diff --git a/CHANGELOG.md b/CHANGELOG.md index 9d8539df5..4cc952d71 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,20 +9,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Unreleased ### Added - + - - Add Sign metric ([#730](https://github.com/tinkoff-ai/etna/pull/730)) - - - - Add AutoARIMA model ([#679](https://github.com/tinkoff-ai/etna/pull/679)) -- -- -- -- -- +- +- +- +- +- - Add parameters `start`, `end` to some eda methods ([#665](https://github.com/tinkoff-ai/etna/pull/665)) -- -- +- Add BATS and TBATS model adapters ([#678](https://github.com/tinkoff-ai/etna/pull/734)) +- ### Changed - - diff --git a/etna/models/__init__.py b/etna/models/__init__.py index 4a54ec55b..cdb97b3d6 100644 --- a/etna/models/__init__.py +++ b/etna/models/__init__.py @@ -19,6 +19,8 @@ from etna.models.seasonal_ma import SeasonalMovingAverageModel from etna.models.sklearn import SklearnMultiSegmentModel from etna.models.sklearn import SklearnPerSegmentModel +from etna.models.tbats import BATSModel +from etna.models.tbats import TBATSModel if SETTINGS.prophet_required: from etna.models.prophet import ProphetModel diff --git a/etna/models/tbats.py b/etna/models/tbats.py new file mode 100644 index 000000000..9f51f6223 --- /dev/null +++ b/etna/models/tbats.py @@ -0,0 +1,178 @@ +from typing import Iterable +from typing import Optional +from typing import Tuple + +import pandas as pd +from tbats.abstract import ContextInterface +from tbats.abstract import Estimator +from tbats.bats import BATS +from tbats.tbats import TBATS +from tbats.tbats.Model import Model + +from etna.models.base import BaseAdapter +from etna.models.base import PerSegmentPredictionIntervalModel + + +class _TBATSAdapter(BaseAdapter): + def __init__(self, model: Estimator): + self.model = model + self._fitted_model: Optional[Model] = None + + def fit(self, df: pd.DataFrame, regressors: Iterable[str]): + target = df["target"] + self._fitted_model = self.model.fit(target) + return self + + def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Iterable[float]) -> pd.DataFrame: + if self._fitted_model is None: + raise ValueError("Model is not fitted! Fit the model before calling predict method!") + y_pred = pd.DataFrame() + if prediction_interval: + for quantile in quantiles: + pred, confidence_intervals = self._fitted_model.forecast(steps=df.shape[0], confidence_level=quantile) + y_pred["target"] = pred + if quantile < 1 / 2: + y_pred[f"target_{quantile:.4g}"] = confidence_intervals["lower_bound"] + else: + y_pred[f"target_{quantile:.4g}"] = confidence_intervals["upper_bound"] + else: + pred = self._fitted_model.forecast(steps=df.shape[0]) + y_pred["target"] = pred + return y_pred + + def get_model(self) -> Estimator: + return self.model + + +class BATSModel(PerSegmentPredictionIntervalModel): + """Class for holding segment interval BATS model.""" + + def __init__( + self, + use_box_cox: Optional[bool] = None, + box_cox_bounds: Tuple[int, int] = (0, 1), + use_trend: Optional[bool] = None, + use_damped_trend: Optional[bool] = None, + seasonal_periods: Optional[Iterable[int]] = None, + use_arma_errors: bool = True, + show_warnings: bool = True, + n_jobs: Optional[int] = None, + multiprocessing_start_method: str = "spawn", + context: Optional[ContextInterface] = None, + ): + """Create BATSModel with given parameters. + + Parameters + ---------- + use_box_cox: bool or None, optional (default=None) + If Box-Cox transformation of original series should be applied. + When None both cases shall be considered and better is selected by AIC. + box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1)) + Minimal and maximal Box-Cox parameter values. + use_trend: bool or None, optional (default=None) + Indicates whether to include a trend or not. + When None both cases shall be considered and better is selected by AIC. + use_damped_trend: bool or None, optional (default=None) + Indicates whether to include a damping parameter in the trend or not. + Applies only when trend is used. + When None both cases shall be considered and better is selected by AIC. + seasonal_periods: iterable or array-like of int values, optional (default=None) + Length of each of the periods (amount of observations in each period). + BATS accepts only int values here. + When None or empty array, non-seasonal model shall be fitted. + use_arma_errors: bool, optional (default=True) + When