# -*- python -*-
# -*- coding: utf-8 -*-
#
# This file is part of DREAMTools software
#
# Copyright (c) 2015, DREAMTools Development Team
# All rights reserved
#
# Distributed under the BSD 3-Clause License.
# See accompanying file LICENSE distributed with this software
#
# File author(s): Thomas Cokelaer <cokelaer@ebi.ac.uk>
#
# website: http://github.com/dreamtools
#
##############################################################################
"""D2C4 scoring function
original code in MATLAB by Gustavo Stolovitzky
"""
import os
import pandas as pd
from dreamtools.core.challenge import Challenge
from dreamtools.core.rocs import D3D4ROC, DREAM2
[docs]class D2C4(Challenge, D3D4ROC, DREAM2):
"""A class dedicated to D2C4 challenge
::
from dreamtools import D2C4
s = D2C4()
subname = 'DIRECTED-UNSIGNED_InSilico1'
filename = s.download_template(subname)
s.score(filename, subname)
"""
def __init__(self, verbose=True, download=True, **kargs):
""".. rubric:: constructor"""
super(D2C4, self).__init__('D2C4', verbose, download, **kargs)
self._init()
# although there is no sub challenges per se,
# 12 different GS were used to score 12 types
# of network. We use those TAGS
#: 12 different sub challenges
self.sub_challenges = []
for name in ['InSilico1', 'InSilico2', 'InSilico3']:
self.sub_challenges.append('DIRECTED-UNSIGNED_%s' % name)
self.sub_challenges.append('UNDIRECTED-UNSIGNED_%s' % name)
self.sub_challenges.append('UNDIRECTED-SIGNED_EXCITATORY_%s' % name)
self.sub_challenges.append('UNDIRECTED-SIGNED_INHIBITORY_%s' % name)
self.sub_challenges.append('DIRECTED-SIGNED_EXCITATORY_%s' % name)
self.sub_challenges.append('DIRECTED-SIGNED_INHIBITORY_%s' % name)
def _init(self):
# should download files from synapse if required.
if self._standalone is True:
return
self._download_data('D2C4_goldstandard.zip', 'syn4565570')
self.unzip('D2C4_goldstandard.zip')
[docs] def download_goldstandard(self, subname=None):
self._check_subname(subname)
gold = self.get_pathname('D2C4_goldstandard_%s.txt' % subname)
return gold
[docs] def download_template(self, subname=None):
# not template found when developing this code.
# We will use the GS instead
msg = "! Note that there is no template per se. Here is the "
msg += "goldstandard that would work as well."
print(msg)
self._check_subname(subname)
return self.download_goldstandard(subname)
[docs] def score(self, filename, subname=None, goldstandard=None):
if goldstandard is None:
goldstandard = self.download_goldstandard(subname)
self.gold_edges = pd.read_csv(goldstandard, sep='\t', header=None)
self.prediction = pd.read_csv(filename, sep='\t', header=None)
newtest = pd.merge(self.prediction, self.gold_edges, how='inner', on=[0,1])
test = list(newtest['2_x'])
gold_index = list(newtest['2_y'])
AUC, AUROC, prec, rec, tpr, fpr = self.get_statistics(self.gold_edges,
self.prediction, gold_index)
P = self.gold_edges[2].sum()
spec_prec = self.compute_specific_precision_values(P, rec)
# for plotting
self.metrics = {'AUPR': AUC, 'AUROC': AUROC ,
'tpr': tpr, 'fpr': fpr,
'rec': rec, 'prec': prec,
'precision at nth correct prediction': spec_prec}
results = {'AUPR': AUC, 'AUROC': AUROC }
results['precision at nth correct prediction'] = spec_prec
return results