Merge remote-tracking branch 'origin/master'

fastNLP/loader/dataset_loader.py

@@ -11,8 +11,24 @@ class DatasetLoader(BaseLoader):
 
 
 class POSDatasetLoader(DatasetLoader):
-    """loader for pos data sets"""
-
+    """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
+        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.
+    """
     def __init__(self, data_name, data_path):
         super(POSDatasetLoader, self).__init__(data_name, data_path)
 
@@ -23,10 +39,42 @@ class POSDatasetLoader(DatasetLoader):
         return line
 
     def load_lines(self):
-        assert (os.path.exists(self.data_path))
+        """
+        :return data: three-level list
+            [
+                [ [word_11, word_12, ...], [label_1, label_1, ...] ],
+                [ [word_21, word_22, ...], [label_2, label_1, ...] ],
+                ...
+            ]
+        """
         with open(self.data_path, "r", encoding="utf-8") as f:
             lines = f.readlines()
-        return lines
+        return self.parse(lines)
+
+    @staticmethod
+    def parse(lines):
+        data = []
+        sentence = []
+        for line in lines:
+            line = line.strip()
+            if len(line) > 1:
+                sentence.append(line.split('\t'))
+            else:
+                words = []
+                labels = []
+                for tokens in sentence:
+                    words.append(tokens[0])
+                    labels.append(tokens[1])
+                data.append([words, labels])
+                sentence = []
+        if len(sentence) != 0:
+            words = []
+            labels = []
+            for tokens in sentence:
+                words.append(tokens[0])
+                labels.append(tokens[1])
+            data.append([words, labels])
+        return data
 
 
 class ClassDatasetLoader(DatasetLoader):
@@ -112,3 +160,10 @@ class LMDatasetLoader(DatasetLoader):
         with open(self.data_path, "r", encoding="utf=8") as f:
             text = " ".join(f.readlines())
         return text.strip().split()
+
+
+if __name__ == "__main__":
+    data = POSDatasetLoader("xxx", "../../test/data_for_tests/people.txt").load_lines()
+    for example in data:
+        for w, l in zip(example[0], example[1]):
+            print(w, l)

fastNLP/loader/preprocess.py

@@ -28,33 +28,24 @@ class BasePreprocess(object):
 class POSPreprocess(BasePreprocess):
     """
         This class are used to preprocess the pos 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
-        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.
+
     """
 
-    def __init__(self, data, pickle_path, train_dev_split=0):
+    def __init__(self, data, pickle_path="./", train_dev_split=0):
         """
         Preprocess pipeline, including building mapping from words to index, from index to words,
         from labels/classes to index, from index to labels/classes.
-        :param data:
-        :param pickle_path:
+        :param data: three-level list
+            [
+                [ [word_11, word_12, ...], [label_1, label_1, ...] ],
+                [ [word_21, word_22, ...], [label_2, label_1, ...] ],
+                ...
+            ]
+        :param pickle_path: str, the directory to the pickle files. Default: "./"
         :param train_dev_split: float in [0, 1]. The ratio of dev data split from training data. Default: 0.
 
         To do:
-        1. use @contextmanager to handle pickle dumps and loads
+        1. simplify __init__
         """
         super(POSPreprocess, self).__init__(data, pickle_path)
 
@@ -75,6 +66,7 @@ class POSPreprocess(BasePreprocess):
         else:
             with open(os.path.join(self.pickle_path, "class2id.pkl"), "wb") as f:
                 _pickle.dump(self.label2index, f)
+            #something will be wrong if word2id.pkl is found but class2id.pkl is not found
 
