def main():
try:
base = DiasporaOutput(sys.argv[1])
except:
logging.error("Missing output path")
exit(-1)
try:
busqueda_id = int(sys.argv[2])
except:
logging.error("Missing busqueda_id")
exit(-1)
busqueda = Busqueda.objects.get(pk=busqueda_id)
for persona in busqueda.persona_set.all():
person_file = base.write_personal_feature_matrix(persona)
logging.info("Exporting features matrix for person " + persona.name)
# TODO: reconnaitre 32 bits et 64 bits data model
# j48_path = UNOPORUNO_ROOT + '/resources/classifiers/j48/J48.weka.32.data.model'
nbtree_path = UNOPORUNO_ROOT + "/resources/classifiers/naivebayes/NaiveBayes.data.model"
command = (
"java weka.classifiers.bayes.NaiveBayes -l "
+ nbtree_path
+ " -T "
+ person_file
+ " -p 1 > "
+ person_file
+ ".out"
)
logging.info("classyfying with command=" + command)
try:
result = os.system(command)
except:
exit(-1)
for subdirs, dirs, files in os.walk(sys.argv[1] + "/"):
for file in files:
re_out = re.search("\.out$", file)
if not re_out:
continue
top5 = get_weka_top5(sys.argv[1] + "/" + file)
logging.info("Extracting " + str(top5) + " tuples from file:" + file)
for s in top5:
snippet = Snippet.objects.get(pk=int(s[0]))
snippet.converging_pipelines = 1
snippet.save()
def main():
try:
base = DiasporaOutput(sys.argv[1])
except:
logging.error('Missing output path')
exit(-1)
try:
busqueda_id = int(sys.argv[2])
except:
logging.error('Missing busqueda_id')
exit(-1)
busqueda = Busqueda.objects.get(pk=busqueda_id)
for persona in busqueda.persona_set.all():
person_file = base.write_personal_feature_matrix(persona)
logging.info('Exporting features matrix for person ' + persona.name)
# TODO: reconnaitre 32 bits et 64 bits data model
# j48_path = UNOPORUNO_ROOT + '/resources/classifiers/j48/J48.weka.32.data.model'
smo_path = UNOPORUNO_ROOT + '/resources/classifiers/smo/SMO.data.model'
command = 'java weka.classifiers.functions.SMO -l ' + smo_path + ' -T ' + person_file + ' -p 1 > ' + person_file + '.out'
logging.info('classyfying with command=' + command)
try:
result = os.system(command)
except:
exit(-1)
for subdirs, dirs, files in os.walk(sys.argv[1] + '/'):
for file in files:
re_out = re.search('\.out$', file)
if not re_out:
continue
top5 = get_weka_top5(sys.argv[1] + '/' + file)
logging.info('Extracting ' + str(top5) + ' tuples from file:' +
file)
for s in top5:
snippet = Snippet.objects.get(pk=int(s[0]))
snippet.converging_pipelines = 1
snippet.save()
def main():
try:
base = DiasporaOutput(sys.argv[1])
except:
logging.error('Missing output path')
exit(-1)
try:
busqueda_id = int(sys.argv[2])
except:
logging.error ('Missing busqueda_id')
exit(-1)
busqueda = Busqueda.objects.get(pk=busqueda_id)
for persona in busqueda.persona_set.all():
person_file = base.write_personal_feature_matrix(persona)
logging.info('Exporting features matrix for person ' +persona.name)
# TODO: reconnaitre 32 bits et 64 bits data model
# j48_path = UNOPORUNO_ROOT + '/resources/classifiers/j48/J48.weka.32.data.model'
smo_path = UNOPORUNO_ROOT + '/resources/classifiers/smo/SMO.data.model'
command = 'java weka.classifiers.functions.SMO -l '+smo_path+' -T '+person_file+' -p 1 > '+person_file+'.out'
logging.info('classyfying with command=' + command)
try:
result = os.system(command)
except:
exit(-1)
for subdirs, dirs, files in os.walk(sys.argv[1]+'/'):
for file in files:
re_out = re.search('\.out$', file)
if not re_out:
continue
top5 = get_weka_top5(sys.argv[1]+'/'+file)
logging.info('Extracting ' +str(top5)+ ' tuples from file:' +file )
for s in top5:
snippet = Snippet.objects.get(pk=int(s[0]))
snippet.converging_pipelines=1
snippet.save()
except:
logging.error('No busqueda object with id=' +busqueda_in+ ' in UNOPORUNO database.')
