Merge pull request #82 from choosewhatulike/master

add Vocabulary

Author: FengZiYjun

fastNLP/core/predictor.py

@@ -27,8 +27,8 @@ def __init__(self, pickle_path, task):
         self.batch_output = []
         self.pickle_path = pickle_path
         self._task = task  # one of ("seq_label", "text_classify")
-        self.index2label = load_pickle(self.pickle_path, "id2class.pkl")
-        self.word2index = load_pickle(self.pickle_path, "word2id.pkl")
+        self.label_vocab = load_pickle(self.pickle_path, "class2id.pkl")
+        self.word_vocab = load_pickle(self.pickle_path, "word2id.pkl")
 
     def predict(self, network, data):
         """Perform inference using the trained model.
@@ -82,7 +82,7 @@ def prepare_input(self, data):
         :return data_set: a DataSet instance.
         """
         assert isinstance(data, list)
-        return create_dataset_from_lists(data, self.word2index, has_target=False)
+        return create_dataset_from_lists(data, self.word_vocab, has_target=False)
 
     def prepare_output(self, data):
         """Transform list of batch outputs into strings."""
@@ -97,14 +97,14 @@ def _seq_label_prepare_output(self, batch_outputs):
         results = []
         for batch in batch_outputs:
             for example in np.array(batch):
-                results.append([self.index2label[int(x)] for x in example])
+                results.append([self.label_vocab.to_word(int(x)) for x in example])
         return results
 
     def _text_classify_prepare_output(self, batch_outputs):
         results = []
         for batch_out in batch_outputs:
             idx = np.argmax(batch_out.detach().numpy(), axis=-1)
-            results.extend([self.index2label[i] for i in idx])
+            results.extend([self.label_vocab.to_word(i) for i in idx])
         return results
 
 

fastNLP/core/preprocess.py

@@ -6,16 +6,7 @@
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.field import TextField, LabelField
 from fastNLP.core.instance import Instance
-
-DEFAULT_PADDING_LABEL = '<pad>'  # dict index = 0
-DEFAULT_UNKNOWN_LABEL = '<unk>'  # dict index = 1
-DEFAULT_RESERVED_LABEL = ['<reserved-2>',
-                          '<reserved-3>',
-                          '<reserved-4>']  # dict index = 2~4
-
-DEFAULT_WORD_TO_INDEX = {DEFAULT_PADDING_LABEL: 0, DEFAULT_UNKNOWN_LABEL: 1,
-                         DEFAULT_RESERVED_LABEL[0]: 2, DEFAULT_RESERVED_LABEL[1]: 3,
-                         DEFAULT_RESERVED_LABEL[2]: 4}
+from fastNLP.core.vocabulary import Vocabulary
 
 
 # the first vocab in dict with the index = 5
@@ -68,24 +59,22 @@ class BasePreprocess(object):
 
         - "word2id.pkl", a mapping from words(tokens) to indices
         - "id2word.pkl", a reversed dictionary
-        - "label2id.pkl", a dictionary on labels
-        - "id2label.pkl", a reversed dictionary on labels
 
     These four pickle files are expected to be saved in the given pickle directory once they are constructed.
     Preprocessors will check if those files are already in the directory and will reuse them in future calls.
     """
 
     def __init__(self):
-        self.word2index = None
-        self.label2index = None
+        self.data_vocab = Vocabulary()
+        self.label_vocab = Vocabulary()
 
     @property
     def vocab_size(self):
-        return len(self.word2index)
+        return len(self.data_vocab)
 
