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README.md

@@ -1,3 +1,30 @@
 ---
-license: cc
+language:
+- en
+tags:
+- balance_scale
+- tabular_classification
+- multiclass_classification
+pretty_name: Balance scale
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- balance
 ---
+# Balance scale
+The [Balance scale dataset](https://archive.ics.uci.edu/ml/datasets/Balance scale) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Two weights are put on the arms of a scale. Is the scale balanced?
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | Description                                                   |
+|-------------------|---------------------------|---------------------------------------------------------------|
+| balance           | Multiclass classification | Predict if the scale is balanced.                             |
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/balance_scale", "balance")["train"]
+```

balance_scale.data

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balance_scale.py

@@ -0,0 +1,82 @@
+"""Balance_Scale"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "balance",
+    "left_weight",
+    "left_distance",
+    "right_weight",
+    "right_distance",
+]
+
+DESCRIPTION = "Balance_Scale dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Balance_Scale"
+_URLS = ("https://huggingface.co/datasets/mstz/balance_scale/raw/balance_scale.data")
+_CITATION = """
+@misc{misc_balance_scale_12,
+  title        = {{Balance Scale}},
+  year         = {1994},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5488X}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/balance_scale/raw/main/balance_scale.data",
+}
+features_types_per_config = {
+    "balance": {
+        "left_weight": datasets.Value("int64"),
+        "left_distance": datasets.Value("int64"),
+        "right_weight": datasets.Value("int64"),
+        "right_distance": datasets.Value("int64"),
+        "balance": datasets.ClassLabel(num_classes=3, names=("tips_left", "balanced", "tips_right"))
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class Balance_ScaleConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(Balance_ScaleConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Balance_Scale(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "balance"
+    BUILDER_CONFIGS = [
+        Balance_ScaleConfig(name="balance",
+                    description="Multiclass classification of the scale balance."),
+    ]
+
+
+    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 = data[["left_weight", "left_distance", "right_weight", "right_distance", "balance"]]
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row