callback_param

callback_param

Classes

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'}

{None, int, list, tuple, set}

validation frequency during training.

None or int

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

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']

bool, default: False

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

int, default: 1

The callbacks save model every save_freq epoch

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.validation_freqs is not None:
            if type(self.validation_freqs).__name__ not in ["int", "list", "tuple", "set"]:
                raise ValueError(
                    "validation strategy param's validate_freqs's type not supported ,"
                    " should be int or list or tuple or set"
                )
            if type(self.validation_freqs).__name__ == "int" and \
                    self.validation_freqs <= 0:
                raise ValueError("validation strategy param's validate_freqs should greater than 0")

        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.validation_freqs is not None:
        if type(self.validation_freqs).__name__ not in ["int", "list", "tuple", "set"]:
            raise ValueError(
                "validation strategy param's validate_freqs's type not supported ,"
                " should be int or list or tuple or set"
            )
        if type(self.validation_freqs).__name__ == "int" and \
                self.validation_freqs <= 0:
            raise ValueError("validation strategy param's validate_freqs should greater than 0")

    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

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Last update: 2022-07-07