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Support for many new model output types (#93)

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* General model wrappers and methods supporting multi-label classifiers
* Support for pytorch multi-label binary classifier
* New model output types + single implementation of score method that supports multiple output types. 
* Anonymization with pytorch multi-output binary model
* Support for multi-label binary models in minimizer. 
* Support for multi-label logits/probabilities
---------
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailgold 2024-07-03 09:04:59 -04:00 • committed by GitHub
parent e00535d120
commit 57e38ea4fa
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13 changed files with 913 additions and 172 deletions
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apt/utils/datasets/datasets.py
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@ -233,7 +233,7 @@ class ArrayDataset(Dataset):
raise ValueError("The supplied features are not the same as in the data features")
self.features_names = x.columns.to_list()
if self._y is not None and len(self._x) != len(self._y):
if self._y is not None and self._x.shape[0] != self._y.shape[0]:
raise ValueError("Non equivalent lengths of x and y")
def get_samples(self) -> OUTPUT_DATA_ARRAY_TYPE:
@ -266,6 +266,8 @@ class DatasetWithPredictions(Dataset):
Dataset that is based on arrays (e.g., numpy/pandas/list...). Includes predictions from a model, and possibly also
features and true labels.
:param pred: collection of model predictions
:type pred: numpy array or pandas DataFrame or list or pytorch Tensor
:param x: collection of data samples
:type x: numpy array or pandas DataFrame or list or pytorch Tensor
:param y: collection of labels