     @property
     def num_classes(self):
-        return len(self.label2index)
+        return len(self.label_vocab)
 
     def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=0, cross_val=False, n_fold=10):
         """Main pre-processing pipeline.
@@ -102,20 +91,14 @@ def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=
         """
 
         if pickle_exist(pickle_path, "word2id.pkl") and pickle_exist(pickle_path, "class2id.pkl"):
-            self.word2index = load_pickle(pickle_path, "word2id.pkl")
-            self.label2index = load_pickle(pickle_path, "class2id.pkl")
+            self.data_vocab = load_pickle(pickle_path, "word2id.pkl")
+            self.label_vocab = load_pickle(pickle_path, "class2id.pkl")
         else:
-            self.word2index, self.label2index = self.build_dict(train_dev_data)
-            save_pickle(self.word2index, pickle_path, "word2id.pkl")
-            save_pickle(self.label2index, pickle_path, "class2id.pkl")
-
-        if not pickle_exist(pickle_path, "id2word.pkl"):
-            index2word = self.build_reverse_dict(self.word2index)
-            save_pickle(index2word, pickle_path, "id2word.pkl")
+            self.data_vocab, self.label_vocab = self.build_dict(train_dev_data)
+            save_pickle(self.data_vocab, pickle_path, "word2id.pkl")
+            save_pickle(self.label_vocab, pickle_path, "class2id.pkl")
 
-        if not pickle_exist(pickle_path, "id2class.pkl"):
-            index2label = self.build_reverse_dict(self.label2index)
-            save_pickle(index2label, pickle_path, "id2class.pkl")
+        self.build_reverse_dict()
 
         train_set = []
         dev_set = []
@@ -125,13 +108,13 @@ def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=
                     split = int(len(train_dev_data) * train_dev_split)
                     data_dev = train_dev_data[: split]
                     data_train = train_dev_data[split:]
-                    train_set = self.convert_to_dataset(data_train, self.word2index, self.label2index)
-                    dev_set = self.convert_to_dataset(data_dev, self.word2index, self.label2index)
+                    train_set = self.convert_to_dataset(data_train, self.data_vocab, self.label_vocab)
+                    dev_set = self.convert_to_dataset(data_dev, self.data_vocab, self.label_vocab)
 
                     save_pickle(dev_set, pickle_path, "data_dev.pkl")
                     print("{} of the training data is split for validation. ".format(train_dev_split))
                 else:
-                    train_set = self.convert_to_dataset(train_dev_data, self.word2index, self.label2index)
+                    train_set = self.convert_to_dataset(train_dev_data, self.data_vocab, self.label_vocab)
                 save_pickle(train_set, pickle_path, "data_train.pkl")
             else:
                 train_set = load_pickle(pickle_path, "data_train.pkl")
@@ -143,8 +126,8 @@ def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=
                 # cross validation
                 data_cv = self.cv_split(train_dev_data, n_fold)
                 for i, (data_train_cv, data_dev_cv) in enumerate(data_cv):
-                    data_train_cv = self.convert_to_dataset(data_train_cv, self.word2index, self.label2index)
-                    data_dev_cv = self.convert_to_dataset(data_dev_cv, self.word2index, self.label2index)
+                    data_train_cv = self.convert_to_dataset(data_train_cv, self.data_vocab, self.label_vocab)
+                    data_dev_cv = self.convert_to_dataset(data_dev_cv, self.data_vocab, self.label_vocab)
                     save_pickle(
                         data_train_cv, pickle_path,
                         "data_train_{}.pkl".format(i))
@@ -165,7 +148,7 @@ def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=
         test_set = []
         if test_data is not None:
             if not pickle_exist(pickle_path, "data_test.pkl"):
-                test_set = self.convert_to_dataset(test_data, self.word2index, self.label2index)
+                test_set = self.convert_to_dataset(test_data, self.data_vocab, self.label_vocab)
                 save_pickle(test_set, pickle_path, "data_test.pkl")
 
         # return preprocessed results
@@ -180,28 +163,15 @@ def run(self, train_dev_data, test_data=None, pickle_path="./", train_dev_split=
             return tuple(results)
 
     def build_dict(self, data):
-        label2index = DEFAULT_WORD_TO_INDEX.copy()
-        word2index = DEFAULT_WORD_TO_INDEX.copy()
         for example in data:
-            for word in example[0]:
-                if word not in word2index:
-                    word2index[word] = len(word2index)
-            label = example[1]
-            if isinstance(label, str):
-                # label is a string
-                if label not in label2index:
-                    label2index[label] = len(label2index)
-            elif isinstance(label, list):
-                # label is a list of strings
-                for single_label in label:
-                    if single_label not in label2index:
-                        label2index[single_label] = len(label2index)
-        return word2index, label2index
-
-
-    def build_reverse_dict(self, word_dict):
-        id2word = {word_dict[w]: w for w in word_dict}
-        return id2word
+            word, label = example
+            self.data_vocab.update(word)
+            self.label_vocab.update(label)
+        return self.data_vocab, self.label_vocab
+
+    def build_reverse_dict(self):
+        self.data_vocab.build_reverse_vocab()
+        self.label_vocab.build_reverse_vocab()
 
     def data_split(self, data, train_dev_split):
         """Split data into train and dev set."""

