* Very scrappy, likely buggy first-cut pickle implementation, to work on Issue #125: allow pickle for Apache Spark. The current implementation sends stuff to temp files, and does almost nothing to ensure all modifiable state is actually preserved. The Language() instance is a deep tree of extension objects, and if pickling during training, some of the C-data state is hard to preserve.

spacy/_ml.pxd

@@ -29,5 +29,6 @@ cdef class Model:
     cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1
     
     cdef object model_loc
+    cdef object _templates
     cdef Extractor _extractor
     cdef LinearModel _model

spacy/_ml.pyx

@@ -3,6 +3,7 @@ from __future__ import unicode_literals
 from __future__ import division
 
 from os import path
+import tempfile
 import os
 import shutil
 import json
@@ -52,6 +53,7 @@ cdef class Model:
     def __init__(self, n_classes, templates, model_loc=None):
         if model_loc is not None and path.isdir(model_loc):
             model_loc = path.join(model_loc, 'model')
+        self._templates = templates
         self.n_classes = n_classes
         self._extractor = Extractor(templates)
         self.n_feats = self._extractor.n_templ
@@ -60,6 +62,18 @@ cdef class Model:
         if self.model_loc and path.exists(self.model_loc):
             self._model.load(self.model_loc, freq_thresh=0)
 
+    def __reduce__(self):
+        model_loc = tempfile.mkstemp()
+        # TODO: This is a potentially buggy implementation. We're not really
+        # given a good guarantee that all internal state is saved correctly here,
+        # since there are learning parameters for e.g. the model averaging in
+        # averaged perceptron, the gradient calculations in AdaGrad, etc
+        # that aren't necessarily saved. So, if we're part way through training
+        # the model, and then we pickle it, we won't recover the state correctly.
+        self._model.dump(model_loc)
+        return (Model, (self.n_classes, self.templates, model_loc),
+                None, None)
+
     def predict(self, Example eg):
         self.set_scores(eg.c.scores, eg.c.atoms)
         eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)

spacy/language.py

@@ -207,6 +207,12 @@ class Language(object):
         self.entity = entity
         self.matcher = matcher
 
+    def __reduce__(self):
+        return (self.__class__,
+                  (None, self.vocab, self.tokenizer, self.tagger, self.parser,
+                   self.entity, self.matcher, None),
+                None, None)
+
     def __call__(self, text, tag=True, parse=True, entity=True):
         """Apply the pipeline to some text.  The text can span multiple sentences,
         and can contain arbtrary whitespace.  Alignment into the original string

spacy/matcher.pyx

@@ -168,13 +168,7 @@ cdef class Matcher:
     cdef Pool mem
     cdef vector[Pattern*] patterns
     cdef readonly Vocab vocab
-
-    def __init__(self, vocab, patterns):
-        self.vocab = vocab
-        self.mem = Pool()
-        self.vocab = vocab
-        for entity_key, (etype, attrs, specs) in sorted(patterns.items()):
-            self.add(entity_key, etype, attrs, specs)
+    cdef object _patterns
 
     @classmethod
     def from_dir(cls, data_dir, Vocab vocab):
@@ -186,10 +180,22 @@ cdef class Matcher:
         else:
             return cls(vocab, {})
 
+    def __init__(self, vocab, patterns):
+        self.vocab = vocab
+        self.mem = Pool()
+        self.vocab = vocab
+        self._patterns = dict(patterns)
+        for entity_key, (etype, attrs, specs) in sorted(patterns.items()):
+            self.add(entity_key, etype, attrs, specs)
+
+    def __reduce__(self):
+        return (self.__class__, (self.vocab, self._patterns), None, None)
+    
     property n_patterns:
         def __get__(self): return self.patterns.size()
 
     def add(self, entity_key, etype, attrs, specs):
+        self._patterns[entity_key] = (etype, dict(attrs), list(specs))
         if isinstance(entity_key, basestring):
             entity_key = self.vocab.strings[entity_key]
         if isinstance(etype, basestring):

spacy/syntax/parser.pyx

@@ -83,7 +83,6 @@ cdef class Parser:
         model = Model(moves.n_moves, templates, model_dir)
         return cls(strings, moves, model)
 
-
     def __call__(self, Doc tokens):
         cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
         self.moves.initialize_state(stcls)
@@ -93,6 +92,9 @@ cdef class Parser:
         self.parse(stcls, eg.c)
         tokens.set_parse(stcls._sent)
 
+    def __reduce__(self):
+        return (Parser, (self.moves.strings, self.moves, self.model), None, None)
+
     cdef void predict(self, StateClass stcls, ExampleC* eg) nogil:
         memset(eg.scores, 0, eg.nr_class * sizeof(weight_t))
         self.moves.set_valid(eg.is_valid, stcls)

spacy/syntax/transition_system.pxd

@@ -37,6 +37,8 @@ cdef class TransitionSystem:
     cdef public int root_label
     cdef public freqs
 