True BATS will try to improve the model by modelling residuals with ARMA. + Best model will be selected by AIC. + If False, ARMA residuals modeling will not be considered. + show_warnings: bool, optional (default=True) + If warnings should be shown or not. + Also see Model.warnings variable that contains all model related warnings. + n_jobs: int, optional (default=None) + How many jobs to run in parallel when fitting BATS model. + When not provided BATS shall try to utilize all available cpu cores. + multiprocessing_start_method: str, optional (default='spawn') + How threads should be started. + See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods + context: abstract.ContextInterface, optional (default=None) + For advanced users only. Provide this to override default behaviors + """ + self.model = BATS( + use_box_cox=use_box_cox, + box_cox_bounds=box_cox_bounds, + use_trend=use_trend, + use_damped_trend=use_damped_trend, + seasonal_periods=seasonal_periods, + use_arma_errors=use_arma_errors, + show_warnings=show_warnings, + n_jobs=n_jobs, + multiprocessing_start_method=multiprocessing_start_method, + context=context, + ) + super().__init__(base_model=_TBATSAdapter(self.model)) + + +class TBATSModel(PerSegmentPredictionIntervalModel): + """Class for holding segment interval TBATS model.""" + + def __init__( + self, + use_box_cox: Optional[bool] = None, + box_cox_bounds: Tuple[int, int] = (0, 1), + use_trend: Optional[bool] = None, + use_damped_trend: Optional[bool] = None, + seasonal_periods: Optional[Iterable[int]] = None, + use_arma_errors: bool = True, + show_warnings: bool = True, + n_jobs: Optional[int] = None, + multiprocessing_start_method: str = "spawn", + context: Optional[ContextInterface] = None, + ): + """Create TBATSModel with given parameters. + + Parameters + ---------- + use_box_cox: bool or None, optional (default=None) + If Box-Cox transformation of original series should be applied. + When None both cases shall be considered and better is selected by AIC. + box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1)) + Minimal and maximal Box-Cox parameter values. + use_trend: bool or None, optional (default=None) + Indicates whether to include a trend or not. + When None both cases shall be considered and better is selected by AIC. + use_damped_trend: bool or None, optional (default=None) + Indicates whether to include a damping parameter in the trend or not. + Applies only when trend is used. + When None both cases shall be considered and better is selected by AIC. + seasonal_periods: iterable or array-like of floats, optional (default=None) + Length of each of the periods (amount of observations in each period). + TBATS accepts int and float values here. + When None or empty array, non-seasonal model shall be fitted. + use_arma_errors: bool, optional (default=True) + When True BATS will try to improve the model by modelling residuals with ARMA. + Best model will be selected by AIC. + If False, ARMA residuals modeling will not be considered. + show_warnings: bool, optional (default=True) + If warnings should be shown or not. + Also see Model.warnings variable that contains all model related warnings. + n_jobs: int, optional (default=None) + How many jobs to run in parallel when fitting BATS model. + When not provided BATS shall try to utilize all available cpu cores. + multiprocessing_start_method: str, optional (default='spawn') + How threads should be started. + See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods + context: abstract.ContextInterface, optional (default=None) + For advanced users only. Provide this to override default behaviors + """ + self.model = TBATS( + use_box_cox=use_box_cox, + box_cox_bounds=box_cox_bounds, + use_trend=use_trend, + use_damped_trend=use_damped_trend, + seasonal_periods=seasonal_periods, + use_arma_errors=use_arma_errors, + show_warnings=show_warnings, + n_jobs=n_jobs, + multiprocessing_start_method=multiprocessing_start_method, + context=context, + ) + super().__init__(base_model=_TBATSAdapter(self.model)) diff --git a/poetry.lock b/poetry.lock index d0f14d972..a93229fac 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,11 @@ [[package]] name = "absl-py" -version = "1.0.0" +version = "1.1.