diff --git a/fastNLP/core/batch.py b/fastNLP/core/batch.py index 442d1154..7b3b6d11 100644 --- a/fastNLP/core/batch.py +++ b/fastNLP/core/batch.py @@ -10,10 +10,10 @@ class Batch(object): for batch_x, batch_y in Batch(data_set, batch_size=16, sampler=SequentialSampler()): # ... - :param dataset: a DataSet object - :param batch_size: int, the size of the batch - :param sampler: a Sampler object - :param as_numpy: bool. If True, return Numpy array. Otherwise, return torch tensors. + :param DataSet dataset: a DataSet object + :param int batch_size: the size of the batch + :param Sampler sampler: a Sampler object + :param bool as_numpy: If True, return Numpy array. Otherwise, return torch tensors. """ diff --git a/fastNLP/io/base_loader.py b/fastNLP/io/base_loader.py index b01c233a..ccfa1169 100644 --- a/fastNLP/io/base_loader.py +++ b/fastNLP/io/base_loader.py @@ -3,7 +3,9 @@ import os class BaseLoader(object): + """Base loader for all loaders. + """ def __init__(self): super(BaseLoader, self).__init__() @@ -32,7 +34,9 @@ class BaseLoader(object): class DataLoaderRegister: - """"register for data sets""" + """Register for all data sets. + + """ _readers = {} @classmethod diff --git a/fastNLP/io/config_io.py b/fastNLP/io/config_io.py index 52c5e789..8be59a35 100644 --- a/fastNLP/io/config_io.py +++ b/fastNLP/io/config_io.py @@ -6,7 +6,11 @@ from fastNLP.io.base_loader import BaseLoader class ConfigLoader(BaseLoader): - """loader for configuration files""" + """Loader for configuration. + + :param str data_path: path to the config + + """ def __init__(self, data_path=None): super(ConfigLoader, self).__init__() @@ -19,13 +23,15 @@ class ConfigLoader(BaseLoader): @staticmethod def load_config(file_path, sections): - """ - :param file_path: the path of config file - :param sections: the dict of {section_name(string): Section instance} - Example: + """Load section(s) of configuration into the ``sections`` provided. No returns. + + :param str file_path: the path of config file + :param dict sections: the dict of ``{section_name(string): ConfigSection object}`` + Example:: + test_args = ConfigSection() ConfigLoader("config.cfg", "").load_config("./data_for_tests/config", {"POS_test": test_args}) - :return: return nothing, but the value of attributes are saved in sessions + """ assert isinstance(sections, dict) cfg = configparser.ConfigParser() @@ -60,9 +66,12 @@ class ConfigLoader(BaseLoader): class ConfigSection(object): + """ConfigSection is the data structure storing all key-value pairs in one section in a config file. + + """ def __init__(self): - pass + super(ConfigSection, self).__init__() def __getitem__(self, key): """ @@ -132,25 +141,12 @@ class ConfigSection(object): return self.__dict__ -if __name__ == "__main__": - config = ConfigLoader('there is no data') - - section = {'General': ConfigSection(), 'My': ConfigSection(), 'A': ConfigSection()} - """ - General and My can be found in config file, so the attr and - value will be updated - A cannot be found in config file, so nothing will be done - """ - - config.load_config("../../test/data_for_tests/config", section) - for s in section: - print(s) - for attr in section[s].__dict__.keys(): - print(s, attr, getattr(section[s], attr), type(getattr(section[s], attr))) - - class ConfigSaver(object): + """ConfigSaver is used to save config file and solve related conflicts. + + :param str file_path: path to the config file + """ def __init__(self, file_path): self.file_path = file_path if not os.path.exists(self.file_path): @@ -244,9 +240,8 @@ class ConfigSaver(object): def save_config_file(self, section_name, section): """This is the function to be called to change the config file with a single section and its name. - :param section_name: The name of section what needs to be changed and saved. - :param section: The section with key and value what needs to be changed and saved. - :return: + :param str section_name: The name of section what needs to be changed and saved. + :param ConfigSection section: The section with key and value what needs to be changed and saved. """ section_file = self._get_section(section_name) if len(section_file.