updated to datasets 4.*

README.md

@@ -1,24 +1,45 @@
 ---
-language:
-- en
+configs:
+- config_name: vehicles
+  data_files:
+  - path: vehicles/train.csv
+    split: train
+  default: true
+- config_name: glass
+  data_files:
+  - path: glass/train.csv
+    split: train
+  default: false
+- config_name: windows
+  data_files:
+  - path: windows/train.csv
+    split: train
+  default: false
+- config_name: containers
+  data_files:
+  - path: containers/train.csv
+    split: train
+  default: false
+- config_name: tableware
+  data_files:
+  - path: tableware/train.csv
+    split: train
+  default: false
+- config_name: headlamps
+  data_files:
+  - path: headlamps/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Glass
+size_categories: 1M<n<10M
 tags:
-- glass
 - tabular_classification
 - binary_classification
-- UCI
-pretty_name: Glass evaluation
-size_categories:
-- n<1k
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- glass
-- windows
-- vehicles
-- containers
-- tableware
-- headlamps
-license: cc
 ---
 # Glass
 The [Glass dataset](https://archive-beta.ics.uci.edu/dataset/42/glass+identification) from the [UCI repository](https://archive-beta.ics.uci.edu).

containers/train.csv

@@ -0,0 +1,215 @@
+refractive_index,sodium,magnesium,aluminum,silicon,potassium,calcium,barium,iron,is_container_glass
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-1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6
-1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6
-1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6
-1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6
-1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6
-1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6
-1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7
-1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7
-1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7
-1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7
-1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7
-1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7
-1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7
-1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7
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-1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7
-1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7
-1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7
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-1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7
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-1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7

glass.py

@@ -1,188 +0,0 @@
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-
-DESCRIPTION = "Glass efficiency dataset from the UCI repository."
-_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/242/glass+efficiency"
-_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/glass+method+choice")
-_CITATION = """
-@misc{misc_glass_efficiency_242,
-  author       = {Tsanas,Athanasios & Xifara,Angeliki},
-  title        = {{Glass efficiency}},
-  year         = {2012},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C51307}}
-}"""
-
-# Dataset info
-_BASE_FEATURE_NAMES = [
-	"refractive_index",
-	"sodium",
-	"magnesium",
-	"aluminum",
-	"silicon",
-	"potassium",
-	"calcium",
-	"barium",
-	"iron",
-	"glass_type",
-]
-urls_per_split = {
-	"train": "https://huggingface.co/datasets/mstz/glass/raw/main/glass.data"
-}
-features_types_per_config = {
-	"glass": {
-		"refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"glass_type": datasets.ClassLabel(num_classes=7, names=("windows_1", "windows_2",
-																"vehicle_windows_1", "vehicle_windows_2",
-																"containers", "tableware", "headlamps"))
-	},
-	"windows": {
-		"refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"is_windows_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-	},
-	"vehicles": {
-		"refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"is_vehicle_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-	},
-	"containers": {
-		"refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"is_container_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-	},
-	"tableware": {
-		"refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"is_tableware_glass": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-	},
-	"headlamps": {
-	   "refractive_index": datasets.Value("float64"),
-		"sodium": datasets.Value("float64"),
-		"magnesium": datasets.Value("float64"),
-		"aluminum": datasets.Value("float64"),
-		"silicon": datasets.Value("float64"),
-		"potassium": datasets.Value("float64"),
-		"calcium": datasets.Value("float64"),
-		"barium": datasets.Value("int8"),
-		"iron": datasets.Value("float64"),
-		"is_headlamp_glass": 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 GlassConfig(datasets.BuilderConfig):
-	def __init__(self, **kwargs):
-		super(GlassConfig, self).__init__(version=VERSION, **kwargs)
-		self.features = features_per_config[kwargs["name"]]
-
-
-class Glass(datasets.GeneratorBasedBuilder):
-	# dataset versions
-	DEFAULT_CONFIG = "glass"
-	BUILDER_CONFIGS = [
-		GlassConfig(name="glass", description="Glass dataset."),
-		GlassConfig(name="windows", description="Is this windows glass?"),
-		GlassConfig(name="vehicles", description="Is this vehicles glass?"),
-		GlassConfig(name="containers", description="Is this containers glass?"),
-		GlassConfig(name="tableware", description="Is this tableware glass?"),
-		GlassConfig(name="headlamps", description="Is this headlamps glass?")
-	]
-
-
-	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, header=None)
-		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.columns = _BASE_FEATURE_NAMES
-
-		if self.config.name == "windows":
-			data = data.rename(columns={"glass_type": "is_windows_glass"})
-			data.loc[:, "is_windows_glass"] = data.is_windows_glass.apply(lambda x: 1 if x == 1 or x == 2 else 0)
-
-		
-		elif self.config.name == "vehicles":
-			data = data.rename(columns={"glass_type": "is_vehicle_glass"})
-			data.loc[:, "is_vehicle_glass"] = data.is_vehicle_glass.apply(lambda x: 1 if x in {3, 4} else 0)
-		
-		elif self.config.name == "containers":
-			data = data.rename(columns={"glass_type": "is_container_glass"})
-			data.loc[:, "is_container_glass"] = data.is_container_glass.apply(lambda x: 1 if x == 5 else 0)
-		
-		elif self.config.name == "tableware":
-			data = data.rename(columns={"glass_type": "is_tableware_glass"})
-			data.loc[:, "is_tableware_glass"] = data.is_tableware_glass.apply(lambda x: 1 if x == 6 else 0)
-		
-		elif self.config.name == "headlamps":
-			data = data.rename(columns={"glass_type": "is_headlamp_glass"})
-			data.loc[:, "is_headlamp_glass"] = data.is_headlamp_glass.apply(lambda x: 1 if x == 7 else 0)
-		
-		else:
-			data.loc[:, "glass_type"] = data.glass_type.apply(lambda x: x - 1)
-			print(data.glass_type.unique())
-
-		return data

glass/train.csv

@@ -0,0 +1,215 @@
+refractive_index,sodium,magnesium,aluminum,silicon,potassium,calcium,barium,iron,glass_type
+1.52101,13.64,4.49,1.1,71.78,0.06,8.75,0,0.0,0
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