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Merge branch 'develop' of https://github.com/explosion/spaCy into develop
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commit
edc4ffd797
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@ -30,7 +30,8 @@ cdef class Vectors:
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cdef readonly StringStore strings
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cdef public object key2row
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cdef public object keys
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cdef public int i
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cdef public int _i_key
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cdef public int _i_vec
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def __init__(self, strings, width=0, data=None):
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"""Create a new vector store. To keep the vector table empty, pass
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@ -53,7 +54,8 @@ cdef class Vectors:
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self.data = numpy.asarray(data, dtype='f')
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else:
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self.data = numpy.zeros((len(self.strings), width), dtype='f')
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self.i = 0
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self._i_key = 0
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self._i_vec = 0
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self.key2row = {}
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self.keys = numpy.zeros((self.data.shape[0],), dtype='uint64')
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if data is not None:
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@ -105,7 +107,7 @@ cdef class Vectors:
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RETURNS (int): The number of vectors in the data.
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"""
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return self.i
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return self._i_vec
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def __contains__(self, key):
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"""Check whether a key has a vector entry in the table.
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@ -127,20 +129,20 @@ cdef class Vectors:
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"""
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if isinstance(key, basestring_):
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key = self.strings.add(key)
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if key in self.key2row and row is None:
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if row is None and key in self.key2row:
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row = self.key2row[key]
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elif key in self.key2row and row is not None:
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self.key2row[key] = row
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elif row is None:
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row = self.i
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self.i += 1
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if row >= self.keys.shape[0]:
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self.keys.resize((row*2,))
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row = self._i_vec
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self._i_vec += 1
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if row >= self.data.shape[0]:
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self.data.resize((row*2, self.data.shape[1]))
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self.keys[row] = key
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if key not in self.key2row:
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if self._i_key >= self.keys.shape[0]:
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self.keys.resize((self._i_key*2,))
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self.keys[self._i_key] = key
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self._i_key += 1
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self.key2row[key] = row
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self.keys[row] = key
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if vector is not None:
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self.data[row] = vector
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return row
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@ -248,7 +248,7 @@ cdef class Vocab:
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width = self.vectors.data.shape[1]
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self.vectors = Vectors(self.strings, width=width)
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def prune_vectors(self, nr_row, batch_size=1024):
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def prune_vectors(self, nr_row, batch_size=8):
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"""Reduce the current vector table to `nr_row` unique entries. Words
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mapped to the discarded vectors will be remapped to the closest vector
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among those remaining.
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@ -276,22 +276,31 @@ cdef class Vocab:
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xp = get_array_module(self.vectors.data)
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# Work in batches, to avoid memory problems.
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keep = self.vectors.data[:nr_row]
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keep_keys = [key for key, row in self.vectors.key2row.items() if row < nr_row]
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toss = self.vectors.data[nr_row:]
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# Normalize the vectors, so cosine similarity is just dot product.
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# Note we can't modify the ones we're keeping in-place...
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keep = keep / (xp.linalg.norm(keep)+1e-8)
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keep = keep / (xp.linalg.norm(keep, axis=1, keepdims=True)+1e-8)
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keep = xp.ascontiguousarray(keep.T)
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neighbours = xp.zeros((toss.shape[0],), dtype='i')
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scores = xp.zeros((toss.shape[0],), dtype='f')
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for i in range(0, toss.shape[0], batch_size):
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batch = toss[i : i+batch_size]
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batch /= xp.linalg.norm(batch)+1e-8
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neighbours[i:i+batch_size] = xp.dot(batch, keep).argmax(axis=1)
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batch /= xp.linalg.norm(batch, axis=1, keepdims=True)+1e-8
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sims = xp.dot(batch, keep)
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matches = sims.argmax(axis=1)
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neighbours[i:i+batch_size] = matches
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scores[i:i+batch_size] = sims.max(axis=1)
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for lex in self:
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# If we're losing the vector for this word, map it to the nearest
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# vector we're keeping.
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if lex.rank >= nr_row:
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lex.rank = neighbours[lex.rank-nr_row]
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self.vectors.add(lex.orth, row=lex.rank)
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for key in self.vectors.keys:
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row = self.vectors.key2row[key]
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if row >= nr_row:
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self.vectors.key2row[key] = neighbours[row-nr_row]
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# Make copy, to encourage the original table to be garbage collected.
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self.vectors.data = xp.ascontiguousarray(self.vectors.data[:nr_row])
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# TODO: return new mapping
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