predict

Pipeline.predict(population_table, peripheral_tables=None, table_name='')

Forecasts on new, unseen data using the trained predictor.

Returns the predictions generated by the pipeline based on population_table and peripheral_tables or writes them into a data base named table_name.

Args:

population_table (Union[pandas.DataFrame, getml.data.DataFrame]):

Main table corresponding to the population Placeholder instance variable. Its target variable(s) will be ignored.

peripheral_tables (

Union[getml.data.DataFrame, List[getml.data.DataFrame]]): Additional tables corresponding to the peripheral Placeholder instance variable. They have to be provided in the exact same order as their corresponding placeholders! A single DataFrame will be wrapped into a list internally.

table_name (str, optional):

If not an empty string, the resulting predictions will be written into the database of the same name. See Unified import interface for further information.

Raises:

IOError: If the pipeline corresponding to the instance

variable name could not be found on the engine or the pipeline could not be fitted.

TypeError: If any input argument is not of proper type. ValueError: If no valid predictor was set. KeyError: If an unsupported instance variable is

encountered.

TypeError: If any instance variable is of wrong type. ValueError: If any instance variable does not match its

possible choices (string) or is out of the expected bounds (numerical).

Return:

numpy.ndarray:

Resulting predictions provided in an array of the (number of rows in population_table, number of targets in population_table).

Note:

Only fitted pipelines (fit()) can be used for prediction.