Add support for pre-trained vectors in text classifier

spacy/_ml.py

@@ -469,30 +469,80 @@ def build_tagger_model(nr_class, token_vector_width, **cfg):
     return model
 
 
+@layerize
+def SpacyVectors(docs, drop=0.):
+    xp = get_array_module(docs[0].vocab.vectors.data)
+    width = docs[0].vocab.vectors.data.shape[1]
+    batch = []
+    for doc in docs:
+        indices = numpy.zeros((len(doc),), dtype='i')
+        for i, word in enumerate(doc):
+            if word.orth in doc.vocab.vectors.key2row:
+                indices[i] = doc.vocab.vectors.key2row[word.orth]
+            else:
+                indices[i] = 0
+        vectors = doc.vocab.vectors.data[indices]
+        batch.append(vectors)
+    return batch, None
+
+
+def foreach(layer, drop_factor=1.0):
+    '''Map a layer across elements in a list'''
+    def foreach_fwd(Xs, drop=0.):
+        drop *= drop_factor
+        ys = []
+        backprops = []
+        for X in Xs:
+            y, bp_y = layer.begin_update(X, drop=drop)
+            ys.append(y)
+            backprops.append(bp_y)
+        def foreach_bwd(d_ys, sgd=None):
+            d_Xs = []
+            for d_y, bp_y in zip(d_ys, backprops):
+                if bp_y is not None and bp_y is not None:
+                    d_Xs.append(d_y, sgd=sgd)
+                else:
+                    d_Xs.append(None)
+            return d_Xs
+        return ys, foreach_bwd
+    model = wrap(foreach_fwd, layer)
+    return model
+
+
 def build_text_classifier(nr_class, width=64, **cfg):
     nr_vector = cfg.get('nr_vector', 200)
-    with Model.define_operators({'>>': chain, '+': add, '|': concatenate, '**': clone}):
+    with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
-        embed_lower = HashEmbed(width, nr_vector, column=1)
+                                 '**': clone}):
-        embed_prefix = HashEmbed(width//2, nr_vector, column=2)
+        lower = HashEmbed(width, nr_vector, column=1)
-        embed_suffix = HashEmbed(width//2, nr_vector, column=3)
+        prefix = HashEmbed(width//2, nr_vector, column=2)
-        embed_shape = HashEmbed(width//2, nr_vector, column=4)
+        suffix = HashEmbed(width//2, nr_vector, column=3)
+        shape = HashEmbed(width//2, nr_vector, column=4)
+
+        trained_vectors = (
+            FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID])
+            >> with_flatten(
+                (lower | prefix | suffix | shape)
+            )
+        )
+
+        convolution = (
+            ExtractWindow(nW=1)
+            >> LN(Maxout(width, width*3))
+        )
 
         cnn_model = (
-            FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE])
+            # TODO Make concatenate support lists
-            >> _flatten_add_lengths
+            concatenate_lists(trained_vectors, SpacyVectors) 
-            >> with_getitem(0,
+            >> with_flatten(
-                uniqued(
+                LN(Maxout(width, 64+32+32+32+300))
-                  (embed_lower | embed_prefix | embed_suffix | embed_shape)
+                >> convolution ** 4, pad=4)
-                  >> Maxout(width, width+(width//2)*3))
+            >> flatten_add_lengths
-                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
+            >> ParametricAttention(width)
-                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
-                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
-            )
-            >> ParametricAttention(width,)
             >> Pooling(sum_pool)
             >> ReLu(width, width)
             >> zero_init(Affine(nr_class, width, drop_factor=0.0))
         )
+
         linear_model = (
             _preprocess_doc
             >> LinearModel(nr_class, drop_factor=0.)
@@ -507,3 +557,35 @@ def build_text_classifier(nr_class, width=64, **cfg):
     model.lsuv = False
     return model
 
+@layerize
+def flatten(seqs, drop=0.):
+    ops = Model.ops
+    lengths = ops.asarray([len(seq) for seq in seqs], dtype='i')
+    def finish_update(d_X, sgd=None):
+        return ops.unflatten(d_X, lengths, pad=0)
+    X = ops.flatten(seqs, pad=0)
+    return X, finish_update
+
+
+def concatenate_lists(*layers, **kwargs): # pragma: no cover
+    '''Compose two or more models `f`, `g`, etc, such that their outputs are
+    concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
+    '''
+    if not layers:
+        return noop()
+    drop_factor = kwargs.get('drop_factor', 1.0)
+    ops = layers[0].ops
+    layers = [chain(layer, flatten) for layer in layers]
+    concat = concatenate(*layers)
+    def concatenate_lists_fwd(Xs, drop=0.):
+        drop *= drop_factor
+        lengths = ops.asarray([len(X) for X in Xs], dtype='i')
+        flat_y, bp_flat_y = concat.begin_update(Xs, drop=drop)
+        ys = ops.unflatten(flat_y, lengths)
+        def concatenate_lists_bwd(d_ys, sgd=None):
+            return bp_flat_y(ops.flatten(d_ys), sgd=sgd)
+        return ys, concatenate_lists_bwd
+    model = wrap(concatenate_lists_fwd, concat)
+    return model
+
+