diff --git a/.github/workflows/run-manual-pre-commit.yml b/.github/workflows/run-manual-pre-commit.yml index 02694ac98..760973959 100644 --- a/.github/workflows/run-manual-pre-commit.yml +++ b/.github/workflows/run-manual-pre-commit.yml @@ -29,7 +29,12 @@ jobs: - uses: pre-commit/action@v3.0.0 with: extra_args: --all-files --hook-stage manual --verbose - - name: Show dependencies graph + - name: Show dependencies graph (current lock) run: | - pdm install -d -G license --skip=post_install + pdm install --skip=post_install + pdm run pipdeptree --license + - name: Show dependencies graph (newest dependencies) + run: | + pdm lock --lockfile pdm.newest.lock --strategy no_cross_platform -dG:all + pdm install --lockfile pdm.newest.lock --skip=post_install pdm run pipdeptree --license diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fafa7418b..f08d3b5b5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,7 +8,7 @@ repos: - id: conventional-pre-commit stages: [commit-msg] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: 'v0.3.7' + rev: 'v0.4.1' hooks: - id: ruff types_or: [ python, pyi, jupyter ] @@ -28,7 +28,7 @@ repos: args: ["--config-file", "pyproject.toml"] additional_dependencies: ['types-requests', 'types-six'] - repo: https://github.com/pdm-project/pdm - rev: 2.14.0 + rev: 2.15.0 hooks: - id: pdm-lock-check - id: pdm-export diff --git a/.tool-versions b/.tool-versions new file mode 100644 index 000000000..593463174 --- /dev/null +++ b/.tool-versions @@ -0,0 +1 @@ +pdm 2.15.4 diff --git a/.vscode/extensions.json b/.vscode/extensions.json index c2f970320..1ed4ad14d 100644 --- a/.vscode/extensions.json +++ b/.vscode/extensions.json @@ -1,3 +1,3 @@ { - "recommendations": ["njpwerner.autodocstring", "charliermarsh.ruff"] + "recommendations": ["njpwerner.autodocstring", "charliermarsh.ruff", "matangover.mypy"] } diff --git a/.vscode/settings.json.default b/.vscode/settings.json.default index 67bb193b3..93f8427c5 100644 --- a/.vscode/settings.json.default +++ b/.vscode/settings.json.default @@ -1,14 +1,11 @@ { - "python.linting.enabled": true, - "python.linting.mypyEnabled": true, "[python]": { "editor.formatOnSave": true, "editor.codeActionsOnSave": { - "source.fixAll": true, - "source.organizeImports": true + "source.fixAll": "always", + "source.organizeImports": "always" } }, - "python.formatting.provider": "black", "autoDocstring.docstringFormat": "google", "python.testing.pytestArgs": [ "tests" @@ -16,4 +13,5 @@ "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "python.analysis.typeCheckingMode": "off", + "mypy.runUsingActiveInterpreter": true, } diff --git a/CHANGELOG.md b/CHANGELOG.md index f37a7f9bd..4267d4b7f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +### Changed + +### Added + +- Initial implementation of datasets [#430](https://github.com/kraina-ai/srai/pull/430) for feature enrichment and benchmarking. + ## [0.7.4] - 2024-05-05 ### Added diff --git a/examples/datasets/airbnb_multicity.ipynb b/examples/datasets/airbnb_multicity.ipynb new file mode 100644 index 000000000..f8cbe2fdc --- /dev/null +++ b/examples/datasets/airbnb_multicity.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import AirbnbMulticityDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "airbnb_multicity = AirbnbMulticityDataset()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_token = os.getenv(\"HF_TOKEN\")\n", + "airbnb_multicity_gdf = airbnb_multicity.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "airbnb_multicity_gdf.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/brightkite.ipynb b/examples/datasets/brightkite.ipynb new file mode 100644 index 000000000..265f79f86 --- /dev/null +++ b/examples/datasets/brightkite.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import BrightkiteDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "brightkite = BrightkiteDataset()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_token = os.getenv(\"HF_TOKEN\")\n", + "brightkite_gdf = brightkite.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "brightkite_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/chicago_crime.ipynb b/examples/datasets/chicago_crime.ipynb new file mode 100644 index 000000000..752dab4d9 --- /dev/null +++ b/examples/datasets/chicago_crime.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import ChicagoCrimeDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chicago_crime = ChicagoCrimeDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load default data " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chicago_crime_default = chicago_crime.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chicago_crime_default.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data from 2022" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chicago_crime_2022 = chicago_crime.load(hf_token=hf_token, version=\"2022\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chicago_crime_2022.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/foursquare_checkins.ipynb b/examples/datasets/foursquare_checkins.ipynb new file mode 100644 index 000000000..89be4ff1c --- /dev/null +++ b/examples/datasets/foursquare_checkins.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import FoursquareCheckinsDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "foursquare_checkins = FoursquareCheckinsDataset()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_token = os.getenv(\"HF_TOKEN\")\n", + "foursquare_checkins_gdf = foursquare_checkins.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "foursquare_checkins_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/geolife.ipynb b/examples/datasets/geolife.ipynb new file mode 100644 index 000000000..17eeabc1f --- /dev/null +++ b/examples/datasets/geolife.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import GeolifeDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_token = os.getenv(\"HF_TOKEN\")\n", + "geolife = GeolifeDataset()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geolide_gdf = geolife.