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README.md

@@ -1,3 +1,42 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- glass
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Glass evaluation
+size_categories:
+- n<1k
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- glass
+- windows
+- vehicles
+- containers
+- tableware
+- headlamps
 ---
+# Glass
+The [Glass dataset](https://archive-beta.ics.uci.edu/dataset/42/glass+identification) from the [UCI repository](https://archive-beta.ics.uci.edu).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**          |
+|-------------------|---------------------------|--------------------------|
+| glass             | Multiclass classification | Classify glass type.     |
+| windows           | Binary classification     | Is this windows glass?   |
+| vehicles          | Binary classification     | Is this vehicles glass?  |
+| containers        | Binary classification     | Is this containers glass?|
+| tableware         | Binary classification     | Is this tableware glass? |
+| headlamps         | Binary classification     | Is this headlamps glass? |
+
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/glass", "glass")["train"]
+```

glass.data

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glass.py

@@ -0,0 +1,186 @@
+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 = [
+    "relative_compactness",
+    "surface_area",
+    "wall_area",
+    "roof_area",
+    "overall_height",
+    "orientation",
+    "glazing_area",
+    "glazing_area_distribution",
+    "heating_load",
+    "cooling_load"
+]
+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=6, 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: 0 if data.is_windows_glass in {1, 2} else 0)
+        
+        elif self.config.name == "vehicles":
+            data = data.rename(columns={"glass_type", "is_vehicles_glass"})
+            data.loc[:, "is_vehicles_glass"] = data.is_vehicles_glass.apply(lambda x: 0 if data.is_vehicles_glass in {3, 4} else 0)
+        
+        elif self.config.name == "containers":
+            data = data.rename(columns={"glass_type", "is_containers_glass"})
+            data.loc[:, "is_containers_glass"] = data.is_containers_glass.apply(lambda x: 0 if data.is_containers_glass == 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: 0 if data.is_tableware_glass == 6 else 0)
+        
+        elif self.config.name == "headlamps":
+            data = data.rename(columns={"glass_type", "is_headlamps_glass"})
+            data.loc[:, "is_headlamps_glass"] = data.is_headlamps_glass.apply(lambda x: 0 if data.is_headlamps_glass == 7 else 0)
+        
+        else:
+            data.loc[:, "glass_type"] = data.glass_type.apply(lambda x: x - 1)
+
+		return data