implemented multi-label support

Revert "minor fix"

This reverts commit 91e9847.

 On branch master
 Your branch is up to date with 'origin/master'.

 Changes to be committed:
	modified:   hanlp/common/component.py
	modified:   hanlp/layers/transformers/loader.py

(cherry picked from commit 7bae452)

minor fix

(cherry picked from commit 91e9847)

minor fix

(cherry picked from commit d4104d7)

multi-label support cherry picked to master

(cherry picked from commit 62f7b3d)

implemented multi-label support

(cherry picked from commit a844481)

.gitignore

@@ -284,4 +284,5 @@ fabric.properties
 .idea/caches/build_file_checksums.ser
 .idea
 *.iml
-data
+data
+.vscode/settings.json

hanlp/common/vocab.py

@@ -79,7 +79,10 @@ def update(self, tokens: Iterable[str]) -> None:
             self.add(token)
 
     def get_idx(self, token: str) -> int:
-        idx = self.token_to_idx.get(token, None)
+        if type(token) is list:
+            idx = [self.get_idx(t) for t in token]
+        else:
+            idx = self.token_to_idx.get(token, None)
         if idx is None:
             if self.mutable:
                 idx = len(self.token_to_idx)

hanlp/components/classifiers/transformer_classifier.py

@@ -15,13 +15,14 @@
 from hanlp.transform.table import TableTransform
 from hanlp.utils.log_util import logger
 from hanlp.utils.util import merge_locals_kwargs
+import numpy as np
 
 
 class TransformerTextTransform(TableTransform):
 
     def __init__(self, config: SerializableDict = None, map_x=False, map_y=True, x_columns=None,
-                 y_column=-1, skip_header=True, delimiter='auto', **kwargs) -> None:
-        super().__init__(config, map_x, map_y, x_columns, y_column, skip_header, delimiter, **kwargs)
+                 y_column=-1, skip_header=True, delimiter='auto', multi_label=False, **kwargs) -> None:
+        super().__init__(config, map_x, map_y, x_columns, y_column, multi_label, skip_header, delimiter, **kwargs)
         self.tokenizer: FullTokenizer = None
 
     def inputs_to_samples(self, inputs, gold=False):
@@ -61,26 +62,40 @@ def inputs_to_samples(self, inputs, gold=False):
                 segment_ids += [0] * diff
 
             assert len(token_ids) == max_length, "Error with input length {} vs {}".format(len(token_ids), max_length)
-            assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask),
-                                                                                                max_length)
-            assert len(segment_ids) == max_length, "Error with input length {} vs {}".format(len(segment_ids),
-                                                                                             max_length)
+            assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask), max_length)
+            assert len(segment_ids) == max_length, "Error with input length {} vs {}".format(len(segment_ids), max_length)
+
+
             label = Y
             yield (token_ids, attention_mask, segment_ids), label
 
     def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
         max_length = self.config.max_length
         types = (tf.int32, tf.int32, tf.int32), tf.string
-        shapes = ([max_length], [max_length], [max_length]), []
+        shapes = ([max_length], [max_length], [max_length]), [None,] if self.config.multi_label else []
         values = (0, 0, 0), self.label_vocab.safe_pad_token
         return types, shapes, values
 
     def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
         logger.fatal('map_x should always be set to True')
         exit(1)
 
+    def y_to_idx(self, y) -> tf.Tensor:
+        if self.config.multi_label:
+            #need to change index to binary vector
+            mapped = tf.map_fn(fn=lambda x: tf.cast(self.label_vocab.lookup(x), tf.int32), elems=y, fn_output_signature=tf.TensorSpec(dtype=tf.dtypes.int32, shape=[None,]))
+            one_hots = tf.one_hot(mapped, len(self.label_vocab))
+            idx = tf.reduce_sum(one_hots, -2)
+        else:
+            idx = self.label_vocab.lookup(y)
+        return idx
+
     def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=False, inputs=None, X=None, batch=None) -> Iterable:
-        preds = tf.argmax(Y, axis=-1)
+        # Prediction to be Y > 0:
+        if self.config.multi_label:
+            preds = Y
+        else:
+            preds = tf.argmax(Y, axis=-1)
         for y in preds:
             yield self.label_vocab.idx_to_token[y]
 
