From bc7fc71faa4e0b3e8e08a5d16a430327c5beb3cf Mon Sep 17 00:00:00 2001 From: FengZiYjun Date: Thu, 3 Jan 2019 18:59:52 +0800 Subject: [PATCH 1/3] * update version number in doc * update fastNLP.core.rst * refine all docstrings in core/ --- docs/source/conf.py | 4 +-- docs/source/fastNLP.core.rst | 14 +++----- fastNLP/core/batch.py | 18 +++++----- fastNLP/core/dataset.py | 21 ++++++------ fastNLP/core/fieldarray.py | 18 +++++----- fastNLP/core/instance.py | 15 ++++----- fastNLP/core/losses.py | 62 ++++++++++++++-------------------- fastNLP/core/metrics.py | 61 +++++++++++++++++++++++----------- fastNLP/core/optimizer.py | 23 ++++++++----- fastNLP/core/sampler.py | 22 ++++++++++--- fastNLP/core/tester.py | 22 ++++++++++--- fastNLP/core/trainer.py | 64 +++++++++++++++++------------------- fastNLP/core/vocabulary.py | 19 ++++++----- 13 files changed, 196 insertions(+), 167 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index e521b3d6..3bf42c53 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,8 +16,6 @@ import os import sys sys.path.insert(0, os.path.abspath('../../')) -import sphinx_rtd_theme - # -- Project information ----------------------------------------------------- project = 'fastNLP' @@ -27,7 +25,7 @@ author = 'xpqiu' # The short X.Y version version = '' # The full version, including alpha/beta/rc tags -release = '1.0' +release = '2.0' # -- General configuration --------------------------------------------------- diff --git a/docs/source/fastNLP.core.rst b/docs/source/fastNLP.core.rst index 5c941e55..b70b6798 100644 --- a/docs/source/fastNLP.core.rst +++ b/docs/source/fastNLP.core.rst @@ -13,10 +13,10 @@ fastNLP.core.dataset .. automodule:: fastNLP.core.dataset :members: -fastNLP.core.field +fastNLP.core.fieldarray ------------------- -.. automodule:: fastNLP.core.field +.. automodule:: fastNLP.core.fieldarray :members: fastNLP.core.instance @@ -25,10 +25,10 @@ fastNLP.core.instance .. automodule:: fastNLP.core.instance :members: -fastNLP.core.loss +fastNLP.core.losses ------------------ -.. automodule:: fastNLP.core.loss +.. automodule:: fastNLP.core.losses :members: fastNLP.core.metrics @@ -49,12 +49,6 @@ fastNLP.core.predictor .. automodule:: fastNLP.core.predictor :members: -fastNLP.core.preprocess ------------------------- - -.. automodule:: fastNLP.core.preprocess - :members: - fastNLP.core.sampler --------------------- diff --git a/fastNLP/core/batch.py b/fastNLP/core/batch.py index 1bb26129..442d1154 100644 --- a/fastNLP/core/batch.py +++ b/fastNLP/core/batch.py @@ -5,21 +5,19 @@ import torch class Batch(object): """Batch is an iterable object which iterates over mini-batches. - :: - for batch_x, batch_y in Batch(data_set, batch_size=16, sampler=SequentialSampler()): + Example:: + 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. """ def __init__(self, dataset, batch_size, sampler, as_numpy=False): - """ - - :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. - - """ self.dataset = dataset self.batch_size = batch_size self.sampler = sampler diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py index 52dac2fc..c798a422 100644 --- a/fastNLP/core/dataset.py +++ b/fastNLP/core/dataset.py @@ -118,7 +118,7 @@ class DataSet(object): def __len__(self): """Fetch the length of the dataset. - :return int length: + :return length: """ if len(self.field_arrays) == 0: return 0 @@ -170,7 +170,7 @@ class DataSet(object): def delete_field(self, name): """Delete a field based on the field name. - :param str name: the name of the field to be deleted. + :param name: the name of the field to be deleted. """ self.field_arrays.pop(name) @@ -182,14 +182,14 @@ class DataSet(object): def get_all_fields(self): """Return all the fields with their names. - :return dict field_arrays: the internal data structure of DataSet. + :return field_arrays: the internal data structure of DataSet. """ return self.field_arrays def get_length(self): """Fetch the length of the dataset. - :return int length: + :return length: """ return len(self) @@ -232,14 +232,14 @@ class DataSet(object): def