def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'SampleDataC')
self.inputs = [("Data", Orange.data.Table, self.set_data)]
# [start-snippet-1]
self.outputs = [("Sampled Data", Orange.data.Table),
("Other Data", Orange.data.Table)]
# [end-snippet-1]
self.proportion = 50
self.commitOnChange = 0
self.loadSettings()
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input yet, waiting to get something.')
self.infob = OWGUI.widgetLabel(box, '')
OWGUI.separator(self.controlArea)
self.optionsBox = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(self.optionsBox, self, 'proportion', min=10, max=90, step=10,
label='Sample Size [%]:', callback=[self.selection, self.checkCommit])
OWGUI.checkBox(self.optionsBox, self, 'commitOnChange', 'Commit data on selection change')
OWGUI.button(self.optionsBox, self, "Commit", callback=self.commit)
self.optionsBox.setDisabled(1)
self.resize(100,50)
def __init__(self, parent=None, signalManager=None, title="Gaussin Mixture"):
OWWidget.__init__(self, parent, signalManager, title)
self.inputs = [("Data", Orange.data.Table, self.set_data)]
self.outputs = [("Data with Indicator Matrix", Orange.data.Table)]
self.init_method = 0
self.n = 3
self.auto_commit = True
self.loadSettings()
#####
# GUI
#####
OWGUI.spin(self.controlArea, self, "n", min=1, max=10, step=1,
box="Settings",
label="Number of gaussians",
tooltip="The number of gaussians in the mixture ",
callback=self.on_params_changed)
OWGUI.comboBox(self.controlArea, self, "init_method",
box="Initialization",
items=["K-means", "Random"],
tooltip="Method used to initialize the mixture", callback=self.on_params_changed)
OWGUI.button(self.controlArea, self, "Apply", callback=self.commit)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'SampleDataB')
self.inputs = [("Data", ExampleTable, self.data)]
self.outputs = [("Sampled Data", ExampleTable)]
self.proportion = 50
self.commitOnChange = 0
self.loadSettings()
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(
box, 'No data on input yet, waiting to get something.')
self.infob = OWGUI.widgetLabel(box, '')
OWGUI.separator(self.controlArea)
self.optionsBox = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(self.optionsBox,
self,
'proportion',
min=10,
max=90,
step=10,
label='Sample Size [%]:',
callback=[self.selection, self.checkCommit])
OWGUI.checkBox(self.optionsBox, self, 'commitOnChange',
'Commit data on selection change')
OWGUI.button(self.optionsBox, self, "Commit", callback=self.commit)
self.optionsBox.setDisabled(1)
self.resize(100, 50)
def __init__(self, parent=None, signalManager=None, name="Ensemble"):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea=False)
self.inputs = [("Learner", orange.Learner, self.setLearner), ("Data", ExampleTable, self.setData)]
self.outputs = [("Learner", orange.Learner), ("Classifier", orange.Classifier)]
self.method = 0
self.t = 10
self.loadSettings()
box = OWGUI.radioButtonsInBox(self.controlArea, self, "method",
[name for name, _ in self.METHODS],
box="Ensemble",
callback=self.onChange)
i_box = OWGUI.indentedBox(box, sep=OWGUI.checkButtonOffsetHint(box.buttons[0]))
OWGUI.spin(i_box, self, "t", min=1, max=100, step=1, label="Number of created classifiers:")
OWGUI.rubber(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.commit)
self.data = None
self.learner = None
self.resize(100, 100)
def __init__(self, parent=None, signalManager=None, title="PLS Regression"):
OWWidget.__init__(self, parent, signalManager, title, wantMainArea=False)
self.inputs = [("Data", Orange.data.Table, self.set_data),
("Preprocessor", PreprocessedLearner, self.set_preprocessor)]
self.outputs = [("Learner", Orange.core.Learner),
("Predictor", Orange.core.Classifier)]
##########
# Settings
##########
self.name = "PLS Regression"
self.n_comp = 2
self.deflation_mode = "Regression"
self.mode = "PLS"
self.algorithm = "svd"
self.loadSettings()
#####
# GUI
#####
box = OWGUI.widgetBox(self.controlArea, "Learner/Predictor Name",
addSpace=True)
OWGUI.lineEdit(box, self, "name",
tooltip="Name to use for the learner/predictor.")
box = OWGUI.widgetBox(self.controlArea, "Settings", addSpace=True)
OWGUI.spin(box, self, "n_comp", 2, 15, 1,
label="Number of components:",
tooltip="Number of components to keep.")
OWGUI.comboBox(box, self, "deflation_mode",
label="Deflation mode",
items=["Regression", "Canonical"],
# tooltip="",
sendSelectedValue=True)
OWGUI.comboBox(box, self, "mode",
label="Mode",
items=["PLS", "CCA"],
# tooltip="",
sendSelectedValue=True)
OWGUI.rubber(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply",
callback=self.apply,
tooltip="Send the learner on",
autoDefault=True)
self.data = None
self.preprocessor = None
self.apply()
def __init__(self, parent=None, signalManager = None, name='Histogram'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0)
self.callbackDeposit = []
self.inputs = [("Data", Clusters, self.setData)]
self.outputs = [("Histogram", Histograms)] # , ("Histograms", ExampleTable)]
self.useLazyEvaluation = pynopticon.useLazyEvaluation
# Settings
self.name = name
self.histogram = None
self.bins = 200
self.loadSettings()
self.data = None # input data set
wbN = OWGUI.widgetBox(self.controlArea, "Histogram Settings")
OWGUI.spin(wbN, self, "bins", 1, 100000, 100, None, "Number of bins ", orientation="horizontal")
OWGUI.separator(self.controlArea)
wbS = OWGUI.widgetBox(self.controlArea, "Widget Settings")
OWGUI.checkBox(wbS, self, "useLazyEvaluation", "Use lazy evaluation")
OWGUI.button(self.controlArea, self, "&Apply Settings", callback = self.applySettings, disabled=0)
self.resize(100,150)
def addHistogramControls(self, parent=None):
# set default settings
self.spinLowerThreshold = 0
self.spinLowerChecked = False
self.spinUpperThreshold = 0
self.spinUpperChecked = False
self.netOption = 0
self.dstWeight = 0
self.kNN = 0
self.andor = 0
self.matrix = None
self.excludeLimit = 2
self.percentil = 0
self.graph = None
self.graph_matrix = None
if parent is None:
parent = self.controlArea
boxGeneral = OWGUI.widgetBox(parent, box="Distance boundaries")
ribg = OWGUI.widgetBox(boxGeneral, None, orientation="horizontal", addSpace=False)
OWGUI.lineEdit(ribg, self, "spinLowerThreshold", "Lower", orientation='horizontal', callback=self.changeLowerSpin, valueType=float, enterPlaceholder=True, controlWidth=100)
OWGUI.lineEdit(ribg, self, "spinUpperThreshold", "Upper ", orientation='horizontal', callback=self.changeUpperSpin, valueType=float, enterPlaceholder=True, controlWidth=100)
ribg.layout().addStretch(1)
#ribg = OWGUI.radioButtonsInBox(boxGeneral, self, "andor", [], orientation='horizontal', callback = self.generateGraph)
#OWGUI.appendRadioButton(ribg, self, "andor", "OR", callback = self.generateGraph)
#b = OWGUI.appendRadioButton(ribg, self, "andor", "AND", callback = self.generateGraph)
#b.setEnabled(False)
#ribg.hide(False)
ribg = OWGUI.widgetBox(boxGeneral, None, orientation="horizontal", addSpace=False)
OWGUI.spin(ribg, self, "kNN", 0, 1000, 1, label="kNN ", orientation='horizontal', callback=self.generateGraph, callbackOnReturn=1, controlWidth=100)
OWGUI.doubleSpin(ribg, self, "percentil", 0, 100, 0.1, label="Percentile", orientation='horizontal', callback=self.setPercentil, callbackOnReturn=1, controlWidth=100)
ribg.layout().addStretch(1)
# Options
self.attrColor = ""
ribg = OWGUI.radioButtonsInBox(parent, self, "netOption", [], "Options", callback=self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "All vertices", callback=self.generateGraph)
hb = OWGUI.widgetBox(ribg, None, orientation="horizontal", addSpace=False)
OWGUI.appendRadioButton(ribg, self, "netOption", "Large components only. Min nodes:", insertInto=hb, callback=self.generateGraph)
OWGUI.spin(hb, self, "excludeLimit", 2, 100, 1, callback=(lambda h=True: self.generateGraph(h)))
OWGUI.appendRadioButton(ribg, self, "netOption", "Largest connected component only", callback=self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "Connected component with vertex")
self.attribute = None
self.attributeCombo = OWGUI.comboBox(parent, self, "attribute", box="Filter attribute", orientation='horizontal')#, callback=self.setVertexColor)
ribg = OWGUI.radioButtonsInBox(parent, self, "dstWeight", [], "Distance -> Weight", callback=self.generateGraph)
hb = OWGUI.widgetBox(ribg, None, orientation="horizontal", addSpace=False)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := distance", insertInto=hb, callback=self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := 1 - distance", insertInto=hb, callback=self.generateGraph)
self.label = ''
self.searchString = OWGUI.lineEdit(self.attributeCombo.box, self, "label", callback=self.setSearchStringTimer, callbackOnType=True)
self.searchStringTimer = QTimer(self)
self.connect(self.searchStringTimer, SIGNAL("timeout()"), self.generateGraph)
if str(self.netOption) != '3':
self.attributeCombo.box.setEnabled(False)
