mindspore.dataset.AGNewsDataset
- class mindspore.dataset.AGNewsDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)[source]
AG News dataset.
The generated dataset has three columns:
[index, title, description], and the data type of three columns is string type.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Acceptable usages include
'train','test'and'all'. Default:None, all samples.num_samples (int, optional) – Number of samples (rows) to read. Default:
None, reads the full dataset.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. Bool type and Shuffle enum are both supported to pass in. Default:
Shuffle.GLOBAL. If shuffle isFalse, no shuffling will be performed. If shuffle isTrue, it is equivalent to setting shuffle tomindspore.dataset.Shuffle.GLOBAL. Set the mode of data shuffling by passing in enumeration variables:Shuffle.GLOBAL: Shuffle both the files and samples.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 max sample number of per shard. Used in data parallel training .shard_id (int, optional) – The shard ID within num_shards . This argument can only be specified when num_shards is also specified. Default:
None.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_dir does not contain data files.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If num_parallel_workers exceeds the max thread numbers.
- Tutorial Examples:
Examples
>>> import mindspore.dataset as ds >>> ag_news_dataset_dir = "/path/to/ag_news_dataset_file" >>> dataset = ds.AGNewsDataset(dataset_dir=ag_news_dataset_dir, usage='all')
About AGNews dataset:
AG is a collection of over 1 million news articles. The news articles were collected by ComeToMyHead from over 2,000 news sources in over 1 year of activity. ComeToMyHead is an academic news search engine that has been in operation since July 2004. The dataset is provided by academics for research purposes such as data mining (clustering, classification, etc.), information retrieval (ranking, searching, etc.), xml, data compression, data streaming, and any other non-commercial activities. AG's news topic classification dataset was constructed by selecting the four largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 test samples. The total number of training samples in train.csv is 120,000 and the number of test samples in test.csv is 7,600.
You can unzip the dataset files into the following structure and read by MindSpore's API:
. └── ag_news_dataset_dir ├── classes.txt ├── train.csv ├── test.csv └── readme.txtCitation:
@misc{zhang2015characterlevel, title={Character-level Convolutional Networks for Text Classification}, author={Xiang Zhang and Junbo Zhao and Yann LeCun}, year={2015}, eprint={1509.01626}, archivePrefix={arXiv}, primaryClass={cs.LG} }
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. |