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1e1faf0
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1 Parent(s): 56f86c7

Upload student_performance.py

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  1. 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("string"),
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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"))
@@ -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("string"),
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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")),
@@ -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("string"),
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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")),
@@ -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 = "cut") -> 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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  "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)