Platform Dynamic Engagement Plugin
The Platform Dynamic Engagement plugin is used to format the output of a Dynamic Interaction predictor before it is returned to the client. This plugin is responsible for converting the predictor’s output into a format that can be easily consumed by the client. The Platform Dynamic Engagement plugin is a default post-predict plugin that is available in the ecosystem.Ai platform and can be extended to create custom post-scoring logic plugins.
How does it work?
The Platform Dynamic Engagement plugin takes the output of the predictor and formats it into a JSON object. The JSON object contains the predictor’s output along with any additional metadata that is required by the client. The plugin can be customized to add additional post-scoring logic to the predictor’s output. This can include filtering the output, sorting the output, or adding additional information to the output.
Java Code
The class PlatformDynamicEngagement extends the PostScoreSuper class, which provides functionality that can be used across different post-scoring plugins.
The getPostPredict() method accepts a JSONObject with prediction results, parameters for the scoring operation, a session object for a Cassandra database, and an array of preloaded models. The method then processes the results and prediction parameters, including extracting features, evaluating offer eligibility and constructing an array of modified offer scores.
After processing, the results are sorted based on score and the top scores are retrieved. There is also a time tracking operation which logs the time taken to execute the method.
A Logger object is initialized for logging purposes. Logging can be done at a variety of levels, including ERROR, WARN, INFO and DEBUG. It is recommended to add detailed logging at the DEBUG level to assist with troubleshooting once the plugin is deployed.
The following is the java implementation of the Platform Dynamic Engagement plugin:
package com.ecosystem.plugin.customer;
import com.datastax.oss.driver.api.core.CqlSession;
import com.ecosystem.plugin.DynamicClassLoader;
import com.ecosystem.plugin.business.BusinessLogic;
import com.ecosystem.utils.DataTypeConversions;
import com.ecosystem.utils.JSONArraySort;
import hex.genmodel.easy.EasyPredictModelWrapper;
import com.ecosystem.utils.log.LogManager;
import com.ecosystem.utils.log.Logger;
import org.json.JSONArray;
import org.json.JSONObject;
import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;
/**
* ECOSYSTEM.AI INTERNAL PLATFORM SCORING
* Use this class to score with dynamic sampling configurations. This class is configured to work with no model.
*/
public class PlatformDynamicEngagement extends PostScoreSuper {
private static final Logger LOGGER = LogManager.getLogger(PlatformDynamicEngagement.class.getName());
public PlatformDynamicEngagement() {
}
/**
* Pre-post predict logic
*/
public void getPostPredict () {
}
/**
* getPostPredict
* Example params:
* {"contextual_variable_one":"Easy Income Gold|Thin|Senior", "contextual_variable_two":"", "batch": true}
*
* @param predictModelMojoResult Result from scoring
* @param params Params carried from input
* @param session Session variable for Cassandra
* @return JSONObject result to further post-scoring logic
*/
public static JSONObject getPostPredict(JSONObject predictModelMojoResult, JSONObject params, CqlSession session, EasyPredictModelWrapper[] models) {
double startTimePost = System.nanoTime();
try {
/** Setup JSON objects for specific prediction case */
JSONObject featuresObj = predictModelMojoResult.getJSONObject("featuresObj");
//JSONObject domainsProbabilityObj = predictModelMojoResult.getJSONObject("domainsProbabilityObj");
JSONObject offerMatrixWithKey = new JSONObject();
boolean om = false;
if (params.has("offerMatrixWithKey")) {
offerMatrixWithKey = params.getJSONObject("offerMatrixWithKey");
om = true;
} else {
LOGGER.info("No Offer Matrix with key configured, using generated defaults.");
}
JSONObject work = params.getJSONObject("in_params");
/***************************************************************************************************/
/** Standardized approach to access dynamic datasets in plugin.
* The options array is the data set/feature_store that's keeping track of the dynamic changes.
* The optionParams is the parameter set that will influence the real-time behavior through param changes.
