lint: default pre-commit hooks & fixing

.github/codecov.yml

@@ -1,4 +1,4 @@
 coverage:
   status:
     project: off
-    patch: off
+    patch: off

.github/pull_request_template.md

@@ -1,7 +1,7 @@
 Checklist before merging this PR:
 - [ ] Mentioned all issues that this PR fixes or addresses.
 - [ ] Summarized the updates of this PR under **Summary**.
-- [ ] Added an entry under **Unreleased** in the [Changelog](../CHANGELOG.md). 
+- [ ] Added an entry under **Unreleased** in the [Changelog](../CHANGELOG.md).
 
 <!-- Please mention an issue this pull request addresses. -->
 Fixes #.

.pre-commit-config.yaml

@@ -1,4 +1,24 @@
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
+  autoupdate_schedule: quarterly
+  # submodules: true
+
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-json
+      - id: check-yaml
+        exclude: "conda_recipe/darts/meta.yaml"
+      - id: check-toml
+      - id: detect-private-key
+
   - repo: https://github.com/psf/black
     rev: 24.3.0
     hooks:

CONTRIBUTING.md

@@ -80,5 +80,5 @@ To ensure you don't need to worry about formatting and linting when contributing
 Please follow the procedure described in [INSTALL.md](https://github.com/unit8co/darts/blob/master/INSTALL.md#test-environment-appple-m1-processor)
 to set up a x_64 emulated environment. For the development environment, instead of installing Darts with
 `pip install darts`, instead go to the darts cloned repo location and install the packages with: `pip install -r requirements/dev-all.txt`.
-If necessary, follow the same steps to setup libomp for lightgbm. 
+If necessary, follow the same steps to setup libomp for lightgbm.
 Finally, verify your overall environment setup by successfully running all unitTests with gradlew or pytest.

Dockerfile

@@ -21,4 +21,4 @@ RUN pip install -e .
 
 # assuming you are working from inside your darts directory:
 # docker build . -t darts-test:latest
-# docker run -it -v $(pwd)/:/app/ darts-test:latest bash
+# docker run -it -v $(pwd)/:/app/ darts-test:latest bash

INSTALL.md

@@ -5,7 +5,7 @@ Below, we detail how to install Darts using either `conda` or `pip`.
 ## From PyPI
 Install Darts with all models except the ones from optional dependencies (Prophet, LightGBM, CatBoost, see more on that [here](#enabling-optional-dependencies)): `pip install darts`.
 
-If this fails on your platform, please follow the official installation 
+If this fails on your platform, please follow the official installation
 guide for [PyTorch](https://pytorch.org/get-started/locally/), then try installing Darts again.
 
 As some dependencies are relatively big or involve non-Python dependencies,
@@ -37,8 +37,8 @@ As some models have relatively heavy dependencies, we provide four conda-forge p
 ## Other Information
 
 ### Enabling Optional Dependencies
-As of version 0.25.0, the default `darts` package does not install Prophet, CatBoost, and LightGBM dependencies anymore, because their 
-build processes were too often causing issues. We continue supporting the model wrappers `Prophet`, `CatBoostModel`, and `LightGBMModel` in Darts though. If you want to use any of them, you will need to manually install the corresponding packages (or install a Darts flavor as described above).  
+As of version 0.25.0, the default `darts` package does not install Prophet, CatBoost, and LightGBM dependencies anymore, because their
+build processes were too often causing issues. We continue supporting the model wrappers `Prophet`, `CatBoostModel`, and `LightGBMModel` in Darts though. If you want to use any of them, you will need to manually install the corresponding packages (or install a Darts flavor as described above).
 
 #### Prophet
 Install the `prophet` package (version 1.1.1 or more recent) using the [Prophet install guide](https://facebook.github.io/prophet/docs/installation.html#python)
@@ -99,4 +99,4 @@ To build documentation locally just run
 ```bash
 ./gradlew buildDocs
 ```
-After that docs will be available in `./docs/build/html` directory. You can just open `./docs/build/html/index.html` using your favourite browser.
+After that docs will be available in `./docs/build/html` directory. You can just open `./docs/build/html/index.html` using your favourite browser.

