Datasets:
Upload breast.py
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breast.py
CHANGED
@@ -10,26 +10,26 @@ import pandas
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VERSION = datasets.Version("1.0.0")
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_ORIGINAL_FEATURE_NAMES = [
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"id",
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"mitoses",
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"is_cancer"
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]
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_BASE_FEATURE_NAMES = [
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"mitoses",
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"is_cancer"
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]
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@@ -62,14 +62,14 @@ features_types_per_config = {
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},
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"cancer": {
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"mitoses": datasets.Value("int8"),
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"is_cancer": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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}
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@@ -121,6 +121,8 @@ class Breast(datasets.GeneratorBasedBuilder):
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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.columns = _BASE_FEATURE_NAMES
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data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1)
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VERSION = datasets.Version("1.0.0")
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_ORIGINAL_FEATURE_NAMES = [
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"id",
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"clump_thickness",
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"uniformity_of_cell_size",
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"uniformity_of_cell_shape",
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"marginal_adhesion",
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"single_epithelial_cell_size",
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"bare_nuclei",
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"bland_chromatin",
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"normal_nucleoli",
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"mitoses",
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"is_cancer"
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]
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_BASE_FEATURE_NAMES = [
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"clump_thickness",
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"uniformity_of_cell_size",
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"uniformity_of_cell_shape",
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"marginal_adhesion",
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"single_epithelial_cell_size",
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"bare_nuclei",
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"bland_chromatin",
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"normal_nucleoli",
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"mitoses",
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"is_cancer"
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]
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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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"uniformity_of_cell_shape": datasets.Value("int8"),
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"marginal_adhesion": datasets.Value("int8"),
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"single_epithelial_cell_size": datasets.Value("int8"),
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"bare_nuclei": datasets.Value("int8"),
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"bland_chromatin": datasets.Value("int8"),
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"normal_nucleoli": datasets.Value("int8"),
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"mitoses": datasets.Value("int8"),
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"is_cancer": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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}
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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.isna()]
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data.columns = _BASE_FEATURE_NAMES
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data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1)
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