-
Notifications
You must be signed in to change notification settings - Fork 6
/
pcfg_logprob.py
129 lines (109 loc) · 4.01 KB
/
pcfg_logprob.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
import random
import numpy as np
from math import exp
import os
import sys
import vose
from type_system import Type, PolymorphicType, PrimitiveType, Arrow, List, UnknownType, INT, BOOL
from program import Program, Function, Variable, BasicPrimitive, New
from pcfg import PCFG
# make sure hash is deterministic
hashseed = os.getenv('PYTHONHASHSEED')
if not hashseed:
os.environ['PYTHONHASHSEED'] = '0'
os.execv(sys.executable, [sys.executable] + sys.argv)
class LogProbPCFG:
"""
Object that represents a probabilistic context-free grammar with lob probabilities
rules: a dictionary of type {S: D}
with S a non-terminal and D a dictionary : {P : l, w}
with P a program, l a list of non-terminals, and w a weight
representing the derivation S -> P(S1, S2, ...) with weight w for l' = [S1, S2, ...]
"""
def __init__(self, start, rules, max_program_depth=4):
self.start = start
self.rules = rules
self.max_program_depth = max_program_depth
self.hash = hash(str(rules))
def __hash__(self):
return self.hash
def clean(self):
self.remove_non_productive()
self.remove_non_reachable()
def remove_non_productive(self):
"""
remove non-terminals which do not produce programs
"""
new_rules = {}
for S in reversed(self.rules):
for P in self.rules[S]:
args_P, w = self.rules[S][P]
if all([arg in new_rules for arg in args_P]):
if S not in new_rules:
new_rules[S] = {}
new_rules[S][P] = self.rules[S][P]
for S in set(self.rules):
if S in new_rules:
self.rules[S] = new_rules[S]
else:
del self.rules[S]
def remove_non_reachable(self):
"""
remove non-terminals which are not reachable from the initial non-terminal
"""
reachable = set()
reachable.add(self.start)
reach = set()
new_reach = set()
reach.add(self.start)
for i in range(self.max_program_depth):
new_reach.clear()
for S in reach:
for P in self.rules[S]:
args_P, _ = self.rules[S][P]
for arg in args_P:
new_reach.add(arg)
reachable.add(arg)
reach.clear()
reach = new_reach.copy()
for S in set(self.rules):
if S not in reachable:
del self.rules[S]
def __hash__(self):
return self.hash
def __repr__(self):
s = "Print a LogProbPCFG\n"
s += "start: {}\n".str(self.start)
for S in reversed(self.rules):
s += "#\n {}\n".str(S)
for P in self.rules[S]:
args_P, w = self.rules[S][P]
s += " {} - {}: {} {}\n".str(P, P.type, args_P, w)
return s
def log_probability_program(self, S, P):
"""
Compute the log probability of a program P generated from the non-terminal S
"""
if isinstance(P, Function):
F = P.function
args_P = P.arguments
probability = self.rules[S][F][1]
for i, arg in enumerate(args_P):
probability = probability + self.log_probability_program(self.rules[S][F][0][i], arg)
return probability
if isinstance(P, (Variable, BasicPrimitive, New)):
return self.rules[S][P][1]
assert False
def normalise(self):
self.clean()
normalised_rules = {}
for S in self.rules:
s = sum(exp(self.rules[S][P][1]) for P in self.rules[S])
if s > 0:
normalised_rules[S] = {}
for P in self.rules[S]:
normalised_rules[S][P] = \
self.rules[S][P][0], exp(self.rules[S][P][1]) / s
return PCFG(self.start,
normalised_rules,
max_program_depth=self.max_program_depth, clean=True)