README.md

@@ -19,8 +19,8 @@ on time series. It contains a variety of models, from classics such as ARIMA to
 deep neural networks. The forecasting models can all be used in the same way,
 using `fit()` and `predict()` functions, similar to scikit-learn.
 The library also makes it easy to backtest models,
-combine the predictions of several models, and take external data into account. 
-Darts supports both univariate and multivariate time series and models. 
+combine the predictions of several models, and take external data into account.
+Darts supports both univariate and multivariate time series and models.
 The ML-based models can be trained on potentially large datasets containing multiple time
 series, and some of the models offer a rich support for probabilistic forecasting.
 
@@ -59,7 +59,7 @@ Once your environment is set up you can install darts using pip:
 
     pip install darts
 
-For more details you can refer to our 
+For more details you can refer to our
 [installation instructions](https://github.com/unit8co/darts/blob/master/INSTALL.md).
 
 ## Example Usage
@@ -166,15 +166,15 @@ series.plot()
 * **Multivariate Support:** `TimeSeries` can be multivariate - i.e., contain multiple time-varying
   dimensions instead of a single scalar value. Many models can consume and produce multivariate series.
 
-* **Multiple series training (global models):** All machine learning based models (incl. all neural networks) 
+* **Multiple series training (global models):** All machine learning based models (incl. all neural networks)
   support being trained on multiple (potentially multivariate) series. This can scale to large datasets too.
 
 * **Probabilistic Support:** `TimeSeries` objects can (optionally) represent stochastic
   time series; this can for instance be used to get confidence intervals, and many models support different
   flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles).
   Some anomaly detection scorers are also able to exploit these predictive distributions.
 
-* **Past and Future Covariates support:** Many models in Darts support past-observed and/or future-known 
+* **Past and Future Covariates support:** Many models in Darts support past-observed and/or future-known
   covariate (external data) time series as inputs for producing forecasts.
 
 * **Static Covariates support:** In addition to time-dependent data, `TimeSeries` can also contain
@@ -262,7 +262,7 @@ on bringing more models and features.
 
 
 ## Community & Contact
-Anyone is welcome to join our [Gitter room](https://gitter.im/u8darts/darts) to ask questions, make proposals, 
+Anyone is welcome to join our [Gitter room](https://gitter.im/u8darts/darts) to ask questions, make proposals,
 discuss use-cases, and more. If you spot a bug or have suggestions, GitHub issues are also welcome.
 
 If what you want to tell us is not suitable for Gitter or Github,

darts/ad/__init__.py

@@ -11,14 +11,14 @@
   or for series accompanied by some predictions (``score_from_prediction()``).
   Scorers can be trainable (e.g., ``KMeansScorer``) or not (e.g., ``NormScorer``).
 
-* `Anomaly Models <https://unit8co.github.io/darts/generated_api/darts.ad.anomaly_model.html>`_ 
+* `Anomaly Models <https://unit8co.github.io/darts/generated_api/darts.ad.anomaly_model.html>`_
   offer a convenient way to produce anomaly scores from any of Darts
   forecasting models (``ForecastingAnomalyModel``) or filtering models (``FilteringAnomalyModel``),
   by comparing models' predictions with actual observations.
   These classes take as parameters one Darts model, and one or multiple scorers, and can be readily
   used to produce anomaly scores with the ``score()`` method.
 
-* `Anomaly Detectors <https://unit8co.github.io/darts/generated_api/darts.ad.detectors.html>`_: 
+* `Anomaly Detectors <https://unit8co.github.io/darts/generated_api/darts.ad.detectors.html>`_:
   transform raw time series (such as anaomly scores) into binary anomaly time series.
 
