# Data Reader Interface and DataSets

## DataTypes

`paddle.v2.data_type.``dense_vector`(dim, seq_type=0)

  Dense Vector. It means the input feature is dense float vector. For example, if the input is an image with 28*28 pixels, the input of Paddle neural network should be a dense vector with dimension 784.

  Parameters:

  - dim (int) – dimension of this vector.
  - seq_type (int) – sequence type of input.

  Returns: | An input type object.
  Return type: | InputType

`paddle.v2.data_type.``dense_vector_sequence`(dim)

  Data type of a sequence of dense vector.

  Parameters: | dim (int) – dimension of dense vector.
  Returns: | An input type object
  Return type: | InputType

`paddle.v2.data_type.``integer_value`(value_range, seq_type=0)

  Data type of integer.

  Parameters:

  - seq_type (int) – sequence type of this input.
  - value_range (int) – range of this integer.

  Returns: | An input type object
  Return type: | InputType

`paddle.v2.data_type.``integer_value_sequence`(value_range)

  Data type of a sequence of integer.

  Parameters: | value_range (int) – range of each element.

`paddle.v2.data_type.``sparse_binary_vector`(dim, seq_type=0)

  Sparse binary vector. It means the input feature is a sparse vector and the every element in this vector is either zero or one.

  Parameters:

  - dim (int) – dimension of this vector.
  - seq_type (int) – sequence type of this input.

  Returns: | An input type object.
  Return type: | InputType

`paddle.v2.data_type.``sparse_binary_vector_sequence`(dim)
  Data type of a sequence of sparse vector, which every element is either zero
    or one.

  Parameters: | dim (int) – dimension of sparse vector.
  Returns: | An input type object
  Return type: | InputType

`paddle.v2.data_type.``sparse_non_value_slot`(dim, seq_type=0)

  Sparse binary vector. It means the input feature is a sparse vector and the every element in this vector is either zero or one.

  Parameters:

  - dim (int) – dimension of this vector.
  - seq_type (int) – sequence type of this input.

  Returns: | An input type object.
  Return type: | InputType

`paddle.v2.data_type.``sparse_value_slot`(dim, seq_type=0)

  Sparse vector. It means the input feature is a sparse vector. Most of the elements in this vector are zero, others could be any float value.

  Parameters:

  - dim (int) – dimension of this vector.
  - seq_type (int) – sequence type of this input.

  Returns: | An input type object.
  Return type: | InputType

`paddle.v2.data_type.``sparse_vector`(dim, seq_type=0)

  Sparse vector. It means the input feature is a sparse vector. Most of the elements in this vector are zero, others could be any float value.

  Parameters:

  - dim (int) – dimension of this vector.
  - seq_type (int) – sequence type of this input.

  Returns: | An input type object.
  Return type: | InputType

`paddle.v2.data_type.``sparse_vector_sequence`(dim)

  Data type of a sequence of sparse vector, which most elements are zero, others could be any float value.

  Parameters: | dim (int) – dimension of sparse vector.
  Returns: | An input type object
  Return type: | InputType

class `paddle.v2.data_type.``InputType`(dim, seq_type, tp)

  InputType is the base class for paddle input types.

  Note

  this is a base class, and should never be used by user.

  Parameters:

  - dim (int) – dimension of input. If the input is an integer, it means the value range. Otherwise, it means the size of layer.
  - seq_type (int) – sequence type of input. 0 means it is not a sequence. 1 means it is a variable length sequence. 2 means it is a nested sequence.
  - type (int) – data type of input.

## DataFeeder

class `paddle.v2.data_feeder.``DataFeeder`(data_types, feeding=None)

  DataFeeder converts the data returned by paddle.reader into a data structure of Arguments which is defined in the API. The paddle.reader usually returns a list of mini-batch data entries. Each data entry in the list is one sample. Each sample is a list or a tuple with one feature or multiple features. DataFeeder converts this mini-batch data entries into Arguments in order to feed it to C++ interface.