get_input_name(self): """Get all field names with `is_input` as True. - :return list field_names: a list of str + :return field_names: a list of str """ return [name for name, field in self.field_arrays.items() if field.is_input] def get_target_name(self): """Get all field names with `is_target` as True. - :return list field_names: a list of str + :return field_names: a list of str """ return [name for name, field in self.field_arrays.items() if field.is_target] @@ -294,8 +294,9 @@ class DataSet(object): """Split the dataset into training and development(validation) set. :param float dev_ratio: the ratio of test set in all data. - :return DataSet train_set: the training set - DataSet dev_set: the development set + :return (train_set, dev_set): + train_set: the training set + dev_set: the development set """ assert isinstance(dev_ratio, float) assert 0 < dev_ratio < 1 @@ -326,7 +327,7 @@ class DataSet(object): :param List[str] or Tuple[str] headers: headers of the CSV file :param str sep: delimiter in CSV file. Default: "," :param bool dropna: If True, drop rows that have less entries than headers. - :return DataSet dataset: + :return dataset: the read data set """ with open(csv_path, "r") as f: @@ -370,7 +371,7 @@ class DataSet(object): """Load a DataSet object from pickle. :param str path: the path to the pickle - :return DataSet data_set: + :return data_set: """ with open(path, 'rb') as f: return pickle.load(f) diff --git a/fastNLP/core/fieldarray.py b/fastNLP/core/fieldarray.py index 5fa8276e..c1a2db1c 100644 --- a/fastNLP/core/fieldarray.py +++ b/fastNLP/core/fieldarray.py @@ -2,20 +2,18 @@ import numpy as np class FieldArray(object): - """FieldArray is the collection of Instances of the same Field. - It is the basic element of DataSet class. + """``FieldArray`` is the collection of ``Instance``s of the same field. + It is the basic element of ``DataSet`` class. + + :param str name: the name of the FieldArray + :param list content: a list of int, float, str or np.ndarray, or a list of list of one, or a np.ndarray. + :param int padding_val: the integer for padding. Default: 0. + :param bool is_target: If True, this FieldArray is used to compute loss. + :param bool is_input: If True, this FieldArray is used to the model input. """ def __init__(self, name, content, padding_val=0, is_target=None, is_input=None): - """ - - :param str name: the name of the FieldArray - :param list content: a list of int, float, str or np.ndarray, or a list of list of one, or a np.ndarray. - :param int padding_val: the integer for padding. Default: 0. - :param bool is_target: If True, this FieldArray is used to compute loss. - :param bool is_input: If True, this FieldArray is used to the model input. - """ self.name = name if isinstance(content, list): content = content diff --git a/fastNLP/core/instance.py b/fastNLP/core/instance.py index dc65fa82..a2877a8f 100644 --- a/fastNLP/core/instance.py +++ b/fastNLP/core/instance.py @@ -1,23 +1,22 @@ class Instance(object): - """An Instance is an example of data. It is the collection of Fields. + """An Instance is an example of data. + Example:: + ins = Instance(field_1=[1, 1, 1], field_2=[2, 2, 2]) + ins["field_1"] + >>[1, 1, 1] + ins.add_field("field_3", [3, 3, 3]) - :: - Instance(field_1=[1, 1, 1], field_2=[2, 2, 2]) + :param fields: a dict of (str: list). """ def __init__(self, **fields): - """ - - :param fields: a dict of (str: list). - """ self.fields = fields def add_field(self, field_name, field): """Add a new field to the instance. :param field_name: str, the name of the field. - :param field: """ self.fields[field_name] = field diff --git a/fastNLP/core/losses.py b/fastNLP/core/losses.py index 057b03f2..04f8b73e 100644 --- a/fastNLP/core/losses.py +++ b/fastNLP/core/losses.py @@ -13,6 +13,9 @@ from fastNLP.core.utils import get_func_signature class LossBase(object): + """Base class for all losses. + + """ def __init__(self): self.param_map = {} self._checked = False @@ -68,10 +71,9 @@ class LossBase(object): # f"positional argument.).") def _fast_param_map(self, pred_dict, target_dict): - """ - - Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. + """Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. such as pred_dict has one element, target_dict has one element + :param pred_dict: :param target_dict: :return: dict, if dict is not {}, pass it to self.evaluate. Otherwise do mapping. @@ -265,27 +267,22 @@ def _prepare_losser(losser): def squash(predict, truth, **kwargs): - """To reshape tensors in order to fit loss functions in pytorch - - :param predict : Tensor, model output - :param truth : Tensor, truth from dataset - :param **kwargs : extra arguments + """To reshape tensors in order to fit loss functions in PyTorch. + :param predict: Tensor, model output + :param truth: Tensor, truth from dataset + :param **kwargs: extra arguments :return predict , truth: predict & truth after processing """ return predict.view(-1, predict.size()[-1]), truth.view(-1, ) def unpad(predict, truth, **kwargs): - """To process padded sequence output to get true loss - Using pack_padded_sequence() method - This method contains squash() + """To process padded sequence output to get true loss. - :param predict : Tensor, [batch_size , max_len , tag_size] - :param truth : Tensor, [batch_size , max_len] - :param **kwargs : extra arguments, kwargs["lens"] is expected to be exsist - kwargs["lens"] : list or LongTensor, [batch_size] - the i-th element is true lengths of i-th sequence + :param predict: Tensor, [batch_size , max_len , tag_size] + :param truth: Tensor, [batch_size , max_len] + :param kwargs: kwargs["lens"] is a list or LongTensor, with size [batch_size]. The i-th element is true lengths of i-th sequence. :return predict , truth: predict & truth after processing """ @@ -299,15 +296,11 @@ def unpad(predict, truth, **kwargs): def unpad_mask(predict, truth, **kwargs): - """To process padded sequence output to get true loss - Using mask() method - This method contains squash() + """To process padded sequence output to get true loss. - :param predict : Tensor, [batch_size , max_len , tag_size] - :param truth : Tensor, [batch_size , max_len] - :param **kwargs : extra arguments, kwargs["lens"] is expected to be exsist - kwargs["lens"] : list or LongTensor, [batch_size] - the i-th element is true lengths of i-th sequence + :param predict: Tensor, [batch_size , max_len , tag_size] + :param truth: Tensor, [batch_size , max_len] + :param kwargs: kwargs["lens"] is a list or LongTensor, with size [batch_size]. The i-th element is true lengths of i-th sequence. :return predict , truth: predict & truth after processing """ @@ -318,14 +311,11 @@ def unpad_mask(predict, truth, **kwargs): def mask(predict, truth, **kwargs): - """To select specific elements from Tensor - This method contains squash() + """To select specific elements from Tensor. This method calls ``squash()``. - :param predict : Tensor, [batch_size , max_len , tag_size] - :param truth : Tensor, [batch_size , max_len] - :param **kwargs : extra arguments, kwargs["mask"] is expected to be exsist - kwargs["mask"] : ByteTensor, [batch_size , max_len] - the mask Tensor , the position that is 1 will be selected + :param predict: Tensor, [batch_size , max_len , tag_size] + :param truth: Tensor, [batch_size , max_len] + :param **kwargs: extra arguments, kwargs["mask"]: ByteTensor, [batch_size , max_len], the mask Tensor. The position that is 1 will be selected. :return predict , truth: predict & truth after processing """ @@ -343,13 +333,11 @@ def mask(predict, truth, **kwargs): def make_mask(lens, tar_len): - """to generate a mask that select [:lens[i]] for i-th element - embezzle from fastNLP.models.sequence_modeling.seq_mask - - :param lens : list or LongTensor, [batch_size] - :param tar_len : int + """To generate a mask over a sequence. - :return mask : ByteTensor + :param lens: list or LongTensor, [batch_size] + :param tar_len: int + :return mask: ByteTensor """ lens = torch.LongTensor(lens) mask = [torch.ge(lens, i + 1) for i in range(tar_len)] diff --git a/fastNLP/core/metrics.py b/fastNLP/core/metrics.py