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

Before Change

        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}"
            for j, value in feature_map.items():
                j_index = self.feature_id_to_index.get(j, None)
                if j_index is None:
                    continue
                dense_data[i][j_index] = value
                dense_presence[i][j_index] = 1
        if self.set_missing_value_to_zero:
            // When we set missing values to 0, we don"t know what is and isn"t missing
            dense_presence = dense_data != 0.0
        return (dense_data, dense_presence)

After Change

        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:
            // 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:

Instances