def update_grammar_rules(grammar, res, delete=False, threshold=0.01, exclude = []):
hyps = [res[h][0] for h in xrange(len(res))]
probs = [res[h][1] for h in xrange(len(res))]
cc = create_counts(grammar, hyps)
# print cc[1]
sigs = [i.get_rule_signature() for i in grammar]
dif_sigs = []
for s in sigs:
if s[0] not in dif_sigs:
dif_sigs.append(s[0])
for g in grammar:
sig = g.get_rule_signature()
if sig[0] not in exclude:
indx = cc[1][sig]
g.p = 0
for h in xrange(len(hyps)):
rule_count = cc[0][sig[0]][h][indx]
hyp = hyps[h]
prob = probs[h]
cplx = np.sum([np.sum(cc[0][s][h]) for s in dif_sigs])
new_prior = (1.0 + rule_count) / (grammar.nrules() + cplx)
g.p += new_prior * prob
if delete and g.p < threshold:
deleteRule(hyp)
grammar.renormalize()
print "# Loaded human data" from Model.Data import concept2data print "# Loaded concept2data" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Get the rule count matrices # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ from LOTlib.GrammarInference.Precompute import create_counts from Model.Grammar import grammar trees = [h.value for h in hypotheses] nt2counts, sig2idx, prior_offset = create_counts(grammar, trees, log=None) print "# Computed counts for each hypothesis & nonterminal" # print counts # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Build up the info about the data # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ concepts = concept2data.keys() NYes = [] NNo = [] # Make NYes and Nno
from LOTlib.GrammarInference.Precompute import create_counts
which_rules = [r for r in grammar if r.nt not in ['START']]
output_rules_helpers(which_rules, "grammar_inference/header.csv")
#for l1 in lsts:
#for l2 in lsts:
#l1 = lsts[0]
#l2 = lsts[1]
for trntrans in ["train", "trans"]:
for training in xrange(len(conditioned_on)):
counts, sig2idx, prior_offset = create_counts(grammar,
hypotheses,
which_rules=which_rules)
##############################################################
#####################GET HUMAN YN COUNTS######################
#data_dct = get_training_data("data/outR.csv", str(trntrans))
#data_dct = get_data_by_condition("data/outR.csv",
#l1, l2, str(trntrans))
if trntrans == "train":
data_dct = get_data_conditioned(
"data/outR.csv",
#conditioned_on[training],
"all",
'0')
else:
from Model.Data import concept2data print "# Loaded concept2data" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Get the rule count matrices # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ from LOTlib.GrammarInference.Precompute import create_counts from Model.Grammar import grammar trees = [h.value for h in hypotheses] nt2counts, sig2idx, prior_offset = create_counts(grammar, trees, log=None) print "# Computed counts for each hypothesis & nonterminal" # print counts # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Build up the info about the data # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ concepts = concept2data.keys() NYes = [] NNo = [] # Make NYes and Nno
hypotheses = list(set([MyHypothesis(grammar=grammar, maxnodes=100) for _ in xrange(1000)])) # list so order is maintained
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get the rule count matrices
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# This stores each hypothesis vs a vector of counts of how often each nonterminal is used
# this is used via a matrix product with the log priors on the GPU to compute the prior
# (the (log)priors are the things we are trying to infer)
from LOTlib.GrammarInference.Precompute import create_counts
# Decide which rules to use
which_rules = [r for r in grammar if r.nt not in ['START']]
counts, sig2idx, prior_offset = create_counts(grammar, hypotheses, which_rules=which_rules)
print "# Computed counts for each hypothesis & nonterminal"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load the human data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.DataAndObjects import make_all_objects
objects = make_all_objects(size=['small', 'medium', 'large'],
color=['red', 'green', 'blue'],
shape=['square', 'triangle', 'circle'])
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The data that learners observed
# For now, we'll just sample from the prior
hypotheses = list(set([MyHypothesis(grammar=grammar, maxnodes=100) for _ in xrange(1000)])) # list so order is maintained
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get the rule count matrices
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# This stores each hypothesis vs a vector of counts of how often each nonterminal is used
# this is used via a matrix product with the log priors on the GPU to compute the prior
# (the (log)priors are the things we are trying to infer)
from LOTlib.GrammarInference.Precompute import create_counts
trees = [h.value for h in hypotheses]
nt2counts, sig2idx, prior_offset = create_counts(grammar, trees, log=None)
print "# Computed counts for each hypothesis & nonterminal"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load the human data
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.DataAndObjects import make_all_objects
objects = make_all_objects(size=['small', 'medium', 'large'],
color=['red', 'green', 'blue'],
shape=['square', 'triangle', 'circle'])
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The data that learners observed
