updated to datasets 4.*

README.md

@@ -1,23 +1,35 @@
 ---
-language:
-- en
+configs:
+- config_name: wine_origin
+  data_files:
+  - path: wine_origin/train.csv
+    split: train
+  default: true
+- config_name: wine_origin_0
+  data_files:
+  - path: wine_origin_0/train.csv
+    split: train
+  default: false
+- config_name: wine_origin_1
+  data_files:
+  - path: wine_origin_1/train.csv
+    split: train
+  default: false
+- config_name: wine_origin_2
+  data_files:
+  - path: wine_origin_2/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Wine origin
+size_categories: 1M<n<10M
 tags:
-- wine_origin
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: Wine Origin
-size_categories:
-- n<1K
 task_categories:
 - tabular-classification
-configs:
-- wine_origin
-- wine_origin_0
-- wine_origin_1
-- wine_origin_2
-license: cc
 ---
 # Wine Origin
 The [Wine Origin dataset](https://archive-beta.ics.uci.edu/dataset/109/wine) from the [UCI repository](https://archive-beta.ics.uci.edu/).

wine.data

@@ -1,180 +0,0 @@
-malic_acid,ash,alcalinity_of_ash,magnesium,phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,diluted_wines,proline,unknown,class
-14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065,1
-14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065,1
-13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050,1
-13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185,1
-14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480,1
-13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735,1
-14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450,1
-14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290,1
-14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295,1
-14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045,1
-13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045,1
-14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510,1
-14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280,1
-13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320,1
-14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150,1
-14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547,1
-13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310,1
-14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280,1
-13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130,1
-14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680,1
-13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845,1
-14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780,1
-12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770,1
-13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035,1
-12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015,1
-13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845,1
-13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830,1
-13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195,1
-13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285,1
-13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915,1
-14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035,1
-13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285,1
-13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515,1
-13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990,1
-13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235,1
-13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095,1
-13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920,1
-13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880,1
-13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105,1
-13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020,1
-14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760,1
-13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795,1
-13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035,1
-13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095,1
-13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680,1
-13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885,1
-14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080,1
-14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065,1
-13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985,1
-14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060,1
-13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260,1
-13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150,1
-13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265,1
-13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190,1
-13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375,1
-13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060,1
-13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120,1
-14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970,1
-13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270,1
-13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285,1
-12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520,2
-12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680,2
-12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450,2
-13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630,2
-12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420,2
-12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355,2
-12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678,2
-13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502,2
-12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510,2
-13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750,2
-12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718,2
-12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870,2
-13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410,2
-13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472,2
-12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985,2
-11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886,2
-11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428,2
-13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392,2
-11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500,2
-12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750,2
-12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463,2
-12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278,2
-12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714,2
-12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630,2
-13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515,2
-11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520,2
-12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450,2
-12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495,2
-11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562,2
-11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680,2
-12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625,2
-12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480,2
-12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450,2
-12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495,2
-12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290,2
-11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345,2
-12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937,2
-11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625,2
-12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428,2
-12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660,2
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-12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510,2
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-12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312,2
-11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680,2
-11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562,2
-12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325,2
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-12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580,2
-12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630,3
-12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530,3
-12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560,3
-12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600,3
-12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650,3
-12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695,3
-12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720,3
-12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515,3
-13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580,3
-12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590,3
-12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600,3
-13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780,3
-13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520,3
-13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550,3
-12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855,3
-13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830,3
-13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415,3
-12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625,3
-13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650,3
-13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550,3
-13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500,3
-12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480,3
-13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425,3
-13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675,3
-12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640,3
-13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725,3
-13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480,3
-12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880,3
-14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660,3
-13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620,3
-12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520,3
-13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680,3
-12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570,3
-12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675,3
-13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615,3
-13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520,3
-13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695,3
-12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685,3
-13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750,3
-13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630,3
-12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510,3
-12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470,3
-14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660,3
-13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740,3
-13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750,3
-13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835,3
-13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840,3
-14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560,3

