def __init__(self, parent=None, signalManager = None, name='Random Forests'):
OWWidget.__init__(self, parent, signalManager, name)
self.inputs = [("Examples", ExampleTable, self.setData)]
self.outputs = [("Learner", orange.Learner),("Random Forests Classifier", orange.Classifier),("Choosen Tree", orange.TreeClassifier) ]
self.name = 'Random Forests'
self.trees = 10
self.attributes = 0
self.attributesP = 5
self.preNodeInst = 1
self.preNodeInstP = 5
self.limitDepth = 0
self.limitDepthP = 3
self.rseed = 0
self.outtree = 0
self.maxTrees = 10000
self.loadSettings()
self.data = None
self.preprocessor = None
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
self.bBox = QVGroupBox(self.controlArea)
self.bBox.setTitle('Basic Properties')
self.treesBox = OWGUI.spin(self.bBox, self, "trees", 1, self.maxTrees, orientation="horizontal", label="Number of trees in forest ")
self.attributesBox, self.attributesPBox = OWGUI.checkWithSpin(self.bBox, self, "Consider exactly", 1, 10000, "attributes", "attributesP", " random attributes at each split.")
self.rseedBox = OWGUI.spin(self.bBox, self, "rseed", 0, 100000, orientation="horizontal", label="Seed for random generator ")
OWGUI.separator(self.controlArea)
self.pBox = QVGroupBox(self.controlArea)
self.pBox.setTitle('Growth Control')
self.limitDepthBox, self.limitDepthPBox = OWGUI.checkWithSpin(self.pBox, self, "Maximum depth of individual trees ", 1, 1000, "limitDepth", "limitDepthP", "")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes with ", 1, 1000, "preNodeInst", "preNodeInstP", " or fewer instances")
OWGUI.separator(self.controlArea)
#self.sBox = QVGroupBox(self.controlArea)
#self.sBox.setTitle('Single Tree Output')
self.streesBox = OWGUI.spin(self.controlArea, self, "outtree", -1, self.maxTrees, orientation="horizontal", label="Index of tree on the output ", callback=[self.period, self.extree])
#self.streesBox.setDisabled(True)
self.streeEnabled(False)
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea, self, "&Apply Changes", callback = self.setLearner, disabled=0)
self.resize(100,200)
self.setLearner()
def __init__(self, parent=None):
OWWidget.__init__(self, parent, "CheckSpin")
self.val = 20
self.chk = 1
OWGUI.checkWithSpin(self.controlArea, self, "Prunning, m=", 0, 100, "chk", "val", posttext="%")
self.resize(100, 100)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Regression Tree", wantMainArea = 0, resizingEnabled = 0)
self.Name="Regression Tree"
self.MinInstCheck=1
self.MinInstVal=5
self.MinNodeCheck=1
self.MinNodeVal=10
self.MaxMajCheck=1
self.MaxMajVal=70
self.PostMaj=1
self.PostMPCheck=1
self.PostMPVal=5
self.Bin=1
self.loadSettings()
self.data=None
self.preprocessor = None
self.inputs=[("Data",ExampleTable,self.dataset),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs=[("Learner", orange.Learner),
("Regressor", orange.Classifier),
("Regression Tree", Orange.regression.tree.TreeClassifier)]
##
#GUI
##
OWGUI.lineEdit(self.controlArea, self, "Name", box="Learner/Classifier name")
OWGUI.separator(self.controlArea)
OWGUI.checkBox(self.controlArea, self, "Bin", label="Binarization", box ="Tree structure")
OWGUI.separator(self.controlArea)
self.prePBox=OWGUI.widgetBox(self.controlArea, "Pre-Pruning")
#OWGUI.checkWithSpin(self.prePBox, self, "Min. instances in leaves: ", 1, 1000,
# "MinInstCheck", "MinInstVal")
OWGUI.checkWithSpin(self.prePBox, self, "Do not split nodes with less instances than", 1, 1000,
"MinNodeCheck", "MinNodeVal")
#OWGUI.checkWithSpin(self.prePBox, self, "Stop splitting nodes with ", 1, 100,
# "MaxMajCheck", "MaxMajVal", "% of majority class")
#OWGUI.checkBox(self.postPBox, self, 'PostMaj', 'Recursively merge leaves with same majority class')
OWGUI.separator(self.controlArea)
self.postPBox=OWGUI.widgetBox(self.controlArea, "Post-Pruning")
OWGUI.checkWithSpin(self.postPBox, self, "Pruning with m-estimate, m:", 0, 1000, 'PostMPCheck', 'PostMPVal')
OWGUI.button(self.controlArea, self, "&Apply settings", callback=self.setLearner, default=True)
OWGUI.rubber(self.controlArea)
self.setLearner()
self.resize(100,100)
def __init__(self, parent=None, signalManager = None, name='Random Forest'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea=False, resizingEnabled=False)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),
("Random Forest Classifier", orange.Classifier)]
self.name = 'Random Forest'
self.trees = 10
self.attributes = 0
self.attributesP = 5
self.preNodeInst = 1
self.preNodeInstP = 5
self.limitDepth = 0
self.limitDepthP = 3
self.rseed = 0
self.maxTrees = 10000
self.loadSettings()
self.data = None
self.preprocessor = None
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
self.bBox = OWGUI.widgetBox(self.controlArea, 'Basic Properties')
self.treesBox = OWGUI.spin(self.bBox, self, "trees", 1, self.maxTrees, orientation="horizontal", label="Number of trees in forest")
self.attributesBox, self.attributesPBox = OWGUI.checkWithSpin(self.bBox, self, "Consider exactly", 1, 10000, "attributes", "attributesP", " "+"random attributes at each split.")
self.rseedBox = OWGUI.spin(self.bBox, self, "rseed", 0, 100000, orientation="horizontal", label="Seed for random generator ")
OWGUI.separator(self.controlArea)
self.pBox = OWGUI.widgetBox(self.controlArea, 'Growth Control')
self.limitDepthBox, self.limitDepthPBox = OWGUI.checkWithSpin(self.pBox, self, "Maximal depth of individual trees", 1, 1000, "limitDepth", "limitDepthP", "")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes with ", 1, 1000, "preNodeInst", "preNodeInstP", " or fewer instances")
OWGUI.separator(self.controlArea)
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea, self, "&Apply Changes", callback = self.doBoth, disabled=0, default=True)
self.resize(100,200)
self.setLearner()
def __init__(self, parent=None, signalManager = None, name='C4.5'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),
("Classification Tree", Orange.classification.tree.TreeClassifier)]#, ("C45 Tree", orange.C45Classifier)]
# Settings
self.name = 'C4.5'
self.infoGain = 0; self.subset = 0; self.probThresh = 0;
self.useMinObjs = 1; self.minObjs = 2; self.prune = 1; self.cf = 25
self.iterative = 0; self.manualWindow = 0; self.window = 50; self.manualIncrement = 0; self.increment = 10; self.trials = 10
self.convertToOrange = 1
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name',
tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
self.wbSplit = OWGUI.widgetBox(self.controlArea, "Splitting")
OWGUI.checkBox(self.wbSplit, self, 'infoGain', 'Use information gain instead of ratio (-g)')
OWGUI.checkBox(self.wbSplit, self, 'subset', 'Subsetting (-s)')
OWGUI.checkBox(self.wbSplit, self, 'probThresh', 'Probabilistic threshold for continuous attributes (-p)')
OWGUI.separator(self.controlArea)
self.wbPruning = OWGUI.widgetBox(self.controlArea, "Pruning")
OWGUI.checkWithSpin(self.wbPruning, self, 'Minimal examples in leaves (-m)', 1, 1000, 'useMinObjs', 'minObjs', '', 1, labelWidth = 225)
OWGUI.checkWithSpin(self.wbPruning, self, 'Post pruning with confidence level (-cf) of ', 0, 100, 'prune', 'cf', '', 5, labelWidth = 225)
OWGUI.separator(self.controlArea)
self.wbIterative = OWGUI.widgetBox(self.controlArea, "Iterative generation")
self.cbIterative = OWGUI.checkBox(self.wbIterative, self, 'iterative', 'Generate the tree iteratively (-i, -t, -w)')
self.spTrial = OWGUI.spin(self.wbIterative, self, 'trials', 1, 30, 1, '', " Number of trials (-t)", orientation = "horizontal", labelWidth = 225)
self.csWindow = OWGUI.checkWithSpin(self.wbIterative, self, "Manually set initial window size (-w) to ", 10, 1000, 'manualWindow', 'window', '', 10, labelWidth = 225)
self.csIncrement = OWGUI.checkWithSpin(self.wbIterative, self, "Manually set window increment (-i) to ", 10, 1000, 'manualIncrement', 'increment', '', 10, labelWidth = 225)
self.cbIterative.disables = [self.spTrial, self.csWindow, self.csIncrement]
self.cbIterative.makeConsistent()
# OWGUI.separator(self.controlArea)
# OWGUI.checkBox(self.controlArea, self, 'convertToOrange', 'Convert to orange tree structure', box = 1)
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback = self.setLearner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.setLearner()
def __init__(self, parent=None, signalManager=None, name='Classification Tree'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea=0, resizingEnabled=0)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", Orange.classification.tree.TreeLearner),
("Classification Tree", Orange.classification.tree.TreeClassifier), ]
self.name = 'Classification Tree'
self.estim = 0; self.relK = 5; self.relM = 100; self.limitRef = True
self.bin = 0; self.subset = 0
self.preLeafInstP = 2; self.preNodeInstP = 5; self.preNodeMajP = 95
self.preLeafInst = 1; self.preNodeInst = 0; self.preNodeMaj = 0
self.postMaj = 1; self.postMPruning = 1; self.postM = 2.0
self.limitDepth = False; self.maxDepth = 100
self.loadSettings()
self.data = None
self.preprocessor = None
self.setLearner()
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
qBox = OWGUI.widgetBox(self.controlArea, 'Attribute selection criterion')
self.qMea = OWGUI.comboBox(qBox, self, "estim", items=[m[0] for m in self.measures], callback=self.measureChanged)
b1 = OWGUI.widgetBox(qBox, orientation="horizontal")
OWGUI.separator(b1, 16, 0)
b2 = OWGUI.widgetBox(b1)
self.cbLimitRef, self.hbxRel1 = OWGUI.checkWithSpin(b2, self, "Limit the number of reference examples to ", 1, 1000, "limitRef", "relM")
OWGUI.separator(b2)
self.hbxRel2 = OWGUI.spin(b2, self, "relK", 1, 50, orientation="horizontal", label="Number of neighbours in ReliefF ")
OWGUI.separator(self.controlArea)
OWGUI.radioButtonsInBox(self.controlArea, self, 'bin', self.binarizationOpts, "Binarization")
OWGUI.separator(self.controlArea)
self.measureChanged()
self.pBox = OWGUI.widgetBox(self.controlArea, 'Pre-Pruning')
self.preLeafInstBox, self.preLeafInstPBox = OWGUI.checkWithSpin(self.pBox, self, "Min. instances in leaves ", 1, 1000, "preLeafInst", "preLeafInstP")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes with less instances than ", 1, 1000, "preNodeInst", "preNodeInstP")
self.preNodeMajBox, self.preNodeMajPBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes with a majority class of (%)", 1, 100, "preNodeMaj", "preNodeMajP")
self.cbLimitDepth, self.maxDepthBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes at depth", 0, 1000, "limitDepth", "maxDepth")
OWGUI.separator(self.controlArea)
self.mBox = OWGUI.widgetBox(self.controlArea, 'Post-Pruning')
OWGUI.checkBox(self.mBox, self, 'postMaj', 'Recursively merge leaves with same majority class')
self.postMPruningBox, self.postMPruningPBox = OWGUI.checkWithSpin(self.mBox, self, "Pruning with m-estimate, m=", 0, 1000, 'postMPruning', 'postM')
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea, self, "&Apply", callback=self.setLearner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(200, 200)
def __init__ (self, parent=None, signalManager = None, name = "Logistic regression"):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.inputs = [("Data", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner), ("Classifier", orange.Classifier), ("Features", list)]
from orngTree import TreeLearner
imputeByModel = orange.ImputerConstructor_model()
imputeByModel.learnerDiscrete = TreeLearner(measure = "infoGain", minSubset = 50)
imputeByModel.learnerContinuous = TreeLearner(measure = "retis", minSubset = 50)
self.imputationMethods = [imputeByModel, orange.ImputerConstructor_average(), orange.ImputerConstructor_minimal(), orange.ImputerConstructor_maximal(), None]
self.imputationMethodsStr = ["Classification/Regression trees", "Average values", "Minimal value", "Maximal value", "None (skip examples)"]
self.name = "Logistic regression"
self.univariate = 0
self.stepwiseLR = 0
self.addCrit = 10
self.removeCrit = 10
self.numAttr = 10
self.limitNumAttr = False
self.zeroPoint = 0
self.imputation = 1
self.data = None
self.preprocessor = None
self.loadSettings()
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Attribute selection")
stepwiseCb = OWGUI.checkBox(box, self, "stepwiseLR", "Stepwise attribute selection")
ibox = OWGUI.indentedBox(box, sep=OWGUI.checkButtonOffsetHint(stepwiseCb))
addCritSpin = OWGUI.spin(ibox, self, "addCrit", 1, 50, label="Add threshold [%]", labelWidth=155, tooltip="Requested significance for adding an attribute")
remCritSpin = OWGUI.spin(ibox, self, "removeCrit", 1, 50, label="Remove threshold [%]", labelWidth=155, tooltip="Requested significance for removing an attribute")
limitAttSpin = OWGUI.checkWithSpin(ibox, self, "Limit number of attributes to ", 1, 100, "limitNumAttr", "numAttr", step=1, labelWidth=155, tooltip="Maximum number of attributes. Algorithm stops when it selects specified number of attributes.")
stepwiseCb.disables += [addCritSpin, remCritSpin, limitAttSpin]
stepwiseCb.makeConsistent()
OWGUI.separator(self.controlArea)
self.imputationCombo = OWGUI.comboBox(self.controlArea, self, "imputation", box="Imputation of unknown values", items=self.imputationMethodsStr)
OWGUI.separator(self.controlArea)
applyButton = OWGUI.button(self.controlArea, self, "&Apply", callback=self.applyLearner, default=True)
OWGUI.rubber(self.controlArea)
#self.adjustSize()
self.applyLearner()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'SampleData', wantMainArea = 0)
self.inputs = [("Data", ExampleTable, self.setData)]
self.outputs = [("Data Sample", ExampleTable), ("Remaining Data", ExampleTable)]
# initialization of variables
self.data = None # dataset (incoming stream)
self.indices = None # indices that control sampling
self.ind = None # indices that control sampling
self.Stratified = 1 # use stratified sampling if possible?
self.Repeat = 0 # can elements repeat in a sample?
self.UseSpecificSeed = 0 # use a specific random seed?
self.RandomSeed = 1 # specific seed used
self.GroupSeed = 1 # current seed for multiple group selection
self.outFold = 1 # folder/group to output
self.Folds = 1 # total number of folds/groups
self.SelectType = 0 # sampling type (LOO, CV, ...)
self.useCases = 0 # use a specific number of cases?
self.nCases = 25 # number of cases to use
self.selPercentage = 30 # sample size in %
self.LOO = 1 # use LOO?
self.CVFolds = 10 # number of CV folds
self.CVFoldsInternal = 10 # number of CV folds (for internal use)
self.nGroups = 3 # number of groups
self.pGroups = [0.1,0.25,0.5] # sizes of groups
self.GroupText = '0.1,0.25,0.5' # assigned to Groups Control (for internal use)
self.loadSettings()
# GUI
# Info Box
box1 = OWGUI.widgetBox(self.controlArea, "Information", addSpace=True)
self.infoa = OWGUI.widgetLabel(box1, 'No data on input.')
self.infob = OWGUI.widgetLabel(box1, ' ')
self.infoc = OWGUI.widgetLabel(box1, ' ')
# Options Box
box2 = OWGUI.widgetBox(self.controlArea, 'Options', addSpace=True)
OWGUI.checkBox(box2, self, 'Stratified', 'Stratified (if possible)', callback=self.settingsChanged)
OWGUI.checkWithSpin(box2, self, 'Set random seed:', 0, 32767, 'UseSpecificSeed', 'RandomSeed', checkCallback=self.settingsChanged, spinCallback=self.settingsChanged)
# Sampling Type Box
self.s = [None, None, None, None]
self.sBox = OWGUI.widgetBox(self.controlArea, "Sampling type", addSpace=True)
self.sBox.buttons = []
# Random Sampling
self.s[0] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Random sampling')
# indent
indent = sep=OWGUI.checkButtonOffsetHint(self.s[0])
# repeat checkbox
self.h1Box = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.checkBox(self.h1Box, self, 'Repeat', 'With replacement', callback=self.settingsChanged)
# specified number of elements checkbox
self.h2Box = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.checkWithSpin(self.h2Box, self, 'Sample size (instances):', 1, 1000000000, 'useCases', 'nCases', checkCallback=[self.uCases, self.settingsChanged], spinCallback=self.settingsChanged)
OWGUI.rubber(self.h2Box)
# percentage slider
self.h3Box = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.widgetLabel(self.h3Box, "Sample size:")
self.slidebox = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.hSlider(self.slidebox, self, 'selPercentage', minValue=1, maxValue=100, step=1, ticks=10, labelFormat=" %d%%", callback=self.settingsChanged)
# Cross Validation
self.s[1] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Cross validation')
box = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.spin(box, self, 'CVFolds', 2, 100, step=1, label='Number of folds: ', callback=[self.changeCombo, self.settingsChanged])
OWGUI.rubber(box)
# Leave-One-Out
self.s[2] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Leave-one-out')
# Multiple Groups
self.s[3] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Multiple subsets')
gbox = OWGUI.indentedBox(self.sBox, sep=indent, orientation = "horizontal")
OWGUI.lineEdit(gbox, self, 'GroupText', label='Subset sizes (e.g. "0.1, 0.2, 0.5"):', callback=self.multipleChanged)
# Output Group Box
self.foldcombo = OWGUI.comboBox(self.controlArea, self, "outFold", 'Output Data for Fold / Group', 'Fold / group:', orientation = "horizontal", items = range(1,101), callback = self.foldChanged, sendSelectedValue = 1, valueType = int)
self.foldcombo.setEnabled(False)
# Select Data Button
OWGUI.rubber(self.controlArea)
self.sampleButton = OWGUI.button(self.controlArea, self, 'Sample &Data', callback = self.process, addToLayout=False, default=True)
self.buttonBackground.layout().setDirection(QBoxLayout.TopToBottom)
self.buttonBackground.layout().insertWidget(0, self.sampleButton)
self.buttonBackground.show()
self.s[self.SelectType].setChecked(True) # set initial radio button on (default sample type)
# CONNECTIONS
# set connections for RadioButton (SelectType)
self.dummy1 = [None]*len(self.s)
for i in range(len(self.s)):
self.dummy1[i] = lambda x, v=i: self.sChanged(x, v)
self.connect(self.s[i], SIGNAL("toggled(bool)"), self.dummy1[i])
# final touch
self.resize(200, 275)
def __init__(self, parent=None, signalManager = None, name='TreeViewer2D'):
OWWidget.__init__(self, parent, signalManager, name, wantGraph=True)
self.root = None
self.selectedNode = None
self.inputs = [("Classification Tree", orange.TreeClassifier, self.ctree)]
self.outputs = [("Examples", ExampleTable)]
#set default settings
self.ZoomAutoRefresh = 0
self.AutoArrange = 0
self.ToolTipsEnabled = 1
self.MaxTreeDepth = 5; self.MaxTreeDepthB = 0
self.LineWidth = 5; self.LineWidthMethod = 2
self.NodeSize = 5
self.MaxNodeWidth = 150
self.LimitNodeWidth = True
self.NodeInfo = [0, 1]
self.Zoom = 5
self.VSpacing = 5; self.HSpacing = 5
self.TruncateText = 1
self.loadSettings()
self.NodeInfo.sort()
# Changed when the GUI was simplified - added here to override any saved settings
self.VSpacing = 1; self.HSpacing = 1
self.ToolTipsEnabled = 1
self.LineWidth = 15 # Also reset when the LineWidthMethod is changed!
# GUI definition
# self.tabs = OWGUI.tabWidget(self.controlArea)
# GENERAL TAB
# GeneralTab = OWGUI.createTabPage(self.tabs, "General")
# GeneralTab = TreeTab = OWGUI.createTabPage(self.tabs, "Tree")
# NodeTab = OWGUI.createTabPage(self.tabs, "Node")
GeneralTab = NodeTab = TreeTab = self.controlArea
self.infBox = OWGUI.widgetBox(GeneralTab, 'Info', sizePolicy = QSizePolicy(QSizePolicy.Minimum , QSizePolicy.Fixed ), addSpace=True)
self.infoa = OWGUI.widgetLabel(self.infBox, 'No tree.')
self.infob = OWGUI.widgetLabel(self.infBox, " ")
self.sizebox = OWGUI.widgetBox(GeneralTab, "Size", addSpace=True)
OWGUI.hSlider(self.sizebox, self, 'Zoom', label='Zoom', minValue=1, maxValue=10, step=1,
callback=self.toggleZoomSlider, ticks=1)
OWGUI.separator(self.sizebox)
cb, sb = OWGUI.checkWithSpin(self.sizebox, self, "Max node width:", 50, 200, "LimitNodeWidth", "MaxNodeWidth",
tooltip="Limit the width of tree nodes",
checkCallback=self.toggleNodeSize,
spinCallback=self.toggleNodeSize,
step=10)
b = OWGUI.checkBox(OWGUI.indentedBox(self.sizebox, sep=OWGUI.checkButtonOffsetHint(cb)), self, "TruncateText", "Truncate text", callback=self.toggleTruncateText)
cb.disables.append(b)
cb.makeConsistent()
OWGUI.checkWithSpin(self.sizebox, self, 'Max tree depth:', 1, 20, 'MaxTreeDepthB', "MaxTreeDepth",
tooltip='Defines the depth of the tree displayed',
checkCallback=self.toggleTreeDepth,
spinCallback=self.toggleTreeDepth)
self.edgebox = OWGUI.widgetBox(GeneralTab, "Edge Widths", addSpace=True)
OWGUI.comboBox(self.edgebox, self, 'LineWidthMethod',
items=['Equal width', 'Root node', 'Parent node'],
callback=self.toggleLineWidth)
# Node information
grid = QGridLayout()
grid.setContentsMargins(*self.controlArea.layout().getContentsMargins())
navButton = OWGUI.button(self.controlArea, self, "Navigator", self.toggleNavigator, debuggingEnabled = 0, addToLayout=False)
# findbox = OWGUI.widgetBox(self.controlArea, orientation = "horizontal")
self.centerRootButton=OWGUI.button(self.controlArea, self, "Find Root", addToLayout=False,
callback=lambda :self.rootNode and \
self.sceneView.centerOn(self.rootNode.x(), self.rootNode.y()))
self.centerNodeButton=OWGUI.button(self.controlArea, self, "Find Selected", addToLayout=False,
callback=lambda :self.selectedNode and \
self.sceneView.centerOn(self.selectedNode.scenePos()))
grid.addWidget(navButton, 0, 0, 1, 2)
grid.addWidget(self.centerRootButton, 1, 0)
grid.addWidget(self.centerNodeButton, 1, 1)
self.leftWidgetPart.layout().insertLayout(1, grid)
self.NodeTab=NodeTab
self.TreeTab=TreeTab
self.GeneralTab=GeneralTab
# OWGUI.rubber(NodeTab)
self.rootNode=None
self.tree=None
self.resize(800, 500)
self.connect(self.graphButton, SIGNAL("clicked()"), self.saveGraph)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,parent,signalManager,"CN2", wantMainArea = 0, resizingEnabled = 0)
self.inputs = [("Data", ExampleTable, self.dataset), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),("Classifier",orange.Classifier),("Unordered CN2 Classifier", orngCN2.CN2UnorderedClassifier)]
self.QualityButton = 0
self.CoveringButton = 0
self.Alpha = 0.05
self.stepAlpha = 0.2
self.BeamWidth = 5
self.MinCoverage = 0
self.MaxRuleLength = 0
self.useMaxRuleLength = False
self.Weight = 0.9
self.m = 2
self.name = "CN2 rules"
self.loadSettings()
self.data=None
self.preprocessor = None
##GUI
labelWidth = 150
self.learnerName = OWGUI.lineEdit(self.controlArea, self, "name", box="Learner/classifier name", tooltip="Name to be used by other widgets to identify the learner/classifier")
#self.learnerName.setText(self.name)
OWGUI.separator(self.controlArea)
self.ruleQualityBG = OWGUI.widgetBox(self.controlArea, "Rule quality estimation")
self.ruleQualityBG.buttons = []
OWGUI.separator(self.controlArea)
self.ruleValidationGroup = OWGUI.widgetBox(self.controlArea, "Pre-prunning (LRS)")
OWGUI.separator(self.controlArea)
OWGUI.spin(self.controlArea, self, "BeamWidth", 1, 100, box="Beam width", tooltip="The width of the search beam\n(number of rules to be specialized)")
OWGUI.separator(self.controlArea)
self.coveringAlgBG = OWGUI.widgetBox(self.controlArea, "Covering algorithm")
self.coveringAlgBG.buttons = []
"""
self.ruleQualityBG=OWGUI.radioButtonsInBox(self.ruleQualityGroup, self, "QualityButton",
btnLabels=["Laplace","m-estimate","WRACC"],
box="Rule quality", callback=self.qualityButtonPressed,
tooltips=["Laplace rule evaluator", "m-estimate rule evaluator",
"WRACC rule evaluator"])
self.mSpin=Spin=OWGUI.spin(self.ruleQualityGroup, self, "m", 0, 100, label="m",
orientation="horizontal", labelWidth=labelWidth-100, tooltip="m value for m estimate rule evaluator")
"""
b1 = QRadioButton("Laplace", self.ruleQualityBG); self.ruleQualityBG.layout().addWidget(b1)
g = OWGUI.widgetBox(self.ruleQualityBG, orientation = "horizontal");
b2 = QRadioButton("m-estimate", g)
g.layout().addWidget(b2)
self.mSpin = OWGUI.doubleSpin(g,self,"m",0,100)
b3 = QRadioButton("EVC", self.ruleQualityBG)
self.ruleQualityBG.layout().addWidget(b3)
b4 = QRadioButton("WRACC", self.ruleQualityBG)
self.ruleQualityBG.layout().addWidget(b4)
self.ruleQualityBG.buttons = [b1, b2, b3, b4]
for i, button in enumerate([b1, b2, b3, b4]):
self.connect(button, SIGNAL("clicked()"), lambda v=i: self.qualityButtonPressed(v))
OWGUI.doubleSpin(self.ruleValidationGroup, self, "Alpha", 0, 1,0.001, label="Alpha (vs. default rule)",
orientation="horizontal", labelWidth=labelWidth,
tooltip="Required significance of the difference between the class distribution on all example and covered examples")
OWGUI.doubleSpin(self.ruleValidationGroup, self, "stepAlpha", 0, 1,0.001, label="Stopping Alpha (vs. parent rule)",
orientation="horizontal", labelWidth=labelWidth,
tooltip="Required significance of each specialization of a rule.")
OWGUI.spin(self.ruleValidationGroup, self, "MinCoverage", 0, 100,label="Minimum coverage",
orientation="horizontal", labelWidth=labelWidth, tooltip=
"Minimum number of examples a rule must\ncover (use 0 for not setting the limit)")
OWGUI.checkWithSpin(self.ruleValidationGroup, self, "Maximal rule length", 0, 100, "useMaxRuleLength", "MaxRuleLength", labelWidth=labelWidth,
tooltip="Maximal number of conditions in the left\npart of the rule (use 0 for don't care)")
"""
self.coveringAlgBG=OWGUI.radioButtonsInBox(self.coveringAlgGroup, self, "CoveringButton",
btnLabels=["Exclusive covering ","Weighted Covering"],
tooltips=["Each example will only be used once\n for the construction of a rule",
"Examples can take part in the construction\n of many rules(CN2-SD Algorithm)"],
box="Covering algorithm", callback=self.coveringAlgButtonPressed)
self.weightSpin=OWGUI.doubleSpin(self.coveringAlgGroup, self, "Weight",0, 0.95,0.05,label= "Weight",
orientation="horizontal", labelWidth=labelWidth, tooltip=
"Multiplication constant by which the weight of\nthe example will be reduced")
"""
B1 = QRadioButton("Exclusive covering", self.coveringAlgBG); self.coveringAlgBG.layout().addWidget(B1)
g = OWGUI.widgetBox(self.coveringAlgBG, orientation = "horizontal")
B2 = QRadioButton("Weighted covering", g); g.layout().addWidget(B2)
self.coveringAlgBG.buttons = [B1, B2]
self.weightSpin=OWGUI.doubleSpin(g,self,"Weight",0,0.95,0.05)
for i, button in enumerate([B1, B2]):
self.connect(button, SIGNAL("clicked()"), lambda v=i: self.coveringAlgButtonPressed(v))
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea, self, "&Apply", callback=self.applySettings, default=True)
self.Alpha=float(self.Alpha)
self.stepAlpha=float(self.stepAlpha)
self.Weight=float(self.Weight)
#self.ruleQualityBG.buttons[self.QualityButton].setChecked(1)
self.qualityButtonPressed(self.QualityButton)
self.coveringAlgButtonPressed(self.CoveringButton)
self.resize(100,100)
self.setLearner()
def __init__(self,
parent=None,
signalManager=None,
name='Classification Tree'):
OWWidget.__init__(self,
parent,
signalManager,
name,
wantMainArea=0,
resizingEnabled=0)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner,
self.setPreprocessor)]
self.outputs = [("Learner", orange.TreeLearner),
("Classification Tree", orange.TreeClassifier),
("Classification Tree Graph", Orange.network.Graph)]
self.name = 'Classification Tree'
self.estim = 0
self.relK = 5
self.relM = 100
self.limitRef = True
self.bin = 0
self.subset = 0
self.preLeafInstP = 2
self.preNodeInstP = 5
self.preNodeMajP = 95
self.preLeafInst = 1
self.preNodeInst = 0
self.preNodeMaj = 0
self.postMaj = 1
self.postMPruning = 1
self.postM = 2.0
self.limitDepth = False
self.maxDepth = 100
self.loadSettings()
self.data = None
self.preprocessor = None
self.setLearner()
OWGUI.lineEdit(
self.controlArea,
self,
'name',
box='Learner/Classifier Name',
tooltip=
'Name to be used by other widgets to identify your learner/classifier.'
)
OWGUI.separator(self.controlArea)
qBox = OWGUI.widgetBox(self.controlArea,
'Attribute selection criterion')
self.qMea = OWGUI.comboBox(qBox,
self,
"estim",
items=[m[0] for m in self.measures],
callback=self.measureChanged)
b1 = OWGUI.widgetBox(qBox, orientation="horizontal")
OWGUI.separator(b1, 16, 0)
b2 = OWGUI.widgetBox(b1)
self.cbLimitRef, self.hbxRel1 = OWGUI.checkWithSpin(
b2, self, "Limit the number of reference examples to ", 1, 1000,
"limitRef", "relM")
OWGUI.separator(b2)
self.hbxRel2 = OWGUI.spin(b2,
self,
"relK",
1,
50,
orientation="horizontal",
label="Number of neighbours in ReliefF ")
OWGUI.separator(self.controlArea)
OWGUI.radioButtonsInBox(self.controlArea, self, 'bin',
self.binarizationOpts, "Binarization")
OWGUI.separator(self.controlArea)
self.measureChanged()
self.pBox = OWGUI.widgetBox(self.controlArea, 'Pre-Pruning')
self.preLeafInstBox, self.preLeafInstPBox = OWGUI.checkWithSpin(
self.pBox, self, "Min. instances in leaves ", 1, 1000,
"preLeafInst", "preLeafInstP")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(
self.pBox, self, "Stop splitting nodes with less instances than ",
1, 1000, "preNodeInst", "preNodeInstP")
self.preNodeMajBox, self.preNodeMajPBox = OWGUI.checkWithSpin(
self.pBox, self,
"Stop splitting nodes with a majority class of (%)", 1, 100,
"preNodeMaj", "preNodeMajP")
self.cbLimitDepth, self.maxDepthBox = OWGUI.checkWithSpin(
self.pBox, self, "Stop splitting nodes at depth", 0, 1000,
"limitDepth", "maxDepth")
OWGUI.separator(self.controlArea)
self.mBox = OWGUI.widgetBox(self.controlArea, 'Post-Pruning')
OWGUI.checkBox(self.mBox, self, 'postMaj',
'Recursively merge leaves with same majority class')
self.postMPruningBox, self.postMPruningPBox = OWGUI.checkWithSpin(
self.mBox, self, "Pruning with m-estimate, m=", 0, 1000,
'postMPruning', 'postM')
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea,
self,
"&Apply",
callback=self.setLearner,
disabled=0,
default=True)
OWGUI.rubber(self.controlArea)
self.resize(200, 200)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "SampleData", wantMainArea=0)
self.inputs = [("Data", ExampleTable, self.setData)]
self.outputs = [("Data Sample", ExampleTable), ("Remaining Data", ExampleTable)]
# initialization of variables
self.data = None # dataset (incoming stream)
self.indices = None # indices that control sampling
self.Stratified = 1 # use stratified sampling if possible?
self.Repeat = 0 # can elements repeat in a sample?
self.UseSpecificSeed = 0 # use a specific random seed?
self.RandomSeed = 1 # specific seed used
self.GroupSeed = 1 # current seed for multiple group selection
self.outFold = 1 # folder/group to output
self.Folds = 1 # total number of folds/groups
self.SelectType = 0 # sampling type (LOO, CV, ...)
self.useCases = 0 # use a specific number of cases?
self.nCases = 25 # number of cases to use
self.selPercentage = 30 # sample size in %
self.CVFolds = 10 # number of CV folds
self.nGroups = 3 # number of groups
self.pGroups = [0.1, 0.25, 0.5] # sizes of groups
self.GroupText = "0.1,0.25,0.5" # assigned to Groups Control (for internal use)
self.autocommit = False
# Invalidated settings flag.
self.outputInvalidateFlag = False
self.loadSettings()
# GUI
# Info Box
box1 = OWGUI.widgetBox(self.controlArea, "Information", addSpace=True)
# Input data set info
self.infoa = OWGUI.widgetLabel(box1, "No data on input.")
# Sampling type/parameters info
self.infob = OWGUI.widgetLabel(box1, " ")
# Output data set info
self.infoc = OWGUI.widgetLabel(box1, " ")
# Options Box
box2 = OWGUI.widgetBox(self.controlArea, "Options", addSpace=True)
OWGUI.checkBox(box2, self, "Stratified", "Stratified (if possible)", callback=self.settingsChanged)
OWGUI.checkWithSpin(
box2,
self,
"Set random seed:",
0,
32767,
"UseSpecificSeed",
"RandomSeed",
checkCallback=self.settingsChanged,
spinCallback=self.settingsChanged,
)
# Sampling Type Box
self.s = [None, None, None, None]
self.sBox = OWGUI.widgetBox(self.controlArea, "Sampling type", addSpace=True)
self.sBox.buttons = []
# Random Sampling
self.s[0] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", "Random sampling")
# indent
indent = OWGUI.checkButtonOffsetHint(self.s[0])
# repeat checkbox
self.h1Box = OWGUI.indentedBox(self.sBox, sep=indent, orientation="horizontal")
OWGUI.checkBox(self.h1Box, self, "Repeat", "With replacement", callback=self.settingsChanged)
# specified number of elements checkbox
self.h2Box = OWGUI.indentedBox(self.sBox, sep=indent, orientation="horizontal")
check, _ = OWGUI.checkWithSpin(
self.h2Box,
self,
"Sample size (instances):",
1,
1000000000,
"useCases",
"nCases",
checkCallback=self.settingsChanged,
spinCallback=self.settingsChanged,
)
# percentage slider
self.h3Box = OWGUI.indentedBox(self.sBox, sep=indent)
OWGUI.widgetLabel(self.h3Box, "Sample size:")
self.slidebox = OWGUI.widgetBox(self.h3Box, orientation="horizontal")
OWGUI.hSlider(
self.slidebox,
self,
"selPercentage",
minValue=1,
maxValue=100,
step=1,
ticks=10,
labelFormat=" %d%%",
callback=self.settingsChanged,
)
# Sample size (instances) check disables the Percentage slider.
# TODO: Should be an exclusive option (radio buttons)
check.disables.extend([(-1, self.h3Box)])
check.makeConsistent()
# Cross Validation sampling options
self.s[1] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", "Cross validation")
box = OWGUI.indentedBox(self.sBox, sep=indent, orientation="horizontal")
OWGUI.spin(box, self, "CVFolds", 2, 100, step=1, label="Number of folds: ", callback=self.settingsChanged)
# Leave-One-Out
self.s[2] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", "Leave-one-out")
# Multiple Groups
self.s[3] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", "Multiple subsets")
gbox = OWGUI.indentedBox(self.sBox, sep=indent, orientation="horizontal")
OWGUI.lineEdit(
gbox, self, "GroupText", label='Subset sizes (e.g. "0.1, 0.2, 0.5"):', callback=self.multipleChanged
)
# Output Group Box
box = OWGUI.widgetBox(self.controlArea, "Output Data for Fold / Group", addSpace=True)
self.foldcombo = OWGUI.comboBox(
box,
self,
"outFold",
items=range(1, 101),
label="Fold / group:",
orientation="horizontal",
sendSelectedValue=1,
valueType=int,
callback=self.invalidate,
)
self.foldcombo.setEnabled(self.SelectType != 0)
# Sample Data box
OWGUI.rubber(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Sample Data")
cb = OWGUI.checkBox(box, self, "autocommit", "Sample on any change")
self.sampleButton = OWGUI.button(box, self, "Sample &Data", callback=self.sdata, default=True)
OWGUI.setStopper(self, self.sampleButton, cb, "outputInvalidateFlag", callback=self.sdata)
# set initial radio button on (default sample type)
self.s[self.SelectType].setChecked(True)
# Connect radio buttons (SelectType)
for i, button in enumerate(self.s):
button.toggled[bool].connect(lambda state, i=i: self.samplingTypeChanged(state, i))
self.process()
self.resize(200, 275)
def __init__(self, visualizationWidget = None, signalManager = None):
OWWidget.__init__(self, None, signalManager, "Sieve Evaluation Dialog", savePosition = True, wantMainArea = 0, wantStatusBar = 1)
orngMosaic.__init__(self)
self.resize(390,620)
self.setCaption("Sieve Diagram Evaluation Dialog")
# loaded variables
self.visualizationWidget = visualizationWidget
self.useTimeLimit = 0
self.useProjectionLimit = 0
self.qualityMeasure = CHI_SQUARE # we will always compute only chi square with sieve diagram
self.optimizationType = EXACT_NUMBER_OF_ATTRS
self.attributeCount = 2
self.attrCondition = None
self.attrConditionValue = None
self.lastSaveDirName = os.getcwd()
self.attrLenDict = {}
self.shownResults = []
self.loadSettings()
self.layout().setMargin(0)
self.tabs = OWGUI.tabWidget(self.controlArea)
self.MainTab = OWGUI.createTabPage(self.tabs, "Main")
self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
self.ManageTab = OWGUI.createTabPage(self.tabs, "Manage")
# ###########################
# MAIN TAB
box = OWGUI.widgetBox(self.MainTab, box = "Condition")
self.attrConditionCombo = OWGUI.comboBoxWithCaption(box, self, "attrCondition", "Attribute:", callback = self.updateConditionAttr, sendSelectedValue = 1, valueType = str, labelWidth = 70)
self.attrConditionValueCombo = OWGUI.comboBoxWithCaption(box, self, "attrConditionValue", "Value:", sendSelectedValue = 1, valueType = str, labelWidth = 70)
self.optimizationBox = OWGUI.widgetBox(self.MainTab, "Evaluate")
self.buttonBox = OWGUI.widgetBox(self.optimizationBox, orientation = "horizontal")
self.resultsBox = OWGUI.widgetBox(self.MainTab, "Projection List Ordered by Chi-Square")
# self.label1 = OWGUI.widgetLabel(self.buttonBox, 'Projections with ')
# self.optimizationTypeCombo = OWGUI.comboBox(self.buttonBox, self, "optimizationType", items = [" exactly ", " maximum "] )
# self.attributeCountCombo = OWGUI.comboBox(self.buttonBox, self, "attributeCount", items = range(1, 5), tooltip = "Evaluate only projections with exactly (or maximum) this number of attributes", sendSelectedValue = 1, valueType = int)
# self.attributeLabel = OWGUI.widgetLabel(self.buttonBox, ' attributes')
self.startOptimizationButton = OWGUI.button(self.optimizationBox, self, "Start Evaluating Projections", callback = self.evaluateProjections)
f = self.startOptimizationButton.font(); f.setBold(1); self.startOptimizationButton.setFont(f)
self.stopOptimizationButton = OWGUI.button(self.optimizationBox, self, "Stop Evaluation", callback = self.stopEvaluationClick)
self.stopOptimizationButton.setFont(f)
self.stopOptimizationButton.hide()
self.resultList = OWGUI.listBox(self.resultsBox, self, callback = self.showSelectedAttributes)
self.resultList.setMinimumHeight(200)
# ##########################
# SETTINGS TAB
OWGUI.checkBox(self.SettingsTab, self, "ignoreTooSmallCells", "Ignore cells where expected number of cases is less than 5", box = "Ignore small cells", tooltip = "Statisticians advise that in cases when the number of expected examples is less than 5 we ignore the cell \nsince it can significantly influence the chi-square value.")
OWGUI.comboBoxWithCaption(self.SettingsTab, self, "percentDataUsed", "Percent of data used: ", box = "Data settings", items = self.percentDataNums, sendSelectedValue = 1, valueType = int, tooltip = "In case that we have a large dataset the evaluation of each projection can take a lot of time.\nWe can therefore use only a subset of randomly selected examples, evaluate projection on them and thus make evaluation faster.")
self.stopOptimizationBox = OWGUI.widgetBox(self.SettingsTab, "When to Stop Evaluation or Optimization?")
OWGUI.checkWithSpin(self.stopOptimizationBox, self, "Time limit: ", 1, 1000, "useTimeLimit", "timeLimit", " (minutes)", debuggingEnabled = 0) # disable debugging. we always set this to 1 minute
OWGUI.checkWithSpin(self.stopOptimizationBox, self, "Use projection count limit: ", 1, 1000000, "useProjectionLimit", "projectionLimit", " (projections)", debuggingEnabled = 0)
OWGUI.rubber(self.SettingsTab)
# ##########################
# SAVE TAB
# self.visualizedAttributesBox = OWGUI.widgetBox(self.ManageTab, "Number of Concurrently Visualized Attributes")
self.dialogsBox = OWGUI.widgetBox(self.ManageTab, "Dialogs")
self.manageResultsBox = OWGUI.widgetBox(self.ManageTab, "Manage projections")
# self.attrLenList = OWGUI.listBox(self.visualizedAttributesBox, self, selectionMode = QListWidget.MultiSelection, callback = self.attrLenListChanged)
# self.attrLenList.setMinimumHeight(60)
self.buttonBox7 = OWGUI.widgetBox(self.dialogsBox, orientation = "horizontal")
OWGUI.button(self.buttonBox7, self, "Attribute Ranking", self.attributeAnalysis, debuggingEnabled = 0)
OWGUI.button(self.buttonBox7, self, "Graph Projection Scores", self.graphProjectionQuality, debuggingEnabled = 0)
hbox = OWGUI.widgetBox(self.manageResultsBox, orientation = "horizontal")
OWGUI.button(hbox, self, "Load", self.load, debuggingEnabled = 0)
OWGUI.button(hbox, self, "Save", self.save, debuggingEnabled = 0)
hbox = OWGUI.widgetBox(self.manageResultsBox, orientation = "horizontal")
OWGUI.button(hbox, self, "Clear results", self.clearResults)
OWGUI.rubber(self.ManageTab)
# reset some parameters if we are debugging so that it won't take too much time
if orngDebugging.orngDebuggingEnabled:
self.useTimeLimit = 1
self.timeLimit = 0.3
self.useProjectionLimit = 1
self.projectionLimit = 100
self.icons = self.createAttributeIconDict()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,
parent,
signalManager,
"CN2",
wantMainArea=0,
resizingEnabled=0)
self.inputs = [("Example Table", ExampleTable, self.dataset)]
self.outputs = [("Learner", orange.Learner),
("Classifier", orange.Classifier),
("Unordered CN2 Classifier",
orngCN2.CN2UnorderedClassifier)]
self.QualityButton = 0
self.CoveringButton = 0
self.Alpha = 0.05
self.stepAlpha = 0.2
self.BeamWidth = 5
self.MinCoverage = 0
self.MaxRuleLength = 0
self.useMaxRuleLength = False
self.Weight = 0.9
self.m = 2
self.name = "CN2 rules"
self.loadSettings()
self.data = None
##GUI
labelWidth = 150
self.learnerName = OWGUI.lineEdit(
self.controlArea,
self,
"name",
box="Learner/classifier name",
tooltip=
"Name to be used by other widgets to identify the learner/classifier"
)
#self.learnerName.setText(self.name)
OWGUI.separator(self.controlArea)
self.ruleQualityBG = OWGUI.widgetBox(self.controlArea,
"Rule quality estimation")
self.ruleQualityBG.buttons = []
OWGUI.separator(self.controlArea)
self.ruleValidationGroup = OWGUI.widgetBox(self.controlArea,
"Pre-prunning (LRS)")
OWGUI.separator(self.controlArea)
OWGUI.spin(
self.controlArea,
self,
"BeamWidth",
1,
100,
box="Beam width",
tooltip=
"The width of the search beam\n(number of rules to be specialized)"
)
OWGUI.separator(self.controlArea)
self.coveringAlgBG = OWGUI.widgetBox(self.controlArea,
"Covering algorithm")
self.coveringAlgBG.buttons = []
"""
self.ruleQualityBG=OWGUI.radioButtonsInBox(self.ruleQualityGroup, self, "QualityButton",
btnLabels=["Laplace","m-estimate","WRACC"],
box="Rule quality", callback=self.qualityButtonPressed,
tooltips=["Laplace rule evaluator", "m-estimate rule evaluator",
"WRACC rule evaluator"])
self.mSpin=Spin=OWGUI.spin(self.ruleQualityGroup, self, "m", 0, 100, label="m",
orientation="horizontal", labelWidth=labelWidth-100, tooltip="m value for m estimate rule evaluator")
"""
b1 = QRadioButton("Laplace", self.ruleQualityBG)
self.ruleQualityBG.layout().addWidget(b1)
g = OWGUI.widgetBox(self.ruleQualityBG, orientation="horizontal")
b2 = QRadioButton("m-estimate", g)
g.layout().addWidget(b2)
self.mSpin = OWGUI.doubleSpin(g, self, "m", 0, 100)
b3 = QRadioButton("WRACC", self.ruleQualityBG)
self.ruleQualityBG.layout().addWidget(b3)
self.ruleQualityBG.buttons = [b1, b2, b3]
for i, button in enumerate([b1, b2, b3]):
self.connect(button,
SIGNAL("clicked()"),
lambda v=i: self.qualityButtonPressed(v))
OWGUI.doubleSpin(
self.ruleValidationGroup,
self,
"Alpha",
0,
1,
0.001,
label="Alpha (vs. default rule)",
orientation="horizontal",
labelWidth=labelWidth,
tooltip=
"Required significance of the difference between the class distribution on all example and covered examples"
)
OWGUI.doubleSpin(
self.ruleValidationGroup,
self,
"stepAlpha",
0,
1,
0.001,
label="Stopping Alpha (vs. parent rule)",
orientation="horizontal",
labelWidth=labelWidth,
tooltip="Required significance of each specialization of a rule.")
OWGUI.spin(
self.ruleValidationGroup,
self,
"MinCoverage",
0,
100,
label="Minimum coverage",
orientation="horizontal",
labelWidth=labelWidth,
tooltip=
"Minimum number of examples a rule must\ncover (use 0 for not setting the limit)"
)
OWGUI.checkWithSpin(
self.ruleValidationGroup,
self,
"Maximal rule length",
0,
100,
"useMaxRuleLength",
"MaxRuleLength",
labelWidth=labelWidth,
tooltip=
"Maximal number of conditions in the left\npart of the rule (use 0 for don't care)"
)
"""
self.coveringAlgBG=OWGUI.radioButtonsInBox(self.coveringAlgGroup, self, "CoveringButton",
btnLabels=["Exclusive covering ","Weighted Covering"],
tooltips=["Each example will only be used once\n for the construction of a rule",
"Examples can take part in the construction\n of many rules(CN2-SD Algorithm)"],
box="Covering algorithm", callback=self.coveringAlgButtonPressed)
self.weightSpin=OWGUI.doubleSpin(self.coveringAlgGroup, self, "Weight",0, 0.95,0.05,label= "Weight",
orientation="horizontal", labelWidth=labelWidth, tooltip=
"Multiplication constant by which the weight of\nthe example will be reduced")
"""
B1 = QRadioButton("Exclusive covering", self.coveringAlgBG)
self.coveringAlgBG.layout().addWidget(B1)
g = OWGUI.widgetBox(self.coveringAlgBG, orientation="horizontal")
B2 = QRadioButton("Weighted covering", g)
g.layout().addWidget(B2)
self.coveringAlgBG.buttons = [B1, B2]
self.weightSpin = OWGUI.doubleSpin(g, self, "Weight", 0, 0.95, 0.05)
for i, button in enumerate([B1, B2]):
self.connect(button,
SIGNAL("clicked()"),
lambda v=i: self.coveringAlgButtonPressed(v))
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea,
self,
"&Apply",
callback=self.applySettings)
self.Alpha = float(self.Alpha)
self.stepAlpha = float(self.stepAlpha)
self.Weight = float(self.Weight)
#self.ruleQualityBG.buttons[self.QualityButton].setChecked(1)
self.qualityButtonPressed(self.QualityButton)
self.coveringAlgButtonPressed(self.CoveringButton)
self.resize(100, 100)
self.setLearner()
def __init__(self,
parent=None,
signalManager=None,
name="Logistic regression"):
OWWidget.__init__(self,
parent,
signalManager,
name,
wantMainArea=0,
resizingEnabled=0)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner,
self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),
("Classifier", orange.Classifier), ("Features", list)]
from orngTree import TreeLearner
imputeByModel = orange.ImputerConstructor_model()
imputeByModel.learnerDiscrete = TreeLearner(measure="infoGain",
minSubset=50)
imputeByModel.learnerContinuous = TreeLearner(measure="retis",
minSubset=50)
self.imputationMethods = [
imputeByModel,
orange.ImputerConstructor_average(),
orange.ImputerConstructor_minimal(),
orange.ImputerConstructor_maximal(), None
]
self.imputationMethodsStr = [
"Classification/Regression trees", "Average values",
"Minimal value", "Maximal value", "None (skip examples)"
]
self.name = "Logistic regression"
self.univariate = 0
self.stepwiseLR = 0
self.addCrit = 10
self.removeCrit = 10
self.numAttr = 10
self.limitNumAttr = False
self.zeroPoint = 0
self.imputation = 1
self.data = None
self.preprocessor = None
self.loadSettings()
OWGUI.lineEdit(
self.controlArea,
self,
'name',
box='Learner/Classifier Name',
tooltip=
'Name to be used by other widgets to identify your learner/classifier.'
)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Attribute selection")
stepwiseCb = OWGUI.checkBox(box, self, "stepwiseLR",
"Stepwise attribute selection")
ibox = OWGUI.indentedBox(box,
sep=OWGUI.checkButtonOffsetHint(stepwiseCb))
addCritSpin = OWGUI.spin(
ibox,
self,
"addCrit",
1,
50,
label="Add threshold [%]",
labelWidth=155,
tooltip="Requested significance for adding an attribute")
remCritSpin = OWGUI.spin(
ibox,
self,
"removeCrit",
1,
50,
label="Remove threshold [%]",
labelWidth=155,
tooltip="Requested significance for removing an attribute")
limitAttSpin = OWGUI.checkWithSpin(
ibox,
self,
"Limit number of attributes to ",
1,
100,
"limitNumAttr",
"numAttr",
step=1,
labelWidth=155,
tooltip=
"Maximum number of attributes. Algorithm stops when it selects specified number of attributes."
