updated to datasets 4.*

README.md

@@ -1,18 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: twonorm
+  data_files:
+  - path: twonorm/train.csv
+    split: train
+  default: true
+language: en
+license: unknown
+pretty_name: Twonorm
+size_categories: 1M<n<10M
 tags:
-- twonorm
 - tabular_classification
 - binary_classification
-pretty_name: Two Norm
-size_categories:
-- 1K<n<10K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- multiclass_classification
+task_categories:
 - tabular-classification
-configs:
-- 8hr
-- 1hr
 ---
 # TwoNorm
 The [TwoNorm dataset](https://www.openml.org/search?type=data&status=active&id=1507) from the [OpenML repository](https://www.openml.org/).

twonorm.py

@@ -1,84 +0,0 @@
-"""TwoNorm"""
-
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-DESCRIPTION = "TwoNorm dataset from the OpenML repository."
-_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=1507"
-_URLS = ("https://www.openml.org/search?type=data&status=active&id=1507")
-_CITATION = """"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/twonorm/raw/main/twonorm.csv"
-}
-features_types_per_config = {
-    "twonorm": {
-		"V1": datasets.Value("float64"),
-		"V2": datasets.Value("float64"),
-		"V3": datasets.Value("float64"),
-		"V4": datasets.Value("float64"),
-		"V5": datasets.Value("float64"),
-		"V6": datasets.Value("float64"),
-		"V7": datasets.Value("float64"),
-		"V8": datasets.Value("float64"),
-		"V9": datasets.Value("float64"),
-		"V10": datasets.Value("float64"),
-		"V11": datasets.Value("float64"),
-		"V12": datasets.Value("float64"),
-		"V13": datasets.Value("float64"),
-		"V14": datasets.Value("float64"),
-		"V15": datasets.Value("float64"),
-		"V16": datasets.Value("float64"),
-		"V17": datasets.Value("float64"),
-		"V18": datasets.Value("float64"),
-		"V19": datasets.Value("float64"),
-		"V20": datasets.Value("float64"),
-		"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class TwoNormConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(TwoNormConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class TwoNorm(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "twonorm"
-    BUILDER_CONFIGS = [
-        TwoNormConfig(name="twonorm",
-                      description="TwoNorm for binary classification.")
-        ]
-
-
-    def _info(self):       
-        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
-                                    features=features_per_config[self.config.name])
-
-        return info
-    
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloads = dl_manager.download_and_extract(urls_per_split)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row