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):
BaseEditor.__init__(self, parent)
self.measureInd = 0
self.selectBy = 0
self.bestN = 10
self.bestP = 10
box = OWGUI.radioButtonsInBox(self,
self,
"selectBy", [],
"Feature selection",
callback=self.onChange)
OWGUI.comboBox(box,
self,
"measureInd",
items=[name for (name, _) in self.MEASURES],
label="Measure",
callback=self.onChange)
hbox1 = OWGUI.widgetBox(box, orientation="horizontal", margin=0)
rb1 = OWGUI.appendRadioButton(box,
self,
"selectBy",
"Best",
insertInto=hbox1,
callback=self.onChange)
self.spin1 = OWGUI.spin(OWGUI.widgetBox(hbox1),
self,
"bestN",
1,
10000,
step=1,
controlWidth=75,
callback=self.onChange,
posttext="features")
OWGUI.rubber(hbox1)
hbox2 = OWGUI.widgetBox(box, orientation="horizontal", margin=0)
rb2 = OWGUI.appendRadioButton(box,
self,
"selectBy",
"Best",
insertInto=hbox2,
callback=self.onChange)
self.spin2 = OWGUI.spin(OWGUI.widgetBox(hbox2),
self,
"bestP",
1,
100,
step=1,
controlWidth=75,
callback=self.onChange,
posttext="% features")
OWGUI.rubber(hbox2)
self.updateSpinStates()
OWGUI.rubber(box)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveA')
# [start-snippet-1]
self.inputs = [("Data", Orange.data.Table, self.set_dataset),
("Learner", Orange.classification.Learner, self.set_learner,
Multiple + Default)]
# [end-snippet-1]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.loadSettings()
self.updateCurvePoints() # sets self.curvePoints, self.steps equidistant points from 1/self.steps to 1
# [start-snippet-2]
self.scoring = [("Classification Accuracy", Orange.evaluation.scoring.CA),
("AUC", Orange.evaluation.scoring.AUC),
("BrierScore", Orange.evaluation.scoring.Brier_score),
("Information Score", Orange.evaluation.scoring.IS),
("Sensitivity", Orange.evaluation.scoring.Sensitivity),
("Specificity", Orange.evaluation.scoring.Specificity)]
# [end-snippet-2]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve() if self.commitOnChange else None)
OWGUI.spin(box, self, 'steps', 2, 100, step=1,
label='Learning curve points: ',
callback=[self.updateCurvePoints,
lambda: self.computeCurve() if self.commitOnChange else None])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting",
callback=self.computeCurve, disabled=1)
OWGUI.rubber(self.controlArea)
# table widget
self.table = OWGUI.table(self.mainArea,
selectionMode=QTableWidget.NoSelection)
self.resize(500,200)
def __init__(self, parent=None, signalManager=None, name='BR-kNN'):
OWWidget.__init__(self,
parent,
signalManager,
name,
wantMainArea=0,
resizingEnabled=0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.set_data),
("Preprocess", PreprocessedLearner,
self.set_preprocessor)]
self.outputs = [("Learner", orange.Learner),
("BR-kNN Classifier",
Orange.multilabel.BRkNNClassifier)]
# Settings
self.name = 'BR-kNN'
self.k = 1
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
self.set_learner() # this just sets the learner, no data
# has come to the input yet
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)
wbN = OWGUI.widgetBox(self.controlArea, "Neighbours")
OWGUI.spin(wbN,
self,
"k",
1,
100,
1,
None,
"Number of neighbours",
orientation="horizontal")
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea,
self,
"&Apply",
callback=self.set_learner,
disabled=0,
default=True)
OWGUI.rubber(self.controlArea)
self.resize(100, 250)
def __init__(self, parent=None):
OWWidget.__init__(self, parent, title='Labels')
self.val = 5
self.line = "a parrot"
OWGUI.spin(self.controlArea, self, "val", 1, 10, label="Value")
OWGUI.lineEdit(self.controlArea, self, "line", label="Line: ", orientation="horizontal")
OWGUI.label(self.controlArea, self, "Value is %(val)i and line edit contains %(line)s")
self.resize(100,100)
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, 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,
title="Earth"):
OWWidget.__init__(self, parent, signalManager, title,
wantMainArea=False)
self.inputs = [("Data", Orange.data.Table, self.set_data),
("Preprocessor", PreprocessedLearner,
self.set_preprocessor)]
self.outputs = [("Learner", earth.EarthLearner, Default),
("Predictor", earth.EarthClassifier, Default),
("Basis Matrix", Orange.data.Table)]
self.name = "Earth Learner"
self.degree = 1
self.terms = 21
self.penalty = 2
self.loadSettings()
#####
# GUI
#####
OWGUI.lineEdit(self.controlArea, self, "name",
box="Learner/Classifier Name",
tooltip="Name for the learner/predictor")
box = OWGUI.widgetBox(self.controlArea, "Forward Pass", addSpace=True)
OWGUI.spin(box, self, "degree", 1, 3, step=1,
label="Max. term degree",
tooltip="Maximum degree of the terms derived "
"(number of hinge functions).")
s = OWGUI.spin(box, self, "terms", 1, 200, step=1,
label="Max. terms",
tooltip="Maximum number of terms derived in the "
"forward pass.")
s.control.setSpecialValueText("Automatic")
box = OWGUI.widgetBox(self.controlArea, "Pruning Pass", addSpace=True)
OWGUI.doubleSpin(box, self, "penalty", min=0.0, max=10.0, step=0.25,
label="Knot penalty")
OWGUI.button(self.controlArea, self, "&Apply",
callback=self.apply)
self.data = None
self.preprocessor = None
self.resize(300, 200)
self.apply()
def __init__(self, parent=None, signalManager = None, name='kNN'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),("KNN Classifier", orange.kNNClassifier)]
self.metricsList = [("Euclidean", orange.ExamplesDistanceConstructor_Euclidean),
("Hamming", orange.ExamplesDistanceConstructor_Hamming),
("Manhattan", orange.ExamplesDistanceConstructor_Manhattan),
("Maximal", orange.ExamplesDistanceConstructor_Maximal),
# ("Dynamic time warp", orange.ExamplesDistanceConstructor_DTW)
]
# Settings
self.name = 'kNN'
self.k = 5; self.metrics = 0; self.ranks = 0
self.ignoreUnknowns = 0
self.normalize = self.oldNormalize = 1
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
self.setLearner() # this just sets the learner, no data
# has come to the input yet
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)
wbN = OWGUI.widgetBox(self.controlArea, "Neighbours")
OWGUI.spin(wbN, self, "k", 1, 100, 1, None, "Number of neighbours ", orientation="horizontal")
OWGUI.checkBox(wbN, self, "ranks", "Weighting by ranks, not distances")
OWGUI.separator(self.controlArea)
wbM = OWGUI.widgetBox(self.controlArea, "Metrics")
OWGUI.comboBox(wbM, self, "metrics", items = [x[0] for x in self.metricsList], valueType = int, callback = self.metricsChanged)
self.cbNormalize = OWGUI.checkBox(wbM, self, "normalize", "Normalize continuous attributes")
OWGUI.checkBox(wbM, self, "ignoreUnknowns", "Ignore unknown values")
self.metricsChanged()
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback = self.setLearner, disabled=0)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def __init__(self, parent=None, signalManager = None, name='kNN'):
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),("kNN Classifier", orange.kNNClassifier)]
self.metricsList = [("Euclidean", orange.ExamplesDistanceConstructor_Euclidean),
("Hamming", orange.ExamplesDistanceConstructor_Hamming),
("Manhattan", orange.ExamplesDistanceConstructor_Manhattan),
("Maximal", orange.ExamplesDistanceConstructor_Maximal),
# ("Dynamic time warp", orange.ExamplesDistanceConstructor_DTW)
]
# Settings
self.name = 'kNN'
self.k = 5; self.metrics = 0; self.ranks = 0
self.ignoreUnknowns = 0
self.normalize = self.oldNormalize = 1
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
self.setLearner() # this just sets the learner, no data
# has come to the input yet
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)
wbN = OWGUI.widgetBox(self.controlArea, "Neighbours")
OWGUI.spin(wbN, self, "k", 1, 100, 1, None, "Number of neighbours ", orientation="horizontal")
OWGUI.checkBox(wbN, self, "ranks", "Weighting by ranks, not distances")
OWGUI.separator(self.controlArea)
wbM = OWGUI.widgetBox(self.controlArea, "Metrics")
OWGUI.comboBox(wbM, self, "metrics", items = [x[0] for x in self.metricsList], valueType = int, callback = self.metricsChanged)
self.cbNormalize = OWGUI.checkBox(wbM, self, "normalize", "Normalize continuous attributes")
OWGUI.checkBox(wbM, self, "ignoreUnknowns", "Ignore unknown values")
self.metricsChanged()
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.setLearner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Pubmed Network View", wantMainArea=0)
self.inputs = []
self.outputs = [("Nx View", Orange.network.NxView)]
self._nhops = 2
self._edge_threshold = 0.5
self._n_max_neighbors = 20
self.selected_titles = []
self.titles = []
self.filter = ""
self.ids = []
self._selected_nodes = []
self._algorithm = 0
self._k_algorithm = 0.3
self.loadSettings()
box = OWGUI.widgetBox(self.controlArea, "Paper Selection", orientation="vertical")
OWGUI.lineEdit(box, self, "filter", callback=self.filter_list, callbackOnType=True)
self.list_titles = OWGUI.listBox(
box, self, "selected_titles", "titles", selectionMode=QListWidget.MultiSelection, callback=self.update_view
