Merge branch 'main' into neuralforcast

.all-contributorsrc

@@ -2717,6 +2717,15 @@
         "doc"
       ]
     },
+    {
+      "login": "MMTrooper",
+      "name": "Michael Mwimali",
+      "avatar_url": "https://avatars.githubusercontent.com/u/89777534?v=4",
+      "profile": "https://github.com/MMTrooper",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "manuel-munoz-aguirre",
       "name": "Manuel Muñoz Aguirre",
@@ -2725,6 +2734,15 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "anteemony",
+      "name": "Anthony Okonneh",
+      "avatar_url": "https://avatars.githubusercontent.com/u/90141191?v=4",
+      "profile": "https://github.com/Anteemony",
+      "contributions": [
+        "doc"
+      ]
     }
   ]
 }

docs/source/api_reference/split.rst

@@ -35,6 +35,7 @@ Time index splitters
 
 Time index splitters split one or multiple time series by temporal order.
 They are typically used in both evaluation and tuning of forecasters.
+They have tag ``"split_type"="temporal"``.
 
 .. currentmodule:: sktime.split
 
@@ -72,3 +73,20 @@ more complex time index based splitting strategies.
 
     SameLocSplitter
     TestPlusTrainSplitter
+
+
+Instance splitters
+------------------
+
+Instance splitters split panels or hierarchical time series by
+the instance index, i.e., identifiers for entire series.
+Train and test sets contain entire series from the original panel.
+Instance splitters have tag ``"split_type"="instance"``.
+
+.. currentmodule:: sktime.split
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    InstanceSplitter

examples/02_classification.ipynb

@@ -1094,7 +1094,7 @@
     "X_test, y_test = load_italy_power_demand(split=\"test\", return_type=\"numpy3D\")\n",
     "\n",
     "# step 3-5 are the same\n",
-    "from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
+    "\n",
     "from sktime.dists_kernels import FlatDist, ScipyDist\n",
     "\n",
     "eucl_dist = FlatDist(ScipyDist())\n",

pyproject.toml

@@ -207,7 +207,7 @@ regression = [
 transformations = [
   'esig<0.10,>=0.9.7; python_version < "3.11"',
   "filterpy<1.5,>=1.4.5",
-  "holidays>=0.29,<0.46",
+  "holidays>=0.29,<0.47",
   "mne>=1.5,<1.7",
   'numba<0.60,>=0.53',
   "pycatch22>=0.4,<0.4.4",

sktime/classification/deep_learning/inceptiontime.py

@@ -41,6 +41,16 @@ class InceptionTimeClassifier(BaseDeepClassifier):
 
     Adapted from the implementation from Fawaz et. al
     https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
+
+    Examples
+    --------
+    >>> from sktime.classification.deep_learning import InceptionTimeClassifier
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train")
+    >>> X_test, y_test = load_unit_test(split="test")
+    >>> clf = InceptionTimeClassifier()  # doctest: +SKIP
+    >>> clf.fit(X_train, y_train)  # doctest: +SKIP
+    InceptionTimeClassifier(...)
     """
 
     _tags = {

sktime/classification/feature_based/_matrix_profile_classifier.py

@@ -194,4 +194,7 @@ def get_test_params(cls, parameter_set="default"):
             instance.
             ``create_test_instance`` uses the first (or only) dictionary in ``params``.
         """
-        return {"subsequence_length": 4}
+        return [
+            {"subsequence_length": 4},
+            {"subsequence_length": 6, "estimator": KNeighborsClassifier(n_neighbors=3)},
+        ]

sktime/forecasting/base/adapters/_neuralforecast.py

@@ -2,25 +2,29 @@
 """Implements adapter for NeuralForecast models."""
 import abc
 import functools
-import typing
+
 from inspect import signature
+from typing import List, Literal, Optional, Union
 
+import numpy as np
 import pandas
 
 from sktime.forecasting.base import BaseForecaster, ForecastingHorizon
 from sktime.utils.warnings import warn
 
 __all__ = ["_NeuralForecastAdapter"]
-__author__ = ["yarnabrina", "pranavvp16"]
+__author__ = ["yarnabrina", "geetu040", "pranavvp16"]
+
 
 
 class _NeuralForecastAdapter(BaseForecaster):
     """Base adapter class for NeuralForecast models.
 