__dict__.keys()) == 0: # the section not in the file before diff --git a/fastNLP/io/dataset_loader.py b/fastNLP/io/dataset_loader.py index fedf8058..61ba1b29 100644 --- a/fastNLP/io/dataset_loader.py +++ b/fastNLP/io/dataset_loader.py @@ -9,11 +9,12 @@ def convert_seq_dataset(data): """Create an DataSet instance that contains no labels. :param data: list of list of strings, [num_examples, *]. - :: - [ - [word_11, word_12, ...], - ... - ] + Example:: + + [ + [word_11, word_12, ...], + ... + ] :return: a DataSet. """ @@ -24,15 +25,16 @@ def convert_seq_dataset(data): def convert_seq2tag_dataset(data): - """Convert list of data into DataSet + """Convert list of data into DataSet. :param data: list of list of strings, [num_examples, *]. - :: - [ - [ [word_11, word_12, ...], label_1 ], - [ [word_21, word_22, ...], label_2 ], - ... - ] + Example:: + + [ + [ [word_11, word_12, ...], label_1 ], + [ [word_21, word_22, ...], label_2 ], + ... + ] :return: a DataSet. """ @@ -43,15 +45,16 @@ def convert_seq2tag_dataset(data): def convert_seq2seq_dataset(data): - """Convert list of data into DataSet + """Convert list of data into DataSet. :param data: list of list of strings, [num_examples, *]. - :: - [ - [ [word_11, word_12, ...], [label_1, label_1, ...] ], - [ [word_21, word_22, ...], [label_2, label_1, ...] ], - ... - ] + Example:: + + [ + [ [word_11, word_12, ...], [label_1, label_1, ...] ], + [ [word_21, word_22, ...], [label_2, label_1, ...] ], + ... + ] :return: a DataSet. """ @@ -62,20 +65,31 @@ def convert_seq2seq_dataset(data): class DataSetLoader: - """"loader for data sets""" + """Interface for all DataSetLoaders. + + """ def load(self, path): - """ load data in `path` into a dataset + """Load data from a given file. + + :param str path: file path + :return: a DataSet object """ raise NotImplementedError def convert(self, data): - """convert list of data into dataset + """Optional operation to build a DataSet. + + :param data: inner data structure (user-defined) to represent the data. + :return: a DataSet object """ raise NotImplementedError class NativeDataSetLoader(DataSetLoader): + """A simple example of DataSetLoader + + """ def __init__(self): super(NativeDataSetLoader, self).__init__() @@ -90,6 +104,9 @@ DataLoaderRegister.set_reader(NativeDataSetLoader, 'read_naive') class RawDataSetLoader(DataSetLoader): + """A simple example of raw data reader + + """ def __init__(self): super(RawDataSetLoader, self).__init__() @@ -108,37 +125,35 @@ DataLoaderRegister.set_reader(RawDataSetLoader, 'read_rawdata') class POSDataSetLoader(DataSetLoader): - """Dataset Loader for POS Tag datasets. - - In these datasets, each line are divided by '\t' - while the first Col is the vocabulary and the second - Col is the label. - Different sentence are divided by an empty line. - e.g: - Tom label1 - and label2 - Jerry label1 - . label3 - (separated by an empty line) - Hello label4 - world label5 - ! label3 - In this file, there are two sentence "Tom and Jerry ." - and "Hello world !". Each word has its own label from label1 - to label5. + """Dataset Loader for a POS Tag dataset. + + In these datasets, each line are divided by "\t". The first Col is the vocabulary and the second + Col is the label. Different sentence are divided by an empty line. + E.g:: + + Tom label1 + and label2 + Jerry label1 + . label3 + (separated by an empty line) + Hello label4 + world label5 + ! label3 + + In this example, there are two sentences "Tom and Jerry ." and "Hello world !". Each word has its own label. """ - def __init__(self): super(POSDataSetLoader, self).