# =================================================================
#
# Last update of any of the components of this module:
#
# March 25, 2008.
#
# Users are encouraged to download periodically updated versions of
# this code at the TANGO home page:
#
# www.ime.usp.br/~egbirgin/tango/
#
# *****************************************************************
# *****************************************************************
# Load the problem definition file
from toyprob import *
# Import the Python module
import algencan
# Set some optional params
algencan.param['iprint'] = 10
# Call the solver
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,
evalhlp,inip,endp)
def main() :
global fractional_counts_language ,fractional_counts_channel,probabilities_channel,probabilities_language,sigma,alpha,current_fractional_counts,current_optimization_params,init_option,initial_parameter_args,constraint_tags_dict,parameter_counter,current_optimization_tag
num_iterations = int(sys.argv[1])
alpha = float(sys.argv[2])
sigma = float(sys.argv[3])
dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
word_lines= emMethods.readWordLines('test.words.new-formatted')
#word_list_five = emMethods.readWordList('TEXT.3.linear')
gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = {}
free_parameters_language = {}
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
for line in word_lines :
#print 'created parameters for a line'
(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
#print language_parameters
#print channel_parameters
total_language_parameters += language_parameters
total_channel_parameters += channel_parameters
print 'total language parameters is %d' %(total_language_parameters)
print 'total channel parameters is %d' %(total_channel_parameters)
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
print 'constraint_lengths are being printed'
for tag in free_parameters_language.keys() :
print len(free_parameters_language[tag].keys())
#raw_input()
sys.exit()
# print len(word_list)
# num_taggings = emMethods.getNumTaggings(word_list,dictionary)
# print 'num_taggings '
# print type(num_taggings)
#print num_taggings
fractional_counts_language = copy.deepcopy(free_parameters_language)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
probabilities_language = copy.deepcopy(free_parameters_language)
emMethods.initUniformProbs(probabilities_channel,probabilities_language)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
emMethods.writeFsa('tagging.fsa',probabilities_language)
emMethods.writeFst('tagging.fst',probabilities_channel)
run_training = r'./carmel.static --train-cascade -M 0 -m -HJ test.words.new-formatted.training tagging.fsa tagging.fst'
# skel_size += len(col)
#running the EM iterations
#we are creating the indexes for algencan . Notice that here, the probabilities language is already uniform and therefore none of them will be zero
createParametersForScaling(probabilities_language)
for i in range (0,num_iterations) :
'''
print 'checking the initial zeros inlanguage'
checkZeros(probabilities_language)
print 'checking the initial zeros in channel'
checkZeros(probabilities_channel)
'''
#best_tag_sequence = emMethods.viterbiSearch(start,end,probabilities_channel,probabilities_language,lattice_skeleton)
#emMethods.calcAccuracy(gold_tag_sequence,best_tag_sequence)
#raw_input()
#this will create the parameters
total_corpus_probability = 0.0
(status,output) = commands.getstatusoutput(run_training)
print 'we just ran the training'
prob_match = probability_re.search(output)
if prob_match == None :
print'we should have found a probability'
else :
print 'the probability is %s'%prob_match.group(1)
total_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
print 'reading language fractional counts'
emMethods.readCarmelFractionalCounts('tagging.fsa.trained',fractional_counts_language,'bigram')
print 'read the fsa'
print 'reading channel fractional counts'
emMethods.readCarmelFractionalCounts('tagging.fst.trained',fractional_counts_channel,'channel')
print 'read the fst'
print' the probability of the corpus was %f' %total_corpus_probability
