Interpretation Parameters

The following parameters can be specified when starting a new interpretation using the Python API:

dataset : Union[str, Path, ExplainableDataset, datatable.Frame, Any]
- Dataset source: explainable dataset instance, datatable frame, string (path to CSV, .jay or any other file type supported by datatable), handle to a remote dataset hosted by Driverless AI or a dictionary (used to construct frame).
model : Union[str, Path, ExplainableModel, Any]
- Path to model (str, Path), explainable model (ExplainableModel) , handle to a remote model hosted by Driverless AI or an instance of 3rd party model (like Scikit) to interpret.
target_col : str
- Target column name - must be valid dataset column name.
explainers : Optional[List[Union[str, commons.ExplainerToRun]]]
- Explainer IDs to run within the interpretation or ExplainerToRun instances with explainer parameters. In case of None or empty list are run all compatible explainers.
explainer_keywords: Optional[List[str]]
- Run compatible explainers which have given keyword (AND). This setting is used only in case that explainers parameter is empty list (or None).
validset : Optional[Union[src, Path, ExplainableDataset, datatable.Frame, Any]]
- Optional validation dataset - sources might the same as in case of dataset.
testset : Optional[Union[src, Path, ExplainableDataset, datatable.Frame, Any]]
- Optional test dataset - sources might the same as in case of dataset.
use_raw_features : bool
- True to use original features, False to use transformed features.
used_features : Optional[List]
- Optional parameter specifying features (dataset columns) used by the model. This parameter is used in case that an instance of the model (not ExplainableModel) is provided by the user - therefore ExplainableModel’s metadata are not available.
weight_col : str
- Name of the weight column to be used by explainers.
prediction_col : str
- Name of the predictions column - in case of 3rd party model (standalone MLI).
drop_cols : Optional[List]
- List of the columns to drop from the interpretation i.e. columns names which should not be explained.
sample_num_rows : Optional[int]
- Sample the dataset to given number of rows. By default, the dataset is sampled based on the RAM size (or to 25000 rows). Use 0 to disable sampling or use an integer value greater than 0 to sample to the specified number of rows.
sampler : Optional[DatasetSampler]
Sampling method (implementation) to be used - see h2o_sonar.utils.sampling module (documentation) for available sampling methods. Use a sampler instance to use the specific sampling method.
container : Optional[Union[str, explainer_container.ExplainerContainer]]
- Optional explainer container name (str) or container instance to be used to run the interpretation.
results_location : Optional[Union[str, pathlib.Path, Dict, Any]]
- Where to store interpretation results - filesystem (path as string or Path), memory (dictionary) or DB. If None, then results are stored to the current directory.
persistence_type : persist.PersistenceType
- Optional choice of the persistence type: file-system (default), in-memory or database. This option does not override persistence type in case that container is provided.
args_as_json_location : Optional[Union[str, pathlib.Path]]
- Load all positional arguments and keyword arguments from JSon file. This is useful when input is generated, persisted, repeated and used from CLI (which doesn’t support all the options). IMPORTANT: if this argument is specified, then all other function parameters are ignored.
upload_to : Union[str, config.ConnectionConfig]
- Upload the interpretation report to the H2O GPT Enterprise in order to talk to the report.
log_level : int
- Optional container and explainers log level.