  The simple usage shows below

      feeding = ['image', 'label']
      data_types = enumerate_data_types_of_data_layers(topology)
      feeder = DataFeeder(data_types=data_types, feeding=feeding)

      minibatch_data = [([1.0, 2.0, 3.0, ...], 5)]

      arg = feeder(minibatch_data)

  If mini-batch data and data layers are not one to one mapping, we could pass a dictionary to feeding parameter to represent the mapping relationship.

      data_types = [('image', paddle.data_type.dense_vector(784)),
                    ('label', paddle.data_type.integer_value(10))]
      feeding = {'image':0, 'label':1}
      feeder = DataFeeder(data_types=data_types, feeding=feeding)
      minibatch_data = [
                         ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ),  # first sample
                         ( [1.0,2.0,3.0,4.0], 5, [6,7,8] )   # second sample
                       ]
      # or minibatch_data = [
      #                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample
      #                       [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample
      #                     ]
      arg = feeder.convert(minibatch_data)

  Note

  This module is for internal use only. Users should use the reader interface.

  Parameters:

  - data_types (list) – A list to specify data name and type. Each item is a tuple of (data_name, data_type).
  - feeding (dict|collections.Sequence|None) – A dictionary or a sequence to specify the position of each data in the input data.

  `convert`(dat, argument=None)

    Parameters:

    - dat (list) – A list of mini-batch data. Each sample is a list or tuple one feature or multiple features.
    - argument (py_paddle.swig_paddle.Arguments) – An Arguments object contains this mini-batch data with one or multiple features. The Arguments definition is in the API.

## Reader

At training and testing time, PaddlePaddle programs need to read data. To ease the users’ work to write data reading code, we define that

- A reader is a function that reads data (from file, network, random number generator, etc) and yields data items.
- A reader creator is a function that returns a reader function.
- A reader decorator is a function, which accepts one or more readers, and returns a reader.
- A batch reader is a function that reads data (from reader, file, network, random number generator, etc) and yields a batch of data items.

### Data Reader Interface

Indeed, data reader doesn’t have to be a function that reads and yields data items. It can be any function with no parameter that creates a iterable (anything can be used in `for x in iterable`):

    iterable = data_reader()

Element produced from the iterable should be a single entry of data, not a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of supported type (e.g., numpy 1d array of float32, int, list of int)

An example implementation for single item data reader creator:

    def reader_creator_random_image(width, height):
        def reader():
            while True:
                yield numpy.random.uniform(-1, 1, size=width*height)
    return reader

An example implementation for multiple item data reader creator:

    def reader_creator_random_image_and_label(width, height, label):
        def reader():
            while True:
                yield numpy.random.uniform(-1, 1, size=width*height), label
    return reader

TODO(yuyang18): Should we add whole design doc here?

`paddle.v2.reader.``map_readers`(func, *readers)

  Creates a data reader that outputs return value of function using output of each data readers as arguments.

  Parameters:

  - func – function to use. The type of func should be (Sample) => Sample
  - readers – readers whose outputs will be used as arguments of func.

  Type: | callable
  Returns: | the created data reader.
  Return type: | callable

`paddle.v2.reader.``buffered`(reader, size)

  Creates a buffered data reader.

  The buffered data reader will read and save data entries into a buffer. Reading from the buffered data reader will proceed as long as the buffer is not empty.

  Parameters:

  - reader (callable) – the data reader to read from.
  - size (int) – max buffer size.

  Returns: | the buffered data reader.

`paddle.v2.reader.``compose`(*readers, **kwargs)

  Creates a data reader whose output is the combination of input readers.

  If input readers output following data entries: (1, 2) 3 (4, 5) The composed reader will output: (1, 2, 3, 4, 5)

  Parameters:

  - readers – readers that will be composed together.
  - check_alignment (bool) – if True, will check if input readers are aligned correctly. If False, will not check alignment and trailing outputs will be discarded. Defaults to True.

  Returns: | the new data reader.
  Raises: | ComposeNotAligned – outputs of readers are not aligned. Will not raise when check_alignment is set to False.

`paddle.v2.reader.``chain`(*readers)

  Creates a data reader whose output is the outputs of input data readers chained together.

