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* Work on integrating a greedy dependency parser
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0
spacy/parser/__init__.pxd
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0
spacy/parser/__init__.pxd
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0
spacy/parser/__init__.py
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spacy/parser/__init__.py
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78
spacy/parser/_state.pxd
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spacy/parser/_state.pxd
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from cymem.cymem cimport Pool
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cdef struct Subtree:
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int[5] kids
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int[5] labels
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int length
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cdef struct State:
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double score
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size_t i
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size_t n
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size_t stack_len
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size_t top
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size_t* stack
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Subtree* lefts
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Subtree* rights
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int* heads
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int* labels
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cdef int add_dep(const State *s, size_t head, size_t child, size_t label) except -1
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cdef size_t pop_stack(State *s) except 0
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cdef int push_stack(State *s) except -1
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cdef inline size_t get_s1(const State *s) nogil:
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if s.stack_len < 2:
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return 0
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return s.stack[s.stack_len - 2]
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cdef inline size_t get_l(const State *s, size_t head) nogil:
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cdef const Subtree* subtree = &s.lefts[head]
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if subtree.length == 0:
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return 0
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return subtree.kids[subtree.length - 1]
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cdef inline size_t get_l2(const State *s, size_t head) nogil:
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cdef const Subtree* subtree = &s.lefts[head]
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if subtree.length < 2:
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return 0
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return subtree.kids[subtree.length - 2]
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cdef inline size_t get_r(const State *s, size_t head) nogil:
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cdef const Subtree* subtree = &s.rights[head]
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if subtree.length == 0:
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return 0
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return subtree.kids[subtree.length - 1]
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cdef inline size_t get_r2(const State *s, size_t head) nogil:
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cdef const Subtree* subtree = &s.rights[head]
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if subtree.length < 2:
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return 0
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return subtree.kids[subtree.length - 2]
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cdef inline bint at_eol(const State *s) nogil:
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return s.i >= (s.n - 1)
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cdef inline bint is_final(State *s) nogil:
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return at_eol(s) and s.stack_len == 0
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cdef int has_child_in_buffer(State *s, size_t word, int* gold) except -1
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cdef int has_head_in_buffer(State *s, size_t word, int* gold) except -1
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cdef int has_child_in_stack(State *s, size_t word, int* gold) except -1
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cdef int has_head_in_stack(State *s, size_t word, int* gold) except -1
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cdef State* init_state(Pool mem, const int sent_length) except NULL
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spacy/parser/_state.pyx
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spacy/parser/_state.pyx
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# cython: profile=True
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from libc.string cimport memmove
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from cymem.cymem cimport Pool
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cdef int add_dep(State *s, size_t head, size_t child, size_t label) except -1:
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s.heads[child] = head
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s.labels[child] = label
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cdef Subtree* subtree = &s.lefts[head] if child < head else &s.rights[head]
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if subtree.length == 5:
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memmove(subtree.kids, &subtree.kids[1], 4 * sizeof(int))
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memmove(subtree.labels, &subtree.labels[1], 4 * sizeof(int))
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subtree.kids[4] = child
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subtree.labels[4] = label
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else:
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subtree.kids[subtree.length - 1] = child
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subtree.kids[subtree.length - 1] = label
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subtree.length += 1
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cdef size_t pop_stack(State *s) except 0:
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cdef size_t popped
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assert s.stack_len >= 1
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popped = s.top
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s.top = get_s1(s)
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s.stack_len -= 1
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assert s.top <= s.n, s.top
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assert popped != 0
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return popped
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cdef int push_stack(State *s) except -1:
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s.top = s.i
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s.stack[s.stack_len] = s.i
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s.stack_len += 1
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assert s.top <= s.n
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s.i += 1
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cdef int has_child_in_buffer(State *s, size_t word, int* gold_heads) except -1:
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assert word != 0
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cdef size_t buff_i
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cdef int n = 0
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for buff_i in range(s.i, s.n):
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if s.heads[buff_i] == 0 and gold_heads[buff_i] == word:
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n += 1
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return n
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cdef int has_head_in_buffer(State *s, size_t word, int* gold_heads) except -1:
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assert word != 0
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cdef size_t buff_i
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for buff_i in range(s.i, s.n):
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if s.heads[buff_i] == 0 and gold_heads[word] == buff_i:
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return 1
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return 0
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cdef int has_child_in_stack(State *s, size_t word, int* gold_heads) except -1:
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assert word != 0
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cdef size_t i, stack_i
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cdef int n = 0
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for i in range(s.stack_len):
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stack_i = s.stack[i]
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# Should this be sensitive to whether the word has a head already?
