/
Train_params.py
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/
Train_params.py
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__author__ = 'tu'
import SimilarityWordnetOxford
import OxfordParser
import Parameters
import random
import WriteParametersAndResult
dict_ox_nouns = OxfordParser.readOxfordNouns();
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_1_MIN(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_1_MIN = 0.05
def _change_params_for_step(cur_step,alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_1_MIN += cur_step*alpha
_reset_params()
_alpha = 0.02
_max_step = 15
_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_MIN_FIRST(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_MIN_FIRST = 0.05
def _change_params_for_step(cur_step,alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_MIN_FIRST += cur_step*alpha
_reset_params()
_alpha = 0.02
_max_step = 15
_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 choice_N_N_MIN_FIRST(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_MIN_FIRST = 0.05
def _change_params_for_step(cur_step,alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_MIN_FIRST += cur_step*alpha
_reset_params()
_alpha = 0.02
_max_step = 15
_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_N_N_RANGE_FIRST(dict_nouns):
def _reset_params():
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_RANGE_FIRST = 1.01
def _change_params_for_step(cur_step, alpha):
_reset_params()
Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_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 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 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_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 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 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)
# train()
# train_from_base(dict_ox_nouns)
# feature_wn(dict_ox_nouns)
# feature_dict(dict_ox_nouns)
# feature_POS(dict_ox_nouns)
# nbest_similarity(dict_ox_nouns)
# jaccard_weight(dict_ox_nouns)
# choice_1_COL_RANGE_FIRST(dict_ox_nouns)
# choice_N_N_RANGE_FIRST(dict_ox_nouns)