# cython: infer_types=True # cython: profile=True from __future__ import unicode_literals from libcpp.vector cimport vector from libc.stdint cimport int32_t, uint64_t, uint16_t from preshed.maps cimport PreshMap from cymem.cymem cimport Pool from murmurhash.mrmr cimport hash64 from .typedefs cimport attr_t, hash_t from .structs cimport TokenC from .lexeme cimport attr_id_t from .vocab cimport Vocab from .tokens.doc cimport Doc from .tokens.doc cimport get_token_attr from .attrs cimport ID, attr_id_t, NULL_ATTR, ORTH from .errors import Errors, TempErrors, Warnings, deprecation_warning from .attrs import IDS from .attrs import FLAG61 as U_ENT from .attrs import FLAG60 as B2_ENT from .attrs import FLAG59 as B3_ENT from .attrs import FLAG58 as B4_ENT from .attrs import FLAG43 as L2_ENT from .attrs import FLAG42 as L3_ENT from .attrs import FLAG41 as L4_ENT from .attrs import FLAG43 as I2_ENT from .attrs import FLAG42 as I3_ENT from .attrs import FLAG41 as I4_ENT DELIMITER = '||' INDEX_HEAD = 1 INDEX_RELOP = 0 cdef enum action_t: REJECT = 0000 MATCH = 1000 ADVANCE = 0100 RETRY = 0010 RETRY_EXTEND = 0011 MATCH_EXTEND = 1001 MATCH_REJECT = 2000 cdef enum quantifier_t: ZERO ZERO_ONE ZERO_PLUS ONE ONE_PLUS cdef struct AttrValueC: attr_id_t attr attr_t value cdef struct TokenPatternC: AttrValueC* attrs int32_t nr_attr quantifier_t quantifier hash_t key cdef struct PatternStateC: TokenPatternC* pattern int32_t start int32_t length cdef struct MatchC: attr_t pattern_id int32_t start int32_t length cdef find_matches(TokenPatternC** patterns, int n, Doc doc): cdef vector[PatternStateC] states cdef vector[MatchC] matches cdef PatternStateC state cdef Pool mem = Pool() # TODO: Prefill this with the extra attribute values. extra_attrs = mem.alloc(len(doc), sizeof(attr_t*)) # Main loop cdef int i, j for i in range(doc.length): for j in range(n): states.push_back(PatternStateC(patterns[j], i, 0)) transition_states(states, matches, &doc.c[i], extra_attrs[i]) # Handle matches that end in 0-width patterns finish_states(matches, states) return [(matches[i].pattern_id, matches[i].start, matches[i].start+matches[i].length) for i in range(matches.size())] cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil: # The code was originally designed to always have pattern[1].attrs.value # be the ent_id when we get to the end of a pattern. However, Issue #2671 # showed this wasn't the case when we had a reject-and-continue before a # match. I still don't really understand what's going on here, but this # workaround does resolve the issue. while pattern.attrs.attr != ID and pattern.nr_attr > 0: pattern += 1 return pattern.attrs.value cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& matches, const TokenC* token, const attr_t* extra_attrs) except *: cdef int q = 0 cdef vector[PatternStateC] new_states for i in range(states.size()): action = get_action(states[i], token, extra_attrs) if action == REJECT: continue state = states[i] states[q] = state while action in (RETRY, RETRY_EXTEND): if action == RETRY_EXTEND: new_states.push_back( PatternStateC(pattern=state.pattern, start=state.start, length=state.length+1)) states[q].pattern += 1 action = get_action(states[q], token, extra_attrs) if action == REJECT: pass elif action == ADVANCE: states[q].pattern += 1 states[q].length += 1 q += 1 else: ent_id = get_ent_id(&state.pattern[1]) if action == MATCH: matches.push_back( MatchC(pattern_id=ent_id, start=state.start, length=state.length+1)) elif action == MATCH_REJECT: matches.push_back( MatchC(pattern_id=ent_id, start=state.start, length=state.length)) elif action == MATCH_EXTEND: matches.push_back( MatchC(pattern_id=ent_id, start=state.start, length=state.length)) states[q].length += 1 q += 1 states.resize(q) for i in range(new_states.size()): states.push_back(new_states[i]) cdef void finish_states(vector[MatchC]& matches, vector[PatternStateC]& states) except *: '''Handle states that end in zero-width patterns.''' cdef PatternStateC state for i in range(states.size()): state = states[i] while get_quantifier(state) in (ZERO_PLUS, ZERO_ONE): is_final = get_is_final(state) if is_final: ent_id = get_ent_id(state.pattern) matches.push_back( MatchC(pattern_id=ent_id, start=state.start, length=state.length)) break