         if not self.pickle_exist("id2word.pkl"):
             index2word = self.build_reverse_dict(self.word2index)
@@ -98,25 +90,23 @@ class POSPreprocess(BasePreprocess):
     def build_dict(self, data):
         """
         Add new words with indices into self.word_dict, new labels with indices into self.label_dict.
-        :param data: list of list [word, label]
-        :return word2index: dict of (str, int)
-                label2index: dict of (str, int)
+        :param data: three-level list
+            [
+                [ [word_11, word_12, ...], [label_1, label_1, ...] ],
+                [ [word_21, word_22, ...], [label_2, label_1, ...] ],
+                ...
+            ]
+        :return word2index: dict of {str, int}
+                label2index: dict of {str, int}
         """
         label2index = {}
         word2index = DEFAULT_WORD_TO_INDEX
-        for line in data:
-            line = line.strip()
-            if len(line) <= 1:
-                continue
-            tokens = line.split('\t')
-
-            if tokens[0] not in word2index:
-                # add (word, index) into the dict
-                word2index[tokens[0]] = len(word2index)
-
-            # for label in tokens[1: ]:
-            if tokens[1] not in label2index:
-                label2index[tokens[1]] = len(label2index)
+        for example in data:
+            for word, label in zip(example[0], example[1]):
+                if word not in word2index:
+                    word2index[word] = len(word2index)
+                if label not in label2index:
+                    label2index[label] = len(label2index)
         return word2index, label2index
 
     def pickle_exist(self, pickle_name):
@@ -139,24 +129,31 @@ class POSPreprocess(BasePreprocess):
     def to_index(self, data):
         """
         Convert word strings and label strings into indices.
-        :param data: list of str. Each string is a line, described above.
-        :return data_index: list of tuple (word index, label index)
+        :param data: three-level list
+            [
+                [ [word_11, word_12, ...], [label_1, label_1, ...] ],
+                [ [word_21, word_22, ...], [label_2, label_1, ...] ],
+                ...
+            ]
+        :return data_index: the shape of data, but each string is replaced by its corresponding index
         """
-        data_train = []
-        sentence = []
-        for w in data:
-            w = w.strip()
-            if len(w) <= 1:
-                wid = []
-                lid = []
-                for i in range(len(sentence)):
-                    wid.append(self.word2index[sentence[i][0]])
-                    lid.append(self.label2index[sentence[i][1]])
-                data_train.append((wid, lid))
-                sentence = []
-                continue
-            sentence.append(w.split('\t'))
-        return data_train
+        data_index = []
+        for example in data:
+            word_list = []
+            label_list = []
+            for word, label in zip(example[0], example[1]):
+                word_list.append(self.word2index[word])
+                label_list.append(self.label2index[label])
+            data_index.append([word_list, label_list])
+        return data_index
+
+    @property
+    def vocab_size(self):
+        return len(self.word2index)
+
+    @property
+    def num_classes(self):
+        return len(self.label2index)
 
 
 class ClassPreprocess(BasePreprocess):

test/test_loader.py

@@ -1,9 +1,23 @@
 import unittest
 
+from fastNLP.loader.dataset_loader import POSDatasetLoader
 
-class MyTestCase(unittest.TestCase):
-    def test_something(self):
-        self.assertEqual(True, False)
+
+class TestPreprocess(unittest.TestCase):
+    def test_case_1(self):
+        data = [[["Tom", "and", "Jerry", "."], ["T", "F", "T", "F"]],
+                ["Hello", "world", "!"], ["T", "F", "F"]]
+        pickle_path = "./data_for_tests/"
+        # POSPreprocess(data, pickle_path)
+
+
+class TestDatasetLoader(unittest.TestCase):
+    def test_case_1(self):
+        data = """Tom\tT\nand\tF\nJerry\tT\n.\tF\n\nHello\tT\nworld\tF\n!\tF"""
+        lines = data.split("\n")
+        answer = POSDatasetLoader.parse(lines)
+        truth = [[["Tom", "and", "Jerry", "."], ["T", "F", "T", "F"]], [["Hello", "world", "!"], ["T", "F", "F"]]]
+        self.assertListEqual(answer, truth, "POS Dataset Loader")
 
 
 if __name__ == '__main__':