print 'we are now checking the accuracies'
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
viterbi_command = r'cat test.words.new-formatted.quotes | ./carmel.static -srbk -QEWI 1 tagging.fsa.noe tagging.fst > tagging_output'
(status,output) = commands.getstatusoutput(viterbi_command)
tagged_sequence = emMethods.readTaggingOutput('tagging_output')
accuracy = emMethods.calcAccuracy(gold_tag_sequence,tagged_sequence)
print 'The accuracy was %s and the objective function value was %s'%(str(accuracy),str(evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)))
#emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#fractional_counts_language = copy.deepcopy(free_parameters_language)
#fractional_counts_channel = copy.deepcopy(free_parameters_channel)
#first optimizing the tag bigrams and doing it per parameter
for tag in initial_parameter_args.keys() :
if len(initial_parameter_args[tag].keys()) == 1 :
continue
#current_initial_parameter_args = initial_parameter_args[tag]
current_optimization_tag = tag
parameter_counter = len(initial_parameter_args[tag].keys())
current_fractional_counts = fractional_counts_language
constraint_tags_dict[1] = tag
temp_language_probs = dict(probabilities_language)
#optimizing per constraint
init_option = 'current_prob'
current_optimization_params = 'tag_bigrams'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
#I should check if the objective function is increasing
language_probs_after_init_current_prob = copy.deepcopy(probabilities_language)
#obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)
total_obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)
obj_val1 = evaluateOptimizationFunction(initial_parameter_args,probabilities_language,fractional_counts_language,alpha,sigma)
print 'the function value was obj 1 %f'%obj_val1
#emMethods.clearAlphaBeta(lattice_skeleton)
init_option = 'zeros'
current_optimization_params = 'tag_bigrams'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
language_probs_after_init_zeros = copy.deepcopy(probabilities_language)
#obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)a
total_obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)
obj_val2 = evaluateOptimizationFunction(initial_parameter_args,probabilities_language,fractional_counts_language,alpha,sigma)
print 'the function value was obj 2 %f'%obj_val2
#emMethods.clearAlphaBeta(lattice_skeleton)
if (total_obj_val1 >= total_obj_val2) :
#init_option = 'current_prob'
#current_optimization_params = 'tag_bigrams'
if (obj_val1 < obj_val2) :
print 'the final objective function value was opposite'
probabilities_language = copy.deepcopy(language_probs_after_init_current_prob)
#algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
print 'the final objective function value was obj 1 %f'%total_obj_val1
else :
#init_option = 'zeros'
#current_optimization_params = 'tag_bigrams'
probabilities_language = copy.deepcopy(language_probs_after_init_zeros)
#algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
if (obj_val2 < obj_val1) :
print 'the final objective function value was opposite'
print 'the final objective function value was obj 2 %f'%total_obj_val2
#raw_input()
# emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
print 'writing the fsa'
#now writing the fsa back again
emMethods.writeFsa('tagging.fsa',probabilities_language)
emMethods.writeFst('tagging.fst',probabilities_channel)
'''
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
viterbi_command = r'cat test.words.new-formatted.quotes | ./carmel.static -srbk -QEWI 1 tagging.fsa.noe tagging.fst > tagging_output'
(status,output) = commands.getstatusoutput(viterbi_command)
tagged_sequence = emMethods.readTaggingOutput('tagging_output')
emMethods.calcAccuracy(gold_tag_sequence,tagged_sequence)
'''
print 'checking the zeros in tag bigram model'
checkZeros(probabilities_language)
print 'checking the initial zeros in channel model'
checkZeros(probabilities_channel)
#fractional_counts_language.clear()
#fractional_counts_channel.clear()
fractional_counts_language = copy.deepcopy(free_parameters_language)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
