def __init__(self, training):
self.training = training
self.wnparents = trainer.WordnetParentsEngine(training)
labels = ["Larger", "Smaller", "Equal", "None"]
self.cls_variable = orange.EnumVariable("class", values=labels)
alist = []
for var in self.wnparents.domain.attributes:
if isinstance(var, orange.FloatVariable):
v1 = orange.FloatVariable(name="%s_w1" % var.name)
v2 = orange.FloatVariable(name="%s_w2" % var.name)
alist.append(v1)
alist.append(v2)
elif isinstance(var, orange.EnumVariable):
v1 = orange.EnumVariable(name="%s_w1" % var.name,
values=var.values)
v2 = orange.EnumVariable(name="%s_w2" % var.name,
values=var.values)
alist.append(v1)
alist.append(v2)
else:
raise ValueError("Unhandled attribute: " + `var`)
self.domain = orange.Domain(alist,
self.cls_variable)
self.training_table = self.makeTable(self.training)
def __call__(self, rule, examples, weights, targetClass):
if not weights:
weights = orange.newmetaid()
examples.addMetaAttribute(weights, 1.)
examples.domain.addmeta(
weights, orange.FloatVariable("weights-" + str(weights)), True)
try:
coverage = examples.domain.getmeta("Coverage")
except:
coverage = orange.FloatVariable("Coverage")
examples.domain.addmeta(orange.newmetaid(), coverage, True)
examples.addMetaAttribute(coverage, 0.0)
newWeightsID = orange.newmetaid()
examples.addMetaAttribute(newWeightsID, 1.)
examples.domain.addmeta(
newWeightsID, orange.FloatVariable("weights-" + str(newWeightsID)),
True)
for example in examples:
if rule(example) and example.getclass() == rule.classifier(
example, orange.GetValue):
try:
example[coverage] += 1.0
except:
example[coverage] = 1.0
example[newWeightsID] = 1.0 / (example[coverage] + 1)
else:
example[newWeightsID] = example[weights]
return (examples, newWeightsID)
def sendList(self, selectedInd):
if self.data and type(self.data[0]) == str:
xAttr=orange.FloatVariable("X")
yAttr=orange.FloatVariable("Y")
nameAttr= orange.StringVariable("name")
if self.selectionOptions == 1:
domain = orange.Domain([xAttr, yAttr, nameAttr])
selection = orange.ExampleTable(domain)
for i in range(len(selectedInd)):
selection.append(list(self.mds.points[selectedInd[i]]) + [self.data[i]])
else:
domain = orange.Domain([nameAttr])
if self.selectionOptions:
domain.addmeta(orange.newmetaid(), xAttr)
domain.addmeta(orange.newmetaid(), yAttr)
selection = orange.ExampleTable(domain)
for i in range(len(selectedInd)):
selection.append([self.data[i]])
if self.selectionOptions:
selection[i][xAttr]=self.mds.points[selectedInd[i]][0]
selection[i][yAttr]=self.mds.points[selectedInd[i]][1]
self.send("Data", selection)
return
if not selectedInd:
self.send("Structured Data Files", None)
else:
datasets=[self.data[i] for i in selectedInd]
names=list(set([d.dirname for d in datasets]))
data=[(name, [d for d in filter(lambda a:a.strain==name, datasets)]) for name in names]
self.send("Structured Data Files",data)
def to_network(self, terms=None):
"""
Return an Orange.network.Network instance constructed from
this ontology.
