diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 562a1463..9f564db7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,7 +9,7 @@ repos: - id: check-yaml - id: check-ast - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 + rev: 6.1.0 hooks: - id: flake8 - repo: https://github.com/pre-commit/mirrors-isort @@ -17,11 +17,11 @@ repos: hooks: - id: isort - repo: https://github.com/psf/black - rev: 23.1.0 + rev: 23.9.0 hooks: - id: black - repo: https://github.com/nbQA-dev/nbQA - rev: 1.6.4 + rev: 1.7.0 hooks: - id: nbqa-black - id: nbqa-isort diff --git a/docs/requirements.txt b/docs/requirements.txt index 06882a95..17a82e77 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,7 +5,7 @@ docutils pydata-sphinx-theme lightning>=2.0.0 cloudpickle -torch>=2.0 +torch>=2.0,!=2.0.1 optuna>=3.1.0 scipy pandas>=1.3 diff --git a/docs/source/conf.py b/docs/source/conf.py index acc624a1..f6ce9562 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -172,3 +172,7 @@ def setup(app: Sphinx): "../../CHANGELOG.md", "CHANGELOG.md", ) + +intersphinx_mapping = { + "sklearn": ("https://scikit-learn.org/stable/", None), +} diff --git a/docs/source/tutorials/ar.ipynb b/docs/source/tutorials/ar.ipynb index b07b8e44..ae76f8ef 100644 --- a/docs/source/tutorials/ar.ipynb +++ b/docs/source/tutorials/ar.ipynb @@ -231,7 +231,8 @@ ], "source": [ "# calculate baseline absolute error\n", - "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)]).to(device)\n", "baseline_predictions = Baseline().predict(val_dataloader)\n", "SMAPE()(baseline_predictions, actuals)" ] @@ -519,7 +520,7 @@ } ], "source": [ - "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)])\n", + "actuals = torch.cat([y[0] for x, y in iter(val_dataloader)]).to(device)\n", "predictions = best_model.predict(val_dataloader)\n", "(actuals - predictions).abs().mean()" ] @@ -538,7 +539,7 @@ "metadata": {}, "outputs": [], "source": [ - "raw_predictions, x = best_model.predict(val_dataloader, mode=\"raw\", return_x=True)" + "raw_predictions = best_model.predict(val_dataloader, mode=\"raw\", return_x=True)" ] }, { @@ -649,7 +650,7 @@ ], "source": [ "for idx in range(10): # plot 10 examples\n", - " best_model.plot_prediction(x, raw_predictions, idx=idx, add_loss_to_title=True)" + " best_model.plot_prediction(raw_predictions.x, raw_predictions.output, idx=idx, add_loss_to_title=True)" ] }, { @@ -797,7 +798,7 @@ ], "source": [ "for idx in range(10): # plot 10 examples\n", - " best_model.plot_interpretation(x, raw_predictions, idx=idx)" + " best_model.plot_interpretation(raw_predictions.x, raw_predictions.output, idx=idx)" ] }, { diff --git a/poetry.lock b/poetry.lock index 0d3eaecb..4b703960 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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"pytest-ruff"] [extras] github-actions = ["pytest-github-actions-annotate-failures"] @@ -6072,4 +5656,4 @@ mqf2 = ["cpflows"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "612a2416108ae92e1cf8d5082c7558615af5c244564576c747d6b15897bd4c9c" +content-hash = "acf0ee98a7ed5f9c84477905279c6eb686064b786d51ba5e5dab7501cb3252f9" diff --git a/pyproject.toml b/pyproject.toml index 8fa956cb..826fcf26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,24 +28,25 @@ black = 1 [tool.poetry] name = "pytorch_forecasting" -readme = "README.md" # Markdown files are supported -version = "0.0.0" # is being replaced automatically +readme = "README.md" # Markdown files are supported +version = "0.0.0" # is being replaced automatically authors = ["Jan Beitner"] classifiers = [ - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Topic :: Scientific/Engineering", - "Topic :: Scientific/Engineering :: Mathematics", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Software Development", - "Topic :: Software Development :: Libraries", - "Topic :: Software Development :: Libraries :: Python Modules", - "License :: OSI Approved :: MIT License"] + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Software Development", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development :: Libraries :: Python Modules", + "License :: OSI Approved :: MIT License", +] description = "Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models" repository = "https://github.com/jdb78/pytorch-forecasting" documentation = "https://pytorch-forecasting.readthedocs.io" @@ -54,7 +55,7 @@ homepage = "https://pytorch-forecasting.readthedocs.io" [tool.poetry.dependencies] python = ">=3.8,<3.11" -torch = "^2.0.0" +torch = "^2.0.0,!