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if gold_heads[stack_i] == word:
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n += 1
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return n
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cdef int has_head_in_stack(State *s, size_t word, int* gold_heads) except -1:
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assert word != 0
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cdef size_t i, stack_i
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for i in range(s.stack_len):
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stack_i = s.stack[i]
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if gold_heads[word] == stack_i:
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return 1
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return 0
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DEF PADDING = 5
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cdef State* init_state(Pool mem, const int sent_length) except NULL:
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cdef size_t i
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cdef State* s = <State*>mem.alloc(1, sizeof(State))
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s.n = sent_length
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s.i = 1
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s.top = 0
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s.stack_len = 0
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n = s.n + PADDING
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s.stack = <size_t*>mem.alloc(n, sizeof(size_t))
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s.heads = <int*>mem.alloc(n, sizeof(int))
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s.labels = <int*>mem.alloc(n, sizeof(int))
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s.lefts = <Subtree*>mem.alloc(n, sizeof(Subtree))
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s.rights = <Subtree*>mem.alloc(n, sizeof(Subtree))
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return s
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23
spacy/parser/arc_eager.pxd
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spacy/parser/arc_eager.pxd
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from cymem.cymem cimport Pool
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from thinc.typedefs cimport weight_t
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from ._state cimport State
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cdef struct Transition:
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int move
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int label
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cdef class TransitionSystem:
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cdef Pool mem
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cdef readonly int n_moves
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cdef const Transition* _moves
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cdef int best_valid(self, const weight_t* scores, const State* s) except -1
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cdef int best_gold(self, const weight_t* scores, const State* s,
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list gold_heads, list gold_labels) except -1
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cdef int transition(self, State *s, const int clas) except -1
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spacy/parser/arc_eager.pyx
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spacy/parser/arc_eager.pyx
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# cython: profile=True
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from ._state cimport State
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from ._state cimport at_eol, pop_stack, push_stack, add_dep
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from ._state cimport has_head_in_buffer, has_child_in_buffer
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from ._state cimport has_head_in_stack, has_child_in_stack
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import index.hashes
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cdef enum:
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ERR
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SHIFT
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REDUCE
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LEFT
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RIGHT
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N_MOVES
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cdef inline bint _can_shift(const State* s) nogil:
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return not at_eol(s)
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cdef inline bint _can_right(const State* s) nogil:
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return s.stack_len >= 1 and not at_eol(s)
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cdef inline bint _can_left(const State* s) nogil:
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return s.stack_len >= 1 and s.heads[s.top] == 0
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cdef inline bint _can_reduce(const State* s) nogil:
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return s.stack_len >= 2 and s.heads[s.top] != 0
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cdef int _shift_cost(const State* s, list gold) except -1:
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assert not at_eol(s)
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cost = 0
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cost += has_head_in_stack(s, s.i, gold)
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cost += has_child_in_stack(s, s.i, gold)
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return cost
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cdef int _right_cost(const State* s, list gold) except -1:
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assert s.stack_len >= 1
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cost = 0
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if gold[s.i] == s.top:
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return cost
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cost += has_head_in_buffer(s, s.i, gold)
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cost += has_child_in_stack(s, s.i, gold)
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cost += has_head_in_stack(s, s.i, gold)
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return cost
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cdef int _left_cost(const State* s, list gold) except -1:
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assert s.stack_len >= 1
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cost = 0
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if gold[s.top] == s.i:
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return cost
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cost += has_head_in_buffer(s, s.top, gold)
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cost += has_child_in_buffer(s, s.top, gold)
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return cost
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cdef int _reduce_cost(const State* s, list gold) except -1:
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assert s.stack_len >= 2
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cost = 0
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cost += has_child_in_buffer(s, s.top, gold)
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return cost
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cdef class TransitionSystem:
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def __init__(self, list left_labels, list right_labels):
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self.mem = Pool()
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self.n_moves = 2 + len(left_labels) + len(right_labels)
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moves = <Transition*>self.mem.alloc(self.n_moves, sizeof(Transition))
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cdef int i = 0
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moves[i].move = SHIFT
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moves[i].label = 0
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i += 1
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moves[i].move = REDUCE
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moves[i].label = 0
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i += 1
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cdef int label
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for label in left_labels:
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moves[i].move = LEFT
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moves[i].label = label
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i += 1
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for label in right_labels:
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moves[i].move = RIGHT
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moves[i].label = label
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i += 1
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self._moves = moves
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cdef int transition(self, State *s, const int clas) except -1:
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cdef const Transition* t = &self._moves[clas]
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if t.move == SHIFT:
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push_stack(s)
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elif t.move == LEFT:
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add_dep(s, s.i, s.top, t.label)
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pop_stack(s)
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elif t.move == RIGHT:
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add_dep(s, s.top, s.i, t.label)
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push_stack(s)
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elif t.move == REDUCE:
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pop_stack(s)
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else:
