def LATITUDE(instanceCLASS):
instanceCLASS = val(instanceCLASS)
instanceCLASS = instanceCLASS.split() # convert to list
instanceRES = pd.DataFrame(instanceCLASS, columns=['CODDEPAL'])
COOR_DEP2["CODDEPAL"] = COOR_DEP2["CODDEPAL"].astype('object')
COOR_DEP2["CODDEPAL"] = COOR_DEP2["CODDEPAL"].apply(
lambda x: prepare_text.formatingText(x))
COOR_DEP2["CODDEPAL"] = COOR_DEP2["CODDEPAL"].apply(
lambda x: prepare_text.deletestopwords(x))
COOR_DEP2['CODDEPAL'] = COOR_DEP2['CODDEPAL'].astype(str)
COOR_DEP2["CODDEPAL"] = COOR_DEP2["CODDEPAL"].apply(
lambda x: prepare_text.formatingText(x))
#COOR_DEP2.head(3)
instanceRES = pd.merge(instanceRES, COOR_DEP2, on='CODDEPAL')
#instanceRES
lat = instanceRES['DEPlatitude'][0]
return lat
def MODELOREGION(inst, loaded_model):
instanceCLASS = ''
if pd.notnull(inst):
# Se limpia el texto para ingresar al modelo
inst = prepare_text.formatingText(inst)
inst = prepare_text.deletestopwords(inst)
inst = ' '.join(inst)
#inst = formatingText(inst)
inst = prepare_text.onlyWORDS(inst, stop_mundep)
inst = ' '.join(inst)
instance = inst
###### CARGAR AL MODELO LAS PALABRAS
instance_pred = loaded_model.predict(
newprediction(instance, 9, tokenizer))
instance_pred = prepare_text.convertprobtobin(instance_pred, 0.30)
instanceCLASS = lemod.inverse_transform(instance_pred)
instanceCLASS = str(instanceCLASS).strip('[()]')
instanceCLASS = prepare_text.formatingText(instanceCLASS)
return instanceCLASS
def MODELODEPARTAMENTO(inst, modeloDepart):
instanceCLASS = ''
if pd.notnull(inst):
# Se limpia el texto para ingresar al modelo
inst = prepare_text.formatingText(inst)
inst = prepare_text.deletestopwords(inst)
inst = ' '.join(inst)
#inst = formatingText(inst)
inst = prepare_text.onlyWORDS(inst, stop_mundep)
inst = ' '.join(inst)
instance = inst
###### CARGAR AL MODELO LAS PALABRAS
# instance_pred = loaded_modelDEP.predict(newpredictionDEP(instance,9,tokenizerDEP))
# modeloDepart = find_location.modelo_dep()
instance_pred = modeloDepart.predict(
newpredictionDEP(instance, 9, tokenizerDEP))
instance_pred = prepare_text.convertprobtobin(instance_pred, 0.30)
instanceCLASS = lemodDEP.inverse_transform(instance_pred)
instanceCLASS = str(instanceCLASS).strip('[()]')
instanceCLASS = prepare_text.formatingText(instanceCLASS)
return instanceCLASS
return lat
#### CARGAR EL MODELO
## Se crean las categorias para el modelo
## Dataframe with the name of categories
data = {
'CODRegionlist2':
['amazonia', 'andina', 'caribe', 'orinoquia', 'pacifico']
}
df = pd.DataFrame(data, columns=['CODRegionlist2'])
df["CODRegionlist2"] = df["CODRegionlist2"].astype('object')
df["CODRegionlist2"] = df["CODRegionlist2"].apply(
lambda x: prepare_text.formatingText(x))
df["CODRegionlist2"] = df["CODRegionlist2"].apply(
lambda x: prepare_text.deletestopwords(x))
df['CODRegionlist2'] = df['CODRegionlist2'].astype(str)
df['CODRegionlist2'] = df['CODRegionlist2'].map(lambda x: eval(x))
## leer archivo spara el modelo
### CARGAR BIBLIOTECA DE MUNICIPIOS Y DEPARTAMENTO
with open('ModReg/stop_mundep.txt', 'rb') as fp:
list_1 = pickle.load(fp)
stop_mundep = list_1
# loading CARGAR EL ARCHIVO DEL TOKENIZER
with open('ModReg/tokenizermundep.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)