updated to datasets 4.*

README.md

@@ -1,20 +1,25 @@
 ---
-language:
-- en
+configs:
+- config_name: page_blocks_binary
+  data_files:
+  - path: page_blocks_binary/train.csv
+    split: train
+  default: true
+- config_name: page_blocks
+  data_files:
+  - path: page_blocks/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Page blocks
+size_categories: 1M<n<10M
 tags:
-- page_blocks
 - tabular_classification
 - binary_classification
 - multiclass_classification
-pretty_name: Page Blocks
-size_categories:
-- 1K<n<10K
 task_categories:
 - tabular-classification
-configs:
-- page_blocks
-- page_blocks_binary
-license: cc
 ---
 # PageBlocks
 The [PageBlocks dataset](https://archive-beta.ics.uci.edu/dataset/76/page_blocks) from the [UCI repository](https://archive-beta.ics.uci.edu/).

page_blocks.py

@@ -1,118 +0,0 @@
-"""PageBlocks Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {
-}
-
-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.ClassLabel(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)
-        
-        data = data.reset_index()
-        data.drop("index", axis="columns", inplace=True)
-                
-        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}")