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 __call__(self, ex, what=orange.Classifier.GetValue):
val = ex[self.var1] * self.noValues2 + ex[self.var2]
if what == orange.Classifier.GetValue:
return orange.Value(self.classVar, val)
probs = orange.DiscDistribution(self.classVar)
probs[val] = 1.0
if what == orange.Classifier.GetProbabilities:
return probs
else:
return (orange.Value(self.classVar, val), probs)
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 all_discrete_clauses(self, attr, max_card=None):
all_vals = self.col_to_clauses[attr]
attrobj = self.full_table.domain[attr]
idx = self.full_table.domain.index(attrobj)
if max_card:
for card in xrange(1, max_card + 1):
for vals in combinations(all_vals, card):
vals = [orange.Value(attrobj, value) for value in vals]
yield orange.ValueFilter_discrete(position=idx,
values=vals)
else:
for vals in powerset(all_vals):
vals = [orange.Value(attrobj, value) for value in vals]
yield orange.ValueFilter_discrete(position=idx, values=vals)
def __call__(self, example, resultType=orange.GetValue):
from operator import add
# voting for class probabilities
if resultType == orange.GetProbabilities or resultType == orange.GetBoth:
cprob = [0.] * len(self.domain.classVar.values)
for c in self.classifiers:
a = [x for x in c(example, orange.GetProbabilities)]
cprob = list(map(add, cprob, a))
norm = sum(cprob)
for i in range(len(cprob)):
cprob[i] = cprob[i] / norm
# voting for crisp class membership, notice that
# this may not be the same class as one obtaining the
# highest probability through probability voting
if resultType == orange.GetValue or resultType == orange.GetBoth:
cfreq = [0] * len(self.domain.classVar.values)
for c in self.classifiers:
cfreq[int(c(example))] += 1
index = cfreq.index(max(cfreq))
cvalue = orange.Value(self.domain.classVar, index)
if resultType == orange.GetValue: return cvalue
elif resultType == orange.GetProbabilities: return cprob
else: return (cvalue, cprob)
def fill_in_rule(self, table, ref_bounds):
domain = table.domain
# if there are any cols not in the rule, fill them in with table bounds
conds = {}
for c in self.filter.conditions:
attr = domain[c.position]
name = attr.name
conds[name] = True
for col, bounds in ref_bounds.iteritems():
if col in conds:
continue
attr = domain[col]
pos = domain.index(attr)
if bounds is None:
vals = range(len(attr.values))
vals = [orange.Value(attr, attr.values[v]) for v in vals]
cond = orange.ValueFilter_discrete(position=pos, values=vals)
else:
(minv, maxv) = bounds
cond = orange.ValueFilter_continuous(
position=pos,
oper=orange.ValueFilter.Between,
min=minv - 1,
max=maxv + 1)
self.filter.conditions.append(cond)
def __call__(self, example, resultType=orange.GetValue):
# 1. calculate sum of distributions of examples that cover the example
num_cover = 0.0
distribution = [0] * len(self.data.domain.classVar.values)
for rsc in self.rulesClass:
for rule in rsc.rules.rules:
if rule.covers(example):
num_cover += 1
tmp_dist = rule(example, orange.GetProbabilities)
for i in range(len(distribution)):
distribution[i] += tmp_dist[i]
# 2. calculate average of distributions of rules that cover example
if num_cover != 0:
max_index = 0
for i in range(len(distribution)):
distribution[i] = distribution[i] / num_cover
if distribution[i] > distribution[max_index]:
max_index = i
dist = orange.DiscDistribution(distribution)
value = orange.Value(self.data.domain.classVar,
self.data.domain.classVar.values[max_index])
# if no rule fiers
else:
value, dist = self.majorityClassifier(example, orange.GetBoth)
# 3. -----------return
if resultType == orange.GetValue:
return value
elif resultType == orange.GetBoth:
return (value, dist)
else:
return dist
def __call__(self, example, result_type=orange.GetValue):
# compute the class probabilities
p = map(None, self.p_class)
for c in range(len(self.domain.classVar.values)):
for a in range(len(self.domain.attributes)):
if not example[a].isSpecial():
p[c] *= self.p_cond[a][int(example[a])][c]
# normalize probabilities to sum to 1
sum = 0.