)
OWGUI.separator(self.controlArea)
box_pref = OWGUI.widgetBox(self.controlArea, "Preferences", orientation="vertical")
OWGUI.spin(box_pref, self, "_nhops", 1, 6, 1, label="Number of hops: ", callback=self.update_view)
OWGUI.spin(
box_pref, self, "_n_max_neighbors", 1, 100, 1, label="Max number of neighbors: ", callback=self.update_view
)
OWGUI.doubleSpin(
box_pref, self, "_edge_threshold", 0, 1, step=0.01, label="Edge threshold: ", callback=self.update_view
)
OWGUI.separator(self.controlArea)
box_alg = OWGUI.widgetBox(self.controlArea, "Interest Propagation Algorithm", orientation="vertical")
radio_box = OWGUI.radioButtonsInBox(box_alg, self, "_algorithm", [], callback=self.update_view)
OWGUI.appendRadioButton(radio_box, self, "_algorithm", "Without Clustering", callback=self.update_view)
OWGUI.doubleSpin(
OWGUI.indentedBox(radio_box),
self,
"_k_algorithm",
0,
1,
step=0.01,
label="Parameter k: ",
callback=self.update_view,
)
OWGUI.appendRadioButton(radio_box, self, "_algorithm", "With Clustering", callback=self.update_view)
self.inside_view = PubmedNetworkView(self)
self.send("Nx View", self.inside_view)
def addHistogramControls(self, parent=None):
# set default settings
self.spinLowerThreshold = 0
self.spinLowerChecked = False
self.spinUpperThreshold = 0
self.spinUpperChecked = False
self.netOption = 0
self.dstWeight = 0
self.kNN = 0
self.andor = 0
self.matrix = None
self.excludeLimit = 1
self.percentil = 0
if parent is None:
parent = self.controlArea
boxGeneral = OWGUI.widgetBox(parent, box = "Distance boundaries")
OWGUI.lineEdit(boxGeneral, self, "spinLowerThreshold", "Lower:", orientation='horizontal', callback=self.changeLowerSpin, valueType=float)
OWGUI.lineEdit(boxGeneral, self, "spinUpperThreshold", "Upper:", orientation='horizontal', callback=self.changeUpperSpin, valueType=float)
ribg = OWGUI.radioButtonsInBox(boxGeneral, self, "andor", [], orientation='horizontal', callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "andor", "OR", callback = self.generateGraph)
b = OWGUI.appendRadioButton(ribg, self, "andor", "AND", callback = self.generateGraph)
b.setEnabled(False)
OWGUI.spin(boxGeneral, self, "kNN", 0, 1000, 1, label="kNN:", orientation='horizontal', callback=self.generateGraph)
OWGUI.doubleSpin(boxGeneral, self, "percentil", 0, 100, 0.1, label="Percentil:", orientation='horizontal', callback=self.setPercentil, callbackOnReturn=1)
# Options
self.attrColor = ""
ribg = OWGUI.radioButtonsInBox(parent, self, "netOption", [], "Options", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "All vertices", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "Exclude small components", callback = self.generateGraph)
OWGUI.spin(OWGUI.indentedBox(ribg), self, "excludeLimit", 1, 100, 1, label="Less vertices than: ", callback = (lambda h=True: self.generateGraph(h)))
OWGUI.appendRadioButton(ribg, self, "netOption", "Largest connected component only", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "Connected component with vertex")
self.attribute = None
self.attributeCombo = OWGUI.comboBox(ribg, self, "attribute", box = "Filter attribute")#, callback=self.setVertexColor)
ribg = OWGUI.radioButtonsInBox(parent, self, "dstWeight", [], "Distance -> Weight", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := distance", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := 1 - distance", callback = self.generateGraph)
self.label = ''
self.searchString = OWGUI.lineEdit(self.attributeCombo.box, self, "label", callback=self.setSearchStringTimer, callbackOnType=True)
self.searchStringTimer = QTimer(self)
self.connect(self.searchStringTimer, SIGNAL("timeout()"), self.generateGraph)
if str(self.netOption) != '3':
self.attributeCombo.box.setEnabled(False)
def addHistogramControls(self, parent=None):
# set default settings
self.spinLowerThreshold = 0
self.spinLowerChecked = False
self.spinUpperThreshold = 0
self.spinUpperChecked = False
self.netOption = 0
self.dstWeight = 0
self.kNN = 0
self.andor = 0
self.matrix = None
self.excludeLimit = 1
self.percentil = 0
if parent is None:
parent = self.controlArea
boxGeneral = OWGUI.widgetBox(parent, box = "Distance boundaries")
OWGUI.lineEdit(boxGeneral, self, "spinLowerThreshold", "Lower:", orientation='horizontal', callback=self.changeLowerSpin, valueType=float)
OWGUI.lineEdit(boxGeneral, self, "spinUpperThreshold", "Upper:", orientation='horizontal', callback=self.changeUpperSpin, valueType=float)
ribg = OWGUI.radioButtonsInBox(boxGeneral, self, "andor", [], orientation='horizontal', callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "andor", "OR", callback = self.generateGraph)
b = OWGUI.appendRadioButton(ribg, self, "andor", "AND", callback = self.generateGraph)
b.setEnabled(False)
OWGUI.spin(boxGeneral, self, "kNN", 0, 1000, 1, label="kNN:", orientation='horizontal', callback=self.generateGraph)
OWGUI.doubleSpin(boxGeneral, self, "percentil", 0, 100, 0.1, label="Percentil:", orientation='horizontal', callback=self.setPercentil, callbackOnReturn=1)
# Options
self.attrColor = ""
ribg = OWGUI.radioButtonsInBox(parent, self, "netOption", [], "Options", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "All vertices", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "Exclude small components", callback = self.generateGraph)
OWGUI.spin(OWGUI.indentedBox(ribg), self, "excludeLimit", 1, 100, 1, label="Less vertices than: ", callback = (lambda h=True: self.generateGraph(h)))
OWGUI.appendRadioButton(ribg, self, "netOption", "Largest connected component only", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "netOption", "Connected component with vertex")
self.attribute = None
self.attributeCombo = OWGUI.comboBox(ribg, self, "attribute", box = "Filter attribute")#, callback=self.setVertexColor)
ribg = OWGUI.radioButtonsInBox(parent, self, "dstWeight", [], "Distance -> Weight", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := distance", callback = self.generateGraph)
OWGUI.appendRadioButton(ribg, self, "dstWeight", "Weight := 1 - distance", callback = self.generateGraph)
self.label = ''
self.searchString = OWGUI.lineEdit(self.attributeCombo.box, self, "label", callback=self.setSearchStringTimer, callbackOnType=True)
self.searchStringTimer = QTimer(self)
self.connect(self.searchStringTimer, SIGNAL("timeout()"), self.generateGraph)
if str(self.netOption) != '3':
self.attributeCombo.box.setEnabled(False)
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Itemsets explorer")
self.inputs = [("Itemsets", Itemsets, self.setItemsets)]
self.outputs = [("Itemsets", Itemsets), ("Data", ExampleTable)]
self.showWholeItemsets = 1
self.treeDepth = 2
self.autoSend = True
self.dataChanged = False
self.purgeAttributes = True
self.purgeClasses = True
self.nItemsets = self.nSelectedItemsets = self.nSelectedExamples = ""
self.loadSettings()
self.treeItemsets = QTreeWidget(self.mainArea)
self.mainArea.layout().addWidget(self.treeItemsets)
self.treeItemsets.setSelectionMode (QTreeWidget.ExtendedSelection)
self.treeItemsets.setHeaderLabels(["Itemsets", "Examples"])
self.treeItemsets.setAllColumnsShowFocus ( 1)
self.treeItemsets.setAlternatingRowColors(1)
self.treeItemsets.setColumnCount(2)
self.connect(self.treeItemsets,SIGNAL("itemSelectionChanged()"),self. selectionChanged)
box = OWGUI.widgetBox(self.controlArea, "Info", addSpace = True)
OWGUI.label(box, self, "Number of itemsets: %(nItemsets)s")
OWGUI.label(box, self, "Selected itemsets: %(nSelectedItemsets)s")
OWGUI.label(box, self, "Selected examples: %(nSelectedExamples)s")
box = OWGUI.widgetBox(self.controlArea, "Tree", addSpace = True)
OWGUI.spin(box, self, "treeDepth", label = "Tree depth", min = 1, max = 10, step = 1, callback = self.populateTree, callbackOnReturn = True)
OWGUI.checkBox(box, self, "showWholeItemsets", "Display whole itemsets", callback = self.setWholeItemsets)
OWGUI.button(box, self, "Expand All", callback = lambda: self.treeItemsets.expandAll())
OWGUI.button(box, self, "Collapse", callback = lambda: self.treeItemsets.collapseAll())
OWGUI.rubber(self.controlArea)
boxSettings = OWGUI.widgetBox(self.controlArea, 'Send selection')
OWGUI.checkBox(boxSettings, self, "purgeAttributes", "Purge attribute values/attributes", box=None, callback=self.purgeChanged)
self.purgeClassesCB = OWGUI.checkBox(OWGUI.indentedBox(boxSettings), self, "purgeClasses", "Purge class attribute", callback=self.purgeChanged)
if not self.purgeAttributes:
self.purgeClassesCB.setEnabled(False)
self.oldPurgeClasses = False
cbSendAuto = OWGUI.checkBox(boxSettings, self, "autoSend", "Send immediately", box=None)
btnUpdate = OWGUI.button(boxSettings, self, "Send", self.sendData, default=True)
OWGUI.setStopper(self, btnUpdate, cbSendAuto, "dataChanged", self.sendData)
self.itemsets= None
def __init__(self, parent=None):
BaseEditor.__init__(self, parent)
self.methodInd = 0
self.sampleN = 100
self.sampleP = 25
box = OWGUI.radioButtonsInBox(self,
self,
"methodInd", [],
box="Sample",
callback=self.onChange)
w1 = OWGUI.widgetBox(box, orientation="horizontal", margin=0)
rb1 = OWGUI.appendRadioButton(box,
self,
"methodInd",
"Sample",
insertInto=w1)
self.sb1 = OWGUI.spin(OWGUI.widgetBox(w1),
self,
"sampleN",
min=1,
max=100000,
step=1,
controlWidth=75,
callback=self.onChange,
posttext="data instances")
OWGUI.rubber(w1)
w2 = OWGUI.widgetBox(box, orientation="horizontal", margin=0)
rb2 = OWGUI.appendRadioButton(box,
self,
"methodInd",
"Sample",
insertInto=w2)
self.sb2 = OWGUI.spin(OWGUI.widgetBox(w2),
self,
"sampleP",
min=1,
max=100,
step=1,
controlWidth=75,
callback=self.onChange,
posttext="% data instances")
OWGUI.rubber(w2)
self.updateSpinStates()
OWGUI.rubber(box)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'Net Inside View')
self.inputs = []
self.outputs = [("Nx View", Orange.network.NxView)]
self._nhops = 2
self.loadSettings()
ib = OWGUI.widgetBox(self.controlArea, "Preferences", orientation="vertical")
OWGUI.spin(ib, self, "_nhops", 1, 6, 1, label="Number of hops: ", callback=self.update_view)
self.inside_view = NxInsideView(self._nhops)
self.send("Nx View", self.inside_view)
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):
OWWidget.__init__(self, parent, 'Check')
# GUI
self.spinval = 10
self.chkA = 1
self.chkB = 0
self.dx = 15
box = OWGUI.widgetBox(self.controlArea, "Settings")
gridbox = OWGUI.widgetBox(self.controlArea, "Grid Opions")
gridbox.setEnabled(self.chkB)
OWGUI.checkBox(box, self, "chkA", "Verbose")
OWGUI.checkBox(box, self, "chkB", "Display Grid", disables=[gridbox])
OWGUI.spin(gridbox, self, "dx", 10, 20)
self.resize(100, 50)
def __init__(self, parent=None):
OWWidget.__init__(self, parent, 'Check')
# GUI
self.spinval = 10
self.chkA = 1
self.chkB = 0
self.dx = 15
box = OWGUI.widgetBox(self.controlArea, "Settings")
gridbox = OWGUI.widgetBox(self.controlArea, "Grid Opions")
gridbox.setEnabled(self.chkB)
OWGUI.checkBox(box, self, "chkA", "Verbose")
OWGUI.checkBox(box, self, "chkB", "Display Grid", disables=[gridbox])
OWGUI.spin(gridbox, self, "dx", 10, 20)
self.resize(100,50)
def __init__(self, parent=None, signalManager = None, name='ML-kNN'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.set_data),
("Preprocess", PreprocessedLearner, self.set_preprocessor)
]
self.outputs = [("Learner", orange.Learner),("ML-kNN Classifier", Orange.multilabel.MLkNNClassifier)]
# Settings
self.name = 'ML-kNN'
self.k = 1
self.smooth = 1.0
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
self.set_learner() # this just sets the learner, no data
# has come to the input yet
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)
wbN = OWGUI.widgetBox(self.controlArea, "Neighbours")
OWGUI.spin(wbN, self, "k", 1, 100, 1, None, "Number of neighbours", orientation="horizontal")
OWGUI.separator(self.controlArea)
OWGUI.widgetLabel(self.controlArea, 'Smoothing parameter')
kernelSizeValid = QDoubleValidator(self.controlArea)
kernelSizeValid.setRange(0,10,3)
OWGUI.lineEdit(self.controlArea, self, 'smooth',
tooltip='Smoothing parameter controlling the strength of uniform prior (Default value is set to 1 which yields the Laplace smoothing).',
valueType = float, validator = kernelSizeValid)
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.set_learner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def __init__(self, parent=None, signalManager = None, name='Classification Tree'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.inputs = [("Examples", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.TreeLearner),("Classification Tree", orange.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)
OWGUI.rubber(self.controlArea)
self.resize(200,200)
def __init__(self, parent=None, signalManager = None, name='C4.5'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),("Classification Tree", orange.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)
OWGUI.rubber(self.controlArea)
self.setLearner()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveA')
self.inputs = [("Data", ExampleTable, self.dataset),
("Learner", orange.Learner, self.learner, Multiple)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.loadSettings()
self.setCurvePoints() # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA), ("AUC", orngStat.AUC), ("BrierScore", orngStat.BrierScore), ("Information Score", orngStat.IS), ("Sensitivity", orngStat.sens), ("Specificity", orngStat.spec)]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames, callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1, label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box, self, 'steps', 2, 100, step=1, label='Learning curve points: ',
callback=[self.setCurvePoints, lambda: self.computeCurve(self.commitOnChange)])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting", callback=self.computeCurve, disabled=1)
# table widget
self.table = OWGUI.table(self.mainArea, selectionMode=QTableWidget.NoSelection)
self.resize(500,200)
def __init__(self, parent=None):
OWWidget.__init__(self, parent, 'Spin')
# GUI
self.spinval = 10
OWGUI.spin(self.controlArea, self, "spinval", 0, 100, box="Value A")
box = OWGUI.widgetBox(self.controlArea, "Options")
self.alpha = 30
self.beta = 4
OWGUI.spin(box,
self,
"alpha",
0,
100,
label="Alpha:",
labelWidth=60,
orientation="horizontal",
callback=self.setInfo)
OWGUI.spin(box,
self,
"beta",
-10,
10,
label="Beta:",
labelWidth=60,
orientation="horizontal",
callback=self.setInfo)
box = OWGUI.widgetBox(self.controlArea, "Info")
self.info = OWGUI.widgetLabel(box, "")
self.setInfo()
self.resize(100, 50)
def __init__(self, parent=None, signalManager = None, name='kmeans'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0)
self.callbackDeposit = []
self.inputs = [("Data", Descriptors, self.setData)]
self.outputs = [("Codebook", Codebook)] # , ("Histograms", ExampleTable)]
self.useLazyEvaluation = pynopticon.useLazyEvaluation
# Settings
self.name = name
self.kmeans = None
self.loadSettings()
self.numClusters = 20
self.maxiter = 0
self.numruns = 1
self.sampleFromData = 1.0
self.loadSettings()
wbN = OWGUI.widgetBox(self.controlArea, "kMeans Settings")
OWGUI.spin(wbN, self, "numClusters", 1, 100000, 100, None, "Number of clusters ", orientation="horizontal")
OWGUI.spin(wbN, self, "maxiter", 0, 100000, 1, None, "Maximum number of iterations", orientation="horizontal")
OWGUI.spin(wbN, self, "numruns", 0, 100000, 1, None, "Number of runs ", orientation="horizontal")
OWGUI.widgetLabel(wbN, 'Use x% of the data')
OWGUI.lineEdit(wbN, self, 'sampleFromData', valueType=float)
OWGUI.separator(self.controlArea)
wbS = OWGUI.widgetBox(self.controlArea, "Widget Settings")
OWGUI.checkBox(wbS, self, "useLazyEvaluation", "Use lazy evaluation")
OWGUI.button(self.controlArea, self, "&Apply Settings", callback = self.applySettings, disabled=0)
self.resize(100,150)
def __init__(self,
parent=None,
signalManager=None,
title="Gaussin Mixture"):
OWWidget.__init__(self, parent, signalManager, title)
self.inputs = [("Data", Orange.data.Table, self.set_data)]
self.outputs = [("Data with Indicator Matrix", Orange.data.Table)]
self.init_method = 0
self.n = 3
self.auto_commit = True
self.loadSettings()
#####
# GUI
#####
OWGUI.spin(self.controlArea,
self,
"n",
min=1,
max=10,
step=1,
box="Settings",
label="Number of gaussians",
tooltip="The number of gaussians in the mixture ",
callback=self.on_params_changed)
OWGUI.comboBox(self.controlArea,
self,
"init_method",
box="Initialization",
items=["K-means", "Random"],
tooltip="Method used to initialize the mixture",
callback=self.on_params_changed)
OWGUI.button(self.controlArea, self, "Apply", callback=self.commit)
def __init__(self, parent=None, signalManager = None, title = "Calibrated Classifier"):
OWWidget.__init__(self, parent, signalManager, title)
self.inputs = [("Data", ExampleTable, self.setData), ("Base Learner", orange.Learner, self.setBaseLearner)]
self.outputs = [("Learner", orange.Learner),("Classifier", orange.Classifier)]
# Settings
self.name = 'Calibrated Learner'
self.optimalThreshold = 0
self.threshold = self.accuracy = 50
self.loadSettings()
self.learner = None
self.baseLearner = None
self.data = 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.wbThreshold = OWGUI.widgetBox(self.controlArea, "Threshold", addSpace=True)
self.cbOptimal = OWGUI.checkBox(self.wbThreshold, self, "optimalThreshold",
"Use optimal threshold",
callback=self.setThreshold)
self.spThreshold = OWGUI.spin(self.wbThreshold, self, "threshold", 1, 99, step=5,
label = "Threshold",
orientation = "horizontal",
callback = self.setThreshold)
self.lbNotice = OWGUI.widgetLabel(self.wbThreshold, "Notice: If the widget is connected to a widget that takes a Learner, not a Classifier (eg 'Test Learners'), the automatically computed threshold can differ from the above.")
