From 4112a991ec012b175a1a97add51ce04d09351886 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 30 Oct 2017 19:44:40 +0100
Subject: [PATCH] Fix vector pruning

---
 spacy/vectors.pyx | 26 ++++++++++++++------------
 spacy/vocab.pyx   | 17 +++++++++++++----
 2 files changed, 27 insertions(+), 16 deletions(-)

diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 368b73866..552a6bcf3 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -30,7 +30,8 @@ cdef class Vectors:
     cdef readonly StringStore strings
     cdef public object key2row
     cdef public object keys
-    cdef public int i
+    cdef public int _i_key
+    cdef public int _i_vec
 
     def __init__(self, strings, width=0, data=None):
         """Create a new vector store. To keep the vector table empty, pass
@@ -53,7 +54,8 @@ cdef class Vectors:
             self.data = numpy.asarray(data, dtype='f')
         else:
             self.data = numpy.zeros((len(self.strings), width), dtype='f')
-        self.i = 0
+        self._i_key = 0
+        self._i_vec = 0
         self.key2row = {}
         self.keys = numpy.zeros((self.data.shape[0],), dtype='uint64')
         if data is not None:
@@ -105,7 +107,7 @@ cdef class Vectors:
 
         RETURNS (int): The number of vectors in the data.
         """
-        return self.i
+        return self._i_vec
 
     def __contains__(self, key):
         """Check whether a key has a vector entry in the table.
@@ -127,20 +129,20 @@ cdef class Vectors:
         """
         if isinstance(key, basestring_):
             key = self.strings.add(key)
-        if key in self.key2row and row is None:
+        if row is None and key in self.key2row:
             row = self.key2row[key]
-        elif key in self.key2row and row is not None:
-            self.key2row[key] = row
         elif row is None:
-            row = self.i
-            self.i += 1
-        if row >= self.keys.shape[0]:
-            self.keys.resize((row*2,))
+            row = self._i_vec
+            self._i_vec += 1
+        if row >= self.data.shape[0]:
             self.data.resize((row*2, self.data.shape[1]))
-            self.keys[row] = key
+        if key not in self.key2row:
+            if self._i_key >= self.keys.shape[0]:
+                self.keys.resize((self._i_key*2,))
+                self.keys[self._i_key] = key
+                self._i_key += 1
 
         self.key2row[key] = row
-        self.keys[row] = key
         if vector is not None:
             self.data[row] = vector
         return row
diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index ff6c5b844..ecf1ad9d9 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -248,7 +248,7 @@ cdef class Vocab:
             width = self.vectors.data.shape[1]
         self.vectors = Vectors(self.strings, width=width)
 
-    def prune_vectors(self, nr_row, batch_size=1024):
+    def prune_vectors(self, nr_row, batch_size=8):
         """Reduce the current vector table to `nr_row` unique entries. Words
         mapped to the discarded vectors will be remapped to the closest vector
         among those remaining.
@@ -267,22 +267,31 @@ cdef class Vocab:
         xp = get_array_module(self.vectors.data)
         # Work in batches, to avoid memory problems.
         keep = self.vectors.data[:nr_row]
+        keep_keys = [key for key, row in self.vectors.key2row.items() if row < nr_row]
         toss = self.vectors.data[nr_row:]
         # Normalize the vectors, so cosine similarity is just dot product.
         # Note we can't modify the ones we're keeping in-place...
-        keep = keep / (xp.linalg.norm(keep)+1e-8)
+        keep = keep / (xp.linalg.norm(keep, axis=1, keepdims=True)+1e-8)
         keep = xp.ascontiguousarray(keep.T)
         neighbours = xp.zeros((toss.shape[0],), dtype='i')
+        scores = xp.zeros((toss.shape[0],), dtype='f')
         for i in range(0, toss.shape[0], batch_size):
             batch = toss[i : i+batch_size]
-            batch /= xp.linalg.norm(batch)+1e-8
-            neighbours[i:i+batch_size] = xp.dot(batch, keep).argmax(axis=1)
+            batch /= xp.linalg.norm(batch, axis=1, keepdims=True)+1e-8
+            sims = xp.dot(batch, keep)
+            matches = sims.argmax(axis=1)
+            neighbours[i:i+batch_size] = matches
+            scores[i:i+batch_size] = sims.max(axis=1)
         for lex in self:
             # If we're losing the vector for this word, map it to the nearest
             # vector we're keeping.
             if lex.rank >= nr_row:
                 lex.rank = neighbours[lex.rank-nr_row]
                 self.vectors.add(lex.orth, row=lex.rank)
+        for key in self.vectors.keys:
+            row = self.vectors.key2row[key]
+            if row >= nr_row:
+                self.vectors.key2row[key] = neighbours[row-nr_row]
         # Make copy, to encourage the original table to be garbage collected.
         self.vectors.data = xp.ascontiguousarray(self.vectors.data[:nr_row])