Table of Contents
WhyLogsRun Objects
class WhyLogsRun(object)
log_pandas
| log_pandas(df: pd.DataFrame, dataset_name: Optional[str] = None)
Log the statistics of a Pandas dataframe. Note that this method is additive within a run: calling this method with a specific dataset name will not generate a new profile; instead, data will be aggregated into the existing profile.
In order to create a new profile, please specify a dataset_name
Arguments:
df
: the Pandas dataframe to logdataset_name
: the name of the dataset (Optional). If not specified, the experiment name is used
log
| log(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).
Arguments:
features
: a map of key value feature for model inputfeature_name
: name of a single feature. Cannot be specified if 'features' is specifiedvalue
: value of as single feature. Cannot be specified if 'features' is specifieddataset_name
: the name of the dataset. If not specified, we fall back to using the experiment name
new_model_log
new_model_log(**kwargs)
Hijack the mlflow.models.Model.log method and upload the .whylogs.yaml configuration to the model path This will allow us to pick up the configuration later under /opt/ml/model/.whylogs.yaml path
enable_mlflow
enable_mlflow() -> bool
Enable whylogs in mlflow
module via mlflow.whylogs
.
Returns:
True if MLFlow has been patched. False otherwise.