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geolide_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/gowalla.ipynb b/examples/datasets/gowalla.ipynb new file mode 100644 index 000000000..fec6a39b9 --- /dev/null +++ b/examples/datasets/gowalla.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import GowallaDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gowalla = GowallaDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gowalla_gdf = gowalla.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gowalla_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/house_sales_in_king_country.ipynb b/examples/datasets/house_sales_in_king_country.ipynb new file mode 100644 index 000000000..f3995a94d --- /dev/null +++ b/examples/datasets/house_sales_in_king_country.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import HouseSalesInKingCountryDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hskc_dataset = HouseSalesInKingCountryDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = hskc_dataset.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/nyc_bike.ipynb b/examples/datasets/nyc_bike.ipynb new file mode 100644 index 000000000..2019629c7 --- /dev/null +++ b/examples/datasets/nyc_bike.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import NYCBikeDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nyc_bike = NYCBikeDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data from 2013 year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = nyc_bike.load(hf_token=hf_token, version=\"nyc_bike_2013\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Easily get data from another year, e.g. 2022." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2022 = nyc_bike.load(hf_token=hf_token, version=\"nyc_bike_2022\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2022.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compatibile with other datasets' .load() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = nyc_bike.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/philadelphia_crime.ipynb b/examples/datasets/philadelphia_crime.ipynb new file mode 100644 index 000000000..915e36d6c --- /dev/null +++ b/examples/datasets/philadelphia_crime.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import PhiladelphiaCrimeDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_token = os.getenv(\"HF_TOKEN\")\n", + "philadelphia_crime = PhiladelphiaCrimeDataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data using .load() method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = philadelphia_crime.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data from 2013 year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2013 = philadelphia_crime.load(hf_token=hf_token, version=\"incidents_2013\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2013.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data from 2022 year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2022 = philadelphia_crime.load(hf_token=hf_token, version=\"incidents_2022\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_2022.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/police_department_incidents.ipynb b/examples/datasets/police_department_incidents.ipynb new file mode 100644 index 000000000..b4932d4bd --- /dev/null +++ b/examples/datasets/police_department_incidents.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import PoliceDepartmentIncidentsDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "police_department_incidents = PoliceDepartmentIncidentsDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = police_department_incidents.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/porto_taxi.ipynb b/examples/datasets/porto_taxi.ipynb new file mode 100644 index 000000000..91c64664c --- /dev/null +++ b/examples/datasets/porto_taxi.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import PortoTaxiDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "porto_taxi = PortoTaxiDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = porto_taxi.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/datasets/t_drive.ipynb b/examples/datasets/t_drive.ipynb new file mode 100644 index 000000000..d50276d7f --- /dev/null +++ b/examples/datasets/t_drive.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from srai.datasets import TDriveDataset\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_drive = TDriveDataset()\n", + "hf_token = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = t_drive.load(hf_token=hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/loaders/hf_loader.ipynb b/examples/loaders/hf_loader.ipynb new file mode 100644 index 000000000..7a33ccc04 --- /dev/null +++ b/examples/loaders/hf_loader.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "from shapely import LineString, MultiPoint\n", + "\n", + "from srai.loaders import HFLoader\n", + "\n", + "%load_ext dotenv\n", + "%dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "token = os.environ[\"HF_access_token\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf_loader = HFLoader(hf_token=token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = hf_loader.load(dataset_name=\"nyc_bike\", name=\"nyc_bike_2014\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_geodataframe_previous(dataframe: pd.DataFrame) -> gpd.GeoDataFrame:\n", + " \"\"\"\n", + " Function to get GeoDataFrame with location data.\n", + "\n", + " Args:\n", + " dataframe: Data of NYC Bike trajectories dataset.\n", + "\n", + " Returns:\n", + " GeoDataFrame of dataset, contatins location data.