Merge branch 'main' into parallel-hierarchical-backendparams

sktime · Oct 7, 2023 · 7bdd05f · 7bdd05f
2 parents 467fba6 + ac6bf41
commit 7bdd05f
Show file tree

Hide file tree

Showing 167 changed files with 9,665 additions and 7,263 deletions.
diff --git a/.all-contributorsrc b/.all-contributorsrc
@@ -19,6 +19,7 @@
       "avatar_url": "https://avatars1.githubusercontent.com/u/7985502?v=4",
       "profile": "https://github.com/fkiraly",
       "contributions": [
+        "blog",
         "bug",
         "business",
         "code",
@@ -2356,6 +2357,15 @@
         "maintenance"
       ]
     },
+    {
+      "login": "ali-parizad",
+      "name": "Ali Parizad",
+      "avatar_url": "https://avatars.githubusercontent.com/u/13907016?v=4",
+      "profile": "https://github.com/ali-parizad",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "BensHamza",
       "name": "Hamza Benslimane",
@@ -2365,6 +2375,36 @@
         "bug",
         "code"
       ]
+    },
+    {
+      "login": "Vasudeva-bit",
+      "name": "Vasudeva Kilaru",
+      "avatar_url": "https://avatars.githubusercontent.com/u/70791259?v=4",
+      "profile": "https://github.com/Vasudeva-bit",
+      "contributions": [
+        "code",
+        "doc"
+      ]
+    },
+    {
+      "login": "geronimos",
+      "name": "Geronimo Bergk",
+      "avatar_url": "https://avatars.githubusercontent.com/u/29955288?s=96&v=4",
+      "profile": "https://github.com/geronimos",
+      "contributions": [
+        "bug",
+        "code"
+      ]
+    },
+    {
+      "login": "julnow",
+      "name": "Julian Nowak",
+      "avatar_url": "https://avatars.githubusercontent.com/u/21206185?v=4",
+      "profile": "https://github.com/julnow",
+      "contributions": [
+        "bug",
+        "code"
+      ]
     }
   ]
 }
diff --git a/.github/workflows/cancel.yml b/.github/workflows/cancel.yml
@@ -8,6 +8,6 @@ jobs:
   cancel:
     runs-on: ubuntu-latest
     steps:
-    - uses: styfle/cancel-workflow-action@0.11.0
+    - uses: styfle/cancel-workflow-action@0.12.0
       with:
         workflow_id: ${{ github.event.workflow.id }}
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -169,63 +169,13 @@ jobs:
       - name: Publish code coverage
         uses: codecov/codecov-action@v3
 
-  test-windows:
+  test-full:
     needs: test-nosoftdeps
-    runs-on: windows-latest
     strategy:
       fail-fast: false  # to not fail all combinations if just one fail
       matrix:
         python-version: ['3.8', '3.9', '3.10', '3.11']
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - run: git remote set-branches origin 'main'
-
-      - run: git fetch --depth 1
-
-      - uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-update-conda: true
-          python-version: ${{ matrix.python-version }}
-          channels: anaconda, conda-forge,
-
-      - run: conda --version
-      - run: which python
-
-      - name: Fix windows paths
-        if: ${{ runner.os == 'Windows' }}
-        run: echo "C:\Program Files (x86)\Windows Kits\10\bin\10.0.18362.0\x64" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-
-      - name: Install conda libpython
-        run: conda install -c anaconda libpython
-
-      - name: Install sktime and dependencies
-        run: python -m pip install .[all_extras_pandas2,dev]
-
-      - name: Show dependencies
-        run: python -m pip list
-
-      - name: Show available branches
-        run: git branch -a
-
-      - name: Run tests
-        run: |
-          mkdir -p testdir/
-          cp .coveragerc testdir/
-          cp setup.cfg testdir/
-          python -m pytest
-
-      - name: Publish code coverage
-        uses: codecov/codecov-action@v3
-
-  test-unix:
-    needs: test-nosoftdeps
-    strategy:
-      fail-fast: false  # to not fail all combinations if just one fail
-      matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11']
-        os: [ubuntu-latest, macos-latest]
+        os: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v4

diff --git a/CODEOWNERS b/CODEOWNERS
@@ -38,6 +38,7 @@ sktime/classification/sklearn/_continuous_interval_tree.py @MatthewMiddlehurst
 sktime/classification/sklearn/_rotation_forest.py @MatthewMiddlehurst
 
 sktime/forecasting/adapters/_hcrystalball.py @MichalChromcak
+sktime/forecasting/arch/_uarch.py @Vasudeva-bit
 sktime/forecasting/arima.py @HYang1996
 sktime/forecasting/base/adapters/_statsforecast.py @FedericoGarza
 sktime/forecasting/bats.py @aiwalter

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
diff --git a/README.md b/README.md
@@ -12,7 +12,7 @@ sktime is a library for time series analysis in Python. It provides a unified in
 
 | Overview | |
 |---|---|
-| <nobr>**Open Source**</nobr> | [![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE) |
+| **Open&#160;Source** | [![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE) |
 | **Tutorials** | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) [![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
 | **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/scikit-time/)  |
 | **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/sktime/sktime/wheels.yml?logo=github)](https://github.com/sktime/sktime/actions/workflows/wheels.yml) [![!codecov](https://img.shields.io/codecov/c/github/sktime/sktime?label=codecov&logo=codecov)](https://codecov.io/gh/sktime/sktime) [![readthedocs](https://img.shields.io/readthedocs/sktime?logo=readthedocs)](https://www.sktime.net/en/latest/?badge=latest) [![platform](https://img.shields.io/conda/pn/conda-forge/sktime)](https://github.com/sktime/sktime) |

diff --git a/docs/source/api_reference/datasets.rst b/docs/source/api_reference/datasets.rst
@@ -105,8 +105,10 @@ use common utilities such as ``pandas.read_csv``
     load_from_tsfile
     load_from_tsfile_to_dataframe
     load_from_ucr_tsv_to_dataframe
+    load_from_long_to_dataframe
     load_tsf_to_dataframe
     write_panel_to_tsfile
     write_dataframe_to_tsfile
     write_ndarray_to_tsfile
     write_tabular_transformation_to_arff
+    write_results_to_uea_format
diff --git a/docs/source/api_reference/forecasting.rst b/docs/source/api_reference/forecasting.rst
@@ -149,6 +149,7 @@ Trend forecasters
     TrendForecaster
     PolynomialTrendForecaster
     STLForecaster
+    CurveFitForecaster
 
 .. currentmodule:: sktime.forecasting.statsforecast
 
@@ -281,7 +282,7 @@ ARCH models
 
     StatsForecastARCH
     StatsForecastGARCH
-
+    ARCH
 
 Structural time series models
 -----------------------------

diff --git a/docs/source/api_reference/transformations.rst b/docs/source/api_reference/transformations.rst
@@ -491,6 +491,14 @@ Seasonality and Date-Time Features
 
