def choice_1_COL_RANGE_FIRST(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST = 1.00
def _change_params_for_step(cur_step,alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST += cur_step*alpha
_reset_params()
_alpha = 0.05
_max_step = 8
_current_step = 0
global __continue
if __continue == 1:
return
_current_step = 8
_change_params_for_step(_current_step, _alpha)
__continue = 1
while _current_step <= _max_step:
SimilarityWordnetOxford.similarityWords(dict_nouns)
_current_step += 1
_change_params_for_step(_current_step, _alpha)
_reset_params()
def jaccard_weight(dict_nouns):
def _reset_params():
Parameters.PARAMETERS.JACCARD_WEIGHT = 0.01
def _change_params_for_step(cur_step, alpha):
_reset_params()
Parameters.PARAMETERS.JACCARD_WEIGHT += cur_step*alpha
_reset_params()
_alpha = 0.02
_max_step = 20
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step,_alpha)
_reset_params()
return (_best_result,_best_params)
def choice_1_COL_RANGE_FIRST(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST = 1.01
def _change_params_for_step(cur_step,alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST += cur_step*alpha
_reset_params()
_alpha = 0.02
_max_step = 10
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step, _alpha)
_reset_params()
return (_best_result,_best_params)
def nbest_similarity(dict_nouns):
def _reset_params():
Parameters.PARAMETERS.N_BEST_CALCULATE_SIMILARITY = 1
def _change_params_for_step(cur_step, alpha):
_reset_params()
Parameters.PARAMETERS.N_BEST_CALCULATE_SIMILARITY += cur_step*alpha
_reset_params()
_alpha = 1
_max_step = 9
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step,_alpha)
_reset_params()
return (_best_result,_best_params)
def feature_POS(dict_nouns):
def _reset_params():
Parameters.PARAMETERS.POS_FEATURE_n = 1
Parameters.PARAMETERS.POS_FEATURE_v = 0
def _change_params_for_step(cur_step):
_reset_params()
if cur_step == 1:
Parameters.PARAMETERS.POS_FEATURE_v = 1
_reset_params()
_max_step = 1
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step)
_reset_params()
return (_best_result,_best_params)
def feature_dict(dict_nouns):
def _reset_params():
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_sd = 1
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_d = 1
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_xh = 1
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_x = 1
def _change_params_for_step(cur_step):
_reset_params()
if cur_step == 1:
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_xh = 0
elif cur_step == 2:
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_x = 0
elif cur_step == 3:
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_xh = 0
Parameters.PARAMETERS.DICT_OX_FEATURE_RELATION_x = 0
_reset_params()
_max_step = 3
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step)
_reset_params()
return (_best_result,_best_params)
def feature_wn(dict_nouns):
def _reset_params():
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_definition = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 0
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 0
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms = 0
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms = 0
def _change_params_for_step(cur_step):
_reset_params()
if cur_step == 1:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
elif cur_step == 2:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 1
elif cur_step == 3:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms = 1
elif cur_step == 4:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms = 1
elif cur_step == 5:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 1
elif cur_step == 6:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms = 1
elif cur_step == 7:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms = 1
elif cur_step == 8:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms = 1
elif cur_step == 9:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms = 1
elif cur_step == 10:
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms = 1
Parameters.PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms = 1
_reset_params()
_max_step = 10
_current_step = 0
_best_result = 0
_best_params = Parameters.get_current_params()
while _current_step <= _max_step:
(precision, recall, accuracy) = SimilarityWordnetOxford.similarityWords(dict_nouns)
if accuracy > _best_result:
_best_result = accuracy
_best_params = Parameters.get_current_params()
_current_step += 1
_change_params_for_step(_current_step)
_reset_params()
return (_best_result,_best_params)
def get_Ox_means(key, key_lemmas):
means = []
for noun in key_lemmas:
if not __dict_Ox__.has_key(noun):
filted_dict = SimilarityWordnetOxford.cal_similarity_for_word(noun)
__dict_Ox__[noun] = filted_dict
for noun in key_lemmas:
if __dict_Ox__[noun].has_key(key):
means = __dict_Ox__[noun][key]
break
return means
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)