wine_origin.py

@@ -1,161 +0,0 @@
-"""WineOrigin Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {}
-
-DESCRIPTION = "WineOrigin dataset."
-_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2"
-_URLS = ("https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2")
-_CITATION = """
-@misc{misc_wine_origin_database_generator_(version_2)_108,
-  author       = {Breiman,L. & Stone,C.J.},
-  title        = {{Waveform Database Generator (Version 2)}},
-  year         = {1988},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C56014}}
-}
-"""
-
-# Dataset info
-urls_per_split = {
-	"train": "https://huggingface.co/datasets/mstz/wine_origin/raw/main/wine.data"
-}
-features_types_per_config = {
-	"wine_origin": {
-		"malic_acid": datasets.Value("float64"),
-		"ash": datasets.Value("float64"),
-		"alcalinity_of_ash": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"phenols": datasets.Value("float64"),
-		"flavanoids": datasets.Value("float64"),
-		"nonflavanoid_phenols": datasets.Value("float64"),
-		"proanthocyanins": datasets.Value("float64"),
-		"color_intensity": datasets.Value("float64"),
-		"hue": datasets.Value("float64"),
-		"diluted_wines": datasets.Value("float64"),
-		"proline": datasets.Value("float64"),
-		"unknown": datasets.Value("int64"),
-		"class": datasets.ClassLabel(num_classes=3)
-	},
-	"wine_origin_0": {
-		"malic_acid": datasets.Value("float64"),
-		"ash": datasets.Value("float64"),
-		"alcalinity_of_ash": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"phenols": datasets.Value("float64"),
-		"flavanoids": datasets.Value("float64"),
-		"nonflavanoid_phenols": datasets.Value("float64"),
-		"proanthocyanins": datasets.Value("float64"),
-		"color_intensity": datasets.Value("float64"),
-		"hue": datasets.Value("float64"),
-		"diluted_wines": datasets.Value("float64"),
-		"proline": datasets.Value("float64"),
-		"unknown": datasets.Value("int64"),
-		"class": datasets.ClassLabel(num_classes=2)
-	},
-	"wine_origin_1": {
-		"malic_acid": datasets.Value("float64"),
-		"ash": datasets.Value("float64"),
-		"alcalinity_of_ash": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"phenols": datasets.Value("float64"),
-		"flavanoids": datasets.Value("float64"),
-		"nonflavanoid_phenols": datasets.Value("float64"),
-		"proanthocyanins": datasets.Value("float64"),
-		"color_intensity": datasets.Value("float64"),
-		"hue": datasets.Value("float64"),
-		"diluted_wines": datasets.Value("float64"),
-		"proline": datasets.Value("float64"),
-		"unknown": datasets.Value("int64"),
-		"class": datasets.ClassLabel(num_classes=2)
-	},
-	"wine_origin_2": {
-		"malic_acid": datasets.Value("float64"),
-		"ash": datasets.Value("float64"),
-		"alcalinity_of_ash": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"phenols": datasets.Value("float64"),
-		"flavanoids": datasets.Value("float64"),
-		"nonflavanoid_phenols": datasets.Value("float64"),
-		"proanthocyanins": datasets.Value("float64"),
-		"color_intensity": datasets.Value("float64"),
-		"hue": datasets.Value("float64"),
-		"diluted_wines": datasets.Value("float64"),
-		"proline": datasets.Value("float64"),
-		"unknown": datasets.Value("int64"),
-		"class": datasets.ClassLabel(num_classes=2)
-	},
-
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class WineOriginConfig(datasets.BuilderConfig):
-	def __init__(self, **kwargs):
-		super(WineOriginConfig, self).__init__(version=VERSION, **kwargs)
-		self.features = features_per_config[kwargs["name"]]
-
-
-class WineOrigin(datasets.GeneratorBasedBuilder):
-	# dataset versions
-	DEFAULT_CONFIG = "wine_origin"
-	BUILDER_CONFIGS = [
-		WineOriginConfig(name="wine_origin", description="WineOrigin for multiclass classification."),
-		WineOriginConfig(name="wine_origin_0", description="WineOrigin for binary classification."),
-		WineOriginConfig(name="wine_origin_1", description="WineOrigin for binary classification."),
-		WineOriginConfig(name="wine_origin_2", description="WineOrigin 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)
-		data = self.preprocess(data)
-
-		for row_id, row in data.iterrows():
-			data_row = dict(row)
-
-			yield row_id, data_row
-
-	def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
-		data["class"] = data["class"].apply(lambda x: x - 1)
-
-		if self.config.name == "wine_origin_0":
-			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
-		elif self.config.name == "wine_origin_1":
-			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
-		elif self.config.name == "wine_origin_2":
-			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
-
-		for feature in _ENCODING_DICS:
-			encoding_function = partial(self.encode, feature)
-			data.loc[:, feature] = data[feature].apply(encoding_function)
-				
-		return data[list(features_types_per_config[self.config.name].keys())]
-
-	def encode(self, feature, value):
-		if feature in _ENCODING_DICS:
-			return _ENCODING_DICS[feature][value]
-		raise ValueError(f"Unknown feature: {feature}")

wine_origin/train.csv

@@ -0,0 +1,180 @@
+malic_acid,ash,alcalinity_of_ash,magnesium,phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,diluted_wines,proline,unknown,class
+14.23,1.71,2.43,15.6,127.0,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065,0
+14.23,1.71,2.43,15.6,127.0,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065,0
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+14.83,1.64,2.17,14.0,97.0,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045,0
+13.86,1.35,2.27,16.0,98.0,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045,0
+14.1,2.16,2.3,18.0,105.0,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510,0
+14.12,1.48,2.32,16.8,95.0,2.2,2.43,0.26,1.57,5.0,1.17,2.82,1280,0
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+14.38,1.87,2.38,12.0,102.0,3.3,3.64,0.29,2.96,7.5,1.2,3.0,1547,0
+13.63,1.81,2.7,17.2,112.0,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310,0
+14.3,1.92,2.72,20.0,120.0,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280,0
+13.83,1.57,2.62,20.0,115.0,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130,0
+14.19,1.59,2.48,16.5,108.0,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680,0
+13.64,3.1,2.56,15.2,116.0,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845,0
+14.06,1.63,2.28,16.0,126.0,3.0,3.17,0.24,2.1,5.65,1.09,3.71,780,0
+12.93,3.8,2.65,18.6,102.0,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770,0
+13.71,1.86,2.36,16.6,101.0,2.61,2.88,0.27,1.69,3.8,1.11,4.0,1035,0
+12.85,1.6,2.52,17.8,95.0,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015,0
+13.5,1.81,2.61,20.0,96.0,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845,0
+13.05,2.05,3.22,25.0,124.0,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830,0
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+13.73,1.5,2.7,22.5,101.0,3.0,3.25,0.29,2.38,5.7,1.19,2.71,1285,0
+13.58,1.66,2.36,19.1,106.0,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515,0
+13.68,1.83,2.36,17.2,104.0,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990,0
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+13.56,1.71,2.31,16.2,117.0,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795,0
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+14.21,4.04,2.44,18.9,111.0,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080,0
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wine_origin_2/train.csv

@@ -0,0 +1,180 @@
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