def test_RandomizeActivities(self):
class RunDetails(object):
shuffled = False
randomized = False
random.seed(0)
details = RunDetails()
self.setUpGeneralLoad()
dataSet = self.d
orgActivities = [d[-1] for d in dataSet]
DataUtils.RandomizeActivities(dataSet,
shuffle=True,
runDetails=details)
self.assertNotEqual(orgActivities, [d[-1] for d in dataSet])
self.assertEqual(sorted(orgActivities),
sorted([d[-1] for d in dataSet]))
self.assertTrue(details.shuffled)
self.assertFalse(details.randomized)
try:
details = RunDetails()
self.setUpGeneralLoad()
dataSet = self.d
orgActivities = [d[-1] for d in dataSet]
DataUtils.RandomizeActivities(dataSet,
shuffle=False,
runDetails=details)
self.assertNotEqual(orgActivities, [d[-1] for d in dataSet])
self.assertEqual(sorted(orgActivities),
sorted([d[-1] for d in dataSet]))
self.assertFalse(details.randomized)
self.assertTrue(details.shuffled)
except NameError:
# This code branch is not working.
pass
def test5(self):
""" indicesToUse """
probes = [
(.5, 4, 2),
(.7, 3, 3),
(.75, 3, 3),
(.333, 6, 0),
(.25, 4, 2),
]
nPts = len(self.d1)
for frac, nKeep, nRej in probes:
DataUtils.InitRandomNumbers((23, 42))
k, r = DataUtils.FilterData(self.d1,
1,
frac,
indicesToUse=range(nPts))
assert len(k) == nKeep, 'bad nKeep (%d != %d)' % (len(k), nKeep)
assert len(r) == nRej, 'bad nRej (%d != %d)' % (len(r), nRej)
keep, rej = k, r
# make sure the examples are actually correct
DataUtils.InitRandomNumbers((23, 42))
tgtKeep, tgtRej = DataUtils.FilterData(self.d1, 1, frac)
assert keep == tgtKeep, '%.2f: %s!=%s' % (frac, str(keep),
str(tgtKeep))
assert rej == tgtRej, '%.2f: %s!=%s' % (frac, str(rej),
str(tgtRej))
def test4_indicesOnly_indicesToUse(self):
# """ indicesOnly with indicesToUse """
probes = [
(.5, 4, 2),
(.7, 3, 3),
(.75, 3, 3),
(.333, 6, 0),
(.25, 4, 2),
]
nPts = len(self.d1)
for frac, nKeep, nRej in probes:
DataUtils.InitRandomNumbers((23, 42))
k, r = DataUtils.FilterData(self.d1,
1,
frac,
indicesToUse=range(nPts),
indicesOnly=1)
assert len(k) == nKeep, 'bad nKeep (%d != %d)' % (len(k), nKeep)
assert len(r) == nRej, 'bad nRej (%d != %d)' % (len(r), nRej)
# make sure the indices are actually correct
keep = [self.d1[x] for x in k]
rej = [self.d1[x] for x in r]
DataUtils.InitRandomNumbers((23, 42))
tgtKeep, tgtRej = DataUtils.FilterData(self.d1, 1, frac)
assert keep == tgtKeep, '%.2f: %s!=%s' % (frac, str(keep),
str(tgtKeep))
assert rej == tgtRej, '%.2f: %s!=%s' % (frac, str(rej),
str(tgtRej))
def RunIt(details, progressCallback=None, saveIt=1, setDescNames=0):
""" does the actual work of building a composite model
**Arguments**
- details: a _CompositeRun.CompositeRun_ object containing details
(options, parameters, etc.) about the run
- progressCallback: (optional) a function which is called with a single
argument (the number of models built so far) after each model is built.
- saveIt: (optional) if this is nonzero, the resulting model will be pickled
and dumped to the filename specified in _details.outName_
- setDescNames: (optional) if nonzero, the composite's _SetInputOrder()_ method
will be called using the results of the data set's _GetVarNames()_ method;
it is assumed that the details object has a _descNames attribute which
is passed to the composites _SetDescriptorNames()_ method. Otherwise
(the default), _SetDescriptorNames()_ gets the results of _GetVarNames()_.
**Returns**
the composite model constructed
"""
details.rundate = time.asctime()
fName = details.tableName.strip()
if details.outName == '':
details.outName = fName + '.pkl'
if not details.dbName:
if details.qBounds != []:
data = DataUtils.TextFileToData(fName)
else:
data = DataUtils.BuildQuantDataSet(fName)
elif details.useSigTrees or details.useSigBayes:
details.tableName = fName
data = details.GetDataSet(pickleCol=0,
pickleClass=DataStructs.ExplicitBitVect)
elif details.qBounds != [] or not details.useTrees:
details.tableName = fName
data = details.GetDataSet()
else:
data = DataUtils.DBToQuantData(
details.dbName, # Function no longer defined
fName,
quantName=details.qTableName,
user=details.dbUser,
password=details.dbPassword)
composite = RunOnData(details,
data,
progressCallback=progressCallback,
saveIt=saveIt,
setDescNames=setDescNames)
return composite
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
def test2XValClass(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'KNN', 'test_data',
'random_pts.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
npvals = data.GetNPossibleVals()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
numNeigh = 11
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
numNeigh,
silent=1)
self.assertAlmostEqual(err, 0.01075, 4)
neighborList = []
res = mod.ClassifyExample(examples[0], neighborList=neighborList)
self.assertEqual(res, 1)
self.assertEqual(neighborList[0][1], examples[0])
self.assertEqual(mod.GetName(), '')
mod.SetName('name')
self.assertEqual(mod.GetName(), 'name')
self.assertEqual(mod.type(), 'Classification Model')
mod.NameModel('this argument is ignored')
self.assertEqual(mod.GetName(), 'Classification Model')
def testPerm1(self):
""" tests the descriptor remapping stuff in a packager """
from rdkit.Chem import Descriptors
pkg = cPickle.load(
open(os.path.join(self.dataDir, 'Jan9_build3_pkg.pkl'), 'rb'))
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
ref = {}
DataUtils.InitRandomNumbers((23, 42))
for smi, pred, conf in self.testD:
for desc in names:
fn = getattr(Descriptors, desc, lambda x: 777)
m = Chem.MolFromSmiles(smi)
ref[desc] = fn(m)
for i in range(5):
perm = list(names)
random.shuffle(perm)
m = Chem.MolFromSmiles(smi)
for desc in perm:
fn = getattr(Descriptors, desc, lambda x: 777)
val = fn(m)
assert feq(
val, ref[desc],
1e-4), '%s: %s(%s): %f!=%f' % (str(perm), smi, desc,
val, ref[desc])
def testPerm1(self):
""" tests the descriptor remapping stuff in a packager """
from rdkit.Chem import Descriptors
with open(os.path.join(self.dataDir,'Jan9_build3_pkg.pkl'),'r') as pkgTF:
buf = pkgTF.read().replace('\r\n', '\n').encode('utf-8')
pkgTF.close()
with io.BytesIO(buf) as pkgF:
pkg = cPickle.load(pkgF)
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
ref = {}
DataUtils.InitRandomNumbers((23,42))
for smi,pred,conf in self.testD:
for desc in names:
fn = getattr(Descriptors,desc,lambda x:777)
m = Chem.MolFromSmiles(smi)
ref[desc] = fn(m)
for i in range(5):
perm = list(names)
random.shuffle(perm,random=random.random)
m = Chem.MolFromSmiles(smi)
for desc in perm:
fn = getattr(Descriptors,desc,lambda x:777)
val = fn(m)
assert feq(val,ref[desc],1e-4),'%s: %s(%s): %f!=%f'%(str(perm),
smi,
desc,
val,
ref[desc])
def testQuantPickle(self):
# " testing QuantDataSet pickling "
self.setUpQuantLoad()
DataUtils.WritePickledData(
RDConfig.RDCodeDir + '/ML/Data/test_data/testquant.qdat.pkl',
self.d)
with open(RDConfig.RDCodeDir + '/ML/Data/test_data/testquant.qdat.pkl',
'rb') as f:
vNames = pickle.load(f)
qBounds = pickle.load(f)
ptNames = pickle.load(f)
examples = pickle.load(f)
d = MLData.MLQuantDataSet(examples,
varNames=vNames,
qBounds=qBounds,
ptNames=ptNames)
assert self.d.GetNPts() == d.GetNPts(), 'nPts wrong'
assert self.d.GetNVars() == d.GetNVars(), 'nVars wrong'
assert self.d.GetNResults() == d.GetNResults(), 'nResults wrong'
assert self.d.GetVarNames() == d.GetVarNames(), 'varNames wrong'
assert self.d.GetPtNames() == d.GetPtNames(), 'ptNames wrong'
assert self.d.GetNPossibleVals() == d.GetNPossibleVals(
), 'nPossible Wrong'
assert self.d.GetQuantBounds() == d.GetQuantBounds(
), 'quantBounds Wrong'
assert self.d.GetResults() == d.GetResults(), 'GetResults wrong'
assert self.d.GetAllData()[1] == d.GetAllData()[1], 'GetAllData wrong'
assert self.d.GetInputData()[3] == d.GetInputData(
)[3], 'GetInputData wrong'
assert self.d.GetNamedData()[2] == d.GetNamedData(
)[2], 'GetNamedData wrong'
def test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0,
silent=True)
self.assertTrue(isinstance(mod, NaiveBayesClassifier))
self.assertAlmostEqual(err, 0.1818, 4)
self.assertEqual(mod.GetName(), '')
mod.SetName('modelName')
self.assertEqual(mod.GetName(), 'modelName')
mod.NameModel(None)
self.assertEqual(mod.GetName(), 'NaiveBayesClassifier')
self.assertGreater(len(mod.GetExamples()), 0)
self.assertGreater(len(mod.GetTrainingExamples()), 0)
self.assertEqual(sorted(mod.GetTrainingExamples() + mod.GetExamples()),
sorted(examples))
def testPerm1(self):
# """ tests the descriptor remapping stuff in a packager """
pkg = self._loadPackage()
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
ref = {}
DataUtils.InitRandomNumbers((23, 42))
for smi, _, _ in self.testD:
for desc in names:
fn = getattr(Descriptors, desc, lambda x: 777)
m = Chem.MolFromSmiles(smi)
ref[desc] = fn(m)
for _ in range(5):
perm = list(names)
random.shuffle(perm, random=random.random)
m = Chem.MolFromSmiles(smi)
for desc in perm:
fn = getattr(Descriptors, desc, lambda x: 777)
val = fn(m)
assert feq(
val, ref[desc],
1e-4), '%s: %s(%s): %f!=%f' % (str(perm), smi, desc,
val, ref[desc])
def test_CalcNPossibleUsingMap(self):
self.setUpQuantLoad()
order = list(range(5))
self.assertEqual(
DataUtils.CalcNPossibleUsingMap(self.d.data, order,
self.d.GetQuantBounds()),
[3, 3, 2, 2, 2])
def setUp(self):
# here is what we are going to do to test this out
# - generate bit vectrs of length nbits
# - turn on a fraction of the first nbits/2 bits at random
# - for each bit i turned on in the range (0, nbits/2) turn on the bit
# nbits/2 + i
# - basically the first half of a fingerprint is same as the second half of the
# fingerprint
# - if we repeat this process often enough we whould see strong correlation between
# the bits i (i < nbits/2) and (nbits/2 + i)
DataUtils.InitRandomNumbers((100, 23))
self.nbits = 200
self.d = 40
self.nfp = 1000
self.blist = range(self.nbits)
self.fps = []
for fi in range(self.nfp):
fp = DataStructs.ExplicitBitVect(self.nbits)
obits = range(self.nbits / 2)
random.shuffle(obits)
obits = obits[0:self.d]
for bit in obits:
fp.SetBit(bit)
fp.SetBit(bit + self.nbits / 2)
self.fps.append(fp)
def testQuantLoad(self):
" testing QuantDataSet load"
ok = 1
try:
self.d = DataUtils.BuildQuantDataSet(RDConfig.RDCodeDir+'/ML/Data/test_data/test.qdat')
except:
ok = 0
assert ok,'BuildQuantDataSet failed'
def test_WriteData(self):
self.setUpQuantLoad()
with contextlib.closing(StringIO()) as f:
DataUtils.WriteData(f, self.d.GetVarNames(), self.d.GetQuantBounds(), self.d.data)
s = f.getvalue()
self.assertIn('DataUtils', s)
self.assertIn('foo1', s)
self.assertIn('2 2 1 0 1', s)
def testGeneralLoad(self):
" testing DataSet load"
ok = 1
try:
self.d = DataUtils.BuildDataSet(RDConfig.RDCodeDir+'/ML/Data/test_data/test.dat')
except:
ok = 0
assert ok,'BuildDataSet failed'
def _balanced_parallel_build_trees(n_trees, forest, X, y, sample_weight,
sample_mask, X_argsorted, seed, verbose):