"""
edge_types = self.edge_types()
terms = self.terms()
from Orange.orng import orngNetwork
import orange
network = orngNetwork.Network(len(terms), True, len(edge_types))
network.objects = dict([(term.id, i) for i, term in enumerate(terms)])
edges = defaultdict(set)
for term in self.terms():
related = self.related_terms(term)
for relType, relTerm in related:
edges[(term.id, relTerm)].add(relType)
edgeitems = edges.items()
for (src, dst), eTypes in edgeitems:
network[src, dst] = [1 if e in eTypes else 0 for e in edge_types]
domain = orange.Domain([
orange.StringVariable("id"),
orange.StringVariable("name"),
orange.StringVariable("def"),
], False)
items = orange.ExampleTable(domain)
for term in terms:
ex = orange.Example(
domain, [term.id, term.name,
term.values.get("def", [""])[0]])
items.append(ex)
relationships = set(
[", ".join(sorted(eTypes)) for (_, _), eTypes in edgeitems])
domain = orange.Domain([
orange.FloatVariable("u"),
orange.FloatVariable("v"),
orange.EnumVariable("relationship", values=list(edge_types))
], False)
id2index = dict([(term.id, i + 1) for i, term in enumerate(terms)])
links = orange.ExampleTable(domain)
for (src, dst), eTypes in edgeitems:
ex = orange.Example(domain,
[id2index[src], id2index[dst],
eTypes.pop()])
links.append(ex)
network.items = items
network.links = links
network.optimization = None
return network
def sendpredictions(self):
if not self.data or not self.outvar:
self.send("Predictions", None)
return
# predictions, data set with class predictions
classification = self.outvar.varType == orange.VarTypes.Discrete
metas = []
if classification:
if len(self.selectedClasses):
for c in self.predictors.values():
m = [orange.FloatVariable(name=str("%s(%s)" % (c.name, str(self.outvar.values[i]))),
getValueFrom = lambda ex, rw, cindx=i, c=c: orange.Value(c(ex, c.GetProbabilities)[cindx])) \
for i in self.selectedClasses]
metas.extend(m)
if self.showClass:
mc = [
orange.EnumVariable(
name=str(c.name),
values=self.outvar.values,
getValueFrom=lambda ex, rw, c=c: orange.Value(c(ex)))
for c in self.predictors.values()
]
metas.extend(mc)
else:
# regression
mc = [
orange.FloatVariable(
name="%s" % c.name,
getValueFrom=lambda ex, rw, c=c: orange.Value(c(ex)))
for c in self.predictors.values()
]
metas.extend(mc)
classVar = self.outvar
domain = orange.Domain(self.data.domain.attributes + [classVar])
domain.addmetas(self.data.domain.getmetas())
for m in metas:
domain.addmeta(orange.newmetaid(), m)
predictions = orange.ExampleTable(domain, self.data)
if self.doPrediction:
c = self.predictors.values()[0]
for ex in predictions:
ex[classVar] = c(ex)
predictions.name = self.data.name
self.send("Predictions", predictions)
self.changedFlag = False
def expandToFuzzyExamples(self, examples, att, a, b):
"""
Function will return new 'fuzzy' example table. Every example from the input table will get two additional meta attributes ('fuzzy set' and 'u') \
based on 'a' and 'b' threshold (lower and higher) and attribute 'att'. Attribute 'fuzzy set' indicates name of the fuzzy set while atribute 'u' \
reflects example's degree of membership to particular fuzzy set. Note that input examples with values of 'att' lying on the (a,b) will be expanded \
into two fuzzy examples.
"""
mu = orange.FloatVariable("u")
mv = orange.StringVariable("fuzzy set")
examples.domain.addmeta(FUZZYMETAID, mu)
examples.domain.addmeta(FUZZYMETAID - 1, mv)
newexamples = []
for j in range(0, len(examples)):
i = examples[j]
v = float(i[att])
if v > a and v < b: # we have to expand this example
newexamples.append(i)
i["fuzzy set"] = 'yes'
i["u"] = (v - a) / (b - a)
examples.append(i)
examples[-1]["fuzzy set"] = "no"
examples[-1]["u"] = (b - v) / (b - a)
else:
if v > a: # u(yes) = 1.0
i["fuzzy set"] = 'yes'
i["u"] = 1.0
else: # u(no) = 1.0
i["fuzzy set"] = 'no'
i["u"] = 1.0
return examples
def __call__(self, weights=None):
if not weights:
weights = self.user_weights
# New augmented table
norm_data = orange.ExampleTable(self.data)
newid = min(norm_data.domain.get_metas().keys(), 0) - 1
score_attr = orange.FloatVariable('score')
norm_data.domain.add_meta(newid, score_attr)
norm_data.add_meta_attribute(score_attr)
# Normalize the attributes to the proper range
for att, (lower_bound, upper_bound) in self.ranges.items():
for ex in norm_data:
ex[att] = ex[att] / (upper_bound - lower_bound)
# Normalize column-wise
col_sum = {}
for att in norm_data.domain.features:
col_sum[att] = float(sum([ex[att] for ex in norm_data]))
for ex in norm_data:
for att in norm_data.domain.features:
ex[att] = ex[att] / col_sum[att]
# Use the inverse of an attr. value it should be minimized.
inverse = lambda x, att: 1 - x if att in self.minimize else x
for ex in norm_data:
score = sum([
inverse(ex[att].value, att) * weights.get(att, 1)
for att in self.ranges.keys()
])
ex['score'] = score
return norm_data
def __call__(self, dataset):
try:
#retain class attribute
attrDataset = dataset.select(self.domain)
imputer = self.imputer(attrDataset)
attrDataset = imputer(attrDataset)
domain = self.continuizer(attrDataset)
attrDataset = attrDataset.translate(domain)
except TypeError as e:
raise orange.KernelException("One or more attributes form training set are missing!")
dataMatrix, classArray, x = attrDataset.toNumpy()
dataMatrix -= self.center
if self.deviation != None:
dataMatrix *= 1./self.deviation
#save transformed data
self._dataMatrix = numpy.dot(dataMatrix, self.loadings)
attributes = [orange.FloatVariable("PC%d" % (i + 1, )) for i in range(len(self.evalues))]
new_domain = orange.Domain(attributes)
new_table = orange.ExampleTable(new_domain, self._dataMatrix)
if dataset.domain.classVar:
#suboptimal
classTable = dataset.select([dataset.domain.classVar.name])
self._classArray = numpy.array([row.getclass() for row in classTable])
new_table = orange.ExampleTable([new_table, classTable])
return new_table
def applySettings(self):
"""use the setting from the widget, identify the outliers"""
if self.haveInput == 1:
outlier = self.outlier
outlier.setKNN(self.ks[self.k][1])
newdomain = orange.Domain(self.data.domain)
newdomain.addmeta(orange.newmetaid(),
orange.FloatVariable("Z score"))
self.newdata = orange.ExampleTable(newdomain, self.data)
zv = outlier.zValues()
for i, el in enumerate(zv):
self.newdata[i]["Z score"] = el
self.send("Examples with Z-scores", self.newdata)
filterout = orange.Filter_values(domain=self.newdata.domain)
filterout["Z score"] = (orange.Filter_values.Greater,
eval(self.zscore))
outliers = filterout(self.newdata)
filterin = orange.Filter_values(domain=self.newdata.domain)
filterin["Z score"] = (orange.Filter_values.LessEqual,
eval(self.zscore))
inliers = filterin(self.newdata)
self.send("Outliers", outliers)
self.send("Inliers", inliers)
else:
self.send("Examples with Z-scores", None)
self.send("Outliers", None)
self.send("Inliers", None)
def etForAttribute(datal, a):
"""
Builds an example table for a single attribute across multiple
example tables.
"""
tables = len(datal)
def getAttrVals(data, attr):
dom2 = orange.Domain([data.domain[attr]], False)
dataa = orange.ExampleTable(dom2, data)
return [a[0].native() for a in dataa]
domainl = []
valuesl = []
for id, data in enumerate(datal):
v = getAttrVals(data, a)
valuesl.append(v)
domainl.append(orange.FloatVariable(name=("v" + str(id))))
classvals = getAttrVals(data, datal[0].domain.classVar)
valuesl += [classvals]
dom = orange.Domain(domainl, datal[0].domain.classVar)
examples = [list(a) for a in zip(*valuesl)]
datat = orange.ExampleTable(dom, examples)
return datat
def __makeExampleTable(namesDict, data):
import orange
from constants import CLASS_ATRR_NAME, CONTROL_GROUP_KEY, DATA_GROUP_KEY
geneIDs = sorted(data.keys())
