import setup setup.load() import manageFiles import recommender recommender.parameters['threshold'] = 0.2 recommender.parameters['selection-step'] = 3 recommender.parameters['key'] = 'codigo' recommender.parameters['inner-list'] = 'processo_seletivo' recommender.parameters['candidates-list'] = 'candidatos' root = '/home/mnf/reachr/projects/RecommenderSystem/test' manageFiles.parameters['path'] = root + '/support/vagas.json' recommender.parameters['input-jobs'] = manageFiles.read() manageFiles.parameters['path'] = root + '/support/similiars.json' recommender.parameters['input-similars'] = manageFiles.read() output = recommender.run() print(output) manageFiles.parameters['path'] = root + '/support/recommendation.json' manageFiles.write(output)
manageFiles.parameters['path'] = './support/vagas.json'
inputList = manageFiles.read() # Le o arquivo
corpus = []
for doc in inputList: # captura a descrição
corpus.append(doc["desc"])
import preprocessor
preprocessor.parameters['corpus'] = corpus
X, Z = preprocessor.run()
print(X)
print(Z)
output_file = []
for i in range(0, X.shape[0]):
output = {"titulo": "", "texto": "", "tokens": []}
output["titulo"] = 'Código: {} - Título: {}'.format(
inputList[i]["codigo"], inputList[i]["titulo"])
output["texto"] = corpus[i]
for j in range(0, X.shape[1]):
if X[i, j] > 0:
token = {"item": Z[j], "tfidf": X[i, j]}
output['tokens'].append(token)
output_file.append(output)
manageFiles.parameters['path'] = './support/processed.json'
manageFiles.write(output_file)
import setup
setup.load()
import manageFiles
import processor
import similarity
root = '/home/mnf/reachr/projects/RecommenderSystem/test'
processor.parameters['index'] = "codigo"
processor.parameters['properties'] = ["desc", "titulo", "area_atuacao"]
processor.parameters['current_path'] = root + "/support/vagas.json"
processor.parameters['new_path'] = root + "/support/new_vaga.json"
computed_similarity = processor.run()
manageFiles.parameters['path'] = root + '/support/similiars.json'
manageFiles.write(computed_similarity)
#test jaccard
import pandas as pd
similarity.parameters['matrix'] = pd.DataFrame(
[[1, 0, 1, 0, 1], [0, 0, 1, 1, 1]], columns=list('ABCDE')).transpose()
print(similarity.run_jaccard())
def getAllJobs():
#response = requests.get(parameters['url'])
#return response;
obj = manageFiles.read(root + "/support/vagas.json")
return manageFiles.write(obj)
def postJobRecommendation(data):
return manageFiles.write(data)
def getJobToRecommend():
obj = manageFiles.read(root + "/support/new_vaga.json")
return manageFiles.write(obj)
import setup
setup.load()
import manageFiles
import score
root = '/home/mnf/reachr/projects/RecommenderSystem/test'
manageFiles.parameters['path'] = root + '/support/similiars.json'
score.parameters['properties'] = ["desc", "titulo", "area_atuacao"]
score.parameters['weights'] = {"desc": 0.5, "titulo": 0.4, "area_atuacao": 0.1}
score.parameters['input'] = manageFiles.read()
manageFiles.write(score.run())
import setup
setup.load()
import manageFiles
expected_ret = {}
# Teste Leitura
ret = manageFiles.read('./support/vagas.json')
print(ret)
assert (isinstance(ret, list)), 'Erro ao abrir arquivo JSON'
# Teste Gravação
ret = manageFiles.write(ret)
print(ret)
assert (ret != 1), 'Erro ao gravar arquivo JSON'