Upload segment.py

segment.py

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+"""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}")