def train_with_random(dict_nouns):
_best_result = 0;
_best_params = Parameters.get_current_params()
for i in range(0,1000):
# random_all_feature
Parameters.random_params_values()
# calculate_result -> current best
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
(best_result_loop, best_params_loop) = train_from_base(dict_nouns)
if best_result_loop>= _best_result:
_best_result = best_result_loop
_best_params = best_params_loop
Parameters.set_params_from_arr(_best_params)
WriteParametersAndResult.append_params_and_result_to_file(_best_params)
def train_from_base(dict_nouns):
# run this feature in range -> choice best result
lower = -1
previous_random = -1;
best_result_loop = 0
best_params_loop = Parameters.get_current_params()
while lower < 7:
chosen_feature = random.randint(0,6)
while chosen_feature == previous_random:
chosen_feature = random.randint(0,6)
previous_random = chosen_feature
best_result = 0
best_params = []
if lower >= 0:
chosen_feature = lower
if chosen_feature == 0:
(best_result, best_params) = jaccard_weight(dict_nouns)
elif chosen_feature == 1:
(best_result, best_params) = choice_1_COL_RANGE_FIRST(dict_nouns)
elif chosen_feature == 2:
(best_result, best_params) = choice_N_N_RANGE_FIRST(dict_nouns)
elif chosen_feature == 3:
(best_result, best_params) = feature_wn(dict_nouns)
elif chosen_feature == 4:
(best_result, best_params) = feature_dict(dict_nouns)
elif chosen_feature == 5:
(best_result, best_params) = feature_POS(dict_nouns)
else:
(best_result, best_params) = nbest_similarity(dict_nouns)
# compare with _best
if best_result >= best_result_loop:
best_result_loop = best_result
best_params_loop = best_params
lower = -1
else:
lower += 1
Parameters.set_params_from_arr(best_params_loop)
return (best_result_loop, best_params_loop)