=2.0.1" lightning = "^2.0.0" optuna = "^3.1.0" scipy = "^1.8" @@ -63,9 +64,9 @@ scikit-learn = "^1.2" matplotlib = "*" statsmodels = "*" -pytest-github-actions-annotate-failures = {version = "*", optional = true} -networkx = {version = "^3.0.0", optional = true} -cpflows = {version="^0.1.2", optional = true} +pytest-github-actions-annotate-failures = { version = "*", optional = true } +networkx = { version = "^3.0.0", optional = true } +cpflows = { version = "^0.1.2", optional = true } fastapi = ">=0.80" pytorch-optimizer = "^2.5.1" @@ -104,7 +105,7 @@ tensorboard = "^2.12.1" pandoc = "^2.3" -[tool.poetry.extras] # extras +[tool.poetry.extras] # extras github-actions = ["pytest-github-actions-annotate-failures"] graph = ["networkx"] mqf2 = ["cpflows"] @@ -113,8 +114,8 @@ mqf2 = ["cpflows"] enable = true vcs = "git" dirty = false -style = "semver" # semantic versioning +style = "semver" # semantic versioning -[build-system] # make the package pip installable +[build-system] # make the package pip installable requires = ["poetry-core>=1.0.7", "poetry-dynamic-versioning>=0.13.1"] build-backend = "poetry_dynamic_versioning.backend" diff --git a/pytorch_forecasting/data/timeseries.py b/pytorch_forecasting/data/timeseries.py index 8c80f662..c409d2dd 100644 --- a/pytorch_forecasting/data/timeseries.py +++ b/pytorch_forecasting/data/timeseries.py @@ -197,7 +197,7 @@ def __init__( add_relative_time_idx: bool = False, add_target_scales: bool = False, add_encoder_length: Union[bool, str] = "auto", - target_normalizer: Union[NORMALIZER, str, List[NORMALIZER], Tuple[NORMALIZER]] = "auto", + target_normalizer: Union[NORMALIZER, str, List[NORMALIZER], Tuple[NORMALIZER], None] = "auto", categorical_encoders: Dict[str, NaNLabelEncoder] = {}, scalers: Dict[str, Union[StandardScaler, RobustScaler, TorchNormalizer, EncoderNormalizer]] = {}, randomize_length: Union[None, Tuple[float, float], bool] = False, @@ -291,7 +291,7 @@ def __init__( the index will range from -encoder_length to prediction_length) add_target_scales (bool): if to add scales for target to static real features (i.e. add the center and scale of the unnormalized timeseries as features) - add_encoder_length (bool): if to add decoder length to list of static real variables. + add_encoder_length (bool): if to add encoder length to list of static real variables. Defaults to "auto", i.e. ``True`` if ``min_encoder_length != max_encoder_length``. target_normalizer (Union[TorchNormalizer, NaNLabelEncoder, EncoderNormalizer, str, list, tuple]): transformer that take group_ids, target and time_idx to normalize targets. @@ -1559,6 +1559,8 @@ def __getitem__(self, idx: int) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]: data_cat = data_cat[encoder_length - new_encoder_length : encoder_length + new_decoder_length] data_cont = data_cont[encoder_length - new_encoder_length : encoder_length + new_decoder_length] target = [t[encoder_length - new_encoder_length : encoder_length + new_decoder_length] for t in target] + if weight is not None: + weight = weight[encoder_length - new_encoder_length : encoder_length + new_decoder_length] encoder_length = new_encoder_length decoder_length = new_decoder_length diff --git a/pytorch_forecasting/models/base_model.py b/pytorch_forecasting/models/base_model.py index 3612e13b..50a4ad77 100644 --- a/pytorch_forecasting/models/base_model.py +++ b/pytorch_forecasting/models/base_model.py @@ -204,7 +204,7 @@ def __init__( def _reset_data(self, result: bool = True): # reset data objects to save results into self._output = [] - self._decode_lenghts = [] + self._decode_lengths = [] self._x_list = [] self._index = [] self._y = [] @@ -267,8 +267,8 @@ def on_predict_batch_end( self._index.append(trainer.predict_dataloaders.dataset.x_to_index(x)) out["index"] = self._index[-1] if self.return_decoder_lengths: - self._decode_lenghts.append(lengths) - out["decoder_lengths"] = self._decode_lenghts[-1] + self._decode_lengths.append(lengths) + out["decoder_lengths"] = self._decode_lengths[-1] if self.return_y: self._y.append(batch[1]) out["y"] = self._y[-1] @@ -312,7 +312,7 @@ def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningMo if self.return_index: output["index"] = pd.concat(self._index, axis=0, ignore_index=True) if self.return_decoder_lengths: - output["decoder_lengths"] = torch.cat(self._decode_lenghts, dim=0) + output["decoder_lengths"] = torch.cat(self._decode_lengths, dim=0) if self.return_y: y = concat_sequences([yi[0] for yi in self._y]) if self._y[-1][1] is None: @@ -1477,13 +1477,14 @@ def predict_dependency( # set values data.set_overwrite_values(variable=variable, values=value, target=target) # predict - kwargs.setdefault("mode", "prediction") + pred_kwargs = deepcopy(kwargs) + pred_kwargs.setdefault("mode", "prediction") if idx == 0 and mode == "dataframe": # need index for returning as dataframe - res = self.predict(data, return_index=True, **kwargs) + res = self.predict(data, return_index=True, **pred_kwargs) results.append(res.output) else: - results.append(self.predict(data, **kwargs)) + results.append(self.predict(data, **pred_kwargs)) # increment progress progress_bar.update() diff --git a/pytorch_forecasting/models/nhits/sub_modules.py b/pytorch_forecasting/models/nhits/sub_modules.py index 610fd1ee..a9ec4338 100644 --- a/pytorch_forecasting/models/nhits/sub_modules.py +++ b/pytorch_forecasting/models/nhits/sub_modules.py @@ -61,7 +61,7 @@ def forward( def init_weights(module, initialization): - if type(module) == torch.nn.Linear: + if type(module) is torch.nn.Linear: if initialization == "orthogonal": torch.nn.init.orthogonal_(module.weight) elif initialization == "he_uniform":