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callback_param

callback_param

Classes

CallbackParam (BaseParam)

Define callback method that used in federated ml.

Parameters:

Name Type Description Default
callbacks list, default: []

Indicate what kinds of callback functions is desired during the training process. Accepted values: {'EarlyStopping', 'ModelCheckpoint'， 'PerformanceEvaluate'}

 None
validation_freqs {None, int, list, tuple, set}

validation frequency during training.

 None
early_stopping_rounds None or int

Will stop training if one metric doesn’t improve in last early_stopping_round rounds

 None
metrics None, or list

Indicate when executing evaluation during train process, which metrics will be used. If set as empty, default metrics for specific task type will be used. As for binary classification, default metrics are ['auc', 'ks']

 None
use_first_metric_only bool, default: False

Indicate whether use the first metric only for early stopping judgement.

 False
save_freq int, default: 1

The callbacks save model every save_freq epoch

 1
Source code in federatedml/param/callback_param.py 
class CallbackParam(BaseParam):
    """
    Define callback method that used in federated ml.

    Parameters
    ----------
    callbacks : list, default: []
        Indicate what kinds of callback functions is desired during the training process.
        Accepted values: {'EarlyStopping', 'ModelCheckpoint'， 'PerformanceEvaluate'}

    validation_freqs: {None, int, list, tuple, set}
        validation frequency during training.

    early_stopping_rounds: None or int
        Will stop training if one metric doesn’t improve in last early_stopping_round rounds

    metrics: None, or list
        Indicate when executing evaluation during train process, which metrics will be used. If set as empty,
        default metrics for specific task type will be used. As for binary classification, default metrics are
        ['auc', 'ks']

    use_first_metric_only: bool, default: False
        Indicate whether use the first metric only for early stopping judgement.

    save_freq: int, default: 1
        The callbacks save model every save_freq epoch


    """

    def __init__(self, callbacks=None, validation_freqs=None, early_stopping_rounds=None,
                 metrics=None, use_first_metric_only=False, save_freq=1):
        super(CallbackParam, self).__init__()
        self.callbacks = callbacks or []
        self.validation_freqs = validation_freqs
        self.early_stopping_rounds = early_stopping_rounds
        self.metrics = metrics or []
        self.use_first_metric_only = use_first_metric_only
        self.save_freq = save_freq

    def check(self):

        if self.early_stopping_rounds is None:
            pass
        elif isinstance(self.early_stopping_rounds, int):
            if self.early_stopping_rounds < 1:
                raise ValueError("early stopping rounds should be larger than 0 when it's integer")
            if self.validation_freqs is None:
                raise ValueError("validation freqs must be set when early stopping is enabled")

        if self.metrics is not None and not isinstance(self.metrics, list):
            raise ValueError("metrics should be a list")

        if not isinstance(self.use_first_metric_only, bool):
            raise ValueError("use_first_metric_only should be a boolean")

        return True
__init__(self, callbacks=None, validation_freqs=None, early_stopping_rounds=None, metrics=None, use_first_metric_only=False, save_freq=1) special
Source code in federatedml/param/callback_param.py 
def __init__(self, callbacks=None, validation_freqs=None, early_stopping_rounds=None,
             metrics=None, use_first_metric_only=False, save_freq=1):
    super(CallbackParam, self).__init__()
    self.callbacks = callbacks or []
    self.validation_freqs = validation_freqs
    self.early_stopping_rounds = early_stopping_rounds
    self.metrics = metrics or []
    self.use_first_metric_only = use_first_metric_only
    self.save_freq = save_freq
check(self)
Source code in federatedml/param/callback_param.py 
def check(self):

    if self.early_stopping_rounds is None:
        pass
    elif isinstance(self.early_stopping_rounds, int):
        if self.early_stopping_rounds < 1:
            raise ValueError("early stopping rounds should be larger than 0 when it's integer")
        if self.validation_freqs is None:
            raise ValueError("validation freqs must be set when early stopping is enabled")

    if self.metrics is not None and not isinstance(self.metrics, list):
        raise ValueError("metrics should be a list")

    if not isinstance(self.use_first_metric_only, bool):
        raise ValueError("use_first_metric_only should be a boolean")

    return True
Last update: 2021-12-01
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