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raise StandardError(t.move)
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cdef int best_valid(self, const weight_t* scores, const State* s) except -1:
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cdef bint[N_MOVES] valid
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valid[SHIFT] = _can_shift(s)
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valid[LEFT] = _can_left(s)
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valid[RIGHT] = _can_right(s)
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valid[REDUCE] = _can_reduce(s)
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cdef int best = -1
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cdef weight_t score
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cdef int i
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for i in range(self.n_moves):
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if valid[self._moves[i].move] and scores[i] > score:
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best = i
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score = scores[i]
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assert best >= -1, "No valid moves found"
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return best
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cdef int best_gold(self, const weight_t* scores, const State* s,
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list gold_heads, list gold_labels) except -1:
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cdef int[N_MOVES] unl_costs
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unl_costs[SHIFT] = _shift_cost(s, gold_heads) if _can_shift(s) else -1
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unl_costs[LEFT] = _left_cost(s, gold_heads) if _can_left(s) else -1
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unl_costs[RIGHT] = _right_cost(s, gold_heads) if _can_right(s) else -1
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unl_costs[REDUCE] = _reduce_cost(s, gold_heads) if _can_reduce(s) else -1
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cdef int cost
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cdef int label_cost
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cdef int move
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cdef int label
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cdef int best = -1
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cdef weight_t score
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cdef int i
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for i in range(self.n_moves):
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move = self._moves[i].move
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label = self._moves[i].label
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if unl_costs[move] == 0:
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if move == SHIFT or move == REDUCE:
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label_cost = 0
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elif move == LEFT:
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if gold_heads[s.top] == s.i:
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label_cost = label != gold_labels[s.top]
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else:
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label_cost = 0
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elif move == RIGHT:
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if gold_heads[s.i] == s.top:
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label_cost = label != gold_labels[s.i]
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else:
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label_cost = 0
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else:
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raise StandardError("Unknown Move")
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if label_cost == 0 and scores[i] > score:
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best = i
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score = scores[i]
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assert best >= -1, "No gold moves found"
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return best
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15
spacy/parser/parser.pxd
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15
spacy/parser/parser.pxd
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from thinc.features cimport Extractor
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from thinc.learner cimport LinearModel
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from .arc_eager cimport TransitionSystem
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from ..tokens cimport Tokens, TokenC
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cdef class Parser:
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cdef object cfg
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cdef Extractor extractor
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cdef LinearModel model
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cdef TransitionSystem moves
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cpdef int parse(self, Tokens tokens) except -1
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160
spacy/parser/parser.pyx
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160
spacy/parser/parser.pyx
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# cython: profile=True
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"""
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MALT-style dependency parser
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"""
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cimport cython
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import random
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import os.path
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from os.path import join as pjoin
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import shutil
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import json
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from cymem.cymem cimport Pool, Address
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t
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from util import Config
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from thinc.features cimport Extractor
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from thinc.features cimport Feature
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from thinc.features cimport count_feats
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from thinc.learner cimport LinearModel
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from ..tokens cimport Tokens, TokenC
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from .arc_eager cimport TransitionSystem
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from ._state cimport init_state, State, is_final, get_s1
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VOCAB_SIZE = 1e6
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TAG_SET_SIZE = 50
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DEF CONTEXT_SIZE = 50
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DEBUG = False
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def set_debug(val):
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global DEBUG
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DEBUG = val
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cdef str print_state(State* s, list words):
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top = words[s.top]
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second = words[get_s1(s)]
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n0 = words[s.i]
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n1 = words[s.i + 1]
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return ' '.join((second, top, '|', n0, n1))
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def train(sents, golds, model_dir, n_iter=15, feat_set=u'basic', seed=0):
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if os.path.exists(model_dir):
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shutil.rmtree(model_dir)
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os.mkdir(model_dir)
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left_labels, right_labels, dfl_labels = get_labels(golds)
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Config.write(model_dir, 'config', features=feat_set, seed=seed,
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left_labels=left_labels, right_labels=right_labels)
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parser = Parser(model_dir)
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indices = list(range(len(sents)))
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for n in range(n_iter):
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for i in indices:
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parser.train_sent(sents[i], *golds[i])
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#parser.tagger.train_sent(py_sent) # TODO
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acc = float(parser.guide.n_corr) / parser.guide.total
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print(parser.guide.end_train_iter(n) + '\t' +
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parser.tagger.guide.end_train_iter(n))
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random.shuffle(indices)
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parser.guide.end_training()
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parser.tagger.guide.end_training()
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parser.guide.dump(pjoin(model_dir, 'model'), freq_thresh=0)
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parser.tagger.guide.dump(pjoin(model_dir, 'tagger'))
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return acc
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def get_labels(sents):
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'''Get alphabetically-sorted lists of left, right and disfluency labels that
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occur in a sample of sentences. Used to determine the set of legal transitions
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from the training set.
