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Salesforce
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cloudops_tsf

Tasks:

Time Series Forecasting

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cloudops_tsf / cloudops_tsf.py

gorold

remove unnecessary loader kwargs

ef4ad84 about 3 years ago

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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
from dataclasses import dataclass, field
from typing import Iterator, Optional

import datasets
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from gluonts.dataset.field_names import FieldName

_CITATION = """\
@article{woo2023pushing,
  title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
  author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
  journal={arXiv preprint arXiv:2310.05063},
  year={2023}
}
"""


_CONFIGS = {
    "azure_vm_traces_2017": {
        "optional_fields": (
            FieldName.FEAT_STATIC_CAT,
            FieldName.FEAT_STATIC_REAL,
            FieldName.PAST_FEAT_DYNAMIC_REAL,
        ),
        "prediction_length": 48,
        "freq": "5T",
        "stride": 48,
        "univariate": True,
        "multivariate": False,
        "rolling_evaluations": 12,
        "test_split_date": pd.Period(
            year=2016, month=12, day=13, hour=15, minute=55, freq="5T"
        ),
        "_feat_static_cat_cardinalities": {
            "train_test": (
                ("vm_id", 17568),
                ("subscription_id", 2713),
                ("deployment_id", 3255),
                ("vm_category", 3),
            ),
            "pretrain": (
                ("vm_id", 177040),
                ("subscription_id", 5514),
                ("deployment_id", 15208),
                ("vm_category", 3),
            ),
        },
        "target_dim": 1,
        "feat_static_real_dim": 3,
        "past_feat_dynamic_real_dim": 2,
    },
    "borg_cluster_data_2011": {
        "optional_fields": (
            FieldName.FEAT_STATIC_CAT,
            FieldName.PAST_FEAT_DYNAMIC_REAL,
        ),
        "prediction_length": 48,
        "freq": "5T",
        "stride": 48,
        "univariate": False,
        "multivariate": True,
        "rolling_evaluations": 12,
        "test_split_date": pd.Period(
            year=2011, month=5, day=28, hour=18, minute=55, freq="5T"
        ),
        "_feat_static_cat_cardinalities": {
            "train_test": (
                ("job_id", 850),
                ("task_id", 11117),
                ("user", 282),
                ("scheduling_class", 4),
                ("logical_job_name", 718),
            ),
            "pretrain": (
                ("job_id", 6072),
                ("task_id", 154503),
                ("user", 518),
                ("scheduling_class", 4),
                ("logical_job_name", 3899),
            ),
        },
        "target_dim": 2,
        "past_feat_dynamic_real_dim": 5,
    },
    "alibaba_cluster_trace_2018": {
        "optional_fields": (
            FieldName.FEAT_STATIC_CAT,
            FieldName.PAST_FEAT_DYNAMIC_REAL,
        ),
        "prediction_length": 48,
        "freq": "5T",
        "stride": 48,
        "univariate": False,
        "multivariate": True,
        "rolling_evaluations": 12,
        "test_split_date": pd.Period(
            year=2018, month=1, day=8, hour=11, minute=55, freq="5T"
        ),
        "_feat_static_cat_cardinalities": {
            "train_test": (
                ("container_id", 6048),
                ("app_du", 1292),
            ),
            "pretrain": (
                ("container_id", 64457),
                ("app_du", 9484),
            ),
        },
        "target_dim": 2,
        "past_feat_dynamic_real_dim": 6,
    },
}

PRETRAIN = datasets.splits.NamedSplit("pretrain")
TRAIN_TEST = datasets.splits.NamedSplit("train_test")

Cardinalities = tuple[tuple[str, int], ...]


@dataclass
class CloudOpsTSFConfig(datasets.BuilderConfig):
    """BuilderConfig for CloudOpsTSF."""

    # builder kwargs
    prediction_length: int = field(default=None)
    freq: str = field(default=None)
    stride: int = field(default=None)
    univariate: bool = field(default=None)
    multivariate: bool = field(default=None)
    optional_fields: tuple[str, ...] = field(default=None)
    rolling_evaluations: int = field(default=None)
    test_split_date: pd.Period = field(default=None)
    _feat_static_cat_cardinalities: dict[str, Cardinalities] = field(
        default_factory=dict
    )
    target_dim: int = field(default=1)
    feat_static_real_dim: int = field(default=0)
    past_feat_dynamic_real_dim: int = field(default=0)

    def feat_static_cat_cardinalities(
        self, split: str = "train_test"
    ) -> Optional[list[int]]:
        if FieldName.FEAT_STATIC_CAT not in self.optional_fields:
            return None

        return [c[1] for c in self._feat_static_cat_cardinalities[split]]


class CloudOpsTSF(datasets.ArrowBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = []
    for dataset, config in _CONFIGS.items():
        BUILDER_CONFIGS.append(
            CloudOpsTSFConfig(name=dataset, version=VERSION, description="", **config)
        )

    def _info(self) -> datasets.DatasetInfo:
        def sequence_feature(dtype: str, univar: bool) -> datasets.Sequence:
            if univar:
                return datasets.Sequence(datasets.Value(dtype))
            return datasets.Sequence(datasets.Sequence(datasets.Value(dtype)))

        features = {
            FieldName.START: datasets.Value("timestamp[s]"),
            FieldName.TARGET: sequence_feature("float32", self.config.univariate),
            FieldName.ITEM_ID: datasets.Value("string"),
        }

