"""
$Id$

Scoring - Calculate rank statistics

Created by Sereina Riniker, October 2012
after a file from Peter Gedeck, Greg Landrum

\param scores: ordered list with descending similarity containing
               active/inactive information
\param col: column index in scores where active/inactive information is stored
\param fractions: list of fractions at which the value shall be calculated
\param alpha: exponential weight
"""

import math


def CalcROC(scores, col):
  """ Determines a ROC curve """
  numMol = len(scores)
  if numMol == 0:
    raise ValueError('score list is empty')
  TPR = [0] * numMol  # True positive rate: TP/(TP+FP)
  TNR = [0] * numMol  # True negative rate: TN/(TN+FN)
  numActives = 0
  numInactives = 0

  # loop over score list
  for i in range(numMol):
    if scores[i][col]:
      numActives += 1
    else:
      numInactives += 1
    TPR[i] = numActives  # TP
    TNR[i] = numInactives  # TN

  # normalize, check that there are actives and inactives
  if numActives > 0:
    TPR = [1.0 * i / numActives for i in TPR]
  if numInactives > 0:
    TNR = [1.0 * i / numInactives for i in TNR]

  return [TNR, TPR]


def CalcAUC(scores, col):
  """ Determines the area under the ROC curve """
  # determine the ROC curve
  roc = CalcROC(scores, col)
  TNR = roc[0]
  TPR = roc[1]

  numMol = len(scores)
  AUC = 0

  # loop over score list
  for i in range(0, numMol - 1):
    AUC += (TNR[i + 1] - TNR[i]) * (TPR[i + 1] + TPR[i])

  return 0.5 * AUC


def _RIEHelper(scores, col, alpha):
  numMol = len(scores)
  alpha = float(alpha)
  if numMol == 0:
    raise ValueError('score list is empty')
  if alpha <= 0.0:
    raise ValueError('alpha must be greater than zero')

  denom = 1.0 / numMol * ((1 - math.exp(-alpha)) / (math.exp(alpha / numMol) - 1))
  numActives = 0
  sum_exp = 0

  # loop over score list
  for i in range(numMol):
    active = scores[i][col]
    if active:
      numActives += 1
      sum_exp += math.exp(-(alpha * (i + 1)) / numMol)

  if numActives > 0:  # check that there are actives
    RIE = sum_exp / (numActives * denom)
  else:
    RIE = 0.0

  return RIE, numActives


def CalcRIE(scores, col, alpha):
  """ RIE original definded here:
    Sheridan, R.P., Singh, S.B., Fluder, E.M. & Kearsley, S.K.
    Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches.
    J. Chem. Inf. Comp. Sci. 41, 1395-1406 (2001).
    """
  RIE, _ = _RIEHelper(scores, col, alpha)
  return RIE


def CalcBEDROC(scores, col, alpha):
  """ BEDROC original defined here:
    Truchon, J. & Bayly, C.I.
    Evaluating Virtual Screening Methods: Good and Bad Metric for the "Early Recognition"
    Problem. J. Chem. Inf. Model. 47, 488-508 (2007).
    """
  # calculate RIE
  RIE, numActives = _RIEHelper(scores, col, alpha)

  if numActives > 0:
    numMol = len(scores)
    ratio = 1.0 * numActives / numMol
    RIEmax = (1 - math.exp(-alpha * ratio)) / (ratio * (1 - math.exp(-alpha)))
    RIEmin = (1 - math.exp(alpha * ratio)) / (ratio * (1 - math.exp(alpha)))

    if RIEmax != RIEmin:
      BEDROC = (RIE - RIEmin) / (RIEmax - RIEmin)
    else:  # numActives = numMol
      BEDROC = 1.0
  else:
    BEDROC = 0.0

  return BEDROC


def CalcEnrichment(scores, col, fractions):
  """ Determines the enrichment factor for a set of fractions """
  numMol = len(scores)
  if numMol == 0:
    raise ValueError('score list is empty')
  if len(fractions) == 0:
    raise ValueError('fraction list is empty')
  for i in fractions:
    if i > 1 or i < 0:
      raise ValueError('fractions must be between [0,1]')

  numPerFrac = [math.ceil(numMol * f) for f in fractions]
  numPerFrac.append(numMol)
  numActives = 0
  enrich = []

  # loop over score list
  for i in range(numMol):
    if i > (numPerFrac[0] - 1) and i > 0:
      enrich.append(1.0 * numActives * numMol / i)
      numPerFrac.pop(0)
    active = scores[i][col]
    if active:
      numActives += 1

  if numActives > 0:  # check that there are actives
    enrich = [e / numActives for e in enrich]
  else:
    enrich = [0.0] * len(fractions)
  return enrich
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