*/
/***************************************************************************************************/
JSONArray options = getOptions(params);
JSONObject optionParams = getOptionsParams(params);
JSONObject locations = getLocations(params);
JSONObject contextual_variables = optionParams.getJSONObject("contextual_variables");
JSONObject randomisation = optionParams.getJSONObject("randomisation");
/***************************************************************************************************/
/** Test if contextual variable is coming via api or feature store: API takes preference... */
if (!work.has("contextual_variable_one")) {
if (featuresObj.has(contextual_variables.getString("contextual_variable_one_name")))
work.put("contextual_variable_one", featuresObj.get(contextual_variables.getString("contextual_variable_one_name")));
else
work.put("contextual_variable_one", "");
}
if (!work.has("contextual_variable_two")) {
if (featuresObj.has(contextual_variables.getString("contextual_variable_two_name")))
work.put("contextual_variable_two", featuresObj.get(contextual_variables.getString("contextual_variable_two_name")));
else
work.put("contextual_variable_two", "");
}
/***************************************************************************************************/
JSONArray finalOffers = new JSONArray();
int offerIndex = 0;
int explore;
int[] optionsSequence = generateOptionsSequence(options.length(), options.length());
String contextual_variable_one = String.valueOf(work.get("contextual_variable_one"));
String contextual_variable_two = String.valueOf(work.get("contextual_variable_two"));
for(int j : optionsSequence) {
if (j > params.getInt("resultcount")) break;
JSONObject option = options.getJSONObject(j);
/** Skip the item if offer matrix does not contain option */
/*
if (!offerMatrixWithKey.has(option.getString("optionKey")))
continue;
*/
/** GENERATE DEFAULT IF OPTION IS NOT IN OFFER MATRIX! */
String offer = option.getString("optionKey");
if (!offerMatrixWithKey.has(option.getString("optionKey"))) {
JSONObject singleOffer = defaultOffer(offer);
offerMatrixWithKey.put(option.getString("optionKey"), singleOffer);
LOGGER.warn("BEWARE, DEFAULT OFFER GENERATED. IN OPTIONS STORE AND NOT OFFER MATRIX: " + option.getString("optionKey"));
}
/** Test eligibility TODO: CREATE A SEPARATE SUPERCLASS WITH THIS IN IT! */
if (locations != null) {
try {
if (locations.getJSONObject(offer).has("open_times")) {
String day = params.getJSONObject("in_params").getString("day");
String time = params.getJSONObject("in_params").getString("time");
if (locations.getJSONObject(offer).getJSONObject("open_times").has(day)) {
if (locations.getJSONObject(offer).getJSONObject("open_times").getJSONObject(day).has("opening1") &&
locations.getJSONObject(offer).getJSONObject("open_times").getJSONObject(day).has("closing1")) {
LOGGER.info("It's Open!");
if (!locations.getJSONObject(offer).getJSONObject("open_times").getString("operatingStatus").equals("operating"))
continue;
SimpleDateFormat sdf = new SimpleDateFormat("hh:mm a");
Date opening = sdf.parse(locations.getJSONObject(offer).getJSONObject("open_times").getJSONObject(day).getString("opening1"));
Date closing = sdf.parse(locations.getJSONObject(offer).getJSONObject("open_times").getJSONObject(day).getString("closing1"));
if (closing.before(opening)) {
Calendar cal = Calendar.getInstance();
cal.setTime(closing);
cal.add(Calendar.DATE, 1);
closing = cal.getTime();
}
Date time_now = sdf.parse(time);
if (time_now.after(opening) && time_now.before(closing)) {
LOGGER.info("It's Open!");
} else {
continue;
}
}
}
}
} catch (Exception e) {
LOGGER.info("\n\n" + offer + " -> Oh no, there's something wrong with the time range check, and will be ignored! use api params: {day:'monday', 'time': '11.00 AM'} " + e.getMessage() + "\n\n");
}
}
String contextual_variable_one_Option = "";
if (option.has("contextual_variable_one") && !contextual_variable_one.equals(""))
contextual_variable_one_Option = String.valueOf(option.get("contextual_variable_one"));
String contextual_variable_two_Option = "";
if (option.has("contextual_variable_two") && !contextual_variable_two.equals(""))
contextual_variable_two_Option = String.valueOf(option.get("contextual_variable_two"));
