Merge pull request #1391 from explosion/feature/multilabel-textcat

💫 Fix multi-label support for text classification

examples/training/train_textcat.py

@@ -21,7 +21,6 @@ import thinc.neural._classes.layernorm
 thinc.neural._classes.layernorm.set_compat_six_eight(False)
 
 
-
 def train_textcat(tokenizer, textcat,
                   train_texts, train_cats, dev_texts, dev_cats,
                   n_iter=20):
@@ -57,13 +56,15 @@ def evaluate(tokenizer, textcat, texts, cats):
     for i, doc in enumerate(textcat.pipe(docs)):
         gold = cats[i]
         for label, score in doc.cats.items():
-            if score >= 0.5 and label in gold:
+            if label not in gold:
+                continue
+            if score >= 0.5 and gold[label] >= 0.5:
                 tp += 1.
-            elif score >= 0.5 and label not in gold:
+            elif score >= 0.5 and gold[label] < 0.5:
                 fp += 1.
-            elif score < 0.5 and label not in gold:
+            elif score < 0.5 and gold[label] < 0.5:
                 tn += 1
-            if score < 0.5 and label in gold:
+            elif score < 0.5 and gold[label] >= 0.5:
                 fn += 1
     precis = tp / (tp + fp)
     recall = tp / (tp + fn)
@@ -80,7 +81,7 @@ def load_data(limit=0):
     train_data = train_data[-limit:]
 
     texts, labels = zip(*train_data)
-    cats = [(['POSITIVE'] if y else []) for y in labels]
+    cats = [{'POSITIVE': bool(y)} for y in labels]
 
     split = int(len(train_data) * 0.8)
 
@@ -97,7 +98,7 @@ def main(model_loc=None):
     textcat = TextCategorizer(tokenizer.vocab, labels=['POSITIVE'])
 
     print("Load IMDB data")
-    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=1000)
+    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=2000)
 
     print("Itn.\tLoss\tP\tR\tF")
     progress = '{i:d} {loss:.3f} {textcat_p:.3f} {textcat_r:.3f} {textcat_f:.3f}'

spacy/gold.pyx

@@ -387,7 +387,7 @@ cdef class GoldParse:
 
     def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
                  deps=None, entities=None, make_projective=False,
-                 cats=tuple()):
+                 cats=None):
         """Create a GoldParse.
 
         doc (Doc): The document the annotations refer to.
@@ -398,12 +398,15 @@ cdef class GoldParse:
         entities (iterable): A sequence of named entity annotations, either as
             BILUO tag strings, or as `(start_char, end_char, label)` tuples,
             representing the entity positions.
-        cats (iterable): A sequence of labels for text classification. Each
-            label may be a string or an int, or a `(start_char, end_char, label)`
+        cats (dict): Labels for text classification. Each key in the dictionary
+            may be a string or an int, or a `(start_char, end_char, label)`
             tuple, indicating that the label is applied to only part of the
             document (usually a sentence). Unlike entity annotations, label
             annotations can overlap, i.e. a single word can be covered by
-            multiple labelled spans.
+            multiple labelled spans. The TextCategorizer component expects
+            true examples of a label to have the value 1.0, and negative examples
+            of a label to have the value 0.0. Labels not in the dictionary are
+            treated as missing -- the gradient for those labels will be zero.
         RETURNS (GoldParse): The newly constructed object.
         """
         if words is None:
@@ -434,7 +437,7 @@ cdef class GoldParse:
         self.c.sent_start = <int*>self.mem.alloc(len(doc), sizeof(int))
         self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
 
-        self.cats = list(cats)
+        self.cats = {} if cats is None else dict(cats)
         self.words = [None] * len(doc)
         self.tags = [None] * len(doc)
         self.heads = [None] * len(doc)

spacy/pipeline.pyx

@@ -551,7 +551,6 @@ class NeuralLabeller(NeuralTagger):
                     label = self.make_label(i, words, tags, heads, deps, ents)
                     if label is not None and label not in self.labels:
                         self.labels[label] = len(self.labels)
-        print(len(self.labels))
         if self.model is True:
             token_vector_width = util.env_opt('token_vector_width')
             self.model = chain(
@@ -720,11 +719,17 @@ class TextCategorizer(BaseThincComponent):
 
     def get_loss(self, docs, golds, scores):
         truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
+        not_missing = numpy.ones((len(golds), len(self.labels)), dtype='f')
         for i, gold in enumerate(golds):
             for j, label in enumerate(self.labels):
-                truths[i, j] = label in gold.cats
+                if label in gold.cats:
+                    truths[i, j] = gold.cats[label]
+                else:
+                    not_missing[i, j] = 0.
         truths = self.model.ops.asarray(truths)
+        not_missing = self.model.ops.asarray(not_missing)
         d_scores = (scores-truths) / scores.shape[0]
+        d_scores *= not_missing
         mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
         return mean_square_error, d_scores