public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
    """
	Validates that the encoded PMML model received matches expected schema.
	
	@param pmml {@link PMML} encoding of a decision forest
	@param schema expected schema attributes of decision forest
    """
    List<Model> models = pmml.getModels();
    Preconditions.checkArgument(models.size() == 1,
                                "Should have exactly one model, but had %s", models.size());

    Model model = models.get(0);
    MiningFunction function = model.getMiningFunction();
    if (schema.isClassification()) {
      Preconditions.checkArgument(function == MiningFunction.CLASSIFICATION,
                                  "Expected classification function type but got %s",
                                  function);
    } else {
      Preconditions.checkArgument(function == MiningFunction.REGRESSION,
                                  "Expected regression function type but got %s",
                                  function);
    }

    DataDictionary dictionary = pmml.getDataDictionary();
    Preconditions.checkArgument(
        schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
        "Feature names in schema don't match names in PMML");

    MiningSchema miningSchema = model.getMiningSchema();
    Preconditions.checkArgument(schema.getFeatureNames().equals(
        AppPMMLUtils.getFeatureNames(miningSchema)));

    Integer pmmlIndex = AppPMMLUtils.findTargetIndex(miningSchema);
    if (schema.hasTarget()) {
      int schemaIndex = schema.getTargetFeatureIndex();
      Preconditions.checkArgument(
          pmmlIndex != null && schemaIndex == pmmlIndex,
          "Configured schema expects target at index %s, but PMML has target at index %s",
          schemaIndex, pmmlIndex);
    } else {
      Preconditions.checkArgument(pmmlIndex == null);
    }
  }
public static void validatePMMLVsSchema(PMML pmml, InputSchema schema) {
    List<Model> models = pmml.getModels();
    Preconditions.checkArgument(models.size() == 1,
                                "Should have exactly one model, but had %s", models.size());

    Model model = models.get(0);
    MiningFunction function = model.getMiningFunction();
    if (schema.isClassification()) {
      Preconditions.checkArgument(function == MiningFunction.CLASSIFICATION,
                                  "Expected classification function type but got %s",
                                  function);
    } else {
      Preconditions.checkArgument(function == MiningFunction.REGRESSION,
                                  "Expected regression function type but got %s",
                                  function);
    }

    DataDictionary dictionary = pmml.getDataDictionary();
    Preconditions.checkArgument(
        schema.getFeatureNames().equals(AppPMMLUtils.getFeatureNames(dictionary)),
        "Feature names in schema don't match names in PMML");

    MiningSchema miningSchema = model.getMiningSchema();
    Preconditions.checkArgument(schema.getFeatureNames().equals(
        AppPMMLUtils.getFeatureNames(miningSchema)));

    Integer pmmlIndex = AppPMMLUtils.findTargetIndex(miningSchema);
    if (schema.hasTarget()) {
      int schemaIndex = schema.getTargetFeatureIndex();
      Preconditions.checkArgument(
          pmmlIndex != null && schemaIndex == pmmlIndex,
          "Configured schema expects target at index %s, but PMML has target at index %s",
          schemaIndex, pmmlIndex);
    } else {
      Preconditions.checkArgument(pmmlIndex == null);
    }
  }
public static void validatePMMLVsSchema ( PMML pmml , InputSchema schema ) { List < Model > models = pmml . getModels ( ) ; Preconditions . checkArgument ( models . size ( ) == 1 , "Should have exactly one model, but had %s" , models . size ( ) ) ; Model model = models . get ( 0 ) ; MiningFunction function = model . getMiningFunction ( ) ; if ( schema . isClassification ( ) ) { Preconditions . checkArgument ( function == MiningFunction . CLASSIFICATION , "Expected classification function type but got %s" , function ) ; } else { Preconditions . checkArgument ( function == MiningFunction . REGRESSION , "Expected regression function type but got %s" , function ) ; } DataDictionary dictionary = pmml . getDataDictionary ( ) ; Preconditions . checkArgument ( schema . getFeatureNames ( ) . equals ( AppPMMLUtils . getFeatureNames ( dictionary ) ) , "Feature names in schema don't match names in PMML" ) ; MiningSchema miningSchema = model . getMiningSchema ( ) ; Preconditions . checkArgument ( schema . getFeatureNames ( ) . equals ( AppPMMLUtils . getFeatureNames ( miningSchema ) ) ) ; Integer pmmlIndex = AppPMMLUtils . findTargetIndex ( miningSchema ) ; if ( schema . hasTarget ( ) ) { int schemaIndex = schema . getTargetFeatureIndex ( ) ; Preconditions . checkArgument ( pmmlIndex != null && schemaIndex == pmmlIndex , "Configured schema expects target at index %s, but PMML has target at index %s" , schemaIndex , pmmlIndex ) ; } else { Preconditions . checkArgument ( pmmlIndex == null ) ; } }
Validates that the encoded PMML model received matches expected schema.

@param pmml {@link PMML} encoding of a decision forest
@param schema expected schema attributes of decision forest
Validates that the encoded PMML model received matches expected schema .