        CAT_FEATS = (
            FieldName.FEAT_STATIC_CAT,
            FieldName.FEAT_DYNAMIC_CAT,
            FieldName.PAST_FEAT_DYNAMIC,
        )
        REAL_FEATS = (
            FieldName.FEAT_STATIC_REAL,
            FieldName.FEAT_DYNAMIC_REAL,
            FieldName.PAST_FEAT_DYNAMIC_REAL,
        )
        STATIC_FEATS = (FieldName.FEAT_STATIC_CAT, FieldName.FEAT_STATIC_REAL)
        DYNAMIC_FEATS = (
            FieldName.FEAT_DYNAMIC_CAT,
            FieldName.FEAT_DYNAMIC_REAL,
            FieldName.PAST_FEAT_DYNAMIC,
            FieldName.PAST_FEAT_DYNAMIC_REAL,
        )

        for ts_field in self.config.optional_fields:
            # Determine field dtype
            if ts_field in CAT_FEATS:
                dtype = "int32"
            elif ts_field in REAL_FEATS:
                dtype = "float32"
            else:
                raise ValueError(f"Invalid field: {ts_field}")

            # Determine field shape
            if ts_field in STATIC_FEATS:
                univar = True
            elif ts_field in DYNAMIC_FEATS:
                univar = False
            else:
                raise ValueError(f"Invalid field: {ts_field}")

            features[ts_field] = sequence_feature(dtype, univar)

        features = datasets.Features(features)

        return datasets.DatasetInfo(
            features=features,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager) -> list[datasets.SplitGenerator]:
        downloaded_files = dl_manager.download_and_extract(
            [
                f"{self.config.name}/train_test.zip",
                f"{self.config.name}/pretrain.zip",
            ]
        )

        generators = [
            datasets.SplitGenerator(
                name=TRAIN_TEST,
                gen_kwargs={"filepath": downloaded_files[0]},
            ),
            datasets.SplitGenerator(
                name=PRETRAIN,
                gen_kwargs={"filepath": downloaded_files[1]},
            ),
        ]

        return generators

    def _generate_tables(self, filepath: str) -> Iterator[pa.Table]:
        table = pq.read_table(filepath)

        for batch in table.to_batches():
            columns = batch.columns
            schema = batch.schema

            yield batch[FieldName.ITEM_ID].to_pylist(), pa.Table.from_arrays(
                columns, schema=schema
            )

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	from dataclasses import dataclass, field
	from typing import Iterator, Optional

	import datasets
	import pandas as pd
	import pyarrow as pa
	import pyarrow.parquet as pq
	from gluonts.dataset.field_names import FieldName

	_CITATION = """\
	@article{woo2023pushing,
	title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
	author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
	journal={arXiv preprint arXiv:2310.05063},
	year={2023}
	}
	"""


	_CONFIGS = {
	"azure_vm_traces_2017": {
	"optional_fields": (
	FieldName.FEAT_STATIC_CAT,
	FieldName.FEAT_STATIC_REAL,
	FieldName.PAST_FEAT_DYNAMIC_REAL,
	),
	"prediction_length": 48,
	"freq": "5T",
	"stride": 48,
	"univariate": True,
	"multivariate": False,
	"rolling_evaluations": 12,
	"test_split_date": pd.Period(
	year=2016, month=12, day=13, hour=15, minute=55, freq="5T"
	),
	"_feat_static_cat_cardinalities": {
	"train_test": (
	("vm_id", 17568),
	("subscription_id", 2713),
	("deployment_id", 3255),
	("vm_category", 3),
	),
	"pretrain": (
	("vm_id", 177040),
	("subscription_id", 5514),
	("deployment_id", 15208),
	("vm_category", 3),
	),
	},
	"target_dim": 1,
	"feat_static_real_dim": 3,
	"past_feat_dynamic_real_dim": 2,
	},
	"borg_cluster_data_2011": {
	"optional_fields": (
	FieldName.FEAT_STATIC_CAT,
	FieldName.PAST_FEAT_DYNAMIC_REAL,
	),
	"prediction_length": 48,
	"freq": "5T",
	"stride": 48,
	"univariate": False,
	"multivariate": True,
	"rolling_evaluations": 12,
	"test_split_date": pd.Period(
	year=2011, month=5, day=28, hour=18, minute=55, freq="5T"
	),
	"_feat_static_cat_cardinalities": {
	"train_test": (
	("job_id", 850),
	("task_id", 11117),
	("user", 282),
	("scheduling_class", 4),
	("logical_job_name", 718),
	),
	"pretrain": (
	("job_id", 6072),
	("task_id", 154503),
	("user", 518),
	("scheduling_class", 4),
	("logical_job_name", 3899),
	),
	},
	"target_dim": 2,
	"past_feat_dynamic_real_dim": 5,
	},
	"alibaba_cluster_trace_2018": {
	"optional_fields": (
	FieldName.FEAT_STATIC_CAT,
	FieldName.PAST_FEAT_DYNAMIC_REAL,
	),
	"prediction_length": 48,
	"freq": "5T",
	"stride": 48,
	"univariate": False,
	"multivariate": True,
	"rolling_evaluations": 12,
	"test_split_date": pd.Period(
	year=2018, month=1, day=8, hour=11, minute=55, freq="5T"
	),
	"_feat_static_cat_cardinalities": {
	"train_test": (
	("container_id", 6048),
	("app_du", 1292),
	),
	"pretrain": (
	("container_id", 64457),
	("app_du", 9484),
	),
	},
	"target_dim": 2,
	"past_feat_dynamic_real_dim": 6,
	},
	}