\n", + " \"\"\"\n", + " start_station_geometry = gpd.points_from_xy(\n", + " x=dataframe[\"start station longitude\"], y=dataframe[\"start station latitude\"]\n", + " )\n", + " end_station_geometry = gpd.points_from_xy(\n", + " x=dataframe[\"end station longitude\"], y=dataframe[\"end station latitude\"]\n", + " )\n", + " multi_point_stations_geometries = [\n", + " MultiPoint([start, end]) for start, end in zip(start_station_geometry, end_station_geometry)\n", + " ]\n", + " gdf = gpd.GeoDataFrame(\n", + " dataframe.drop(\n", + " [\n", + " \"start station latitude\",\n", + " \"start station longitude\",\n", + " \"end station latitude\",\n", + " \"end station longitude\",\n", + " ],\n", + " axis=1,\n", + " ),\n", + " geometry=multi_point_stations_geometries,\n", + " crs=\"EPSG:4326\",\n", + " )\n", + "\n", + " return gdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = make_geodataframe_previous(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = hf_loader.load(dataset_name=\"geolife\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data[\"geometry\"] = data[\"arrays_geometry\"].map(LineString)\n", + "geolife = gpd.GeoDataFrame(\n", + " data.drop([\"arrays_geometry\"], axis=1),\n", + " geometry=data[\"geometry\"],\n", + " crs=\"EPSG:4326\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "geolife.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + 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"osmnx" ] requires-python = ">=3.9" readme = "README.md" @@ -63,12 +63,14 @@ osm = [ "overpass", "pillow", "beautifulsoup4", - "quackosm>=0.7.1", + "quackosm>=0.7.2", ] # pdm add -G voronoi voronoi = ["pymap3d", "haversine", "scipy", "spherical-geometry"] + # pdm add -G gtfs gtfs = ["gtfs-kit"] + # pdm add -G plotting plotting = [ "folium", @@ -77,9 +79,20 @@ plotting = [ "plotly", "kaleido<=0.2.1", ] # kaleido<=0.2.1 because installation breaks on 0.2.1.post1 + # pdm add -G torch -torch = ["pytorch-lightning", "torch"] -all = ["srai[osm,voronoi,gtfs,plotting,torch]"] +torch = [ + "pytorch-lightning", + "torch<2.3; sys_platform == 'darwin' and platform_machine == 'x86_64'", + "torch; sys_platform != 'darwin' or platform_machine == 'arm64'", +] + +# pdm add -G datasets +datasets = [ + "datasets", +] + +all = ["srai[osm,voronoi,gtfs,plotting,torch,datasets]"] [build-system] @@ -90,7 +103,10 @@ build-backend = "pdm.backend" [tool.pdm] [tool.pdm.dev-dependencies] # pdm add -d -dev = ["bumpver", "types-requests"] +dev = [ + "bumpver", + "types-requests", +] # pdm add -dG lint lint = ["pre-commit", "mypy", "docformatter[tomli]", "ruff"] # pdm add -dG test @@ -105,9 +121,10 @@ test = [ "pyogrio", "six", "pytest-snapshot", + "python-dotenv", ] # pdm add -dG visualization -visualization = ["keplergl", "matplotlib"] +visualization = ["matplotlib"] # pdm add -dG docs docs = [ "mkdocs", @@ -169,13 +186,13 @@ max-doc-length = 100 [tool.ruff.format] docstring-code-format = true +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D100", "D103"] + [tool.mypy] strict = true show_column_numbers = true -disallow_untyped_defs = true no_implicit_optional = true -check_untyped_defs = true -warn_return_any = true ignore_missing_imports = true [tool.docformatter] @@ -223,7 +240,6 @@ ignore_packages = [ 'pymap3d', # uses BSD-2 license, has mismatched license in analysis 'mkdocs-jupyter', # uses Apache-2.0 license, has mismatched license in analysis 'pycountry', # uses LGPLv2, has mismatched license in analysis - 'keplergl', # uses MIT license, has mismatched license in analysis 'nvidia-cublas-cu11', # uses NVIDIA license 'nvidia-cuda-cupti-cu11', # uses NVIDIA license 'nvidia-cuda-nvrtc-cu11', # uses NVIDIA license @@ -247,5 +263,7 @@ ignore_packages = [ 'nvidia-nccl-cu12', # uses NVIDIA license 'nvidia-nvtx-cu12', # uses NVIDIA license 'nvidia-nvjitlink-cu12', # uses NVIDIA license - 'mkl', # Github Action artifact, uses Intel Simplified Software License + 'mkl', # uses Intel Simplified Software License + 'tbb', # uses Apache-2.0 license + 'intel-openmp', # uses BSD 3-Clause and Intel EULA license ] diff --git a/srai/datasets/__init__.py b/srai/datasets/__init__.py new file mode 100644 index 000000000..917b46caa --- /dev/null +++ b/srai/datasets/__init__.py @@ -0,0 +1,35 @@ +""" +This module contains dataset used to load dataset containing spatial information. + +Datasets can be loaded using .load() method. Some of them may need name of version. +""" + +from ._base import HuggingFaceDataset +from .airbnb_multicity.dataset import AirbnbMulticityDataset +from .brightkite.dataset import BrightkiteDataset +from .chicago_crime.dataset import ChicagoCrimeDataset +from .foursquare_checkins.dataset import FoursquareCheckinsDataset +from .geolife.dataset import GeolifeDataset +from .gowalla.dataset import GowallaDataset +from .house_sales_in_king_country.dataset import HouseSalesInKingCountryDataset +from .nyc_bike.dataset import NYCBikeDataset +from .philadelphia_crime.dataset import PhiladelphiaCrimeDataset +from .police_department_incidents.dataset import PoliceDepartmentIncidentsDataset +from .porto_taxi.dataset import PortoTaxiDataset +from .t_drive.dataset import TDriveDataset + +__all__ = [ + "HuggingFaceDataset", + "AirbnbMulticityDataset", + "BrightkiteDataset", + "ChicagoCrimeDataset", + "FoursquareCheckinsDataset", + "GeolifeDataset", + "GowallaDataset", + "HouseSalesInKingCountryDataset", + "NYCBikeDataset", + "PhiladelphiaCrimeDataset", + "PoliceDepartmentIncidentsDataset", + "PortoTaxiDataset", + "TDriveDataset", +] diff --git a/srai/datasets/_base.py b/srai/datasets/_base.py new file mode 100644 index 000000000..3731153a4 --- /dev/null +++ b/srai/datasets/_base.py @@ -0,0 +1,53 @@ +"""Base classes for Datasets.""" + +import abc +from typing import Optional + +import geopandas as gpd +import pandas as pd + +from srai.loaders import HuggingFaceLoader + + +class HuggingFaceDataset(abc.ABC): + """Abstract class for HuggingFace datasets.""" + + def __init__(self, path: str, version: Optional[str] = None) -> None: + self.path = path + self.version = version + + @abc.abstractmethod + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + raise NotImplementedError + + def load( + self, hf_token: Optional[str] = None, version: Optional[str] = None + ) -> gpd.GeoDataFrame: + """ + Method to load dataset. + + Args: + hf_token (str, optional): If needed, a User Access Token needed to authenticate to + the Hugging Face Hub. Environment variable `HF_TOKEN` can be also used. + Defaults to None. + version (str, optional): version of a dataset + + Returns: + gpd.GeoDataFrame: Loaded data. + """ + dataset_name = self.path + version = version or self.version + data = HuggingFaceLoader(hf_token=hf_token).load(dataset_name=dataset_name, name=version) + processed_data = self._preprocessing(data, version=version) + + return processed_data diff --git a/srai/datasets/airbnb_multicity/__init__.py b/srai/datasets/airbnb_multicity/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/airbnb_multicity/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/airbnb_multicity/dataset.py b/srai/datasets/airbnb_multicity/dataset.py new file mode 100644 index 000000000..f0dc2739c --- /dev/null +++ b/srai/datasets/airbnb_multicity/dataset.py @@ -0,0 +1,44 @@ +""" +AirbnbMulticity dataset loader. + +This module contains AirbnbMulticity dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class AirbnbMulticityDataset(HuggingFaceDataset): + """ + AirbnbMulticity dataset. + + Dataset description will be added. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/airbnb_multicity") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocessing to get GeoDataFrame with location data, based on GEO_EDA files. + + Args: + data (pd.DataFrame): Data of AirbnbMulticity dataset. + version (str, optional): version of a dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["latitude", "longitude"], axis=1), + geometry=gpd.points_from_xy(x=data["longitude"], y=data["latitude"]), + crs=WGS84_CRS, + ) + + return gdf diff --git a/srai/datasets/brightkite/__init__.py b/srai/datasets/brightkite/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/brightkite/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/brightkite/dataset.py b/srai/datasets/brightkite/dataset.py new file mode 100644 index 000000000..061edeafa --- /dev/null +++ b/srai/datasets/brightkite/dataset.py @@ -0,0 +1,50 @@ +""" +Brightkite dataset loader. + +This module contains Brightkite Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString +from tqdm.contrib.concurrent import process_map + +from srai.constants import GEOMETRY_COLUMN, WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class BrightkiteDataset(HuggingFaceDataset): + """ + Brightkite dataset. + + Brightkite was once a location-based social networking service provider where users shared their + locations by checking-in. The friendship network was collected using their public API, and + consists of 58,228 nodes and 214,078 edges. + + The network is originally directed but we have constructed a network with undirected edges when + there is a friendship in both ways. We have also collected a total of 4,491,143 checkins of + these users over the period of Apr. 2008 - Oct. 2010. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/brightkite") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocessing to get GeoDataFrame with location data, based on GEO_EDA files. + + Args: + data (pd.DataFrame): Data of Brightkite trajectories dataset. + version (str, optional): version of dataset. + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + df = data.copy() + df[GEOMETRY_COLUMN] = process_map(LineString, df[GEOMETRY_COLUMN], chunksize=1000) + gdf = gpd.GeoDataFrame(data=df, geometry=GEOMETRY_COLUMN, crs=WGS84_CRS) + + return gdf diff --git a/srai/datasets/chicago_crime/__init__.py b/srai/datasets/chicago_crime/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/chicago_crime/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/chicago_crime/dataset.py b/srai/datasets/chicago_crime/dataset.py new file mode 100644 index 000000000..257ad6454 --- /dev/null +++ b/srai/datasets/chicago_crime/dataset.py @@ -0,0 +1,65 @@ +""" +Chicago Crime dataset loader. + +This module contains Chicago Crime Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class ChicagoCrimeDataset(HuggingFaceDataset): + """ + Chicago Crime dataset. + + This dataset reflects reported incidents of crime (with the exception of murders where data + exists for each victim) that occurred in the City of Chicago. Data is extracted from the Chicago + Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. + """ + + available_versions: list[str] = ["2020", "2021", "2022"] # TODO: use + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/chicago_crime") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocessing to get GeoDataFrame with location data, based on GEO_EDA files. + + Args: + data: Data of Chicago Crime dataset. + version: version of a dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["Latitude", "Longitude", "X Coordinate", "Y Coordinate"], axis=1), + geometry=gpd.points_from_xy(x=data["Longitude"], y=data["Latitude"]), + crs=WGS84_CRS, + ) + return gdf + + def load( + self, hf_token: Optional[str] = None, version: Optional[str] = "2020" + ) -> gpd.GeoDataFrame: + """ + Method to load dataset. + + Args: + hf_token (str, optional): If needed, a User Access Token needed to authenticate to + the Hugging Face Hub. Environment variable `HF_TOKEN` can be also used. + Defaults to None. + version (str, optional): version of a dataset. + Available values: `2020`, `2021`, `2022`. Defaults to `2020`. + + Returns: + gpd.GeoDataFrame: Loaded data. + """ + return super().load(hf_token, version) diff --git a/srai/datasets/foursquare_checkins/__init__.py b/srai/datasets/foursquare_checkins/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/foursquare_checkins/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/foursquare_checkins/dataset.py b/srai/datasets/foursquare_checkins/dataset.py new file mode 100644 