Deeppavlov fixes

deeppavlov/core/data/utils.py

@@ -465,7 +465,7 @@ def flatten_str_batch(batch: Union[str, Iterable]) -> Union[list, chain]:
         ['a', 'b', 'c', 'd']
 
     """
-    if isinstance(batch, str):
+    if isinstance(batch, str) or isinstance(batch, int) or isinstance(batch, float):
         return [batch]
     else:
         return chain(*[flatten_str_batch(sample) for sample in batch])

deeppavlov/core/trainers/nn_trainer.py

@@ -20,6 +20,8 @@
 from pathlib import Path
 from typing import List, Tuple, Union, Optional, Iterable
 
+from tqdm import tqdm
+
 from deeppavlov.core.common.errors import ConfigError
 from deeppavlov.core.common.registry import register
 from deeppavlov.core.data.data_learning_iterator import DataLearningIterator
@@ -279,7 +281,8 @@ def train_on_batches(self, iterator: DataLearningIterator) -> None:
         while True:
             impatient = False
             self._send_event(event_name='before_train')
-            for x, y_true in iterator.gen_batches(self.batch_size, data_type='train'):
+            log.info('The model training started')
+            for x, y_true in tqdm(iterator.gen_batches(self.batch_size, data_type='train')):
                 self.last_result = self._chainer.train_on_batch(x, y_true)
                 if self.last_result is None:
                     self.last_result = {}

deeppavlov/dataset_readers/basic_classification_reader.py

@@ -35,6 +35,7 @@ class BasicClassificationDatasetReader(DatasetReader):
     @overrides
     def read(self, data_path: str, url: str = None,
              format: str = "csv", class_sep: str = None,
+             float_labels: bool = False,
              *args, **kwargs) -> dict:
         """
         Read dataset from data_path directory.
@@ -48,6 +49,8 @@ def read(self, data_path: str, url: str = None,
             format: extension of files. Set of Values: ``"csv", "json"``
             class_sep: string separator of labels in column with labels
             sep (str): delimeter for ``"csv"`` files. Default: None -> only one class per sample
+            float_labels (boolean): if True and class_sep is not None, we treat all classes as float
+            quotechar (str): what char we consider as quote in the dataset
             header (int): row number to use as the column names
             names (array): list of column names to use
             orient (str): indication of expected JSON string format
@@ -80,7 +83,7 @@ def read(self, data_path: str, url: str = None,
             file = Path(data_path).joinpath(file_name)
             if file.exists():
                 if format == 'csv':
-                    keys = ('sep', 'header', 'names')
+                    keys = ('sep', 'header', 'names', 'quotechar')
                     options = {k: kwargs[k] for k in keys if k in kwargs}
                     df = pd.read_csv(file, **options)
                 elif format == 'json':
@@ -92,22 +95,27 @@ def read(self, data_path: str, url: str = None,
 
                 x = kwargs.get("x", "text")
                 y = kwargs.get('y', 'labels')
-                if isinstance(x, list):
-                    if class_sep is None:
-                        # each sample is a tuple ("text", "label")
-                        data[data_type] = [([row[x_] for x_ in x], str(row[y]))
-                                           for _, row in df.iterrows()]
-                    else:
-                        # each sample is a tuple ("text", ["label", "label", ...])
-                        data[data_type] = [([row[x_] for x_ in x], str(row[y]).split(class_sep))
-                                           for _, row in df.iterrows()]
-                else:
-                    if class_sep is None:
-                        # each sample is a tuple ("text", "label")
-                        data[data_type] = [(row[x], str(row[y])) for _, row in df.iterrows()]
-                    else:
-                        # each sample is a tuple ("text", ["label", "label", ...])
-                        data[data_type] = [(row[x], str(row[y]).split(class_sep)) for _, row in df.iterrows()]
+                data[data_type] = []
+                i = 0
+                prev_n_classes = 0  # to capture samples with different n_classes
+                for _, row in df.iterrows():
+                     if isinstance(x, list):
+                         sample = [row[x_] for x_ in x]
+                     else:
+                         sample = row[x]
+                     label = str(row[y])
+                     if class_sep:
+                         label = str(row[y]).split(class_sep)
+                         if prev_n_classes == 0:
+                             prev_n_classes = len(label)
+                         assert len(label) == prev_n_classes, f"Wrong class number at {i} row"
+                     if float_labels:
+                         label = [float(k) for k in label]                      
+                     if sample == sample and label == label:  # not NAN
+                         data[data_type].append((sample, label))
+                     else:
+                         log.warning(f'Skipping NAN received in file {file} at {i} row')
+                     i += 1
             else:
                 log.warning("Cannot find {} file".format(file))