2021-04-22 17:08:43 +02:00
|
|
|
#####################################################
|
|
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
|
|
|
|
#####################################################
|
|
|
|
import math
|
|
|
|
import abc
|
|
|
|
import numpy as np
|
|
|
|
from typing import Optional
|
|
|
|
import torch
|
|
|
|
import torch.utils.data as data
|
|
|
|
|
|
|
|
|
|
|
|
class UnifiedSplit:
|
|
|
|
"""A class to unify the split strategy."""
|
|
|
|
|
|
|
|
def __init__(self, total_num, mode):
|
|
|
|
# Training Set 60%
|
|
|
|
num_of_train = int(total_num * 0.6)
|
|
|
|
# Validation Set 20%
|
|
|
|
num_of_valid = int(total_num * 0.2)
|
|
|
|
# Test Set 20%
|
|
|
|
num_of_set = total_num - num_of_train - num_of_valid
|
|
|
|
all_indexes = list(range(total_num))
|
|
|
|
if mode is None:
|
|
|
|
self._indexes = all_indexes
|
|
|
|
elif mode.lower() in ("train", "training"):
|
|
|
|
self._indexes = all_indexes[:num_of_train]
|
|
|
|
elif mode.lower() in ("valid", "validation"):
|
|
|
|
self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
|
|
|
|
elif mode.lower() in ("test", "testing"):
|
|
|
|
self._indexes = all_indexes[num_of_train + num_of_valid :]
|
|
|
|
else:
|
|
|
|
raise ValueError("Unkonwn mode of {:}".format(mode))
|
|
|
|
self._mode = mode
|
|
|
|
|
|
|
|
@property
|
|
|
|
def mode(self):
|
|
|
|
return self._mode
|
|
|
|
|
|
|
|
|
2021-04-26 14:16:38 +02:00
|
|
|
class TimeStamp(UnifiedSplit, data.Dataset):
|
|
|
|
"""The timestamp dataset."""
|
2021-04-22 17:08:43 +02:00
|
|
|
|
|
|
|
def __init__(
|
|
|
|
self,
|
2021-04-26 14:16:38 +02:00
|
|
|
min_timestamp: float = 0.0,
|
|
|
|
max_timestamp: float = 1.0,
|
2021-04-22 17:08:43 +02:00
|
|
|
num: int = 100,
|
|
|
|
mode: Optional[str] = None,
|
|
|
|
):
|
2021-04-26 14:16:38 +02:00
|
|
|
self._min_timestamp = min_timestamp
|
|
|
|
self._max_timestamp = max_timestamp
|
|
|
|
self._interval = (max_timestamp - min_timestamp) / (float(num) - 1)
|
2021-04-22 17:08:43 +02:00
|
|
|
self._total_num = num
|
|
|
|
UnifiedSplit.__init__(self, self._total_num, mode)
|
|
|
|
|
2021-04-28 17:56:25 +02:00
|
|
|
@property
|
|
|
|
def min_timestamp(self):
|
|
|
|
return self._min_timestamp
|
|
|
|
|
|
|
|
@property
|
|
|
|
def max_timestamp(self):
|
|
|
|
return self._max_timestamp
|
|
|
|
|
2021-04-22 17:08:43 +02:00
|
|
|
def __iter__(self):
|
|
|
|
self._iter_num = 0
|
|
|
|
return self
|
|
|
|
|
|
|
|
def __next__(self):
|
|
|
|
if self._iter_num >= len(self):
|
|
|
|
raise StopIteration
|
|
|
|
self._iter_num += 1
|
|
|
|
return self.__getitem__(self._iter_num - 1)
|
|
|
|
|
|
|
|
def __getitem__(self, index):
|
|
|
|
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
|
|
|
|
index = self._indexes[index]
|
2021-04-26 14:16:38 +02:00
|
|
|
timestamp = self._min_timestamp + self._interval * index
|
|
|
|
return index, timestamp
|
2021-04-22 17:08:43 +02:00
|
|
|
|
|
|
|
def __len__(self):
|
|
|
|
return len(self._indexes)
|
|
|
|
|
|
|
|
def __repr__(self):
|
|
|
|
return "{name}({cur_num:}/{total} elements)".format(
|
|
|
|
name=self.__class__.__name__,
|
|
|
|
cur_num=len(self),
|
|
|
|
total=self._total_num,
|
|
|
|
)
|