File size: 6,141 Bytes
c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 0a58567 c4b87d2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
# Copyright (c) 2023, Salesforce, Inc.
# SPDX-License-Identifier: Apache-2
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from collections.abc import Iterable, Iterator
from enum import Enum
from functools import cached_property
from pathlib import Path
import datasets
import pyarrow.compute as pc
from gluonts.dataset import DataEntry
from gluonts.dataset.common import ProcessDataEntry
from gluonts.dataset.split import TestData, TrainingDataset, split
from gluonts.itertools import Map
from gluonts.time_feature import norm_freq_str
from gluonts.transform import Transformation
from pandas.tseries.frequencies import to_offset
from toolz import compose
TEST_SPLIT = 0.1
MAX_WINDOW = 20
M4_PRED_LENGTH_MAP = {
"A": 6,
"Q": 8,
"M": 18,
"W": 13,
"D": 14,
"H": 48,
"h": 48,
"Y": 6,
}
PRED_LENGTH_MAP = {
"M": 12,
"W": 8,
"D": 30,
"H": 48,
"h": 48,
"T": 48,
"S": 60,
"s": 60,
"min": 48,
}
TFB_PRED_LENGTH_MAP = {
"A": 6,
"Y": 6,
"H": 48,
"h": 48,
"Q": 8,
"D": 14,
"M": 18,
"W": 13,
"U": 8,
"T": 8,
"min": 8,
"us": 8,
}
class Term(Enum):
SHORT = "short"
MEDIUM = "medium"
LONG = "long"
@property
def multiplier(self) -> int:
if self == Term.SHORT:
return 1
elif self == Term.MEDIUM:
return 10
elif self == Term.LONG:
return 15
def itemize_start(data_entry: DataEntry) -> DataEntry:
data_entry["start"] = data_entry["start"].item()
return data_entry
class MultivariateToUnivariate(Transformation):
def __init__(self, field):
self.field = field
def __call__(self, data_it: Iterable[DataEntry], is_train: bool = False) -> Iterator:
for data_entry in data_it:
item_id = data_entry["item_id"]
val_ls = list(data_entry[self.field])
for id, val in enumerate(val_ls):
univariate_entry = data_entry.copy()
univariate_entry[self.field] = val
univariate_entry["item_id"] = item_id + "_dim" + str(id)
yield univariate_entry
class Dataset:
def __init__(
self,
name: str,
term: Term | str = Term.SHORT,
to_univariate: bool = False,
storage_path: str = None,
max_windows: int | None = None,
):
storage_path = Path(storage_path)
self.hf_dataset = datasets.load_from_disk(str(storage_path / name)).with_format("numpy")
process = ProcessDataEntry(
self.freq,
one_dim_target=self.target_dim == 1,
)
self.gluonts_dataset = Map(compose(process, itemize_start), self.hf_dataset)
if to_univariate:
self.gluonts_dataset = MultivariateToUnivariate("target").apply(self.gluonts_dataset)
self.term = Term(term)
self.name = name
self.max_windows = max_windows if max_windows is not None else MAX_WINDOW
@cached_property
def prediction_length(self) -> int:
freq = norm_freq_str(to_offset(self.freq).name)
if freq.endswith("E"):
freq = freq[:-1]
pred_len = M4_PRED_LENGTH_MAP[freq] if "m4" in self.name else PRED_LENGTH_MAP[freq]
return self.term.multiplier * pred_len
@cached_property
def freq(self) -> str:
return self.hf_dataset[0]["freq"]
@cached_property
def target_dim(self) -> int:
return target.shape[0] if len((target := self.hf_dataset[0]["target"]).shape) > 1 else 1
@cached_property
def past_feat_dynamic_real_dim(self) -> int:
if "past_feat_dynamic_real" not in self.hf_dataset[0]:
return 0
elif len((past_feat_dynamic_real := self.hf_dataset[0]["past_feat_dynamic_real"]).shape) > 1:
return past_feat_dynamic_real.shape[0]
else:
return 1
@cached_property
def windows(self) -> int:
if "m4" in self.name:
return 1
w = math.ceil(TEST_SPLIT * self._min_series_length / self.prediction_length)
return min(max(1, w), self.max_windows)
@cached_property
def _min_series_length(self) -> int:
if self.hf_dataset[0]["target"].ndim > 1:
lengths = pc.list_value_length(pc.list_flatten(pc.list_slice(self.hf_dataset.data.column("target"), 0, 1)))
else:
lengths = pc.list_value_length(self.hf_dataset.data.column("target"))
return min(lengths.to_numpy())
@cached_property
def sum_series_length(self) -> int:
if self.hf_dataset[0]["target"].ndim > 1:
lengths = pc.list_value_length(pc.list_flatten(self.hf_dataset.data.column("target")))
else:
lengths = pc.list_value_length(self.hf_dataset.data.column("target"))
return sum(lengths.to_numpy())
@property
def training_dataset(self) -> TrainingDataset:
training_dataset, _ = split(self.gluonts_dataset, offset=-self.prediction_length * (self.windows + 1))
return training_dataset
@property
def validation_dataset(self) -> TrainingDataset:
validation_dataset, _ = split(self.gluonts_dataset, offset=-self.prediction_length * self.windows)
return validation_dataset
@property
def test_data(self) -> TestData:
_, test_template = split(self.gluonts_dataset, offset=-self.prediction_length * self.windows)
test_data = test_template.generate_instances(
prediction_length=self.prediction_length,
windows=self.windows,
distance=self.prediction_length,
)
return test_data
|