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README.md

@@ -1,3 +1,26 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- page_blocks
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Page Blocks
+size_categories:
+- 1K<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- page_blocks
+- page_blocks_binary
 ---
+# PageBlocks
+The [PageBlocks dataset](https://archive-beta.ics.uci.edu/dataset/76/page_blocks) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+How many transitions does the page block have?
+
+# Configurations and tasks
+| **Configuration** | **Task**                  |
+|-------------------|---------------------------|
+| page_blocks       | Multiclass classification |
+| page_blocks_binary| Binary classification     |

page_blocks.py

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+"""PageBlocks Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"is_family_financially_stable": {
+		"convenient": True,
+		"inconvenient": False
+	}
+}
+
+DESCRIPTION = "PageBlocks dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification")
+_CITATION = """
+@misc{misc_page_blocks_classification_78,
+  author       = {Malerba,Donato},
+  title        = {{Page Blocks Classification}},
+  year         = {1995},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5J590}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/page_blocks/raw/main/page_blocks.data"
+}
+features_types_per_config = {
+    "page_blocks": {
+		"height": datasets.Value("float64"),
+        "lenght": datasets.Value("float64"),
+        "area": datasets.Value("float64"),
+        "eccentricity": datasets.Value("float64"),
+        "percentage_black_pixels": datasets.Value("float64"),
+        "percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
+        "mean_numer_of_transitions": datasets.Value("float64"),
+        "number_of_black_pixels": datasets.Value("float64"),
+        "number_of_black_pixels_after_rlsa": datasets.Value("float64"),
+        "number_of_transitions": datasets.Value("int8")
+    },
+    "page_blocks_binary": {
+		"height": datasets.Value("float64"),
+        "lenght": datasets.Value("float64"),
+        "area": datasets.Value("float64"),
+        "eccentricity": datasets.Value("float64"),
+        "percentage_black_pixels": datasets.Value("float64"),
+        "percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
+        "mean_numer_of_transitions": datasets.Value("float64"),
+        "number_of_black_pixels": datasets.Value("float64"),
+        "number_of_black_pixels_after_rlsa": datasets.Value("float64"),
+        "has_multiple_transitions": datasets.ClassLabelS(num_classes=2)
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class PageBlocksConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(PageBlocksConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class PageBlocks(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "page_blocks"
+    BUILDER_CONFIGS = [
+        PageBlocksConfig(name="page_blocks",
+                   description="PageBlocks for regression."),
+        PageBlocksConfig(name="page_blocks_binary",
+                   description="PageBlocks 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:
+        if self.config.name == "page_blocks_binary":
+            data["number_of_transitions"] = data["number_of_transitions"].apply(lambda x: 1 if x > 1 else 0)
+            data = data.rename(columns={"number_of_transitions": "has_multiple_transitions"})
+
+        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}")