     Parameters
     ----------
-    freq : str (default="auto")
+    freq : Union[str, int] (default="auto")
         frequency of the data, see available frequencies [1]_ from ``pandas``
+        use int freq when using RangeIndex in ``y``
 
         default ("auto") interprets freq from ForecastingHorizon in ``fit``
     local_scaler_type : str (default=None)
@@ -70,11 +74,11 @@ class _NeuralForecastAdapter(BaseForecaster):
 
     def __init__(
         self: "_NeuralForecastAdapter",
-        freq: str = "auto",
-        local_scaler_type: typing.Optional[
-            typing.Literal["standard", "robust", "robust-iqr", "minmax", "boxcox"]
+        freq: Union[str, int] = "auto",
+        local_scaler_type: Optional[
+            Literal["standard", "robust", "robust-iqr", "minmax", "boxcox"]
         ] = None,
-        futr_exog_list: typing.Optional[typing.List[str]] = None,
+        futr_exog_list: Optional[List[str]] = None,
         verbose_fit: bool = False,
         verbose_predict: bool = False,
     ) -> None:
@@ -191,7 +195,7 @@ def _instantiate_model(self: "_NeuralForecastAdapter", fh: ForecastingHorizon):
     def _fit(
         self: "_NeuralForecastAdapter",
         y: pandas.Series,
-        X: typing.Optional[pandas.DataFrame],
+        X: Optional[pandas.DataFrame],
         fh: ForecastingHorizon,
     ) -> "_NeuralForecastAdapter":
         """Fit forecaster to training data.
@@ -224,15 +228,60 @@ def _fit(
         if not fh.is_all_out_of_sample(cutoff=self.cutoff):
             raise NotImplementedError("in-sample prediction is currently not supported")
 
-        if self.freq == "auto" and fh.freq is None:
-            # when freq cannot be interpreted from ForecastingHorizon
-            raise ValueError(
-                f"Error in {self.__class__.__name__}, "
-                f"could not interpret freq, "
-                f"try passing freq in model initialization"
-            )
-
-        self._freq = fh.freq if self.freq == "auto" else self.freq
+        # A. freq is given {use this}
+        # B. freq is auto
+        #     B1. freq is infered from fh {use this}
+        #     B2. freq is not infered from fh
+        #         B2.1. y is date-like {raise exception}
+        #         B2.2. y is not date-like
+        #             B2.2.1 equispaced integers {use diff in time}
+        #             B2.2.2 non-equispaced integers {raise exception}
+
+        # behavior of different indexes when freq="auto"
+        # | Indexes                 | behavior  |
+        # | ----------------------- | --------- |
+        # | PeriodIndex             | B1        |
+        # | PeriodIndex (Missing)   | B1        |
+        # | DatetimeIndex           | B1        |
+        # | DatetimeIndex (Missing) | B2.1      |
+        # | RangeIndex              | B2.2.1    |
+        # | RangeIndex (Missing)    | B2.2.2    |
+        # | Index                   | B2.2.1    |
+        # | Index (Missing)         | B2.2.2    |
+        # | Other                   | unreached |
+
+        if self.freq != "auto":
+            # A
+            self._freq = self.freq
+        else:
+            # B
+            if fh.freq:
+                # B1
+                self._freq = fh.freq
+            else:
+                # B2
+                if isinstance(y.index, pandas.DatetimeIndex):
+                    # B2.1
+                    raise ValueError(
+                        f"Error in {self.__class__.__name__}, "
+                        "could not interpret freq, "
+                        "try passing freq in model initialization "
+                        "or use a valid offset in index"
+                    )
+                else:
+                    # B2.2
+                    diffs = np.unique(np.diff(y.index))
+                    if diffs.shape[0] > 1:
+                        # B2.2.1
+                        raise ValueError(
+                            f"Error in {self.__class__.__name__}, "
+                            "could not interpret freq, "
+                            "try passing integer freq in model initialization "
+                            "or use a valid integer offset in index"
+                        )
+                    else:
+                        # B2.2.2
+                        self._freq = int(diffs[-1])  # converts numpy.int64 to int
 
         train_indices = y.index
         if isinstance(train_indices, pandas.PeriodIndex):
@@ -262,8 +311,8 @@ def _fit(
 
     def _predict(
         self: "_NeuralForecastAdapter",
-        fh: typing.Optional[ForecastingHorizon],
-        X: typing.Optional[pandas.DataFrame],
+        fh: Optional[ForecastingHorizon],
+        X: Optional[pandas.DataFrame],
     ) -> pandas.Series:
         """Forecast time series at future horizon.