Docs/fix warnings

README.md

@@ -36,11 +36,11 @@ fledged anomaly detection models.
 * [API Reference](https://unit8co.github.io/darts/generated_api/darts.html)
 * [Examples](https://unit8co.github.io/darts/examples.html)
 
-##### High Level Introductions
+### High Level Introductions
 * [Introductory Blog Post](https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878)
 * [Introduction video (25 minutes)](https://youtu.be/g6OXDnXEtFA)
 
-##### Articles on Selected Topics
+### Articles on Selected Topics
 * [Training Models on Multiple Time Series](https://medium.com/unit8-machine-learning-publication/training-forecasting-models-on-multiple-time-series-with-darts-dc4be70b1844)
 * [Using Past and Future Covariates](https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993)
 * [Temporal Convolutional Networks and Forecasting](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
@@ -83,6 +83,7 @@ train, val = series[:-36], series[-36:]
 ```
 
 Fit an exponential smoothing model, and make a (probabilistic) prediction over the validation series' duration:
+
 ```python
 from darts.models import ExponentialSmoothing
 
@@ -92,6 +93,7 @@ prediction = model.predict(len(val), num_samples=1000)
 ```
 
 Plot the median, 5th and 95th percentiles:
+
 ```python
 import matplotlib.pyplot as plt
 
@@ -235,7 +237,7 @@ Here's a breakdown of the forecasting models currently implemented in Darts. Our
 | [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA)                                                                                                                                             |                                                                                                                                                                                                                                   | ✅ 🔴                                                         | 🔴 ✅ 🔴                                                                  | ✅ 🔴                                                                     | 🔴                                        |
 | [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA)                                                                                                                                         |                                                                                                                                                                                                                                   | 🔴 ✅                                                         | 🔴 ✅ 🔴                                                                  | ✅ 🔴                                                                     | 🔴                                        |
 | [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing)                                                                               |                                                                                                                                                                                                                                   | ✅ 🔴                                                         | 🔴 🔴 🔴                                                                 | ✅ 🔴                                                                     | 🔴                                        |
-| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R)                                                                                          | ✅ 🔴                                                         | 🔴 🔴 🔴                                                                 | 🔴 🔴                                                                    | 🔴                                        |
+| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta paper](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta source](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R)                                                                             | ✅ 🔴                                                         | 🔴 🔴 🔴                                                                 | 🔴 🔴                                                                    | 🔴                                        |
 | [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet)                                                                                                                         | [Prophet repo](https://github.com/facebook/prophet)                                                                                                                                                                               | ✅ 🔴                                                         | 🔴 ✅ 🔴                                                                  | ✅ 🔴                                                                     | 🔴                                        |
 | [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform)                                                                                                                            |                                                                                                                                                                                                                                   | ✅ 🔴                                                         | 🔴 🔴 🔴                                                                 | 🔴 🔴                                                                    | 🔴                                        |
 | [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification                                   | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf)                                                                                                                              | ✅ ✅                                                          | 🔴 ✅ 🔴                                                                  | ✅ 🔴                                                                     | 🔴                                        |

darts/ad/__init__.py

@@ -5,23 +5,23 @@
 A suite of tools for performing anomaly detection and classification
 on time series.
 
-- `Anomaly Scorers <https://unit8co.github.io/darts/generated_api/darts.ad.scorers.html>`_ are at the core of the
+- `Anomaly Scorers <https://unit8co.github.io/darts/generated_api/darts.ad.scorers.html>`__ are at the core of the
   anomaly detection module. They produce anomaly scores time series, either for single series (`score()`),
   or for series accompanied by some predictions (`score_from_prediction()`). Scorers can be trainable
   (e.g., :class:`~darts.ad.scorers.kmeans_scorer.KMeansScorer`) or not
   (e.g., :class:`~darts.ad.scorers.norm_scorer.NormScorer`).
 
