Examples of com.senseidb.search.client.req.relevance.Model

Package com.senseidb.search.client.req.relevance

Examples of com.senseidb.search.client.req.relevance.Model

com.senseidb.search.client.req.relevance.Model
The relevance json has two parts, one is the model part, another is the values part. Model part should be relatively static. The values part provides the input required by the static model. Each request may have different values part, but may probably use the same model. Inside the model part, we define 4 items:
1. variables — User provided variable, the variable name and type are defined here, but the actual values has to be filled outside the model part, but in the values part.
2. facets — Define what facet/column will be used in the relevance model. It automatically defined the variable name the same as the facet name.
3. function_params — Define which parameters will be used in the function. All the parameters listed here have to be defined either in the variables part, or the facets part.
4. function — The real function body. Java code here. It must have a return type, and return a float value. No malicious class can be used, a custom class loader will prevent it from being loaded if it is not in the white class list.

      assertEquals("automatic,hybrid,leather,reliable", ret.get(5L).get("tags"));
  }
  @Test
  public void testRelevance() throws Exception
  {
    Model model = Model.builder().addFacets(RelevanceFacetType.type_int, "year","mileage").
        addFacets(RelevanceFacetType.type_long, "groupid").addFacets(RelevanceFacetType.type_string, "color","category").
        addFunctionParams("_INNER_SCORE", "thisYear", "year","goodYear","mileageWeight","mileage","color", "yearcolor", "colorweight", "category", "categorycolor").
        addVariables(VariableType.set_int, "goodYear").addVariables(VariableType.type_int, "thisYear").
        addVariables(VariableType.map_int_float, "mileageWeight").addVariables(VariableType.map_int_string, "yearcolor")
        .addVariables(VariableType.map_string_float, "colorweight").addVariables(VariableType.map_string_string, "categorycolor").saveAs("model", true).

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import com.senseidb.search.client.req.relevance.VariableType;


public class RelevanceExample {
public static void main(String[] args) throws Exception {
  SenseiServiceProxy senseiServiceProxy = new SenseiServiceProxy("localhost", 8080);
  Model model = Model.builder().addFacets(RelevanceFacetType.type_int, "year","mileage").
      addFacets(RelevanceFacetType.type_long, "groupid").addFacets(RelevanceFacetType.type_string, "color","category").
      addFunctionParams("_INNER_SCORE", "thisYear", "year","goodYear","mileageWeight","mileage","color", "yearcolor", "colorweight", "category", "categorycolor").
      addVariables(VariableType.set_int, "goodYear").addVariables(VariableType.type_int, "thisYear").
      addVariables(VariableType.map_int_float, "mileageWeight").addVariables(VariableType.map_int_string, "yearcolor")
      .addVariables(VariableType.map_string_float, "colorweight").addVariables(VariableType.map_string_string, "categorycolor").

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Related Classes of com.senseidb.search.client.req.relevance.Model

com.senseidb.test.client.JavaClientIntegrationTest

com.senseidb.test.client.RelevanceExample

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