else: state.pattern += 1 cdef action_t get_action(PatternStateC state, const TokenC* token, const attr_t* extra_attrs) nogil: '''We need to consider: a) Does the token match the specification? [Yes, No] b) What's the quantifier? [1, 0+, ?] c) Is this the last specification? [final, non-final] We can transition in the following ways: a) Do we emit a match? b) Do we add a state with (next state, next token)? c) Do we add a state with (next state, same token)? d) Do we add a state with (same state, next token)? We'll code the actions as boolean strings, so 0000 means no to all 4, 1000 means match but no states added, etc. 1: Yes, final: 1000 Yes, non-final: 0100 No, final: 0000 No, non-final 0000 0+: Yes, final: 1001 Yes, non-final: 0011 No, final: 1000 (note: Don't include last token!) No, non-final: 0010 ?: Yes, final: 1000 Yes, non-final: 0100 No, final: 1000 (note: Don't include last token!) No, non-final: 0010 Possible combinations: 1000, 0100, 0000, 1001, 0011, 0010, We'll name the bits "match", "advance", "retry", "extend" REJECT = 0000 MATCH = 1000 ADVANCE = 0100 RETRY = 0010 MATCH_EXTEND = 1001 RETRY_EXTEND = 0011 MATCH_REJECT = 2000 # Match, but don't include last token Problem: If a quantifier is matching, we're adding a lot of open partials ''' cdef char is_match is_match = get_is_match(state, token, extra_attrs) quantifier = get_quantifier(state) is_final = get_is_final(state) if quantifier == ZERO: is_match = not is_match quantifier = ONE if quantifier == ONE: if is_match and is_final: # Yes, final: 1000 return MATCH elif is_match and not is_final: # Yes, non-final: 0100 return ADVANCE elif not is_match and is_final: # No, final: 0000 return REJECT else: return REJECT elif quantifier == ZERO_PLUS: if is_match and is_final: # Yes, final: 1001 return MATCH_EXTEND elif is_match and not is_final: # Yes, non-final: 0011 return RETRY_EXTEND elif not is_match and is_final: # No, final 2000 (note: Don't include last token!) return MATCH_REJECT else: # No, non-final 0010 return RETRY elif quantifier == ZERO_ONE: if is_match and is_final: # Yes, final: 1000 return MATCH elif is_match and not is_final: # Yes, non-final: 0100 return ADVANCE elif not is_match and is_final: # No, final 2000 (note: Don't include last token!) return MATCH_REJECT else: # No, non-final 0010 return RETRY cdef char get_is_match(PatternStateC state, const TokenC* token, const attr_t* extra_attrs) nogil: spec = state.pattern for attr in spec.attrs[:spec.nr_attr]: if get_token_attr(token, attr.attr) != attr.value: return 0 else: return 1 cdef char get_is_final(PatternStateC state) nogil: if state.pattern[1].attrs[0].attr == ID and state.pattern[1].nr_attr == 0: return 1 else: return 0 cdef char get_quantifier(PatternStateC state) nogil: return state.pattern.quantifier DEF PADDING = 5 cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id, object token_specs) except NULL: pattern = mem.alloc(len(token_specs) + 1, sizeof(TokenPatternC)) cdef int i for i, (quantifier, spec) in enumerate(token_specs): pattern[i].quantifier = quantifier pattern[i].attrs = mem.alloc(len(spec), sizeof(AttrValueC)) pattern[i].nr_attr = len(spec) for j, (attr, value) in enumerate(spec): pattern[i].attrs[j].attr = attr pattern[i].attrs[j].value = value pattern[i].key = hash64(pattern[i].attrs, pattern[i].nr_attr * sizeof(AttrValueC), 0) i = len(token_specs) pattern[i].attrs = mem.alloc(2, sizeof(AttrValueC)) pattern[i].attrs[0].attr = ID pattern[i].attrs[0].value = entity_id pattern[i].nr_attr = 0 return pattern cdef attr_t get_pattern_key(const TokenPatternC* pattern) nogil: while pattern.nr_attr != 0: pattern += 1 id_attr = pattern[0].attrs[0] if id_attr.attr != ID: with gil: raise ValueError(Errors.E074.format(attr=ID, bad_attr=id_attr.attr)) return id_attr.value def _convert_strings(token_specs, string_store): # Support 'syntactic sugar' operator '+', as combination of ONE, ZERO_PLUS operators = {'*': (ZERO_PLUS,), '+': (ONE, ZERO_PLUS), '?': (ZERO_ONE,), '1': (ONE,), '!': (ZERO,)} tokens = [] op = ONE for spec in token_specs: if not spec: # Signifier for 'any token' tokens.append((ONE, [(NULL_ATTR, 0)])) continue token = [] ops = (ONE,) for attr, value in spec.items(): if isinstance(attr, basestring) and attr.upper() == 'OP': if value in operators: ops = operators[value] else: keys = ', '.join(operators.keys()) raise KeyError(Errors.E011.format(op=value, opts=keys)) if isinstance(attr, basestring): attr = IDS.get(attr.upper()) if isinstance(value, basestring): value = string_store.add(value) if isinstance(value, bool): value = int(value) if attr is not None: token.append((attr, value)) for op in ops: tokens.append((op, token)) return tokens cdef class Matcher: """Match sequences of tokens, based on pattern rules.""" cdef Pool mem cdef vector[TokenPatternC*] patterns cdef readonly Vocab vocab cdef public object _patterns cdef public object _entities cdef public object _callbacks def __init__(self, vocab): """Create the Matcher. vocab (Vocab): The vocabulary object, which must be shared with the documents the matcher will operate on. RETURNS (Matcher): The newly constructed object. """ self._patterns = {} self._entities = {} self._callbacks = {} self.vocab = vocab self.mem = Pool() def __reduce__(self): data = (self.vocab, self._patterns, self._callbacks) return (unpickle_matcher, data, None, None) def __len__(self): """Get the number of rules added to the matcher. Note that this only returns the number of rules (identical with the number of IDs), not the number of individual patterns. RETURNS (int): The number of rules. """ return len(self._patterns) def __contains__(self, key): """Check whether the matcher contains rules for a match ID. key (unicode): The match ID. RETURNS (bool): Whether the matcher contains rules for this match ID. """ return self._normalize_key(key) in self._patterns def add(self, key, on_match, *patterns): """Add a match-rule to the matcher. A match-rule consists of: an ID key, an on_match callback, and one or more patterns. If the key exists, the patterns are appended to the previous ones, and the previous on_match callback is replaced. The `on_match` callback will receive the arguments `(matcher, doc, i, matches)`. You can also set `on_match` to `None` to not perform any actions. A pattern consists of one or more `token_specs`, where a `token_spec` is a dictionary mapping attribute IDs to values, and optionally a quantifier operator under the key "op". The available quantifiers are: '!': Negate the pattern, by requiring it to match exactly 0 times. '?': Make the pattern optional, by allowing it to match 0 or 1 times. '+': Require the pattern to match 1 or more times. '*': Allow the pattern to zero or more times. The + and * operators are usually interpretted "greedily", i.e. longer matches are returned where possible. However, if you specify two '+' and '*' patterns in a row and their matches overlap, the first operator will behave non-greedily. This quirk in the semantics makes the matcher more efficient, by avoiding the need for back-tracking. key (unicode): The match ID. on_match (callable): Callback executed on match. *patterns (list): List of token descriptions. """ for pattern in patterns: if len(pattern) == 0: raise ValueError(Errors.E012.format(key=key)) key = self._normalize_key(key) for pattern in patterns: specs = _convert_strings(pattern, self.vocab.strings) self.patterns.push_back(init_pattern(self.mem, key, specs)) self._patterns.setdefault(key, []) self._callbacks[key] = on_match self._patterns[key].extend(patterns) def remove(self, key): """Remove a rule from the matcher. A KeyError is raised if the key does not exist. key (unicode): The ID of the match rule. """ key = self._normalize_key(key) self._patterns.pop(key) self._callbacks.pop(key) cdef int i = 0 while i < self.patterns.size(): pattern_key = get_pattern_key(self.patterns.at(i)) if pattern_key == key: self.patterns.erase(self.patterns.begin()+i) else: i += 1 def has_key(self, key): """Check whether the matcher has a rule with a given key. key (string or int): The key to check. RETURNS (bool): Whether the matcher has the rule. """ key = self._normalize_key(key) return key in self._patterns def get(self, key, default=None): """Retrieve the pattern stored for a key. key (unicode or int): The key to retrieve. RETURNS (tuple): The rule, as an (on_match, patterns) tuple. """ key = self._normalize_key(key) if key not in self._patterns: return default return (self._callbacks[key], self._patterns[key]) def pipe(self, docs, batch_size=1000, n_threads=2): """Match a stream of documents, yielding them in turn. docs (iterable): A stream of documents. batch_size (int): Number of documents to accumulate into a working set. n_threads (int): The number of threads with which to work on the buffer in parallel, if the implementation supports multi-threading. YIELDS (Doc): Documents, in order. """ for doc in docs: self(doc) yield doc def __call__(self, Doc doc): """Find all token sequences matching the supplied pattern. doc (Doc): The document to match over. RETURNS (list): A list of `(key, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `label_id` and `key` are both integers. """ matches = find_matches(&self.patterns[0], self.patterns.size(), doc) for i, (key, start, end) in enumerate(matches): on_match = self._callbacks.get(key, None) if on_match is not None: on_match(self, doc, i, matches) return matches def _normalize_key(self, key): if isinstance(key, basestring): return self.vocab.strings.add(key) else: return key def unpickle_matcher(vocab, patterns, callbacks): matcher = Matcher(vocab) for key, specs in patterns.items(): callback = callbacks.get(key, None) matcher.add(key, callback, *specs) return matcher def _get_longest_matches(matches): '''Filter out matches that have a longer equivalent.''' longest_matches = {} for pattern_id, start, end in matches: key = (pattern_id, start) length = end-start if key not in longest_matches or length > longest_matches[key]: longest_matches[key] = length return [(pattern_id, start, start+length) for (pattern_id, start), length in longest_matches.items()] def get_bilou(length): if length == 0: raise ValueError("Length must be >= 1") elif length == 1: return [U_ENT] elif length == 2: return [B2_ENT, L2_ENT] elif length == 3: return [B3_ENT, I3_ENT, L3_ENT] else: return [B4_ENT, I4_ENT] + [I4_ENT] * (length-3) + [L4_ENT] cdef class PhraseMatcher: cdef Pool mem cdef Vocab vocab cdef Matcher matcher cdef PreshMap phrase_ids cdef int max_length cdef attr_id_t attr cdef public object _callbacks cdef public object _patterns def __init__(self, Vocab vocab, max_length=0, attr='ORTH'): if max_length != 0: deprecation_warning(Warnings.W010) self.mem = Pool() self.max_length = max_length self.vocab = vocab self.matcher = Matcher(self.vocab) if isinstance(attr, long): self.attr = attr else: self.attr = self.vocab.strings[attr] self.phrase_ids = PreshMap() abstract_patterns = [ [{U_ENT: True}], [{B2_ENT: True}, {L2_ENT: True}], [{B3_ENT: True}, {I3_ENT: True}, {L3_ENT: True}], [{B4_ENT: True}, {I4_ENT: True}, {I4_ENT: True, "OP": "+"}, {L4_ENT: True}], ] self.matcher.add('Candidate', None, *abstract_patterns) self._callbacks = {} def __len__(self): """Get the number of rules added to the matcher. Note that this only returns the number of rules (identical with the number of IDs), not the number of individual patterns. RETURNS (int): The number of rules. """ return len(self.phrase_ids) def __contains__(self, key): """Check whether the matcher contains rules for a match ID. key (unicode): The match ID. RETURNS (bool): Whether the matcher contains rules for this match ID. """ cdef hash_t ent_id = self.matcher._normalize_key(key) return ent_id in self._callbacks def __reduce__(self): return (self.__class__, (self.vocab,), None, None) def add(self, key, on_match, *docs): """Add a match-rule to the phrase-matcher. A match-rule consists of: an ID key, an on_match callback, and one or more patterns. key (unicode): The match ID. on_match (callable): Callback executed on match. *docs (Doc): `Doc` objects representing match patterns. """ cdef Doc doc cdef hash_t ent_id = self.matcher._normalize_key(key) self._callbacks[ent_id] = on_match cdef int length cdef int i cdef hash_t phrase_hash cdef Pool mem = Pool() for doc in docs: length = doc.length if length == 0: continue tags = get_bilou(length) phrase_key = mem.alloc(length, sizeof(attr_t)) for i, tag in enumerate(tags): attr_value = self.get_lex_value(doc, i) lexeme = self.vocab[attr_value] lexeme.set_flag(tag, True) phrase_key[i] = lexeme.orth