__init__() def load(self, data_path): """ :return data: three-level list - [ - [ [word_11, word_12, ...], [label_1, label_1, ...] ], - [ [word_21, word_22, ...], [label_2, label_1, ...] ], - ... - ] + Example:: + [ + [ [word_11, word_12, ...], [label_1, label_1, ...] ], + [ [word_21, word_22, ...], [label_2, label_1, ...] ], + ... + ] """ with open(data_path, "r", encoding="utf-8") as f: lines = f.readlines() @@ -188,17 +203,17 @@ class TokenizeDataSetLoader(DataSetLoader): super(TokenizeDataSetLoader, self).__init__() def load(self, data_path, max_seq_len=32): - """ - load pku dataset for Chinese word segmentation + """Load pku dataset for Chinese word segmentation. CWS (Chinese Word Segmentation) pku training dataset format: - 1. Each line is a sentence. - 2. Each word in a sentence is separated by space. + 1. Each line is a sentence. + 2. Each word in a sentence is separated by space. This function convert the pku dataset into three-level lists with labels . - B: beginning of a word - M: middle of a word - E: ending of a word - S: single character + B: beginning of a word + M: middle of a word + E: ending of a word + S: single character + :param str data_path: path to the data set. :param max_seq_len: int, the maximum length of a sequence. If a sequence is longer than it, split it into several sequences. :return: three-level lists @@ -254,11 +269,9 @@ class ClassDataSetLoader(DataSetLoader): @staticmethod def parse(lines): """ - Params - lines: lines from dataset - Return - list(list(list())): the three level of lists are - words, sentence, and dataset + + :param lines: lines from dataset + :return: list(list(list())): the three level of lists are words, sentence, and dataset """ dataset = list() for line in lines: @@ -280,15 +293,9 @@ class ConllLoader(DataSetLoader): """loader for conll format files""" def __init__(self): - """ - :param str data_path: the path to the conll data set - """ super(ConllLoader, self).__init__() def load(self, data_path): - """ - :return: list lines: all lines in a conll file - """ with open(data_path, "r", encoding="utf-8") as f: lines = f.readlines() data = self.parse(lines) @@ -320,8 +327,8 @@ class ConllLoader(DataSetLoader): class LMDataSetLoader(DataSetLoader): """Language Model Dataset Loader - This loader produces data for language model training in a supervised way. - That means it has X and Y. + This loader produces data for language model training in a supervised way. + That means it has X and Y. """ @@ -467,6 +474,7 @@ class Conll2003Loader(DataSetLoader): return dataset + class SNLIDataSetLoader(DataSetLoader): """A data set loader for SNLI data set. @@ -478,8 +486,8 @@ class SNLIDataSetLoader(DataSetLoader): def load(self, path_list): """ - :param path_list: A list of file name, in the order of premise file, hypothesis file, and label file. - :return: data_set: A DataSet object. + :param list path_list: A list of file name, in the order of premise file, hypothesis file, and label file. + :return: A DataSet object. """ assert len(path_list) == 3 line_set = [] @@ -507,12 +515,14 @@ class SNLIDataSetLoader(DataSetLoader): """Convert a 3D list to a DataSet object. :param data: A 3D tensor. - [ - [ [premise_word_11, premise_word_12, ...], [hypothesis_word_11, hypothesis_word_12, ...], [label_1] ], - [ [premise_word_21, premise_word_22, ...], [hypothesis_word_21, hypothesis_word_22, ...], [label_2] ], - ... - ] - :return: data_set: A DataSet object. + Example:: + [ + [ [premise_word_11, premise_word_12, ...], [hypothesis_word_11, hypothesis_word_12, ...], [label_1] ], + [ [premise_word_21, premise_word_22, ...], [hypothesis_word_21, hypothesis_word_22, ...], [label_2] ], + ... + ] + + :return: A DataSet object. """ data_set = DataSet() diff --git a/fastNLP/io/embed_loader.py b/fastNLP/io/embed_loader.py index 779b7fd0..75b83c11 100644 --- a/fastNLP/io/embed_loader.py +++ b/fastNLP/io/embed_loader.py @@ -38,7 +38,7 @@ class EmbedLoader(BaseLoader): :param str emb_file: the pre-trained embedding file path :param str emb_type: the pre-trained embedding data format - :return dict embedding: `{str: np.array}` + :return: a dict of ``{str: np.array}`` """ if emb_type == 'glove': return EmbedLoader._load_glove(emb_file) @@ -53,8 +53,9 @@ class EmbedLoader(BaseLoader): :param str emb_file: the pre-trained embedding file path. :param str emb_type: the pre-trained embedding format, support glove now :param Vocabulary vocab: a mapping from word to index, can be provided by user or built from pre-trained embedding - :return embedding_tensor: Tensor of shape (len(word_dict), emb_dim) - vocab: input vocab or vocab built by pre-train + :return (embedding_tensor, vocab): + embedding_tensor - Tensor of shape (len(word_dict), emb_dim); + vocab - input vocab or vocab built by pre-train """ pretrain = EmbedLoader._load_pretrain(emb_file, emb_type) @@ -87,7 +88,7 @@ class EmbedLoader(BaseLoader): :param int emb_dim: the dimension of the embedding. Should be the same as pre-trained embedding. :param str emb_file: the pre-trained embedding file path. :param Vocabulary vocab: a mapping from word to index, can be provided by user or built from pre-trained embedding - :return numpy.ndarray embedding_matrix: + :return embedding_matrix: numpy.ndarray """ if vocab is None: diff --git a/fastNLP/io/logger.py b/fastNLP/io/logger.py index 19b2b2c3..9e9730db 100644 --- a/fastNLP/io/logger.py +++ b/fastNLP/io/logger.py @@ -3,15 +3,16 @@ import os def create_logger(logger_name, log_path, log_format=None, log_level=logging.INFO): - """Return a logger. + """Create a logger. - :param logger_name: str - :param log_path: str + :param str logger_name: + :param str log_path: :param log_format: :param log_level: :return: logger - to use a logger: + To use a logger:: + logger.debug("this is a debug message") logger.info("this is a info message") logger.warning("this is a warning message") diff --git a/fastNLP/io/model_io.py b/fastNLP/io/model_io.py index e1264b47..422eb919 100644 --- a/fastNLP/io/model_io.py +++ b/fastNLP/io/model_io.py @@ -13,10 +13,10 @@ class ModelLoader(BaseLoader): @staticmethod def load_pytorch(empty_model, model_path): - """ - Load model parameters from .pkl files into the empty PyTorch model. + """Load model parameters from ".pkl" files into the empty PyTorch model. + :param empty_model: a PyTorch model with initialized parameters. - :param model_path: str, the path to the saved model. + :param str model_path: the path to the saved model. """ empty_model.load_state_dict(torch.load(model_path)) @@ -24,30 +24,30 @@ class ModelLoader(BaseLoader): def load_pytorch_model(model_path): """Load the entire model. + :param str model_path: the path to the saved model. """ return torch.load(model_path) class ModelSaver(object): """Save a model + + :param str save_path: the path to the saving directory. Example:: + saver = ModelSaver("./save/model_ckpt_100.pkl") saver.save_pytorch(model) """ def __init__(self, save_path): - """ - - :param save_path: str, the path to the saving directory. - """ self.save_path = save_path def save_pytorch(self, model, param_only=True): - """Save a pytorch model into .pkl file. + """Save a pytorch model into ".pkl" file. :param model: a PyTorch model - :param param_only: bool, whether only to save the model parameters or the entire model. + :param bool param_only: whether only to save the model parameters or the entire model. """ if param_only is True: