# Description: Demonstrates the use of discretization
# Category: discretization
# Classes: entropyDiscretization, DiscretizedLearner
# Uses: iris.tab
import orange
import orngDisc
data = orange.ExampleTable("iris.tab")
disc_data = orngDisc.entropyDiscretization(data)
disc_learner = orngDisc.DiscretizedLearner(orange.BayesLearner(),
name="disc-bayes")
learner = orange.BayesLearner(name="bayes")
learners = [learner, disc_learner]
import orngTest, orngStat
results = orngTest.crossValidation(learners, data)
print "Classification Accuracy:"
for i in range(len(learners)):
print("%15s: %5.3f") % (learners[i].name, orngStat.CA(results)[i])
# Description: Demonstrates the use of discretization
# Category: discretization
# Classes: entropyDiscretization, DiscretizedLearner
# Uses: iris.tab
import orange
import orngDisc
data = orange.ExampleTable("iris.tab")
disc_data = orngDisc.entropyDiscretization(data)
disc_learner = orngDisc.DiscretizedLearner(orange.BayesLearner(), name="disc-bayes")
learner = orange.BayesLearner(name="bayes")
learners = [learner, disc_learner]
import orngTest, orngStat
results = orngTest.crossValidation(learners, data)
print "Classification Accuracy:"
for i in range(len(learners)):
print ("%15s: %5.3f") % (learners[i].name, orngStat.CA(results)[i])
# Description: Entropy based discretization compared to discretization with equal-frequency
# of instances in intervals
# Category: preprocessing
# Uses: wdbc.tab
# Classes: Preprocessor_discretize, EntropyDiscretization
# Referenced: o_categorization.htm
import orange
def show_values(data, heading):
for a in data.domain.attributes:
print "%s/%d: %s" % (a.name, len(a.values), reduce(lambda x, y: x + ', ' + y, [i for i in a.values]))
data = orange.ExampleTable("wdbc.tab")
print '%d features in original data set, discretized:' % len(data.domain.attributes)
data_ent = orange.Preprocessor_discretize(data, method=orange.EntropyDiscretization())
show_values(data_ent, "Entropy based discretization")
print '\nFeatures with sole value after discretization:'
for a in data_ent.domain.attributes:
if len(a.values) == 1:
print a.name
import orngDisc
data_ent2 = orngDisc.entropyDiscretization(data)
print '%d features after removing features discretized to a constant value' % len(data_ent2.domain.attributes)
# Description: Entropy based discretization compared to discretization with equal-frequency
# of instances in intervals
# Category: preprocessing
# Uses: wdbc.tab
# Classes: Preprocessor_discretize, EntropyDiscretization
# Referenced: o_categorization.htm
import orange
def show_values(data, heading):
for a in data.domain.attributes:
print "%s/%d: %s" % (a.name, len(a.values), reduce(lambda x,y: x+', '+y, [i for i in a.values]))
data = orange.ExampleTable("../datasets/wdbc")
print '%d features in original data set, discretized:' % len(data.domain.attributes)
data_ent = orange.Preprocessor_discretize(data, method=orange.EntropyDiscretization())
show_values(data_ent, "Entropy based discretization")
print '\nFeatures with sole value after discretization:'
for a in data_ent.domain.attributes:
if len(a.values)==1:
print a.name
import orngDisc
reload(orngDisc)
data_ent2 = orngDisc.entropyDiscretization(data)
print '%d features after removing features discretized to a constant value' % len(data_ent2.domain.attributes)