fastNLP/core/vocabulary.py

@@ -0,0 +1,124 @@
+from copy import deepcopy
+
+DEFAULT_PADDING_LABEL = '<pad>'  # dict index = 0
+DEFAULT_UNKNOWN_LABEL = '<unk>'  # dict index = 1
+DEFAULT_RESERVED_LABEL = ['<reserved-2>',
+                          '<reserved-3>',
+                          '<reserved-4>']  # dict index = 2~4
+
+DEFAULT_WORD_TO_INDEX = {DEFAULT_PADDING_LABEL: 0, DEFAULT_UNKNOWN_LABEL: 1,
+                         DEFAULT_RESERVED_LABEL[0]: 2, DEFAULT_RESERVED_LABEL[1]: 3,
+                         DEFAULT_RESERVED_LABEL[2]: 4}
+
+def isiterable(p_object):
+    try:
+        it = iter(p_object)
+    except TypeError: 
+        return False
+    return True
+
+class Vocabulary(object):
+    """Use for word and index one to one mapping
+
+    Example::
+
+        vocab = Vocabulary()
+        word_list = "this is a word list".split()
+        vocab.update(word_list)
+        vocab["word"]
+        vocab.to_word(5)
+    """
+    def __init__(self, need_default=True):
+        """
+        :param bool need_default: set if the Vocabulary has default labels reserved.
+        """
+        if need_default:
+            self.word2idx = deepcopy(DEFAULT_WORD_TO_INDEX)
+            self.padding_label = DEFAULT_PADDING_LABEL
+            self.unknown_label = DEFAULT_UNKNOWN_LABEL
+        else:
+            self.word2idx = {}
+            self.padding_label = None
+            self.unknown_label = None
+
+        self.has_default = need_default
+        self.idx2word = None
+
+    def __len__(self):
+        return len(self.word2idx)
+
+    def update(self, word):
+        """add word or list of words into Vocabulary
+        
+        :param word: a list of str or str
+        """
+        if not isinstance(word, str) and isiterable(word):
+        # it's a nested list
+            for w in word:
+                self.update(w)
+        else:
+        # it's a word to be added
+            if word not in self.word2idx:
+                self.word2idx[word] = len(self)
+                if self.idx2word is not None:
+                    self.idx2word = None
+
+    
+    def __getitem__(self, w):
+        """To support usage like::
+
+            vocab[w]
+        """
+        if w in self.word2idx:
+            return self.word2idx[w]
+        else:
+            return self.word2idx[DEFAULT_UNKNOWN_LABEL]
+
+    def to_index(self, w):
+        """ like to_index(w) function, turn a word to the index
+            if w is not in Vocabulary, return the unknown label
+        
+        :param str w:
+        """
+        return self[w]
+    
+    def unknown_idx(self):
+        if self.unknown_label is None: 
+            return None
+        return self.word2idx[self.unknown_label]
+    
+    def padding_idx(self):
+        if self.padding_label is None:
+            return None
+        return self.word2idx[self.padding_label]
+
+    def build_reverse_vocab(self):
+        """build 'index to word' dict based on 'word to index' dict
+        """
+        self.idx2word = {self.word2idx[w] : w for w in self.word2idx}
+    
+    def to_word(self, idx):
+        """given a word's index, return the word itself
+
+        :param int idx:
+        """
+        if self.idx2word is None:
+            self.build_reverse_vocab()
+        return self.idx2word[idx]
+    
+    def __getstate__(self):
+        """use to prepare data for pickle
+        """
+        state = self.__dict__.copy()
+        # no need to pickle idx2word as it can be constructed from word2idx
+        del state['idx2word']
+        return state
+    
+    def __setstate__(self, state):
+        """use to restore state from pickle
+        """
+        self.__dict__.update(state)
+        self.idx2word = None
+    
+
+    

