Merge branch 'master' into feat/set-up-flake8

Author: tomasvanpottelbergh

darts/dataprocessing/dtw/cost_matrix.py

@@ -1,4 +1,4 @@
-from typing import Tuple, Dict
+from typing import Tuple
 import numpy as np
 
 from .window import Window, CRWindow
@@ -117,8 +117,8 @@ def to_dense(self) -> np.ndarray:
 
             # TODO express only in terms of numpy operations
             for i in range(1, self.n + 1):
-                start = self.window.column_ranges[i * 2 + 0] - 1
-                end = self.window.column_ranges[i * 2 + 1] - 1
+                start = ranges[i * 2 + 0] - 1
+                end = ranges[i * 2 + 1] - 1
                 len = lengths[i]
                 offset = self.offsets[i]
 
@@ -150,7 +150,6 @@ def __setitem__(self, elem, value):
         i, j = elem
 
         start = self.column_ranges[i * 2 + 0]
-        end = self.column_ranges[i * 2 + 1]
 
         self.dense[self.offsets[i] + j - start] = value
 

darts/dataprocessing/dtw/window.py

@@ -1,5 +1,3 @@
-from typing import Iterable, Tuple
-from dataclasses import dataclass
 import numpy as np
 from darts.logging import raise_if_not, raise_if
 from abc import ABC, abstractmethod

darts/dataprocessing/transformers/base_data_transformer.py

@@ -109,8 +109,8 @@ def _transform_iterator(
     ) -> Iterator[Tuple[TimeSeries]]:
         """
         Return an ``Iterator`` object with tuples of inputs for each single call to :func:`ts_transform()`.
-        Additional `args` and `kwargs` from :func:`transform()` (constant across all the calls to :func:`ts_transform()`)
-        are already forwarded, and thus don't need to be included in this generator.
+        Additional `args` and `kwargs` from :func:`transform()` (constant across all the calls to
+        :func:`ts_transform()`) are already forwarded, and thus don't need to be included in this generator.
 
         The basic implementation of this method returns ``zip(series)``, i.e., a generator of single-valued tuples,
         each containing one ``TimeSeries`` object.

darts/dataprocessing/transformers/boxcox.py

@@ -44,11 +44,11 @@ def __init__(
         name
             A specific name for the transformer
         lmbda
-            The parameter :math:`\lambda` of the Box-Cox transform. If a single float is given, the same
-            :math:`\lambda` value will be used for all dimensions of the series, for all the series.
+            The parameter :math:`\\lambda` of the Box-Cox transform. If a single float is given, the same
+            :math:`\\lambda` value will be used for all dimensions of the series, for all the series.
             If a sequence is given, there is one value per component in the series. If a sequence of sequence
             is given, there is one value per component for all series.
-            If `None` given, will automatically find an optimal value of :math:`\lambda` (for each dimension
+            If `None` given, will automatically find an optimal value of :math:`\\lambda` (for each dimension
             of the time series, for each time series) using :func:`scipy.stats.boxcox_normmax`
             with ``method=optim_method``.
         optim_method

darts/datasets/__init__.py

@@ -207,7 +207,8 @@ class TaylorDataset(DatasetLoaderCSV):
 
     References
     ----------
-    .. [1] Taylor, J.W. (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54, 799-805.
+    .. [1] Taylor, J.W. (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing.
+           Journal of the Operational Research Society, 54, 799-805.
 
     .. [2] https://www.rdocumentation.org/packages/forecast/versions/8.13/topics/taylor
     """

darts/datasets/dataset_loaders.py

@@ -5,7 +5,6 @@
 from abc import ABC, abstractmethod
 
 import pandas as pd
-import numpy as np
 import requests
 
 from darts import TimeSeries

darts/metrics/metrics.py

@@ -30,8 +30,8 @@
 def multi_ts_support(func):
     """
     This decorator further adapts the metrics that took as input two univariate/multivariate ``TimeSeries`` instances,
-    adding support for equally-sized sequences of ``TimeSeries`` instances. The decorator computes the pairwise metric for
-    ``TimeSeries`` with the same indices, and returns a float value that is computed as a function of all the
+    adding support for equally-sized sequences of ``TimeSeries`` instances. The decorator computes the pairwise metric
+    for ``TimeSeries`` with the same indices, and returns a float value that is computed as a function of all the
     pairwise metrics using a `inter_reduction` subroutine passed as argument to the metric function.
 