attrList = [orange.FloatVariable(name=str(geneID)) for geneID in geneIDs]
classAttr = orange.EnumVariable(name=CLASS_ATRR_NAME,
values=[CONTROL_GROUP_KEY, DATA_GROUP_KEY])
domain = orange.Domain(attrList, classAttr)
table = orange.ExampleTable(domain)
# first half: group 1
for attrName in namesDict[CONTROL_GROUP_KEY].keys():
exampleValues = [
data[geneID][CONTROL_GROUP_KEY][attrName] for geneID in geneIDs
] + [CONTROL_GROUP_KEY]
example = orange.Example(domain, exampleValues)
table.append(example)
# second half: group 2
for attrName in namesDict[DATA_GROUP_KEY].keys():
exampleValues = [
data[geneID][DATA_GROUP_KEY][attrName] for geneID in geneIDs
] + [DATA_GROUP_KEY]
example = orange.Example(domain, exampleValues)
table.append(example)
return table
def applySettings(self):
if self.haveInput == 1:
outlier = self.outlier
outlier.setKNN(self.ks[self.k][1])
newdomain = orange.Domain(self.data.domain)
newdomain.addmeta(orange.newmetaid(),
orange.FloatVariable("Z score"))
self.newdata = orange.ExampleTable(newdomain, self.data)
zv = outlier.zValues()
for i, el in enumerate(zv):
self.newdata[i]["Z score"] = el
self.send("Examples with Z-scores", self.newdata)
filter = orange.Filter_values(domain=self.newdata.domain)
filter["Z score"] = (orange.Filter_values.Greater,
eval(self.zscore))
self.outliers = filter(self.newdata)
self.send("Outliers", self.outliers)
else:
self.send("Examples with Z-scores", None)
self.send("Outliers", None)
def getSMARTSrecalcDesc(data, smarts):
""" Calculates structural descriptors for test and training data.
In other words, checks for the substructure occurrence (0/1) in the
test or prediction molecules. Uses RDK.
Expects the test/prediction data and a list of SMARTS strings.
Returns the data including the new features.
"""
smilesName = dataUtilities.getSMILESAttr(data)
if not smilesName or type(smarts) != list or not len(smarts):
print "Please check the input parameters"
return None
existingAttrs = [attr for attr in smarts if attr in data.domain]
if existingAttrs:
print "The input data cannot contain the smarts to be calculated!"
return None
newdomain = orange.Domain(data.domain.attributes + \
[orange.FloatVariable(attr, numberOfDecimals=1) for attr in smarts],\
data.domain.classVar )
newdata = orange.ExampleTable(newdomain, data)
for ex in newdata:
smile = str(ex[smilesName].value)
mol = rdk.Chem.MolFromSmiles(smile)
if mol is None:
continue
for smrt in smarts:
patt = rdk.Chem.MolFromSmarts(smrt)
if mol.HasSubstructMatch(patt):
ex[smrt] = 1.0
else:
ex[smrt] = 0.0
return newdata
def addMetaID(data):
meta_id = orange.FloatVariable("meta_id")
mid = orange.newmetaid()
while mid in data.domain.getmetas().keys():
mid = orange.newmetaid()
data.domain.addmeta(mid, meta_id)
for i in range(len(data)):
data[i][meta_id] = i
def __call__(self, rule, examples, weights, targetClass):
if not weights:
weights = orange.newmetaid()
examples.addMetaAttribute(weights, 1.)
examples.domain.addmeta(
weights, orange.FloatVariable("weights-" + str(weights)), True)
newWeightsID = orange.newmetaid()
examples.addMetaAttribute(newWeightsID, 1.)
examples.domain.addmeta(
newWeightsID, orange.FloatVariable("weights-" + str(newWeightsID)),
True)
for example in examples:
if rule(example) and example.getclass() == rule.classifier(
example, orange.GetValue):
example[newWeightsID] = example[weights] * self.mult
else:
example[newWeightsID] = example[weights]
return (examples, newWeightsID)
def __make_rule_term_example_table(tableDict, allTerms):
import orange
import constants as const
attrList = [
orange.EnumVariable(name=str(term),
values=[const.PRESENT, const.ABSENT])
for term in allTerms
]
# three meta attributes
ruleName = orange.StringVariable(const.NAME_ATTR)
mid = orange.newmetaid()
ruleTerms = orange.StringVariable(const.TERMS_ATTR)
mid1 = orange.newmetaid()
#ruleNumber = orange.EnumVariable(SEQ_NUM_ATTR) #StringVariable(SEQ_NUM_ATTR)
ruleNumber = orange.FloatVariable(const.SEQ_NUM_ATTR,
startValue=1,
endValue=len(tableDict),
stepValue=1,
numberOfDecimals=0)
mid2 = orange.newmetaid()
# this is a classless domain
domain = orange.Domain(attrList, False)
# name of the rule is a meta attribute
domain.addmeta(mid, ruleName, False)
domain.addmeta(mid1, ruleTerms, False)
domain.addmeta(mid2, ruleNumber, False)
table = orange.ExampleTable(domain)
for k in sorted(tableDict.keys()):
exampleValues = []
for (i, term) in enumerate(allTerms):
if term in tableDict[k][const.RULETERMS_KEY]:
#exampleValues.append(PRESENT)
exampleValues.append(orange.Value(attrList[i], const.PRESENT))
else:
#exampleValues.append(ABSENT)
exampleValues.append(orange.Value(attrList[i], const.ABSENT))
example = orange.Example(domain, exampleValues)
#example[NAME_ATTR] = tableDict[k][RULENAME_KEY][1:-1] #skip square brackets from the string
#example[TERMS_ATTR] = tableDict[k][RULETERMS_STR_KEY][1:-1]
#example[SEQ_NUM_ATTR] = k
example[const.NAME_ATTR] = orange.Value(ruleName, tableDict[k][
const.RULENAME_KEY][1:-1]) #skip square brackets from the string
example[const.TERMS_ATTR] = orange.Value(
ruleTerms, tableDict[k][const.RULETERMS_STR_KEY][1:-1])
example[const.SEQ_NUM_ATTR] = orange.Value(ruleNumber, k)
table.append(example)
#end
return table
def sendData(self):
self.selectionDirty = False
selected = [(x.row(), x.column())
for x in self.table.selectedIndexes()]
res = self.res
if not res or not selected or not self.selectedLearner:
self.send("Selected Data", None)
return
learnerI = self.selectedLearner[0]
data = None
if hasattr(res, "examples") and isinstance(res.examples,
orange.ExampleTable):
selectionIndices = [
i for i, rese in enumerate(res.results)
if (rese.actualClass, rese.classes[learnerI]) in selected
]
data = res.examples.getitemsref(selectionIndices)
if data is not None and (self.appendPredictions
or self.appendProbabilities):
domain = orange.Domain(data.domain.attributes,
data.domain.classVar)
domain.addmetas(data.domain.getmetas())
data = orange.ExampleTable(domain, data)
if self.appendPredictions:
cname = self.learnerNames[learnerI]
predVar = type(domain.classVar)(
"%s(%s)" % (domain.classVar.name, cname.encode("utf-8")
if isinstance(cname, unicode) else cname))
if hasattr(domain.classVar, "values"):
predVar.values = domain.classVar.values
predictionsId = orange.newmetaid()
domain.addmeta(predictionsId, predVar)
for i, ex in zip(selectionIndices, data):
ex[predictionsId] = res.results[i].classes[learnerI]
if self.appendProbabilities:
probVars = [
orange.FloatVariable("p(%s)" % v)
for v in domain.classVar.values
]
probIds = [orange.newmetaid() for pv in probVars]
domain.addmetas(dict(zip(probIds, probVars)))
for i, ex in zip(selectionIndices, data):
for id, p in zip(probIds,
res.results[i].probabilities[learnerI]):
ex[id] = p
if data is not None:
data.name = self.learnerNames[learnerI]
self.send("Selected Data", data)
def sendExampleTable(self, selectedInd):
if self.selectionOptions==0:
self.send("Data", orange.ExampleTable(self.data.getitems(selectedInd)))
else:
xAttr=orange.FloatVariable("X")