index 07ebe3fe..388f3e84 100644 --- a/fastNLP/core/metrics.py +++ b/fastNLP/core/metrics.py @@ -13,6 +13,24 @@ from fastNLP.core.utils import seq_lens_to_masks class MetricBase(object): + """Base class for all metrics. + + ``MetricBase`` handles validity check of its input dictionaries - ``pred_dict`` and ``target_dict``. + ``pred_dict`` is the output of ``forward()`` or prediction function of a model. + ``target_dict`` is the ground truth from DataSet where ``is_target`` is set ``True``. + ``MetricBase`` will do the following type checks: + + 1. whether self.evaluate has varargs, which is not supported. + 2. whether params needed by self.evaluate is not included in ``pred_dict``, ``target_dict``. + 3. whether params needed by self.evaluate duplicate in ``pred_dict``, ``target_dict``. + 4. whether params in ``pred_dict``, ``target_dict`` are not used by evaluate.(Might cause warning) + + Besides, before passing params into self.evaluate, this function will filter out params from output_dict and + target_dict which are not used in self.evaluate. (but if **kwargs presented in self.evaluate, no filtering + will be conducted.) + However, in some cases where type check is not necessary, ``_fast_param_map`` will be used. + + """ def __init__(self): self.param_map = {} # key is param in function, value is input param. self._checked = False @@ -71,10 +89,9 @@ class MetricBase(object): raise NotImplemented def _fast_param_map(self, pred_dict, target_dict): - """ - - Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. + """Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. such as pred_dict has one element, target_dict has one element + :param pred_dict: :param target_dict: :return: dict, if dict is not {}, pass it to self.evaluate. Otherwise do mapping. @@ -177,6 +194,9 @@ class MetricBase(object): class AccuracyMetric(MetricBase): + """Accuracy Metric + + """ def __init__(self, pred=None, target=None, seq_lens=None): super().__init__() @@ -186,10 +206,9 @@ class AccuracyMetric(MetricBase): self.acc_count = 0 def _fast_param_map(self, pred_dict, target_dict): - """ - - Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. + """Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. such as pred_dict has one element, target_dict has one element + :param pred_dict: :param target_dict: :return: dict, if dict is not None, pass it to self.evaluate. Otherwise do mapping. @@ -230,7 +249,7 @@ class AccuracyMetric(MetricBase): torch.Size([B,]), torch.Size([B,]), torch.Size([B, max_len]), torch.Size([B, max_len]) :param seq_lens: List of (torch.Tensor, or numpy.ndarray). Element's can be: None, None, torch.Size([B], torch.Size([B]). ignored if masks are provided. - :return: dict({'acc': float}) + """ # TODO 这里报错需要更改，因为pred是啥用户并不知道。需要告知用户真实的value if not isinstance(pred, torch.Tensor): @@ -269,6 +288,11 @@ class AccuracyMetric(MetricBase): self.total += np.prod(list(pred.size())) def get_metric(self, reset=True): + """Returns computed metric. + + :param bool reset: whether to recount next time. + :return evaluate_result: {"acc": float} + """ evaluate_result = {'acc': round(self.acc_count / self.total, 6)} if reset: self.acc_count = 0 @@ -308,34 +332,31 @@ def _prepare_metrics(metrics): def accuracy_topk(y_true, y_prob, k=1): - """Compute accuracy of y_true matching top-k probable - labels in y_prob. + """Compute accuracy of y_true matching top-k probable labels in y_prob. - :param y_true: ndarray, true label, [n_samples] - :param y_prob: ndarray, label probabilities, [n_samples, n_classes] - :param k: int, k in top-k - :return :accuracy of top-k - """ + :param y_true: ndarray, true label, [n_samples] + :param y_prob: ndarray, label probabilities, [n_samples, n_classes] + :param k: int, k in top-k + :returns acc: accuracy of top-k + """ y_pred_topk = np.argsort(y_prob, axis=-1)[:, -1:-k - 1:-1] y_true_tile = np.tile(np.expand_dims(y_true, axis=1), (1, k)) y_match = np.any(y_pred_topk == y_true_tile, axis=-1) acc = np.sum(y_match) / y_match.shape[0] - return acc def pred_topk(y_prob, k=1): """Return