nmr-learning/src/posterior.js

Learn a database of chemical shift and coupling constants assignments

const FS = require('fs');
const path = require('path');

const OCLE = require('openchemlib-extended');
const predictor = require('nmr-predictor');

const autoassigner = require('../../nmr-auto-assignment/src/index');

// const cheminfo = require('./preprocess/cheminfo');
// const maybridge = require('./preprocess/maybridge');
const compilePredictionTable = require('./compilePredictionTable');
const stats = require('./stats');


function loadFile(filename) {
  return FS.readFileSync(path.join(__dirname, filename)).toString();
}

async function start() {
  var maxIterations = 15; // Set the number of interations for training
  var ignoreLabile = true;// Set the use of labile protons during training
  var learningRatio = 0.8; // A number between 0 and 1
  const levels = [6, 5, 4, 3, 3];

  var testSet = JSON.parse(loadFile('/../data/assigned298.json'));// File.parse("/data/nmrsignal298.json");//"/Research/NMR/AutoAssign/data/cobasSimulated";
  var dataset1 = JSON.parse(FS.readFileSync('/home/acastillo/Documents/data/procjson/cheminfo443_y.json').toString());
  var dataset2 = JSON.parse(FS.readFileSync('/home/acastillo/Documents/data/procjson/maybridge_y.json').toString());
  var dataset3 = [];// JSON.parse(FS.readFileSync('/home/acastillo/Documents/data/procjson/big0.json').toString());

  // dataset3.splice(0, 500)

  var datasets = [dataset1, dataset2, dataset3];


  var start, date;
  var prevError = 0;
  var prevCont = 0;
  var dataset, max, ds, i, j, k, nAtoms;
  var result, solutions;
  // var fastDB = [];
  var fastDB = JSON.parse(loadFile('/../data/h_15.json'));
  console.log(`Cheminfo All: ${dataset1.length}`);
  console.log(`MayBridge All: ${dataset2.length}`);
  console.log(`Other All: ${dataset3.length}`);


  // Remove the overlap molecules from train and test
  var removed = 0;
  var trainDataset = [];
  for (i = 0; i < testSet.length; i++) {
    for (ds = 0; ds < datasets.length; ds++) {
      dataset = datasets[ds];
      for (j = dataset.length - 1; j >= 0; j--) {
        if (dataset[j].general.ocl.hasLabile || testSet[i].diaID === dataset[j].general.ocl.id) {
          // if (testSet[i].diaID === dataset[j].general.ocl.id) {
          dataset.splice(j, 1);
          removed++;
          break;
        }
      }
    }
  }
  if (start === 0) {
    start += removed;
  }

  for (ds = 0; ds < datasets.length; ds++) {
    dataset = datasets[ds];
    for (j = 0; j < dataset.length; j++) {
      trainDataset.push(dataset[j]);
    }
  }

  console.log(`Cheminfo Final: ${dataset1.length}`);
  console.log(`MayBridge Final: ${dataset2.length}`);
  console.log(`Other Final: ${dataset3.length}`);
  console.log(`Overlaped molecules: ${removed}.  They were removed from training datasets`);


  // Run the learning process. After each iteration the system has seen every single molecule once
  // We have to use another stop criteria like convergence
  var iteration = 0;
  maxIterations = 10;
  var convergence = false;
  try {
    for (let level of levels) {
      date = new Date();
      start = date.getTime();
      var count = 0;
      dataset = trainDataset;// datasets[ds];
      max = dataset.length;
      predictor.setDb(fastDB, 'proton', 'proton');
      // we could now loop on the sdf to add the int index
      for (i = 0; i < max; i++) {
        let predictions = await predictor.proton(dataset[i].general.ocl, {
          condensed: true,
          OCLE: OCLE,
          levels: [level],
          hose: true,
          use: 'median',
          ignoreLabile: ignoreLabile,
          keepMolecule: true
        });
        // console.log(i)
        let ranges = dataset[i].spectra.nmr[0].range;
        for (let k = predictions.length - 1; k >= 0; k--) {
          let pred = predictions[k];
          let hose5 = pred.hose[level - 1];
          if (pred.ncs > 4 && fastDB[level - 1][hose5]) {
            let found = false;
            for (let range of ranges) {
              if (Math.abs((range.from + range.to) - (pred.min + pred.max)) < (Math.abs(range.from - range.to) + Math.abs(pred.min - pred.max))) {
                if (!fastDB[level - 1][hose5].p) {
                  fastDB[level - 1][hose5].p = 1;
                } else {
                  fastDB[level - 1][hose5].p++;
                }
                found = true;
                break;
              }
            }
            // console.log(hose5)
            if (!found) {
              if (!fastDB[level - 1][hose5].n) {
                fastDB[level - 1][hose5].n = 1;
              } else {
                fastDB[level - 1][hose5].n++;
              }
            }
          } else {
            predictions.splice(k, 1);
          }
        }
      }
      let keys = Object.keys(fastDB[level - 1]);
      var deleted = 0;
      keys.forEach((key) => {
        let confidence = 0;
        if (fastDB[level - 1][key].p) {
          confidence = 1;
          if (fastDB[level - 1][key].n) {
            confidence = fastDB[level - 1][key].p / (fastDB[level - 1][key].p + fastDB[level - 1][key].n);
          }
        }
        fastDB[level - 1][key].conf = confidence;
        if (confidence > 0 && confidence < 0.2) {
          console.log(`${key}:${JSON.stringify(fastDB[level - 1][key])}`);
          delete fastDB[level - 1][key];
          deleted++;
          // console.log(fastDB[4][key]);
        }
      });
      console.log(`Deleted at ${level}:${deleted}`);
    }


    FS.writeFileSync(`${__dirname}/../data/h_clean.json`, JSON.stringify(fastDB));
  } catch (e) {
    console.log(e);
  }
  console.log('Done');
}

start();