"""Private function used to build a batch of trees within a job"""
from sklearn.utils import check_random_state
from sklearn.utils.fixes import bincount
import random
MAX_INT = numpy.iinfo(numpy.int32).max
random_state = check_random_state(seed)
trees = []
for i in xrange(n_trees):
if verbose > 1:
print("building tree %d of %d" % (i + 1, n_trees))
seed = random_state.randint(MAX_INT)
tree = forest._make_estimator(append=False)
tree.set_params(compute_importances=forest.compute_importances)
tree.set_params(random_state=check_random_state(seed))
if forest.bootstrap:
n_samples = X.shape[0]
if sample_weight is None:
curr_sample_weight = numpy.ones((n_samples, ),
dtype=numpy.float64)
else:
curr_sample_weight = sample_weight.copy()
ty = list(enumerate(y))
indices = DataUtils.FilterData(ty,
val=1,
frac=0.5,
col=1,
indicesToUse=0,
indicesOnly=1)[0]
indices2 = random_state.randint(0, len(indices), len(indices))
indices = [indices[j] for j in indices2]
sample_counts = bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
curr_sample_mask = sample_mask.copy()
curr_sample_mask[sample_counts == 0] = False
tree.fit(X,
y,
sample_weight=curr_sample_weight,
sample_mask=curr_sample_mask,
X_argsorted=X_argsorted,
check_input=False)
tree.indices = curr_sample_mask
else:
tree.fit(X,
y,
sample_weight=sample_weight,
sample_mask=sample_mask,
X_argsorted=X_argsorted,
check_input=False)
trees.append(tree)
return trees
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True,
calcTotalError=True)
self.assertAlmostEqual(mod._classProbs[0], 0.515151, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.484848, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.40315, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.62185, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], 0.4305, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.80305, 4)
self.assertAlmostEqual(err, 0.14563, 4)
mod, err = CrossValidate.CrossValidationDriver(
examples,
attrs,
npvals,
qBounds,
silent=True,
replacementSelection=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5131578, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.4868421, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.036, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.93465, 4)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.6696, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -1.06785, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.3, 4)
def test2XValClass(self): fName = os.path.join(RDConfig.RDCodeDir,'ML','KNN','test_data','random_pts.csv') data = DataUtils.TextFileToData(fName) examples = data.GetNamedData() npvals = data.GetNPossibleVals() nvars = data.GetNVars() attrs = range(1,nvars+1) numNeigh = 11 mod, err = CrossValidate.CrossValidationDriver(examples, attrs, npvals, numNeigh,silent=1) self.assertAlmostEqual(err,0.01075,4)
def testPerm2(self):
# """ tests the descriptor remapping stuff in a packager """
pkg = self._loadPackage()
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
DataUtils.InitRandomNumbers((23, 42))
perm = list(names)
random.shuffle(perm, random=random.random)
calc.simpleList = perm
calc.descriptorNames = perm
pkg.Init()
self._verify(pkg, self.testD)
def test4XValRegress(self):
fName = os.path.join(RDConfig.RDCodeDir,'ML','KNN','test_data','random_pts.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
npvals = data.GetNPossibleVals()
nvars = data.GetNVars()
attrs = range(1,nvars+1)
numNeigh = 11
mod, err = CrossValidate.CrossValidationDriver(examples, attrs, npvals, numNeigh,silent=1,
modelBuilder=CrossValidate.makeRegressionModel)
# NOTE: this number hasn't been extensively checked
self.assertAlmostEqual(err,0.0777,4)
def test1(self):
""" basics """
probes = [
(.5, 4, 2),
(.7, 3, 3),