top-k predicted labels and corresponding probabilities. - - :param y_prob: ndarray, size [n_samples, n_classes], probabilities on labels - :param k: int, k of top-k - :returns + :param y_prob: ndarray, size [n_samples, n_classes], probabilities on labels + :param k: int, k of top-k + :returns (y_pred_topk, y_prob_topk): y_pred_topk: ndarray, size [n_samples, k], predicted top-k labels y_prob_topk: ndarray, size [n_samples, k], probabilities for top-k labels - """ + """ y_pred_topk = np.argsort(y_prob, axis=-1)[:, -1:-k - 1:-1] x_axis_index = np.tile( np.arange(len(y_prob))[:, np.newaxis], diff --git a/fastNLP/core/optimizer.py b/fastNLP/core/optimizer.py index fddafbad..145f117c 100644 --- a/fastNLP/core/optimizer.py +++ b/fastNLP/core/optimizer.py @@ -2,6 +2,11 @@ import torch class Optimizer(object): + """ + + :param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models. + :param kwargs: additional parameters. + """ def __init__(self, model_params, **kwargs): if model_params is not None and not hasattr(model_params, "__next__"): raise RuntimeError("model parameters should be a generator, rather than {}.".format(type(model_params))) @@ -10,13 +15,14 @@ class Optimizer(object): class SGD(Optimizer): - def __init__(self, lr=0.001, momentum=0, model_params=None): - """ + """ :param float lr: learning rate. Default: 0.01 :param float momentum: momentum. Default: 0 - :param model_params: a generator. E.g. model.parameters() for PyTorch models. - """ + :param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models. + """ + + def __init__(self, lr=0.001, momentum=0, model_params=None): if not isinstance(lr, float): raise TypeError("learning rate has to be float.") super(SGD, self).__init__(model_params, lr=lr, momentum=momentum) @@ -30,13 +36,14 @@ class SGD(Optimizer): class Adam(Optimizer): - def __init__(self, lr=0.001, weight_decay=0, betas=(0.9, 0.999), eps=1e-8, amsgrad=False, model_params=None): - """ + """ :param float lr: learning rate :param float weight_decay: - :param model_params: a generator. E.g. model.parameters() for PyTorch models. - """ + :param model_params: a generator. E.g. ``model.parameters()`` for PyTorch models. + """ + + def __init__(self, lr=0.001, weight_decay=0, betas=(0.9, 0.999), eps=1e-8, amsgrad=False, model_params=None): if not isinstance(lr, float): raise TypeError("learning rate has to be float.") super(Adam, self).__init__(model_params, lr=lr, betas=betas, eps=eps, amsgrad=amsgrad, diff --git a/fastNLP/core/sampler.py b/fastNLP/core/sampler.py index 766d71a7..67ec2a8d 100644 --- a/fastNLP/core/sampler.py +++ b/fastNLP/core/sampler.py @@ -20,8 +20,8 @@ def convert_to_torch_tensor(data_list, use_cuda): class BaseSampler(object): """The base class of all samplers. - Sub-classes must implement the __call__ method. - __call__ takes a DataSet object and returns a list of int - the sampling indices. + Sub-classes must implement the ``__call__`` method. + ``__call__`` takes a DataSet object and returns a list of int - the sampling indices. """ def __call__(self, *args, **kwargs): @@ -32,8 +32,12 @@ class SequentialSampler(BaseSampler): """Sample data in the original order. """ - def __call__(self, data_set): + """ + + :param DataSet data_set: + :return result: a list of integers. + """ return list(range(len(data_set))) @@ -41,13 +45,23 @@ class RandomSampler(BaseSampler): """Sample data in random permutation order. """ - def __call__(self, data_set): + """ + + :param DataSet data_set: + :return result: a list of integers. + """ return list(np.random.permutation(len(data_set))) class BucketSampler(BaseSampler): + """ + + :param int num_buckets: the number of buckets to use. + :param int batch_size: batch size per epoch. + :param str seq_lens_field_name: the field name indicating the field about sequence length. + """ def __init__(self, num_buckets=10, batch_size=32, seq_lens_field_name='seq_lens'): self.num_buckets = num_buckets self.batch_size = batch_size diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py index ef3780a0..48e1f090 100644 --- a/fastNLP/core/tester.py +++ b/fastNLP/core/tester.py @@ -1,5 +1,3 @@ -from collections import defaultdict - import torch from torch import nn @@ -15,7 +13,16 @@ from fastNLP.core.utils import get_func_signature class Tester(object): - """An collection of model inference and evaluation of performance, used over validation/dev set and test set. """ + """An collection of model inference and evaluation of performance, used over validation/dev set and test set. + + :param DataSet data: a validation/development set + :param torch.nn.modules.module model: a PyTorch model + :param MetricBase metrics: a metric object or a list of metrics (List[MetricBase]) + :param int batch_size: batch size for validation + :param bool use_cuda: whether to use CUDA in validation. + :param int verbose: the number of steps after which an information is printed. + + """ def __init__(self, data, model, metrics, batch_size=16, use_cuda=False, verbose=1): super(Tester, self).__init__() @@ -49,6 +56,11 @@ class Tester(object): self._predict_func = self._model.forward def test(self): + """Start test or validation. + + :return eval_results: a dictionary whose keys are the class name of metrics to use, values are the evaluation results of these metrics. + + """ # turn on the testing mode; clean up the history network = self._model self._mode(network, is_test=True) @@ -60,8 +72,8 @@ class Tester(object): _move_dict_value_to_device(batch_x, batch_y, device=self._model_device) pred_dict = self._data_forward(self._predict_func, batch_x) if not isinstance(pred_dict, dict): - raise TypeError(f"The return value of {get_func_signature(self._predict_func)} " - f"must be `dict`, got {type(pred_dict)}.") + raise TypeError(f"The return value of {get_func_signature(self._predict_func)} " + f"must be `dict`, got {type(pred_dict)}.") for metric in self.metrics: metric(pred_dict, batch_y) for metric in self.metrics: diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py index bf32fa6c..3e8360d2 100644 --- a/fastNLP/core/trainer.py +++ b/fastNLP/core/trainer.py @@ -27,39 +27,37 @@ from fastNLP.core.utils import get_func_signature class Trainer(object): - """Main Training Loop - """ - def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch_size=32, print_every=50, - validate_every=-1, dev_data=None, use_cuda=False, save_path=None, - optimizer=Adam(lr=0.01, weight_decay=0), check_code_level=0, - metric_key=None, sampler=RandomSampler(), use_tqdm=True): - """ - :param DataSet train_data: the training data :param torch.nn.modules.module model: a PyTorch model :param LossBase loss: a loss object - :param MetricBase or List[MetricBase] metrics: a metric object or a list of metrics + :param MetricBase metrics: a metric object or a list of metrics (List[MetricBase]) :param int n_epochs: the number of training epochs :param int batch_size: batch size for training and validation :param int print_every: step interval to print next training information. Default: -1(no print). :param int validate_every: step interval to do next validation. Default: -1(validate every epoch). :param DataSet dev_data: the validation data - :param use_cuda: - :param save_path: file path to save models + :param bool use_cuda: whether to use CUDA in training. + :param str save_path: file path to save models :param Optimizer optimizer: an optimizer object - :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict. + :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict.\\ `ignore` will not check unused field; `warning` when warn if some field are not used; `strict` means it will raise error if some field are not used. :param str metric_key: a single indicator used to decide the best model based on metric results. It must be one of the keys returned by the FIRST metric in `metrics`. If the overall result gets better if the indicator gets - smaller, add a `-` character in front of the string. For example - :: + smaller, add "-" in front of the string. For example:: + metric_key="-PPL" # language model gets better as perplexity gets smaller - :param sampler: method used to generate batch data. - :param use_tqdm: boolean, use tqdm to show train progress. - """ + :param BaseSampler sampler: method used to generate batch data. + :param bool use_tqdm: whether to use tqdm to show train progress. + + """ + + def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch_size=32, print_every=50, + validate_every=-1, dev_data=None, use_cuda=False, save_path=None, + optimizer=Adam(lr=0.01, weight_decay=0), check_code_level=0, + metric_key=None, sampler=RandomSampler(), use_tqdm=True): super(Trainer, self).