phrase_hash = hash64(phrase_key, length * sizeof(attr_t), 0) self.phrase_ids.set(phrase_hash, ent_id) def __call__(self, Doc doc): """Find all sequences matching the supplied patterns on the `Doc`. doc (Doc): The document to match over. RETURNS (list): A list of `(key, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `label_id` and `key` are both integers. """ matches = [] if self.attr == ORTH: match_doc = doc else: # If we're not matching on the ORTH, match_doc will be a Doc whose # token.orth values are the attribute values we're matching on, # e.g. Doc(nlp.vocab, words=[token.pos_ for token in doc]) words = [self.get_lex_value(doc, i) for i in range(len(doc))] match_doc = Doc(self.vocab, words=words) for _, start, end in self.matcher(match_doc): ent_id = self.accept_match(match_doc, start, end) if ent_id is not None: matches.append((ent_id, start, end)) for i, (ent_id, start, end) in enumerate(matches): on_match = self._callbacks.get(ent_id) if on_match is not None: on_match(self, doc, i, matches) return matches def pipe(self, stream, batch_size=1000, n_threads=1, return_matches=False, as_tuples=False): """Match a stream of documents, yielding them in turn. docs (iterable): A stream of documents. batch_size (int): Number of documents to accumulate into a working set. n_threads (int): The number of threads with which to work on the buffer in parallel, if the implementation supports multi-threading. return_matches (bool): Yield the match lists along with the docs, making results (doc, matches) tuples. as_tuples (bool): Interpret the input stream as (doc, context) tuples, and yield (result, context) tuples out. If both return_matches and as_tuples are True, the output will be a sequence of ((doc, matches), context) tuples. YIELDS (Doc): Documents, in order. """ if as_tuples: for doc, context in stream: matches = self(doc) if return_matches: yield ((doc, matches), context) else: yield (doc, context) else: for doc in stream: matches = self(doc) if return_matches: yield (doc, matches) else: yield doc def accept_match(self, Doc doc, int start, int end): cdef int i, j cdef Pool mem = Pool() phrase_key = mem.alloc(end-start, sizeof(attr_t)) for i, j in enumerate(range(start, end)): phrase_key[i] = doc.c[j].lex.orth cdef hash_t key = hash64(phrase_key, (end-start) * sizeof(attr_t), 0) ent_id = self.phrase_ids.get(key) if ent_id == 0: return None else: return ent_id def get_lex_value(self, Doc doc, int i): if self.attr == ORTH: # Return the regular orth value of the lexeme return doc.c[i].lex.orth # Get the attribute value instead, e.g. token.pos attr_value = get_token_attr(&doc.c[i], self.attr) if attr_value in (0, 1): # Value is boolean, convert to string string_attr_value = str(attr_value) else: string_attr_value = self.vocab.strings[attr_value] string_attr_name = self.vocab.strings[self.attr] # Concatenate the attr name and value to not pollute lexeme space # e.g. 'POS-VERB' instead of just 'VERB', which could otherwise # create false positive matches return 'matcher:{}-{}'.format(string_attr_name, string_attr_value) cdef class DependencyTreeMatcher: """Match dependency parse tree based on pattern rules.""" cdef Pool mem cdef readonly Vocab vocab cdef readonly Matcher token_matcher cdef public object _patterns cdef public object _keys_to_token cdef public object _root cdef public object _entities cdef public object _callbacks cdef public object _nodes cdef public object _tree def __init__(self, vocab): """Create the DependencyTreeMatcher. vocab (Vocab): The vocabulary object, which must be shared with the documents the matcher will operate on. RETURNS (DependencyTreeMatcher): The newly constructed object. """ size = 20 self.token_matcher = Matcher(vocab) self._keys_to_token = {} self._patterns = {} self._root = {} self._nodes = {} self._tree = {} self._entities = {} self._callbacks = {} self.vocab = vocab self.mem = Pool() def __reduce__(self): data = (self.vocab, self._patterns,self._tree, self._callbacks) return (unpickle_matcher, data, None, None) def __len__(self): """Get the number of rules, which are edges ,added to the dependency tree matcher. RETURNS (int): The number of rules. """ return len(self._patterns) def __contains__(self, key): """Check whether the matcher contains rules for a match ID. key (unicode): The match ID. RETURNS (bool): Whether the matcher contains rules for this match ID. """ return self._normalize_key(key) in self._patterns def validateInput(self, pattern, key): idx = 0 visitedNodes = {} for relation in pattern: if 'PATTERN' not in relation or 'SPEC' not in relation: raise ValueError(Errors.E098.format(key=key)) if idx == 0: if not('NODE_NAME' in relation['SPEC'] and 'NBOR_RELOP' not in relation['SPEC'] and 'NBOR_NAME' not in relation['SPEC']): raise ValueError(Errors.E099.format(key=key)) visitedNodes[relation['SPEC']['NODE_NAME']] = True else: if not('NODE_NAME' in relation['SPEC'] and 'NBOR_RELOP' in relation['SPEC'] and 'NBOR_NAME' in relation['SPEC']): raise ValueError(Errors.E100.format(key=key)) if relation['SPEC']['NODE_NAME'] in visitedNodes or relation['SPEC']['NBOR_NAME'] not in visitedNodes: raise ValueError(Errors.E101.format(key=key)) visitedNodes[relation['SPEC']['NODE_NAME']] = True visitedNodes[relation['SPEC']['NBOR_NAME']] = True idx = idx + 1 def add(self, key, on_match, *patterns): for pattern in patterns: if len(pattern) == 0: raise ValueError(Errors.E012.format(key=key)) self.validateInput(pattern,key) key = self._normalize_key(key) _patterns = [] for pattern in patterns: token_patterns = [] for i in range(len(pattern)): token_pattern = [pattern[i]['PATTERN']] token_patterns.append(token_pattern) # self.patterns.append(token_patterns) _patterns.append(token_patterns) self._patterns.setdefault(key, []) self._callbacks[key] = on_match self._patterns[key].extend(_patterns) # Add each node pattern of all the input patterns individually to the matcher. # This enables only a single instance of Matcher to be used. # Multiple adds are required to track each node pattern. _keys_to_token_list = [] for i in range(len(_patterns)): _keys_to_token = {} # TODO : Better ways to hash edges in pattern? for j in range(len(_patterns[i])): k = self._normalize_key(unicode(key)+DELIMITER+unicode(i)+DELIMITER+unicode(j)) self.token_matcher.add(k,None,_patterns[i][j]) _keys_to_token[k] = j _keys_to_token_list.append(_keys_to_token) self._keys_to_token.setdefault(key, []) self._keys_to_token[key].extend(_keys_to_token_list) _nodes_list = [] for pattern in patterns: nodes = {} for i in range(len(pattern)): nodes[pattern[i]['SPEC']['NODE_NAME']]=i _nodes_list.append(nodes) self._nodes.setdefault(key, []) self._nodes[key].extend(_nodes_list) # Create an object tree to traverse later on. # This datastructure enable easy tree pattern match. # Doc-Token based tree cannot be reused since it is memory heavy and # tightly coupled with doc self.retrieve_tree(patterns,_nodes_list,key) def retrieve_tree(self,patterns,_nodes_list,key): _heads_list = [] _root_list = [] for i in range(len(patterns)): heads = {} root = -1 for j in range(len(patterns[i])): token_pattern = patterns[i][j] if('NBOR_RELOP' not in token_pattern['SPEC']): heads[j] = ('root',j) root = j else: heads[j] = (token_pattern['SPEC']['NBOR_RELOP'],_nodes_list[i][token_pattern['SPEC']['NBOR_NAME']]) _heads_list.append(heads) _root_list.append(root) _tree_list = [] for i in range(len(patterns)): tree = {} for j in range(len(patterns[i])): if(_heads_list[i][j][INDEX_HEAD] == j): continue head = _heads_list[i][j][INDEX_HEAD] if(head not in tree): tree[head] = [] tree[head].append( (_heads_list[i][j][INDEX_RELOP],j) ) _tree_list.append(tree) self._tree.setdefault(key, []) self._tree[key].extend(_tree_list) self._root.setdefault(key, []) self._root[key].extend(_root_list) def has_key(self, key): """Check whether the matcher has a rule with a given key. key (string or int): The key to check. RETURNS (bool): Whether the matcher has the rule. """ key = self._normalize_key(key) return key in self._patterns def get(self, key, default=None): """Retrieve the pattern stored for a key. key (unicode or int): The key to retrieve. RETURNS (tuple): The rule, as an (on_match, patterns) tuple. """ key = self._normalize_key(key) if key not in self._patterns: return default return (self._callbacks[key], self._patterns[key]) def __call__(self, Doc doc): matched_trees = [] matches = self.token_matcher(doc) for