mindspore.dataset.PennTreebankDataset

class mindspore.dataset.PennTreebankDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)

PennTreebank dataset.

The generated dataset has one column [text] . The tensor of column text is of the string type.

Parameters

• dataset_dir (str) – Path to the root directory that contains the dataset.

• usage (str, optional) – Acceptable usages include 'train', 'test', 'valid' and 'all'. 'train' will read from 42,068 train samples of string type, 'test' will read from 3,370 test samples of string type, 'valid' will read from 3,761 test samples of string type, 'all' will read from all 49,199 samples of string type. 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 by mindspore.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 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.

  • 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.

• 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:

• Load & Process Data With Dataset Pipeline

Examples

>>> import mindspore.dataset as ds
>>> penn_treebank_dataset_dir = "/path/to/penn_treebank_dataset_directory"
>>> dataset = ds.PennTreebankDataset(dataset_dir=penn_treebank_dataset_dir, usage='all')

About PennTreebank dataset:

Penn Treebank (PTB) dataset, is widely used in machine learning for NLP (Natural Language Processing) research. Word-level PTB does not contain capital letters, numbers, and punctuations, and the vocabulary is capped at 10k unique words, which is relatively small in comparison to most modern datasets which can result in a larger number of out of vocabulary tokens.

Here is the original PennTreebank dataset structure. You can unzip the dataset files into this directory structure and read by MindSpore's API.

.
└── PennTreebank_dataset_dir
     ├── ptb.test.txt
     ├── ptb.train.txt
     └── ptb.valid.txt

Citation:

@techreport{Santorini1990,
  added-at = {2014-03-26T23:25:56.000+0100},
  author = {Santorini, Beatrice},
  biburl = {https://www.bibsonomy.org/bibtex/234cdf6ddadd89376090e7dada2fc18ec/butonic},
  file = {:Santorini - Penn Treebank tag definitions.pdf:PDF},
  institution = {Department of Computer and Information Science, University of Pennsylvania},
  interhash = {818e72efd9e4b5fae3e51e88848100a0},
  intrahash = {34cdf6ddadd89376090e7dada2fc18ec},
  keywords = {dis pos tagging treebank},
  number = {MS-CIS-90-47},
  timestamp = {2014-03-26T23:25:56.000+0100},
  title = {Part-of-speech tagging guidelines for the {P}enn {T}reebank {P}roject},
  url = {ftp://ftp.cis.upenn.edu/pub/treebank/doc/tagguide.ps.gz},
  year = 1990
}

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.