@@ -126,7 +141,14 @@ def _y_id_to_str(self, Y_pred) -> str:
         return self.transform.label_vocab.idx_to_token[Y_pred.numpy()]
 
     def build_loss(self, loss, **kwargs):
-        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+        if loss:
+            assert isinstance(loss, tf.keras.losses.loss), 'Must specify loss as an instance in tf.keras.losses'
+            return loss
+        elif self.config.multi_label:
+        #Loss to be BinaryCrossentropy for multi-label:
+            loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)
+        else:
+            loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
         return loss
 
     # noinspection PyMethodOverriding
@@ -158,3 +180,10 @@ def build_vocab(self, trn_data, logger):
         warmup_steps_per_epoch = math.ceil(train_examples * self.config.warmup_steps_ratio / self.config.batch_size)
         self.config.warmup_steps = warmup_steps_per_epoch * self.config.epochs
         return train_examples
+
+    def build_metrics(self, metrics, logger, **kwargs):
+        if self.config.multi_label:
+            metric = tf.keras.metrics.BinaryAccuracy('binary_accuracy')
+        else:
+            metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
+        return [metric]

hanlp/transform/table.py

@@ -3,7 +3,7 @@
 # Date: 2019-11-10 21:00
 from abc import ABC
 from typing import Tuple, Union
-
+import numpy as np
 import tensorflow as tf
 
 from hanlp.common.structure import SerializableDict
@@ -16,9 +16,9 @@
 
 class TableTransform(Transform, ABC):
     def __init__(self, config: SerializableDict = None, map_x=False, map_y=True, x_columns=None,
-                 y_column=-1,
+                 y_column=-1, multi_label=False,
                  skip_header=True, delimiter='auto', **kwargs) -> None:
-        super().__init__(config, map_x, map_y, x_columns=x_columns, y_column=y_column,
+        super().__init__(config, map_x, map_y, x_columns=x_columns, y_column=y_column, multi_label=multi_label,
                          skip_header=skip_header,
                          delimiter=delimiter, **kwargs)
         self.label_vocab = create_label_vocab()
@@ -28,6 +28,9 @@ def file_to_inputs(self, filepath: str, gold=True):
         y_column = self.config.y_column
         num_features = self.config.get('num_features', None)
         for cells in read_cells(filepath, skip_header=self.config.skip_header, delimiter=self.config.delimiter):
+            #multi-label: Dataset in .tsv format: x_columns: at most 2 columns being a sentence pair while in most 
+            # cases just one column being the doc content. y_column being the single label, which shall be modified 
+            # to load a list of labels.
             if x_columns:
                 inputs = tuple(c for i, c in enumerate(cells) if i in x_columns), cells[y_column]
             else:
@@ -37,6 +40,15 @@ def file_to_inputs(self, filepath: str, gold=True):
             if num_features is None:
                 num_features = len(inputs[0])
                 self.config.num_features = num_features
+            # multi-label support
+            if self.config.multi_label:
+                assert type(inputs[1]) is str, 'Y value has to be string'
+                if inputs[1][0] == '[':
+                    # multi-label is in literal form of a list
+                    labels = eval(inputs[1])
+                else:
+                    labels = inputs[1].strip().split(',')
+                inputs = inputs[0], labels
             else:
                 assert num_features == len(inputs[0]), f'Numbers of columns {num_features} ' \
                                                        f'inconsistent with current {len(inputs[0])}'
@@ -56,7 +68,11 @@ def y_to_idx(self, y) -> tf.Tensor:
     def fit(self, trn_path: str, **kwargs):
         samples = 0
         for t in self.file_to_samples(trn_path, gold=True):
-            self.label_vocab.add(t[1])  # the second one regardless of t is pair or triple
+            if self.config.multi_label:
+                for l in t[1]:
+                    self.label_vocab.add(l)
+            else:
+                self.label_vocab.add(t[1])  # the second one regardless of t is pair or triple
             samples += 1
         return samples
 

hanlp/utils/tf_util.py

@@ -11,9 +11,7 @@
 
 
 def size_of_dataset(dataset: tf.data.Dataset) -> int:
-    count = 0
-    for element in dataset.unbatch().batch(1):
-        count += 1
+    count = len(list(dataset.unbatch().as_numpy_iterator()))
     return count