	PRETRAIN = datasets.splits.NamedSplit("pretrain")
	TRAIN_TEST = datasets.splits.NamedSplit("train_test")

	Cardinalities = tuple[tuple[str, int], ...]


	@dataclass
	class CloudOpsTSFConfig(datasets.BuilderConfig):
	"""BuilderConfig for CloudOpsTSF."""

	# builder kwargs
	prediction_length: int = field(default=None)
	freq: str = field(default=None)
	stride: int = field(default=None)
	univariate: bool = field(default=None)
	multivariate: bool = field(default=None)
	optional_fields: tuple[str, ...] = field(default=None)
	rolling_evaluations: int = field(default=None)
	test_split_date: pd.Period = field(default=None)
	_feat_static_cat_cardinalities: dict[str, Cardinalities] = field(
	default_factory=dict
	)
	target_dim: int = field(default=1)
	feat_static_real_dim: int = field(default=0)
	past_feat_dynamic_real_dim: int = field(default=0)

	def feat_static_cat_cardinalities(
	self, split: str = "train_test"
	) -> Optional[list[int]]:
	if FieldName.FEAT_STATIC_CAT not in self.optional_fields:
	return None

	return [c[1] for c in self._feat_static_cat_cardinalities[split]]


	class CloudOpsTSF(datasets.ArrowBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = []
	for dataset, config in _CONFIGS.items():
	BUILDER_CONFIGS.append(
	CloudOpsTSFConfig(name=dataset, version=VERSION, description="", **config)
	)

	def _info(self) -> datasets.DatasetInfo:
	def sequence_feature(dtype: str, univar: bool) -> datasets.Sequence:
	if univar:
	return datasets.Sequence(datasets.Value(dtype))
	return datasets.Sequence(datasets.Sequence(datasets.Value(dtype)))

	features = {
	FieldName.START: datasets.Value("timestamp[s]"),
	FieldName.TARGET: sequence_feature("float32", self.config.univariate),
	FieldName.ITEM_ID: datasets.Value("string"),
	}

	CAT_FEATS = (
	FieldName.FEAT_STATIC_CAT,
	FieldName.FEAT_DYNAMIC_CAT,
	FieldName.PAST_FEAT_DYNAMIC,
	)
	REAL_FEATS = (
	FieldName.FEAT_STATIC_REAL,
	FieldName.FEAT_DYNAMIC_REAL,
	FieldName.PAST_FEAT_DYNAMIC_REAL,
	)
	STATIC_FEATS = (FieldName.FEAT_STATIC_CAT, FieldName.FEAT_STATIC_REAL)
	DYNAMIC_FEATS = (
	FieldName.FEAT_DYNAMIC_CAT,
	FieldName.FEAT_DYNAMIC_REAL,
	FieldName.PAST_FEAT_DYNAMIC,
	FieldName.PAST_FEAT_DYNAMIC_REAL,
	)

	for ts_field in self.config.optional_fields:
	# Determine field dtype
	if ts_field in CAT_FEATS:
	dtype = "int32"
	elif ts_field in REAL_FEATS:
	dtype = "float32"
	else:
	raise ValueError(f"Invalid field: {ts_field}")

	# Determine field shape
	if ts_field in STATIC_FEATS:
	univar = True
	elif ts_field in DYNAMIC_FEATS:
	univar = False
	else:
	raise ValueError(f"Invalid field: {ts_field}")

	features[ts_field] = sequence_feature(dtype, univar)

	features = datasets.Features(features)

	return datasets.DatasetInfo(
	features=features,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> list[datasets.SplitGenerator]:
	downloaded_files = dl_manager.download_and_extract(
	[
	f"{self.config.name}/train_test.zip",
	f"{self.config.name}/pretrain.zip",
	]
	)

	generators = [
	datasets.SplitGenerator(
	name=TRAIN_TEST,
	gen_kwargs={"filepath": downloaded_files[0]},
	),
	datasets.SplitGenerator(
	name=PRETRAIN,
	gen_kwargs={"filepath": downloaded_files[1]},
	),
	]

	return generators

	def _generate_tables(self, filepath: str) -> Iterator[pa.Table]:
	table = pq.read_table(filepath)

	for batch in table.to_batches():
	columns = batch.columns
	schema = batch.schema

	yield batch[FieldName.ITEM_ID].to_pylist(), pa.Table.from_arrays(
	columns, schema=schema
	)