 * `Anomaly Aggregators <https://unit8co.github.io/darts/generated_api/darts.ad.aggregators.html>`_:

darts/ad/scorers/__init__.py

@@ -30,7 +30,7 @@
   between the prediction (coming e.g., from a forecasting model) and the series itself.
   When scoring, the scorer will attribute a higher score to residuals that are distant
   from the clusters found during the training phase.
-    
+
 Note that `Anomaly Models <https://unit8co.github.io/darts/generated_api/darts.ad.anomaly_model.html>`_
 can be used to conveniently combine any of Darts forecasting and filtering models with one or multiple scorers.
 

datasets/heart_rate.csv

@@ -1798,4 +1798,4 @@ Heart rate
 101.623
 99.5679
 99.1835
-98.8567
+98.8567

datasets/ice_cream_heater.csv

@@ -196,4 +196,4 @@ Month,heater,ice cream
 2020-03,25,44
 2020-04,25,53
 2020-05,27,70
-2020-06,24,74
+2020-06,24,74

datasets/monthly-milk-incomplete.csv

@@ -154,4 +154,3 @@
 "1975-08",858
 "1975-11",797
 "1975-12",843
-

datasets/monthly-milk.csv

@@ -167,4 +167,3 @@
 "1975-10",827
 "1975-11",797
 "1975-12",843
-

datasets/monthly-sunspots.csv

@@ -2818,4 +2818,4 @@
 "1983-09",50.3
 "1983-10",55.8
 "1983-11",33.3
-"1983-12",33.4
+"1983-12",33.4

datasets/temps.csv

@@ -3648,4 +3648,4 @@ Date,Daily minimum temperatures
 12/28/1990,13.6
 12/29/1990,13.5
 12/30/1990,15.7
-12/31/1990,13
+12/31/1990,13

datasets/us_gasoline.csv

@@ -1576,4 +1576,4 @@ Week,Gasoline
 03/1/1991,7224
 02/22/1991,6582
 02/15/1991,6433
-02/8/1991,6621
+02/8/1991,6621

docs/source/userguide.rst

@@ -25,7 +25,7 @@ You will find here some more detailed information about Darts.
    .. userguide/probabilistic_forecasting.md
 
    .. userguide/ensembling.md
- 
+
    .. userguide/filtering_models.md
 
    .. userguide/preprocessing_and_pipelines.md

docs/userguide/covariates.md

@@ -90,13 +90,13 @@ Let's have a look at some examples of past, future, and static covariates:
     -   daily average **forecasted** temperatures (known in the future)
     -   day of week, month, year, ...
 - `static_covariates`: time independent/constant/static `target` characteristics
-    -   categorical: 
+    -   categorical:
         - location of `target` (country, city, .. name)
         - `target` identifier: (product ID, store ID, ...)
     -   numerical:
         - population of `target`'s country/market area (assuming it stays constant over the forecasting horizon)
         - average temperature of `target`'s region (assuming it stays constant over the forecasting horizon)
- 
+
 
 Temporal attributes are powerful because they are known in advance and can help models capture trends and / or seasonal patterns of the `target` series.
 Static attributes are powerful when working with multiple `targets` (either multiple `TimeSeries`, or multivariate series containing multiple dimensions each). The time independent information can help models identify the nature/environment of the underlying series and improve forecasts across different `targets`.
@@ -148,8 +148,8 @@ GFMs are models that can be trained on multiple target (and covariate) time seri
 | [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel)                                                                                                                                                 | ✅               |                   |                   |
 | [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel)                                                                                                                                             | ✅               |                   |                   |
 | [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel)                                                                                                             | ✅               |                   |                   |
-| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel)                                                                                                                                             | ✅               |         ✅         |         ✅         | 
-| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel)                                                                                                                                         | ✅               |         ✅         |         ✅         | 
+| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel)                                                                                                                                             | ✅               |         ✅         |         ✅         |
+| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel)                                                                                                                                         | ✅               |         ✅         |         ✅         |
 | [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel)                                                                                                                                         | ✅               |         ✅         |         ✅         |
 | [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel)                                                                                                                                         | ✅               |         ✅         |         ✅         |
 | [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel)                                                                                                                             | ✅               |         ✅         |         ✅         |

docs/userguide/forecasting_overview.md

@@ -15,7 +15,7 @@ by calling the `fit()` function, and finally they are used to obtain one or seve
 from darts.models import NaiveSeasonal
 