if (contextual_variable_one_Option.equals(contextual_variable_one) && contextual_variable_two_Option.equals(contextual_variable_two)) {
double alpha = (double) DataTypeConversions.getDoubleFromIntLong(option.get("alpha"));
double beta = (double) DataTypeConversions.getDoubleFromIntLong(option.get("beta"));
double accuracy = 0.001;
if (option.has("accuracy"))
accuracy = (double) DataTypeConversions.getDoubleFromIntLong(option.get("accuracy"));
/***************************************************************************************************/
/* r IS THE RANDOMIZED SCORE VALUE */
double p = 0.0;
double arm_reward = 0.001;
explore = 0;
if (option.has("arm_reward")) {
p = (double) option.get("arm_reward");
} else {
p = arm_reward;
}
arm_reward = p;
/** Check if values are correct */
if (p != p) p = 0.0;
if (alpha != alpha) alpha = 0.0;
if (beta != beta) beta = 0.0;
if (arm_reward != arm_reward) arm_reward = 0.0;
/***************************************************************************************************/
JSONObject singleOffer = new JSONObject();
double offer_value = 1.0;
double offer_cost = 1.0;
double modified_offer_score = p;
if (om) {
if (offerMatrixWithKey.has(offer)) {
singleOffer = offerMatrixWithKey.getJSONObject(offer);
if (singleOffer.has("offer_price"))
offer_value = DataTypeConversions.getDouble(singleOffer, "offer_price");
if (singleOffer.has("price"))
offer_value = DataTypeConversions.getDouble(singleOffer, "price");
if (singleOffer.has("offer_cost"))
offer_cost = singleOffer.getDouble("offer_cost");
if (singleOffer.has("cost"))
offer_cost = singleOffer.getDouble("cost");
modified_offer_score = p * ((double) offer_value - offer_cost);
}
}
JSONObject finalOffersObject = new JSONObject();
finalOffersObject.put("offer", offer);
finalOffersObject.put("offer_name", offer);
finalOffersObject.put("offer_name_desc", option.getString("option"));
/* process final */
finalOffersObject.put("score", p);
finalOffersObject.put("final_score", p);
finalOffersObject.put("modified_offer_score", modified_offer_score);
finalOffersObject.put("offer_value", offer_value);
finalOffersObject.put("price", offer_value);
finalOffersObject.put("cost", offer_cost);
finalOffersObject.put("p", p);
if (option.has("contextual_variable_one"))
finalOffersObject.put("contextual_variable_one", option.getString("contextual_variable_one"));
else
finalOffersObject.put("contextual_variable_one", "");
if (option.has("contextual_variable_two"))
finalOffersObject.put("contextual_variable_two", option.getString("contextual_variable_two"));
else
finalOffersObject.put("contextual_variable_two", "");
finalOffersObject.put("alpha", alpha);
finalOffersObject.put("beta", beta);
finalOffersObject.put("weighting", (double) DataTypeConversions.getDoubleFromIntLong(option.get("weighting")));
finalOffersObject.put("explore", explore);
finalOffersObject.put("uuid", params.get("uuid"));
finalOffersObject.put("arm_reward", arm_reward);
/* Debugging variables */
if (!option.has("expected_takeup"))
finalOffersObject.put("expected_takeup", -1.0);
else
finalOffersObject.put("expected_takeup", (double) DataTypeConversions.getDoubleFromIntLong(option.get("expected_takeup")));
if (!option.has("propensity"))
finalOffersObject.put("propensity", -1.0);
else
finalOffersObject.put("propensity", (double) DataTypeConversions.getDoubleFromIntLong(option.get("propensity")));
if (!option.has("epsilon_nominated"))
finalOffersObject.put("epsilon_nominated", -1.0);
else
finalOffersObject.put("epsilon_nominated", (double) DataTypeConversions.getDoubleFromIntLong(option.get("epsilon_nominated")));
finalOffers.put(offerIndex, finalOffersObject);
offerIndex = offerIndex + 1;
}
}
JSONArray sortJsonArray = JSONArraySort.sortArray(finalOffers, "arm_reward", "double", "d");
predictModelMojoResult.put("final_result", sortJsonArray);
predictModelMojoResult = getTopScores(params, predictModelMojoResult);
double endTimePost = System.nanoTime();
LOGGER.info("PlatformDynamicEngagement:I001: time in ms: ".concat( String.valueOf((endTimePost - startTimePost) / 1000000) ));
} catch (Exception e) {
e.printStackTrace();
LOGGER.error(e);
}
return predictModelMojoResult;
}
}