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README.md

@@ -1,3 +1,38 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- steel_plates
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Landsat
+size_categories:
+- 1K<n<5K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- steel_plates
+- steel_plates_0
+- steel_plates_1
+- steel_plates_2
+- steel_plates_3
+- steel_plates_4
+- steel_plates_5
+- steel_plates_6
+
 ---
+# Landsat
+The [Steel Plates dataset](https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| steel_plates                 | Multiclass classification.|                         |
+| steel_plates_0               | Binary classification.    | Is the input of class 0? |
+| steel_plates_1               | Binary classification.    | Is the input of class 1? |
+| steel_plates_2               | Binary classification.    | Is the input of class 2? |
+| steel_plates_3               | Binary classification.    | Is the input of class 3? |
+| steel_plates_4               | Binary classification.    | Is the input of class 4? |
+| steel_plates_5               | Binary classification.    | Is the input of class 5? |
+| steel_plates_6               | Binary classification.    | Is the input of class 6? |

steel_plates.py

@@ -0,0 +1,347 @@
+"""Landsat Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+
+DESCRIPTION = "Landsat dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults")
+_CITATION = """
+@misc{misc_steel_plates_faults_198,
+  author       = {Buscema,M, Terzi,S & Tastle,W},
+  title        = {{Steel Plates Faults}},
+  year         = {2010},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5J88N}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/steel_plates/raw/main/steel_plates.csv"
+}
+features_types_per_config = {
+    "steel_plates": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=7),
+    },
+    "steel_plates_0": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_1": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_2": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_3": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_4": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_5": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+    "steel_plates_6": {
+        "x_minimum": datasets.Value("int64"),
+		"x_maximum": datasets.Value("int64"),
+		"y_minimum": datasets.Value("int64"),
+		"y_maximum": datasets.Value("int64"),
+		"pixels_areas": datasets.Value("int64"),
+		"x_perimeter": datasets.Value("int64"),
+		"y_perimeter": datasets.Value("int64"),
+		"sum_of_luminosity": datasets.Value("int64"),
+		"minimum_of_luminosity": datasets.Value("int64"),
+		"maximum_of_luminosity": datasets.Value("int64"),
+		"length_of_conveyer": datasets.Value("int64"),
+		"typeofsteel_a300": datasets.Value("int64"),
+		"typeofsteel_a400": datasets.Value("int64"),
+		"steel_plate_thickness": datasets.Value("int64"),
+		"edges_index": datasets.Value("float64"),
+		"empty_index": datasets.Value("float64"),
+		"square_index": datasets.Value("float64"),
+		"outside_x_index": datasets.Value("float64"),
+		"edges_x_index": datasets.Value("float64"),
+		"edges_y_index": datasets.Value("float64"),
+		"outside_global_index": datasets.Value("float64"),
+		"logofareas": datasets.Value("float64"),
+		"log_x_index": datasets.Value("float64"),
+		"log_y_index": datasets.Value("float64"),
+		"orientation_index": datasets.Value("float64"),
+		"luminosity_index": datasets.Value("float64"),
+		"sigmoidofareas": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2),
+    },
+   
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class LandsatConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(LandsatConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Landsat(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "steel_plates"
+    BUILDER_CONFIGS = [
+        LandsatConfig(name="steel_plates", description="Landsat for multiclass classification."),
+        LandsatConfig(name="steel_plates_0", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_1", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_2", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_3", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_4", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_5", description="Landsat for binary classification."),
+        LandsatConfig(name="steel_plates_5", description="Landsat 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 == "steel_plates_0":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
+        elif self.config.name == "steel_plates_1":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+        elif self.config.name == "steel_plates_2":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+        elif self.config.name == "steel_plates_3":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
+        elif self.config.name == "steel_plates_4":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
+        elif self.config.name == "steel_plates_5":
+            data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
+        elif self.config.name == "steel_plates_6":
+            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}")