-- `Anomaly Models <https://unit8co.github.io/darts/generated_api/darts.ad.anomaly_model.html>`_ offer a convenient way
+- `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
   (:class:`~darts.ad.anomaly_model.forecasting_am.ForecastingAnomalyModel`) or filtering models
   (:class:`~darts.ad.anomaly_model.filtering_am.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>`_: transform raw time
+- `Anomaly Detectors <https://unit8co.github.io/darts/generated_api/darts.ad.detectors.html>`__: transform raw time
   series (such as anomaly scores) into binary anomaly time series.
 
-- `Anomaly Aggregators <https://unit8co.github.io/darts/generated_api/darts.ad.aggregators.html>`_: combine multiple
+- `Anomaly Aggregators <https://unit8co.github.io/darts/generated_api/darts.ad.aggregators.html>`__: combine multiple
   binary anomaly time series (in the form of multivariate time series) into a single binary anomaly time series
   applying boolean logic.
 """

darts/ad/aggregators/aggregators.py

@@ -1,5 +1,6 @@
 """
-Anomaly aggregators base classes
+Base Aggregator
+---------------
 """
 
 # TODO:

darts/ad/aggregators/ensemble_sklearn_aggregator.py

@@ -18,7 +18,7 @@ def __init__(self, model: BaseEnsemble) -> None:
         """Ensemble scikit-learn aggregator
 
         Aggregator wrapped around the sklearn ensemble model `sklearn ensemble model
-        <https://scikit-learn.org/stable/modules/ensemble.html>`_.
+        <https://scikit-learn.org/stable/modules/ensemble.html>`__.
 
         Parameters
         ----------

darts/ad/anomaly_model/anomaly_model.py

@@ -1,5 +1,6 @@
 """
-Anomaly models base classes
+Base Anomaly Model
+------------------
 """
 
 import sys

darts/ad/detectors/detectors.py

@@ -1,5 +1,6 @@
 """
-Detector Base Classes
+Base Detector
+-------------
 """
 
 # TODO:

darts/ad/detectors/iqr_detector.py

@@ -1,6 +1,6 @@
 """
 Interquartile Range (IQR) Detector
------------------
+----------------------------------
 
 Flags anomalies that are beyond the IQR (between the third and the first quartile)
 of historical data by some factor of it's difference (typically 1.5).

darts/ad/scorers/__init__.py

@@ -29,7 +29,7 @@
   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>`_
+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.
 
 Most of the scorers have the following main parameters:

darts/ad/scorers/pyod_scorer.py

@@ -1,9 +1,9 @@
 """
 PyOD Scorer
------
+-----------
 
 This scorer can wrap around detection algorithms of PyOD.
-`PyOD https://pyod.readthedocs.io/en/latest/#`_.
+`PyOD <https://pyod.readthedocs.io/en/latest/>`__.
 """
 
 import numpy as np

darts/ad/scorers/scorers.py

@@ -1,5 +1,6 @@
 """
-Scorers Base Classes
+Base Scorer
+-----------
 """
 
 # TODO:
@@ -592,15 +593,17 @@ def show_anomalies(
         Computes the score on the given series input. And plots the results.
 
         The plot will be composed of the following:
-            - the series itself.
-            - the anomaly score of the score.
-            - the actual anomalies, if given.
+
+        - the series itself.
+        - the anomaly score of the score.
+        - the actual anomalies, if given.
 
         It is possible to:
-            - add a title to the figure with the parameter `title`
-            - give personalized name to the scorer with `scorer_name`
-            - show the results of a metric for the anomaly score (AUC_ROC or AUC_PR),
-            if the actual anomalies is provided.
+
+        - add a title to the figure with the parameter `title`
+        - give personalized name to the scorer with `scorer_name`
+        - show the results of a metric for the anomaly score (AUC_ROC or AUC_PR),
+          if the actual anomalies is provided.
 
         Parameters
         ----------

darts/ad/scorers/wasserstein_scorer.py

@@ -1,10 +1,10 @@
 """
 Wasserstein Scorer
------
+------------------
 
 Wasserstein Scorer (distance function defined between probability distributions) [1]_.
 The implementations is wrapped around `scipy.stats
-<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html>`_.
+<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html>`__.
 
