# $Id$
#
#  Copyright (C) 2002-2006  greg Landrum and Rational Discovery LLC
#
#   @@ All Rights Reserved @@
#  This file is part of the RDKit.
#  The contents are covered by the terms of the BSD license
#  which is included in the file license.txt, found at the root
#  of the RDKit source tree.
#
"""Command line tool to construct an enrichment plot from saved composite models

Usage:  EnrichPlot [optional args] -d dbname -t tablename <models>

Required Arguments:
  -d "dbName": the name of the database for screening

  -t "tablename": provide the name of the table with the data to be screened

  <models>: file name(s) of pickled composite model(s).
     If the -p argument is also provided (see below), this argument is ignored.

Optional Arguments:
  - -a "list": the list of result codes to be considered active.  This will be
        eval'ed, so be sure that it evaluates as a list or sequence of
        integers. For example, -a "[1,2]" will consider activity values 1 and 2
        to be active

  - --enrich "list": identical to the -a argument above.

  - --thresh: sets a threshold for the plot.  If the confidence falls below
          this value, picking will be terminated

  - -H: screen only the hold out set (works only if a version of
        BuildComposite more recent than 1.2.2 was used).

  - -T: screen only the training set (works only if a version of
        BuildComposite more recent than 1.2.2 was used).

  - -S: shuffle activity values before screening

  - -R: randomize activity values before screening

  - -F *filter frac*: filters the data before training to change the
     distribution of activity values in the training set.  *filter frac*
     is the fraction of the training set that should have the target value.
     **See note in BuildComposite help about data filtering**

  - -v *filter value*: filters the data before training to change the
     distribution of activity values in the training set. *filter value*
     is the target value to use in filtering.
     **See note in BuildComposite help about data filtering**

  - -p "tableName": provides the name of a db table containing the
      models to be screened.  If you use this argument, you should also
      use the -N argument (below) to specify a note value.

  - -N "note": provides a note to be used to pull models from a db table.

  - --plotFile "filename": writes the data to an output text file (filename.dat)
    and creates a gnuplot input file (filename.gnu) to plot it

  - --showPlot: causes the gnuplot plot constructed using --plotFile to be
    displayed in gnuplot.

"""
# from rdkit.Dbase.DbConnection import DbConnect

import pickle
import sys

import numpy

from rdkit import DataStructs, RDConfig
from rdkit.Dbase.DbConnection import DbConnect
from rdkit.ML import CompositeRun
from rdkit.ML.Data import DataUtils, SplitData, Stats

__VERSION_STRING = "2.4.0"


def cmp(t1, t2):
  return (t1 < t2) * -1 or (t1 > t2) * 1


def message(msg, noRet=0, dest=sys.stderr):
  """ emits messages to _sys.stderr_
      override this in modules which import this one to redirect output

      **Arguments**

        - msg: the string to be displayed

    """
  if noRet:
    dest.write('%s ' % (msg))
  else:
    dest.write('%s\n' % (msg))


def error(msg, dest=sys.stderr):
  """ emits messages to _sys.stderr_
      override this in modules which import this one to redirect output

      **Arguments**

        - msg: the string to be displayed

    """
  sys.stderr.write('ERROR: %s\n' % (msg))


def ScreenModel(mdl, descs, data, picking=[1], indices=[], errorEstimate=0):
  """ collects the results of screening an individual composite model that match
      a particular value

     **Arguments**

       - mdl: the composite model

       - descs: a list of descriptor names corresponding to the data set

       - data: the data set, a list of points to be screened.

       - picking: (Optional) a list of values that are to be collected.
         For examples, if you want an enrichment plot for picking the values
         1 and 2, you'd having picking=[1,2].

