Example #1
0
    def setClassificationTree(self, tree):
        self.closeContext()
        if tree and (not tree.classVar
                     or tree.classVar.varType != orange.VarTypes.Discrete):
            self.error(
                "This viewer only shows trees with discrete classes.\nThere is another viewer for regression trees"
            )
            self.tree = None
        else:
            self.error()
            self.tree = tree

        self.setTreeView()
        self.sliderChanged()

        self.targetCombo.clear()
        if tree:
            self.treeNodes, self.treeLeaves = orngTree.countNodes(
                tree), orngTree.countLeaves(tree)
            self.infoa.setText('Number of nodes: %i' % self.treeNodes)
            self.infob.setText('Number of leaves: %i' % self.treeLeaves)
            self.targetCombo.addItems(
                [name for name in tree.tree.examples.domain.classVar.values])
            self.targetClass = 0
            self.openContext("", tree.domain)
        else:
            self.treeNodes = self.treeLeaves = 0
            self.infoa.setText('No tree on input.')
            self.infob.setText('')
            self.openContext("", None)
Example #2
0
    def ctree(self, tree=None):
        self.clear()
        if not tree:
            self.centerRootButton.setDisabled(1)
            self.centerNodeButton.setDisabled(0)
            self.infoa.setText('No tree.')
            self.infob.setText('')
            self.tree = None
            self.rootNode = None
        else:
            self.tree = tree.tree
            self.infoa.setText('Number of nodes: ' +
                               str(orngTree.countNodes(tree)))
            self.infob.setText('Number of leaves: ' +
                               str(orngTree.countLeaves(tree)))
            if hasattr(self.scene, "colorPalette"):
                self.scene.colorPalette.setNumberOfColors(
                    len(self.tree.distribution))


#            self.scene.setDataModel(GraphicsTree(self.tree))
            self.rootNode = self.walkcreate(self.tree, None)
            #            self.scene.addItem(self.rootNode)
            self.scene.fixPos(self.rootNode, self.HSpacing, self.VSpacing)
            self.activateLoadedSettings()
            self.sceneView.centerOn(self.rootNode.x(), self.rootNode.y())
            self.updateNodeToolTips()
            self.centerRootButton.setDisabled(0)
            self.centerNodeButton.setDisabled(1)

        self.scene.update()
    def ctree(self, tree=None):
        self.clear()
        if not tree:
            self.centerRootButton.setDisabled(1)
            self.centerNodeButton.setDisabled(0)
            self.infoa.setText('No tree.')
            self.infob.setText('')
            self.tree=None
            self.rootNode = None
        else:
            self.tree=tree.tree
            self.infoa.setText('Number of nodes: ' + str(orngTree.countNodes(tree)))
            self.infob.setText('Number of leaves: ' + str(orngTree.countLeaves(tree)))
            if hasattr(self.scene, "colorPalette"):
                self.scene.colorPalette.setNumberOfColors(len(self.tree.distribution))
#            self.scene.setDataModel(GraphicsTree(self.tree))
            self.rootNode=self.walkcreate(self.tree, None)
#            self.scene.addItem(self.rootNode)
            self.scene.fixPos(self.rootNode,self.HSpacing,self.VSpacing)
            self.activateLoadedSettings()
            self.sceneView.centerOn(self.rootNode.x(), self.rootNode.y())
            self.updateNodeToolTips()
            self.centerRootButton.setDisabled(0)
            self.centerNodeButton.setDisabled(1)

        self.scene.update()
    def setClassificationTree(self, tree):
        self.closeContext()
        if tree and (not tree.classVar or tree.classVar.varType != orange.VarTypes.Discrete):
            self.error("This viewer only shows trees with discrete classes.\nThere is another viewer for regression trees")
            self.tree = None
        else:
            self.error()
            self.tree = tree

        self.setTreeView()
        self.sliderChanged()