#probabilities_language = copy.deepcopy(free_parameters_language)
probabilities_channel = copy.deepcopy(free_parameters_channel)
# Module: Main program # ================================================================= # # Last update of any of the components of this module: # # March 25, 2008. # # Users are encouraged to download periodically updated versions of # this code at the TANGO home page: # # www.ime.usp.br/~egbirgin/tango/ # # ***************************************************************** # ***************************************************************** # Load the problem definition file from toyprob import * # Import the Python module import algencan # Set some optional params algencan.param["iprint"] = 10 # Call the solver algencan.solvers(evalf, evalg, evalh, evalc, evaljac, evalhc, evalfc, evalgjac, evalhl, evalhlp, inip, endp)
def main() :
global fractional_counts_language ,fractional_counts_channel,probabilities_channel,sigma,alpha,current_fractional_counts,current_optimization_params,init_option
num_iterations = int(sys.argv[1])
alpha = float(sys.argv[2])
sigma = float(sys.argv[3])
# dictionary = emMethods.createDictionary('DICT2')#.small')
# word_list = emMethods.readWordList('TEXT.linear')
#word_list_five = emMethods.readWordList('TEXT.5.linear')
# gold_tag_sequence = emMethods.readWordList('GOLD.linear')
gold_cipher = emMethods.readCipherFile('cipher.gold.noq')
print gold_cipher
#dictionary = emMethods.createDictionary('complete.dict.new-formatted')#.small')
#word_lines= emMethods.readWordLines('test.words.new-formatted')
cipher_letter_dict = emMethods.getUniqCipherLetters('cipher.data.noq')
#word_list_five = emMethods.readWordList('TEXT.3.linear')
#plaintext = map(chr, range(97, 123))
plaintext = []
for k in range(65, 91):
plaintext.append(chr(k))
print plaintext
print 'the number of unique cipher letter is %d'%len(cipher_letter_dict.keys())
print cipher_letter_dict
#gold_tag_sequence = emMethods.readWordList('test.tags.new-formatted.linear')
free_parameters_channel = {}
free_parameters_language = {}
print 'starting to create parameters'
total_language_parameters = 0
total_channel_parameters = 0
#for line in cipher_lines :
#print 'created parameters for a line'
#(language_parameters,channel_parameters) = emMethods.getFreeParametersBigram(line,dictionary,free_parameters_language,free_parameters_channel)
emMethods.getFreeCipherParametersChannel(cipher_letter_dict,plaintext,free_parameters_channel)
temp = {'_':0.0}
free_parameters_channel['_'] = temp
#print free_parameters_channel
#sys.exit()
#print language_parameters
#print channel_parameters
#total_language_parameters += language_parameters
#total_channel_parameters += channel_parameters
#print 'total language parameters is %d' %(total_language_parameters)
#print 'total channel parameters is %d' %(total_channel_parameters)
#now, we will build all the lattices, and create a special start node and end node for every sentence
start_node_end_node_list = []
# print len(word_list)
# num_taggings = emMethods.getNumTaggings(word_list,dictionary)
# print 'num_taggings '
# print type(num_taggings)
#print num_taggings
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
probabilities_channel = copy.deepcopy(free_parameters_channel)
emMethods.initUniformProbs(probabilities_channel)
# emMethods.initUniformProbs(probabilities_language,probabilities_channel)
emMethods.writeFst('cipher.fst',probabilities_channel)
run_training = r'./carmel.static --train-cascade -M 0 -m -HJ cipher.data cipher.wfsa cipher.fst'
# skel_size += len(col)
#running the EM iterations
for i in range (0,num_iterations) :
'''
print 'checking the initial zeros inlanguage'
checkZeros(probabilities_language)
print 'checking the initial zeros in channel'
checkZeros(probabilities_channel)
'''
#best_tag_sequence = emMethods.viterbiSearch(start,end,probabilities_channel,probabilities_language,lattice_skeleton)
#emMethods.calcAccuracy(gold_tag_sequence,best_tag_sequence)
#raw_input()
#this will create the parameters
total_corpus_probability = 0.0
(status,output) = commands.getstatusoutput(run_training)
print 'we just ran the training'