index 000000000..a237cf677 --- /dev/null +++ b/srai/datasets/foursquare_checkins/dataset.py @@ -0,0 +1,55 @@ +""" +Foursquare Checkins dataset loader. + +This module contains Foursquare Checkins Dataset. +""" + +import itertools +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString, Point + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class FoursquareCheckinsDataset(HuggingFaceDataset): + """ + Foursquare Checkins dataset. + + The Foursquare dataset consists of check-in data for different cities. One subset contains + check-ins in NYC and Tokyo collected for about 10 month (from 12 April 2012 to 16 February + 2013). It contains 227,428 check-ins in New York city and 573,703 check-ins in Tokyo. Each + check-in is associated with its time stamp, its GPS coordinates and its semantic meaning + (represented by fine-grained venue-categories). + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/foursquare_checkins", "tokyo_newyork") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data=data.drop(columns=["latitude", "longitude"]), + geometry=data.apply( + lambda row: LineString( + list(itertools.starmap(Point, zip(row["longitude"], row["latitude"]))) + ), + axis=1, + ), + crs=WGS84_CRS, + ) + + return gdf diff --git a/srai/datasets/geolife/__init__.py b/srai/datasets/geolife/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/geolife/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/geolife/dataset.py b/srai/datasets/geolife/dataset.py new file mode 100644 index 000000000..59d87562c --- /dev/null +++ b/srai/datasets/geolife/dataset.py @@ -0,0 +1,49 @@ +""" +Geolife dataset loader. + +This module contains Geolife Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class GeolifeDataset(HuggingFaceDataset): + """ + Geolife Dataset. + + This GPS trajectory dataset was collected in (Microsoft Research Asia) Geolife project by 182 + users in a period of over five years (from April 2007 to August 2012). A GPS trajectory of this + dataset is represented by sequence of time-stamped points each of which contains the information + of altitude, longitude, latitude. This dataset contains 17,784 trajectories, ~25M Points with a + total distance of 1,292,951 kilometers and a total duration of 50,176 hours. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/geolife") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["arrays_geometry"], axis=1), + geometry=gpd.GeoSeries(data["arrays_geometry"].map(LineString)), + crs=WGS84_CRS, + ) + + return gdf diff --git a/srai/datasets/gowalla/__init__.py b/srai/datasets/gowalla/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/gowalla/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/gowalla/dataset.py b/srai/datasets/gowalla/dataset.py new file mode 100644 index 000000000..9a73f07d9 --- /dev/null +++ b/srai/datasets/gowalla/dataset.py @@ -0,0 +1,47 @@ +""" +Gowalla dataset loader. + +This module contains Geolife Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString +from tqdm.contrib.concurrent import process_map + +from srai.constants import GEOMETRY_COLUMN, WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class GowallaDataset(HuggingFaceDataset): + """ + Gowalla Dataset. + + Gowalla is a location-based social networking website where users share their locations + by checking-in. The friendship network is undirected and was collected using their + public API, and consists of 196,591 nodes and 950,327 edges. We have collected a total + of 6,442,890 check-ins of these users over the period of Feb. 2009 - Oct. 2010. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/gowalla") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + df = data.copy() + df[GEOMETRY_COLUMN] = process_map(LineString, df[GEOMETRY_COLUMN], chunksize=1000) + gdf = gpd.GeoDataFrame(data=df, geometry=GEOMETRY_COLUMN, crs=WGS84_CRS) + + return gdf diff --git a/srai/datasets/house_sales_in_king_country/__init__.py b/srai/datasets/house_sales_in_king_country/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/house_sales_in_king_country/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/house_sales_in_king_country/dataset.py b/srai/datasets/house_sales_in_king_country/dataset.py new file mode 100644 index 000000000..31032c3ed --- /dev/null +++ b/srai/datasets/house_sales_in_king_country/dataset.py @@ -0,0 +1,46 @@ +""" +House Sales in King Country dataset loader. + +This module contains House Sales in King Country Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class HouseSalesInKingCountryDataset(HuggingFaceDataset): + """ + House Sales in King Country dataset. + + This dataset contains house sale prices for King County, which includes Seattle. It includes + homes sold between May 2014 and May 2015. + + It's a great dataset for evaluating simple regression models. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/house_sales_in_king_country") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["lat", "long"], axis=1), + geometry=gpd.points_from_xy(x=data["long"], y=data["lat"]), + crs=WGS84_CRS, + ) + return gdf diff --git a/srai/datasets/nyc_bike/__init__.py b/srai/datasets/nyc_bike/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/nyc_bike/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/nyc_bike/dataset.py b/srai/datasets/nyc_bike/dataset.py new file mode 100644 index 000000000..67d5519c8 --- /dev/null +++ b/srai/datasets/nyc_bike/dataset.py @@ -0,0 +1,105 @@ +""" +New York City Bike dataset loader. + +This module contains New York City Bike Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import MultiPoint + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class NYCBikeDataset(HuggingFaceDataset): + """ + New York City Bike dataset. + + Where do Citi Bikers ride? When do they ride? How far do they go? Which stations are most + popular? What days of the week are most rides taken on? We've heard all of these questions and + more from