# Description: Attribute-based discretization. Shows how different attributes may be discretized with different categorization methods and how the default attribute values names used by these methods may be simply replaced by the list of user-defined names.
# Category:    preprocessing
# Uses:        iris
# Classes:     EquiNDiscretization, EntropyDiscretization
# Referenced:  o_categorization.htm

def printexamples(data, inxs, msg="%i examples"):
  print msg % len(inxs)
  for i in inxs:
    print i, data[i]
  print

import orange
iris = orange.ExampleTable("iris")

equiN = orange.EquiNDiscretization(numberOfIntervals=4)
entropy = orange.EntropyDiscretization()

pl = equiN("petal length", iris)
sl = equiN("sepal length", iris)
pl.values = sl.values = ["very low", "low", "high", "very high"]
sl_ent = entropy("sepal length", iris)

inxs = [0, 15, 35, 50, 98]
d_iris = iris.select(["sepal width", pl, "sepal length",sl, sl_ent, iris.domain.classVar])
printexamples(iris, inxs, "%i examples before discretization")
printexamples(d_iris, inxs, "%i examples before discretization")