Docs/general improvements (#1904)

* udpate dtw example and add dtw to rendered documentation * make all windows inherit from Window * clean up windows * improve dtw documentation * improved forecasting model module documentation * update models and add model links in covariates user guide * add model links to README * update changelog * fix typo in dtw example notebook * remove outdated lines from tide model from before probabilistic support * apply suggestions from PR review * update readme model table * Feat/fit predict encodings (#1925) * added encode_train_inference to encoders * added generate_fit_predict_encodings to ForecastingModel * simplify TransferrableFut..Model.generatice_predict_encodings * update changelog * Apply suggestions from code review Co-authored-by: madtoinou <[email protected]> * apply suggestions from PR review part 2 --------- Co-authored-by: madtoinou <[email protected]> * update readme * apply suggestions from code review and improve README.md * Update README.md Co-authored-by: madtoinou <[email protected]> --------- Co-authored-by: madtoinou <[email protected]>
unit8co · Aug 4, 2023 · 8b88b0d · 8b88b0d
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -35,6 +35,9 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 - Improvements to `Explainability` module:
   - 🚀🚀 New forecasting model explainer: `TFTExplainer` for `TFTModel`. You can now access and visualize the trained model's feature importances and self attention. [#1392](https://github.com/unit8co/darts/issues/1392) by [Sebastian Cattes](https://github.com/Cattes) and [Dennis Bader](https://github.com/dennisbader).
   - Added static covariates support to `ShapeExplainer`. [#1803](https://github.com/unit8co/darts/pull/1803) by [Anne de Vries](https://github.com/anne-devries) and [Dennis Bader](https://github.com/dennisbader).
+- Improvements to documentation [#1904](https://github.com/unit8co/darts/pull/1904) by [Dennis Bader](https://github.com/dennisbader):
+  - made model sections in README.md, covariates user guide and forecasting model API Reference more user friendly by adding model links and reorganizing them into model categories.
+  - added the Dynamic Time Warping (DTW) module and improved its appearance.
 - Other improvements:
   - Improved static covariates column naming when using `StaticCovariatesTransformer` with a `sklearn.preprocessing.OneHotEncoder`. [#1863](https://github.com/unit8co/darts/pull/1863) by [Anne de Vries](https://github.com/anne-devries).
   - Added `MSTL` (Season-Trend decomposition using LOESS for multiple seasonalities) as a `method` option for `extract_trend_and_seasonality()`. [#1879](https://github.com/unit8co/darts/pull/1879) by [Alex Colpitts](https://github.com/alexcolpitts96).

diff --git a/README.md b/README.md
diff --git a/darts/dataprocessing/dtw/__init__.py b/darts/dataprocessing/dtw/__init__.py
@@ -1,3 +1,8 @@
+"""
+Dynamic Time Warping (DTW)
+--------------------------
+"""
+
 from .cost_matrix import CostMatrix
 from .dtw import DTWAlignment, dtw
 from .window import CRWindow, Itakura, NoWindow, SakoeChiba, Window
diff --git a/darts/dataprocessing/dtw/dtw.py b/darts/dataprocessing/dtw/dtw.py
@@ -1,5 +1,10 @@
+"""
+Dynamic Time Warping (DTW)
+--------------------------
+"""
+
 import copy
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -21,7 +26,7 @@
 # CORE ALGORITHM
 def _dtw_cost_matrix(
     x: np.ndarray, y: np.ndarray, dist: DistanceFunc, window: Window
-) -> np.ndarray:
+) -> CostMatrix:
 
     dtw = CostMatrix._from_window(window)
 
@@ -138,16 +143,40 @@ def _fast_dtw(
     return cost
 
 
+def _default_distance_multi(x_values: np.ndarray, y_values: np.ndarray):
+    return np.sum(np.abs(x_values - y_values))
+
+
+def _default_distance_uni(x_value: float, y_value: float):
+    return abs(x_value - y_value)
+
+
 # Public API Functions
 class DTWAlignment:
+    """
+    Dynamic Time Warping (DTW) Alignment.
+
+    Attributes
+    ----------
+    n
+        The length of `series1`
+    m
+        The length of `series2`
+    series1
+        A `TimeSeries` to align with `series2`.
+    series2
+        A `TimeSeries` to align with `series1`.
+    cost
+        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)
         self.series1 = series1
@@ -157,7 +186,8 @@ def __init__(self, series1: TimeSeries, series2: TimeSeries, cost: CostMatrix):
     from ._plot import plot, plot_alignment
 