you, and we're happy to provide the data to help you discover the answers to these + questions and more. We invite developers, engineers, statisticians, artists, academics and other + interested members of the public to use the data we provide for analysis, development, + visualization and whatever else moves you. This data is provided according to the NYCBS Data Use + Policy. + """ + + years_previous: list[int] = [2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020] + years_current: list[int] = [2021, 2022, 2023] + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/nyc_bike") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset. + Available: from `nyc_bike_2013` to `nyc_bike_2023`. + + Returns: + gpd.GeoDataFrame: preprocessed data with Multipoint(StartStation, EndStation) + """ + if version: + dataset_year = int(version[-4:]) + else: + raise ValueError("Dataset version name can't be None") + + if dataset_year in self.years_previous: + c_start_lng = "start station longitude" + c_start_lat = "start station latitude" + c_end_lng = "end station longitude" + c_end_lat = "end station latitude" + elif dataset_year in self.years_current: + c_start_lng = "start_lng" + c_start_lat = "start_lat" + c_end_lng = "end_lng" + c_end_lat = "end_lat" + else: + raise ValueError(f"Dataset version name `{version}` is not valid.") + + start_station_geometry = gpd.points_from_xy(x=data[c_start_lng], y=data[c_start_lat]) + end_station_geometry = gpd.points_from_xy(x=data[c_end_lng], y=data[c_end_lat]) + multi_point_stations_geometries = [ + MultiPoint([start, end]) + for start, end in zip(start_station_geometry, end_station_geometry) + ] + gdf = gpd.GeoDataFrame( + data.drop( + [ + c_start_lng, + c_start_lat, + c_end_lng, + c_end_lat, + ], + axis=1, + ), + geometry=multi_point_stations_geometries, + crs=WGS84_CRS, + ) + + return gdf + + def load( + self, hf_token: Optional[str] = None, version: Optional[str] = "nyc_bike_2023" + ) -> gpd.GeoDataFrame: + """ + Method to load dataset. + + Args: + hf_token (str, optional): If needed, a User Access Token needed to authenticate to + the Hugging Face Hub. Environment variable `HF_TOKEN` can be also used. + Defaults to None. + version (str, optional): version of a dataset. + Available: from `nyc_bike_2013` to `nyc_bike_2023`. Defaults to `nyc_bike_2023`. + + Returns: + gpd.GeoDataFrame: Loaded data. + """ + return super().load(hf_token, version) diff --git a/srai/datasets/philadelphia_crime/__init__.py b/srai/datasets/philadelphia_crime/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/philadelphia_crime/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/philadelphia_crime/dataset.py b/srai/datasets/philadelphia_crime/dataset.py new file mode 100644 index 000000000..15854f564 --- /dev/null +++ b/srai/datasets/philadelphia_crime/dataset.py @@ -0,0 +1,67 @@ +""" +Philadelphia Crime dataset loader. + +This module contains Philadelphia Crime Dataset. +""" + +from typing import Optional + +import geopandas as gpd + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class PhiladelphiaCrimeDataset(HuggingFaceDataset): + """ + Philadelphia Crime dataset. + + Crime incidents from the Philadelphia Police Department. Part I crimes include violent offenses + such as aggravated assault, rape, arson, among others. Part II crimes include simple assault, + prostitution, gambling, fraud, and other non-violent offenses. + """ + + years_previous: list[int] = [2013, 2014, 2015, 2016, 2017, 2018, 2019] + years_current: list[int] = [2020, 2021, 2022, 2023] + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/philadelphia_crime") + + def _preprocessing( + self, data: gpd.GeoDataFrame, version: Optional[str] = None + ) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["lng", "lat"], axis=1), + geometry=gpd.points_from_xy(data["lng"], data["lat"]), + crs=WGS84_CRS, + ) + return gdf + + def load( + self, hf_token: Optional[str] = None, version: Optional[str] = "incidents_2023" + ) -> gpd.GeoDataFrame: + """ + Method to load dataset. + + Args: + hf_token (str, optional): If needed, a User Access Token needed to authenticate to + the Hugging Face Hub. Environment variable `HF_TOKEN` can be also used. + Defaults to None. + version (str, optional): version of a dataset. + Available: `incidents_2013` to `incidents_2023`. Defaults to `incidents_2023`. + + Returns: + gpd.GeoDataFrame: Loaded data. + """ + return super().load(hf_token, version) diff --git a/srai/datasets/police_department_incidents/__init__.py b/srai/datasets/police_department_incidents/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/police_department_incidents/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/police_department_incidents/dataset.py b/srai/datasets/police_department_incidents/dataset.py new file mode 100644 index 000000000..de6de344f --- /dev/null +++ b/srai/datasets/police_department_incidents/dataset.py @@ -0,0 +1,45 @@ +""" +The San Francisco Police Department's (SFPD) Incident Report dataset loader. + +This module contains The San Francisco Police Department's (SFPD) Incident Report Datatset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class PoliceDepartmentIncidentsDataset(HuggingFaceDataset): + """ + The San Francisco Police Department's (SFPD) Incident Report Datatset. + + This dataset includes incident reports that have been filed as of January 1, 2018 till March, + 2024. These reports are filed by officers or self-reported by members of the public using SFPD’s + online reporting system. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/police_department_incidents") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["Latitude", "Longitude"], axis=1), + geometry=gpd.points_from_xy(x=data["Longitude"], y=data["Latitude"]), + crs=WGS84_CRS, + ) + return gdf diff --git a/srai/datasets/porto_taxi/__init__.py b/srai/datasets/porto_taxi/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/porto_taxi/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/porto_taxi/dataset.py b/srai/datasets/porto_taxi/dataset.py new file mode 100644 index 000000000..99d28d18a --- /dev/null +++ b/srai/datasets/porto_taxi/dataset.py @@ -0,0 +1,50 @@ +""" +Porto Taxi dataset loader. + +This module contains Porto Taxi Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString +from tqdm.contrib.concurrent import process_map + +from srai.constants import GEOMETRY_COLUMN, WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class PortoTaxiDataset(HuggingFaceDataset): + """ + Porto Taxi dataset. + + The dataset covers a year of trajectory data for taxis in Porto, Portugal + Each ride is categorized as: + A) taxi central based, + B) stand-based or + C) non-taxi central based. + Each data point represents a completed trip initiated through + the dispatch central, a taxi stand, or a random street. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/porto_taxi") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + df = data.copy() + df[GEOMETRY_COLUMN] = process_map(LineString, df[GEOMETRY_COLUMN], chunksize=1000) + gdf = gpd.GeoDataFrame(data=df, geometry=GEOMETRY_COLUMN, crs=WGS84_CRS) + + return gdf diff --git a/srai/datasets/t_drive/__init__.py b/srai/datasets/t_drive/__init__.py new file mode 100644 index 000000000..acc6702e6 --- /dev/null +++ b/srai/datasets/t_drive/__init__.py @@ -0,0 +1 @@ +"""Dataset.""" diff --git a/srai/datasets/t_drive/dataset.py b/srai/datasets/t_drive/dataset.py new file mode 100644 index 000000000..174c124c9 --- /dev/null +++ b/srai/datasets/t_drive/dataset.py @@ -0,0 +1,58 @@ +""" +T-Drive dataset loader. + +This module contains T-Drive Dataset. +""" + +from typing import Optional + +import geopandas as gpd +import pandas as pd +from shapely.geometry import LineString + +from srai.constants import WGS84_CRS +from srai.datasets import HuggingFaceDataset + + +class TDriveDataset(HuggingFaceDataset): + """ + T-Drive dataset. + + This dataset contains the GPS trajectories of 10,357 taxis during the + period of Feb. 2 to Feb. 8, 2008 within Beijing. The total number of + points in this dataset is about 15 million and the total distance of + the trajectories reaches to 9 million kilometers. + + [1] Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun. Driving with + knowledge from the physical world. In The 17th ACM SIGKDD international + conference on Knowledge Discovery and Data mining, + KDD '11, New York, NY, USA, 2011. ACM. [2] Jing Yuan, Yu Zheng, + Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. + Tdrive: driving directions based on taxi trajectories. In Proceedings + of the 18th SIGSPATIAL International Conference on Advances in + Geographic Information Systems, GIS '10, pages 99'108, New York, NY, USA, + 2010. ACM. + """ + + def __init__(self) -> None: + """Create the dataset.""" + super().__init__("kraina/t_drive") + + def _preprocessing(self, data: pd.DataFrame, version: Optional[str] = None) -> gpd.GeoDataFrame: + """ + Preprocess the dataset from HuggingFace. + + Args: + data (pd.DataFrame): a dataset to preprocess + version (str, optional): version of dataset + + Returns: + gpd.GeoDataFrame: preprocessed data. + """ + gdf = gpd.GeoDataFrame( + data.drop(["arrays_geometry"], axis=1).set_index("taxi_id"), + geometry=gpd.GeoSeries(data["arrays_geometry"].map(LineString)), + crs=WGS84_CRS, + ) + + return gdf diff --git a/srai/loaders/__init__.py b/srai/loaders/__init__.py index 941793b79..4ae331043 100644 --- a/srai/loaders/__init__.py +++ b/srai/loaders/__init__.py @@ -10,6 +10,7 @@ from .download import download_file from .geoparquet_loader import GeoparquetLoader from .gtfs_loader import GTFSLoader +from .hugging_face_loader import HuggingFaceLoader from .osm_loaders import OSMLoader, OSMOnlineLoader, OSMPbfLoader, OSMTileLoader from .osm_way_loader import OSMNetworkType, OSMWayLoader @@ -24,4 +25,5 @@ "OSMTileLoader", "OSMNetworkType", "download_file", + "HuggingFaceLoader", ] diff --git a/srai/loaders/hugging_face_loader.py b/srai/loaders/hugging_face_loader.py new file mode 100644 index 000000000..d2e0a03b4 --- /dev/null +++ b/srai/loaders/hugging_face_loader.py @@ -0,0 +1,51 @@ +""" +Hugging Face loader. + +This module contains Hugging Face loader. +""" + +from typing import Optional + +import pandas as pd + +from srai._optional import import_optional_dependencies +from srai.loaders import Loader + + +class HuggingFaceLoader(Loader): + """ + Hugging Face loader. + + Loader to download dataset from HuggingFace and return a Pandas DataFrame. + """ + + def __init__(self, hf_token: Optional[str] = None) -> None: + """ + Initialize loader. + + Args: + hf_token (str): Token from HuggingFace Hub account. + """ + import_optional_dependencies( + dependency_group="datasets", + modules=["datasets"], + ) + self.hf_token = hf_token + + def load(self, dataset_name: str, name: Optional[str] = None) -> pd.DataFrame: + """ + Method to load data files from Hugging Face. + + Args: + dataset_name (str, optional): Name of dataset. + name (Optional[str], optional): Name of version of dataset e.g. "nyc_bike_2013". \ + Defaults to None. + + Returns: + pd.DataFrame: _description_ + """ + from datasets import load_dataset + + dataset = load_dataset(dataset_name, name=name, token=self.hf_token, trust_remote_code=True) + + return dataset["train"].to_pandas() diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..3a10eefc8 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,8 @@ +import pytest +from dotenv import load_dotenv + + +@pytest.fixture(autouse=True, scope="session") # type: ignore +def env_file() -> None: + """Load environment variables from env file.""" + load_dotenv() diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py new file mode 100644 index 000000000..5471fad63 --- /dev/null +++ b/tests/datasets/test_datasets.py @@ -0,0 +1,150 @@ +from typing import