var MODEL_NAME = 'Query and Conversion Rate';
var PROJECT_ID = '40310033428';
// These are the names of your columns from the training
// data to ignore. Change these to create variations of your
// model for testing
var COLS_TO_IGNORE = [
];
// This is the output column for your training data, or
// what value the model is supposed to predict
var OUTPUT_COLUMN = 'Conv. rate';

function main() {
  var sheet = SpreadsheetApp.openByUrl("https://docs.google.com/spreadsheets/d/1kKLYXMGDNFRJ-sAtW5xAdXoG_dK0pNItXov2FVLOq5g/edit#gid=2144226213").getSheetByName("Initial Training Data"); 
  //createTrainingModel(sheet);
  var sheet = SpreadsheetApp.openByUrl("https://docs.google.com/spreadsheets/d/1kKLYXMGDNFRJ-sAtW5xAdXoG_dK0pNItXov2FVLOq5g/edit#gid=2144226213").getSheetByName("Additional Training Data"); 
  //updateTrainedModelData(sheet);
  var trainingStatus = Prediction.Trainedmodels.get(PROJECT_ID, MODEL_NAME).trainingStatus;
  Logger.log("Training Status: " + trainingStatus);
  
  var queries = [];
  // We are going to test it by querying with training data
  var sheet = SpreadsheetApp.openByUrl("https://docs.google.com/spreadsheets/d/1kKLYXMGDNFRJ-sAtW5xAdXoG_dK0pNItXov2FVLOq5g/edit#gid=2144226213").getSheetByName("Initial Training Data"); 
  var testData = sheet.getDataRange().getValues();
  var headers = testData.shift();
  for(var r in testData) {
    var query = [];
    var row = testData[r];
    for(var i in headers) {
      if(COLS_TO_IGNORE.indexOf(headers[i]) == -1 && headers[i] != OUTPUT_COLUMN) {
        query.push(row[i])
      }
    }
    queries.push(query);
  }
  Logger.log(makePrediction(queries));
  
}


/***********************************
 * This function accepts a sheet full of training
 * data and creates a trained model for you to query.
 ***********************************/


function createTrainingModel(sheet) {
  var trainingInstances = [];
  // get the spreadsheet values
  var trainingData = sheet.getDataRange().getValues();
  var headers = trainingData.shift();
  for(var r in trainingData) {
    var inputs = [];
    var row = trainingData[r];
    for(var i in headers) {
      if(COLS_TO_IGNORE.indexOf(headers[i]) == -1 && headers[i] != OUTPUT_COLUMN) {
        inputs.push(row[i])
      }
    }
    var output = row[headers.indexOf(OUTPUT_COLUMN)];
    trainingInstances.push(createTrainingInstance(inputs,output));
  }

  var insert = Prediction.newInsert();
  insert.id = MODEL_NAME;
  insert.trainingInstances = trainingInstances;

  var insertReply = Prediction.Trainedmodels.insert(insert, PROJECT_ID);
  Logger.log('Trained model with data.');
}

// Helper function to create the training instance.
function createTrainingInstance(inputs,output) {
  
  var trainingInstances = Prediction.newInsertTrainingInstances();
  trainingInstances.csvInstance = inputs;
  trainingInstances.output = output;
  return trainingInstances;
}

function updateTrainedModelData(sheet) {
  var updateData = sheet.getDataRange().getValues();
  var headers = updateData.shift();
  for(var r in updateData) {
    Logger.log("r: " + r);
    var inputs = [];
    var row = updateData[r];
    for(var i in headers) {
      if(COLS_TO_IGNORE.indexOf(headers[i]) == -1 && headers[i] != OUTPUT_COLUMN) {
        inputs.push(row[i])
      }
    }
    
    Logger.log("inputs: " + inputs);
    var output = row[headers.indexOf(OUTPUT_COLUMN)];
    Logger.log("output: " + output);
    var update = createUpdateInstance(inputs,output)
    var updateResponse = Prediction.Trainedmodels.update(update, PROJECT_ID, MODEL_NAME);
    Logger.log('Trained model updated with new data.');
    sheet.deleteRow(2);
  }
}

// Helper function to create the update instance.
function createUpdateInstance(inputs,output) {
  var updateInstance = Prediction.newUpdate();
  updateInstance.csvInstance = inputs;
  updateInstance.output = output;
  return updateInstance;
}

/***************************
 * Accepts a 2d array of query data and returns the
 * predicted output in an array.
 ***************************/
function makePrediction(data) {
  var retVal = [];
  for(var r in data) {
    var request = Prediction.newInput();
    request.input = Prediction.newInputInput();
    request.input.csvInstance = data[r];
    var predictionResult = Prediction.Trainedmodels.predict(
      request, PROJECT_ID, MODEL_NAME);
    Logger.log("Prediction for data: %s is %s",
               JSON.stringify(data[r]), predictionResult.outputValue);
    retVal.push(predictionResult.outputValue);
  }
  return retVal;
}