Datasets:
Upload student_performance.py
Browse files- student_performance.py +4 -4
student_performance.py
CHANGED
@@ -63,7 +63,7 @@ features_types_per_config = {
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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-
"has_completed_preparation_test": datasets.Value("
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"reading_score": datasets.Value("int64"),
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"writing_score": datasets.Value("int64"),
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"has_passed_math_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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@@ -73,7 +73,7 @@ features_types_per_config = {
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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-
"has_completed_preparation_test": datasets.Value("
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"reading_score": datasets.Value("int64"),
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"math_score": datasets.Value("int64"),
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"has_passed_writing_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
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@@ -83,7 +83,7 @@ features_types_per_config = {
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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-
"has_completed_preparation_test": datasets.Value("
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"writing_score": datasets.Value("int64"),
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"math_score": datasets.Value("int64"),
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"has_passed_reading_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
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@@ -143,7 +143,7 @@ class StudentPerformance(datasets.GeneratorBasedBuilder):
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yield row_id, data_row
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-
def preprocess(self, data: pandas.DataFrame, config: str = "
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data.columns = _BASE_FEATURE_NAMES
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for feature in _ENCODING_DICS:
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encoding_function = partial(self.encode, feature)
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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+
"has_completed_preparation_test": datasets.Value("int8"),
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"reading_score": datasets.Value("int64"),
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"writing_score": datasets.Value("int64"),
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"has_passed_math_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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+
"has_completed_preparation_test": datasets.Value("int8"),
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"reading_score": datasets.Value("int64"),
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"math_score": datasets.Value("int64"),
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"has_passed_writing_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
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"ethnicity": datasets.Value("string"),
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"parental_level_of_education": datasets.Value("int8"),
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"has_standard_lunch": datasets.Value("int8"),
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+
"has_completed_preparation_test": datasets.Value("int8"),
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"writing_score": datasets.Value("int64"),
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"math_score": datasets.Value("int64"),
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"has_passed_reading_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
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yield row_id, data_row
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+
def preprocess(self, data: pandas.DataFrame, config: str = "math") -> pandas.DataFrame:
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data.columns = _BASE_FEATURE_NAMES
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for feature in _ENCODING_DICS:
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encoding_function = partial(self.encode, feature)
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