def generate_prim_augmentation(shuffle,nsupport,nquery,input_lang,output_lang,scan_var_tuples,nextra,tabu_list=[]):
# Generate a SCAN episode with primitive augmentation,
# The tabu list identifier is only determined based on the assignment of the "jump" primitive
#
# Input
# shuffle: permute how the input primitives map to the output actions? (true/false)
# scan_var_tuples : scan input/output patterns with placeholder replacement
# nextra: number of abstract input/output primitives to add to the set of possibilities
#
special_prim = 'jump'
count = 0
while True:
D_support, D_query, D_primitive = ge.sample_augment_scan(nsupport,nquery,scan_var_tuples,shuffle,nextra,inc_support_in_query=use_resconstruct_loss)
input_prim_list = [s[0] for s in D_primitive]
try:
index_prim = input_prim_list.index(special_prim)
D_str = D_primitive[index_prim][0] + ' -> ' + D_primitive[index_prim][1]
except ValueError:
D_str = 'no jump'
identifier = D_str
if not shuffle: # ignore tabu list if we aren't shuffling primitive assignments
break
if identifier not in tabu_list:
break
count += 1
if count > max_try_novel:
raise Exception('We were unable to generate an episode that is not on the tabu list')
x_support = [d[0].split(' ') for d in D_support]
y_support = [d[1].split(' ') for d in D_support]
x_query = [d[0].split(' ') for d in D_query]
y_query = [d[1].split(' ') for d in D_query]
return build_sample(x_support,y_support,x_query,y_query,input_lang,output_lang,identifier)
def generate_prim_permutation(shuffle,nsupport,nquery,input_lang,output_lang,scan_var_tuples,nextra,tabu_list=[]):
# Generate a SCAN episode with primitive permutation.
# The tabu list identifier is based on the permutation of primitive inputs to primitive actions.
#
# Input
# shuffle: permute how the input primitives map to the output actions? (true/false)
# scan_var_tuples : scan input/output sequences with placeholder replacement
# nextra: number of abstract input/output primitives to add to the set of possibilities
#
count = 0
while True:
D_support, D_query, D_primitive = ge.sample_augment_scan(nsupport,nquery,scan_var_tuples,shuffle,nextra,inc_support_in_query=use_resconstruct_loss)
D_str = '\n'.join([s[0] + ' -> ' + s[1] for s in D_primitive])
identifier = make_hashable(D_str)
if not shuffle: # ignore tabu list if we aren't shuffling primitive assignments
break
if identifier not in tabu_list:
break
count += 1
if count > max_try_novel:
raise Exception('We were unable to generate an episode that is not on the tabu list')
x_support = [d[0].split(' ') for d in D_support]
y_support = [d[1].split(' ') for d in D_support]
x_query = [d[0].split(' ') for d in D_query]
y_query = [d[1].split(' ') for d in D_query]
return build_sample(x_support,y_support,x_query,y_query,input_lang,output_lang,identifier)
def scan_evaluation_prim_only(mytype, split, input_lang, output_lang):
# Load an entire SCAN split as the query set.
# Use the isolated primitives as the support set
#
# Input
# mytype : type of SCAN experiment
# split : 'train' or 'test'
# ... other inputs are language objects
D_query = ge.load_scan_file(mytype, split)
_, _, D_primitive = ge.sample_augment_scan(0,
0, [],
shuffle=False,
inc_support_in_query=False)
D_support = D_primitive # support set only includes the primitive mappings...
random.shuffle(D_support)
x_support = [d[0].split(' ') for d in D_support]
y_support = [d[1].split(' ') for d in D_support]
x_query = [d[0].split(' ') for d in D_query]
y_query = [d[1].split(' ') for d in D_query]
return build_sample(x_support, y_support, x_query, y_query, input_lang,
output_lang, '')