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." category = "main" optional = true python-versions = ">=3.6" -[package.dependencies] -six = "*" - [[package]] name = "aiohttp" version = "3.8.1" @@ -52,11 +49,11 @@ python-versions = "*" [[package]] name = "alembic" -version = "1.7.7" +version = "1.8.0" description = "A database migration tool for SQLAlchemy." category = "main" optional = true -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} @@ -158,7 +155,7 @@ tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (> [[package]] name = "autopage" -version = "0.5.0" +version = "0.5.1" description = "A library to provide automatic paging for console output" category = "main" optional = true @@ -241,27 +238,27 @@ dev = ["pip-tools (==6.5.1)", "pytest (==7.1.1)", "flake8 (==4.0.1)", "tox (==3. [[package]] name = "boto3" -version = "1.23.7" +version = "1.24.2" description = "The AWS SDK for Python" category = "main" optional = false -python-versions = ">= 3.6" +python-versions = ">= 3.7" [package.dependencies] -botocore = ">=1.26.7,<1.27.0" +botocore = ">=1.27.2,<1.28.0" jmespath = ">=0.7.1,<2.0.0" -s3transfer = ">=0.5.0,<0.6.0" +s3transfer = ">=0.6.0,<0.7.0" [package.extras] crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.26.7" +version = "1.27.2" description = "Low-level, data-driven core of boto 3." category = "main" optional = false -python-versions = ">= 3.6" +python-versions = ">= 3.7" [package.dependencies] jmespath = ">=0.7.1,<2.0.0" @@ -287,7 +284,7 @@ doc = ["numpydoc", "sphinx", "gitpython"] [[package]] name = "cachetools" -version = "5.1.0" +version = "5.2.0" description = "Extensible memoizing collections and decorators" category = "main" optional = true @@ -912,7 +909,7 @@ test = ["pytest (>=6.0)", "pytest-cov", "flaky", "ipyparallel", "pre-commit", "p [[package]] name = "ipython" -version = "7.33.0" +version = "7.34.0" description = "IPython: Productive Interactive Computing" category = "main" optional = true @@ -1031,7 +1028,7 @@ python-versions = ">=3.6" [[package]] name = "jsonschema" -version = "4.5.1" +version = "4.6.0" description = "An implementation of JSON Schema validation for Python" category = "main" optional = true @@ -1046,7 +1043,7 @@ typing-extensions = {version = "*", markers = "python_version < \"3.8\""} [package.extras] format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] -format_nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] +format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] [[package]] name = "jupyter" @@ -1368,7 +1365,7 @@ testing = ["beautifulsoup4", "coverage", "docutils (>=0.17.0,<0.18.0)", "pytest [[package]] name = "nbclient" -version = "0.6.3" +version = "0.6.4" description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." category = "main" optional = true @@ -1378,7 +1375,7 @@ python-versions = ">=3.7.0" jupyter-client = ">=6.1.5" nbformat = ">=5.0" nest-asyncio = "*" -traitlets = ">=5.0.0" +traitlets = ">=5.2.2" [package.extras] sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"] @@ -1490,7 +1487,7 @@ test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium", "pyte [[package]] name = "numba" -version = "0.55.1" +version = "0.55.2" description = "compiling Python code using LLVM" category = "main" optional = false @@ -1498,7 +1495,7 @@ python-versions = ">=3.7,<3.11" [package.dependencies] llvmlite = ">=0.38.0rc1,<0.39" -numpy = ">=1.18,<1.22" +numpy = ">=1.18,<1.23" [[package]] name = "numpy" @@ -1538,7 +1535,7 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"] [[package]] name = "omegaconf" -version = "2.2.1" +version = "2.2.2" description = "A flexible configuration library" category = "main" optional = false @@ -2077,7 +2074,7 @@ github-actions = ["pytest-github-actions-annotate-failures"] [[package]] name = "pytorch-lightning" -version = "1.6.3" +version = "1.6.4" description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." category = "main" optional = true @@ -2087,7 +2084,8 @@ python-versions = ">=3.7" fsspec = {version = ">=2021.05.0,<2021.06.0 || >2021.06.0", extras = ["http"]} numpy = ">=1.17.2" packaging = ">=17.0" -pyDeprecate = ">=0.3.1,<0.4.0" +protobuf = "<=3.20.1" +pyDeprecate = ">=0.3.1" PyYAML = ">=5.4" tensorboard = ">=2.2.0" torch = ">=1.8" @@ -2096,14 +2094,17 @@ tqdm = ">=4.57.0" typing-extensions = ">=4.0.0" [package.extras] -all = ["matplotlib (>3.1)", "horovod (>=0.21.2,!=0.24.0)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>5.2.0,<6.3)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "twine (==3.