mindspore.dataset.NumpySlicesDataset

class mindspore.dataset.NumpySlicesDataset(data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)

Creates a dataset with given data slices, mainly for loading Python data into dataset.

The column names and column types of generated dataset depend on Python data defined by users.

Parameters

• data (Union[list, tuple, dict]) – list, tuple, dict and other NumPy formats. Input data will be sliced along the first dimension and generate additional rows, if input is list, there will be one column in each row, otherwise there tends to be multi columns. Large data is not recommended to be loaded in this way as data is loading into memory.

• column_names (list[str], optional) – List of column names of the dataset. Default: None . If column_names is not provided, the output column names will be named as the keys of dict when the input data is a dict, otherwise they will be named like column_0, column_1 …

• num_samples (int, optional) – The number of samples to be included in the dataset. Default: None , all samples.

• num_parallel_workers (int, optional) – Number of worker subprocesses used to fetch the dataset in parallel. Default: 1.

• shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.

• sampler (Union[Sampler, Iterable], optional) – Object used to choose samples from the dataset. Default: None , expected order behavior shown in the table below.

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

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

Note

• The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.

Sampler obtained by different combinations of parameters sampler and num_samples , shuffle , num_shards , shard_id

Parameter sampler	Parameter num_shards / shard_id	Parameter shuffle	Parameter num_samples	Sampler Used
mindspore.dataset.Sampler type	None	None	None	sampler
numpy.ndarray,list,tuple,int type	/	/	num_samples	SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type	/	/	num_samples	IterSampler(sampler = sampler , num_samples = num_samples )
None	num_shards / shard_id	None / True	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None	num_shards / shard_id	False	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None	None	None / True	None	RandomSampler(num_samples = num_samples )
None	None	None / True	num_samples	RandomSampler(replacement = True , num_samples = num_samples )
None	None	False	num_samples	SequentialSampler(num_samples = num_samples )

Raises

• RuntimeError – If len of column_names does not match output len of data.

• ValueError – If num_parallel_workers exceeds the max thread numbers.

• ValueError – If sampler and shuffle are specified at the same time.

• ValueError – If sampler and sharding are specified at the same time.

• ValueError – If num_shards is specified but shard_id is None.

• ValueError – If shard_id is specified but num_shards is None.

• ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

• Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> # 1) Input data can be a list
>>> data = [1, 2, 3]
>>> dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1"])
>>>
>>> # 2) Input data can be a dictionary, and column_names will be its keys
>>> data = {"a": [1, 2], "b": [3, 4]}
>>> dataset = ds.NumpySlicesDataset(data=data)
>>>
>>> # 3) Input data can be a tuple of lists (or NumPy arrays), each tuple element refers to data in each column
>>> data = ([1, 2], [3, 4], [5, 6])
>>> dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1", "column_2", "column_3"])
>>>
>>> # 4) Load data from CSV file
>>> import pandas as pd
>>> df = pd.read_csv(filepath_or_buffer=csv_dataset_dir[0])
>>> dataset = ds.NumpySlicesDataset(data=dict(df), shuffle=False)

Pre-processing Operation

mindspore.dataset.Dataset.apply	Apply a function in this dataset.
mindspore.dataset.Dataset.concat	Concatenate the dataset objects in the input list.
mindspore.dataset.Dataset.filter	Filter dataset by prediction.
mindspore.dataset.Dataset.flat_map	Map func to each row in dataset and flatten the result.
mindspore.dataset.Dataset.map	Apply each operation in operations to this dataset.
mindspore.dataset.Dataset.project	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
mindspore.dataset.Dataset.rename	Rename the columns in input datasets.
mindspore.dataset.Dataset.repeat	Repeat this dataset count times.
mindspore.dataset.Dataset.reset	Reset the dataset for next epoch.
mindspore.dataset.Dataset.save	Save the dynamic data processed by the dataset pipeline in common dataset format.
mindspore.dataset.Dataset.shuffle	Shuffle the dataset by creating a cache with the size of buffer_size .
mindspore.dataset.Dataset.skip	Skip the first N elements of this dataset.
mindspore.dataset.Dataset.split	Split the dataset into smaller, non-overlapping datasets.
mindspore.dataset.Dataset.take	Take the first specified number of samples from the dataset.
mindspore.dataset.Dataset.zip	Zip the datasets in the sense of input tuple of datasets.

Batch

mindspore.dataset.Dataset.batch	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
mindspore.dataset.Dataset.bucket_batch_by_length	Bucket elements according to their lengths.
mindspore.dataset.Dataset.padded_batch	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

mindspore.dataset.Dataset.create_dict_iterator	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Get the mapping dictionary from category names to category indexes.
mindspore.dataset.Dataset.get_col_names	Return the names of the columns in dataset.
mindspore.dataset.Dataset.get_dataset_size	Return the number of batches in an epoch.
mindspore.dataset.Dataset.get_repeat_count	Get the replication times in RepeatDataset.
mindspore.dataset.Dataset.input_indexs	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
mindspore.dataset.Dataset.num_classes	Get the number of classes in a dataset.
mindspore.dataset.Dataset.output_shapes	Get the shapes of output data.
mindspore.dataset.Dataset.output_types	Get the types of output data.

Apply Sampler

mindspore.dataset.MappableDataset.add_sampler	Add a child sampler for the current dataset.
mindspore.dataset.MappableDataset.use_sampler	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

mindspore.dataset.Dataset.sync_update	Release a blocking condition and trigger callback with given data.
mindspore.dataset.Dataset.sync_wait	Add a blocking condition to the input Dataset and a synchronize action will be applied.
mindspore.dataset.Dataset.to_json	Serialize a pipeline into JSON string and dump into file if filename is provided.