for pp in p:
sum += pp
if sum > 0:
for i in range(len(p)):
p[i] = p[i] / sum
# find the class with highest probability
v_index = p.index(max(p))
v = orange.Value(self.domain.classVar, v_index)
# return the value based on requested return type
if result_type == orange.GetValue:
return v
if result_type == orange.GetProbabilities:
return p
return (v, p)
def __rule__(self):
if self._rule: return self._rule
conds = []
for attr, gid in zip(self.attrs, self.group):
if attr.var_type == Orange.feature.Type.Discrete:
vals = [
orange.Value(attr, v)
for v in self.grouper.id2vals[attr][gid]
]
conds.append(
orange.ValueFilter_discrete(
position=self.grouper.data.domain.index(attr),
values=vals))
else:
vals = self.grouper.id2vals[attr][gid]
minv, maxv = vals[0], vals[1]
conds.append(
Orange.data.filter.ValueFilterContinuous(
oper=orange.ValueFilter.Between,
position=self.grouper.data.domain.index(attr),
min=minv,
max=maxv))
self._rule = SDRule(self.grouper.data, None, conds, None)
self._rule.quality = self.inf
self._rule.inf_state = self.inf_state
return self._rule
def cloneAndAddCondition(self, attribute, value):
'''Returns a copy of this rule which condition part is extended by attribute = value'''
cond = self.filter.conditions[:]
cond.append(
orange.ValueFilter_discrete(
position=self.data.domain.attributes.index(attribute),
values=[orange.Value(attribute, value)]))
return SDRule(self.data, self.targetClass, cond, self.g)
def simplify(self, data=None, cdists=None, ddists=None):
"""
Args:
data: non-filtered! data
cdists: non-filtered Continuous distribution
ddists: non-filtered discrete distribution
Return:
copy of this rule with simplified conditions
"""
subset = data and self(data) or self.examples
data = data or self.data
ret = self.clone()
positions = [cond.position for cond in self.filter.conditions]
cdists = cdists or Orange.statistics.basic.Domain(data)
ddists = ddists or Orange.statistics.distribution.Domain(data)
#scdists = Orange.statistics.basic.Domain(subset)
#sddists = Orange.statistics.distribution.Domain(subset)
conds = []
for old_cond, idx in zip(self.filter.conditions, positions):
attr = data.domain[idx]
# if rule values == full dataset values, then remove rule
# filter down to the values that intersect the subset of data
if attr.var_type == Orange.feature.Type.Discrete:
full_d = ddists[attr.name]
#sub_d = sddists[attr.name]
fvals = [k for k, v in full_d.items() if v]
cvals = set(
[str(attr.values[int(v)]) for v in old_cond.values])
if len(cvals) == len(fvals):
continue
#dvals = [k for k,v in sub_d.items() if v]
#vals = set(cvals).intersection(dvals)
vals = cvals
cond = orange.ValueFilter_discrete(
position=idx,
values=[orange.Value(attr, val) for val in vals])
else:
fb = cdists[attr.name]
#sb = scdists[attr.name]
old_bound = [fb.min, fb.max]
cond_bound = [old_cond.min, old_cond.max]
bound = r_intersect(old_bound, cond_bound)
if r_vol(bound) >= r_vol(old_bound): continue
#bound = r_intersect(bound, [sb.min, sb.max])
cond = old_cond
cond.min, cond.max = bound[0], bound[1]
conds.append(cond)
continue
ret.quality = self.quality
ret.filter.conditions = conds
ret.c_range = list(self.c_range)
return ret
def __call__(self, example, what=orange.Classifier.GetValue):
probs = self.classifier(example, self.GetProbabilities)
if what == self.GetProbabilities:
return probs
value = orange.Value(self.classifier.classVar,
probs[1] > self.threshold)
if what == orange.Classifier.GetValue:
return value
else:
return (value, probs)
def mergeClassValues(data, value):
selection = orange.EnumVariable("Selection", values=["0", "1"])
selectedClassesStr = [value]
nonSelectedClassesStr = []
for val in data.domain.classVar.values:
if val not in selectedClassesStr: nonSelectedClassesStr.append(val)
shortData1 = data.select({data.domain.classVar.name: selectedClassesStr})
shortData2 = data.select(
{data.domain.classVar.name: nonSelectedClassesStr})
d1 = orange.Domain(shortData1.domain.attributes + [selection])
selection.getValueFrom = lambda ex, what: orange.Value(selection, "0")
data1 = orange.ExampleTable(d1, shortData1)
selection.getValueFrom = lambda ex, what: orange.Value(selection, "1")
data2 = orange.ExampleTable(d1, shortData2)
data1.extend(data2)
return data1
def __call__(self, data, targetClass, num_of_rules=0):
'''Returns CN2-SD rules by performing weighted covering algorithm.'''
