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)[source]

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.