featuretools.
EntitySet
Stores all actual data for a entityset
id
entity_dict
relationships
time_type
metadata
__init__
Creates EntitySet
id (str) – Unique identifier to associate with this instance
entities (dict[str -> tuple(pd.DataFrame, str, str, dict[str -> Variable])]) – dictionary of entities. Entries take the format {entity id -> (dataframe, id column, (time_index), (variable_types), (make_index))}. Note that time_index, variable_types and make_index are optional.
relationships (list[(str, str, str, str)]) – List of relationships between entities. List items are a tuple with the format (parent entity id, parent variable, child entity id, child variable).
Example
entities = { "cards" : (card_df, "id"), "transactions" : (transactions_df, "id", "transaction_time") } relationships = [("cards", "id", "transactions", "card_id")] ft.EntitySet("my-entity-set", entities, relationships)
Methods
__init__([id, entities, relationships])
add_interesting_values([max_values, verbose])
add_interesting_values
Find interesting values for categorical variables, to be used to generate “where” clauses
add_last_time_indexes([updated_entities])
add_last_time_indexes
Calculates the last time index values for each entity (the last time an instance or children of that instance were observed). Used when calculating features using training windows :param updated_entities: List of entity ids to update last_time_index for (will update all parents of those entities as well) :type updated_entities: list[str].
add_relationship(relationship)
add_relationship
Add a new relationship between entities in the entityset
add_relationships(relationships)
add_relationships
Add multiple new relationships to a entityset
concat(other[, inplace])
concat
Combine entityset with another to create a new entityset with the combined data of both entitysets.
entity_from_dataframe(entity_id, dataframe)
entity_from_dataframe
Load the data for a specified entity from a Pandas DataFrame.
find_backward_paths(start_entity_id, …)
find_backward_paths
Generator which yields all backward paths between a start and goal entity.
find_forward_paths(start_entity_id, …)
find_forward_paths
Generator which yields all forward paths between a start and goal entity.
get_backward_entities(entity_id[, deep])
get_backward_entities
Get entities that are in a backward relationship with entity
get_backward_relationships(entity_id)
get_backward_relationships
get relationships where entity “entity_id” is the parent.
get_forward_entities(entity_id[, deep])
get_forward_entities
Get entities that are in a forward relationship with entity
get_forward_relationships(entity_id)
get_forward_relationships
Get relationships where entity “entity_id” is the child
has_unique_forward_path(start_entity_id, …)
has_unique_forward_path
Is the forward path from start to end unique?
normalize_entity(base_entity_id, …[, …])
normalize_entity
Create a new entity and relationship from unique values of an existing variable.
plot([to_file])
plot
Create a UML diagram-ish graph of the EntitySet.
query_by_values(entity_id, instance_vals[, …])
query_by_values
Query instances that have variable with given value
reset_data_description()
reset_data_description
to_csv(path[, sep, encoding, engine, …])
to_csv
Write entityset to disk in the csv format, location specified by path.
to_dictionary()
to_dictionary
to_parquet(path[, engine, compression, …])
to_parquet
Write entityset to disk in the parquet format, location specified by path.
to_pickle(path[, compression, profile_name])
to_pickle
Write entityset in the pickle format, location specified by path.
Attributes
entities
Returns the metadata for this EntitySet.