_samples * 0.7)
num_val = num_samples - num_test - num_train
# train
x_train, y_train = x[:num_train], y[:num_train]
# val
x_val, y_val = (
x[num_train : num_train + num_val],
y[num_train : num_train + num_val],
)
# test
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, "%s.npz" % cat),
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 main(args):
print("Generating training data")
generate_train_val_test(args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--output_dir", type=str, default="data/", help="Output directory."
)
parser.add_argument(
"--traffic_df_filename",
type=str,
default="data/metr-la.h5",
help="Raw traffic readings.",
)
args = parser.parse_args()
main(args)
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