fix style problem

neural_seq_qa/reader.py

@@ -8,9 +8,10 @@ from datapoint import DataPoint, Evidence, EecommFeatures
 import utils
 from utils import logger
 
-__all__ = ["Q_IDS", "E_IDS", "LABELS", "QE_COMM", "EE_COMM",
-           "Q_IDS_STR", "E_IDS_STR", "LABELS_STR", "QE_COMM_STR", "EE_COMM_STR",
-           "Settings", "create_reader"]
+__all__ = [
+    "Q_IDS", "E_IDS", "LABELS", "QE_COMM", "EE_COMM", "Q_IDS_STR", "E_IDS_STR",
+    "LABELS_STR", "QE_COMM_STR", "EE_COMM_STR", "Settings", "create_reader"
+]
 
 # slot names
 Q_IDS_STR = "q_ids"
@@ -25,7 +26,6 @@ LABELS = 2
 QE_COMM = 3
 EE_COMM = 4
 
-
 NO_ANSWER = "no_answer"
 
 
@@ -33,6 +33,7 @@ class Settings(object):
     """
     class for storing settings
     """
+
     def __init__(self,
                  vocab,
                  is_training,
@@ -77,8 +78,16 @@ class Settings(object):
         else:
             raise ValueError("label_schema should be BIO/BIO2")
         self.B, self.I, self.O1, self.O2 = B, I, O1, O2
-        self.label_map = {"B":B, "I":I, "O1":O1, "O2":O2,
-                          "b":B, "i":I, "o1":O1, "o2":O2}
+        self.label_map = {
+            "B": B,
+            "I": I,
+            "O1": O1,
+            "O2": O2,
+            "b": B,
+            "i": I,
+            "o1": O1,
+            "o2": O2
+        }
         self.label_num = len(set((B, I, O1, O2)))
 
         # id for OOV
@@ -145,8 +154,7 @@ class SampleStream(object):
                     remove_extra_b(labels)
                 evi[Evidence.GOLDEN_LABELS] = labels
 
-        def get_eecom_feats_list(cur_sample_is_negative,
-                                 eecom_feats_list,
+        def get_eecom_feats_list(cur_sample_is_negative, eecom_feats_list,
                                  evidences):
             if not self.settings.is_training:
                 return [item[EecommFeatures.EECOMM_FEATURES] \
@@ -197,8 +205,7 @@ class SampleStream(object):
 
             eecom_feats_list = get_eecom_feats_list(
                 sample_type != Evidence.POSITIVE,
-                                    evi[Evidence.EECOMM_FEATURES_LIST],
-                                    evidences)
+                evi[Evidence.EECOMM_FEATURES_LIST], evidences)
             if not eecom_feats_list:
                 return None
             else:
@@ -226,6 +233,7 @@ class SampleStream(object):
                     sample = process_evi(q_ids, evi, evidences)
                     if sample: yield q_idx, sample, evi[Evidence.TYPE]
 
+
 class DataReader(object):
     def __iter__(self):
         return self
@@ -251,10 +259,7 @@ class DataReader(object):
 
 
 class TrainingDataReader(DataReader):
-    def __init__(self,
-                 sample_stream,
-                 negative_ratio,
-                 hit_ans_negative_ratio):
+    def __init__(self, sample_stream, negative_ratio, hit_ans_negative_ratio):
         super(TrainingDataReader, self).__init__()
         self.positive_data = []
         self.hit_ans_negative_data = []
@@ -335,7 +340,7 @@ class TrainingDataReader(DataReader):
             self.p_idx = 0
 
         self.p_idx += 1
-        return self.positive_data[self.p_idx-1]
+        return self.positive_data[self.p_idx - 1]
 
     def _next_negative_data(self, idx, negative_data):
         if idx >= len(negative_data):
@@ -352,7 +357,7 @@ class TrainingDataReader(DataReader):
             random.shuffle(bundle[0])
             bundle[1] = 0
         bundle[1] += 1
-        return idx+1, bundle[0][bundle[1]-1]
+        return idx + 1, bundle[0][bundle[1] - 1]
 
     def next_hit_ans_negative_data(self):
         self.hit_idx, data = self._next_negative_data(
@@ -387,16 +392,18 @@ class TestDataReader(DataReader):
 def create_reader(filename, settings, samples_per_pass=sys.maxint):
     if settings.is_training:
         training_reader = TrainingDataReader(
-                SampleStream(filename, settings),
-                settings.negative_sample_ratio,
+            SampleStream(filename, settings), settings.negative_sample_ratio,
             settings.hit_ans_negative_sample_ratio)
 
         def wrapper():
             for i, data in izip(xrange(samples_per_pass), training_reader):
                 yield data
+
         return wrapper
     else:
+
         def wrapper():
             sample_stream = SampleStream(filename, settings)
             return TestDataReader(sample_stream)
+
         return wrapper

neural_seq_qa/test/test_reader.py

@@ -19,11 +19,12 @@ ch = logging.StreamHandler()
 ch.setFormatter(formatter)
 utils.logger.addHandler(ch)
 
+
 class Vocab(object):
     @property
     def data(self):
-        word_dict_path = os.path.join(
-                topdir, "data", "embedding", "wordvecs.vcb")
+        word_dict_path = os.path.join(topdir, "data", "embedding",
+                                      "wordvecs.vcb")
         return utils.load_dict(word_dict_path)