__init__() if not isinstance(train_data, DataSet): @@ -141,30 +139,30 @@ class Trainer(object): def train(self, load_best_model=True): """ - 开始训练过程。主要有以下几个步骤 - for epoch in range(num_epochs): - (1) 使用Batch从DataSet中按批取出数据，并自动对DataSet中dtype为float, int的fields进行padding。并转换为Tensor。非 - float，int类型的参数将不会被转换为Tensor，且不进行padding - for batch_x, batch_y in Batch(DataSet): - # batch_x中为设置为input的field - # batch_y中为设置为target的field - (2) 将batch_x的数据送入到model.forward函数中，并获取结果 - (3) 将batch_y与model.forward的结果一并送入loss中计算loss - (4) 获取到loss之后，进行反向求导并更新梯度 - if dev_data is not None: - 根据metrics进行evaluation，并根据是否提供了save_path判断是否存储模型 - - :param load_best_model: 该参数只有在初始化提供了dev_data的情况下有效，如果True, trainer将在返回之前重新加载dev表现最好的 + 开始训练过程。主要有以下几个步骤:: + + 对于每次循环 + 1. 使用Batch从DataSet中按批取出数据，并自动对DataSet中dtype为float, int的fields进行padding。并转换为Tensor。 + 非float，int类型的参数将不会被转换为Tensor，且不进行padding。 + for batch_x, batch_y in Batch(DataSet) + # batch_x中为设置为input的field + # batch_y中为设置为target的field + 2. 将batch_x的数据送入到model.forward函数中，并获取结果 + 3. 将batch_y与model.forward的结果一并送入loss中计算loss + 4. 获取到loss之后，进行反向求导并更新梯度 + 如果测试集不为空 + 根据metrics进行evaluation，并根据是否提供了save_path判断是否存储模型 + + :param bool load_best_model: 该参数只有在初始化提供了dev_data的情况下有效，如果True, trainer将在返回之前重新加载dev表现最好的 模型参数。 + :return results: 返回一个字典类型的数据, 内含以下内容:: - 将会返回一个字典类型的数据, 内含以下内容： seconds: float, 表示训练时长 以下三个内容只有在提供了dev_data的情况下会有。 best_eval: Dict of Dict, 表示evaluation的结果 best_epoch: int，在第几个epoch取得的最佳值 best_step: int, 在第几个step(batch)更新取得的最佳值 - return dict: """ results = {} try: diff --git a/fastNLP/core/vocabulary.py b/fastNLP/core/vocabulary.py index e8cc0e22..50a79d24 100644 --- a/fastNLP/core/vocabulary.py +++ b/fastNLP/core/vocabulary.py @@ -41,13 +41,13 @@ class Vocabulary(object): vocab.update(word_list) vocab["word"] vocab.to_word(5) + + :param int max_size: set the max number of words in Vocabulary. Default: None + :param int min_freq: set the min occur frequency of words in Vocabulary. Default: None + """ def __init__(self, max_size=None, min_freq=None, unknown='', padding=''): - """ - :param int max_size: set the max number of words in Vocabulary. Default: None - :param int min_freq: set the min occur frequency of words in Vocabulary. Default: None - """ self.max_size = max_size self.min_freq = min_freq self.word_count = Counter() @@ -78,6 +78,7 @@ class Vocabulary(object): """Add a single word into the vocabulary. :param str word: a word or token. + """ self.add(word) @@ -86,11 +87,12 @@ class Vocabulary(object): """Add a list of words into the vocabulary. :param list word_lst: a list of strings + """ self.update(word_lst) def build_vocab(self): - """Build 'word to index' dict, and filter the word using `max_size` and `min_freq`. + """Build a mapping from word to index, and filter the word using ``max_size`` and ``min_freq``. """ self.word2idx = {} @@ -111,7 +113,7 @@ class Vocabulary(object): self.rebuild = False def build_reverse_vocab(self): - """Build 'index to word' dict based on 'word to index' dict. + """Build "index to word" dict based on "word to index" dict. """ self.idx2word = {i: w for w, i in self.word2idx.items()} @@ -146,10 +148,9 @@ class Vocabulary(object): raise ValueError("word {} not in vocabulary".format(w)) def to_index(self, w): - """ Turn a word to an index. - If w is not in Vocabulary, return the unknown label. + """ Turn a word to an index. If w is not in Vocabulary, return the unknown label. - :param str w: + :param str w: a word """ return self.