yAttr=orange.FloatVariable("Y")
if self.selectionOptions==1:
domain=orange.Domain([xAttr, yAttr]+[v for v in self.data.domain.variables])
domain.addmetas(self.data.domain.getmetas())
else:
domain=orange.Domain(self.data.domain)
domain.addmeta(orange.newmetaid(), xAttr)
domain.addmeta(orange.newmetaid(), yAttr)
selection=orange.ExampleTable(domain)
selection.extend(self.data.getitems(selectedInd))
for i in range(len(selectedInd)):
selection[i][xAttr]=self.mds.points[selectedInd[i]][0]
selection[i][yAttr]=self.mds.points[selectedInd[i]][1]
self.send("Data", selection)
def initialize(self, examples, weightID, targetClass, apriori):
self.bestRule = [None] * len(examples)
self.probAttribute = orange.newmetaid()
examples.addMetaAttribute(self.probAttribute, -1.e-6)
examples.domain.addmeta(self.probAttribute,
orange.FloatVariable("Probs"))
for example in examples:
## if targetClass<0 or (example.getclass() == targetClass):
example[self.probAttribute] = apriori[targetClass] / apriori.abs
return examples
def PCAOnExampleTable(table, keepOriginal=1, nPCs=-1):
data = table.toNumpyMA("a")[0]
projData, vectors, values = pca(data, nPCs)
newDomain = orange.Domain(
[orange.FloatVariable("PC %d" % (d + 1)) for d in range(len(vectors))],
0)
newTable = orange.ExampleTable(newDomain, projData.data)
if keepOriginal:
return orange.ExampleTable([table, newTable])
else:
return newTable
class PCAClassifier(object):
def __init__(self, domain, imputer, continuizer, center, deviation,
evalues, loadings):
#data checking and modifying
self.domain = domain
self.imputer = imputer
self.continuizer = continuizer
#PCA properites
self.center = center
self.deviation = deviation
self.evalues = evalues
self.loadings = loadings
#last predicition performed -> used for biplot
self._dataMatrix = None
self._classArray = None
def __call__(self, dataset):
try:
#retain class attribute
attrDataset = dataset.select(self.domain)
imputer = self.imputer(attrDataset)
attrDataset = imputer(attrDataset)
domain = self.continuizer(attrDataset)
attrDataset = attrDataset.translate(domain)
except TypeError, e:
raise orange.KernelException, "One or more attributes form training set are missing!"
dataMatrix, classArray, x = attrDataset.toNumpy()
dataMatrix -= self.center
if self.deviation != None:
dataMatrix *= 1. / self.deviation
#save transformed data
self._dataMatrix = numpy.dot(dataMatrix, self.loadings)
attributes = [
orange.FloatVariable("PC%d" % (i + 1, ))
for i in range(len(self.evalues))
]
new_domain = orange.Domain(attributes)
new_table = orange.ExampleTable(new_domain, self._dataMatrix)
if dataset.domain.classVar:
#suboptimal
classTable = dataset.select([dataset.domain.classVar.name])
self._classArray = numpy.array(
[row.getclass() for row in classTable])
new_table = orange.ExampleTable([new_table, classTable])
return new_table
def makeDomain(names):
attributes = [orange.FloatVariable(n) for n in names]
domain = orange.Domain(
attributes, orange.EnumVariable("class", values=["True", "False"]))
domain.addmeta(orange.newmetaid(), orange.FloatVariable("weight"))
domain.addmeta(orange.newmetaid(),
orange.EnumVariable("isInsane", values=["True", "False"]))
domain.addmeta(orange.newmetaid(), orange.StringVariable("filename"))
domain.addmeta(orange.newmetaid(),
orange.StringVariable("sourceEngineName"))
domain.addmeta(orange.newmetaid(), orange.StringVariable("engineName"))
domain.addmeta(orange.newmetaid(), orange.StringVariable("landmarkName"))
domain.addmeta(orange.newmetaid(), orange.PythonVariable("geometry"))
domain.addmeta(orange.newmetaid(), orange.PythonVariable("track"))
domain.addmeta(orange.newmetaid(), orange.PythonVariable("drawMap"))
domain.addmeta(orange.newmetaid(), orange.PythonVariable("description"))
domain.addmeta(orange.newmetaid(), orange.PythonVariable("farAway"))
return domain
def get_domain_trans(self):
#if(self.domain != None):
# return self.domain
attributes = [