exit(-1)
logging.info('Processing busqueda ' +busqueda.nombre )
try:
results_path = sys.argv[2]
except:
logging.error('Missing parameter path')
logging.error('Usage: python unoporuno_export.py NAME|NUMBER path')
exit(-1)
try:
person_id_list = []
person_id_file = open(sys.argv[3])
for l in person_id_file:
person_id_list.append(l.strip())
except:
person_id_list = None
diaspora_output = DiasporaOutput(results_path)
if person_id_list:
logging.debug('launching export_persona with id_list=' +str(person_id_list))
diaspora_output.export_unoporuno_persona(busqueda, person_id_list)
else:
diaspora_output.export_unoporuno_busqueda(busqueda)
try:
busqueda = Busqueda.objects.get(nombre=busqueda_in)
except:
logging.error('No busqueda object with id=' + busqueda_in +
' in UNOPORUNO database.')
exit(-1)
logging.info('Processing busqueda ' + busqueda.nombre)
try:
results_path = sys.argv[2]
except:
logging.error('Missing parameter path')
logging.error('Usage: python unoporuno_export.py NAME|NUMBER path')
exit(-1)
try:
person_id_list = []
person_id_file = open(sys.argv[3])
for l in person_id_file:
person_id_list.append(l.strip())
except:
person_id_list = None
diaspora_output = DiasporaOutput(results_path)
if person_id_list:
logging.debug('launching export_persona with id_list=' +
str(person_id_list))
diaspora_output.export_unoporuno_persona(busqueda, person_id_list)
else:
diaspora_output.export_unoporuno_busqueda(busqueda)
def classify_person_top5(busqueda_id, path, classifier, data_model_file):
#TODO: validar cuando a) no hay snippets clasificados como positivos y b) hay menos de 5 snippets clasificados como positivos
output_path = path+'/'+classifier+'/'
logging.info('classifying persons with busqueda_id='+str(busqueda_id)+', classifier'\
+classifier+ ' , data_model_file=' +data_model_file)
try:
base = DiasporaOutput(output_path)
except:
logging.error('Error on output path'+output_path)
exit(-1)
try:
busqueda_id = int(busqueda_id)
except:
logging.error ('Missing busqueda_id')
exit(-1)
busqueda = Busqueda.objects.get(pk=busqueda_id)
d_personas = dict()
for persona in busqueda.persona_set.all():
persona_file = persona_file = base.write_personal_feature_matrix_2class(persona)
command = 'java ' +classifier+ ' -l '+data_model_file+' -T '+persona_file+' -p 1 > '+persona_file+'.out'
try:
result = os.system(command)
except:
exit(-1)
logging.info('classyfying with command=' + command)
for subdirs, dirs, files in os.walk(output_path):
for file in files:
re_out = re.search('\.out$', file)
if not re_out:
continue
classed_snippets = get_weka_top5(output_path+'/'+file)
logging.info('Extracting ' +str(len(classed_snippets))+ ' tuples from file:' +file )
logging.info('Classed snippets=' +str(classed_snippets))
if len(classed_snippets):
for tupla in classed_snippets:
logging.info('looking for snippet id =' +str(tupla[0]))
snippet = Snippet.objects.get(id=int(tupla[0]))
if d_personas.has_key(snippet.persona_id):
d_paises = d_personas[snippet.persona_id]
else:
d_paises = dict()
lista_paises = snippet.featured_countries.split(',') if snippet.featured_countries else []
for pais in lista_paises:
u_pais = pais.encode('utf-8')
if d_paises.has_key(u_pais):
d_paises[u_pais] += 1
else:
d_pais = dict({u_pais:1})
d_paises.update(d_pais)
d_persona = dict({snippet.persona_id:d_paises})
d_personas.update(d_persona)
snippet.converging_pipelines=2
snippet.RE_score = get_feature_count(snippet.RE_features)