  If input readers output following data entries: [0, 0, 0] [1, 1, 1] [2, 2, 2] The chained reader will output: [0, 0, 0, 1, 1, 1, 2, 2, 2]

  Parameters: | readers – input readers.
  Returns: | the new data reader.
  Return type: | callable

`paddle.v2.reader.``shuffle`(reader, buf_size)

  Creates a data reader whose data output is shuffled.

  Output from the iterator that created by original reader will be buffered into shuffle buffer, and then shuffled. The size of shuffle buffer is determined by argument buf_size.

  Parameters:

  - reader (callable) – the original reader whose output will be shuffled.
  - buf_size (int) – shuffle buffer size.

  Returns: | the new reader whose output is shuffled.
  Return type: | callable

`paddle.v2.reader.``firstn`(reader, n)

  Limit the max number of samples that reader could return.

  Parameters:

  - reader (callable) – the data reader to read from.
  - n (int) – the max number of samples that return.

  Returns: | the decorated reader.
  Return type: | callable

Creator package contains some simple reader creator, which could be used in user program.

`paddle.v2.reader.creator.``np_array`(x)

  Creates a reader that yields elements of x, if it is a numpy vector. Or rows of x, if it is a numpy matrix. Or any sub-hyperplane indexed by the highest dimension.

  Parameters: | x – the numpy array to create reader from.
  Returns: | data reader created from x.

`paddle.v2.reader.creator.``text_file`(path)

  Creates a data reader that outputs text line by line from given text file. Trailing new line (‘\n’) of each line will be removed.

  Path: | path of the text file.
  Returns: | data reader of text file

## minibatch

`paddle.v2.minibatch.``batch`(reader, batch_size)

  Create a batched reader.

  Parameters:

  - reader (callable) – the data reader to read from.
  - batch_size (int) – size of each mini-batch

  Returns: | the batched reader.
  Return type: | callable

## Dataset

Dataset package.

### mnist

MNIST dataset.

This module will download dataset from http://yann.lecun.com/exdb/mnist/ and parse training set and test set into paddle reader creators.

`paddle.v2.dataset.mnist.``train`()

  MNIST training set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.mnist.``test`()

  MNIST test set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].

  Returns: | Test reader creator.
  Return type: | callable

### cifar

CIFAR dataset.

This module will download dataset from https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into paddle reader creators.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class.

`paddle.v2.dataset.cifar.``train100`()

  CIFAR-100 training set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 99].

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.cifar.``test100`()

  CIFAR-100 test set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].

  Returns: | Test reader creator.
  Return type: | callable

`paddle.v2.dataset.cifar.``train10`()

  CIFAR-10 training set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.cifar.``test10`()

  CIFAR-10 test set creator.

  It returns a reader creator, each sample in the reader is image pixels in [0, 1] and label in [0, 9].

  Returns: | Test reader creator.
  Return type: | callable

### conll05

Conll05 dataset. Paddle semantic role labeling Book and demo use this dataset as an example. Because Conll05 is not free in public, the default downloaded URL is test set of Conll05 (which is public). Users can change URL and MD5 to their Conll dataset. And a pre-trained word vector model based on Wikipedia corpus is used to initialize SRL model.

`paddle.v2.dataset.conll05.``get_dict`()

  Get the word, verb and label dictionary of Wikipedia corpus.

`paddle.v2.dataset.conll05.``get_embedding`()

  Get the trained word vector based on Wikipedia corpus.

`paddle.v2.dataset.conll05.``test`()

  Conll05 test set creator.

  Because the training dataset is not free, the test dataset is used for training. It returns a reader creator, each sample in the reader is nine features, including sentence sequence, predicate, predicate context, predicate context flag and tagged sequence.

  Returns: | Training reader creator
  Return type: | callable

### imdb

IMDB dataset.

This module downloads IMDB dataset from http://ai.stanford.edu/%7Eamaas/data/sentiment/. This dataset contains a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. Besides, this module also provides API for building dictionary.

`paddle.v2.dataset.imdb.``build_dict`(pattern, cutoff)

  Build a word dictionary from the corpus. Keys of the dictionary are words, and values are zero-based IDs of these words.

`paddle.v2.dataset.imdb.``train`(word_idx)

  IMDB training set creator.

  It returns a reader creator, each sample in the reader is an zero-based ID sequence and label in [0, 1].

