def set_model(sem_input_file, sem_name, sem_input_macro = True, semantics_name='TCG_semantics_main',
grammar_name='TCG_grammar_VB_main', model_params = {}):
"""
Sets up a TCG production model.
Args:
- sem_input_file (STR): Semantic input file name
- sem_name (STR): Semantic input name
- sem_input_macro (BOOL): True is the input is an ISRF macro
- semantics_name (STR): Name of the semantic file containing the perceptual, world, and conceptualization knowledge.
- grammar_name (STR): Name of the grammar file to use.
- model_prams (dict): Dictionary defining the model parameters (if different than default)
Returns: (model, semantic input genereator)
"""
SEM_INPUT_PATH = './data/sem_inputs/'
model = TCG_production_system(grammar_name=grammar_name, semantics_name=semantics_name)
if model_params:
model.update_params(model_params)
# Set up semantic input generator
conceptLTM = model.schemas['Concept_LTM']
if not(sem_input_macro):
sem_inputs = TCG_LOADER.load_sem_input(sem_input_file, SEM_INPUT_PATH)
sem_input = {sem_name:sem_inputs[sem_name]}
sem_gen = ls.SEM_GENERATOR(sem_input, conceptLTM, speed_param=1)
if sem_input_macro:
sem_inputs = TCG_LOADER.load_sem_macro(sem_name, sem_input_file, SEM_INPUT_PATH)
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=1)
return (model, sem_gen)
def set_inputs(model, input_name, sem_input_file='diagnostic.json', sem_input_macro=False, speed_param=10):
"""
Sets up a TCG ISRF inputs generator for TCG production model.
Args:
- model (): model to which the inputs will be sent
- input_name (STR): name of the input to be used.
- sem_input_file (STR): Semantic input file name. For non-macro input, set to 'ALL' to load all inputs from file.
- sem_input_macro (BOOL): True is the input is an ISRF macro.
- speed_param (INT): multiplier of the rate defined in the ISRF input (by default the ISFR rate is 1.)
Returns:
- input SEM_GENERATOR object.
"""
SEM_INPUT_PATH = './data/sem_inputs/'
conceptLTM = model.schemas['Concept_LTM']
if not(sem_input_macro):
sem_inputs = TCG_LOADER.load_sem_input(sem_input_file, SEM_INPUT_PATH)
if input_name == 'ALL':
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=speed_param, is_macro=sem_input_macro)
sem_gen.ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
else:
sem_input = {input_name:sem_inputs[input_name]}
sem_gen = ls.SEM_GENERATOR(sem_input, conceptLTM, speed_param=speed_param,is_macro=sem_input_macro)
ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
sem_gen.ground_truths = ground_truths.get(input_name, None)
if sem_input_macro:
sem_inputs = TCG_LOADER.load_sem_macro(input_name, sem_input_file, SEM_INPUT_PATH)
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param=speed_param, is_macro=sem_input_macro)
ground_truths = TCG_LOADER.load_ground_truths(sem_input_file, SEM_INPUT_PATH)
sem_gen.ground_truths = ground_truths.get(input_name, None)
return sem_gen
def test_run(seed=None):
"""
Test run function for the production model.
"""
if not(seed): # Quick trick so that I can have access to the seed used to run the simulation.
random.seed(seed)
seed = random.randint(0,10**9)
print "seed = %i" %seed
random.seed(seed)
SEM_INPUT = 'sem_inputs.json' # semantic input files (no macros)
INPUT_NAME = 'blue_woman_kick_man' # Name of the input to use.
FOLDER = './tmp/TEST_%s_%s/' %(INPUT_NAME, str(seed)) # Folder where the simulation results will be saved.
language_system_P = TCG_production_system(grammar_name='TCG_grammar_VB_main', semantics_name='TCG_semantics_main') # Create model
# Set up semantic input generator
conceptLTM = language_system_P.schemas['Concept_LTM']
sem_inputs = TCG_LOADER.load_sem_input(SEM_INPUT, "./data/sem_inputs/")
speed_param = 1
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param)
generator = sem_gen.sem_generator(INPUT_NAME)
(sem_insts, next_time, prop) = generator.next() #Getting the initial input.
# Test paramters
language_system_P.params['Control']['task']['start_produce'] = 3100
language_system_P.params['Control']['task']['time_pressure'] = 200
language_system_P.params['Grammatical_WM_P']['C2']['confidence_threshold'] = 0.3
set_up_time = -10 # Starts negative to let the system settle before it receives its first input. Also, easier to handle input arriving at t=0.
max_time = 3000
save_states = [30, 700, 2000]
flag = False
for t in range(set_up_time, max_time):
if next_time != None and t>next_time:
(sem_insts, next_time, prop) = generator.next()
print "t:%i, sem: %s (prop: %s)" %(t, ', '.join([inst.name for inst in sem_insts]), prop)
language_system_P.set_input(sem_insts)
language_system_P.update()
output = language_system_P.get_output()
if not(language_system_P.schemas['Grammatical_WM_P'].comp_links) and t>10 and not(flag):
print "t:%i, Competition done" % t
flag = True
TCG_VIEWER.display_lingWM_state(language_system_P.schemas['Semantic_WM'], language_system_P.schemas['Grammatical_WM_P'], concise=True, folder = FOLDER)
language_system_P.params['Control']['task']['start_produce'] = t + 10
if output['Utter']:
print "t:%i, '%s'" %(t, output['Utter'])
if t - set_up_time in save_states:
TCG_VIEWER.display_lingWM_state(language_system_P.schemas['Semantic_WM'], language_system_P.schemas['Grammatical_WM_P'], concise=True, folder = FOLDER)
language_system_P.schemas['Semantic_WM'].show_SemRep()
language_system_P.schemas['Grammatical_WM_P'].show_dynamics(inst_act=True, WM_act=False, c2_levels=True, c2_network=False)
language_system_P.save_sim(FOLDER, 'test_language_output.json')
return language_system_P
def run_model(seed=None):
"""
"""
SEM_INPUT = 'sem_inputs.json'
INPUT_NAME = 'kick_static_focus_agent'
language_system_P = TCG_production_system(grammar_name='TCG_grammar_VB_main', semantics_name='TCG_semantics_main')
# Set up semantic input generator
conceptLTM = language_system_P.schemas['Concept_LTM']
sem_inputs = TCG_LOADER.load_sem_input(SEM_INPUT, "./data/sem_inputs/")
speed_param = 1
sem_gen = ls.SEM_GENERATOR(sem_inputs, conceptLTM, speed_param)
generator = sem_gen.sem_generator(INPUT_NAME)
(sem_insts, next_time, prop) = generator.next()
# Test paramters
language_system_P.params['Control']['task']['start_produce'] = 400
language_system_P.params['Control']['task']['time_pressure'] = 200
language_system_P.params['Grammatical_WM_P']['C2']['confidence_threshold'] = 0.3
set_up_time = -10 # Starts negative to let the system settle before it receives its first input. Also, easier to handle input arriving at t=0.
max_time = 900
out_data = []
for t in range(set_up_time, max_time):
if next_time != None and t>next_time:
(sem_insts, next_time, prop) = generator.next()
language_system_P.set_input(sem_insts)
language_system_P.update()
# Store output
output = language_system_P.get_output()
if output['Grammatical_WM_P']:
out_data.append(output['Grammatical_WM_P'])
if output['Utter']:
print "t:%i, '%s'" %(t, output['Utter'])
# Output analysis
res = prod_analyses(out_data)
return res