public static FieldValue evaluate(
DefineFunction defineFunction, List<FieldValue> values, EvaluationContext context) {
List<ParameterField> parameterFields = defineFunction.getParameterFields();
if (parameterFields.size() < 1) {
throw new InvalidFeatureException(defineFunction);
} // End if
if (parameterFields.size() != values.size()) {
throw new EvaluationException();
}
FunctionEvaluationContext functionContext = new FunctionEvaluationContext(context);
for (int i = 0; i < parameterFields.size(); i++) {
ParameterField parameterField = parameterFields.get(i);
FieldValue value = FieldValueUtil.refine(parameterField, values.get(i));
functionContext.declare(parameterField.getName(), value);
}
Expression expression = defineFunction.getExpression();
if (expression == null) {
throw new InvalidFeatureException(defineFunction);
}
FieldValue result = ExpressionUtil.evaluate(expression, functionContext);
return FieldValueUtil.refine(defineFunction.getDataType(), defineFunction.getOptype(), result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
StorelessUnivariateStatistic statistic = createStatistic();
DataType dataType = null;
for (FieldValue value : values) {
// "Missing values in the input to an aggregate function are simply ignored"
if (value == null) {
continue;
}
statistic.increment((value.asNumber()).doubleValue());
if (dataType != null) {
dataType = TypeUtil.getResultDataType(dataType, value.getDataType());
} else {
dataType = value.getDataType();
}
}
if (statistic.getN() == 0) {
throw new MissingResultException(null);
}
Object result = cast(getResultType(dataType), statistic.getResult());
return FieldValueUtil.create(result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
if (values.size() != 2) {
throw new EvaluationException();
}
FieldValue left = values.get(0);
FieldValue right = values.get(1);
// "If one of the input fields of a simple arithmetic function is a missing value, the result
// evaluates to missing value"
if (left == null || right == null) {
return null;
}
DataType dataType = TypeUtil.getResultDataType(left.getDataType(), right.getDataType());
Number result;
try {
result = evaluate(left.asNumber(), right.asNumber());
} catch (ArithmeticException ae) {
throw new InvalidResultException(null);
}
return FieldValueUtil.create(cast(dataType, result));
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkVariableArguments(values, 2);
Boolean result = evaluate(values.get(0), values.subList(1, values.size()));
return FieldValueUtil.create(result);
}
public static Double getCategoricalResidual(Object object, FieldValue expectedObject) {
HasProbability hasProbability = TypeUtil.cast(HasProbability.class, object);
String value = TypeUtil.format(getPredictedValue(object));
String expectedValue = TypeUtil.format(FieldValueUtil.getValue(expectedObject));
boolean equals = TypeUtil.equals(DataType.STRING, value, expectedValue);
return Double.valueOf((equals ? 1d : 0d) - hasProbability.getProbability(value));
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkArguments(values, 1);
FieldValue value = values.get(0);
String result = evaluate(value.asString());
return FieldValueUtil.create(result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkArguments(values, 1, true);
FieldValue value = values.get(0);
Boolean result = evaluate(value);
return FieldValueUtil.create(result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkArguments(values, 1);
FieldValue value = values.get(0);
Number result = cast(getResultType(value.getDataType()), evaluate(value.asNumber()));
return FieldValueUtil.create(result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkArguments(values, 2);
FieldValue left = values.get(0);
FieldValue right = values.get(1);
Boolean result = evaluate((left).compareToValue(right));
return FieldValueUtil.create(result);
}
@Override
public FieldValue evaluate(List<FieldValue> values) {
checkVariableArguments(values, 2);
Boolean result = (values.get(0)).asBoolean();
for (int i = 1; i < values.size(); i++) {
result = evaluate(result, (values.get(i)).asBoolean());
}
return FieldValueUtil.create(result);
}
private static Object getPredictedDisplayValue(
Object object, DataField dataField, Target target) {
if (object instanceof HasDisplayValue) {
HasDisplayValue hasDisplayValue = TypeUtil.cast(HasDisplayValue.class, object);
return hasDisplayValue.getDisplayValue();
}
object = getPredictedValue(object);
if (target != null) {
TargetValue targetValue = TargetUtil.getTargetValue(target, object);
