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Runtime error
| import pandas as pd | |
| try: | |
| import tempeh.configurations as tc | |
| except ImportError as error: | |
| from logging import warning | |
| warning("{}: fetch_lawschool_gpa will be unavailable. To install, run:\n" | |
| "pip install 'aif360[LawSchoolGPA]'".format(error)) | |
| from aif360.sklearn.datasets.utils import standardize_dataset | |
| def fetch_lawschool_gpa(subset="all", *, usecols=None, dropcols=None, | |
| numeric_only=False, dropna=True): | |
| """Load the Law School GPA dataset | |
| Note: | |
| By default, the data is downloaded from tempeh. See | |
| https://github.com/microsoft/tempeh for details. | |
| Args: | |
| subset ({'train', 'test', or 'all'}, optional): Select the dataset to | |
| load: 'train' for the training set, 'test' for the test set, 'all' | |
| for both. | |
| usecols (single label or list-like, optional): Feature column(s) to | |
| keep. All others are dropped. | |
| dropcols (single label or list-like, optional): Feature column(s) to | |
| drop. | |
| numeric_only (bool): Drop all non-numeric feature columns. | |
| dropna (bool): Drop rows with NAs. FIXME: NAs already dropped by tempeh | |
| Returns: | |
| namedtuple: Tuple containing X, y, and sample_weights for the Law School | |
| GPA dataset accessible by index or name. | |
| """ | |
| if subset not in {'train', 'test', 'all'}: | |
| raise ValueError("subset must be either 'train', 'test', or 'all'; " | |
| "cannot be {}".format(subset)) | |
| dataset = tc.datasets["lawschool_gpa"]() | |
| X_train, X_test = dataset.get_X(format=pd.DataFrame) | |
| y_train, y_test = dataset.get_y(format=pd.Series) | |
| A_train, A_test = dataset.get_sensitive_features(name='race', | |
| format=pd.Series) | |
| all_train = pd.concat([X_train, y_train, A_train], axis=1) | |
| all_test = pd.concat([X_test, y_test, A_test], axis=1) | |
| if subset == "train": | |
| df = all_train | |
| elif subset == "test": | |
| df = all_test | |
| else: | |
| df = pd.concat([all_train, all_test], axis=0) | |
| df.race = df.race.astype('category').cat.set_categories( | |
| ['black', 'white'], ordered=True) | |
| return standardize_dataset(df, prot_attr='race', target='zfygpa', | |
| usecols=usecols, dropcols=dropcols, | |
| numeric_only=numeric_only, dropna=dropna) | |