orange.FloatVariable(name) for name in self.dataset.trans_alphabet
]
alp = [str(i) for i in range(len(self.dataset.label_alphabet)**2)]
classattr = orange.EnumVariable("classname", values=alp)
domain = orange.Domain(attributes + [classattr])
return domain
def get_domain_obs(self):
#if(self.domain != None):
# return self.domain
attributes = [
orange.FloatVariable(name) for name in self.dataset.obs_alphabet
]
alp = [str(s) for s in self.dataset.label_alphabet]
classattr = orange.EnumVariable("classname", values=alp)
domain = orange.Domain(attributes + [classattr])
return domain
def learnModel(self, X, y):
if numpy.unique(y).shape[0] != 2:
raise ValueError("Can only operate on binary data")
classes = numpy.unique(y)
self.worstResponse = classes[classes != self.bestResponse][0]
#We need to convert y into indices
newY = self.labelsToInds(y)
XY = numpy.c_[X, newY]
attrList = []
for i in range(X.shape[1]):
attrList.append(orange.FloatVariable("X" + str(i)))
attrList.append(orange.EnumVariable("y"))
attrList[-1].addValue(str(self.bestResponse))
attrList[-1].addValue(str(self.worstResponse))
self.domain = orange.Domain(attrList)
eTable = orange.ExampleTable(self.domain, XY)
#Weight examples
preprocessor = orange.Preprocessor_addClassWeight(equalize=1)
preprocessor.classWeights = [1 - self.weight, self.weight]
eTable, weightID = preprocessor(eTable)
eTable.domain.addmeta(weightID, orange.FloatVariable("w"))
tree = orngTree.TreeLearner(mForPruning=self.m,
measure="gainRatio",
minExamples=self.minSplit,
maxDepth=self.maxDepth).instance()
self.learner = orngEnsemble.RandomForestLearner(
learner=tree,
trees=self.numTrees,
attributes=numpy.round(X.shape[1] * self.featureSize))
self.classifier = self.learner(eTable, weightID)
def loadLibSVM(filename):
data = [
line.split() for line in open(filename, "rb").read().splitlines()
if line.strip()
]
vars = type(
"attr", (dict, ), {
"__missing__":
lambda self, key: self.setdefault(key, orange.FloatVariable(key))
})()
item = lambda i, v: (vars[i], vars[i](v))
values = [dict([item(*val.split(":")) for val in ex[1:]]) for ex in data]
classes = [ex[0] for ex in data]
disc = all(["." not in c for c in classes])
attributes = sorted(list(vars.values()), key=lambda var: int(var.name))
classVar = orange.EnumVariable("class", values=sorted(
set(classes))) if disc else orange.FloatVariable("target")
domain = orange.Domain(attributes, classVar)
return orange.ExampleTable([
orange.Example(domain,
[ex.get(attr, attr("?")) for attr in attributes] + [c])
for ex, c in zip(values, classes)
])
def learnModel(self, X, y):
if numpy.unique(y).shape[0] != 2:
raise ValueError("Can only operate on binary data")
classes = numpy.unique(y)
self.worstResponse = classes[classes != self.bestResponse][0]
#We need to convert y into indices
newY = self.labelsToInds(y)
XY = numpy.c_[X, newY]
attrList = []
for i in range(X.shape[1]):
attrList.append(orange.FloatVariable("X" + str(i)))
attrList.append(orange.EnumVariable("y"))
attrList[-1].addValue(str(self.bestResponse))
attrList[-1].addValue(str(self.worstResponse))
self.domain = orange.Domain(attrList)
eTable = orange.ExampleTable(self.domain, XY)
#Weight examples and equalise
#Equalizing computes such weights that the weighted number of examples
#in each class is equivalent.
preprocessor = orange.Preprocessor_addClassWeight(equalize=1)
preprocessor.classWeights = [1 - self.weight, self.weight]
eTable, weightID = preprocessor(eTable)
eTable.domain.addmeta(weightID, orange.FloatVariable("w"))
self.learner = orngTree.TreeLearner(m_pruning=self.m,
measure="gainRatio")
self.learner.max_depth = self.maxDepth
self.learner.stop = orange.TreeStopCriteria_common()
self.learner.stop.min_instances = self.minSplit
self.classifier = self.learner(eTable, weightID)
def __parseBBRCoutput(self, res):
#Parse the results to an orange tab file
if self.verbose: print "Parsing BBRC results. Please wait..."