snippet.save()
LA = ['AR','BZ','BO','CL','CO','CR','C','DO','SV','MX','GT','HT','JM','NI','PY','PE','VE','TT','PY','HN','PA','UY']
for persona in busqueda.persona_set.all():
if not d_personas.has_key(persona.id):
d_paises = dict()
d_persona = dict({persona.id:d_paises})
d_personas.update(d_persona)
logging.info('Persona ' +persona.name+ ' has the following country frequencies:' +str(d_personas[persona.id])+ \
' and prediction='+str(persona.prediction))
LA_freq = ('',0)
mundo_freq = ('',0)
for pais in d_personas[persona.id].keys():
u_pais = pais.encode('utf-8')
if u_pais in LA:
if d_personas[persona.id][u_pais] > LA_freq[1]:
LA_freq = (u_pais,d_personas[persona.id][u_pais])
else:
if d_personas[persona.id][u_pais] > mundo_freq[1]:
mundo_freq = (u_pais, d_personas[persona.id][u_pais])
logging.info('Pais LA mas frequente:' +str(LA_freq))
logging.info('Pais no LA mas frequente:' +str(mundo_freq))
#3 del país móvil más frecuente
#2 del país latinoamericano más frecuente
#los demás con móviles
if mundo_freq[1]>0 and LA_freq[1]>0:
persona.prediction=1
logging.info(persona.name+' is movil! with prediction=' +str(persona.prediction))
elif mundo_freq[1]>0 or LA_freq[1]>0:
persona.prediction = 2
logging.info('local!')
else:
persona.prediction = 3
logging.info('no sé!')
mobile_snippets = persona.snippet_set.filter(converging_pipelines=2).order_by('-RE_score')
local_snippets = persona.snippet_set.filter(converging_pipelines=3).order_by('-RE_score')
converging_count = [0,0,0] #[converging_count, world_count, LA_count]
if mundo_freq[1]>0:
mobile_limit = min(3,mundo_freq[1])
LA_limit = min(2,LA_freq[1])
for s in mobile_snippets:
if converging_count[0]>=5:
break
if converging_count[1]<mobile_limit:
if mundo_freq[0] in str(s.featured_countries):
s.converging_pipelines=1
s.save()
converging_count[0]+=1
converging_count[1]+=1
logging.info('world hit!')
elif converging_count[2]<LA_limit:
if LA_freq[0] in str(s.featured_countries):
s.converging_pipelines=1
s.save()
converging_count[0]+=1
converging_count[2]+=1
logging.info('LA hit!')
if converging_count[0]<5:
for s in local_snippets:
if converging_count[0]>=5:
break
if converging_count[1]<mobile_limit:
if mundo_freq[0] in str(s.featured_countries):
s.converging_pipelines=1
s.save()
converging_count[0]+=1
converging_count[1]+=1
logging.info('world local hit!')
elif converging_count[2]<LA_limit:
if LA_freq[0] in str(s.featured_countries):
s.converging_pipelines=1
s.save()
converging_count[0]+=1
converging_count[2]+=1
logging.info('LA local hit!')
if converging_count[0]<5:
for s in mobile_snippets:
if converging_count[0]>=5:
break
if s.converging_pipelines==1:
continue
s.converging_pipelines=1
s.save()
converging_count[0]+=1
if converging_count[0]<5:
for s in local_snippets:
if converging_count[0]>=5:
break
if s.converging_pipelines==1:
continue
s.converging_pipelines=1
s.save()
converging_count[0]+=1
if converging_count[0]<5:
todos = persona.snippet_set.filter(FG=1).exclude(RE=1).order_by('-RE_features')
for s in local_snippets:
if converging_count[0]>=5:
break
if s.converging_pipelines==1:
continue
s.converging_pipelines=1
s.save()
converging_count[0]+=1
if converging_count<5:
for s in local_snippets:
if converging_count>=5:
break
s.converging_pipelines=1
s.save()
converging_count +=1
logging.info(persona.name+' is movil! with prediction=' +str(persona.prediction))
persona.save()