fastNLP/fastnlp.py

@@ -69,7 +69,7 @@ def __init__(self, model_dir="./"):
         :param model_dir: this directory should contain the following files:
             1. a pre-trained model
             2. a config file
-            3. "id2class.pkl"
+            3. "class2id.pkl"
             4. "word2id.pkl"
         """
         self.model_dir = model_dir
@@ -99,10 +99,10 @@ def load(self, model_name, config_file="config", section_name="model"):
         print("Restore model hyper-parameters {}".format(str(model_args.data)))
 
         # fetch dictionary size and number of labels from pickle files
-        word2index = load_pickle(self.model_dir, "word2id.pkl")
-        model_args["vocab_size"] = len(word2index)
-        index2label = load_pickle(self.model_dir, "id2class.pkl")
-        model_args["num_classes"] = len(index2label)
+        word_vocab = load_pickle(self.model_dir, "word2id.pkl")
+        model_args["vocab_size"] = len(word_vocab)
+        label_vocab = load_pickle(self.model_dir, "class2id.pkl")
+        model_args["num_classes"] = len(label_vocab)
 
         # Construct the model
         model = model_class(model_args)

reproduction/chinese_word_segment/run.py

@@ -32,7 +32,7 @@ def infer():
     # fetch dictionary size and number of labels from pickle files
     word2index = load_pickle(pickle_path, "word2id.pkl")
     test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "id2class.pkl")
+    index2label = load_pickle(pickle_path, "class2id.pkl")
     test_args["num_classes"] = len(index2label)
 
 
@@ -105,7 +105,7 @@ def test():
     # fetch dictionary size and number of labels from pickle files
     word2index = load_pickle(pickle_path, "word2id.pkl")
     test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "id2class.pkl")
+    index2label = load_pickle(pickle_path, "class2id.pkl")
     test_args["num_classes"] = len(index2label)
 
     # load dev data

reproduction/pos_tag_model/train_pos_tag.py

@@ -33,7 +33,7 @@ def infer():
     # fetch dictionary size and number of labels from pickle files
     word2index = load_pickle(pickle_path, "word2id.pkl")
     test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "id2class.pkl")
+    index2label = load_pickle(pickle_path, "class2id.pkl")
     test_args["num_classes"] = len(index2label)
 
     # Define the same model
@@ -105,7 +105,7 @@ def test():
     # fetch dictionary size and number of labels from pickle files
     word2index = load_pickle(pickle_path, "word2id.pkl")
     test_args["vocab_size"] = len(word2index)
-    index2label = load_pickle(pickle_path, "id2class.pkl")
+    index2label = load_pickle(pickle_path, "class2id.pkl")
     test_args["num_classes"] = len(index2label)
 
     # load dev data

test/core/test_predictor.py

@@ -4,6 +4,7 @@
 from fastNLP.core.predictor import Predictor
 from fastNLP.core.preprocess import save_pickle
 from fastNLP.models.sequence_modeling import SeqLabeling
+from fastNLP.core.vocabulary import Vocabulary
 
 
 class TestPredictor(unittest.TestCase):
@@ -23,10 +24,14 @@ def test_seq_label(self):
             ['a', 'b', 'c', 'd', '$'],
             ['!', 'b', 'c', 'd', 'e']
         ]
-        vocab = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
+
+        vocab = Vocabulary()
+        vocab.word2idx = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
+        class_vocab = Vocabulary()
+        class_vocab.word2idx = {"0":0, "1":1, "2":2, "3":3, "4":4}
 
         os.system("mkdir save")
-        save_pickle({0: "0", 1: "1", 2: "2", 3: "3", 4: "4"}, "./save/", "id2class.pkl")
+        save_pickle(class_vocab, "./save/", "class2id.pkl")
         save_pickle(vocab, "./save/", "word2id.pkl")
 
         model = SeqLabeling(model_args)