Esempio n. 1
0
def learning_pipeline(data, conf, config_path='snapw.config'):
    master_server, server_list = get_servers()
    data_c = threading.Thread(target=poll_for_kv,
                              args=(data, server_list, master_server))
    data_c.start()
    while data_c.isAlive():
        time.sleep(1)
    logging.info("Learning data captured.")
    # Conf is a dictionary with key as paramter, value as value, i.e. k-v file.
    # Data is dictionary with key as machine, value as the k-k-v file.
    # Setting is a dictionary with key as parameter, value as the category.
    logging.critical("DATA: %s", data)
    label_rt = 1.0
    setting = {
        "GenTasks": "average",
        "GenStubs": "average",
        "GenGraph": "average",
        "GetNbr": "average"
    }

    logging.info("Generating features.")
    features, target = generate_features(label_rt, conf["var"], data, setting)
    logging.info("Training model.")
    train(features, target)
    logging.info("Generating new configuration file")
    new_conf_d = predict(conf["var"])
    return new_conf_d
Esempio n. 2
0
def learning_pipeline(data, conf, config_path='snapw.config'):
    master_server, server_list = get_servers()
    data_c = threading.Thread(target=poll_for_kv, args=(data, server_list, master_server ))
    data_c.start()
    while data_c.isAlive():
        time.sleep(1)
    logging.info("Learning data captured.")
    # Conf is a dictionary with key as paramter, value as value, i.e. k-v file.
    # Data is dictionary with key as machine, value as the k-k-v file.
    # Setting is a dictionary with key as parameter, value as the category.
    logging.critical("DATA: %s", data)
    label_rt = 1.0
    setting = {"GenTasks": "average", "GenStubs":"average", "GenGraph":"average", "GetNbr":"average"}

    logging.info("Generating features.")
    features, target = generate_features(label_rt, conf["var"], data, setting)
    logging.info("Training model.")
    train(features, target)
    logging.info("Generating new configuration file")
    new_conf_d = predict(conf["var"])
    return new_conf_d
Esempio n. 3
0
    # Conf is a dictionary with key as paramter, value as value, i.e. k-v file,
    # Data is dictionary with key as machine, value as the k-k-v file, 
    # setting is a dictionary with key as parameter, value as the category,
    # features, target = generate_features(conf, data, setting)
    # train(features, target)
    # new_conf_d = predict(features)
    # Generate new configuration file and pass to other machines.

    label = 1.0
    conf = {"nodes":10000, "range":1, "stat_tasks":1, "gen_tasks":10000, "drange":10000}
    data = {"GenTasks":{1:2.0, 2:3.0, 4:2.4}, "GenStubs":{"a":1.0, "b":2.0, "c":3}, "GenGraph":{"1":101.0, "2":99.0, "4":2.4}, "GetNbr":{"1":1.0, "2":9.0, "4":1.4}, "GetDist":{"1":101.0, "2":99.0, "3":2.4}}
    setting = {"GenTasks": "average", "GenStubs":"average", "GenGraph":"average", "GetNbr":"average"}

    features, target = generate_features(label, conf, data, setting)
    train(features, target)
    new_conf_d = predict(conf)
    print new_conf_d

'''
route   __Start__       GenTasks
route   GenTasks        GenStubs
route   GenStubs        GenGraph
route   GenGraph        GetNbr
route   GetNbr:1        GetDist
route   GetNbr:2        GetTargets
route   GetTargets      GetDist
route   GetDist:1       GetNbr
route   GetDist:2       __Finish__
'''
Esempio n. 4
0
    # new_conf_d = predict(features)
    # Generate new configuration file and pass to other machines.

    label = 1.0
    conf = {"nodes": 10000, "range": 1, "stat_tasks": 1, "gen_tasks": 10000, "drange": 10000}
    data = {
        "GenTasks": {1: 2.0, 2: 3.0, 4: 2.4},
        "GenStubs": {"a": 1.0, "b": 2.0, "c": 3},
        "GenGraph": {"1": 101.0, "2": 99.0, "4": 2.4},
        "GetNbr": {"1": 1.0, "2": 9.0, "4": 1.4},
        "GetDist": {"1": 101.0, "2": 99.0, "3": 2.4},
    }
    setting = {"GenTasks": "average", "GenStubs": "average", "GenGraph": "average", "GetNbr": "average"}

    features, target = generate_features(label, conf, data, setting)
    train(features, target)
    new_conf_d = predict(conf)
    print new_conf_d

"""
route   __Start__       GenTasks
route   GenTasks        GenStubs
route   GenStubs        GenGraph
route   GenGraph        GetNbr
route   GetNbr:1        GetDist
route   GetNbr:2        GetTargets
route   GetTargets      GetDist
route   GetDist:1       GetNbr
route   GetDist:2       __Finish__
"""