        self.targetCombo.clear()
        if tree:
            self.treeNodes, self.treeLeaves = orngTree.countNodes(tree), orngTree.countLeaves(tree) 
            self.infoa.setText('Number of nodes: %i' % self.treeNodes)
            self.infob.setText('Number of leaves: %i' % self.treeLeaves)
            self.targetCombo.addItems([name for name in tree.tree.examples.domain.classVar.values])
            self.targetClass = 0
            self.openContext("", tree.domain)
        else:
            self.treeNodes = self.treeLeaves = 0
            self.infoa.setText('No tree on input.')
            self.infob.setText('')
            self.openContext("", None)
Example #5
0
    def updateTree(self):
        self.setTreeView()
        self.learner = FixedTreeLearner(self.tree, self.captionTitle)
        self.infoa.setText("Number of nodes: %i" % orngTree.countNodes(self.tree))
        self.infob.setText("Number of leaves: %i" % orngTree.countLeaves(self.tree))
#        self.send("Data", self.tree)
        self.send("Classifier", self.tree)
        self.send("Tree Learner", self.learner)
Example #6
0
# Description: Builds a classification tree, and prunes it using minimal error
#              prunning with different values of parameter m. Prints
#              out m and the size of the tree.
#              a tree in text and dot format
# Category:    modelling
# Uses:        iris
# Referenced:  orngTree.htm

import orange, orngTree
data = orange.ExampleTable("../datasets/adult_sample.tab")

tree = orange.TreeLearner(data)
prunner = orange.TreePruner_m()
trees = [(0, tree.tree)]
for m in [0.0, 0.1, 0.5, 1, 5, 10, 50, 100]:
    prunner.m = m
    trees.append((m, prunner(tree)))

for m, t in trees:
    print "m = %5.3f: %i nodes, %i leaves" % (m, t.treesize(), orngTree.countLeaves(t))
Example #7
0
 def sendReport(self):
     self.reportData(self.data)
     self.treeNodes, self.treeLeaves = orngTree.countNodes(
         self.tree), orngTree.countLeaves(self.tree)
     super(OWITree, self).sendReport()
Example #8
0
 def sendReport(self):
     self.reportData(self.data)
     self.treeNodes, self.treeLeaves = orngTree.countNodes(self.tree), orngTree.countLeaves(self.tree)
     super(OWITree, self).sendReport()
Example #9
0
def tree(data, 
         classify,
         sameMajorityPruning=False,
         mForPruning=0,
         maxMajority= 1,
         minSubset = 0,
         minExamples = 0):
    '''
    make a classification tree using orange
    
    For more details see `orange tree <http://orange.biolab.si/doc/modules/orngTree.htm>`_
    
    :param data: data from :meth:`perform_experiments`
    :param classify: function for classifying runs
    :param sameMajorityPruning: If true, invokes a bottom-up post-pruning by 
                                removing the subtrees of which all leaves 
                                classify to the same class (default: False).
    :param mForPruning: If non-zero, invokes an error-based bottom-up 
                        post-pruning, where m-estimate is used to estimate 
                        class probabilities (default: 0).
    :param maxMajority: Induction stops when the proportion of majority class 
                        in the node exceeds the value set by this parameter
                        (default: 1.0). 
    :param minSubset: Minimal number of examples in non-null leaves 
                      (default: 0).
    :param minExamples: Data subsets with less than minExamples examples are 
                        not split any further, that is, all leaves in the tree 
                        will contain at least that many of examples 
                        (default: 0).
    :rtype: a classification tree
    
    in order to print the results one can for example use `graphiv <http://www.graphviz.org/>`_.
    
    >>> import orgnTree
    >>> tree = tree(input, classify)
    >>> orngTree.printDot(tree, r'..\..\models\\tree.dot', 
                      leafStr="%V (%M out of %N)") 
    
    this generates a .dot file that can be opened and displayed using graphviz.
    the leafStr keyword argument specifies the format of the string for
    each leaf. See on this also the more detailed discussion on the orange 
    web site.
    
    At some future state, a convenience function might be added for turning
    a tree into a `networkx graph <http://networkx.lanl.gov/>`_. However, this
    is a possible future addition. 
    
