DEVELOPMENT... { "data_id": "44775", "name": "sf-police-incidents_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True", "exact_name": "sf-police-incidents_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True", "version": 1, "version_label": "f46b4179-48b3-42a1-8638-4a985e6c0dbe", "description": "Subsampling of the dataset sf-police-incidents (42732) with\n\nseed=2\nargs.nrows=2000\nargs.ncols=100\nargs.nclasses=10\nargs.no_stratify=True\nGenerated with the following source code:\n\n\n```python\n def subsample(\n self,\n seed: int,\n nrows_max: int = 2_000,\n ncols_max: int = 100,\n nclasses_max: int = 10,\n stratified: bool = True,\n ) -> Dataset:\n rng = np.random.default_rng(seed)\n\n x = self.x\n y = self.y\n\n # Uniformly sample\n classes = y.unique()\n if len(classes) > nclasses_max:\n vcs = y.value_counts()\n selected_classes = rng.choice(\n classes,\n size=nclasses_max,\n replace=False,\n p=vcs \/ sum(vcs),\n )\n\n # Select the indices where one of these classes is present\n idxs = y.index[y.isin(classes)]\n x = x.iloc[idxs]\n y = y.iloc[idxs]\n\n # Uniformly sample columns if required\n if len(x.columns) > ncols_max:\n columns_idxs = rng.choice(\n list(range(len(x.columns))), size=ncols_max, replace=False\n )\n sorted_column_idxs = sorted(columns_idxs)\n selected_columns = list(x.columns[sorted_column_idxs])\n x = x[selected_columns]\n else:\n sorted_column_idxs = list(range(len(x.columns)))\n\n if len(x) > nrows_max:\n # Stratify accordingly\n target_name = y.name\n data = pd.concat((x, y), axis=\"columns\")\n _, subset = train_test_split(\n data,\n test_size=nrows_max,\n stratify=data[target_name],\n shuffle=True,\n random_state=seed,\n )\n x = subset.drop(target_name, axis=\"columns\")\n y = subset[target_name]\n\n # We need to convert categorical columns to string for openml\n categorical_mask = [self.categorical_mask[i] for i in sorted_column_idxs]\n columns = list(x.columns)\n\n return Dataset(\n # Technically this is not the same but it's where it was derived from\n dataset=self.dataset,\n x=x,\n y=y,\n categorical_mask=categorical_mask,\n columns=columns,\n )\n```", "format": "arff", "uploader": "David Wilson", "uploader_id": 32840, "visibility": "public", "creator": "\"Eddie Bergman\"", "contributor": null, "date": "2022-11-17 18:50:29", "update_comment": null, "last_update": "2022-11-17 18:50:29", "licence": "CC0", "status": "active", "error_message": null, "url": "https:\/\/api.openml.org\/data\/download\/22111537\/dataset", "default_target_attribute": "ViolentCrime", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "sf-police-incidents_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True", "Subsampling of the dataset sf-police-incidents (42732) with seed=2 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10, stratified: bool = True, ) -> Dataset: rng = np.random.default_rng(seed) x = self.x y = self.y # Uniformly sample classes = y.unique() if len(classes) > nclasses_max: vcs = y.value_counts() selected_classes = " ], "weight": 5 }, "qualities": { "NumberOfInstances": 2000, "NumberOfFeatures": 9, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 3, "NumberOfSymbolicFeatures": 6, "PercentageOfSymbolicFeatures": 66.66666666666666, "AutoCorrelation": 0.7848924462231116, "PercentageOfNumericFeatures": 33.33333333333333, "PercentageOfMissingValues": 0, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfBinaryFeatures": 11.11111111111111, "NumberOfBinaryFeatures": 1, "MinorityClassSize": 243, "MinorityClassPercentage": 12.15, "MajorityClassSize": 1757, "MajorityClassPercentage": 87.85, "Dimensionality": 0.0045 }, "tags": [], "features": [ { "name": "ViolentCrime", "index": "8", "type": "nominal", "distinct": "2", "missing": "0", "target": "1", "distr": [ [ "Yes", "No" ], [ [ "243", "0" ], [ "0", "1757" ] ] ] }, { "name": "Hour", "index": "0", "type": "numeric", "distinct": "24", "missing": "0", "min": "0", "max": "23", "mean": "13", "stdev": "7" }, { "name": "DayOfWeek", "index": "1", "type": "nominal", "distinct": "7", "missing": "0", "distr": [ [ "1", "2", "3", "4", "5", "6", "7" ], [ [ "33", "256" ], [ "34", "264" ], [ "34", "244" ], [ "26", "230" ], [ "42", "252" ], [ "41", "266" ], [ "33", "245" ] ] ] }, { "name": "Month", "index": "2", "type": "nominal", "distinct": "12", "missing": "0", "distr": [ [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12" ], [ [ "19", "185" ], [ "30", "137" ], [ "17", "154" ], [ "24", "148" ], [ "19", "166" ], [ "18", "128" ], [ "20", "140" ], [ "22", "138" ], [ "20", "136" ], [ "24", "145" ], [ "19", "142" ], [ "11", "138" ] ] ] }, { "name": "Year", "index": "3", "type": "nominal", "distinct": "16", "missing": "0", "distr": [ [ "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018" ], [ [ "13", "121" ], [ "14", "108" ], [ "13", "104" ], [ "10", "100" ], [ "10", "118" ], [ "26", "128" ], [ "9", "113" ], [ "14", "104" ], [ "14", "112" ], [ "26", "114" ], [ "24", "120" ], [ "18", "131" ], [ "10", "125" ], [ "20", "103" ], [ "15", "125" ], [ "7", "31" ] ] ] }, { "name": "PdDistrict", "index": "4", "type": "nominal", "distinct": "10", "missing": "0", "distr": [ [ "BAYVIEW", "CENTRAL", "INGLESIDE", "MISSION", "NORTHERN", "PARK", "RICHMOND", "SOUTHERN", "TARAVAL", "TENDERLOIN" ], [ [ "33", "175" ], [ "28", "177" ], [ "24", "145" ], [ "35", "228" ], [ "29", "224" ], [ "10", "106" ], [ "7", "113" ], [ "32", "329" ], [ "18", "125" ], [ "27", "135" ] ] ] }, { "name": "Address", "index": "5", "type": "nominal", "distinct": "1531", "missing": "0", "distr": [] }, { "name": "X", "index": "6", "type": "numeric", "distinct": "1618", "missing": "0", "min": "-123", "max": "0", "mean": "-122", "stdev": "0" }, { "name": "Y", "index": "7", "type": "numeric", "distinct": "1618", "missing": "0", "min": "38", "max": "38", "mean": "38", "stdev": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }