Datasets:
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README.md
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- breast
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- tabular_classification
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- binary_classification
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pretty_name:
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size_categories:
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- 100<n<1K
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task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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- cancer
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---
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# Breast cancer
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The [Breast cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29)
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- breast
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- tabular_classification
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- binary_classification
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pretty_name: Breast
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size_categories:
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- 100<n<1K
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task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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- cancer
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---
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# Breast cancer
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The [Breast cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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# Configurations and tasks
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- `cancer` Binary classification task to classify the cell clump as cancerous or not.
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# Features
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| **Name** |**Type**|**Description** |
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|-------------------------------|--------|----------------------------|
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|`clump_thickness` |`int8` |Thickness of the clump |
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|`uniformity_of_cell_size` |`int8` |Uniformity of cell size |
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|`uniformity_of_cell_shape` |`int8` |Uniformity of cell shape |
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|`marginal_adhesion` |`int8` |Marginal adhesion |
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|`single_epithelial_cell_size` |`int8` |single_epithelial_cell_size |
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|`bare_nuclei` |`int8` |bare_nuclei |
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|`bland_chromatin` |`int8` |bland_chromatin |
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|`normal_nucleoli` |`int8` |normal_nucleoli |
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|`mitoses` |`int8` |mitoses |
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|`is_cancer` |`int8` |Is the clump cancer |
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breast.py
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"train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data",
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}
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features_types_per_config = {
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"encoding": {
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"feature": datasets.Value("string"),
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"original_value": datasets.Value("string"),
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"encoded_value": datasets.Value("int8"),
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},
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"cancer": {
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"clump_thickness": datasets.Value("int8"),
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"uniformity_of_cell_size": datasets.Value("int8"),
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]
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def _info(self):
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if self.config.name not in features_per_config:
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raise ValueError(f"Unknown configuration: {self.config.name}")
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
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features=features_per_config[self.config.name])
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]
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def _generate_examples(self, filepath: str):
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data_row = dict(row)
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def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
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data.drop("id", axis="columns", inplace=True)
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print(data.dtypes)
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data = data[data.bare_nuclei != "?"]
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for c in data.columns:
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data.loc[:, c] = data[c].astype(int)
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"train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data",
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}
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features_types_per_config = {
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"cancer": {
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"clump_thickness": datasets.Value("int8"),
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"uniformity_of_cell_size": datasets.Value("int8"),
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]
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def _info(self):
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
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features=features_per_config[self.config.name])
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]
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def _generate_examples(self, filepath: str):
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if self.config.name == "cancer":
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data = pandas.read_csv(filepath, header=None)
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data.columns=_ORIGINAL_FEATURE_NAMES
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data = self.preprocess(data, config=self.config.name)
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for row_id, row in data.iterrows():
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data_row = dict(row)
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yield row_id, data_row
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else:
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raise ValueError(f"Unknown config: {self.config.name}")
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def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
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data.drop("id", axis="columns", inplace=True)
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data = data[data.bare_nuclei != "?"]
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for c in data.columns:
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data.loc[:, c] = data[c].astype(int)
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