     FourierTransform
 
+.. currentmodule:: sktime.transformations.series.peak
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    PeakTimeFeature
+
 Auto-correlation series
 ~~~~~~~~~~~~~~~~~~~~~~~
 

diff --git a/examples/01_forecasting.ipynb b/examples/01_forecasting.ipynb
@@ -2296,7 +2296,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sktime.forecasting.model_selection import temporal_train_test_split"
+    "from sktime.split import temporal_train_test_split"
    ]
   },
   {
@@ -2541,9 +2541,9 @@
    "source": [
     "from sktime.datasets import load_airline\n",
     "from sktime.forecasting.base import ForecastingHorizon\n",
-    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
     "from sktime.forecasting.naive import NaiveForecaster\n",
-    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error"
+    "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
+    "from sktime.split import temporal_train_test_split"
    ]
   },
   {
@@ -2599,9 +2599,9 @@
    "source": [
     "from sktime.datasets import load_airline\n",
     "from sktime.forecasting.base import ForecastingHorizon\n",
-    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
     "from sktime.forecasting.naive import NaiveForecaster\n",
-    "from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError"
+    "from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError\n",
+    "from sktime.split import temporal_train_test_split"
    ]
   },
   {
@@ -3010,7 +3010,7 @@
    "source": [
     "# from sktime.datasets import load_airline\n",
     "# from sktime.forecasting.ets import AutoETS\n",
-    "# from sktime.forecasting.model_selection import ExpandingWindowSplitter\n",
+    "# from sktime.split import ExpandingWindowSplitter\n",
     "# from sktime.utils.plotting import plot_series"
    ]
   },
@@ -3076,7 +3076,7 @@
    "source": [
     "from sktime.forecasting.arima import AutoARIMA\n",
     "from sktime.forecasting.model_evaluation import evaluate\n",
-    "from sktime.forecasting.model_selection import ExpandingWindowSplitter"
+    "from sktime.split import ExpandingWindowSplitter"
    ]
   },
   {
@@ -4579,8 +4579,8 @@
     "# imports necessary for this chapter\n",
     "from sktime.datasets import load_airline\n",
     "from sktime.forecasting.base import ForecastingHorizon\n",
-    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
     "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
+    "from sktime.split import temporal_train_test_split\n",
     "from sktime.utils.plotting import plot_series\n",
     "\n",
     "# data loading for illustration (see section 1 for explanation)\n",
@@ -5214,8 +5214,8 @@
     "# imports necessary for this chapter\n",
     "from sktime.datasets import load_airline\n",
     "from sktime.forecasting.base import ForecastingHorizon\n",
-    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
     "from sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n",
+    "from sktime.split import temporal_train_test_split\n",
     "from sktime.utils.plotting import plot_series\n",
     "\n",
     "# data loading for illustration (see section 1 for explanation)\n",
@@ -5852,10 +5852,8 @@
     "from sklearn.neighbors import KNeighborsRegressor\n",
     "\n",
     "from sktime.forecasting.compose import make_reduction\n",
-    "from sktime.forecasting.model_selection import (\n",
-    "    ForecastingGridSearchCV,\n",
-    "    SlidingWindowSplitter,\n",
-    ")"
+    "from sktime.forecasting.model_selection import ForecastingGridSearchCV\n",
+    "from sktime.split import SlidingWindowSplitter"
    ]
   },
   {
@@ -5993,10 +5991,8 @@
     "from sklearn.neighbors import KNeighborsRegressor\n",
     "\n",
     "from sktime.forecasting.compose import make_reduction\n",
-    "from sktime.forecasting.model_selection import (\n",
-    "    ForecastingGridSearchCV,\n",
-    "    SlidingWindowSplitter,\n",
-    ")"
+    "from sktime.forecasting.model_selection import ForecastingGridSearchCV\n",
+    "from sktime.split import SlidingWindowSplitter"
    ]
   },
   {
@@ -6380,10 +6376,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sktime.forecasting.model_selection import (\n",
-    "    ForecastingGridSearchCV,\n",
-    "    SlidingWindowSplitter,\n",
-    ")"
+    "from sktime.forecasting.model_selection import ForecastingGridSearchCV\n",
+    "from sktime.split import SlidingWindowSplitter"
    ]
   },
   {
@@ -6517,11 +6511,9 @@
     "\n",
     "from sktime.datasets import load_airline\n",
     "from sktime.forecasting.compose import TransformedTargetForecaster\n",
-    "from sktime.forecasting.model_selection import (\n",
-    "    ForecastingGridSearchCV,\n",
-    "    SlidingWindowSplitter,\n",
-    ")\n",
+    "from sktime.forecasting.model_selection import ForecastingGridSearchCV\n",
     "from sktime.forecasting.naive import NaiveForecaster\n",
+    "from sktime.split import SlidingWindowSplitter\n",
     "from sktime.transformations.compose import OptionalPassthrough\n",
     "from sktime.transformations.series.adapt import TabularToSeriesAdaptor\n",
     "from sktime.transformations.series.detrend import Deseasonalizer"
@@ -6690,23 +6682,15 @@
   {
    "cell_type": "code",
    "execution_count": 288,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "hedge_expert = NormalHedgeEnsemble(n_estimators=3, loss_func=mean_squared_error)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "We can then create the forecaster by defining the individual forecasters and specifying the `PredictionWeightedEnsembler` we are using. Then by fitting our forecasters and performing updates and prediction with the `update_predict` function, we get:"
    ]

diff --git a/examples/01a_forecasting_sklearn.ipynb b/examples/01a_forecasting_sklearn.ipynb
@@ -75,7 +75,7 @@
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from sktime.datasets import load_airline\n",
-    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
+    "from sktime.split import temporal_train_test_split\n",
     "from sktime.utils.plotting import plot_series"
    ]
   },
@@ -166,6 +166,8 @@
    "outputs": [],
    "source": [
     "# slightly modified code from the M4 competition\n",
+    "\n",
+    "\n",
     "def split_into_train_test(data, in_num, fh):\n",
     "    \"\"\"\n",
     "    Splits the series into train and test sets.\n",