  Parameters: | word_idx (dict) – word dictionary
  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.imdb.``test`(word_idx)

  IMDB test set creator.

  It returns a reader creator, each sample in the reader is an zero-based ID sequence and label in [0, 1].

  Parameters: | word_idx (dict) – word dictionary
  Returns: | Test reader creator
  Return type: | callable

### imikolov

imikolov’s simple dataset.

This module will download dataset from http://www.fit.vutbr.cz/~imikolov/rnnlm/ and parse training set and test set into paddle reader creators.

`paddle.v2.dataset.imikolov.``build_dict`(min_word_freq=50)

  Build a word dictionary from the corpus, Keys of the dictionary are words, and values are zero-based IDs of these words.

`paddle.v2.dataset.imikolov.``train`(word_idx, n, data_type=1)

  imikolov training set creator.

  It returns a reader creator, each sample in the reader is a word ID tuple.

  Parameters:

  - word_idx (dict) – word dictionary
  - n (int) – sliding window size if type is ngram, otherwise max length of sequence
  - data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.imikolov.``test`(word_idx, n, data_type=1)

  imikolov test set creator.

  It returns a reader creator, each sample in the reader is a word ID tuple.

  Parameters:

  - word_idx (dict) – word dictionary
  - n (int) – sliding window size if type is ngram, otherwise max length of sequence
  - data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)

  Returns: | Test reader creator
  Return type: | callable

### movielens

Movielens 1-M dataset.

Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000 movies, which was collected by GroupLens Research. This module will download Movielens 1-M dataset from http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training set and test set into paddle reader creators.

`paddle.v2.dataset.movielens.``get_movie_title_dict`()

  Get movie title dictionary.

`paddle.v2.dataset.movielens.``max_movie_id`()

  Get the maximum value of movie id.

`paddle.v2.dataset.movielens.``max_user_id`()

  Get the maximum value of user id.

`paddle.v2.dataset.movielens.``max_job_id`()

  Get the maximum value of job id.

`paddle.v2.dataset.movielens.``movie_categories`()

  Get movie categoriges dictionary.

`paddle.v2.dataset.movielens.``user_info`()

  Get user info dictionary.

`paddle.v2.dataset.movielens.``movie_info`()

  Get movie info dictionary.

class `paddle.v2.dataset.movielens.``MovieInfo`(index, categories, title)

  Movie id, title and categories information are stored in MovieInfo.

class `paddle.v2.dataset.movielens.``UserInfo`(index, gender, age, job_id)

  User id, gender, age, and job information are stored in UserInfo.

### sentiment

The script fetch and preprocess movie_reviews data set that provided by NLTK

TODO(yuyang18): Complete dataset.

`paddle.v2.dataset.sentiment.``get_word_dict`()

  Sorted the words by the frequency of words which occur in sample :return:

  > words_freq_sorted

`paddle.v2.dataset.sentiment.``train`()

  Default training set reader creator

`paddle.v2.dataset.sentiment.``test`()

  Default test set reader creator

### uci_housing

UCI Housing dataset.

This module will download dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and parse training set and test set into paddle reader creators.

`paddle.v2.dataset.uci_housing.``train`()

  UCI_HOUSING training set creator.

  It returns a reader creator, each sample in the reader is features after normalization and price number.

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.uci_housing.``test`()

  UCI_HOUSING test set creator.

  It returns a reader creator, each sample in the reader is features after normalization and price number.

  Returns: | Test reader creator
  Return type: | callable

### wmt14

WMT14 dataset. The original WMT14 dataset is too large and a small set of data for set is provided. This module will download dataset from http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and parse training set and test set into paddle reader creators.

`paddle.v2.dataset.wmt14.``train`(dict_size)

  WMT14 training set creator.

  It returns a reader creator, each sample in the reader is source language word ID sequence, target language word ID sequence and next word ID sequence.

  Returns: | Training reader creator
  Return type: | callable

`paddle.v2.dataset.wmt14.``test`(dict_size)

  WMT14 test set creator.

  It returns a reader creator, each sample in the reader is source language word ID sequence, target language word ID sequence and next word ID sequence.

  Returns: | Test reader creator
  Return type: | callable