self.lbNotice.setWordWrap(True)
self.cbOptimal.disables = [self.lbNotice]
self.cbOptimal.makeConsistent()
self.spThreshold.setDisabled(self.optimalThreshold)
OWGUI.rubber(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply Setting",
callback = self.btApplyCallback,
disabled=0)
self.btSave = OWGUI.button(self.controlArea, self, "&Save Graph", callback = self.saveToFile, disabled=1)
self.graph = ThresholdGraph()
self.mainArea.layout().addWidget(self.graph)
self.resize(700, 330)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'Nx Clustering')
self.inputs = [("Network", Orange.network.Graph,
self.setNetwork, Default)]
self.outputs = [("Network", Orange.network.Graph)]
self.net = None
self.method = 0
self.iterationHistory = 0
self.autoApply = 0
self.iterations = 1000
self.hop_attenuation = 0.1
self.loadSettings()
OWGUI.spin(self.controlArea, self, "iterations", 1,
100000, 1, label="Iterations: ")
ribg = OWGUI.radioButtonsInBox(self.controlArea, self, "method",
[], "Method", callback=self.cluster)
OWGUI.appendRadioButton(ribg, self, "method",
"Label propagation clustering (Raghavan et al., 2007)",
callback=self.cluster)
OWGUI.appendRadioButton(ribg, self, "method",
"Label propagation clustering (Leung et al., 2009)",
callback=self.cluster)
OWGUI.doubleSpin(OWGUI.indentedBox(ribg), self, "hop_attenuation",
0, 1, 0.01, label="Hop attenuation (delta): ")
self.info = OWGUI.widgetLabel(self.controlArea, ' ')
OWGUI.checkBox(self.controlArea, self, "iterationHistory",
"Append clustering data on each iteration",
callback=self.cluster)
OWGUI.checkBox(self.controlArea, self, "autoApply",
"Commit automatically")
OWGUI.button(self.controlArea, self, "Commit",
callback=lambda b=True: self.cluster(b))
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'Pubmed Network View', wantMainArea=0)
self.inputs = []
self.outputs = [("Nx View", Orange.network.NxView)]
self._nhops = 2
self._edge_threshold = 0.5
self._n_max_neighbors = 20
self.selected_titles = []
self.titles = []
self.filter = ''
self.ids = []
self._selected_nodes = []
self._algorithm = 0
self._k_algorithm = 0.3
self.loadSettings()
box = OWGUI.widgetBox(self.controlArea, "Paper Selection", orientation="vertical")
OWGUI.lineEdit(box, self, "filter", callback=self.filter_list, callbackOnType=True)
self.list_titles = OWGUI.listBox(box, self, "selected_titles", "titles", selectionMode=QListWidget.MultiSelection, callback=self.update_view)
OWGUI.separator(self.controlArea)
box_pref = OWGUI.widgetBox(self.controlArea, "Preferences", orientation="vertical")
OWGUI.spin(box_pref, self, "_nhops", 1, 6, 1, label="Number of hops: ", callback=self.update_view)
OWGUI.spin(box_pref, self, "_n_max_neighbors", 1, 100, 1, label="Max number of neighbors: ", callback=self.update_view)
OWGUI.doubleSpin(box_pref, self, "_edge_threshold", 0, 1, step=0.01, label="Edge threshold: ", callback=self.update_view)
OWGUI.separator(self.controlArea)
box_alg = OWGUI.widgetBox(self.controlArea, "Interest Propagation Algorithm", orientation="vertical")
radio_box = OWGUI.radioButtonsInBox(box_alg, self, "_algorithm", [], callback=self.update_view)
OWGUI.appendRadioButton(radio_box, self, "_algorithm", "Without Clustering", callback=self.update_view)
OWGUI.doubleSpin(OWGUI.indentedBox(radio_box), self, "_k_algorithm", 0, 1, step=0.01, label="Parameter k: ", callback=self.update_view)
OWGUI.appendRadioButton(radio_box, self, "_algorithm", "With Clustering", callback=self.update_view)
self.inside_view = PubmedNetworkView(self)
self.send("Nx View", self.inside_view)
def __init__(self, parent=None, signalManager = None, name='BR-kNN'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.set_data),
("Preprocess", PreprocessedLearner, self.set_preprocessor)
]
self.outputs = [("Learner", orange.Learner),("BR-kNN Classifier", Orange.multilabel.BRkNNClassifier)]
# Settings
self.name = 'BR-kNN'
self.k = 1
self.loadSettings()
self.data = None # input data set
self.preprocessor = None # no preprocessing as default
self.set_learner() # this just sets the learner, no data
# has come to the input yet
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)
wbN = OWGUI.widgetBox(self.controlArea, "Neighbours")
OWGUI.spin(wbN, self, "k", 1, 100, 1, None, "Number of neighbours", orientation="horizontal")
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.set_learner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def __init__(self, parent=None):
OWWidget.__init__(self, parent, 'Spin')
# GUI
self.spinval = 10
OWGUI.spin(self.controlArea, self, "spinval", 0, 100, box="Value A")
box = OWGUI.widgetBox(self.controlArea, "Options")
self.alpha = 30
self.beta = 4
OWGUI.spin(box, self, "alpha", 0, 100, label="Alpha:", labelWidth=60,
orientation="horizontal", callback=self.setInfo)
OWGUI.spin(box, self, "beta", -10, 10, label="Beta:", labelWidth=60,
orientation="horizontal", callback=self.setInfo)
box = OWGUI.widgetBox(self.controlArea, "Info")
self.info = OWGUI.widgetLabel(box, "")
self.setInfo()
self.resize(100,50)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveC')
self.inputs = [("Data", ExampleTable, self.dataset), ("Learner", orange.Learner, self.learner, 0)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.graphPointSize = 5 # size of points in the graphs
self.graphDrawLines = 1 # draw lines between points in the graph
self.graphShowGrid = 1 # show gridlines in the graph
self.selectedLearners = []
self.loadSettings()
warnings.filterwarnings("ignore", ".*builtin attribute.*", orange.AttributeWarning)
self.setCurvePoints() # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA), ("AUC", orngStat.AUC), ("BrierScore", orngStat.BrierScore), ("Information Score", orngStat.IS), ("Sensitivity", orngStat.sens), ("Specificity", orngStat.spec)]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
## class selection (classQLB)
OWGUI.separator(self.controlArea)
self.cbox = OWGUI.widgetBox(self.controlArea, "Learners")
self.llb = OWGUI.listBox(self.cbox, self, "selectedLearners", selectionMode=QListWidget.MultiSelection, callback=self.learnerSelectionChanged)
self.llb.setMinimumHeight(50)
self.blockSelectionChanges = 0
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box, self, 'steps', 2, 100, step=1,
label='Learning curve points: ',
callback=[self.setCurvePoints, lambda: self.computeCurve(self.commitOnChange)])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting", callback=self.computeCurve, disabled=1)
# start of content (right) area
tabs = OWGUI.tabWidget(self.mainArea)
# graph widget
tab = OWGUI.createTabPage(tabs, "Graph")
self.graph = OWPlot(tab)
self.graph.set_axis_autoscale(xBottom)
self.graph.set_axis_autoscale(yLeft)
tab.layout().addWidget(self.graph)
self.setGraphGrid()
# table widget
tab = OWGUI.createTabPage(tabs, "Table")
self.table = OWGUI.table(tab, selectionMode=QTableWidget.NoSelection)
self.resize(550,200)
def __init__(self, parent=None, signalManager=None):
"""Widget creator."""
