def test5Bug29(self):
""" a more extensive test of the cmp stuff using hand-built trees """
import copy
t1 = QuantTreeNode(None, 't1')
t1.SetQuantBounds([1.])
c1 = QuantTreeNode(t1, 'c1')
c1.SetQuantBounds([2.])
t1.AddChildNode(c1)
c2 = QuantTreeNode(t1, 'c2')
c2.SetQuantBounds([2.])
t1.AddChildNode(c2)
c11 = QuantTreeNode(c1, 'c11')
c11.SetQuantBounds([3.])
c1.AddChildNode(c11)
c12 = QuantTreeNode(c1, 'c12')
c12.SetQuantBounds([3.])
c1.AddChildNode(c12)
assert not cmp(t1, copy.deepcopy(t1)), 'self equality failed'
t2 = QuantTreeNode(None, 't1')
t2.SetQuantBounds([1.])
c1 = QuantTreeNode(t2, 'c1')
c1.SetQuantBounds([2.])
t2.AddChildNode(c1)
c2 = QuantTreeNode(t2, 'c2')
c2.SetQuantBounds([2.])
t2.AddChildNode(c2)
c11 = QuantTreeNode(c1, 'c11')
c11.SetQuantBounds([3.])
c1.AddChildNode(c11)
c12 = QuantTreeNode(c1, 'c12')
c12.SetQuantBounds([3.00003])
c1.AddChildNode(c12)
assert cmp(t1, t2), 'inequality failed'
def test5Bug29(self):
""" a more extensive test of the cmp stuff using hand-built trees """
import copy
t1 = QuantTreeNode(None, 't1')
t1.SetQuantBounds([1.])
c1 = QuantTreeNode(t1, 'c1')
c1.SetQuantBounds([2.])
t1.AddChildNode(c1)
c2 = QuantTreeNode(t1, 'c2')
c2.SetQuantBounds([2.])
t1.AddChildNode(c2)
c11 = QuantTreeNode(c1, 'c11')
c11.SetQuantBounds([3.])
c1.AddChildNode(c11)
c12 = QuantTreeNode(c1, 'c12')
c12.SetQuantBounds([3.])
c1.AddChildNode(c12)
assert not cmp(t1, copy.deepcopy(t1)), 'self equality failed'
t2 = QuantTreeNode(None, 't1')
t2.SetQuantBounds([1.])
c1 = QuantTreeNode(t2, 'c1')
c1.SetQuantBounds([2.])
t2.AddChildNode(c1)
c2 = QuantTreeNode(t2, 'c2')
c2.SetQuantBounds([2.])
t2.AddChildNode(c2)
c11 = QuantTreeNode(c1, 'c11')
c11.SetQuantBounds([3.])
c1.AddChildNode(c11)
c12 = QuantTreeNode(c1, 'c12')
c12.SetQuantBounds([3.00003])
c1.AddChildNode(c12)
assert cmp(t1, t2), 'inequality failed'
def test6Bug29_2(self):
""" a more extensive test of the cmp stuff using pickled trees"""
import os
with open(os.path.join(RDConfig.RDCodeDir,'ML','DecTree','test_data','CmpTree1.pkl'),'rb') as t1File:
t1 = cPickle.load(t1File)
with open(os.path.join(RDConfig.RDCodeDir,'ML','DecTree','test_data','CmpTree2.pkl'),'rb') as t2File:
t2 = cPickle.load(t2File)
assert cmp(t1,t2),'equality failed'
def _getBondAttachmentCoordinates(self, p1, p2, labelSize):
newpos = [None, None]
if labelSize != None:
labelSizeOffset = [
labelSize[0][0] / 2 + (cmp(p2[0], p1[0]) * labelSize[0][2]),
labelSize[0][1] / 2
]
if p1[1] == p2[1]:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * labelSizeOffset[0]
else:
if abs(labelSizeOffset[1] * (p2[0] - p1[0]) /
(p2[1] - p1[1])) < labelSizeOffset[0]:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * abs(
labelSizeOffset[1] * (p2[0] - p1[0]) / (p2[1] - p1[1]))
else:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * labelSizeOffset[0]
if p1[0] == p2[0]:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * labelSizeOffset[1]
else:
if abs(labelSizeOffset[0] * (p1[1] - p2[1]) /
(p2[0] - p1[0])) < labelSizeOffset[1]:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * abs(
labelSizeOffset[0] * (p1[1] - p2[1]) / (p2[0] - p1[0]))
else:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * labelSizeOffset[1]
else:
newpos = copy.deepcopy(p1)
return newpos
def Compare(self, other, ignoreExtras=1):
""" not as choosy as self==other
"""
tv1, tv2 = str(type(self)), str(type(other))
tv = cmp(tv1, tv2)
if tv:
return tv
tv1, tv2 = len(self), len(other)
tv = cmp(tv1, tv2)
if tv:
return tv
if not ignoreExtras:
m1, m2 = self.GetMetric(), other.GetMetric()
if abs(m1 - m2) > CMPTOL:
return cmp(m1, m2)
if cmp(self.GetName(), other.GetName()):
return cmp(self.GetName(), other.GetName())
sP = self.GetPosition()
oP = other.GetPosition()
try:
r = cmp(len(sP), len(oP))
except Exception:
pass
else:
if r:
return r
try:
r = cmp(sP, oP)
except Exception:
r = sum(sP - oP)
if r:
return r
c1, c2 = self.GetChildren(), other.GetChildren()
if cmp(len(c1), len(c2)):
return cmp(len(c1), len(c2))
for i in range(len(c1)):
t = c1[i].Compare(c2[i], ignoreExtras=ignoreExtras)
if t:
return t
return 0
def Compare(self,other,ignoreExtras=1):
""" not as choosy as self==other
"""
tv1,tv2 = str(type(self)),str(type(other))
tv = cmp(tv1,tv2)
if tv:
return tv
tv1,tv2 = len(self),len(other)