data_discretized = False
# If any of the attributes are continuous, discretize them
if data.domain.hasContinuousAttributes():
original_data = data
data_discretized = True
new_domain = []
discretize = orange.EntropyDiscretization(forceAttribute=True)
for attribute in data.domain.attributes:
if attribute.varType == orange.VarTypes.Continuous:
d_attribute = discretize(attribute, data)
# An attribute is irrelevant, if it is discretized into a single interval
# if len(d_attribute.getValueFrom.transformer.points) > 0:
new_domain.append(d_attribute)
else:
new_domain.append(attribute)
data = original_data.select(new_domain +
[original_data.domain.classVar])
self.data = data
self.max_rules = num_of_rules
rules = []
tc = orange.Value(data.domain.classVar, targetClass)
# weighted covering
self.data.addMetaAttribute(
self.weightID) # set weights of all examples to 1
self.data.addMetaAttribute(
self.counter) # set counters of all examples to 0
targetClassRule = SDRule(data, targetClass, conditions=[], g=1)
tmpRule = self.rbf(data, self.weightID, targetClass, None)
while (tmpRule.quality > 0) and (self.max_rules == 0
or len(rules) < self.max_rules):
bestRule = SDRule(self.data, tc, tmpRule.filter.conditions)
bestRule.quality = tmpRule.quality
self.decreaseExampleWeights(bestRule)
rules.append(bestRule)
tmpRule = self.rbf(data, self.weightID, targetClass, None)
if data_discretized:
targetClassRule = SDRule(original_data,
targetClass,
conditions=[],
g=1)
# change beam so the rules apply to original data
rules = [rule.getUndiscretized(original_data) for rule in rules]
else:
targetClassRule = SDRule(data, targetClass, conditions=[], g=1)
return SDRules(rules, targetClassRule, "CN2-SD")
def __call__(self, example, resultType = orange.GetValue):
freq = [0.] * len(self.domain.classVar.values)
for c in self.classifiers:
freq[int(c(example))] += 1
index = freq.index(max(freq))
value = orange.Value(self.domain.classVar, index)
for i in range(len(freq)):
freq[i] = freq[i]/len(self.classifiers)
if resultType == orange.GetValue: return value
elif resultType == orange.GetProbabilities: return freq
else: return (value, freq)
def __call__(self, example, what=orange.Classifier.GetValue):
probability = self.classifier.orange_classify(example)
answer = orange.Value(self.classVar, int(round(probability)))
probabilities = orange.DiscDistribution(self.classVar)
probabilities[answer] = probability
if what == orange.Classifier.GetValue:
return answer
elif what == orange.Classifier.GetProbabilities:
return probabilities
else:
return answer, probabilities
def lookupFromFunction(attribute, bound, function):
"""
Constructs ClassifierByExampleTable or ClassifierByLookupTable mirroring the given function
"""
lookup = lookupFromBound(attribute, bound)
if lookup:
lookup.lookupTable = [orange.Value(attribute, function(attributes)) for attributes in orngMisc.LimitedCounter([len(attr.values) for attr in bound])]
return lookup
else:
examples = orange.ExampleTable(orange.Domain(bound, attribute))
for attributes in orngMisc.LimitedCounter([len(attr.values) for attr in dom.attributes]):
examples.append(orange.Example(dom, attributes + [function(attributes)]))
return orange.LookupLearner(examples)
def __call__(self, ex, what=orange.Classifier.GetValue):
value = self.classify(ex)
result = orange.Value(ex.domain.classVar, str(value))
probs = orange.DiscDistribution(ex.domain.classVar)
probs[value] = 1.0
if what == orange.Classifier.GetValue:
return result
elif what == orange.Classifier.GetProbabilities:
return probs
elif what == orange.Classifier.GetBoth:
return result, probs
else:
raise ValueError("Bad what argument: %s" % ` what `)
def __call__(self, example, result_type=orange.GetValue):
if result_type == orange.GetValue:
return orange.Value(self.domain.classVar,
self.model.predict(extract_features(example)))
else:
# build a label map, which will be used to sort the outputted
# probabilities
class_map = {}
for pos, label in enumerate(self.domain.classVar.values):
class_map[label] = pos
result = self.model.eval_all(extract_features(example))
if len(result) > 0:
if result_type == orange.GetProbabilities:
r = [None] * len(result)
for label, prob in result:
r[class_map[label]] = prob
return r
