예제 #1
0
            'label_inappropriate',
            'label_offtopic',
            'label_personalstories',
            'label_possiblyfeedback',
            'label_sentimentnegative',
            'label_sentimentpositive',
        ]

        IS_DEVELOPMENT = False

        mlflow_logger = m.MLFlowLogger(uri=TRACKING_URI,
                                       experiment=EXPERIMENT_NAME,
                                       is_dev=IS_DEVELOPMENT,
                                       params=mlflow_params,
                                       tags=mlflow_tags)
        training = m.Modeling(data, gs, mlflow_logger)
        for method, strat in trans_os.items():
            for strategy in strat:
                print(method, strategy)
                for label in TARGET_LABELS:
                    logger.info(f"-" * 20)
                    logger.info(f"Target: {label}")
                    data.set_label(label=label)
                    data.set_balance_method(balance_method=method,
                                            sampling_strategy=strategy)
                    training.train()
                    training.evaluate(["train", "val"])
                    #if True:
                    with mlflow.start_run() as run:
                        mlflow_logger.log()
        'label_discriminating',
        'label_inappropriate',
        'label_sentimentnegative',
        'label_needsmoderation',
    ]
    mlflow_params = dict()
    mlflow_params["model"] = "DummyClassifier"
    mlflow_tags = {
        "cycle4": True,
    }

    IS_DEVELOPMENT = False

    data = m.Posts()
    mlflow_logger = m.MLFlowLogger(uri=TRACKING_URI,
                                   experiment=EXPERIMENT_NAME,
                                   is_dev=IS_DEVELOPMENT,
                                   params=mlflow_params,
                                   tags=mlflow_tags)
    training = m.Modeling(data, pipeline, mlflow_logger)
    for label in TARGET_LABELS:
        logger.info(f"-" * 20)
        logger.info(f"Target: {label}")
        data.set_label(label=label)
        data.set_balance_method(balance_method=None, sampling_strategy=None)
        training.train()
        training.evaluate(["train", "val"])
        #if True:
        with mlflow.start_run(run_name='dummy_classifier') as run:
            mlflow_logger.log()