'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()