OWWidget.__init__(
self,
parent,
signalManager,
wantMainArea=0,
wantStateInfoWidget=0,
)
# DEFINE OUTPUT
self.outputs = [('Text data', Segmentation)]
# Settings and other attribute initializations...
self.segment_label = u'search_results'
self.nb_tweet = 50
self.include_RT = False
self.word_to_search = ''
self.autoSend = False
self.useTwitterLicenseKey = False
self.twitterLicenseKeys = False
self.twitterLicenseKeysConsumerKey = ''
self.twitterLicenseKeysConsumerSecret = ''
self.twitterLicenseKeysAccessToken = ''
self.twitterLicenseKeysAccessTokenSecret = ''
self.service = u'Twitter'
self.wiki_section = False
self.wiki_type_of_text = u'Plain text'
self.nb_bing_entry = 50
self.language = 'English'
self.createdInputs = list()
self.dico_lang = {
'English': 'en',
'French': 'fr',
'German': 'de',
'Spanish': 'es',
'Italian': 'it',
'Dutch': 'nl'
}
# BASIC SETTING
self.uuid = None
self.loadSettings()
self.uuid = getWidgetUuid(self)
self.inputData = None
self.infoBox = InfoBox(widget=self.controlArea)
self.sendButton = SendButton(
widget=self.controlArea,
master=self,
callback=self.sendData,
infoBoxAttribute='infoBox'
)
# CONFIG BOXES
optionsBox = OWGUI.widgetBox(self.controlArea, 'Options')
OWGUI.separator(widget=self.controlArea, height=3)
self.twitterBox = OWGUI.widgetBox(self.controlArea, 'Twitter')
self.wikipediaBox = OWGUI.widgetBox(self.controlArea, 'Wikipedia')
self.bingBox = OWGUI.widgetBox(self.controlArea, 'Bing')
OWGUI.separator(widget=self.controlArea, height=3)
self.serviceBoxes = [self.twitterBox, self.wikipediaBox, self.bingBox]
# OPTION BOX
OWGUI.comboBox(
widget = optionsBox,
master = self,
value = 'service',
items = [u'Twitter', u'Wikipedia', u'Bing'],
sendSelectedValue = True,
orientation = 'horizontal',
label = u'Service:',
labelWidth = 160,
callback = self.set_service_box_visibility,
tooltip = (
u"Select a service."
),
)
OWGUI.separator(widget=optionsBox, height=3)
OWGUI.comboBox(
widget = optionsBox,
master = self,
value = 'language',
items = ['English', 'French', 'German', 'Spanish', 'Italian', 'Dutch'],
sendSelectedValue = True,
orientation = 'horizontal',
label = u'Language:',
labelWidth = 160,
callback = self.sendButton.settingsChanged,
tooltip = (
u"Select language."
),
)
OWGUI.separator(widget=optionsBox, height=3)
OWGUI.lineEdit(
widget = optionsBox,
master = self,
value = 'word_to_search',
orientation = 'horizontal',
label = u'Query:',
callback = self.sendButton.settingsChanged,
labelWidth = 160,
)
OWGUI.separator(widget=optionsBox, height=3)
# TWITTER BOX
OWGUI.spin(
widget=self.twitterBox,
master=self,
value='nb_tweet',
label='Number of tweets:',
labelWidth=160,
tooltip='Select a number of tweet.',
callback= self.sendButton.settingsChanged,
min= 1,
max= 3000,
step=1,
)
OWGUI.separator(widget=self.twitterBox, height=3)
OWGUI.checkBox(
widget = self.twitterBox,
master = self,
value = 'include_RT',
label = u'Include retweets',
labelWidth = 160,
callback = self.sendButton.settingsChanged,
tooltip = (
u"Include re-tweets or not."
),
)
OWGUI.separator(widget=self.twitterBox, height=3)
# TWITTER LICENSE KEY BOX
OWGUI.checkBox(
widget = self.twitterBox,
master = self,
value = 'useTwitterLicenseKey',
label = u'Use license key',
labelWidth = 160,
callback = self.changeTwitterLicenseKeyBox,
tooltip = (
u"Use private license key or not."
),
)
self.twitterLicenseBox = OWGUI.indentedBox(self.twitterBox, sep=20)
OWGUI.separator(widget=self.twitterLicenseBox, height=3)
OWGUI.lineEdit(
widget=self.twitterLicenseBox,
master=self,
value='twitterLicenseKeysConsumerKey',
label=u'Consumer key: ',
orientation='horizontal',
callback=self.sendButton.settingsChanged,
labelWidth=140,
tooltip=(
u"Your twitter Consumer key."
),
)
OWGUI.separator(widget=self.twitterLicenseBox, height=3)
OWGUI.lineEdit(
widget=self.twitterLicenseBox,
master=self,
value='twitterLicenseKeysConsumerSecret',
label=u'Consumer secret: ',
orientation='horizontal',
callback=self.sendButton.settingsChanged,
labelWidth=140,
tooltip=(
u"Your private twitter license key."
),
)
OWGUI.separator(widget=self.twitterLicenseBox, height=3)
OWGUI.lineEdit(
widget=self.twitterLicenseBox,
master=self,
value='twitterLicenseKeysAccessToken',
label=u'Access token: ',
orientation='horizontal',
callback=self.sendButton.settingsChanged,
labelWidth=140,
tooltip=(
u"Your private twitter Access token."
),
)
OWGUI.separator(widget=self.twitterLicenseBox, height=3)
OWGUI.lineEdit(
widget=self.twitterLicenseBox,
master=self,
value='twitterLicenseKeysAccessTokenSecret',
label=u'Access token secret: ',
orientation='horizontal',
callback=self.sendButton.settingsChanged,
labelWidth=140,
tooltip=(
u"Your private twitter access token secret."
),
)
OWGUI.separator(widget=self.twitterLicenseBox, height=3)
# WIKIPEDIA BOX
OWGUI.checkBox(
widget = self.wikipediaBox,
master = self,
value = 'wiki_section',
label = u'Segment into sections',
labelWidth = 160,
callback = self.sendButton.settingsChanged,
tooltip = (
u"Segment into Wikipedia sections:"
),
)
OWGUI.separator(widget=self.wikipediaBox, height=3)
OWGUI.comboBox(
widget = self.wikipediaBox,
master = self,
value = 'wiki_type_of_text',
items = [u'Plain text', u'HTML'],
sendSelectedValue = True,
orientation = 'horizontal',
label = u'Output format:',
labelWidth = 160,
callback = self.sendButton.settingsChanged,
tooltip = (
u"Select type of text."