 References
 ----------

darts/dataprocessing/dtw/cost_matrix.py

@@ -1,3 +1,8 @@
+"""
+DTW Cost Matrix
+---------------
+"""
+
 import array
 from abc import ABC, abstractmethod
 from itertools import repeat

darts/dataprocessing/dtw/dtw.py

@@ -166,12 +166,6 @@ class DTWAlignment:
         The `CostMatrix` for DTW.
     """
 
-    n: int
-    m: int
-    series1: TimeSeries
-    series2: TimeSeries
-    cost: CostMatrix
-
     def __init__(self, series1: TimeSeries, series2: TimeSeries, cost: CostMatrix):
         self.n = len(series1)
         self.m = len(series2)

darts/dataprocessing/encoders/encoder_base.py

@@ -53,19 +53,20 @@ def __init__(
         scenarios described below. With user `covariates`, it simply copies and returns the `covariates` time index.
 
         It can be used:
-        A   in combination with :class:`LocalForecastingModel`, or in a model agnostic scenario:
-                All parameters can be ignored. This scenario is only supported by
-                :class:`FutureCovariatesIndexGenerator`.
-        B   in combination with :class:`SKLearnModel`:
-                Set `input_chunk_length`, `output_chunk_length`, and `lags_covariates`.
-                `input_chunk_length` is the absolute value of the minimum target lag `abs(min(lags))` used with the
-                `SKLearnModel`.
-                Set `output_chunk_length`, and `lags_covariates` with the identical values used at forecasting model
-                creation. For the covariates lags, use `lags_past_covariates` for class:`PastCovariatesIndexGenerator`,
-                and `lags_future_covariates` for class:`PastCovariatesIndexGenerator`.
-        C   in combination with :class:`TorchForecastingModel`:
-                Set `input_chunk_length`, and `output_chunk_length` with the identical values used at forecasting model
-                creation.
+
+        - in combination with :class:`LocalForecastingModel`, or in a model agnostic scenario:
+          All parameters can be ignored. This scenario is only supported by
+          :class:`FutureCovariatesIndexGenerator`.
+        - in combination with :class:`SKLearnModel`:
+          Set `input_chunk_length`, `output_chunk_length`, and `lags_covariates`.
+          `input_chunk_length` is the absolute value of the minimum target lag `abs(min(lags))` used with the
+          `SKLearnModel`.
+          Set `output_chunk_length`, and `lags_covariates` with the identical values used at forecasting model
+          creation. For the covariates lags, use `lags_past_covariates` for class:`PastCovariatesIndexGenerator`,
+          and `lags_future_covariates` for class:`PastCovariatesIndexGenerator`.
+        - in combination with :class:`TorchForecastingModel`:
+          Set `input_chunk_length`, and `output_chunk_length` with the identical values used at forecasting model
+          creation.
 
         Parameters
         ----------

darts/dataprocessing/pipeline.py

@@ -186,17 +186,17 @@ def inverse_transform(
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
         """
         For each data transformer in the pipeline, inverse-transform data. Then inverse transformed data is passed to
-        the next transformer. Transformers are traversed in reverse order. Raises value error if not all of the
-        transformers are invertible and ``partial`` is set to `False`. Set ``partial`` to True for inverting only the
-        InvertibleDataTransformer in the pipeline.
+        the next transformer. Transformers are traversed in reverse order. Raises value error if not all transformers
+        are invertible and `partial` is set to `False`. Set `partial` to True for inverting only the
+        `InvertibleDataTransformer` in the pipeline.
 
         Parameters
         ----------
         data
             (Sequence of) TimeSeries to be inverse transformed.
         partial
             If set to `True`, the inverse transformation is applied even if the pipeline is not fully invertible,
-            calling `inverse_transform()` only on the `InvertibleDataTransformer`s
+            calling `inverse_transform()` only on transformers of type `InvertibleDataTransformer`.
         series_idx
             Optionally, the index(es) of each series corresponding to their positions within the series used to fit
             the transformer (to retrieve the appropriate transformer parameters).