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 _setupMultiTree(self):
examples = [[0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 2], [0, 1, 1, 2],
[1, 0, 0, 2], [1, 0, 1, 2], [1, 1, 0, 2], [1, 1, 1, 0]]
data = MLData.MLQuantDataSet(examples)
attrs = range(0, data.GetNVars())
t1 = ID3.ID3Boot(data.GetAllData(), attrs, data.GetNPossibleVals())
self.t1 = t1
self.examples = examples
def DBToData(dbName, tableName, user='******', password='******', dupCol=-1, what='*', where='',
join='', pickleCol=-1, pickleClass=None, ensembleIds=None):
""" constructs an _MLData.MLDataSet_ from a database
**Arguments**
- dbName: the name of the database to be opened
- tableName: the table name containing the data in the database
- user: the user name to be used to connect to the database
- password: the password to be used to connect to the database
- dupCol: if nonzero specifies which column should be used to recognize
duplicates.
**Returns**
an _MLData.MLDataSet_
**Notes**
- this uses Dbase.DataUtils functionality
"""
conn = DbConnect(dbName, tableName, user, password)
res = conn.GetData(fields=what, where=where, join=join, removeDups=dupCol, forceList=1)
nPts = len(res)
vals = [None] * nPts
ptNames = [None] * nPts
classWorks = True
for i in range(nPts):
tmp = list(res[i])
ptNames[i] = tmp.pop(0)
if pickleCol >= 0:
if not pickleClass or not classWorks:
tmp[pickleCol] = cPickle.loads(str(tmp[pickleCol]))
else:
try:
tmp[pickleCol] = pickleClass(str(tmp[pickleCol]))
except Exception:
tmp[pickleCol] = cPickle.loads(str(tmp[pickleCol]))
classWorks = False
if ensembleIds:
tmp[pickleCol] = BitUtils.ConstructEnsembleBV(tmp[pickleCol], ensembleIds)
else:
if ensembleIds:
tmp = TakeEnsemble(tmp, ensembleIds, isDataVect=True)
vals[i] = tmp
varNames = conn.GetColumnNames(join=join, what=what)
data = MLData.MLDataSet(vals, varNames=varNames, ptNames=ptNames)
return data
def _setupPyMultiTree(self):
from rdkit.ML.InfoTheory import entropy
ID3.entropy.InfoEntropy = entropy.PyInfoEntropy
ID3.entropy.InfoGain = entropy.PyInfoGain
examples = [[0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 2], [0, 1, 1, 2],
[1, 0, 0, 2], [1, 0, 1, 2], [1, 1, 0, 2], [1, 1, 1, 0]]
data = MLData.MLQuantDataSet(examples)
attrs = range(0, data.GetNVars())
t1 = ID3.ID3Boot(data.GetAllData(), attrs, data.GetNPossibleVals())
self.t1 = t1
self.examples = examples
def TextToData(reader, ignoreCols=[], onlyCols=None):
""" constructs an _MLData.MLDataSet_ from a bunch of text
#DOC
**Arguments**
- reader needs to be iterable and return lists of elements
(like a csv.reader)
**Returns**
an _MLData.MLDataSet_
"""
varNames = next(reader)
if not onlyCols:
keepCols = []
for i, name in enumerate(varNames):
if name not in ignoreCols:
keepCols.append(i)
else:
keepCols = [-1] * len(onlyCols)
for i, name in enumerate(varNames):
if name in onlyCols:
keepCols[onlyCols.index(name)] = i
nCols = len(varNames)
varNames = tuple([varNames[x] for x in keepCols])
nVars = len(varNames)
vals = []
ptNames = []
for splitLine in reader:
if len(splitLine):
if len(splitLine) != nCols:
raise ValueError('unequal line lengths')
tmp = [splitLine[x] for x in keepCols]
ptNames.append(tmp[0])
pt = [None] * (nVars - 1)
for j in range(nVars - 1):
try:
val = int(tmp[j + 1])
except ValueError:
try:
val = float(tmp[j + 1])
except ValueError:
val = str(tmp[j + 1])
pt[j] = val
vals.append(pt)
data = MLData.MLDataSet(vals, varNames=varNames, ptNames=ptNames)
return data
def BuildDataSet(fileName):
""" builds a data set from a .dat file
**Arguments**
- fileName: the name of the .dat file
**Returns**
an _MLData.MLDataSet_
"""
with open(fileName, 'r') as inFile:
varNames, qBounds = ReadVars(inFile)
ptNames, examples = ReadGeneralExamples(inFile)
data = MLData.MLDataSet(examples, qBounds=qBounds, varNames=varNames, ptNames=ptNames)
return data
def BuildQuantDataSet(fileName):
""" builds a data set from a .qdat file
**Arguments**
- fileName: the name of the .qdat file
**Returns**
an _MLData.MLQuantDataSet_
"""
inFile = open(fileName, 'r')
varNames, qBounds = ReadVars(inFile)
ptNames, examples = ReadQuantExamples(inFile)
data = MLData.MLQuantDataSet(examples,
qBounds=qBounds,
varNames=varNames,
ptNames=ptNames)
return data
