Update parquet files

README.md

@@ -1,34 +0,0 @@
----
-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

@@ -1,97 +0,0 @@
-"""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):
-        data = pandas.read_csv(filepath, header=None)
-        data.columns = _BASE_FEATURE_NAMES
-        data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == "g" else 0)
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row

magic/magic-train.parquet

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