 naive_model = NaiveSeasonal(K=1)            # init
-naive_model.fit(train)                      # fit  
+naive_model.fit(train)                      # fit
 naive_forecast = naive_model.predict(n=36)  # predict
 ```
 
@@ -111,7 +111,7 @@ These models are shown with a "✅" under the `Multivariate` column on the [mode
 ## Handling multiple series
 
 Some models support being fit on multiple time series. To do this, it is enough to simply provide a Python `Sequence` of `TimeSeries` (for instance a list of `TimeSeries`) to `fit()`. When a model is fit this way, the `predict()` function will expect the argument `series` to be set, containing
-one or several `TimeSeries` (i.e., a single or a `Sequence` of `TimeSeries`) that need to be forecasted. 
+one or several `TimeSeries` (i.e., a single or a `Sequence` of `TimeSeries`) that need to be forecasted.
 The advantage of training on multiple series is that a single model can be exposed to more patterns occurring across all series in the training dataset. That can often be beneficial, especially for larger models with more capacity.
 
 In turn, the advantage of having `predict()` providing forecasts for potentially several series at once is that the computation can often be batched and vectorized across the multiple series, which is computationally faster than calling `predict()` multiple times on isolated series.
@@ -178,9 +178,9 @@ pred.plot(label='forecast')
 ![Exponential Smoothing](./images/probabilistic/example_ets.png)
 
 ### Probabilistic neural networks
-All neural networks (torch-based models) in Darts have a rich support to estimate different kinds of probability distributions. 
-When creating the model, it is possible to provide one of the *likelihood models* available in [darts.utils.likelihood_models](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html), which determine the distribution that will be estimated by the model. 
-In such cases, the model will output the parameters of the distribution, and it will be trained by minimising the negative log-likelihood of the training samples. 
+All neural networks (torch-based models) in Darts have a rich support to estimate different kinds of probability distributions.
+When creating the model, it is possible to provide one of the *likelihood models* available in [darts.utils.likelihood_models](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html), which determine the distribution that will be estimated by the model.
+In such cases, the model will output the parameters of the distribution, and it will be trained by minimising the negative log-likelihood of the training samples.
 Most of the likelihood models also support prior values for the distribution's parameters, in which case the training loss is regularized by a Kullback-Leibler divergence term pushing the resulting distribution in the direction of the distribution specified by the prior parameters.
 The strength of this regularization term can also be specified when creating the likelihood model object.
 
@@ -201,7 +201,7 @@ train = scaler.fit_transform(train)
 val = scaler.transform(val)
 series = scaler.transform(series)
 
-model = TCNModel(input_chunk_length=30, 
+model = TCNModel(input_chunk_length=30,
                  output_chunk_length=12,
                  likelihood=LaplaceLikelihood(prior_b=0.1))
 model.fit(train, epochs=400)
@@ -232,7 +232,7 @@ train = scaler.fit_transform(train)
 val = scaler.transform(val)
 series = scaler.transform(series)
 
-model = TCNModel(input_chunk_length=30, 
+model = TCNModel(input_chunk_length=30,
                  output_chunk_length=12,
                  likelihood=QuantileRegression(quantiles=[0.01, 0.05, 0.2, 0.5, 0.8, 0.95, 0.99]))
 model.fit(train, epochs=400)
@@ -291,8 +291,8 @@ from darts.models import LinearRegressionModel
 series = AirPassengersDataset().load()
 train, val = series[:-36], series[-36:]
 
-model = LinearRegressionModel(lags=30, 
-                              likelihood="quantile", 
+model = LinearRegressionModel(lags=30,
+                              likelihood="quantile",
                               quantiles=[0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95])
 model.fit(train)
 pred = model.predict(n=36, num_samples=500)
@@ -304,4 +304,4 @@ pred.plot(label='forecast')
 ![quantile linear regression](./images/probabilistic/example_linreg_quantile.png)
 
 
-[1] Yarin Gal, Zoubin Ghahramani, ["Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"](https://arxiv.org/abs/1506.02142)
+[1] Yarin Gal, Zoubin Ghahramani, ["Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"](https://arxiv.org/abs/1506.02142)