src/whylogs/mlflow/patcher.py
def log(
        self,
        features: Dict[str, any] = None,
        feature_name: str = None,
        value: any = None,
        dataset_name: Optional[str] = None,
    ):
        """
        Logs a collection of features or a single feature (must specify one or the other).

        :param features: a map of key value feature for model input
        :param feature_name: a dictionary of key->value for multiple features. Each entry represent a single columnar feature
        :param feature_name: name of a single feature. Cannot be specified if 'features' is specified
        :param value: value of as single feature. Cannot be specified if 'features' is specified
        :param dataset_name: the name of the dataset. If not specified, we fall back to using the experiment name
        """
        ylogs = self._get_or_create_logger(dataset_name)
        if ylogs is None:
            logger.warning(
                "Unable to get an active logger. Are you in an active MLFlow run?"
            )
            return

        ylogs.log(features, feature_name, value)

        Logs a collection of features or a single feature (must specify one or the other).

        :param features: a map of key value feature for model input
        :param feature_name: a dictionary of key->value for multiple features. Each entry represent a single columnar feature
        :param feature_name: name of a single feature. Cannot be specified if 'features' is specified
        :param value: value of as single feature. Cannot be specified if 'features' is specified
        :param dataset_name: the name of the dataset. If not specified, we fall back to using the experiment name
        
Logs a collection of features or a single feature (must specify one or the other).
Logs a collection of features or a single feature ( must specify one or the other ) .
def log(
        self,
        features: Dict[str, any] = None,
        feature_name: str = None,
        value: any = None,
        dataset_name: Optional[str] = None,
    ):
        ylogs = self._get_or_create_logger(dataset_name)
        if ylogs is None:
            logger.warning(
                "Unable to get an active logger. Are you in an active MLFlow run?"
            )
            return
        ylogs.log(features, feature_name, value)
def log ( self , features : Dict [ str , any ] = None , feature_name : str = None , value : any = None , dataset_name : Optional [ str ] = None , ) : ylogs = self . _get_or_create_logger ( dataset_name ) if ylogs is None : logger . warning ( "Unable to get an active logger. Are you in an active MLFlow run?" ) return ylogs . log ( features , feature_name , value )
Logs a collection of features or a single feature (must specify one or the other).
Logs a collection of features or a single feature ( must specify one or the other ) .
"""
        Logs a collection of features or a single feature (must specify one or the other).

        :param features: a map of key value feature for model input
        :param feature_name: a dictionary of key->value for multiple features. Each entry represent a single columnar feature
        :param feature_name: name of a single feature. Cannot be specified if 'features' is specified
        :param value: value of as single feature. Cannot be specified if 'features' is specified
        :param dataset_name: the name of the dataset. If not specified, we fall back to using the experiment name
        """
self
features
Dict[str, any]
a map of key value feature for model input
a map of key value feature for model input
feature_name
str
name of a single feature. Cannot be specified if 'features' is specified
name of a single feature . Cannot be specified if ' features ' is specified
value
any
value of as single feature. Cannot be specified if 'features' is specified
value of as single feature . Cannot be specified if ' features ' is specified
dataset_name
Optional[str]
the name of the dataset. If not specified, we fall back to using the experiment name
the name of the dataset . If not specified we fall back to using the experiment name