     def path(self) -> np.ndarray:
-        """
+        """Gives the index paths from `series1` to `series2`.
+
         Returns
         -------
         np.ndarray of shape `(len(path), 2)`
@@ -172,7 +202,8 @@ def path(self) -> np.ndarray:
         return self._path
 
     def distance(self) -> float:
-        """
+        """Gives the total distance between pair-wise elements in the two series after warping.
+
         Returns
         -------
         float
@@ -181,7 +212,8 @@ def distance(self) -> float:
         return self.cost[(self.n, self.m)]
 
     def mean_distance(self) -> float:
-        """
+        """Gives the mean distance between pair-wise elements in the two series after warping.
+
         Returns
         -------
         float
@@ -195,9 +227,8 @@ def mean_distance(self) -> float:
         return self._mean_distance
 
     def warped(self) -> (TimeSeries, TimeSeries):
-        """
-        Warps the two time series according to the warp path returned by .path(), which minimizes
-        the pair-wise distance.
+        """Warps the two time series according to the warp path returned by `DTWAlignment.path()`, which minimizes the
+        pair-wise distance.
         This will bring two time series that are out-of-phase back into phase.
 
         Returns
@@ -254,24 +285,16 @@ def warped(self) -> (TimeSeries, TimeSeries):
         )
 
 
-def default_distance_multi(x_values: np.ndarray, y_values: np.ndarray):
-    return np.sum(np.abs(x_values - y_values))
-
-
-def default_distance_uni(x_value: float, y_value: float):
-    return abs(x_value - y_value)
-
-
 def dtw(
     series1: TimeSeries,
     series2: TimeSeries,
-    window: Window = NoWindow(),
+    window: Optional[Window] = None,
     distance: Union[DistanceFunc, None] = None,
     multi_grid_radius: int = -1,
 ) -> DTWAlignment:
     """
-    Determines the optimal alignment between two time series series1 and series2,
-    according to the Dynamic Time Warping algorithm.
+    Determines the optimal alignment between two time series `series1` and `series2`, according to the Dynamic Time
+    Warping algorithm.
     The alignment minimizes the distance between pair-wise elements after warping.
     All elements in the two series are matched and are in strictly monotonically increasing order.
     Considers only the values in the series, ignoring the time axis.
@@ -282,24 +305,24 @@ def dtw(
     Parameters
     ----------
     series1
-        `TimeSeries`
+        A `TimeSeries` to align with `series2`.
     series2
-        A `TimeSeries`
+        A `TimeSeries` to align with `series1`.
     window
-        Used to constrain the search for the optimal alignment: see SakoeChiba and Itakura.
-        Default considers all possible alignments.
+        Optionally, a `Window` used to constrain the search for the optimal alignment: see `SakoeChiba` and `Itakura`.
+        Default considers all possible alignments (`NoWindow`).
     distance
         Function taking as input either two `floats` for univariate series or two `np.ndarray`,
         and returning the distance between them.
 
         Defaults to the abs difference for univariate-data and the
         sum of the abs difference for multi-variate series.
     multi_grid_radius
-        Default radius of -1 results in an exact evaluation of the dynamic time warping algorithm.
+        Default radius of `-1` results in an exact evaluation of the dynamic time warping algorithm.
         Without constraints DTW runs in O(nxm) time where n,m are the size of the series.
         Exact evaluation with no constraints, will result in a performance warning on large datasets.
 
-        Setting multi_grid_radius to a value other than -1, will enable the approximate multi-grid solver,
+        Setting `multi_grid_radius` to a value other than `-1`, will enable the approximate multi-grid solver,
         which executes in linear time, vs quadratic time for exact evaluation.
         Increasing radius trades solution accuracy for performance.
 
@@ -308,6 +331,8 @@ def dtw(
     DTWAlignment
         Helper object for getting warp path, mean_distance, distance and warped time series
     """
+    if window is None:
+        window = NoWindow()
 
     if (
         multi_grid_radius == -1
@@ -328,7 +353,7 @@ def dtw(
             logger,
         )
 
-        distance = default_distance_uni if both_univariate else default_distance_multi
+        distance = _default_distance_uni if both_univariate else _default_distance_multi
 
     if both_univariate:
         values_x = series1.univariate_values(copy=False)