      **Returns**

        a list of 4-tuples containing:

           - the id of the point

           - the true result (from the data set)

           - the predicted result

           - the confidence value for the prediction

    """
  mdl.SetInputOrder(descs)

  for j in range(len(mdl)):
    tmp = mdl.GetModel(j)
    if hasattr(tmp, '_trainIndices') and not isinstance(tmp._trainIndices, dict):
      tis = {}
      if hasattr(tmp, '_trainIndices'):
        for v in tmp._trainIndices:
          tis[v] = 1
      tmp._trainIndices = tis

  res = []
  if mdl.GetQuantBounds():
    needsQuant = 1
  else:
    needsQuant = 0

  if not indices:
    indices = list(range(len(data)))
  nTrueActives = 0
  for i in indices:
    if errorEstimate:
      use = []
      for j in range(len(mdl)):
        tmp = mdl.GetModel(j)
        if not tmp._trainIndices.get(i, 0):
          use.append(j)
    else:
      use = None
    pt = data[i]
    pred, conf = mdl.ClassifyExample(pt, onlyModels=use)
    if needsQuant:
      pt = mdl.QuantizeActivity(pt[:])
    trueRes = pt[-1]
    if trueRes in picking:
      nTrueActives += 1
    if pred in picking:
      res.append((pt[0], trueRes, pred, conf))
  return nTrueActives, res


def AccumulateCounts(predictions, thresh=0, sortIt=1):
  """  Accumulates the data for the enrichment plot for a single model

      **Arguments**

        - predictions: a list of 3-tuples (as returned by _ScreenModels_)

        - thresh: a threshold for the confidence level.  Anything below
          this threshold will not be considered

        - sortIt: toggles sorting on confidence levels


      **Returns**

        - a list of 3-tuples:

          - the id of the active picked here

          - num actives found so far

          - number of picks made so far

    """
  if sortIt:
    predictions.sort(lambda x, y: cmp(y[3], x[3]))
  res = []
  nCorrect = 0
  nPts = 0
  for i in range(len(predictions)):
    ID, real, pred, conf = predictions[i]
    if conf > thresh:
      if pred == real:
        nCorrect += 1
      nPts += 1
      res.append((ID, nCorrect, nPts))

  return res


def MakePlot(details, final, counts, pickVects, nModels, nTrueActs=-1):
  if not hasattr(details, 'plotFile') or not details.plotFile:
    return

  dataFileName = '%s.dat' % (details.plotFile)
  outF = open(dataFileName, 'w+')
  i = 0
  while i < len(final) and counts[i] != 0:
    if nModels > 1:
      _, sd = Stats.MeanAndDev(pickVects[i])
      confInterval = Stats.GetConfidenceInterval(sd, len(pickVects[i]), level=90)
      outF.write('%d %f %f %d %f\n' %
                 (i + 1, final[i][0] / counts[i], final[i][1] / counts[i], counts[i], confInterval))
    else:
      outF.write('%d %f %f %d\n' %
                 (i + 1, final[i][0] / counts[i], final[i][1] / counts[i], counts[i]))
    i += 1
  outF.close()
  plotFileName = '%s.gnu' % (details.plotFile)
  gnuF = open(plotFileName, 'w+')
  gnuHdr = """# Generated by EnrichPlot.py version: %s
  set size square 0.7
  set xr [0:]
  set data styl points
  set ylab 'Num Correct Picks'
  set xlab 'Num Picks'
  set grid
  set nokey
  set term postscript enh color solid "Helvetica" 16
  set term X
  """ % (__VERSION_STRING)
  print(gnuHdr, file=gnuF)
  if nTrueActs > 0:
    print('set yr [0:%d]' % nTrueActs, file=gnuF)
  print('plot x with lines', file=gnuF)
  if nModels > 1:
    everyGap = i / 20
    print('replot "%s" using 1:2 with lines,' % (dataFileName), end='', file=gnuF)
    print('"%s" every %d using 1:2:5 with yerrorbars' % (dataFileName, everyGap), file=gnuF)
  else:
    print('replot "%s" with points' % (dataFileName), file=gnuF)
  gnuF.close()

