mindspore.dataset.YelpReviewDataset
- class mindspore.dataset.YelpReviewDataset(dataset_dir, usage=None, num_samples=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, num_parallel_workers=None, cache=None)[source]
Yelp Review Polarity and Yelp Review Full datasets.
The generated dataset has two columns:
[label, text], and the data type of two 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'. For Polarity,'train'will read from 560,000 train samples,'test'will read from 38,000 test samples,'all'will read from all 598,000 samples. For Full,'train'will read from 650,000 train samples,'test'will read from 50,000 test samples,'all'will read from all 700,000 samples. Default:None, all samples.num_samples (int, optional) – Number of samples (rows) to read. Default:
None, reads 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 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 . 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 bymindspore.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.
- Tutorial Examples:
Examples
>>> import mindspore.dataset as ds >>> yelp_review_dataset_dir = "/path/to/yelp_review_dataset_dir" >>> dataset = ds.YelpReviewDataset(dataset_dir=yelp_review_dataset_dir, usage='all')
About YelpReview Dataset:
The Yelp Review Full dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data, and it is mainly used for text classification.
The Yelp Review Polarity dataset is constructed from the above dataset, by considering stars 1 and 2 negative, and 3 and 4 positive.
The directory structures of these two datasets are the same. You can unzip the dataset files into the following structure and read by MindSpore's API:
. └── yelp_review_dir ├── train.csv ├── test.csv └── readme.txtCitation:
For Yelp Review Polarity:
@article{zhangCharacterlevelConvolutionalNetworks2015, archivePrefix = {arXiv}, eprinttype = {arxiv}, eprint = {1509.01626}, primaryClass = {cs}, title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.}, journal = {arXiv:1509.01626 [cs]}, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, month = sep, year = {2015}, }
Citation:
For Yelp Review Full:
@article{zhangCharacterlevelConvolutionalNetworks2015, archivePrefix = {arXiv}, eprinttype = {arxiv}, eprint = {1509.01626}, primaryClass = {cs}, title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.}, journal = {arXiv:1509.01626 [cs]}, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, month = sep, year = {2015}, }
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. |