9cf8f6cdf6a2008843cb37da6e34b8d10353b0bf,ml/rl/preprocessing/sparse_to_dense.py,PythonSparseToDenseProcessor,process,#PythonSparseToDenseProcessor#Any#,73

Before Change

    ) -> Tuple[torch.Tensor, torch.Tensor]:
        dense_data = torch.ones([len(sparse_data), len(self.feature_id_to_index)])
        dense_presence = torch.zeros(
            [len(sparse_data), len(self.feature_id_to_index)]
        ).byte()
        for i, feature_map in enumerate(sparse_data):
            assert (

After Change

        ).float()
        if self.set_missing_value_to_zero:
            // When we set missing values to 0, we don"t know what is and isn"t missing
            presence = torch.ones_like(values, dtype=torch.bool)
        else:
            presence = values != missing_value
        return values, presence

Instances