  if hasattr(details, 'showPlot') and details.showPlot:
    try:
      from Gnuplot import Gnuplot
      p = Gnuplot()
      p('load "%s"' % (plotFileName))
      input('press return to continue...\n')
    except Exception:
      import traceback
      traceback.print_exc()


def Usage():
  """ displays a usage message and exits """
  sys.stderr.write(__doc__)
  sys.exit(-1)


if __name__ == '__main__':
  import getopt
  try:
    args, extras = getopt.getopt(sys.argv[1:], 'd:t:a:N:p:cSTHF:v:',
                                 ('thresh=', 'plotFile=', 'showPlot', 'pickleCol=', 'OOB', 'noSort',
                                  'pickBase=', 'doROC', 'rocThresh=', 'enrich='))
  except Exception:
    import traceback
    traceback.print_exc()
    Usage()

  details = CompositeRun.CompositeRun()
  CompositeRun.SetDefaults(details)

  details.activeTgt = [1]
  details.doTraining = 0
  details.doHoldout = 0
  details.dbTableName = ''
  details.plotFile = ''
  details.showPlot = 0
  details.pickleCol = -1
  details.errorEstimate = 0
  details.sortIt = 1
  details.pickBase = ''
  details.doROC = 0
  details.rocThresh = -1
  for arg, val in args:
    if arg == '-d':
      details.dbName = val
    if arg == '-t':
      details.dbTableName = val
    elif arg == '-a' or arg == '--enrich':
      details.activeTgt = eval(val)
      if not isinstance(details.activeTgt, (tuple, list)):
        # if (type(details.activeTgt) not in (types.TupleType, types.ListType)):
        details.activeTgt = (details.activeTgt, )

    elif arg == '--thresh':
      details.threshold = float(val)
    elif arg == '-N':
      details.note = val
    elif arg == '-p':
      details.persistTblName = val
    elif arg == '-S':
      details.shuffleActivities = 1
    elif arg == '-H':
      details.doTraining = 0
      details.doHoldout = 1
    elif arg == '-T':
      details.doTraining = 1
      details.doHoldout = 0
    elif arg == '-F':
      details.filterFrac = float(val)
    elif arg == '-v':
      details.filterVal = float(val)
    elif arg == '--plotFile':
      details.plotFile = val
    elif arg == '--showPlot':
      details.showPlot = 1
    elif arg == '--pickleCol':
      details.pickleCol = int(val) - 1
    elif arg == '--OOB':
      details.errorEstimate = 1
    elif arg == '--noSort':
      details.sortIt = 0
    elif arg == '--doROC':
      details.doROC = 1
    elif arg == '--rocThresh':
      details.rocThresh = int(val)
    elif arg == '--pickBase':
      details.pickBase = val

  if not details.dbName or not details.dbTableName:
    Usage()
    print('*******Please provide both the -d and -t arguments')

  message('Building Data set\n')
  dataSet = DataUtils.DBToData(details.dbName, details.dbTableName, user=RDConfig.defaultDBUser,
                               password=RDConfig.defaultDBPassword, pickleCol=details.pickleCol,
                               pickleClass=DataStructs.ExplicitBitVect)

  descs = dataSet.GetVarNames()
  nPts = dataSet.GetNPts()
  message('npts: %d\n' % (nPts))
  final = numpy.zeros((nPts, 2), float)
  counts = numpy.zeros(nPts, numpy.integer)
  selPts = [None] * nPts

