Upload 2 files

.gitattributes

@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+covtype.data filter=lfs diff=lfs merge=lfs -text

covertype.py

@@ -0,0 +1,126 @@
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+
+DESCRIPTION = "Covertype dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/31/covertype"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/31/covertype")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/covertype/raw/main/covtype.data"
+}
+features_types_per_config = {
+    "covertype": {
+        "elevation": datasets.Value("float32"),
+		"aspect": datasets.Value("float32"),
+		"slope": datasets.Value("float32"),
+		"horizontal_distance_to_hydrology": datasets.Value("float32"),
+		"vertical_distance_to_hydrology": datasets.Value("float32"),
+		"horizontal_distance_to_roadways": datasets.Value("float32"),
+		"hillshade_9am": datasets.Value("float32"),
+		"hillshade_noon": datasets.Value("float32"),
+		"hillshade_3pm": datasets.Value("float32"),
+		"horizontal_distance_to_fire_points": datasets.Value("float32"),
+		"is_a_wilderness_area": datasets.Value("bool"),
+		"soil_type_id_0": datasets.Value("bool"),
+		"soil_type_id_1": datasets.Value("bool"),
+		"soil_type_id_2": datasets.Value("bool"),
+		"soil_type_id_3": datasets.Value("bool"),
+		"soil_type_id_4": datasets.Value("bool"),
+		"soil_type_id_5": datasets.Value("bool"),
+		"soil_type_id_6": datasets.Value("bool"),
+		"soil_type_id_7": datasets.Value("bool"),
+		"soil_type_id_8": datasets.Value("bool"),
+		"soil_type_id_9": datasets.Value("bool"),
+		"soil_type_id_10": datasets.Value("bool"),
+		"soil_type_id_11": datasets.Value("bool"),
+		"soil_type_id_12": datasets.Value("bool"),
+		"soil_type_id_13": datasets.Value("bool"),
+		"soil_type_id_14": datasets.Value("bool"),
+		"soil_type_id_15": datasets.Value("bool"),
+		"soil_type_id_16": datasets.Value("bool"),
+		"soil_type_id_17": datasets.Value("bool"),
+		"soil_type_id_18": datasets.Value("bool"),
+		"soil_type_id_19": datasets.Value("bool"),
+		"soil_type_id_20": datasets.Value("bool"),
+		"soil_type_id_21": datasets.Value("bool"),
+		"soil_type_id_22": datasets.Value("bool"),
+		"soil_type_id_23": datasets.Value("bool"),
+		"soil_type_id_24": datasets.Value("bool"),
+		"soil_type_id_25": datasets.Value("bool"),
+		"soil_type_id_26": datasets.Value("bool"),
+		"soil_type_id_27": datasets.Value("bool"),
+		"soil_type_id_28": datasets.Value("bool"),
+		"soil_type_id_29": datasets.Value("bool"),
+		"soil_type_id_30": datasets.Value("bool"),
+		"soil_type_id_31": datasets.Value("bool"),
+		"soil_type_id_32": datasets.Value("bool"),
+		"soil_type_id_33": datasets.Value("bool"),
+		"soil_type_id_34": datasets.Value("bool"),
+		"soil_type_id_35": datasets.Value("bool"),
+		"soil_type_id_36": datasets.Value("bool"),
+		"soil_type_id_37": datasets.Value("bool"),
+		"soil_type_id_38": datasets.Value("bool"),
+		"soil_type_id_39": datasets.Value("bool"),
+        "soil_type": datasets.Value("string"),
+		"cover_type
+    }
+}
+
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class CovertypeConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(CovertypeConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Covertype(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "covertype"
+    BUILDER_CONFIGS = [
+        CovertypeConfig(name="covertype",
+                    description="Covertype for multiclass classification.")
+    ]
+
+
+    def _info(self):
+        if self.config.name not in features_per_config:
+            raise ValueError(f"Unknown configuration: {self.config.name}")
+        
+        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, config=self.config.name)
+
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
+
+                yield row_id, data_row
+
+    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data["500"] = data["500"].apply(lambda x: max(0, x)).astype(int)
+        if "0.1" in data:
+            data.drop("0.1", axis="columns", inplace=True)
+
+        return data

covtype.data

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1825eb1a33610ad72b10ea943f20aed7951f503553ab0b872348ceb116f62c1
+size 75170171