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AutoML Python API reference | Databricks Documentation

This article describes the AutoML Python API, which provides methods to start classification, regression, and forecasting AutoML runs. Each method call trains a set of models and generates a trial notebook for each model.

For more information on AutoML, including a low-code UI option, see What is AutoML?.

The databricks.automl.classify method configures an AutoML run to train a classification model.

note

The max_trials parameter is deprecated in Databricks Runtime 10.4 ML and is not supported in Databricks Runtime 11.0 ML and above. Use timeout_minutes to control the duration of an AutoML run.

Python

databricks.automl.classify(
  dataset: Union[pyspark.sql.DataFrame, pandas.DataFrame, pyspark.pandas.DataFrame, str],
  *,
  target_col: str,
  primary_metric: str = "f1",
  data_dir: Optional[str] = None,
  experiment_dir: Optional[str] = None,                             
  experiment_name: Optional[str] = None,                            
  exclude_cols: Optional[List[str]] = None,                         
  exclude_frameworks: Optional[List[str]] = None,                   
  feature_store_lookups: Optional[List[Dict]] = None,               
  imputers: Optional[Dict[str, Union[str, Dict[str, Any]]]] = None, 
  pos_label: Optional[Union[int, bool, str]] = None,                 
  time_col: Optional[str] = None,
  split_col: Optional[str] = None,                                  
  sample_weight_col: Optional[str] = None                           
  max_trials: Optional[int] = None,                                 
  timeout_minutes: Optional[int] = None,
) -> AutoMLSummary

The databricks.automl.regress method configures an AutoML run to train a regression model. This method returns an AutoMLSummary.

note

The max_trials parameter is deprecated in Databricks Runtime 10.4 ML and is not supported in Databricks Runtime 11.0 ML and above. Use timeout_minutes to control the duration of an AutoML run.

Python

databricks.automl.regress(
  dataset: Union[pyspark.sql.DataFrame, pandas.DataFrame, pyspark.pandas.DataFrame, str],
  *,
  target_col: str,
  primary_metric: str = "r2",
  data_dir: Optional[str] = None,
  experiment_dir: Optional[str] = None,                             
  experiment_name: Optional[str] = None,                            
  exclude_cols: Optional[List[str]] = None,                         
  exclude_frameworks: Optional[List[str]] = None,                   
  feature_store_lookups: Optional[List[Dict]] = None,               
  imputers: Optional[Dict[str, Union[str, Dict[str, Any]]]] = None, 
  time_col: Optional[str] = None,
  split_col: Optional[str] = None,                                  
  sample_weight_col: Optional[str] = None,                          
  max_trials: Optional[int] = None,                                 
  timeout_minutes: Optional[int] = None,
) -> AutoMLSummary

The databricks.automl.forecast method configures an AutoML run for training a forecasting model. This method returns an AutoMLSummary. To use Auto-ARIMA, the time series must have a regular frequency (that is, the interval between any two points must be the same throughout the time series). The frequency must match the frequency unit specified in the API call. AutoML handles missing time steps by filling in those values with the previous value.

Python

databricks.automl.forecast(
  dataset: Union[pyspark.sql.DataFrame, pandas.DataFrame, pyspark.pandas.DataFrame, str],
  *,
  target_col: str,
  time_col: str,
  primary_metric: str = "smape",
  country_code: str = "US",                                         
  frequency: str = "D",
  horizon: int = 1,
  data_dir: Optional[str] = None,
  experiment_dir: Optional[str] = None,
  experiment_name: Optional[str] = None,                            
  exclude_frameworks: Optional[List[str]] = None,
  feature_store_lookups: Optional[List[Dict]] = None,               
  identity_col: Optional[Union[str, List[str]]] = None,
  sample_weight_col: Optional[str] = None,                          
  output_database: Optional[str] = None,                            
  timeout_minutes: Optional[int] = None,
) -> AutoMLSummary

The databricks.automl.import_notebook method imports a notebook that has been saved as an MLflow artifact. This method returns an ImportNotebookResult.

Python

databricks.automl.import_notebook(
  artifact_uri: str,
  path: str,
  overwrite: bool = False
) -> ImportNotebookResult:

Python

summary = databricks.automl.classify(...)
result = databricks.automl.import_notebook(summary.trials[5].artifact_uri, "/Users/you@yourcompany.com/path/to/directory")
print(result.path)
print(result.url)

Summary object for an AutoML run that describes the metrics, parameters, and other details for each of the trials. You also use this object to load the model trained by a specific trial.

Summary object for each individual trial.

TrialInfo has a method to load the model generated for the trial.

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