  models = []
  if details.persistTblName:
    conn = DbConnect(details.dbName, details.persistTblName)
    message('-> Retrieving models from database')
    curs = conn.GetCursor()
    curs.execute("select model from %s where note='%s'" % (details.persistTblName, details.note))
    message('-> Reconstructing models')
    try:
      blob = curs.fetchone()
    except Exception:
      blob = None
    while blob:
      message(' Building model %d' % len(models))
      blob = blob[0]
      try:
        models.append(pickle.loads(str(blob)))
      except Exception:
        import traceback
        traceback.print_exc()
        print('Model failed')
      else:
        message('  <-Done')
      try:
        blob = curs.fetchone()
      except Exception:
        blob = None
    curs = None
  else:
    for modelName in extras:
      try:
        model = pickle.load(open(modelName, 'rb'))
      except Exception:
        import traceback
        print('problems with model %s:' % modelName)
        traceback.print_exc()
      else:
        models.append(model)
  nModels = len(models)
  pickVects = {}
  halfwayPts = [1e8] * len(models)
  for whichModel, model in enumerate(models):
    tmpD = dataSet
    try:
      seed = model._randomSeed
    except AttributeError:
      pass
    else:
      DataUtils.InitRandomNumbers(seed)
    if details.shuffleActivities:
      DataUtils.RandomizeActivities(tmpD, shuffle=1)
    if hasattr(model, '_splitFrac') and (details.doHoldout or details.doTraining):
      trainIdx, testIdx = SplitData.SplitIndices(tmpD.GetNPts(), model._splitFrac, silent=1)
      if details.filterFrac != 0.0:
        trainFilt, temp = DataUtils.FilterData(tmpD, details.filterVal, details.filterFrac, -1,
                                               indicesToUse=trainIdx, indicesOnly=1)
        testIdx += temp
        trainIdx = trainFilt
      if details.doTraining:
        testIdx, trainIdx = trainIdx, testIdx
    else:
      testIdx = list(range(tmpD.GetNPts()))

    message('screening %d examples' % (len(testIdx)))
    nTrueActives, screenRes = ScreenModel(model, descs, tmpD, picking=details.activeTgt,
                                          indices=testIdx, errorEstimate=details.errorEstimate)
    message('accumulating')
    runningCounts = AccumulateCounts(screenRes, sortIt=details.sortIt, thresh=details.threshold)
    if details.pickBase:
      pickFile = open('%s.%d.picks' % (details.pickBase, whichModel + 1), 'w+')
    else:
      pickFile = None

    for i, entry in enumerate(runningCounts):
      entry = runningCounts[i]
      selPts[i] = entry[0]
      final[i][0] += entry[1]
      final[i][1] += entry[2]
      v = pickVects.get(i, [])
      v.append(entry[1])
      pickVects[i] = v
      counts[i] += 1
      if pickFile:
        pickFile.write('%s\n' % (entry[0]))
      if entry[1] >= nTrueActives / 2 and entry[2] < halfwayPts[whichModel]:
        halfwayPts[whichModel] = entry[2]
    message('Halfway point: %d\n' % halfwayPts[whichModel])

  if details.plotFile:
    MakePlot(details, final, counts, pickVects, nModels, nTrueActs=nTrueActives)
  else:
    if nModels > 1:
      print('#Index\tAvg_num_correct\tConf90Pct\tAvg_num_picked\tNum_picks\tlast_selection')
    else:
      print('#Index\tAvg_num_correct\tAvg_num_picked\tNum_picks\tlast_selection')

    i = 0
    while i < nPts and counts[i] != 0:
      if nModels > 1:
        mean, sd = Stats.MeanAndDev(pickVects[i])
        confInterval = Stats.GetConfidenceInterval(sd, len(pickVects[i]), level=90)
        print('%d\t%f\t%f\t%f\t%d\t%s' % (i + 1, final[i][0] / counts[i], confInterval,
                                          final[i][1] / counts[i], counts[i], str(selPts[i])))
      else:
        print('%d\t%f\t%f\t%d\t%s' %
              (i + 1, final[i][0] / counts[i], final[i][1] / counts[i], counts[i], str(selPts[i])))
      i += 1

  mean, sd = Stats.MeanAndDev(halfwayPts)
  print('Halfway point: %.2f(%.2f)' % (mean, sd))