__getitem__(w) From 5d8f6960a711ea04735f6155061a3136034a88be Mon Sep 17 00:00:00 2001 From: FengZiYjun Date: Thu, 3 Jan 2019 19:25:22 +0800 Subject: [PATCH 2/3] * fix README figure * refine code style --- docs/source/figures/text_classification.png | Bin 73437 -> 73948 bytes fastNLP/io/dataset_loader.py | 22 ++++++++++---------- test/io/test_dataset_loader.py | 9 ++++---- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/source/figures/text_classification.png b/docs/source/figures/text_classification.png index 183aaba9ed8cbd45cdb31f6675a6b304cb8b262a..0d36a2a1a1819b56d32d378ae9e11a3e56aba2b9 100644 GIT binary patch literal 73948 zcmeFZ2UJsCxGfsHsHlKQ6+}8n3lIezJvn zj(!Y3q)7c5By4z-{Ni0XOIq_-#l&q>jP3N?clJW?Xmd+*3&HJ5O+g>ZWwzSbL;~Hn ztk)hNq63)u`)B2->4o3#K%kZ5|L=HzlP$#c^SCG$Q94}Gy;0Orl}zZ#n&0d>H@Y#h zdhy!lhwsOpp62Cf;9|TX{&>J7x>(c)uJ3Hpe=b3Oomv4MT$qG)#oJ4++Ot=wG{QqR 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zWc%!v4t2KPS@*0$GZ?+EyEp4JEB1h{k27GnRbH9NGui_12S}blOLo0pXX^Xm3WAu0 zMf$K8kY=*?u=|vvt&k?i-*UI*f|7%g^BhE=^<_vN=i%W{X=0IZ6gRQ$ExSIBS@J!Zn(!UfN=R?kHTID1wxHuYZtlVL>7;s>QxNqqwQ#Iak}7AA@fGCls)k2LJ#7 diff --git a/fastNLP/io/dataset_loader.py b/fastNLP/io/dataset_loader.py index 76b9584d..fedf8058 100644 --- a/fastNLP/io/dataset_loader.py +++ b/fastNLP/io/dataset_loader.py @@ -417,7 +417,7 @@ class PeopleDailyCorpusLoader(DataSetLoader): data_set.set_input("seq_len") return data_set - + class Conll2003Loader(DataSetLoader): """Self-defined loader of conll2003 dataset @@ -425,14 +425,14 @@ class Conll2003Loader(DataSetLoader): https://sites.google.com/site/ermasoftware/getting-started/ne-tagging-conll2003-data """ - + def __init__(self): super(Conll2003Loader, self).__init__() - + def load(self, dataset_path): with open(dataset_path, "r", encoding="utf-8") as f: lines = f.readlines() - + ##Parse the dataset line by line parsed_data = [] sentence = [] @@ -444,13 +444,13 @@ class Conll2003Loader(DataSetLoader): sentence = [] tokens = [] continue - + temp = line.strip().split(" ") - sentence.append(temp[0]) + sentence.append(temp[0]) tokens.append(temp[1:4]) - + return self.convert(parsed_data) - + def convert(self, parsed_data): dataset = DataSet() for sample in parsed_data: @@ -460,11 +460,11 @@ class Conll2003Loader(DataSetLoader): lambda labels: labels[1], sample[1])) label2_list = list(map( lambda labels: labels[2], sample[1])) - dataset.append(Instance(token_list=sample[0], - label0_list=label0_list, + dataset.append(Instance(token_list=sample[0], + label0_list=label0_list, label1_list=label1_list, label2_list=label2_list)) - + return dataset class SNLIDataSetLoader(DataSetLoader): diff --git a/test/io/test_dataset_loader.py b/test/io/test_dataset_loader.py index 9bee175b..cf38c973 100644 --- a/test/io/test_dataset_loader.py +++ b/test/io/test_dataset_loader.py @@ -1,9 +1,10 @@ -import os import unittest from fastNLP.io.dataset_loader import Conll2003Loader + + class TestDatasetLoader(unittest.TestCase): - + def test_case_1(self): ''' Test the the loader of Conll2003 dataset @@ -12,7 +13,7 @@ class TestDatasetLoader(unittest.TestCase): dataset_path = "test/data_for_tests/conll_2003_example.txt" loader = Conll2003Loader() dataset_2003 = loader.load(dataset_path) - + for item in dataset_2003: len0 = len(item["label0_list"]) len1 = len(item["label1_list"]) @@ -20,4 +21,4 @@ class TestDatasetLoader(unittest.TestCase): lentoken = len(item["token_list"]) self.assertNotEqual(len0, 0) self.assertEqual(len0, len1) - self.assertEqual(len1, len2) \ No newline at end of file + self.assertEqual(len1, len2) From 07c2b87caf002275b8ef6e07867f30ed76717ac4 Mon Sep 17 00:00:00 2001 From: FengZiYjun Date: Thu, 3 Jan 2019 19:52:14 +0800 Subject: [PATCH 3/3] fix version number --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 3bf42c53..e449a9f8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -23,9 +23,9 @@ copyright = '2018, xpqiu' author = 'xpqiu' # The short X.Y version -version = '' +version = '0.2' # The full version, including alpha/beta/rc tags -release = '2.0' +release = '0.2' # -- General configuration ---------------------------------------------------