if (targetValue != null) {
String displayValue = targetValue.getDisplayValue();
if (displayValue != null) {
return displayValue;
}
}
}
OpType opType = dataField.getOpType();
switch (opType) {
case CONTINUOUS:
break;
case CATEGORICAL:
case ORDINAL:
{
Value value = FieldValueUtil.getValidValue(dataField, object);
if (value != null) {
String displayValue = value.getDisplayValue();
if (displayValue != null) {
return displayValue;
}
}
}
break;
default:
throw new UnsupportedFeatureException(dataField, opType);
}
// "If the display value is not specified explicitly, then the raw predicted value is used by
// default"
return object;
}
static Object evaluateRegressionInternal(
FieldName name, Object value, ModelEvaluationContext context) {
ModelEvaluator<?> evaluator = context.getModelEvaluator();
if (Objects.equals(Evaluator.DEFAULT_TARGET, name)) {
DataField dataField = evaluator.getDataField();
if (value != null) {
value = TypeUtil.cast(dataField.getDataType(), value);
}
} else {
Target target = evaluator.getTarget(name);
if (target != null) {
if (value == null) {
value = getDefaultValue(target);
} // End if
if (value != null) {
value = processValue(target, (Double) value);
}
}
DataField dataField = evaluator.getDataField(name);
if (dataField == null) {
throw new MissingFieldException(name);
} // End if
if (value != null) {
value = TypeUtil.cast(dataField.getDataType(), value);
}
MiningField miningField = evaluator.getMiningField(name);
context.declare(
name, FieldValueUtil.createTargetValue(dataField, miningField, target, value));
}
return value;
}
static Classification evaluateClassificationInternal(
FieldName name, Classification value, ModelEvaluationContext context) {
ModelEvaluator<?> evaluator = context.getModelEvaluator();
if (Objects.equals(Evaluator.DEFAULT_TARGET, name)) {
DataField dataField = evaluator.getDataField();
if (value != null) {
value.computeResult(dataField.getDataType());
}
} else {
Target target = evaluator.getTarget(name);
if (target != null) {
if (value == null) {
value = getPriorProbabilities(target);
}
}
DataField dataField = evaluator.getDataField(name);
if (dataField == null) {
throw new MissingFieldException(name);
} // End if
if (value != null) {
value.computeResult(dataField.getDataType());
}
MiningField miningField = evaluator.getMiningField(name);
context.declare(
name,
FieldValueUtil.createTargetValue(
dataField, miningField, target, value != null ? value.getResult() : null));
}
return value;
}
/**
* Evaluates the {@link Output} element.
*
* @param predictions A map of {@link Evaluator#getTargetFields() target field} values.
* @return A map of {@link Evaluator#getTargetFields() target field} values together with {@link
* Evaluator#getOutputFields() output field} values.
*/
@SuppressWarnings(value = {"fallthrough"})
public static Map<FieldName, ?> evaluate(
Map<FieldName, ?> predictions, ModelEvaluationContext context) {
ModelEvaluator<?> modelEvaluator = context.getModelEvaluator();
Model model = modelEvaluator.getModel();
Output output = model.getOutput();
if (output == null) {
return predictions;
}
Map<FieldName, Object> result = new LinkedHashMap<>(predictions);
List<OutputField> outputFields = output.getOutputFields();
outputFields:
for (OutputField outputField : outputFields) {
FieldName targetFieldName = outputField.getTargetField();
Object targetValue = null;
ResultFeature resultFeature = outputField.getResultFeature();
String segmentId = outputField.getSegmentId();
SegmentResult segmentPredictions = null;
// Load the target value of the specified segment
if (segmentId != null) {
if (!(model instanceof MiningModel)) {
throw new InvalidFeatureException(outputField);
}
MiningModelEvaluationContext miningModelContext = (MiningModelEvaluationContext) context;
segmentPredictions = miningModelContext.getResult(segmentId);
// "If there is no Segment matching segmentId or if the predicate of the matching Segment
// evaluated to false, then the result delivered by this OutputField is missing"
if (segmentPredictions == null) {
continue outputFields;
} // End if
if (targetFieldName != null) {
if (!segmentPredictions.containsKey(targetFieldName)) {
throw new MissingValueException(targetFieldName, outputField);
}
targetValue = segmentPredictions.get(targetFieldName);
} else {
targetValue = segmentPredictions.getTargetValue();
}
} else
// Load the target value
{
switch (resultFeature) {
case ENTITY_ID:
{
// "Result feature entityId returns the id of the winning segment"
if (model instanceof MiningModel) {
targetValue = TypeUtil.cast(HasEntityId.class, predictions);
break;
}
}
// Falls through
default:
{
if (targetFieldName == null) {
targetFieldName = modelEvaluator.getTargetFieldName();
} // End if
if (!predictions.containsKey(targetFieldName)) {
throw new MissingValueException(targetFieldName, outputField);
}
targetValue = predictions.get(targetFieldName);
}
break;
}
}
// "If the target value is missing, then the result delivered by this OutputField is missing"
if (targetValue == null) {
continue outputFields;
}
Object value;
// Perform the requested computation on the target value
switch (resultFeature) {
case PREDICTED_VALUE:
{
value = getPredictedValue(targetValue);
}
break;
case PREDICTED_DISPLAY_VALUE:
{
DataField dataField = modelEvaluator.getDataField(targetFieldName);
if (dataField == null) {
throw new MissingFieldException(targetFieldName, outputField);
}
Target target = modelEvaluator.getTarget(targetFieldName);
value = getPredictedDisplayValue(targetValue, dataField, target);
}
break;
case TRANSFORMED_VALUE:
case DECISION:
{
if (segmentId != null) {
String name = outputField.getValue();
if (name == null) {
throw new InvalidFeatureException(outputField);
}
Expression expression = outputField.getExpression();
if (expression != null) {
throw new InvalidFeatureException(outputField);
}
value = segmentPredictions.get(FieldName.create(name));
break;
}
Expression expression = outputField.getExpression();
if (expression == null) {
throw new InvalidFeatureException(outputField);
}
value = FieldValueUtil.getValue(ExpressionUtil.evaluate(expression, context));
}
break;
case PROBABILITY:
{
value = getProbability(targetValue, outputField);
}
break;
case RESIDUAL:
{
FieldValue expectedTargetValue = context.evaluate(targetFieldName);
if (expectedTargetValue == null) {
throw new MissingValueException(targetFieldName, outputField);
}
DataField dataField = modelEvaluator.getDataField(targetFieldName);
OpType opType = dataField.getOpType();
switch (opType) {
case CONTINUOUS:
value = getContinuousResidual(targetValue, expectedTargetValue);
break;
case CATEGORICAL:
value = getCategoricalResidual(targetValue, expectedTargetValue);
break;
default:
throw new UnsupportedFeatureException(dataField, opType);
}
}
break;
case CLUSTER_ID:
{
value = getClusterId(targetValue);
}
break;
case ENTITY_ID:
{
if (targetValue instanceof HasRuleValues) {
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.RULE_ID);
break;
}
value = getEntityId(targetValue, outputField);
}
break;
case AFFINITY:
{
value = getAffinity(targetValue, outputField);
}
break;
case CLUSTER_AFFINITY:
case ENTITY_AFFINITY:
{
String entityId = outputField.getValue();
// Select the specified entity instead of the winning entity
if (entityId != null) {
value = getAffinity(targetValue, outputField);
break;
}
value = getEntityAffinity(targetValue);
}
break;
case REASON_CODE:
{
value = getReasonCode(targetValue, outputField);
}
break;
case RULE_VALUE:
{
value = getRuleValue(targetValue, outputField);
}
break;
case ANTECEDENT:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.ANTECEDENT);
}
break;
case CONSEQUENT:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.CONSEQUENT);
}
break;
case RULE:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.RULE);
}
break;
case RULE_ID:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.RULE_ID);
}
break;
case CONFIDENCE:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.CONFIDENCE);
}
break;
case SUPPORT:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.SUPPORT);
}
break;
case LIFT:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.LIFT);
}
break;
case LEVERAGE:
{
value = getRuleValue(targetValue, outputField, OutputField.RuleFeature.LEVERAGE);
}
break;
case WARNING:
{
value = context.getWarnings();
}
break;
default:
throw new UnsupportedFeatureException(outputField, resultFeature);
}
FieldValue outputValue = FieldValueUtil.create(outputField, value);
// The result of one output field becomes available to other output fields
context.declare(outputField.getName(), outputValue);
result.put(outputField.getName(), FieldValueUtil.getValue(outputValue));
}
return result;
}
private static Double getContinuousResidual(Object object, FieldValue expectedObject) {
Number value = (Number) getPredictedValue(object);
Number expectedValue = (Number) FieldValueUtil.getValue(expectedObject);
return Double.valueOf(expectedValue.doubleValue() - value.doubleValue());
}
