* final clean up

* remove conflicts
* all tests passed

fastNLP/core/dataset.py

@@ -98,10 +98,10 @@ class DataSet(object):
             raise KeyError("Unrecognized type {} for idx in __getitem__ method".format(type(idx)))
 
     def __getattr__(self, item):
+        # Not tested. Don't use !!
         if item == "field_arrays":
             raise AttributeError
-        # TODO dataset.x
-        if item in self.field_arrays:
+        if isinstance(item, str) and item in self.field_arrays:
             return self.field_arrays[item]
         try:
             reader = DataLoaderRegister.get_reader(item)

fastNLP/core/trainer.py

@@ -85,7 +85,7 @@ class Trainer(object):
         if metric_key is not None:
             self.increase_better = False if metric_key[0] == "-" else True
             self.metric_key = metric_key[1:] if metric_key[0] == "+" or metric_key[0] == "-" else metric_key
-        elif metrics is not None:
+        elif len(metrics) > 0:
             self.metric_key = metrics[0].__class__.__name__.lower().strip('metric')
 
         # prepare loss

fastNLP/io/base_loader.py

@@ -31,22 +31,6 @@ class BaseLoader(object):
             return obj
 
 
-class ToyLoader0(BaseLoader):
-    """
-        For CharLM
-    """
-
-    def __init__(self, data_path):
-        super(ToyLoader0, self).__init__(data_path)
-
-    def load(self):
-        with open(self.data_path, 'r') as f:
-            corpus = f.read().lower()
-        import re
-        corpus = re.sub(r"<unk>", "unk", corpus)
-        return corpus.split()
-
-
 class DataLoaderRegister:
     """"register for data sets"""
     _readers = {}

fastNLP/io/dataset_loader.py

@@ -75,7 +75,6 @@ class DataSetLoader:
         raise NotImplementedError
 
 
-@DataSet.set_reader("read_naive")
 class NativeDataSetLoader(DataSetLoader):
     def __init__(self):
         super(NativeDataSetLoader, self).__init__()
@@ -87,7 +86,9 @@ class NativeDataSetLoader(DataSetLoader):
         return ds
 
 
-@DataSet.set_reader('read_raw')
+DataLoaderRegister.set_reader(NativeDataSetLoader, 'read_naive')
+
+
 class RawDataSetLoader(DataSetLoader):
     def __init__(self):
         super(RawDataSetLoader, self).__init__()
@@ -101,6 +102,8 @@ class RawDataSetLoader(DataSetLoader):
 
     def convert(self, data):
         return convert_seq_dataset(data)
+
+
 DataLoaderRegister.set_reader(RawDataSetLoader, 'read_rawdata')
 
 
@@ -171,6 +174,8 @@ class POSDataSetLoader(DataSetLoader):
         """Convert lists of strings into Instances with Fields.
         """
         return convert_seq2seq_dataset(data)
+
+
 DataLoaderRegister.set_reader(POSDataSetLoader, 'read_pos')
 
 
@@ -348,7 +353,6 @@ class LMDataSetLoader(DataSetLoader):
         pass
 
 
-@DataSet.set_reader('read_people_daily')
 class PeopleDailyCorpusLoader(DataSetLoader):
     """
         People Daily Corpus: Chinese word segmentation, POS tag, NER

test/core/test_dataset.py

@@ -178,6 +178,20 @@ class TestDataSet(unittest.TestCase):
         self.assertTrue(isinstance(ans, FieldArray))
         self.assertEqual(ans.content, [[5, 6]] * 10)
 
+    def test_reader(self):
+        # 跑通即可
+        ds = DataSet().read_naive("test/data_for_tests/tutorial_sample_dataset.csv")
+        self.assertTrue(isinstance(ds, DataSet))
+        self.assertTrue(len(ds) > 0)
+
+        ds = DataSet().read_rawdata("test/data_for_tests/people_daily_raw.txt")
+        self.assertTrue(isinstance(ds, DataSet))
+        self.assertTrue(len(ds) > 0)
+
+        ds = DataSet().read_pos("test/data_for_tests/people.txt")
+        self.assertTrue(isinstance(ds, DataSet))
+        self.assertTrue(len(ds) > 0)
+
 
 class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):

test/core/test_optimizer.py

@@ -7,7 +7,7 @@ from fastNLP.core.optimizer import SGD, Adam
 
 class TestOptim(unittest.TestCase):
     def test_SGD(self):
-        optim = SGD(torch.nn.Linear(10, 3).parameters())
+        optim = SGD(model_params=torch.nn.Linear(10, 3).parameters())
         self.assertTrue("lr" in optim.__dict__["settings"])
         self.assertTrue("momentum" in optim.__dict__["settings"])
         res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
@@ -22,13 +22,18 @@ class TestOptim(unittest.TestCase):
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.002)
         self.assertEqual(optim.__dict__["settings"]["momentum"], 0.989)
 
-        with self.assertRaises(RuntimeError):
+        optim = SGD(0.001)
+        self.assertEqual(optim.__dict__["settings"]["lr"], 0.001)
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.SGD))
+
+        with self.assertRaises(TypeError):
             _ = SGD("???")
-        with self.assertRaises(RuntimeError):
+        with self.assertRaises(TypeError):
             _ = SGD(0.001, lr=0.002)
 
     def test_Adam(self):
-        optim = Adam(torch.nn.Linear(10, 3).parameters())
+        optim = Adam(model_params=torch.nn.Linear(10, 3).parameters())
         self.assertTrue("lr" in optim.__dict__["settings"])
         self.assertTrue("weight_decay" in optim.__dict__["settings"])
         res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
@@ -42,3 +47,8 @@ class TestOptim(unittest.TestCase):
         optim = Adam(lr=0.002, weight_decay=0.989)
         self.assertEqual(optim.__dict__["settings"]["lr"], 0.002)
         self.assertEqual(optim.__dict__["settings"]["weight_decay"], 0.989)
+
+        optim = Adam(0.001)
+        self.assertEqual(optim.__dict__["settings"]["lr"], 0.001)
+        res = optim.construct_from_pytorch(torch.nn.Linear(10, 3).parameters())
+        self.assertTrue(isinstance(res, torch.optim.Adam))