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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 log
  • dataset_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 input
  • feature_name: name of a single feature. Cannot be specified if 'features' is specified
  • value: value of as single feature. Cannot be specified if 'features' is specified
  • dataset_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.

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