mindelec.data.Dataset

class mindelec.data.Dataset(geometry_dict=None, existed_data_list=None, dataset_list=None)[source]

Combine datasets together.

Parameters

  • geometry_dict (dict, optional) – specifies geometry datasets to be merged. The key is geometry instance and value is a list of type of geometry. For example, geometry_dict = {geom : [“domain”, “BC”, “IC”]}. Default: None.

  • existed_data_list (Union[list, tuple, ExistedDataConfig], optional) – specifies existed datasets to be merged. For example, existed_data_list = [ExistedDataConfig_Instance1, ExistedDataConfig_Instance2]. Default: None.

  • dataset_list (Union[list, tuple, Data], optional) – specifies instances of data to be merged. For example, dataset_list=[BoundaryIC_Instance, Equation_Instance, BoundaryBC_Instance and ExistedData_Instance]. Default: None.

Raises

  • ValueError – If geometry_dict, existed_data_list and dataset_list are all None.

  • TypeError – If the type of geometry_dict is not dict.

  • TypeError – If the type of key of geometry_dict is not instance of mindelec.geometry.Geometry.

  • TypeError – If the type of existed_data_list is not list, tuple or instance of ExistedDataConfig.

  • TypeError – If the element of existed_data_list is not instance of ExistedDataConfig.

  • TypeError – If the element of dataset_list is not instance of class Data.

Supported Platforms:

Ascend

Examples

>>> from easydict import EasyDict as edict
>>> from mindelec.geometry import Rectangle, create_config_from_edict
>>> from mindelec.data import Dataset
>>> rectangle_mesh = edict({'domain': edict({'random_sampling': False, 'size': [50, 25]})})
>>> rect_space = Rectangle("rectangle", coord_min=[0, 0], coord_max=[5, 5],
...                        sampling_config=create_config_from_edict(rectangle_mesh))
>>> geom_dict = {rect_space: ["domain"]}
>>> dataset = Dataset(geometry_dict=geom_dict)
create_dataset(batch_size=1, preprocess_fn=None, input_output_columns_map=None, shuffle=True, drop_remainder=True, prebatched_data=False, num_parallel_workers=1, num_shards=None, shard_id=None, python_multiprocessing=False)[source]

create the final mindspore type dataset to merge all the sub-datasets.

Parameters

  • batch_size (int, optional) – An int number of rows each batch is created with. Default: 1.

  • preprocess_fn (Union[list[TensorOp], list[functions]], optional) – List of operations to be applied on the dataset. Operations are applied in the order they appear in this list. Default: None.

  • input_output_columns_map (dict, optional) – specifies which columns to replace and to what. The key is the column name to be replaced and the value is the name you want to replace with. There’s no need to set this argument if all columns are not changed after mapping. Default: None.

  • shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Random accessible input is required. Default: True, expected order behavior shown in the table.

  • drop_remainder (bool, optional) – Determines whether or not to drop the last block whose data row number is less than batch size. If True, and if there are less than batch_size rows available to make the last batch, then those rows will be dropped and not propagated to the child node. Default: True.

  • prebatched_data (bool, optional) – Generate pre-batched data before create mindspore dataset. If True, pre-batched data will be returned when get each sub-dataset data by index. Else, the batch operation will be done by mindspore dataset interface: dataset.batch. When batch_size is very large, it’s recommended to set this option to be True in order to improve performance on host. Default: False.

  • num_parallel_workers (int, optional) – Number of workers(threads) to process the dataset in parallel. Default: 1.

  • num_shards (int, optional) – Number of shards that the dataset will be divided into. Random accessible input is required. When this argument is specified, num_samples reflects the maximum sample number of per shard. Default: None.

  • shard_id (int, optional) – The shard ID within num_shards. This argument must be specified only when num_shards is also specified. Random accessible input is required. Default: None.

  • python_multiprocessing (bool, optional) – Parallelize Python function per_batch_map with multi-processing. This option could be beneficial if the function is computational heavy. Default: False.

Returns

BatchDataset, dataset batched.

Examples

>>> data = dataset.create_dataset()
get_columns_list()[source]

get columns list.

Args:

Returns

list[str]. column names list of the final unified dataset.

Examples

>>> columns_list = dataset.get_columns_list()
set_constraint_type(constraint_type='Equation')[source]

set constraint type of dataset.

Parameters

constraint_type (Union[str, dict]) – The constraint type of specified dataset. If it is string, the constraint type of all subdataset will be set to the same one, e.g. “Equation”, “Label” and “Function”. If it is dict, the subdataset and it’s constraint type is specified by the pair (key, value). Default: “Equation”.

Examples

>>> dataset.set_constraint_type("Equation")