twonorm.csv → twonorm/train.csv

@@ -2,7 +2,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
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--1.2444,1.099,-1.4684,-1.7003,0.451,-0.9695,-0.5761,0.0877,0.1058,-1.9614,1.8583,-0.88,1,-1.756,-0.6727,0.6034,-0.6858,-0.9101,0.0747,1.1082,1
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@@ -792,7 +792,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 0.3807,0.3453,0.3047,-0.0514,-1.1675,2.2279,-1.4779,-0.4234,1.6976,-1.8873,-0.6293,0.4479,-0.7891,1.1261,1.7353,1.0249,-1.549,0.2746,-0.8685,0.3783,0
 1.8547,-0.0061,0.204,1.5581,-0.9164,0.5495,-1.6938,-0.7807,-0.5041,0.0501,0.4643,-0.5072,0.0841,1.2426,1.162,1.2052,1.271,1.1174,0.7284,2.0638,0
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-0.8209,1.517,0.2671,-0.4589,1.0058,0.5059,1.0323,2,0.4673,0.1641,1.4012,1.5502,-0.1617,0.6099,1.7676,0.3154,2.2202,1.1725,2.1021,0.4476,0
+0.8209,1.517,0.2671,-0.4589,1.0058,0.5059,1.0323,2.0,0.4673,0.1641,1.4012,1.5502,-0.1617,0.6099,1.7676,0.3154,2.2202,1.1725,2.1021,0.4476,0
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@@ -919,7 +919,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 -1.3312,-1.117,-1.0556,-0.9584,-1.077,0.343,0.2116,-0.5628,-1.6583,-1.2753,1.1278,-0.0289,0.2584,-2.5667,0.7014,-0.0129,-1.0656,-1.5668,0.8056,-0.4703,1
 -0.7132,0.1249,0.4259,-0.0378,1.1377,0.524,2.0164,1.1471,-1.7185,0.2649,0.0732,-0.0141,-0.2566,-0.0517,1.0223,0.4106,0.3245,0.659,0.1093,1.4898,0
 -0.0863,1.1184,-1.5256,1.0514,0.2223,0.501,1.8419,-0.1054,-0.4548,0.4941,1.4736,-1.3878,0.8002,2.0246,-0.1222,1.1331,0.504,-0.8794,-0.2399,1.6104,0
-0.5308,0.439,-0.6116,2.491,1.7936,1.6373,-0.4551,0.0336,0.3977,-0.2819,1.8751,1.6223,0.883,-0.36,-0.8321,1.0778,0.3583,1.0679,1,1.7242,0
+0.5308,0.439,-0.6116,2.491,1.7936,1.6373,-0.4551,0.0336,0.3977,-0.2819,1.8751,1.6223,0.883,-0.36,-0.8321,1.0778,0.3583,1.0679,1.0,1.7242,0
 -0.6969,-2.4191,-0.0273,0.0379,-0.9396,0.1123,-1.1632,1.7071,-1.4978,0.3773,-1.7652,-0.0842,0.0783,-1.7575,-1.9349,-1.81,0.1423,-0.5371,-0.5731,-1.1299,1
 -0.3583,1.532,0.1204,-0.4549,-0.2315,0.5134,0.0797,0.813,1.3131,-0.5691,-0.58,1.6298,-0.0571,1.9013,-0.0629,0.3317,1.13,0.8352,0.1509,0.8242,0
 1.3925,0.2431,-0.9108,0.5423,1.2458,0.7686,-0.0718,1.2001,0.5926,0.0753,-0.8198,-0.1177,1.0624,-1.1337,0.5718,0.648,-1.2633,0.8703,0.2042,0.304,0
@@ -2237,7 +2237,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 0.8534,0.6513,1.5296,0.6482,0.8763,-2.2019,0.416,-1.0573,-0.2282,-0.6964,-2.0675,-1.0239,-0.7484,-1.5868,0.4737,-1.0268,-0.2094,0.4162,-0.6616,-1.1343,1
 -0.0207,4.3451,1.5243,0.9309,-1.5935,0.7737,-1.7958,-1.5624,-0.9895,0.314,1.2552,0.5894,0.4949,1.8349,-0.5581,-0.6153,1.0671,-0.4343,0.1033,0.2479,0
 0.5394,1.6581,0.0715,1.1937,0.5243,-0.7458,1.5859,1.3763,1.1325,-1.9111,0.1958,1.3912,0.798,0.2422,2.0146,1.1686,-0.7217,0.8335,-0.0867,0.939,0
--0.3932,-2.034,-1.0199,-0.2203,-1.6503,-0.9222,-0.5304,0.6705,0.3463,-0.3467,0.1226,-1.3777,0,-0.8549,-1.5049,0.0891,-0.0669,-0.4342,0.3815,-0.56,1