nCompounds = len(self.data)
allDesc = []
allIDs = []
for line in res:
allDesc.append(line.split("\t")[0].strip())
allIDs.append(
[int(x) for x in line.split("\t")[1][1:-1].strip().split(" ")])
# Find the Descriptors that are required to be at the output file, but they are not among allDesc
missingDesc = []
desAttr = []
selDesc = [x for x in allDesc]
newDomainAttrs = [attr for attr in self.data.domain.attributes] + \
[orange.FloatVariable(name) for name in selDesc]
newDomain = orange.Domain(newDomainAttrs, self.data.domain.classVar)
if self.verbose:
print "Original domain lenght: ", len(self.data.domain)
print "New domain lenght : ", len(newDomain)
print "\n0%" + " " * 98 + "100%"
print "|" + "-" * 100 + "|"
sys.stdout.write("|")
sys.stdout.flush()
newData = dataUtilities.DataTable(newDomain)
for idx, ex in enumerate(self.data):
newEx = orange.Example(newDomain, ex)
if self.verbose:
if nCompounds < 100:
sys.stdout.write("=")
elif idx % (int(nCompounds / 100)) == 0:
sys.stdout.write("=")
sys.stdout.flush()
ID = idx + 1 # ID is the number of coumpound in self.data which is the number os the example (1 based!)
for dIdx, d in enumerate(selDesc):
if ID in allIDs[dIdx]:
newEx[d] = 1.0
else:
newEx[d] = 0.0
newData.append(newEx)
if self.verbose:
if nCompounds < 100:
sys.stdout.write("=" * (100 - nCompounds + 1))
print ""
return newData
def createLogRegExampleTable(data, weightID):
finalData = orange.ExampleTable(data)
origData = orange.ExampleTable(data)
for at in data.domain.attributes:
# za vsak atribut kreiraj nov newExampleTable newData
# v dataOrig, dataFinal in newData dodaj nov atribut -- continuous variable
if at.varType == orange.VarTypes.Continuous:
atDisc = orange.FloatVariable(at.name + "Disc")
newDomain = orange.Domain(origData.domain.attributes +
[atDisc, data.domain.classVar])
newDomain.addmetas(newData.domain.getmetas())
finalData = orange.ExampleTable(newDomain, finalData)
newData = orange.ExampleTable(newDomain, origData)
origData = orange.ExampleTable(newDomain, origData)
for d in origData:
d[atDisc] = 0
for d in finalData:
d[atDisc] = 0
for i, d in enumerate(newData):
d[atDisc] = 1
d[at] = 0
d[weightID] = 100 * data[i][weightID]
elif at.varType == orange.VarTypes.Discrete:
# v dataOrig, dataFinal in newData atributu "at" dodaj ee eno vreednost, ki ima vrednost kar ime atributa + "X"
atNew = orange.EnumVariable(at.name,
values=at.values +
[at.name + "X"])
newDomain = orange.Domain(
filter(lambda x: x != at, origData.domain.attributes) +
[atNew, origData.domain.classVar])
newDomain.addmetas(origData.domain.getmetas())
temp_finalData = orange.ExampleTable(finalData)
finalData = orange.ExampleTable(newDomain, finalData)
newData = orange.ExampleTable(newDomain, origData)
temp_origData = orange.ExampleTable(origData)
origData = orange.ExampleTable(newDomain, origData)
for i, d in enumerate(origData):
d[atNew] = temp_origData[i][at]
for i, d in enumerate(finalData):
d[atNew] = temp_finalData[i][at]
for i, d in enumerate(newData):
d[atNew] = at.name + "X"
d[weightID] = 10 * data[i][weightID]
finalData.extend(newData)
return finalData
def test():
app = QApplication(sys.argv)
w = OWHierarchicalClustering()
w.show()
data = orange.ExampleTable("../../doc/datasets/iris.tab")
id = orange.newmetaid()
data.domain.addmeta(id, orange.FloatVariable("a"))
data.addMetaAttribute(id)
matrix = orange.SymMatrix(len(data))
dist = orange.ExamplesDistanceConstructor_Euclidean(data)
matrix = orange.SymMatrix(len(data))
matrix.setattr('items', data)
for i in range(len(data)):
for j in range(i + 1):
matrix[i, j] = dist(data[i], data[j])
w.set_matrix(matrix)
app.exec_()