key in list(self._patterns.keys()): _patterns_list = self._patterns[key] _keys_to_token_list = self._keys_to_token[key] _root_list = self._root[key] _tree_list = self._tree[key] _nodes_list = self._nodes[key] length = len(_patterns_list) for i in range(length): _keys_to_token = _keys_to_token_list[i] _root = _root_list[i] _tree = _tree_list[i] _nodes = _nodes_list[i] id_to_position = {} for i in range(len(_nodes)): id_to_position[i]=[] # This could be taken outside to improve running time..? for match_id, start, end in matches: if match_id in _keys_to_token: id_to_position[_keys_to_token[match_id]].append(start) _node_operator_map = self.get_node_operator_map(doc,_tree,id_to_position,_nodes,_root) length = len(_nodes) if _root in id_to_position: candidates = id_to_position[_root] for candidate in candidates: isVisited = {} self.dfs(candidate,_root,_tree,id_to_position,doc,isVisited,_node_operator_map) # To check if the subtree pattern is completely identified. This is a heuristic. # This is done to reduce the complexity of exponential unordered subtree matching. # Will give approximate matches in some cases. if(len(isVisited) == length): matched_trees.append((key,list(isVisited))) for i, (ent_id, nodes) in enumerate(matched_trees): on_match = self._callbacks.get(ent_id) if on_match is not None: on_match(self, doc, i, matches) return matched_trees def dfs(self,candidate,root,tree,id_to_position,doc,isVisited,_node_operator_map): if(root in id_to_position and candidate in id_to_position[root]): # color the node since it is valid isVisited[candidate] = True if root in tree: for root_child in tree[root]: if candidate in _node_operator_map and root_child[INDEX_RELOP] in _node_operator_map[candidate]: candidate_children = _node_operator_map[candidate][root_child[INDEX_RELOP]] for candidate_child in candidate_children: result = self.dfs( candidate_child.i, root_child[INDEX_HEAD], tree, id_to_position, doc, isVisited, _node_operator_map ) # Given a node and an edge operator, to return the list of nodes # from the doc that belong to node+operator. This is used to store # all the results beforehand to prevent unnecessary computation while # pattern matching # _node_operator_map[node][operator] = [...] def get_node_operator_map(self,doc,tree,id_to_position,nodes,root): _node_operator_map = {} all_node_indices = nodes.values() all_operators = [] for node in all_node_indices: if node in tree: for child in tree[node]: all_operators.append(child[INDEX_RELOP]) all_operators = list(set(all_operators)) all_nodes = [] for node in all_node_indices: all_nodes = all_nodes + id_to_position[node] all_nodes = list(set(all_nodes)) for node in all_nodes: _node_operator_map[node] = {} for operator in all_operators: _node_operator_map[node][operator] = [] # Used to invoke methods for each operator switcher = { '<':self.dep, '>':self.gov, '>>':self.dep_chain, '<<':self.gov_chain, '.':self.imm_precede, '$+':self.imm_right_sib, '$-':self.imm_left_sib, '$++':self.right_sib, '$--':self.left_sib } for operator in all_operators: for node in all_nodes: _node_operator_map[node][operator] = switcher.get(operator)(doc,node) return _node_operator_map def dep(self,doc,node): return list(doc[node].head) def gov(self,doc,node): return list(doc[node].children) def dep_chain(self,doc,node): return list(doc[node].ancestors) def gov_chain(self,doc,node): return list(doc[node].subtree) def imm_precede(self,doc,node): if node>0: return [doc[node-1]] return [] def imm_right_sib(self,doc,node): for idx in range(list(doc[node].head.children)): if idx == node-1: return [doc[idx]] return [] def imm_left_sib(self,doc,node): for idx in range(list(doc[node].head.children)): if idx == node+1: return [doc[idx]] return [] def right_sib(self,doc,node): candidate_children = [] for idx in range(list(doc[node].head.children)): if idx < node: candidate_children.append(doc[idx]) return candidate_children def left_sib(self,doc,node): candidate_children = [] for idx in range(list(doc[node].head.children)): if idx > node: candidate_children.append(doc[idx]) return candidate_children def _normalize_key(self, key): if isinstance(key, basestring): return self.vocab.strings.add(key) else: return key