prob_match = probability_re.search(output)
if prob_match == None :
print'we should have found a probability'
else :
print 'the probability is %s'%prob_match.group(1)
total_corpus_probability = float(prob_match.group(1)[2:len(prob_match.group(1))])
print 'reading channel fractional counts'
emMethods.readCarmelFractionalCounts('cipher.fst.trained',fractional_counts_channel,'channel')
print 'read the fst'
print' the probability of the corpus was %f' %total_corpus_probability
print 'we are now checking the accuracies'
noe_command = 'cat tagging.fsa | sed \'s/*e*//g\' > tagging.fsa.noe'
(status,output) = commands.getstatusoutput(noe_command)
print 'we wrote the noe fsa'
viterbi_command = r'cat cipher.data | ./carmel.static -srbk -QEWI 1 cipher.wfsa.noe cipher.fst > decipherment_output'
(status,output) = commands.getstatusoutput(viterbi_command)
#tagged_sequence = emMethods.readTaggingOutput('tagging_output')
deciphered_sequence = emMethods.readCipherFile('decipherment_output')
accuracy = emMethods.calcAccuracy(gold_cipher,deciphered_sequence)
print 'The accuracy was %s and the objective function value was %s'%(str(accuracy),str(evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)))
#first optimizing the channel
current_fractional_counts = fractional_counts_channel
createParameters(probabilities_channel,current_fractional_counts,free_parameters_channel,alpha,sigma)
temp_channel_probs = dict(probabilities_channel)
init_option = 'current_prob'
current_optimization_params = 'channel'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
#I should check if the objective function is increasing
channel_probs_after_init_current_prob = copy.deepcopy(probabilities_channel)
#obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)
total_obj_val1 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)
obj_val1 = evaluateOptimizationFunction(initial_parameter_args,probabilities_channel,fractional_counts_channel,alpha,sigma)
print 'the function value was obj 1 %f'%obj_val1
#emMethods.clearAlphaBeta(lattice_skeleton)
init_option = 'zeros'
current_optimization_params = 'channel'
algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
channel_probs_after_init_zeros = copy.deepcopy(probabilities_channel)
#obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_language,probabilities_channel,alpha,sigma)a
total_obj_val2 = evaluateObjectiveFuncValue(total_corpus_probability,probabilities_channel,alpha,sigma)
obj_val2 = evaluateOptimizationFunction(initial_parameter_args,probabilities_channel,fractional_counts_channel,alpha,sigma)
print 'the function value was obj 2 %f'%obj_val2
#emMethods.clearAlphaBeta(lattice_skeleton)
if (total_obj_val1 >= total_obj_val2) :
#init_option = 'current_prob'
#current_optimization_params = 'tag_bigrams'
if (obj_val1 < obj_val2) :
print 'the final objective function value was opposite'
probabilities_channel = copy.deepcopy(channel_probs_after_init_current_prob)
#algencan.solvers(evalf,evalg,evalh,evalc,evaljac,evalhc,evalfc,evalgjac,evalhl,evalhlp,inip,endp)
print 'the final objective function value was obj 1 %f'%total_obj_val1
else :
#init_option = 'zeros'
#current_optimization_params = 'tag_bigrams'
probabilities_channel = copy.deepcopy(channel_probs_after_init_zeros)
if (obj_val2 < obj_val1) :
print 'the final objective function value was opposite'
print 'the final objective function value was obj 2 %f'%total_obj_val2
#raw_input()
# emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#emMethods.reEstimateProbabilities(probabilities_channel,probabilities_language,fractional_counts_channel,fractional_counts_language)
#print 'writing the fsa'
#now writing the fsa back again
#emMethods.writeFsa('tagging.fsa',probabilities_language)
emMethods.writeFst('cipher.fst',probabilities_channel)
#print 'checking the zeros in tag bigram model'
#checkZeros(probabilities_language)
print 'checking the zeros in channel model'
checkZeros(probabilities_channel)
#fractional_counts_language.clear()
#fractional_counts_channel.clear()
#fractional_counts_language = copy.deepcopy(free_parameters_language)
fractional_counts_channel = copy.deepcopy(free_parameters_channel)