+-0.3932,-2.034,-1.0199,-0.2203,-1.6503,-0.9222,-0.5304,0.6705,0.3463,-0.3467,0.1226,-1.3777,0.0,-0.8549,-1.5049,0.0891,-0.0669,-0.4342,0.3815,-0.56,1
 1.3349,2.0145,1.1715,0.3088,0.5248,-1.053,-0.1697,2.1394,1.5354,1.1925,1.0147,1.6968,-0.8378,-0.716,0.0178,-0.292,0.4367,1.8142,1.0035,-0.8676,0
 0.381,0.0188,1.3279,2.0884,2.3604,0.6593,2.5147,0.3718,-0.0922,-2.3908,-0.8556,-0.7975,0.0995,-0.6223,0.5962,1.3722,0.1562,-0.992,-1.1451,-0.1792,0
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@@ -3377,7 +3377,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 -1.5779,0.5991,-0.7042,-1.3589,-1.4586,-1.2853,0.0725,-0.2867,0.726,1.4115,0.9365,-0.3323,0.4553,0.9723,-0.5017,-0.3129,-1.856,-0.6272,-1.4438,-0.1363,1
 1.5758,1.237,0.9602,0.3167,0.7583,2.3048,1.3174,0.6587,-0.3355,0.0065,-0.3085,-0.0759,-0.3379,-0.5813,-0.3714,-0.6977,0.6559,1.1559,1.1675,0.2199,0
 -1.4916,0.4581,-1.3629,-1.0683,1.0127,-0.6068,0.206,0.5256,0.4575,-0.6338,0.0802,-0.7571,-1.8125,0.7333,0.379,1.6569,0.5412,0.1648,-0.5044,-1.8502,1
--1.9345,-1.4739,-0.4101,0.2334,-0.2457,0.9,0.0525,-0.7644,0.4082,0.2434,1.3298,-0.2613,-0.7819,-2.5275,-2,-0.0155,-0.7892,-0.5669,-1.6783,-0.1552,1
+-1.9345,-1.4739,-0.4101,0.2334,-0.2457,0.9,0.0525,-0.7644,0.4082,0.2434,1.3298,-0.2613,-0.7819,-2.5275,-2.0,-0.0155,-0.7892,-0.5669,-1.6783,-0.1552,1
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@@ -3432,7 +3432,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 0.4907,-0.7633,-1.9074,-0.2736,-2.4056,-1.2264,-0.5598,-0.4938,-0.3165,-0.7103,-0.8065,-0.4174,-0.1255,0.1437,-0.5703,-0.8363,-0.6932,0.4244,-1.224,-0.8051,1
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--0.7418,0,-0.8362,0.1454,1.2218,0.233,-0.876,-0.9514,-0.5214,0.1166,0.2838,1.0623,-0.5958,-1.2059,0.3452,-0.5751,-0.8912,-0.5885,-1.0752,0.9555,1
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@@ -5709,7 +5709,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 1.0573,-1.3747,-1.1436,-1.262,0.069,-1.4971,0.4559,-0.0667,-0.2636,-0.6466,-1.0221,-0.3812,-1.059,-0.4383,-0.8228,-1.4084,-1.2055,-1.8274,0.1082,0.8635,1
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--0.7951,0.0706,-0.3979,-0.6124,1.1276,-1.1874,1.6545,-0.6933,-0.1816,0.091,0.3333,0.0244,-1.5831,0.0282,-1,-0.4488,0.4688,-2.0431,-1.3009,-2.3099,1
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 2.2415,1.7057,1.0542,0.0078,0.9165,-0.7549,-0.9456,-0.7126,-1.2618,0.8001,0.8894,-0.4867,0.7521,0.2775,1.6132,-0.9332,2.4609,1.0383,0.2151,1.6876,0
@@ -6220,7 +6220,7 @@ V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,class
 -0.757,-1.2057,-1.1183,1.2385,1.7733,-0.9695,1.5182,0.1889,0.5788,0.0902,-1.5449,1.2693,-0.9741,0.0996,0.1532,0.9261,2.7201,-0.0931,0.0761,0.1838,0
 -0.6701,-0.0418,-0.453,0.0665,-0.0787,2.9085,0.4637,-0.5247,1.6706,-0.2222,1.5213,-0.2616,1.0776,-0.4763,0.6935,-0.0052,1.0137,1.3112,0.5197,1.9777,0
 2.9941,0.1272,0.833,0.2112,0.6341,-0.0551,-0.1668,1.7523,-0.1158,0.654,-0.7147,1.0125,2.341,1.0893,-0.0632,1.1825,-2.0159,0.0242,-0.9702,-0.3163,0
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