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Ranking
Ranking models are often used in recommendation systems, ads, search engines, etc. In Aporia, these models are represented with the
ranking model type.If you have a ranking or recommendations model, then your database may look like the following:
id | feature1 (numeric) | feature2 (boolean) | scores (array) | relevance (array) | timestamp (datetime) |
|---|---|---|---|---|---|
1 | 13.5 | True | [9, 8, 10, ...] | [2, 0, 1, ...] | 2014-10-19 10:23:54 |
2 | -8 | False | [4.5, 8.7, 9, ...] | [0, 1, 2, ...] | 2014-10-19 10:24:24 |
To monitor a ranking model, create a new model version with an
array field(s):apr_model = aporia.create_model_version(
model_id="<MODEL_ID>", # You will need to create a model with this MODEL_ID in advance
model_version="v1",
model_type="ranking"
features={
...
},
predictions={
"scores": "array"
},
)
To connect your data source to this model in Aporia, please call the
connect_serving(...) API: apr_model.connect_serving(
data_source=my_data_source,
id_column="id",
timestamp_column="timestamp",
predictions={
# Prediction name -> Column name representing
"relevance": "scores"
}
)
Check out the Data Sources section for further reading on the available data sources and how to connect to each one of them.
Last modified 10mo ago