     If a 'Sequence[TimeSeries]' is passed as input, this decorator provides also parallelisation of the metric
@@ -1146,7 +1146,7 @@ def rho_risk(
     where :math:`L_{\\rho} \\left( Z, \\hat{Z}_{\\rho} \\right)` is the :math:`\\rho`-loss function:
 
     .. math:: L_{\\rho} \\left( Z, \\hat{Z}_{\\rho} \\right) = 2 \\left( Z - \\hat{Z}_{\\rho} \\right)
-        \\left( \\rho I_{\\hat{Z}_{\\rho} < Z} - \\left( 1 - \\rho \\right) I_{\\hat{Z}_{\\rho} \geq Z} \\right),
+        \\left( \\rho I_{\\hat{Z}_{\\rho} < Z} - \\left( 1 - \\rho \\right) I_{\\hat{Z}_{\\rho} \\geq Z} \\right),
 
     where :math:`Z = \\sum_{t=1}^{T} y_t` (1) is the aggregated target value and :math:`\\hat{Z}_{\\rho}` is the
     :math:`\\rho`-quantile of the predicted values. For this, each sample realization :math:`i \\in N` is first

darts/models/filtering/gaussian_process_filter.py

@@ -10,7 +10,6 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import Kernel
 
-from darts.utils.utils import raise_if_not
 from darts.models.filtering.filtering_model import FilteringModel
 
 

darts/models/filtering/kalman_filter.py

@@ -35,8 +35,8 @@ def __init__(self, dim_x: int = 1, kf: Optional[Kalman] = None):
         This implementation uses Kalman from the NFourSID package. More information can be found here:
         https://nfoursid.readthedocs.io/en/latest/source/kalman.html.
 
-        The dimensionality of the measurements z and optional control signal (covariates) u is automatically inferred upon
-        calling `filter()`.
+        The dimensionality of the measurements z and optional control signal (covariates) u is automatically inferred
+        upon calling `filter()`.
 
         Parameters
         ----------
@@ -47,7 +47,7 @@ def __init__(self, dim_x: int = 1, kf: Optional[Kalman] = None):
             If this is provided, the parameter dim_x is ignored. This instance will be copied for every
             call to `filter()`, so the state is not carried over from one time series to another across several
             calls to `filter()`.
-            The various dimensionalities of the filter must match those of the `TimeSeries` used when calling `filter()`.
+            The dimensionalities of the filter must match those of the `TimeSeries` used when calling `filter()`.
         """
         # TODO: Add support for x_init. Needs reimplementation of NFourSID.
 
@@ -85,8 +85,8 @@ def fit(
             The series of outputs (observations) used to infer the underlying state space model.
             This must be a deterministic series (containing one sample).
         covariates : Optional[TimeSeries]
-            An optional series of inputs (control signal) that will also be used to infer the underlying state space model.
-            This must be a deterministic series (containing one sample).
+            An optional series of inputs (control signal) that will also be used to infer the underlying state space
+            model. This must be a deterministic series (containing one sample).
         num_block_rows : Optional[int]
             The number of block rows to use in the block Hankel matrices used in the N4SID algorithm.
             See the documentation of nfoursid.nfoursid.NFourSID for more information.
@@ -142,11 +142,11 @@ def filter(
         Parameters
         ----------
         series : TimeSeries
-            The series of outputs (observations) used to infer the underlying outputs according to the specified Kalman process.
-            This must be a deterministic series (containing one sample).
+            The series of outputs (observations) used to infer the underlying outputs according to the specified Kalman
+            process. This must be a deterministic series (containing one sample).
         covariates : Optional[TimeSeries]
-            An optional series of inputs (control signal), necessary if the Kalman filter was initialized with covariates.
-            This must be a deterministic series (containing one sample).
+            An optional series of inputs (control signal), necessary if the Kalman filter was initialized with
+            covariates. This must be a deterministic series (containing one sample).
         num_samples : int, default: 1
             The number of samples to generate from the inferred distribution of the output z. If this is set to 1, the
             output is a `TimeSeries` containing a single sample using the mean of the distribution.

darts/models/forecasting/block_rnn_model.py

@@ -226,9 +226,9 @@ def __init__(
             Default: ``torch.nn.MSELoss()``.
         model_name
             Name of the model. Used for creating checkpoints and saving tensorboard data. If not specified,
-            defaults to the following string ``"YYYY-mm-dd_HH:MM:SS_torch_model_run_PID"``, where the initial part of the
-            name is formatted with the local date and time, while PID is the processed ID (preventing models spawned at
-            the same time by different processes to share the same model_name). E.g.,
+            defaults to the following string ``"YYYY-mm-dd_HH:MM:SS_torch_model_run_PID"``, where the initial part of
+            the name is formatted with the local date and time, while PID is the processed ID (preventing models spawned
+            at the same time by different processes to share the same model_name). E.g.,
             ``"2021-06-14_09:53:32_torch_model_run_44607"``.
         work_dir
             Path of the working directory, where to save checkpoints and Tensorboard summaries.