    '''

    data = build_orange_data(data, classify)

    #make the actually tree, for details on the meaning of the parameters, see 
    #the orange webpage
    info("executing tree learner")
    tree = orngTree.TreeLearner(data,
                                sameMajorityPruning=sameMajorityPruning,
                                mForPruning=mForPruning,
                                maxMajority=maxMajority,
                                minSubset = minSubset ,
                                minExamples = minExamples)
    info("tree contains %s leaves" % orngTree.countLeaves(tree))
    
    return tree
    

#def multi_dimensional_scaling(data,
#                              classify,
#                              runs=100):
#    '''
#    Perform multi dimensional scaling using the `orngMDS<http://orange.biolab.si/doc/modules/orngMDS.htm>`_
#    module. 
#    
#    :param data: data from :meth:`perform_experiments`
#    :param classify: function for classifying runs
#    :param runs: the number of iterations to be used in MDS
#    :returns: a `figure<http://matplotlib.sourceforge.net/api/figure_api.html#matplotlib.figure.Figure>`_ instance
#    
#    
#    '''
#    data = build_orange_data(data, classify)
#    
#    euclidean = orange.ExamplesDistanceConstructor_Euclidean(data) 
#    distance = orange.SymMatrix(len(data)) 
#    for i in range(len(data)): 
#        for j in range(i+1): 
#            distance[i, j] = euclidean(data[i], data[j]) 
#    
#    mds=orngMDS.MDS(distance) 
#    mds.run(runs)
#    
#
#    colors = graphs.COLOR_LIST
#    points = [] 
#    
#    figure = plt.figure()
#    ax = figure.add_subplot(111)
#    
#    for (i,d) in enumerate(data): 
#        points.append((mds.points[i][0], mds.points[i][1], d.getclass())) 
#    
#    for c in range(len(data.domain.classVar.values)): 
#        sel = filter(lambda x: x[-1]==c, points) 
#        x = [s[0] for s in sel] 
#        y = [s[1] for s in sel] 
#        ax.scatter(x, y, c=colors[c]) 
#    
#    return figure
Example #10
0
def tree(data, 
         classify,
         sameMajorityPruning=False,
         mForPruning=0,
         maxMajority= 1,
         minSubset = 0,
         minExamples = 0):
    '''
    make a classification tree using orange
    
    For more details see `orange tree <http://orange.biolab.si/doc/modules/orngTree.htm>`_
    
    :param data: data from :meth:`perform_experiments`
    :param classify: function for classifying runs
    :param sameMajorityPruning: If true, invokes a bottom-up post-pruning by 
                                removing the subtrees of which all leaves 
                                classify to the same class (default: False).
    :param mForPruning: If non-zero, invokes an error-based bottom-up 
                        post-pruning, where m-estimate is used to estimate 
                        class probabilities (default: 0).
    :param maxMajority: Induction stops when the proportion of majority class 
                        in the node exceeds the value set by this parameter
                        (default: 1.0). 
    :param minSubset: Minimal number of examples in non-null leaves 
                      (default: 0).
    :param minExamples: Data subsets with less than minExamples examples are 
                        not split any further, that is, all leaves in the tree 
                        will contain at least that many of examples 
                        (default: 0).
    :rtype: a classification tree
    
    in order to print the results one can for example use `graphiv <http://www.graphviz.org/>`_.
    
    >>> import orgnTree
    >>> tree = tree(input, classify)
    >>> orngTree.printDot(tree, r'..\..\models\\tree.dot', 
                      leafStr="%V (%M out of %N)") 
    
    this generates a .dot file that can be opened and displayed using graphviz.
    the leafStr keyword argument specifies the format of the string for
    each leaf. See on this also the more detailed discussion on the orange 
    web site.
    
    At some future state, a convenience function might be added for turning
    a tree into a `networkx graph <http://networkx.lanl.gov/>`_. However, this
    is a possible future addition. 
    
    '''

    data = build_orange_data(data, classify)

    #make the actually tree, for details on the meaning of the parameters, see 
    #the orange webpage
    info("executing tree learner")
    tree = orngTree.TreeLearner(data,
                                sameMajorityPruning=sameMajorityPruning,
                                mForPruning=mForPruning,
                                maxMajority=maxMajority,
                                minSubset = minSubset ,
                                minExamples = minExamples)
    info("tree contains %s leaves" % orngTree.countLeaves(tree))
    
    return tree