)
stepwiseCb.disables += [addCritSpin, remCritSpin, limitAttSpin]
stepwiseCb.makeConsistent()
OWGUI.separator(self.controlArea)
self.imputationCombo = OWGUI.comboBox(
self.controlArea,
self,
"imputation",
box="Imputation of unknown values",
items=self.imputationMethodsStr)
OWGUI.separator(self.controlArea)
applyButton = OWGUI.button(self.controlArea,
self,
"&Apply",
callback=self.applyLearner,
default=True)
OWGUI.rubber(self.controlArea)
#self.adjustSize()
self.applyLearner()
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Nomogram", 1)
#self.setWFlags(Qt.WResizeNoErase | Qt.WRepaintNoErase) #this works like magic.. no flicker during repaint!
self.parent = parent
# self.setWFlags(self.getWFlags()+Qt.WStyle_Maximize)
self.callbackDeposit = [] # deposit for OWGUI callback functions
self.alignType = 0
self.contType = 0
self.yAxis = 0
self.probability = 0
self.verticalSpacing = 60
self.verticalSpacingContinuous = 100
self.diff_between_ordinal = 30
self.fontSize = 9
self.lineWidth = 1
self.histogram = 0
self.histogram_size = 10
self.data = None
self.cl = None
self.confidence_check = 0
self.confidence_percent = 95
self.sort_type = 0
self.loadSettings()
self.pointsName = ["Total", "Total"]
self.totalPointsName = ["Probability", "Probability"]
self.bnomogram = None
self.inputs=[("Classifier", orange.Classifier, self.classifier)]
self.TargetClassIndex = 0
self.targetCombo = OWGUI.comboBox(self.controlArea, self, "TargetClassIndex", " Target Class ", addSpace=True, tooltip='Select target (prediction) class in the model.', callback = self.setTarget)
self.alignRadio = OWGUI.radioButtonsInBox(self.controlArea, self, 'alignType', ['Align left', 'Align by zero influence'], box='Attribute placement',
tooltips=['Attributes in nomogram are left aligned', 'Attributes are not aligned, top scale represents true (normalized) regression coefficient value'],
addSpace=True,
callback=self.showNomogram)
self.verticalSpacingLabel = OWGUI.spin(self.alignRadio, self, 'verticalSpacing', 15, 200, label = 'Vertical spacing:', orientation = 0, tooltip='Define space (pixels) between adjacent attributes.', callback = self.showNomogram)
self.ContRadio = OWGUI.radioButtonsInBox(self.controlArea, self, 'contType', ['1D projection', '2D curve'], 'Continuous attributes',
tooltips=['Continuous attribute are presented on a single scale', 'Two dimensional space is used to present continuous attributes in nomogram.'],
addSpace=True,
callback=[lambda:self.verticalSpacingContLabel.setDisabled(not self.contType), self.showNomogram])
self.verticalSpacingContLabel = OWGUI.spin(OWGUI.indentedBox(self.ContRadio, sep=OWGUI.checkButtonOffsetHint(self.ContRadio.buttons[-1])), self, 'verticalSpacingContinuous', 15, 200, label = "Height", orientation=0, tooltip='Define space (pixels) between adjacent 2d presentation of attributes.', callback = self.showNomogram)
self.verticalSpacingContLabel.setDisabled(not self.contType)
self.yAxisRadio = OWGUI.radioButtonsInBox(self.controlArea, self, 'yAxis', ['Point scale', 'Log odds ratios'], 'Scale',
tooltips=['values are normalized on a 0-100 point scale','values on top axis show log-linear contribution of attribute to full model'],
addSpace=True,
callback=self.showNomogram)
layoutBox = OWGUI.widgetBox(self.controlArea, "Display", orientation=1, addSpace=True)
self.probabilityCheck = OWGUI.checkBox(layoutBox, self, 'probability', 'Show prediction', tooltip='', callback = self.setProbability)
self.CICheck, self.CILabel = OWGUI.checkWithSpin(layoutBox, self, 'Confidence intervals (%):', min=1, max=99, step = 1, checked='confidence_check', value='confidence_percent', checkCallback=self.showNomogram, spinCallback = self.showNomogram)
self.histogramCheck, self.histogramLabel = OWGUI.checkWithSpin(layoutBox, self, 'Show histogram, size', min=1, max=30, checked='histogram', value='histogram_size', step = 1, tooltip='-(TODO)-', checkCallback=self.showNomogram, spinCallback = self.showNomogram)
OWGUI.separator(layoutBox)
self.sortOptions = ["No sorting", "Absolute importance", "Positive influence", "Negative influence"]
self.sortBox = OWGUI.comboBox(layoutBox, self, "sort_type", label="Sort by ", items=self.sortOptions, callback = self.sortNomogram, orientation="horizontal")
OWGUI.rubber(self.controlArea)
self.connect(self.graphButton, SIGNAL("clicked()"), self.menuItemPrinter)
#add a graph widget
self.header = OWNomogramHeader(None, self.mainArea)
self.header.setFixedHeight(60)
self.header.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.header.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graph = OWNomogramGraph(self.bnomogram, self.mainArea)
self.graph.setMinimumWidth(200)
self.graph.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.footer = OWNomogramHeader(None, self.mainArea)
self.footer.setFixedHeight(60*2+10)
self.footer.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.footer.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.mainArea.layout().addWidget(self.header)
self.mainArea.layout().addWidget(self.graph)
self.mainArea.layout().addWidget(self.footer)
self.resize(700,500)
#self.repaint()
#self.update()
# mouse pressed flag
self.mousepr = False
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Nomogram", 1)
#self.setWFlags(Qt.WResizeNoErase | Qt.WRepaintNoErase) #this works like magic.. no flicker during repaint!
self.parent = parent
# self.setWFlags(self.getWFlags()+Qt.WStyle_Maximize)
self.callbackDeposit = [] # deposit for OWGUI callback functions
self.alignType = 0
self.contType = 0
self.yAxis = 0
self.probability = 0
self.verticalSpacing = 60
self.verticalSpacingContinuous = 100
self.diff_between_ordinal = 30
self.fontSize = 9
self.lineWidth = 1
self.histogram = 0
self.histogram_size = 10
self.data = None
self.cl = None
self.confidence_check = 0
self.confidence_percent = 95
self.sort_type = 0
self.loadSettings()
self.pointsName = ["Total", "Total"]
self.totalPointsName = ["Probability", "Probability"]
self.bnomogram = None
self.inputs = [("Classifier", orange.Classifier, self.classifier)]
self.TargetClassIndex = 0
self.targetCombo = OWGUI.comboBox(
self.controlArea,
self,
"TargetClassIndex",
" Target Class ",
addSpace=True,
tooltip='Select target (prediction) class in the model.',
callback=self.setTarget)
self.alignRadio = OWGUI.radioButtonsInBox(
self.controlArea,
self,
'alignType', ['Align left', 'Align by zero influence'],
box='Attribute placement',
tooltips=[
'Attributes in nomogram are left aligned',
'Attributes are not aligned, top scale represents true (normalized) regression coefficient value'
],
addSpace=True,
callback=self.showNomogram)
self.verticalSpacingLabel = OWGUI.spin(
self.alignRadio,
self,
'verticalSpacing',
15,
200,
label='Vertical spacing:',
orientation=0,
tooltip='Define space (pixels) between adjacent attributes.',
callback=self.showNomogram)
self.ContRadio = OWGUI.radioButtonsInBox(
self.controlArea,
self,
'contType', ['1D projection', '2D curve'],
'Continuous attributes',
tooltips=[
'Continuous attribute are presented on a single scale',
'Two dimensional space is used to present continuous attributes in nomogram.'