),
)
OWGUI.separator(widget=self.wikipediaBox, height=3)
# BING BOX
OWGUI.spin(
widget=self.bingBox,
master=self,
value='nb_bing_entry',
label='Number of results:',
labelWidth=160,
tooltip='Select a number of results.',
callback= self.sendButton.settingsChanged,
min= 1,
max= 1000,
step=1,
)
OWGUI.separator(widget=self.bingBox, height=3)
# CONFIG WIDGET
OWGUI.rubber(self.controlArea)
self.sendButton.draw()
self.infoBox.draw()
self.set_service_box_visibility()
self.changeTwitterLicenseKeyBox()
self.sendButton.sendIf()
self.adjustSizeWithTimer()
def __init__(self, parent=None, signalManager = None, name='sift'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0)
self.callbackDeposit = []
self.inputs = [("Images PIL", SeqContainer, self.setData)]
self.outputs = [("Descriptors", SeqContainer)]
# Settings
self.name = name
self.sift = None
self.useLazyEvaluation = armor.useLazyEvaluation
self.Octave = 6
self.Levels = 3
self.FirstOctave = 0
self.PeakThresh = 0
self.EdgeThresh = 10
self.NormThresh = 0
self.Orientations = 0
self.loadSettings()
self.data = None # input data set
wbN = OWGUI.widgetBox(self.controlArea, "Sift Settings")
OWGUI.spin(wbN, self, "Octave", -1, 8, 1, None, "Octaves ", orientation="horizontal")
OWGUI.spin(wbN, self, "Levels", 1, 8, 1, None, "Levels ", orientation="horizontal")
OWGUI.spin(wbN, self, "FirstOctave", 0, 8, 1, None, "First Octave ", orientation="horizontal")
OWGUI.spin(wbN, self, "PeakThresh", -1, 8, 1, None, "PeakThresh", orientation="horizontal")
OWGUI.spin(wbN, self, "EdgeThresh", -1, 8, 1, None, "EdgeThresh", orientation="horizontal")
OWGUI.spin(wbN, self, "NormThresh", -1, 8, 1, None, "NormThresh", orientation="horizontal")
OWGUI.spin(wbN, self, "Orientations", 0, 1, 1, None, "Force computation of orientations", orientation="horizontal")
wbS = OWGUI.widgetBox(self.controlArea, "Widget Settings")
OWGUI.checkBox(wbS, self, "useLazyEvaluation", "Use lazy evaluation")
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self, "&Apply Settings", callback = self.applySettings, disabled=0)
self.resize(100,250)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,
parent,
signalManager,
'Hierarchical Clustering',
wantGraph=True)
self.inputs = [("Distances", Orange.misc.SymMatrix, self.set_matrix)]
self.outputs = [("Selected Data", Orange.data.Table),
("Other Data", Orange.data.Table),
("Centroids", Orange.data.Table)]
self.linkage = [
("Single linkage", hierarchical.HierarchicalClustering.Single),
("Average linkage", hierarchical.HierarchicalClustering.Average),
("Ward's linkage", hierarchical.HierarchicalClustering.Ward),
("Complete linkage", hierarchical.HierarchicalClustering.Complete),
]
self.Linkage = 3
self.Annotation = 0
self.PrintDepthCheck = 0
self.PrintDepth = 10
# initial horizontal and vertical dendrogram size
self.HDSize = 500
self.VDSize = 800
self.ManualHorSize = 0
self.AutoResize = 0
self.TextSize = 8
self.LineSpacing = 4
self.SelectionMode = 0
self.AppendClusters = 0
self.CommitOnChange = 0
self.ClassifyName = "HC_class"
self.addIdAs = 0
self.loadSettings()
self.inputMatrix = None
self.root_cluster = None
self.selectedExamples = None
self.selectionChanged = False
self.linkageMethods = [a[0] for a in self.linkage]
#################################
##GUI
#################################
#HC Settings
OWGUI.comboBox(self.controlArea,
self,
"Linkage",
box="Linkage",
items=self.linkageMethods,
tooltip="Choose linkage method",
callback=self.run_clustering,
addSpace=True)
#Label
box = OWGUI.widgetBox(self.controlArea, "Annotation", addSpace=True)
self.labelCombo = OWGUI.comboBox(box,
self,
"Annotation",
items=["None"],
tooltip="Choose label attribute",
callback=self.update_labels)
OWGUI.spin(box,
self,
"TextSize",
label="Text size",
min=5,
max=15,
step=1,
callback=self.update_font,
controlWidth=40,
keyboardTracking=False)
# Dendrogram graphics settings
dendrogramBox = OWGUI.widgetBox(self.controlArea,
"Limits",
addSpace=True)
form = QFormLayout()
form.setLabelAlignment(Qt.AlignLeft)
# Depth settings
sw = OWGUI.widgetBox(dendrogramBox,
orientation="horizontal",
addToLayout=False)
cw = OWGUI.widgetBox(dendrogramBox,
orientation="horizontal",
addToLayout=False)
OWGUI.hSlider(sw,
self,
"PrintDepth",
minValue=1,
maxValue=50,
callback=self.on_depth_change)
cblp = OWGUI.checkBox(cw,
self,
"PrintDepthCheck",
"Show to depth",
callback=self.on_depth_change,
disables=[sw])
form.addRow(cw, sw)
checkWidth = OWGUI.checkButtonOffsetHint(cblp)
# Width settings
sw = OWGUI.widgetBox(dendrogramBox,
orientation="horizontal",
addToLayout=False)
cw = OWGUI.widgetBox(dendrogramBox,
orientation="horizontal",
addToLayout=False)
hsb = OWGUI.spin(sw,
self,
"HDSize",
min=200,
max=10000,
step=10,
callback=self.on_width_changed,
callbackOnReturn=False,
keyboardTracking=False)
OWGUI.checkBox(cw,
self,
"ManualHorSize",
"Horizontal size",
callback=self.on_width_changed,
disables=[sw])
sw.setEnabled(self.ManualHorSize)
self.hSizeBox = hsb
form.addRow(cw, sw)
dendrogramBox.layout().addLayout(form)
# Selection settings
box = OWGUI.widgetBox(self.controlArea, "Selection")
OWGUI.checkBox(box,
self,
"SelectionMode",
"Show cutoff line",
callback=self.update_cutoff_line)
cb = OWGUI.checkBox(box,
self,
"AppendClusters",
"Append cluster IDs",
callback=self.commit_data_if)
self.classificationBox = ib = OWGUI.widgetBox(box, margin=0)
form = QWidget()
le = OWGUI.lineEdit(form,
self,
"ClassifyName",
None,
callback=None,
orientation="horizontal")
self.connect(le, SIGNAL("editingFinished()"), self.commit_data_if)
aa = OWGUI.comboBox(
form,
self,
"addIdAs",
label=None,
orientation="horizontal",
items=["Class attribute", "Attribute", "Meta attribute"],
callback=self.commit_data_if)
layout = QFormLayout()
layout.setSpacing(8)
layout.setContentsMargins(0, 5, 0, 5)
layout.setFieldGrowthPolicy(QFormLayout.AllNonFixedFieldsGrow)
layout.setLabelAlignment(Qt.AlignLeft)
layout.addRow("Name ", le)
layout.addRow("Place ", aa)
form.setLayout(layout)
ib.layout().addWidget(form)
ib.layout().setContentsMargins(checkWidth, 5, 5, 5)
cb.disables.append(ib)
cb.makeConsistent()
OWGUI.separator(box)
cbAuto = OWGUI.checkBox(box, self, "CommitOnChange",
"Commit on change")
btCommit = OWGUI.button(box,
self,
"&Commit",
self.commit_data,
default=True)
OWGUI.setStopper(self, btCommit, cbAuto, "selectionChanged",
self.commit_data)
OWGUI.rubber(self.controlArea)
self.connect(self.graphButton, SIGNAL("clicked()"), self.saveGraph)
self.scale_scene = scale = ScaleScene(self, self)
self.headerView = ScaleView(scale, self)
self.footerView = ScaleView(scale, self)
self.dendrogram = DendrogramScene(self)
self.dendrogramView = DendrogramView(self.dendrogram, self.mainArea)
self.connect(self.dendrogram, SIGNAL("clusterSelectionChanged()"),
self.on_selection_change)
self.connect(self.dendrogram, SIGNAL("sceneRectChanged(QRectF)"),
scale.scene_rect_update)
self.connect(self.dendrogram,
SIGNAL("dendrogramGeometryChanged(QRectF)"),
self.on_dendrogram_geometry_change)
self.connect(self.dendrogram, SIGNAL("cutoffValueChanged(float)"),
self.on_cuttof_value_changed)
self.connect(self.dendrogramView, SIGNAL("viewportResized(QSize)"),
self.on_width_changed)
self.connect(self.dendrogramView,
SIGNAL("transformChanged(QTransform)"),
self.headerView.setTransform)
self.connect(self.dendrogramView,
SIGNAL("transformChanged(QTransform)"),
self.footerView.setTransform)
self.mainArea.layout().addWidget(self.headerView)
self.mainArea.layout().addWidget(self.dendrogramView)
self.mainArea.layout().addWidget(self.footerView)
self.dendrogram.header = self.headerView
self.dendrogram.footer = self.footerView
self.connect(self.dendrogramView.horizontalScrollBar(),
SIGNAL("valueChanged(int)"),
self.footerView.horizontalScrollBar().setValue)
self.connect(self.dendrogramView.horizontalScrollBar(),
SIGNAL("valueChanged(int)"),
self.headerView.horizontalScrollBar().setValue)
self.dendrogram.setSceneRect(0, 0, self.HDSize, self.VDSize)
self.dendrogram.update()
self.resize(800, 500)
self.natural_dendrogram_width = 800
self.dendrogramView.set_fit_to_width(not self.ManualHorSize)
self.matrix = None
self.selectionList = []
self.selected_clusters = []
def __init__(self, parent=None, signalManager=None):
"Constructor"
OWWidget.__init__(self, parent, signalManager, "&Distributions", TRUE)
# settings
self.numberOfBars = 5
self.barSize = 50
self.showContinuousClassGraph = 1
self.showProbabilities = 1
self.showConfidenceIntervals = 0
self.smoothLines = 0
self.lineWidth = 1
self.showMainTitle = 0
self.showXaxisTitle = 1
self.showYaxisTitle = 1
self.showYPaxisTitle = 1
self.attribute = ""
self.targetValue = 0
self.visibleOutcomes = []
self.outcomes = []
# tmp values
self.mainTitle = ""
self.xaxisTitle = ""
self.yaxisTitle = "frequency"
self.yPaxisTitle = ""
# GUI
# self.tabs = OWGUI.tabWidget(self.controlArea)
# self.GeneralTab = OWGUI.createTabPage(self.tabs, "Main")
# self.SettingsTab = OWGUI.createTabPage(self.tabs, "Settings")
self.GeneralTab = self.SettingsTab = self.controlArea
self.graph = OWDistributionGraph(self, self.mainArea)