Callable, Optional + +import pandas as pd +from parametrization import Parametrization as P +from pytest_mock import MockerFixture + +from srai.constants import GEOMETRY_COLUMN, WGS84_CRS +from srai.datasets import ( + AirbnbMulticityDataset, + BrightkiteDataset, + ChicagoCrimeDataset, + FoursquareCheckinsDataset, + GeolifeDataset, + GowallaDataset, + HouseSalesInKingCountryDataset, + HuggingFaceDataset, + NYCBikeDataset, + PhiladelphiaCrimeDataset, + PoliceDepartmentIncidentsDataset, + PortoTaxiDataset, + TDriveDataset, +) +from srai.loaders import HuggingFaceLoader + + +@P.parameters("dataset_fn", "loaded_df", "version") # type: ignore +@P.case( # type: ignore + "AirbnbMulticityDataset", + AirbnbMulticityDataset, + pd.DataFrame({"f1": ["a", "b"], "latitude": [10, 15], "longitude": [20, 30]}), + None, +) +@P.case( # type: ignore + "BrightkiteDataset", + BrightkiteDataset, + pd.DataFrame({"f1": ["a", "b"], "geometry": [[[1, 2], [3, 4]], [[10, 11], [23, 24]]]}), + None, +) +@P.case( # type: ignore + "ChicagoCrimeDataset", + ChicagoCrimeDataset, + pd.DataFrame( + { + "f1": ["a", "b"], + "Latitude": [10, 15], + "Longitude": [20, 30], + "X Coordinate": [2, 3], + "Y Coordinate": [4, 5], + } + ), + None, +) +@P.case( # type: ignore + "FoursquareCheckinsDataset", + FoursquareCheckinsDataset, + pd.DataFrame( + {"f1": ["a", "b"], "latitude": [[10, 15], [20, 25]], "longitude": [[20, 30], [45, 50]]} + ), + None, +) +@P.case( # type: ignore + "GeolifeDataset", + GeolifeDataset, + pd.DataFrame({"f1": ["a", "b"], "arrays_geometry": [[[1, 2], [3, 4]], [[10, 11], [23, 24]]]}), + None, +) +@P.case( # type: ignore + "GowallaDataset", + GowallaDataset, + pd.DataFrame({"f1": ["a", "b"], "geometry": [[[1, 2], [3, 4]], [[10, 11], [23, 24]]]}), + None, +) +@P.case( # type: ignore + "HouseSalesInKingCountryDataset", + HouseSalesInKingCountryDataset, + pd.DataFrame({"f1": ["a", "b"], "lat": [10, 15], "long": [20, 30]}), + None, +) +@P.case( # type: ignore + "NYCBikeDataset_2013", + NYCBikeDataset, + pd.DataFrame( + { + "f1": ["a", "b"], + "start station latitude": [10, 15], + "start station longitude": [20, 30], + "end station latitude": [10, 15], + "end station longitude": [20, 30], + } + ), + "nyc_bike_2013", +) +@P.case( # type: ignore + "NYCBikeDataset_2023", + NYCBikeDataset, + pd.DataFrame( + { + "f1": ["a", "b"], + "start_lat": [10, 15], + "start_lng": [20, 30], + "end_lat": [10, 15], + "end_lng": [20, 30], + } + ), + "nyc_bike_2023", +) +@P.case( # type: ignore + "PhiladelphiaCrimeDataset", + PhiladelphiaCrimeDataset, + pd.DataFrame({"f1": ["a", "b"], "lat": [10, 15], "lng": [20, 30]}), + None, +) +@P.case( # type: ignore + "PoliceDepartmentIncidentsDataset", + PoliceDepartmentIncidentsDataset, + pd.DataFrame({"f1": ["a", "b"], "Latitude": [10, 15], "Longitude": [20, 30]}), + None, +) +@P.case( # type: ignore + "PortoTaxiDataset", + PortoTaxiDataset, + pd.DataFrame({"f1": ["a", "b"], "geometry": [[[1, 2], [3, 4]], [[10, 11], [23, 24]]]}), + None, +) +@P.case( # type: ignore + "TDriveDataset", + TDriveDataset, + pd.DataFrame( + { + "taxi_id": ["taxi_1", "taxi_2"], + "f1": ["a", "b"], + "arrays_geometry": [[[1, 2], [3, 4]], [[10, 11], [23, 24]]], + } + ), + None, +) +def test_hugging_face_dataset( + mocker: MockerFixture, + dataset_fn: Callable[[], HuggingFaceDataset], + loaded_df: pd.DataFrame, + version: Optional[str], +) -> None: + mocker.patch.object(HuggingFaceLoader, "load").return_value = loaded_df + + dataset = dataset_fn() + gdf = dataset.load(version=version) + assert not gdf.empty + assert GEOMETRY_COLUMN in gdf + assert gdf.crs == WGS84_CRS + assert "f1" in gdf diff --git a/tests/loaders/test_hugging_face_loader.py b/tests/loaders/test_hugging_face_loader.py new file mode 100644 index 000000000..dc83765c5 --- /dev/null +++ b/tests/loaders/test_hugging_face_loader.py @@ -0,0 +1,27 @@ +import pandas as pd +import pyarrow as pa +import pytest +from datasets import Dataset, DatasetDict +from pytest_mock import MockerFixture + +from srai.loaders import HuggingFaceLoader + + +@pytest.fixture() # type: ignore +def dataset_dict() -> DatasetDict: + metric = pa.array([200, 4, 50, 100]) + cities = pa.array(["Wrocław", "Berlin", "Gdańsk", "Amsterdam"]) + names = ["f1", "f2"] + table = pa.Table.from_arrays([metric, cities], names=names) + + return DatasetDict(train=Dataset(table)) + + +def test_loader(mocker: MockerFixture, dataset_dict: DatasetDict) -> None: + mocker.patch("datasets.load_dataset").return_value = dataset_dict + + loader = HuggingFaceLoader() + df = loader.load("test/dataset") + + assert isinstance(df, pd.DataFrame) + assert len(df) == 4 diff --git a/tests/miscellaneous/test_optional_dependencies.py b/tests/miscellaneous/test_optional_dependencies.py index ae871c381..9502b17af 100644 --- a/tests/miscellaneous/test_optional_dependencies.py +++ b/tests/miscellaneous/test_optional_dependencies.py @@ -16,20 +16,21 @@ def optional_packages() -> list[str]: """Get a list with optional packages.""" return [ - "quackosm", - "osmnx", - "overpass", "beautifulsoup4", - "pymap3d", - "haversine", - "scipy", - "spherical_geometry", - "gtfs_kit", + "datasets", "folium", + "gtfs_kit", + "haversine", + "kaleido", "mapclassify", + "osmnx", + "overpass", "plotly", - "kaleido", + "pymap3d", "pytorch-lightning", + "quackosm", + "scipy", + "spherical_geometry", "torch", ] @@ -126,6 +127,12 @@ def _test_gtfs() -> None: GTFSLoader() +def _test_datasets() -> None: + from srai.loaders import HuggingFaceLoader + + HuggingFaceLoader() + + def _get_regions_gdf() -> gpd.GeoDataFrame: return gpd.GeoDataFrame( data={ @@ -151,6 +158,7 @@ def _get_regions_gdf() -> gpd.GeoDataFrame: (_test_torch), (_test_osm), (_test_gtfs), + (_test_datasets), ], ) def test_optional_available(test_fn): @@ -167,6 +175,7 @@ def test_optional_available(test_fn): (_test_torch), (_test_osm), (_test_gtfs), + (_test_datasets), ], ) def test_optional_missing(test_fn):