],
addSpace=True,
callback=[
lambda: self.verticalSpacingContLabel.setDisabled(
not self.contType), self.showNomogram
])
self.verticalSpacingContLabel = OWGUI.spin(
OWGUI.indentedBox(self.ContRadio,
sep=OWGUI.checkButtonOffsetHint(
self.ContRadio.buttons[-1])),
self,
'verticalSpacingContinuous',
15,
200,
label="Height",
orientation=0,
tooltip=
'Define space (pixels) between adjacent 2d presentation of attributes.',
callback=self.showNomogram)
self.verticalSpacingContLabel.setDisabled(not self.contType)
self.yAxisRadio = OWGUI.radioButtonsInBox(
self.controlArea,
self,
'yAxis', ['Point scale', 'Log odds ratios'],
'Scale',
tooltips=[
'values are normalized on a 0-100 point scale',
'values on top axis show log-linear contribution of attribute to full model'
],
addSpace=True,
callback=self.showNomogram)
layoutBox = OWGUI.widgetBox(self.controlArea,
"Display",
orientation=1,
addSpace=True)
self.probabilityCheck = OWGUI.checkBox(layoutBox,
self,
'probability',
'Show prediction',
tooltip='',
callback=self.setProbability)
self.CICheck, self.CILabel = OWGUI.checkWithSpin(
layoutBox,
self,
'Confidence intervals (%):',
min=1,
max=99,
step=1,
checked='confidence_check',
value='confidence_percent',
checkCallback=self.showNomogram,
spinCallback=self.showNomogram)
self.histogramCheck, self.histogramLabel = OWGUI.checkWithSpin(
layoutBox,
self,
'Show histogram, size',
min=1,
max=30,
checked='histogram',
value='histogram_size',
step=1,
tooltip='-(TODO)-',
checkCallback=self.showNomogram,
spinCallback=self.showNomogram)
OWGUI.separator(layoutBox)
self.sortOptions = [
"No sorting", "Absolute importance", "Positive influence",
"Negative influence"
]
self.sortBox = OWGUI.comboBox(layoutBox,
self,
"sort_type",
label="Sort by ",
items=self.sortOptions,
callback=self.sortNomogram,
orientation="horizontal")
OWGUI.rubber(self.controlArea)
self.connect(self.graphButton, SIGNAL("clicked()"),
self.menuItemPrinter)
#add a graph widget
self.header = OWNomogramHeader(None, self.mainArea)
self.header.setFixedHeight(self.verticalSpacing)
self.header.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.header.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graph = OWNomogramGraph(self.bnomogram, self.mainArea)
self.graph.setMinimumWidth(200)
self.graph.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.footer = OWNomogramHeader(None, self.mainArea)
self.footer.setFixedHeight(self.verticalSpacing * 2 + 10)
self.footer.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.footer.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.mainArea.layout().addWidget(self.header)
self.mainArea.layout().addWidget(self.graph)
self.mainArea.layout().addWidget(self.footer)
self.resize(700, 500)
#self.repaint()
#self.update()
# mouse pressed flag
self.mousepr = False
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'SampleData', wantMainArea = 0)
self.inputs = [("Data", ExampleTable, self.setData)]
self.outputs = [("Sample", ExampleTable), ("Remaining Examples", ExampleTable)]
# initialization of variables
self.data = None # dataset (incoming stream)
self.indices = None # indices that control sampling
self.ind = None # indices that control sampling
self.Stratified = 1 # use stratified sampling if possible?
self.Repeat = 0 # can elements repeat in a sample?
self.UseSpecificSeed = 0 # use a specific random seed?
self.RandomSeed = 1 # specific seed used
self.GroupSeed = 1 # current seed for multiple group selection
self.outFold = 1 # folder/group to output
self.Folds = 1 # total number of folds/groups
self.SelectType = 0 # sampling type (LOO, CV, ...)
self.useCases = 0 # use a specific number of cases?
self.nCases = 25 # number of cases to use
self.selPercentage = 30 # sample size in %
self.LOO = 1 # use LOO?
self.CVFolds = 10 # number of CV folds
self.CVFoldsInternal = 10 # number of CV folds (for internal use)
self.nGroups = 3 # number of groups
self.pGroups = [0.1,0.25,0.5] # sizes of groups
self.GroupText = '0.1,0.25,0.5' # assigned to Groups Control (for internal use)
self.loadSettings()
# GUI
# Info Box
box1 = OWGUI.widgetBox(self.controlArea, "Information")
self.infoa = OWGUI.widgetLabel(box1, 'No data on input.')
self.infob = OWGUI.widgetLabel(box1, ' ')
self.infoc = OWGUI.widgetLabel(box1, ' ')
# Options Box
box2 = OWGUI.widgetBox(self.controlArea, 'Options')
OWGUI.checkBox(box2, self, 'Stratified', 'Stratified (if possible)', callback=self.settingsChanged)
OWGUI.checkWithSpin(box2, self, 'Set random seed:', 0, 32767, 'UseSpecificSeed', 'RandomSeed', checkCallback=self.settingsChanged, spinCallback=self.settingsChanged)
OWGUI.separator(self.controlArea)
# Sampling Type Box
self.s = [None, None, None, None]
self.sBox = OWGUI.widgetBox(self.controlArea, "Sampling type")
self.sBox.buttons = []
# Random Sampling
self.s[0] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Random sampling')
# repeat checkbox
self.h1Box = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.checkBox(self.h1Box, self, 'Repeat', 'With replacement', callback=self.settingsChanged)
# specified number of elements checkbox
self.h2Box = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.checkWithSpin(self.h2Box, self, 'Sample size (instances):', 1, 1000000000, 'useCases', 'nCases', checkCallback=[self.uCases, self.settingsChanged], spinCallback=self.settingsChanged)
OWGUI.rubber(self.h2Box)
# percentage slider
self.h3Box = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.widgetLabel(self.h3Box, "Sample size:")
self.slidebox = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.hSlider(self.slidebox, self, 'selPercentage', minValue=1, maxValue=100, step=1, ticks=10, labelFormat=" %d%%", callback=self.settingsChanged)
# Cross Validation
self.s[1] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Cross validation')
box = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.spin(box, self, 'CVFolds', 2, 100, step=1, label='Number of folds: ', callback=[self.changeCombo, self.settingsChanged])
OWGUI.rubber(box)
# Leave-One-Out
self.s[2] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Leave-one-out')
# Multiple Groups
self.s[3] = OWGUI.appendRadioButton(self.sBox, self, "SelectType", 'Multiple subsets')
gbox = OWGUI.indentedBox(self.sBox, orientation = "horizontal")
OWGUI.lineEdit(gbox, self, 'GroupText', label='Subset sizes (e.g. "0.1, 0.2, 0.5"):', callback=self.multipleChanged)
# Output Group Box
OWGUI.separator(self.controlArea)
self.foldcombo = OWGUI.comboBox(self.controlArea, self, "outFold", 'Output Data for Fold / Group', 'Fold / group:', orientation = "horizontal", items = range(1,101), callback = self.foldChanged, sendSelectedValue = 1, valueType = int)
self.foldcombo.setEnabled(False)
# Select Data Button
OWGUI.separator(self.controlArea)
self.sampleButton = OWGUI.button(self.controlArea, self, 'Sample &Data', callback = self.process)
self.s[self.SelectType].setChecked(True) # set initial radio button on (default sample type)
OWGUI.separator(self.controlArea)
# CONNECTIONS
# set connections for RadioButton (SelectType)
self.dummy1 = [None]*len(self.s)
for i in range(len(self.s)):
self.dummy1[i] = lambda x, v=i: self.sChanged(x, v)
self.connect(self.s[i], SIGNAL("toggled(bool)"), self.dummy1[i])
# final touch
self.resize(200, 275)
def __init__(self, parent=None, signalManager=None, name='Random Forest'):
OWWidget.__init__(self,
parent,
signalManager,
name,
wantMainArea=False,
resizingEnabled=False)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner,
self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),
("Random Forest Classifier", orange.Classifier)]
self.name = 'Random Forest'
self.trees = 10
self.attributes = 0
self.attributesP = 5
self.preNodeInst = 1
self.preNodeInstP = 5
self.limitDepth = 0
self.limitDepthP = 3
self.rseed = 0
self.maxTrees = 10000
self.loadSettings()
self.data = None
self.preprocessor = None
OWGUI.lineEdit(
self.controlArea,
self,
'name',
box='Learner/Classifier Name',
tooltip=
'Name to be used by other widgets to identify your learner/classifier.'
)
OWGUI.separator(self.controlArea)
self.bBox = OWGUI.widgetBox(self.controlArea, 'Basic Properties')
self.treesBox = OWGUI.spin(self.bBox,
self,
"trees",
1,
self.maxTrees,
orientation="horizontal",
label="Number of trees in forest")
self.attributesBox, self.attributesPBox = OWGUI.checkWithSpin(
self.bBox, self, "Consider exactly", 1, 10000, "attributes",
"attributesP", " " + "random attributes at each split.")
self.rseedBox = OWGUI.spin(self.bBox,
self,
"rseed",
0,
100000,
orientation="horizontal",
label="Seed for random generator ")
OWGUI.separator(self.controlArea)
self.pBox = OWGUI.widgetBox(self.controlArea, 'Growth Control')
self.limitDepthBox, self.limitDepthPBox = OWGUI.checkWithSpin(
self.pBox, self, "Maximal depth of individual trees", 1, 1000,
"limitDepth", "limitDepthP", "")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(
self.pBox, self, "Stop splitting nodes with ", 1, 1000,
"preNodeInst", "preNodeInstP", " or fewer instances")
OWGUI.separator(self.controlArea)
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea,
self,
"&Apply Changes",
callback=self.doBoth,
disabled=0,
default=True)
self.resize(100, 200)
self.setLearner()