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "sklearn", "jsonargparse", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas", "torchvision (>=0.9)", "gym[classic_control] (>=0.17.0)", "ipython"] -cpu = ["matplotlib (>3.1)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>5.2.0,<6.3)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "twine (==3.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "sklearn", "jsonargparse", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas", "torchvision (>=0.9)", "gym[classic_control] (>=0.17.0)", "ipython"] -cpu-extra = ["matplotlib (>3.1)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)"] -dev = ["matplotlib (>3.1)", "horovod (>=0.21.2,!=0.24.0)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>5.2.0,<6.3)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "twine (==3.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "sklearn", "jsonargparse", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas"] +all = ["matplotlib (>3.1)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas", "torchvision (>=0.9)", "gym[classic_control] (>=0.17.0)", "ipython", "fairscale (>=0.4.5)", "deepspeed", "horovod (>=0.21.2,!=0.24.0)", "hivemind (>=1.0.1)"] +deepspeed = ["deepspeed"] +dev = ["matplotlib (>3.1)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas"] examples = ["torchvision (>=0.9)", "gym[classic_control] (>=0.17.0)", "ipython"] -extra = ["matplotlib (>3.1)", "horovod (>=0.21.2,!=0.24.0)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)"] +extra = ["matplotlib (>3.1)", "torchtext (>=0.9)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.7.1)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2,<10.15.0 || >=10.16.0)"] +fairscale = ["fairscale (>=0.4.5)"] +hivemind = ["hivemind (>=1.0.1)"] +horovod = ["horovod (>=0.21.2,!=0.24.0)"] loggers = ["neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)"] -test = ["coverage (>5.2.0,<6.3)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "twine (==3.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "sklearn", "jsonargparse", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas"] +strategies = ["fairscale (>=0.4.5)", "deepspeed", "horovod (>=0.21.2,!=0.24.0)", "hivemind (>=1.0.1)"] +test = ["coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "mypy (>=0.920)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "pytest-forked", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas"] [[package]] name = "pytz" @@ -2139,7 +2140,7 @@ python-versions = ">=3.6" [[package]] name = "pyzmq" -version = "23.0.0" +version = "23.1.0" description = "Python bindings for 0MQ" category = "main" optional = true @@ -2187,7 +2188,7 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] [[package]] name = "regex" -version = "2022.4.24" +version = "2022.6.2" description = "Alternative regular expression module, to replace re." category = "main" optional = true @@ -2257,11 +2258,11 @@ test = ["pytest", "pytest-cov"] [[package]] name = "s3transfer" -version = "0.5.2" +version = "0.6.0" description = "An Amazon S3 Transfer Manager" category = "main" optional = false -python-versions = ">= 3.6" +python-versions = ">= 3.7" [package.dependencies] botocore = ">=1.12.36,<2.0a.0" @@ -2571,7 +2572,7 @@ test = ["pytest"] [[package]] name = "sqlalchemy" -version = "1.4.36" +version = "1.4.37" description = "Database Abstraction Library" category = "main" optional = true @@ -2596,7 +2597,7 @@ mysql_connector = ["mysql-connector-python"] oracle = ["cx_oracle (>=7,<8)", "cx_oracle (>=7)"] postgresql = ["psycopg2 (>=2.7)"] postgresql_asyncpg = ["greenlet (!=0.4.17)", "asyncpg"] -postgresql_pg8000 = ["pg8000 (>=1.16.6)"] +postgresql_pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"] postgresql_psycopg2binary = ["psycopg2-binary"] postgresql_psycopg2cffi = ["psycopg2cffi"] pymysql = ["pymysql (<1)", "pymysql"] @@ -2634,6 +2635,23 @@ python-versions = ">=3.6" importlib-metadata = {version = ">=1.7.0", markers = "python_version < \"3.8\""} pbr = ">=2.0.0,<2.1.0 || >2.1.0" +[[package]] +name = "tbats" +version = "1.1.0" +description = "BATS and TBATS for time series forecasting" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +numpy = "*" +pmdarima = "*" +scikit-learn = "*" +scipy = "*" + +[package.extras] +dev = ["pip-tools", "rpy2"] + [[package]] name = "tenacity" version = "8.0.1" @@ -2750,27 +2768,27 @@ typing-extensions = "*" [[package]] name = "torchmetrics" -version = "0.8.2" +version = "0.9.0" description = "PyTorch native Metrics" category = "main" optional = true -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] numpy = ">=1.17.2" packaging = "*" -pyDeprecate = ">=0.3.0,<0.4.0" torch = ">=1.3.1" +typing-extensions = {version = "*", markers = "python_version < \"3.8\""} [package.extras] -all = ["sacrebleu (>=2.0.0)", "check-manifest", "cloudpickle (>=1.3)", "rouge-score (>=0.0.4)", "fire", "codecov (>=2.1)", "scikit-image (>0.17.1)", "pytest-doctestplus (>=0.9.0)", "mypy (>=0.790)", 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100644 index 000000000..b0316b91c --- /dev/null +++ b/tests/test_models/test_tbats.py @@ -0,0 +1,117 @@ +import numpy as np +import pandas as pd +import pytest + +from etna.datasets import TSDataset +from etna.metrics import MAE +from etna.models.tbats import BATSModel +from etna.models.tbats import TBATSModel +from etna.transforms import LagTransform +from tests.test_models.test_linear_model import linear_segments_by_parameters + + +@pytest.fixture() +def linear_segments_ts_unique(random_seed): + alpha_values = [np.random.rand() * 4 - 2 for _ in range(3)] + intercept_values = [np.random.rand() * 4 + 1 for _ in range(3)] + return linear_segments_by_parameters(alpha_values, intercept_values) + + +@pytest.fixture() +def sinusoid_ts(): + horizon = 14 + periods = 100 + sinusoid_ts_1 = pd.DataFrame( + { + "segment": np.zeros(periods), + "timestamp": pd.date_range(start="1/1/2018", periods=periods), + "target": [np.sin(i) for i in range(periods)], + } + ) + sinusoid_ts_2 = pd.DataFrame( + { + "segment": np.ones(periods), + "timestamp": pd.date_range(start="1/1/2018", periods=periods), + "target": [np.sin(i + 1) for i in range(periods)], + } + ) + df = pd.concat((sinusoid_ts_1, sinusoid_ts_2)) + df = TSDataset.to_dataset(df) + ts = TSDataset(df, freq="D") + return ts.train_test_split(test_size=horizon) + + +@pytest.mark.parametrize( + "model_class, model_class_repr", + ((TBATSModel, "TBATSModel"), (BATSModel, "BATSModel")), +) +def test_reper(model_class, model_class_repr): + kwargs = { + "use_box_cox": None, + "box_cox_bounds": None, + "use_trend": None, + "use_damped_trend": None, + "seasonal_periods": None, + "use_arma_errors": None, + "show_warnings": None, + "n_jobs": None, + "multiprocessing_start_method": None, + "context": None, + } + kwargs_repr = ( + "use_box_cox = None, " + + "box_cox_bounds = None, " + + "use_trend = None, " + + "use_damped_trend = None, " + + "seasonal_periods = None, " + + "use_arma_errors = None, " + + "show_warnings = None, " + + "n_jobs = None, " + + "multiprocessing_start_method = None, " + + "context = None" + ) + model = model_class(**kwargs) + model_repr = model.__repr__() + true_repr = f"{model_class_repr}({kwargs_repr}, )" + assert model_repr == true_repr + + +@pytest.mark.parametrize("model", (TBATSModel(), BATSModel())) +def test_not_fitted(model, linear_segments_ts_unique): + train, test = linear_segments_ts_unique + to_forecast = train.make_future(3) + with pytest.raises(ValueError, match="model is not fitted!"): + model.forecast(to_forecast) + + +@pytest.mark.parametrize("model", [TBATSModel(), BATSModel()]) +def test_format(model, new_format_df): + df = new_format_df + ts = TSDataset(df, "1d") + lags = LagTransform(lags=[3, 4, 5], in_column="target") + ts.fit_transform([lags]) + model.fit(ts) + future_ts = ts.make_future(3) + model.forecast(future_ts) + assert not future_ts.isnull().values.any() + + +@pytest.mark.parametrize("model", [TBATSModel(), BATSModel()]) +def test_dummy(model, sinusoid_ts): + train, test = sinusoid_ts + model.fit(train) + future_ts = train.make_future(14) + y_pred = model.forecast(future_ts) + metric = MAE("macro") + value_metric = metric(y_pred, test) + assert value_metric < 0.33 + + +@pytest.mark.parametrize("model", [TBATSModel(), BATSModel()]) +def test_prediction_interval(model, example_tsds): + model.fit(example_tsds) + forecast = model.forecast(example_tsds, prediction_interval=True, quantiles=[0.025, 0.975]) + for segment in forecast.segments: + segment_slice = forecast[:, segment, :][segment] + assert {"target_0.025", "target_0.975", "target"}.issubset(segment_slice.columns) + assert (segment_slice["target_0.975"] - segment_slice["target_0.025"] >= 0).all()