mindspore.dataset.SogouNewsDataset

class mindspore.dataset.SogouNewsDataset(dataset_dir, usage=None, num_samples=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, num_parallel_workers=None, cache=None)[source]

Sogou News dataset.

The generated dataset has three columns: [index, title, content] , and the data type of three columns is string.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Usage of this dataset, can be ‘train’, ‘test’ or ‘all’ . ‘train’ will read from 450,000 train samples, ‘test’ will read from 60,000 test samples, ‘all’ will read from all 510,000 samples. Default: None, all samples.
num_samples (int, optional) – Number of samples (rows) to read. Default: None, read all samples.
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 is False, no shuffling will be performed. If shuffle is True, it is equivalent to setting shuffle to mindspore.dataset.Shuffle.GLOBAL. Set the mode of data shuffling by passing in enumeration variables:
- 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 max sample number of per shard.
shard_id (int, optional) – The shard ID within num_shards . Default: None. This argument can only be specified when num_shards is also specified.
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 by mindspore.dataset.config.set_num_parallel_workers .
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.

Examples

>>> sogou_news_dataset_dir = "/path/to/sogou_news_dataset_dir"
>>> dataset = ds.SogouNewsDataset(dataset_dir=sogou_news_dataset_dir, usage='all')

About SogouNews Dataset:

SogouNews dataset includes 3 columns, corresponding to class index (1 to 5), title and content. The title and content are escaped using double quotes (“), and any internal double quote is escaped by 2 double quotes (“”). New lines are escaped by a backslash followed with an “n” character, that is “n”.

You can unzip the dataset files into the following structure and read by MindSpore’s API:

.
└── sogou_news_dir
     ├── classes.txt
     ├── readme.txt
     ├── test.csv
     └── train.csv

Citation:

@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

`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`	Takes at most given numbers of elements 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.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Return the class index.
`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.device_que`	Return a transferred Dataset that transfers data through a device.
`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.