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Args:
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sentences (list[Input]): A list of Input objects, usually the training set.
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Returns:
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labels (tuple[list, list, list]): Sorted lists of left, right and disfluency
|
||||
labels.
|
||||
'''
|
||||
left_labels = set()
|
||||
right_labels = set()
|
||||
# TODO
|
||||
return list(sorted(left_labels)), list(sorted(right_labels))
|
||||
|
||||
|
||||
def get_templates(feats_str):
|
||||
'''Interpret feats_str, returning a list of template tuples. Each template
|
||||
is a tuple of numeric indices, referring to positions in the context
|
||||
array. See _parse_features.pyx for examples. The templates are applied by
|
||||
thinc.features.Extractor, which picks out the appointed values and hashes
|
||||
the resulting array, to produce a single feature code.
|
||||
'''
|
||||
return tuple()
|
||||
|
||||
|
||||
cdef class Parser:
|
||||
def __init__(self, model_dir):
|
||||
assert os.path.exists(model_dir) and os.path.isdir(model_dir)
|
||||
self.cfg = Config.read(model_dir, 'config')
|
||||
self.extractor = Extractor(get_templates(self.cfg.features))
|
||||
self.moves = TransitionSystem(self.cfg.left_labels, self.cfg.right_labels)
|
||||
|
||||
self.model = LinearModel(self.moves.n_moves, self.extractor.n_templ)
|
||||
if os.path.exists(pjoin(model_dir, 'model')):
|
||||
self.model.load(pjoin(model_dir, 'model'))
|
||||
|
||||
cpdef int parse(self, Tokens tokens) except -1:
|
||||
cdef:
|
||||
Feature* feats
|
||||
weight_t* scores
|
||||
|
||||
cdef atom_t[CONTEXT_SIZE] context
|
||||
cdef int n_feats
|
||||
cdef Pool mem = Pool()
|
||||
cdef State* state = init_state(mem, tokens.length)
|
||||
while not is_final(state):
|
||||
fill_context(context, state, tokens.data) # TODO
|
||||
feats = self.extractor.get_feats(context, &n_feats)
|
||||
scores = self.model.get_scores(feats, n_feats)
|
||||
|
||||
guess = self.moves.best_valid(scores, state)
|
||||
|
||||
self.moves.transition(state, guess)
|
||||
# TODO output
|
||||
|
||||
def train_sent(self, Tokens tokens, list gold_heads, list gold_labels):
|
||||
cdef:
|
||||
Feature* feats
|
||||
weight_t* scores
|
||||
|
||||
cdef int n_feats
|
||||
cdef atom_t[CONTEXT_SIZE] context
|
||||
cdef Pool mem = Pool()
|
||||
cdef State* state = init_state(mem, tokens.length)
|
||||
|
||||
while not is_final(state):
|
||||
fill_context(context, state, tokens.data) # TODO
|
||||
feats = self.extractor.get_feats(context, &n_feats)
|
||||
scores = self.model.get_scores(feats, n_feats)
|
||||
|
||||
guess = self.moves.best_valid(scores, state)
|
||||
best = self.moves.best_gold(scores, state, gold_heads, gold_labels)
|
||||
|
||||
counts = {guess: {}, best: {}}
|
||||
if guess != best:
|
||||
count_feats(counts[guess], feats, n_feats, -1)
|
||||
count_feats(counts[best], feats, n_feats, 1)
|
||||
self.model.update(counts)
|
||||
|
||||
self.moves.transition(state, guess)
|
||||
|
||||
|
||||
cdef int fill_context(atom_t* context, State* s, TokenC* sent) except -1:
|
||||
pass
|
Loading…
Reference in New Issue
Block a user