Recipes

class datarobot.models.recipe.Recipe

Data wrangling entity, which contains all information needed to transform dataset and generate SQL.

classmethod update_downsampling(recipe_id, downsampling)

Set downsampling for the recipe, applied during publishing.

Return type:

Recipe

retrieve_preview(max_wait=600, number_of_operations_to_use=None)

Retrieve preview and compute it, if absent.

Parameters:

max_wait (int) – The number of seconds to wait for the result.

number_of_operations_to_use (Optional[int]) – Request preview for particular number of operations.

Returns:

preview

Return type:

dict

retrieve_insights(max_wait=600, number_of_operations_to_use=None)

Retrieve insights for the sample. When preview is requested, the insights job starts automatically.

Parameters:

max_wait (int) – The number of seconds to wait for the result.

number_of_operations_to_use (Optional[int]) – Retrieves insights for the specified number of operations. First, preview computation for the same number of operations must be submitted.

Return type:

Any

classmethod set_inputs(recipe_id, inputs)

Set inputs for the recipe.

Return type:

Recipe

classmethod set_operations(recipe_id, operations)

Set operations for the recipe.

Return type:

Recipe

get_sql(operations=None)

Generate sql for the given recipe in a transient way, recipe is not modified. if operations is None, recipe operations are used to generate sql. if operations = [], recipe operations are ignored during sql generation. if operations is not empty list, generate sql for them.

Return type:

str

classmethod from_data_store(use_case, data_store, data_source_type, dialect, data_source_inputs)

Create a wrangling recipe from data store.

Return type:

Recipe

classmethod from_dataset(use_case, dataset, dialect=None, inputs=None, recipe_type=RecipeType.WRANGLING)

Create a wrangling recipe from dataset.

Return type:

Recipe

class datarobot.models.recipe.RecipeSettings

Settings, for example to apply at downsampling stage.

class datarobot.models.recipe.RecipeDatasetInput

Object, describing inputs for recipe transformations.

class datarobot.models.recipe.DatasetInput

class datarobot.models.recipe.DataSourceInput

Inputs required to create a new recipe from data store.

Recipe Operations

class datarobot.models.recipe_operation.WranglingOperation

class datarobot.models.recipe_operation.DownsamplingOperation

class datarobot.models.recipe_operation.SamplingOperation

class datarobot.models.recipe_operation.BaseTimeAwareTask

class datarobot.models.recipe_operation.TaskPlanElement

class datarobot.models.recipe_operation.CategoricalStats

class datarobot.models.recipe_operation.NumericStats

class datarobot.models.recipe_operation.Lags

class datarobot.models.recipe_operation.LagsOperation

Generate lags in a window.

class datarobot.models.recipe_operation.WindowCategoricalStatsOperation

Generate rolling statistics in a window for categorical features.

class datarobot.models.recipe_operation.WindowNumericStatsOperation

Generate various rolling numeric statistics in a window. Output could be a several columns.

class datarobot.models.recipe_operation.TimeSeriesOperation

Operation to generate a dataset ready for time series modeling: with forecast point, forecast distances, known in advance columns, etc.

class datarobot.models.recipe_operation.ComputeNewOperation

class datarobot.models.recipe_operation.RenameColumnsOperation

class datarobot.models.recipe_operation.FilterCondition

class datarobot.models.recipe_operation.FilterOperation

Filter rows.

class datarobot.models.recipe_operation.DropColumnsOperation

class datarobot.models.recipe_operation.RandomSamplingOperation

class datarobot.models.recipe_operation.DatetimeSamplingOperation