tv = cmp(tv1,tv2)
if tv:
return tv
if not ignoreExtras:
m1,m2=self.GetMetric(),other.GetMetric()
if abs(m1-m2)>CMPTOL:
return cmp(m1,m2)
if cmp(self.GetName(),other.GetName()):
return cmp(self.GetName(),other.GetName())
sP = self.GetPosition()
oP = other.GetPosition()
try:
r = cmp(len(sP),len(oP))
except Exception:
pass
else:
if r:
return r
try:
r = cmp(sP,oP)
except Exception:
r = sum(sP-oP)
if r:
return r
c1,c2=self.GetChildren(),other.GetChildren()
if cmp(len(c1),len(c2)):
return cmp(len(c1),len(c2))
for i in range(len(c1)):
t = c1[i].Compare(c2[i],ignoreExtras=ignoreExtras)
if t:
return t
return 0
def test6Bug29_2(self):
""" a more extensive test of the cmp stuff using pickled trees"""
import os
with open(
os.path.join(RDConfig.RDCodeDir, 'ML', 'DecTree', 'test_data',
'CmpTree1.pkl'), 'rb') as t1File:
t1 = cPickle.load(t1File)
with open(
os.path.join(RDConfig.RDCodeDir, 'ML', 'DecTree', 'test_data',
'CmpTree2.pkl'), 'rb') as t2File:
t2 = cPickle.load(t2File)
assert cmp(t1, t2), 'equality failed'
def test6Bug29_2(self):
""" a more extensive test of the cmp stuff using pickled trees"""
import os
with open(os.path.join(RDConfig.RDCodeDir,'ML','DecTree','test_data','CmpTree1.pkl'),'r') as t1TFile:
buf = t1TFile.read().replace('\r\n', '\n').encode('utf-8')
t1TFile.close()
with io.BytesIO(buf) as t1File:
t1 = cPickle.load(t1File)
with open(os.path.join(RDConfig.RDCodeDir,'ML','DecTree','test_data','CmpTree2.pkl'),'r') as t2TFile:
buf = t2TFile.read().replace('\r\n', '\n').encode('utf-8')
t2TFile.close()
with io.BytesIO(buf) as t2File:
t2 = cPickle.load(t2File)
assert cmp(t1,t2),'equality failed'
def test6Bug29_2(self):
""" a more extensive test of the cmp stuff using pickled trees"""
import os
with open(
os.path.join(RDConfig.RDCodeDir, 'ML', 'DecTree', 'test_data',
'CmpTree1.pkl'), 'r') as t1TFile:
buf = t1TFile.read().replace('\r\n', '\n').encode('utf-8')
t1TFile.close()
with io.BytesIO(buf) as t1File:
t1 = cPickle.load(t1File)
with open(
os.path.join(RDConfig.RDCodeDir, 'ML', 'DecTree', 'test_data',
'CmpTree2.pkl'), 'r') as t2TFile:
buf = t2TFile.read().replace('\r\n', '\n').encode('utf-8')
t2TFile.close()
with io.BytesIO(buf) as t2File:
t2 = cPickle.load(t2File)
assert cmp(t1, t2), 'equality failed'
def __cmp__(self, other):
""" allows _cluster1 == cluster2_ to work
"""
if cmp(type(self), type(other)):
return cmp(type(self), type(other))
m1, m2 = self.GetMetric(), other.GetMetric()
if abs(m1 - m2) > CMPTOL:
return cmp(m1, m2)
if cmp(self.GetName(), other.GetName()):
return cmp(self.GetName(), other.GetName())
c1, c2 = self.GetChildren(), other.GetChildren()
return cmp(c1, c2)
def __cmp__(self,other):
""" allows _cluster1 == cluster2_ to work
"""
if cmp(type(self),type(other)):
return cmp(type(self),type(other))
m1,m2=self.GetMetric(),other.GetMetric()
if abs(m1-m2)>CMPTOL:
return cmp(m1,m2)
if cmp(self.GetName(),other.GetName()):
return cmp(self.GetName(),other.GetName())
c1,c2=self.GetChildren(),other.GetChildren()
return cmp(c1,c2)
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 _getBondAttachmentCoordinates(self, p1, p2, labelSize):
newpos = [None, None]
if labelSize != None:
labelSizeOffset = [labelSize[0][0]/2 + (cmp(p2[0], p1[0]) * labelSize[0][2]), labelSize[0][1]/2]
if p1[1] == p2[1]:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * labelSizeOffset[0]
else:
if abs(labelSizeOffset[1] * (p2[0] - p1[0]) / (p2[1] - p1[1])) < labelSizeOffset[0]:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * abs(labelSizeOffset[1] * (p2[0] - p1[0]) / (p2[1] - p1[1]))
else:
newpos[0] = p1[0] + cmp(p2[0], p1[0]) * labelSizeOffset[0]
if p1[0] == p2[0]:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * labelSizeOffset[1]
else:
if abs(labelSizeOffset[0] * (p1[1] - p2[1]) / (p2[0] - p1[0])) < labelSizeOffset[1]:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * abs(labelSizeOffset[0] * (p1[1] - p2[1]) / (p2[0] - p1[0]))
else:
newpos[1] = p1[1] + cmp(p2[1], p1[1]) * labelSizeOffset[1]
else:
newpos = copy.deepcopy(p1)
return newpos
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 sortHelp(x, y):
if x[2] == y[2]:
return cmp(x[1], y[1])
else:
return cmp(x[2], y[2])
def sortHelp(x, y):
if x[2] == y[2]:
return cmp(x[1], y[1])
else:
return cmp(x[2], y[2])
