Datasets:
Update balloons.py
Browse files- balloons.py +6 -8
balloons.py
CHANGED
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@@ -1,5 +1,3 @@
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"""Balloons."""
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from typing import List
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import datasets
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@@ -13,7 +11,7 @@ _BASE_FEATURE_NAMES = [
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"size",
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"act",
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"age",
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"
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]
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@@ -40,28 +38,28 @@ features_types_per_config = {
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"
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},
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"adult_and_stretch": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"
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},
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"yellow_and_small": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"
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},
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"yellow_and_small_or_adult_and_stretch": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"
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}
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}
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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@@ -104,7 +102,7 @@ class Balloons(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, filepath: str):
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data = pandas.read_csv(filepath, header=None)
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data.columns = _BASE_FEATURE_NAMES
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data.loc[:, "
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for row_id, row in data.iterrows():
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data_row = dict(row)
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from typing import List
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import datasets
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"size",
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"act",
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"age",
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"is_inflated"
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]
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"is_inflated": datasets.ClassLabel(num_classes=2)
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},
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"adult_and_stretch": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"is_inflated": datasets.ClassLabel(num_classes=2)
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},
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"yellow_and_small": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"is_inflated": datasets.ClassLabel(num_classes=2)
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},
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"yellow_and_small_or_adult_and_stretch": {
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"color": datasets.Value("string"),
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"size": datasets.Value("string"),
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"act": datasets.Value("string"),
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"age": datasets.Value("string"),
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"is_inflated": datasets.ClassLabel(num_classes=2)
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}
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}
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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def _generate_examples(self, filepath: str):
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data = pandas.read_csv(filepath, header=None)
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data.columns = _BASE_FEATURE_NAMES
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data.loc[:, "is_inflated"] = data.inflated.apply(lambda x: 1 if x == "T" else 0)
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for row_id, row in data.iterrows():
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data_row = dict(row)
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