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

Before Change

        self, sparse_data: List[Dict[int, float]]
    ) -> 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 (
                feature_map is not None
            ), f"Please make sure that features are not NULL; row {i}"

After Change

        state_features_df = pd.DataFrame(sparse_data).fillna(missing_value)
        // Add columns identified by normalization, but not present in batch
        for col in self.sorted_features:
            if col not in state_features_df.columns:
                state_features_df[col] = missing_value
        values = torch.from_numpy(
            state_features_df[self.sorted_features].values
        ).float()
        if self.set_missing_value_to_zero:

Instances