mindspore.dataset.CSVDataset
- class mindspore.dataset.CSVDataset(dataset_files, field_delim=',', column_defaults=None, column_names=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)[source]
A source dataset that reads and parses comma-separated values (CSV) files as dataset.
The columns of generated dataset depend on the source CSV files.
- Parameters
dataset_files (Union[str, list[str]]) – String or list of files to be read or glob strings to search for a pattern of files. The list will be sorted in a lexicographical order.
field_delim (str, optional) – A string that indicates the char delimiter to separate fields. Default:
','
.column_defaults (list, optional) – List of default values for the CSV field. Default:
None
. Each item in the list is either a valid type (float, int, or string). If this is not provided, treats all columns as string type.column_names (list[str], optional) – List of column names of the dataset. Default:
None
. If this is not provided, infers the column_names from the first row of CSV file.num_samples (int, optional) – The number of samples to be included in the dataset. Default:
None
, will include all images.num_parallel_workers (int, optional) – Number of worker threads to read the data. Default:
None
, will use global default workers(8), it can be set bymindspore.dataset.config.set_num_parallel_workers()
.shuffle (Union[bool, Shuffle], optional) –
Perform reshuffling of the data every epoch. Default:
Shuffle.GLOBAL
. Bool type and Shuffle enum are both supported to pass in. If shuffle isFalse
, no shuffling will be performed. If shuffle isTrue
, performs global shuffle. There are three levels of shuffling, desired shuffle enum defined bymindspore.dataset.Shuffle
.Shuffle.GLOBAL
: Shuffle both the files and samples, same as setting shuffle to True.Shuffle.FILES
: Shuffle files only.
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 maximum sample number of per shard. Used in data parallel training .shard_id (int, optional) – The shard ID within num_shards . Default:
None
. This argument can only be specified when num_shards is also specified.cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default:
None
, which means no cache is used.
- Raises
RuntimeError – If dataset_files are not valid or do not exist.
ValueError – If field_delim is invalid.
ValueError – If num_parallel_workers exceeds the max thread numbers.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If shard_id is not in range of [0, num_shards ).
Examples
>>> import mindspore.dataset as ds >>> csv_dataset_dir = ["/path/to/csv_dataset_file"] # contains 1 or multiple csv files >>> dataset = ds.CSVDataset(dataset_files=csv_dataset_dir, column_names=['col1', 'col2', 'col3', 'col4'])
Pre-processing Operation
Apply a function in this dataset. |
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Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Take the first specified number of samples from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
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. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |