updated to datasets 4.*

README.md

@@ -1,3 +1,53 @@
 ---
+configs:
+- config_name: segment
+  data_files:
+  - path: segment/train.csv
+    split: train
+  default: true
+- config_name: brickface
+  data_files:
+  - path: brickface/train.csv
+    split: train
+  default: false
+- config_name: sky
+  data_files:
+  - path: sky/train.csv
+    split: train
+  default: false
+- config_name: foliage
+  data_files:
+  - path: foliage/train.csv
+    split: train
+  default: false
+- config_name: cement
+  data_files:
+  - path: cement/train.csv
+    split: train
+  default: false
+- config_name: window
+  data_files:
+  - path: window/train.csv
+    split: train
+  default: false
+- config_name: path
+  data_files:
+  - path: path/train.csv
+    split: train
+  default: false
+- config_name: grass
+  data_files:
+  - path: grass/train.csv
+    split: train
+  default: false
+language: en
 license: cc-by-4.0
+pretty_name: Segment
+size_categories: 1M<n<10M
+tags:
+- tabular_classification
+- binary_classification
+- multiclass_classification
+task_categories:
+- tabular-classification
 ---

segment.py

@@ -1,277 +0,0 @@
-"""Segment Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {}
-
-DESCRIPTION = "Segment 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_statlog_(image_segmentation)_147,
-  title        = {{Statlog (Image Segmentation)}},
-  year         = {1990},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5P01G}}
-}
-"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/segment/raw/main/segment.csv"
-}
-features_types_per_config = {
-    "segment": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=7,
-                                     names=("brickface", "sky", "foliage", "cement", "window", "path", "grass")),
-    },
-    "brickface": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "sky": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "foliage": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "cement": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "window": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "path": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "grass": {
-        "region_centroid_col": datasets.Value("float64"),
-        "region_centroid_row": datasets.Value("float64"),
-        "region_centroid_pixel_count": datasets.Value("float64"),
-        "short_line_density": datasets.Value("float64"),
-        "vedge_mean": datasets.Value("float64"),
-        "vedge_std": datasets.Value("float64"),
-        "hedge_mean": datasets.Value("float64"),
-        "hedge_std": datasets.Value("float64"),
-        "intensity_mean": datasets.Value("float64"),
-        "rawred_mean": datasets.Value("float64"),
-        "rawblue_mean": datasets.Value("float64"),
-        "rawgreen_mean": datasets.Value("float64"),
-        "exred_mean": datasets.Value("float64"),
-        "exblue_mean": datasets.Value("float64"),
-        "exgreen_mean": datasets.Value("float64"),
-        "value_mean": datasets.Value("float64"),
-        "saturation_mean": datasets.Value("float64"),
-        "hue_mean": datasets.Value("float64"),
-        "class": 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 SegmentConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(SegmentConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Segment(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "segment"
-    BUILDER_CONFIGS = [
-        SegmentConfig(name="segment", description="Segment for multiclass classification."),
-        SegmentConfig(name="brickface", description="Segment for binary classification."),
-        SegmentConfig(name="sky", description="Segment for binary classification."),
-        SegmentConfig(name="foliage", description="Segment for binary classification."),
-        SegmentConfig(name="cement", description="Segment for binary classification."),
-        SegmentConfig(name="window", description="Segment for binary classification."),
-        SegmentConfig(name="path", description="Segment for binary classification."),
-        SegmentConfig(name="grass", description="Segment 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:
-        data["class"] = data["class"].apply(lambda x: x - 1)
-        data = data.reset_index()
-        data.drop("index", axis="columns", inplace=True)
-
-        if self.config.name == "brickface":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
-        if self.config.name == "sky":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
-        if self.config.name == "foliage":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
-        if self.config.name == "cement":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
-        if self.config.name == "window":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
-        if self.config.name == "path":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
-        if self.config.name == "grass":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
-
-        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}")