def run(strategy, explanation_score_name = "mat_score", prediction_score_name = "mato_score",
show_me = False, test_problems = None, test_name = "spm", test_anlgs = None, test_trans = None):
start_time = time.time()
probs = prob_anlg_tran_new.get_probs(test_problems, test_name)
for prob in probs:
print(prob.name)
# initialize cache
jaccard.load_jaccard_cache(prob.name)
asymmetric_jaccard.load_asymmetric_jaccard_cache(prob.name)
# run strategy
anlg_tran_data, pred_data, pred_d = strategy(prob,
explanation_score_name = explanation_score_name,
prediction_score_name = prediction_score_name,
test_anlgs = test_anlgs,
test_trans = test_trans)
# save data
prob.data = utils.save_data(prob, anlg_tran_data, pred_data, pred_d,
"./data/" + test_name + "_" + strategy.__name__ + "_" + prediction_score_name + "_" + prob.name,
show_me)
# update cache
jaccard.save_jaccard_cache(prob.name)
asymmetric_jaccard.save_asymmetric_jaccard_cache(prob.name)
# generate report
report.create_report(probs, test_name + "_" + strategy.__name__ + "_" + prediction_score_name + "_")
end_time = time.time()
print(end_time - start_time)
def run_raven_greedy(show_me=False, test_problems=None):
start_time = time.time()
print("run raven in greedy mode.")
probs = prob_anlg_tran.get_probs(test_problems)
for prob in probs:
print(prob.name)
jaccard.load_jaccard_cache(prob.name)
asymmetric_jaccard.load_asymmetric_jaccard_cache(prob.name)
anlgs = prob_anlg_tran.get_anlgs(prob)
anlg_tran_data = []
anlg_data = []
for anlg in anlgs:
# score all transformations given an analogy
tran_data = run_prob_anlg(prob, anlg)
anlg_tran_data.extend(tran_data)
# optimize w.r.t. transformations for this analogy
anlg_tran_d = utils.find_best(tran_data, "pat_score")
anlg_data.append(anlg_tran_d)
pred_data = []
for anlg_d in anlg_data:
# predict with an analogy, and score all options with the prediction
anlg_pred_data = predict(prob, anlg_d)
pred_data.extend(anlg_pred_data)
# optimize w.r.t. options
pred_d = utils.find_best(pred_data, "pat_score", "pato_score")
# imaging
save_image(prob, pred_d.get("pred"),
prob.options[pred_d.get("optn") - 1], "greedy", show_me)
# data aggregation progression, TODO maybe save them as images
for d in anlg_tran_data:
del d["diff"]
for d in anlg_data:
del d["diff"]
for d in pred_data:
del d["diff"]
del d["pred"]
del pred_d["diff"]
del pred_d["pred"]
aggregation_progression = {
"anlg_tran_data": anlg_tran_data,
"anlg_data": anlg_data,
"pred_data": pred_data,
"pred_d": pred_d
}
with open("./data/greedy_" + prob.name + ".json", 'w+') as outfile:
json.dump(aggregation_progression, outfile)
outfile.close()
# update cache
jaccard.save_jaccard_cache(prob.name)
asymmetric_jaccard.save_asymmetric_jaccard_cache(prob.name)
prob.data = aggregation_progression
# output report
if test_problems is None:
report.create_report(probs, "greedy_")
end_time = time.time()
print(end_time - start_time)