+    cdef object _labels_by_action
+
     cdef int initialize_state(self, StateClass state) except -1
     cdef int finalize_state(self, StateClass state) nogil
 

spacy/syntax/transition_system.pyx

@@ -15,7 +15,8 @@ class OracleError(Exception):
 
 
 cdef class TransitionSystem:
-    def __init__(self, StringStore string_table, dict labels_by_action):
+    def __init__(self, StringStore string_table, dict labels_by_action, _freqs=None):
+        self._labels_by_action = labels_by_action
         self.mem = Pool()
         self.n_moves = sum(len(labels) for labels in labels_by_action.values())
         self._is_valid = <bint*>self.mem.alloc(self.n_moves, sizeof(bint))
@@ -30,7 +31,7 @@ cdef class TransitionSystem:
                 i += 1
         self.c = moves
         self.root_label = self.strings['ROOT']
-        self.freqs = {}
+        self.freqs = {} if _freqs is None else _freqs
         for attr in (TAG, HEAD, DEP, ENT_TYPE, ENT_IOB):
             self.freqs[attr] = defaultdict(int)
             self.freqs[attr][0] = 1
@@ -39,6 +40,11 @@ cdef class TransitionSystem:
             self.freqs[HEAD][i] = 1
             self.freqs[HEAD][-i] = 1
 
+    def __reduce__(self):
+        return (self.__class__,
+                (self.strings, self._labels_by_action, self.freqs),
+                None, None)
+
     cdef int initialize_state(self, StateClass state) except -1:
         pass
 

spacy/tagger.pyx

@@ -148,6 +148,9 @@ cdef class Tagger:
         tokens.is_tagged = True
         tokens._py_tokens = [None] * tokens.length
 
+    def __reduce__(self):
+        return (self.__class__, (self.vocab, self.model), None, None)
+
     def tag_from_strings(self, Doc tokens, object tag_strs):
         cdef int i
         for i in range(tokens.length):

spacy/vocab.pyx

@@ -99,16 +99,18 @@ cdef class Vocab:
         return self.length
 
     def __reduce__(self):
+        # TODO: Dump vectors
         tmp_dir = tempfile.mkdtemp()
         lex_loc = path.join(tmp_dir, 'lexemes.bin')
         str_loc = path.join(tmp_dir, 'strings.txt')
-        map_loc = path.join(tmp_dir, 'tag_map.json')
+        vec_loc = path.join(self.data_dir, 'vec.bin') if self.data_dir is not None else None
 
         self.dump(lex_loc)
         self.strings.dump(str_loc)
-        json.dump(self.morphology.tag_map, open(map_loc, 'w'))
-
-        return (unpickle_vocab, (tmp_dir,), None, None)
+        
+        state = (str_loc, lex_loc, vec_loc, self.morphology, self.get_lex_attr,
+                 self.serializer_freqs, self.data_dir)
+        return (unpickle_vocab, state, None, None)
 
     cdef const LexemeC* get(self, Pool mem, unicode string) except NULL:
         '''Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
@@ -353,11 +355,21 @@ cdef class Vocab:
         return vec_len
 
 
-def unpickle_vocab(data_dir):
-    # TODO: This needs fixing --- the trouble is, we can't pickle staticmethods,
-    # so we need to fiddle with the design of Language a little bit.
-    from .language import Language
-    return Vocab.from_dir(data_dir, Language.default_lex_attrs())
+def unpickle_vocab(strings_loc, lex_loc, vec_loc, morphology, get_lex_attr,
+                   serializer_freqs, data_dir):
+    cdef Vocab vocab = Vocab()
+
+    vocab.get_lex_attr = get_lex_attr
+    vocab.morphology = morphology
+    vocab.strings = morphology.strings
+    vocab.data_dir = data_dir
+    vocab.serializer_freqs = serializer_freqs
+
+    vocab.load_lexemes(strings_loc, lex_loc)
+    if vec_loc is not None:
+        vocab.load_vectors_from_bin_loc(vec_loc)
+    return vocab
+ 
 
 copy_reg.constructor(unpickle_vocab)
 

tests/parser/test_pickle.py

@@ -0,0 +1,16 @@
+import pytest
+
+import pickle
+import cloudpickle
+import StringIO
+
+
+@pytest.mark.models
+def test_pickle(EN):
+    file_ = StringIO.StringIO()
+    cloudpickle.dump(EN.parser, file_)
+
+    file_.seek(0)
+
+    loaded = pickle.load(file_)
+

tests/test_pickle.py

@@ -0,0 +1,15 @@
+import pytest
+import StringIO
+import cloudpickle
+import pickle
+
+
+@pytest.mark.models
+def test_pickle_english(EN):
+    file_ = StringIO.StringIO()
+    cloudpickle.dump(EN, file_)
+
+    file_.seek(0)
+
+    loaded = pickle.load(file_)
+

tests/vocab/test_vocab.py

@@ -1,13 +1,13 @@
 from __future__ import unicode_literals
 import pytest
 import StringIO
+import cloudpickle
 import pickle
 
 from spacy.attrs import LEMMA, ORTH, PROB, IS_ALPHA
 from spacy.parts_of_speech import NOUN, VERB
 
 
-
 def test_neq(en_vocab):
     addr = en_vocab['Hello']
     assert en_vocab['bye'].orth != addr.orth
@@ -44,7 +44,7 @@ def test_symbols(en_vocab):
 
 def test_pickle_vocab(en_vocab):
     file_ = StringIO.StringIO()
-    pickle.dump(en_vocab, file_)
+    cloudpickle.dump(en_vocab, file_)
 
     file_.seek(0)