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decoder.py
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decoder.py
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# This file contains routines from Lisbon Machine Learning summer school.
# The code is freely distributed under a MIT license. https://github.com/LxMLS/lxmls-toolkit/
import numpy as np
import sys
from collections import defaultdict, namedtuple
from operator import itemgetter
def parse_proj(scores, gold=None):
'''
Parse using Eisner's algorithm.
'''
nr, nc = np.shape(scores)
if nr != nc:
raise ValueError("scores must be a squared matrix with nw+1 rows")
N = nr - 1 # Number of words (excluding root).
# Initialize CKY table.
complete = np.zeros([N+1, N+1, 2]) # s, t, direction (right=1).
incomplete = np.zeros([N+1, N+1, 2]) # s, t, direction (right=1).
complete_backtrack = -np.ones([N+1, N+1, 2], dtype=int) # s, t, direction (right=1).
incomplete_backtrack = -np.ones([N+1, N+1, 2], dtype=int) # s, t, direction (right=1).
incomplete[0, :, 0] -= np.inf
# Loop from smaller items to larger items.
for k in xrange(1,N+1):
for s in xrange(N-k+1):
t = s+k
# First, create incomplete items.
# left tree
incomplete_vals0 = complete[s, s:t, 1] + complete[(s+1):(t+1), t, 0] + scores[t, s] + (0.0 if gold is not None and gold[s]==t else 1.0)
incomplete[s, t, 0] = np.max(incomplete_vals0)
incomplete_backtrack[s, t, 0] = s + np.argmax(incomplete_vals0)
# right tree
incomplete_vals1 = complete[s, s:t, 1] + complete[(s+1):(t+1), t, 0] + scores[s, t] + (0.0 if gold is not None and gold[t]==s else 1.0)
incomplete[s, t, 1] = np.max(incomplete_vals1)
incomplete_backtrack[s, t, 1] = s + np.argmax(incomplete_vals1)
# Second, create complete items.
# left tree
complete_vals0 = complete[s, s:t, 0] + incomplete[s:t, t, 0]
complete[s, t, 0] = np.max(complete_vals0)
complete_backtrack[s, t, 0] = s + np.argmax(complete_vals0)
# right tree
complete_vals1 = incomplete[s, (s+1):(t+1), 1] + complete[(s+1):(t+1), t, 1]
complete[s, t, 1] = np.max(complete_vals1)
complete_backtrack[s, t, 1] = s + 1 + np.argmax(complete_vals1)
value = complete[0][N][1]
heads = [-1 for _ in range(N+1)] #-np.ones(N+1, dtype=int)
backtrack_eisner(incomplete_backtrack, complete_backtrack, 0, N, 1, 1, heads)
value_proj = 0.0
for m in xrange(1,N+1):
h = heads[m]
value_proj += scores[h,m]
return heads
def backtrack_eisner(incomplete_backtrack, complete_backtrack, s, t, direction, complete, heads):
'''
Backtracking step in Eisner's algorithm.
- incomplete_backtrack is a (NW+1)-by-(NW+1) numpy array indexed by a start position,
an end position, and a direction flag (0 means left, 1 means right). This array contains
the arg-maxes of each step in the Eisner algorithm when building *incomplete* spans.
- complete_backtrack is a (NW+1)-by-(NW+1) numpy array indexed by a start position,
an end position, and a direction flag (0 means left, 1 means right). This array contains
the arg-maxes of each step in the Eisner algorithm when building *complete* spans.
- s is the current start of the span
- t is the current end of the span
- direction is 0 (left attachment) or 1 (right attachment)
- complete is 1 if the current span is complete, and 0 otherwise
- heads is a (NW+1)-sized numpy array of integers which is a placeholder for storing the
head of each word.
'''
if s == t:
return
if complete:
r = complete_backtrack[s][t][direction]
if direction == 0:
backtrack_eisner(incomplete_backtrack, complete_backtrack, s, r, 0, 1, heads)
backtrack_eisner(incomplete_backtrack, complete_backtrack, r, t, 0, 0, heads)
return
else:
backtrack_eisner(incomplete_backtrack, complete_backtrack, s, r, 1, 0, heads)
backtrack_eisner(incomplete_backtrack, complete_backtrack, r, t, 1, 1, heads)
return
else:
r = incomplete_backtrack[s][t][direction]
if direction == 0:
heads[s] = t
backtrack_eisner(incomplete_backtrack, complete_backtrack, s, r, 1, 1, heads)
backtrack_eisner(incomplete_backtrack, complete_backtrack, r+1, t, 0, 1, heads)
return
else:
heads[t] = s
backtrack_eisner(incomplete_backtrack, complete_backtrack, s, r, 1, 1, heads)
backtrack_eisner(incomplete_backtrack, complete_backtrack, r+1, t, 0, 1, heads)
return