elif result_type == orange.GetBoth:
return (orange.Value(self.domain.classVar,
result[0][0]), result[0][1])
else:
return None
def to_rule(self, table, cont_dists=None, disc_dists=None):
"""
@param cols list of attribute names
"""
if not self.rule:
domain = table.domain
attrnames = [attr.name for attr in domain]
cont_dists = cont_dists or dict(
zip(attrnames, Orange.statistics.basic.Domain(table)))
disc_dists = disc_dists or dict(
zip(attrnames, Orange.statistics.distribution.Domain(table)))
conds = []
for col, bound in zip(self.cols, zip(*self.bbox)):
attr = domain[col]
pos = domain.index(attr)
table_bound = cont_dists[attr.name]
minv, maxv = r_intersect(bound,
[table_bound.min, table_bound.max])
if maxv - minv > 0.99 * (table_bound.max - table_bound.min):
continue
conds.append(
orange.ValueFilter_continuous(position=pos,
max=bound[1],
min=bound[0]))
for disc_name, vidxs in self.discretes.iteritems():
attr = domain[disc_name]
disc_pos = domain.index(attr)
vals = [
orange.Value(attr, attr.values[int(vidx)])
for vidx in vidxs if int(vidx) < len(attr.values)
]
if not vals or len(vals) == len(disc_dists[attr.name]):
continue
conds.append(
orange.ValueFilter_discrete(position=disc_pos,
values=vals))
rule = SDRule(table, None, conditions=conds)
self.rule = rule
rule = self.rule
rule.quality = rule.score = self.error
rule.inf_state = self.inf_state
rule.c_range = self.c_range
return rule
def getFixed(self, original_data):
cond = []
for c in self.filter.conditions:
feature = self.data.domain.attributes[c.position]
position = original_data.domain.attributes.index(feature.attribute)
if feature.cond == '==':
cond.append(
orange.ValueFilter_discrete(position=position,
values=[
orange.Value(
feature.attribute,
feature.value)
]))
elif feature.cond == '!=':
cond.append(
orange.ValueFilter_discrete(
position=position,
values=[
orange.Value(feature.attribute, value)
for value in feature.attribute.values
if value != feature.value
]))
elif feature.cond == '<=':
cond.append(
orange.ValueFilter_continuous(position=position,
max=feature.value,
min=float(-infinity),
outside=False))
elif feature.cond == '>':
cond.append(
orange.ValueFilter_continuous(position=position,
max=feature.value,
min=float(-infinity),
outside=True))
return SDRule(original_data, self.targetClass, cond, self.g)
def __call__(self, learndata, testdata = None, weight = None):
# because of preprocessing
if testdata:
classifier = SD_Classifier(testdata)
else:
classifier = SD_Classifier(learndata)
for targetClassValue in learndata.domain.classVar.values:
targetClass = orange.Value(learndata.domain.classVar, targetClassValue)
beam = self.learner (learndata, targetClass, self.max_rules)
classifier.addRulesForClass(beam, targetClass)
classifier.name = self.name
classifier.algorithm = self.algorithm
return classifier
def __call__(self, example, resultType=orange.GetValue):
votes = [0.] * len(self.classVar.values)
for c, e in self.classifiers:
votes[int(c(example))] += e
index = orngMisc.selectBestIndex(votes)
value = orange.Value(self.classVar, index)
if resultType == orange.GetValue:
return value
sv = sum(votes)
for i in range(len(votes)):
votes[i] = votes[i] / sv
if resultType == orange.GetProbabilities:
return votes
else:
return (value, votes)
def create_clause(table, attr, val, bdists, cmp='='):
cmps = ['<', '<=', '>', '>=', '=']
if attr.varType == Orange.feature.Type.Discrete:
if not isinstance(val, (list, tuple)):
val = [val]
vals = [orange.Value(attr, v) for v in val]
filt = orange.ValueFilter_discrete(position=table.domain.index(attr),
values=vals)
return filt
else:
# it may be a discretized continuous condition (e.g., "<= 5")
isnumerical = False
for c in cmps:
try:
if val.startswith(c):
val = float(val.split(c)[1])
cmp = c
isnumerical = True
break
except:
pass
if not isnumerical:
val = float(val)
bdist = bdists[attr]
minv, maxv = bdist.min, bdist.max
op = None
if cmp == '>=':
minv = val
elif cmp == '>':
minv = val
elif cmp == '<=':
maxv = val
elif cmp == '<':
maxv = val
elif cmp == '=':
maxv = minv = val
else:
raise
return Orange.data.filter.ValueFilterContinuous(
position=table.domain.index(attr),
oper=orange.ValueFilter.Between,
min=minv,
max=maxv)
def __call__(self, example, resultType=orange.GetValue):
ex = orange.Example(self.domain, example)
ex = self.imputer(ex)
ex = numpy.array(ex.native())
if self.beta0:
if len(self.beta) > 1:
yhat = self.beta[0] + dot(self.beta[1:], ex[:-1])
else:
yhat = self.beta[0]
else:
yhat = dot(self.beta, ex[:-1])
yhat = orange.Value(yhat)
if resultType == orange.GetValue:
return yhat
if resultType == orange.GetProbabilities:
return orange.ContDistribution({1.0: yhat})
return (yhat, orange.ContDistribution({1.0: yhat}))
def dictToCond(d, data):
if d['type'] == 'num':
return orange.ValueFilter_continuous(
position=d['pos'],
oper=orange.ValueFilter.Between,
min=d['vals'][0],
max=d['vals'][1])
# XXX: NULL hack
attr = data.domain[d['col']]
vals = []
for v in d['vals']:
if v is None:
if 'NULL' in attr.values:
v = 'NULL'
elif 'None' in attr.values:
v = 'None'
vals.append(orange.Value(attr, v))
return orange.ValueFilter_discrete(position=d['pos'], values=vals)
def __call__(self, table, bound, weightID=0):
if not len(bound):
raise AttributeError, "no bound attributes"
bound = [table.domain[a] for a in bound]
newVar = orange.EnumVariable("-".join([a.name for a in bound]))
if (len(bound) == 1):
newVar.values = list(bound[0].values)
clsfr = orange.ClassifierByLookupTable(newVar, bound[0])
else:
import orngMisc
for vs in orngMisc.LimitedCounter([len(a.values) for a in bound]):
newVar.values.append("-".join(
[bound[i].values[v] for i, v in enumerate(vs)]))
clsfr = orange.ClassifierByLookupTable(newVar, bound)
## elif (len(bound)==2):
## for v1 in bound[0].values:
## for v2 in bound[1].values:
## newVar.values.append(v1+"-"+v2)
## clsfr = orange.ClassifierByLookupTable2(newVar, bound[0], bound[1])
## elif (len(bound)==3):
## for v1 in bound[0].values:
## for v2 in bound[1].values:
## for v3 in bound[2].values:
## newVar.values.append(v1+"-"+v2+"-"+v3)
## clsfr = orange.ClassifierByLookupTable3(newVar, bound[0], bound[1], bound[2])
## else:
## raise AttributeError, "cannot deal with more than 3 bound attributes"
for i in range(len(newVar.values)):
clsfr.lookupTable[i] = orange.Value(newVar, i)
newVar.getValueFrom = clsfr
if self.measure:
meas = self.measure(newVar, table)
else:
meas = 0
return newVar, meas
def cloneAndAddCondition(self,
attribute,
values,
used=False,
negate=False):
'''Returns a copy of this rule which condition part is extended by attribute = value'''
conds = list(self.filter.conditions)
if not (values):
return self
if not isinstance(values, list):
values = [values]
pos = self.data.domain.index(attribute)
conds = filter(lambda cond: cond.position != pos, conds)
values = [orange.Value(attribute, value) for value in values]
conds.append(
orange.ValueFilter_discrete(
position=self.data.domain.index(attribute), values=values))
conds.sort(key=lambda c: c.position)
return SDRule(self.data, self.targetClass, conds, self.g)
def __call__(self, example, resultType=orange.GetValue):
if self.classVar.varType == orange.VarTypes.Discrete:
freq = [0.] * len(self.classVar.values)
for c in self.classifiers:
freq[int(c(example))] += 1
index = freq.index(max(freq))
value = orange.Value(self.classVar, index)
if resultType == orange.GetValue:
return value
for i in range(len(freq)):
freq[i] = freq[i] / len(self.classifiers)
if resultType == orange.GetProbabilities:
return freq
else:
return (value, freq)
elif self.classVar.varType == orange.VarTypes.Continuous:
votes = [
c(
example, orange.GetBoth
if resultType == orange.GetProbabilities else resultType)
for c in self.classifiers
]
wsum = float(len(self.classifiers))
if resultType in [orange.GetBoth, orange.GetProbabilities]:
pred = sum([float(c) for c, p in votes]) / wsum
# prob = sum([float(p.modus()) for c, p in votes]) / wsum
from collections import defaultdict
prob = defaultdict(float)
for c, p in votes:
try:
prob[float(c)] += p[c] / wsum
except IndexError: # p[c] sometimes fails with index error
prob[float(c)] += 1.0 / wsum
prob = orange.ContDistribution(prob)
return self.classVar(
pred), prob if resultType == orange.GetBoth else prob
elif resultType == orange.GetValue:
pred = sum([float(c) for c in votes]) / wsum
return self.classVar(pred)
def booleanToOrange(bool, var):
if bool:
txt = "True"
else:
txt = "False"
return orange.Value(var, txt)
