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README.md

@@ -1,3 +1,34 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- magic
+- tabular_classification
+- binary_classification
+pretty_name: Magic
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- magic
 ---
+# Magic
+The [Magic dataset](https://archive.ics.uci.edu/ml/datasets/Magic) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**                                               |
+|-------------------|---------------------------|---------------------------------------------------------------|
+| magic             | Binary classification     | Classify the person's magic as over or under the threshold.  |
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/magic", "magic")["train"]
+```
+
+# Features
+|**Feature**        |**Type**   | **Description**                                           |
+

magic.py

@@ -0,0 +1,159 @@
+"""Magic"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "major_axis_length"
+    "minor_axis_length"
+    "log_of_sum_of_content"
+    "ratio_of_sum_of_highest_pixels_and_size"
+    "ratio_of_highest_pixel_and_size"
+    "projected_distance_highest_to_center_pixel"
+    "third_root_of_third_moment_along_major_axis"
+    "third_root_of_third_moment_along_minor_axis"
+    "angle_major_axis_to_origin"
+    "distance_origin_to_center"
+    "class"
+]
+
+
+DESCRIPTION = "Magic dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Magic"
+_URLS = ("https://archive.ics.uci.edu/ml/datasets/Magic")
+_CITATION = """
+@misc{misc_magic_gamma_telescope_159,
+  author       = {Bock,R.},
+  title        = {{MAGIC Gamma Telescope}},
+  year         = {2007},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C52C8B}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/magic/raw/main/magic04.data"
+}
+features_types_per_config = {
+    "magic": {
+        "major_axis_length": datasets.Value("float64"),
+        "minor_axis_length": datasets.Value("float64"),
+        "log_of_sum_of_content": datasets.Value("float64"),
+        "ratio_of_sum_of_highest_pixels_and_size": datasets.Value("float64"),
+        "ratio_of_highest_pixel_and_size": datasets.Value("float64"),
+        "projected_distance_highest_to_center_pixel": datasets.Value("float64"),
+        "third_root_of_third_moment_along_major_axis": datasets.Value("float64"),
+        "third_root_of_third_moment_along_minor_axis": datasets.Value("float64"),
+        "angle_major_axis_to_origin": datasets.Value("float64"),
+        "distance_origin_to_center": 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 MagicConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(MagicConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Magic(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "magic"
+    BUILDER_CONFIGS = [
+        MagicConfig(name="magic",
+                    description="Magic 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):
+        if self.config.name == "encoding":
+            data = self.encodings()
+
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
+
+                yield row_id, data_row
+
+        elif self.config.name in ["magic", "magic-no race", "race"]:               
+            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
+
+        else:
+            raise ValueError(f"Unknown config: {self.config.name}")
+
+    def encodings(self):
+        data = [pandas.DataFrame([(feature, original_value, encoded_value)
+                                   for original_value, encoded_value in d.items()],
+                                 columns=["feature", "original_value", "encoded_value"])
+                for feature, d in _ENCODING_DICS.items()]
+        data.append(pandas.DataFrame([("race", original_value, encoded_value)
+                                       for original_value, encoded_value in _RACE_ENCODING.items()],
+                    columns=["feature", "original_value", "encoded_value"]))
+        data.append(pandas.DataFrame([("education", original_value, encoded_value)
+                                       for original_value, encoded_value in _EDUCATION_ENCODING.items()],
+                    columns=["feature", "original_value", "encoded_value"]))
+        data = pandas.concat(data, axis="rows").reset_index()
+        data.drop("index", axis="columns", inplace=True)
+
+        return data
+
+
+    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data.drop("education", axis="columns", inplace=True)
+        data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"})
+
+        data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
+                     "hours_per_week", "marital_status", "native_country", "occupation",
+                     "race", "relationship", "is_male", "workclass", "over_threshold"]]
+        data.columns = _BASE_FEATURE_NAMES
+
+        for feature in _ENCODING_DICS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
+        
+
+        if config == "magic":
+            return data[list(features_types_per_config["magic"].keys())]
+        elif config == "magic-no race":
+            return data[list(features_types_per_config["magic-no race"].keys())]
+        elif config =="race":
+            data.loc[:, "race"] = data.race.apply(self.encode_race)
+            data = data[list(features_types_per_config["race"].keys())]
+
+            return data
+        else:
+            raise ValueError(f"Unknown config: {config}")
+    
+    def encode(self, feature, value):
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")
+    
+    def encode_race(self, race):
+        return _RACE_ENCODING[race]