# -*- python -*-
#
# This file is part of DREAMTools software
#
# Copyright (c) 2014-2015 - EBI-EMBL
#
# File author(s): Thomas Cokelaer <cokelaer@ebi.ac.uk>
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# http://www.gnu.org/licenses/gpl-3.0.html
#
# website: http://github.org/dreamtools
#
##############################################################################
from dreamtools.core.challenge import Challenge
from dreamtools.core.rtools import RTools
import os
__all__ = ['D8C2_sc2']
[docs]class D8C2_sc2(Challenge, RTools):
"""D8C2 Tox challenge scoring (sub challenge 2)
::
from dreamtools import D8C2_s2
s = D8C2_sc2(filename)
s.run()
s.df
see `github README <https://github.com/dreamtools/dreamtools/tree/master/dreamtools/dream8/D8C2>`_ for details
"""
def __init__(self, filename, verboseR=True):
Challenge.__init__(self, challenge_name='D8C2')
RTools.__init__(self, verboseR=verboseR)
self.filename = filename
[docs] def run(self):
"""Compute the score and populates :attr:`df` attribute with results"""
script = """
load("%(path)sdata/data_sch2.RData")
source("%(path)sfunctionsLeaderboard_sch2.R")
source("%(path)sprob_Cidx.R")
load("%(path)stestSubmissions_sch2.RData")
yourSubmission = read.table("%(filename)s")
#scoreByCompound<-computeByCompound(submission=randomSubmission2,
# observed=observed, metric="pearson", keepCompounds=toxCompoundsID)
# example2, compute prob C-index for the best Performer (note: prob C-index
# computation takes some time)
#scoreByCompound<-computeByCompound(submission=bestPerformer, observed=observed,
# metric="probCindex", keepCompounds=toxCompoundsID, pooled_std=pooled_std)
submissions<-list(bestPerformer=bestPerformer, randomSubmission1=randomSubmission1,
yourSubmission=yourSubmission)
res <- computeOverallScores(submissions)
"""
print("Running the scoring function. This may take a couple of minutes.")
params = {'filename':self.filename, 'path':self.classpath + os.sep}
self.session.run(script % params)
self.df = self.session.res.copy()
self.df.index = ['bestPerformer','randomSubmission','yourSubmission']
self.df.columns = [c.strip() for c in self.df.columns]
return self.df