(.75, 3, 3),
(.333, 6, 0),
(.25, 4, 2),
]
for frac, nKeep, nRej in probes:
k, r = DataUtils.FilterData(self.d1, 1, frac)
assert len(k) == nKeep, 'bad nKeep (%d != %d)' % (len(k), nKeep)
assert len(r) == nRej, 'bad nRej (%d != %d)' % (len(r), nRej)
def testPerm2(self):
""" tests the descriptor remapping stuff in a packager """
pkg = cPickle.load(
open(os.path.join(self.dataDir, 'Jan9_build3_pkg.pkl'), 'rb'))
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
DataUtils.InitRandomNumbers((23, 42))
perm = list(names)
random.shuffle(perm)
calc.simpleList = perm
calc.descriptorNames = perm
pkg.Init()
self._verify(pkg, self.testD)
def test_SplitData(self):
self.assertRaises(ValueError, SplitData.SplitDataSet, None, -1.1)
self.assertRaises(ValueError, SplitData.SplitDataSet, None, 1.1)
data = list(range(10))
DataUtils.InitRandomNumbers((23, 42))
f = StringIO()
with redirect_stdout(f):
result = SplitData.SplitDataSet(data, 0.5)
self.assertEqual(set(result[0]).intersection(result[1]), set())
self.assertEqual(len(result[0]), 5)
s = f.getvalue()
self.assertIn('Training', s)
self.assertIn('hold-out', s)
def setUp(self):
#print '\n%s: '%self.shortDescription(),
self.examples = cPickle.load(
open(RDConfig.RDCodeDir + '/ML/Composite/test_data/ferro.pkl',
'rb'))
self.varNames = [
'composition', 'max_atomic', 'has3d', 'has4d', 'has5d', 'elconc',
'atvol', 'isferro'
]
self.qBounds = [[], [1.89, 3.53], [], [], [], [0.55, 0.73],
[11.81, 14.52], []]
self.nPoss = [0, 3, 2, 2, 2, 3, 3, 2]
self.attrs = range(1, len(self.varNames) - 1)
from rdkit.ML.Data import DataUtils
DataUtils.InitRandomNumbers((23, 43))
def testPerm2(self):
""" tests the descriptor remapping stuff in a packager """
with open(os.path.join(self.dataDir,'Jan9_build3_pkg.pkl'),'r') as pkgTF:
buf = pkgTF.read().replace('\r\n', '\n').encode('utf-8')
pkgTF.close()
with io.BytesIO(buf) as pkgF:
pkg = cPickle.load(pkgF)
calc = pkg.GetCalculator()
names = calc.GetDescriptorNames()
DataUtils.InitRandomNumbers((23,42))
perm = list(names)
random.shuffle(perm,random=random.random)
calc.simpleList = perm
calc.descriptorNames = perm
pkg.Init()
self._verify(pkg,self.testD)
def GetDataSet(self, **kwargs):
""" Returns a MLDataSet pulled from a database using our stored
values.
"""
from rdkit.ML.Data import DataUtils
data = DataUtils.DBToData(self.dbName,
self.tableName,
user=self.dbUser,
password=self.dbPassword,
what=self.dbWhat,
where=self.dbWhere,
join=self.dbJoin,
**kwargs)
return data
def setUp(self):
with open(RDConfig.RDCodeDir + '/ML/Composite/test_data/ferro.pkl',
'r') as pklTF:
buf = pklTF.read().replace('\r\n', '\n').encode('utf-8')
pklTF.close()
with io.BytesIO(buf) as pklF:
self.examples = cPickle.load(pklF)
self.varNames = [
'composition', 'max_atomic', 'has3d', 'has4d', 'has5d', 'elconc',
'atvol', 'isferro'
]
self.qBounds = [[], [1.89, 3.53], [], [], [], [0.55, 0.73],
[11.81, 14.52], []]
self.nPoss = [0, 3, 2, 2, 2, 3, 3, 2]
self.attrs = list(range(1, len(self.varNames) - 1))
from rdkit.ML.Data import DataUtils
DataUtils.InitRandomNumbers((23, 43))
def test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0)
assert feq(err, 0.19354)
def test1Neighbors(self):
fName = os.path.join(RDConfig.RDCodeDir,'ML','KNN','test_data','random_pts.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
npvals = data.GetNPossibleVals()
nvars = data.GetNVars()
attrs = range(1,nvars+1)
numNeigh = 11
metric = DistFunctions.EuclideanDist
mdl = KNNModel.KNNModel(numNeigh,attrs,metric)
pt = examples.pop(0)
tgt = [(metric(pt,ex,attrs),ex) for ex in examples]
tgt.sort()
mdl.SetTrainingExamples(examples)
neighbors = mdl.GetNeighbors(pt)
for i in range(numNeigh):
assert feq(-tgt[i][0],neighbors[i][0])
assert tgt[i][1][0]==neighbors[i][1][0]
