relevanceai.api.endpoints.aggregate

Module Contents

Classes

AggregateClient

Aggregate service
class relevanceai.api.endpoints.aggregate.AggregateClient(project, api_key)

Bases: relevanceai.base._Base

Aggregate service

aggregate(self, dataset_id: str, metrics: list = [], groupby: list = [], filters: list = [], page_size: int = 20, page: int = 1, asc: bool = False, flatten: bool = True, alias: str = 'default')

Aggregation/Groupby of a collection using an aggregation query. The aggregation query is a json body that follows the schema of:

>>> {
>>>        "groupby" : [
>>>            {"name": <alias>, "field": <field in the collection>, "agg": "category"},
>>>            {"name": <alias>, "field": <another groupby field in the collection>, "agg": "numeric"}
>>>        ],
>>>        "metrics" : [
>>>            {"name": <alias>, "field": <numeric field in the collection>, "agg": "avg"}
>>>            {"name": <alias>, "field": <another numeric field in the collection>, "agg": "max"}
>>>        ]
>>>    }
>>>    For example, one can use the following aggregations to group score based on region and player name.
>>>    {
>>>        "groupby" : [
>>>            {"name": "region", "field": "player_region", "agg": "category"},
>>>            {"name": "player_name", "field": "name", "agg": "category"}
>>>        ],
>>>        "metrics" : [
>>>            {"name": "average_score", "field": "final_score", "agg": "avg"},
>>>            {"name": "max_score", "field": "final_score", "agg": "max"},
>>>            {'name':'total_score','field':"final_score", 'agg':'sum'},
>>>            {'name':'average_deaths','field':"final_deaths", 'agg':'avg'},
>>>            {'name':'highest_deaths','field':"final_deaths", 'agg':'max'},
>>>        ]
>>>    }

“groupby” is the fields you want to split the data into. These are the available groupby types:

  • category : groupby a field that is a category

  • numeric: groupby a field that is a numeric

“metrics” is the fields and metrics you want to calculate in each of those, every aggregation includes a frequency metric. These are the available metric types:

  • “avg”, “max”, “min”, “sum”, “cardinality”

The response returned has the following in descending order.

If you want to return documents, specify a “group_size” parameter and a “select_fields” parameter if you want to limit the specific fields chosen. This looks as such:

>>>    {
>>>    'groupby':[
>>>        {'name':'Manufacturer','field':'manufacturer','agg':'category',
>>>        'group_size': 10, 'select_fields': ["name"]},
>>>    ],
>>>    'metrics':[
>>>        {'name':'Price Average','field':'price','agg':'avg'},
>>>    ],
>>>    }
>>>
>>>    {"title": {"title": "books", "frequency": 200, "documents": [{...}, {...}]}, {"title": "books", "frequency": 100, "documents": [{...}, {...}]}}

For array-aggregations, you can add “agg”: “array” into the aggregation query.

Parameters

  • dataset_id (string) – Unique name of dataset

  • metrics (list) – Fields and metrics you want to calculate

  • groupby (list) – Fields you want to split the data into

  • filters (list) – Query for filtering the search results

  • page_size (int) – Size of each page of results

  • page (int) – Page of the results

  • asc (bool) – Whether to sort results by ascending or descending order

  • flatten (bool) – Whether to flatten

  • alias (string) – Alias used to name a vector field. Belongs in field_{alias} vector