{"id":13426996,"url":"https://github.com/SamComber/spacv","last_synced_at":"2025-03-15T22:31:28.738Z","repository":{"id":48126692,"uuid":"271744073","full_name":"SamComber/spacv","owner":"SamComber","description":"Spatial cross-validation in Python.","archived":false,"fork":false,"pushed_at":"2024-05-19T11:17:27.000Z","size":3939,"stargazers_count":43,"open_issues_count":4,"forks_count":14,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-03T02:36:42.339Z","etag":null,"topics":["cross-validation","data-science","geographic-data-science","machine-learning","python","scikit-learn","scikitlearn-machine-learning","sklearn","spatial-data-science"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SamComber.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-12T08:07:15.000Z","updated_at":"2024-12-28T13:26:47.000Z","dependencies_parsed_at":"2024-09-22T07:00:37.912Z","dependency_job_id":null,"html_url":"https://github.com/SamComber/spacv","commit_stats":{"total_commits":62,"total_committers":2,"mean_commits":31.0,"dds":"0.016129032258064502","last_synced_commit":"3cb92f1e6bf272919081af5e6fe4072f00336945"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamComber%2Fspacv","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamComber%2Fspacv/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamComber%2Fspacv/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamComber%2Fspacv/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SamComber","download_url":"https://codeload.github.com/SamComber/spacv/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243801600,"owners_count":20350105,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cross-validation","data-science","geographic-data-science","machine-learning","python","scikit-learn","scikitlearn-machine-learning","sklearn","spatial-data-science"],"created_at":"2024-07-31T00:01:50.496Z","updated_at":"2025-03-15T22:31:23.723Z","avatar_url":"https://github.com/SamComber.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# `spacv`: spatial cross-validation in Python\r\n\r\n`spacv` is a small Python 3 (3.6 and above) package for cross-validation of models\r\nthat assess generalization performance to datasets with spatial dependence. `spacv` provides\r\na familiar sklearn-like API to expose a suite of tools useful for points-based spatial prediction tasks.\r\nSee the notebook `spacv_guide.ipynb` for usage.\r\n\r\n\u003cp align=\"center\"\u003e\r\n\u003cimg src=\"demo_viz_buffer.gif\" width=\"300\" height=\"250\"/\u003e\r\n\u003c/p\u003e\r\n\r\n## Dependencies\r\n\r\n* `numpy`\r\n* `matplotlib`\r\n* `pandas`\r\n* `geopandas`\r\n* `shapely`\r\n* `scikit-learn`\r\n* `scipy`\r\n\r\n## Installation and usage\r\n\r\nTo install use pip:\r\n\r\n    $ pip install spacv\r\n\r\nThen build quick spatial cross-validation workflows with `sklearn` as:\r\n\r\n```python\r\nimport spacv\r\nimport geopandas as gpd\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.svm import SVC\r\n\r\ndf = gpd.read_file('data/baltim.geojson')\r\n\r\nXYs = df['geometry']\r\nX = df[['NROOM', 'BMENT', 'NBATH', 'PRICE', 'LOTSZ', 'SQFT']]\r\ny = df['PATIO']\r\n\r\n# Build fold indices as a generator\r\nskcv = spacv.SKCV(n_splits=4, buffer_radius=10).split(XYs)\r\n\r\nsvc = SVC()\r\n\r\ncross_val_score(svc,       # Model \r\n                X,         # Features\r\n                y,         # Labels\r\n                cv = skcv) # Fold indices\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSamComber%2Fspacv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSamComber%2Fspacv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSamComber%2Fspacv/lists"}