# Set a fixed minimum width. This disables the dynamic minimumSizeHint
# from the layout, which can return a uselessly large width for the
# x axis when showing a discrete variable with many values.
self.graph.setMinimumWidth(250)
self.mainArea.layout().addWidget(self.graph)
self.graph.setYRlabels(None)
self.graph.setAxisScale(QwtPlot.yRight, 0.0, 1.0, 0.1)
self.connect(self.graphButton, SIGNAL("clicked()"),
self.graph.saveToFile)
self.loadSettings()
self.barSize = 50
# inputs
# data and graph temp variables
self.inputs = [("Data", ExampleTable, self.setData, Default)]
self.data = None
self.outcomenames = []
self.probGraphValues = []
b = OWGUI.widgetBox(self.controlArea, "Variable", addSpace=True)
self.variablesQCB = OWGUI.comboBox(b,
self,
"attribute",
valueType=str,
sendSelectedValue=True,
callback=self.setVariable)
OWGUI.widgetLabel(b, "Displayed outcomes")
self.outcomesQLB = OWGUI.listBox(
b,
self,
"visibleOutcomes",
"outcomes",
selectionMode=QListWidget.MultiSelection,
callback=self.outcomeSelectionChange)
# GUI connections
# options dialog connections
# b = OWGUI.widgetBox(self.SettingsTab, "Bars")
# OWGUI.spin(b, self, "numberOfBars", label="Number of bars", min=5, max=60, step=5, callback=self.setNumberOfBars, callbackOnReturn=True)
# self.numberOfBarsSlider = OWGUI.hSlider(self.SettingsTab, self, 'numberOfBars', box='Number of bars', minValue=5, maxValue=60, step=5, callback=self.setNumberOfBars, ticks=5)
# self.numberOfBarsSlider.setTracking(0) # no change until the user stop dragging the slider
# self.barSizeSlider = OWGUI.hSlider(self.SettingsTab, self, 'barSize', box="Bar size", minValue=30, maxValue=100, step=5, callback=self.setBarSize, ticks=10)
# OWGUI.spin(b, self, "barSize", label="Bar size", min=30, max=100, step=5, callback=self.setBarSize, callbackOnReturn=True)
box = OWGUI.widgetBox(self.SettingsTab,
"General graph settings",
addSpace=True)
box.setMinimumWidth(180)
box2 = OWGUI.widgetBox(box, orientation="horizontal")
OWGUI.checkBox(box2,
self,
'showMainTitle',
'Main title',
callback=self.setShowMainTitle)
OWGUI.lineEdit(box2,
self,
'mainTitle',
callback=self.setMainTitle,
enterPlaceholder=True)
box3 = OWGUI.widgetBox(box, orientation="horizontal")
OWGUI.checkBox(box3,
self,
'showXaxisTitle',
'X axis title',
callback=self.setShowXaxisTitle)
OWGUI.lineEdit(box3,
self,
'xaxisTitle',
callback=self.setXaxisTitle,
enterPlaceholder=True)
box4 = OWGUI.widgetBox(box, orientation="horizontal")
OWGUI.checkBox(box4,
self,
'showYaxisTitle',
'Y axis title',
callback=self.setShowYaxisTitle)
OWGUI.lineEdit(box4,
self,
'yaxisTitle',
callback=self.setYaxisTitle,
enterPlaceholder=True)
OWGUI.checkBox(box,
self,
'graph.showContinuousClassGraph',
'Show continuous class graph',
callback=self.setShowContinuousClassGraph)
OWGUI.spin(box,
self,
"numberOfBars",
label="Number of bars",
min=5,
max=60,
step=5,
callback=self.setNumberOfBars,
callbackOnReturn=True)
self.probabilityPlotBox = box5 = OWGUI.widgetBox(
self.SettingsTab, "Probability plot")
self.showProb = OWGUI.checkBox(box5,
self,
'showProbabilities',
'Show probabilities',
callback=self.setShowProbabilities)
self.targetQCB = OWGUI.comboBox(OWGUI.indentedBox(
box5, sep=OWGUI.checkButtonOffsetHint(self.showProb)),
self,
"targetValue",
label="Target value",
valueType=int,
callback=self.setTarget)
box6 = OWGUI.widgetBox(box5, orientation="horizontal")
self.showYPaxisCheck = OWGUI.checkBox(box6,
self,
'showYPaxisTitle',
'Axis title',
callback=self.setShowYPaxisTitle)
self.yPaxisEdit = OWGUI.lineEdit(box6,
self,
'yPaxisTitle',
callback=self.setYPaxisTitle,
enterPlaceholder=True)
self.confIntCheck = OWGUI.checkBox(
box5,
self,
'showConfidenceIntervals',
'Show confidence intervals',
callback=self.setShowConfidenceIntervals)
self.cbSmooth = OWGUI.checkBox(box5,
self,
'smoothLines',
'Smooth probability lines',
callback=self.setSmoothLines)
self.showProb.disables = [
self.showYPaxisCheck, self.yPaxisEdit, self.confIntCheck,
self.targetQCB, self.cbSmooth
]
self.showProb.makeConsistent()
# self.barSizeSlider = OWGUI.hSlider(box5, self, 'lineWidth', box='Line width', minValue=1, maxValue=9, step=1, callback=self.setLineWidth, ticks=1)
OWGUI.rubber(self.SettingsTab)
#add controls to self.controlArea widget
self.icons = self.createAttributeIconDict()
self.graph.numberOfBars = self.numberOfBars
self.graph.barSize = self.barSize
self.graph.setShowMainTitle(self.showMainTitle)
self.graph.setShowXaxisTitle(self.showXaxisTitle)
self.graph.setShowYLaxisTitle(self.showYaxisTitle)
self.graph.setShowYRaxisTitle(self.showYPaxisTitle)
self.graph.setMainTitle(self.mainTitle)
self.graph.setXaxisTitle(self.xaxisTitle)
self.graph.setYLaxisTitle(self.yaxisTitle)
self.graph.setYRaxisTitle(self.yPaxisTitle)
self.graph.showProbabilities = self.showProbabilities
self.graph.showConfidenceIntervals = self.showConfidenceIntervals
self.graph.smoothLines = self.smoothLines
self.graph.lineWidth = self.lineWidth
#self.graph.variableContinuous = self.VariableContinuous
self.graph.targetValue = self.targetValue
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Association rules viewer")
self.inputs = [("Association Rules", orange.AssociationRules, self.arules)]
self.outputs = [("Association Rules", orange.AssociationRules), ("Covered Data", ExampleTable), ("Matching Data", ExampleTable), ("Mismatched Data", ExampleTable)]
self.showWholeRules = 1
self.treeDepth = 2
self.autoSend = True
self.dataChanged = False
self.purgeAttributes = True
self.purgeClasses = True
self.nRules = self.nSelectedRules = self.nSelectedExamples = self.nMatchingExamples = self.nMismatchingExamples = ""
self.showsupport = self.showconfidence = 1
self.showlift = self.showleverage = self.showstrength = self.showcoverage = 0
self.loadSettings()
# self.grid = QGridLayout()
# rightUpRight = OWGUI.widgetBox(self.mainArea, box="Shown measures", orientation = self.grid)
# self.cbMeasures = [OWGUI.checkBox(rightUpRight, self, "show"+attr, long, callback = self.showHideColumn, addToLayout = 0) for long, short, attr in self.measures]
# for i, cb in enumerate(self.cbMeasures):
# self.grid.addWidget(cb, i % 2, i / 2)
box = OWGUI.widgetBox(self.mainArea, orientation = 0)
OWGUI.widgetLabel(box, "Shown measures: ")
self.cbMeasures = [OWGUI.checkBox(box, self, "show"+attr, long+" ", callback = self.showHideColumn) for long, short, attr in self.measures]
OWGUI.rubber(box)
self.treeRules = QTreeWidget(self.mainArea)
self.mainArea.layout().addWidget(self.treeRules)
self.treeRules.setSelectionMode (QTreeWidget.ExtendedSelection)
self.treeRules.setHeaderLabels(["Rules"] + [m[1] for m in self.measures])
self.treeRules.setAllColumnsShowFocus ( 1)
self.treeRules.setAlternatingRowColors(1)
self.showHideColumn()
self.connect(self.treeRules,SIGNAL("itemSelectionChanged()"),self. selectionChanged)
box = OWGUI.widgetBox(self.controlArea, "Info", addSpace = True)
OWGUI.label(box, self, "Number of rules: %(nRules)s")
OWGUI.label(box, self, "Selected rules: %(nSelectedRules)s")
OWGUI.label(box, self, "Selected examples: %(nSelectedExamples)s")
ibox = OWGUI.indentedBox(box)
OWGUI.label(ibox, self, "... matching: %(nMatchingExamples)s")
OWGUI.label(ibox, self, "... mismatching: %(nMismatchingExamples)s")
box = OWGUI.widgetBox(self.controlArea, "Options", addSpace = True)
OWGUI.spin(box, self, "treeDepth", label = "Tree depth", min = 0, max = 10, step = 1, callback = self.displayRules, callbackOnReturn = True)
OWGUI.separator(box)
OWGUI.checkBox(box, self, "showWholeRules", "Display whole rules", callback = self.setWholeRules)
OWGUI.rubber(self.controlArea)
boxSettings = OWGUI.widgetBox(self.controlArea, 'Send selection')
OWGUI.checkBox(boxSettings, self, "purgeAttributes", "Purge attribute values/attributes", box=None, callback=self.purgeChanged)
self.purgeClassesCB = OWGUI.checkBox(OWGUI.indentedBox(boxSettings), self, "purgeClasses", "Purge class attribute", callback=self.purgeChanged)
if not self.purgeAttributes:
self.purgeClassesCB.setEnabled(False)
self.oldPurgeClasses = False
cbSendAuto = OWGUI.checkBox(boxSettings, self, "autoSend", "Send immediately", box=None)
btnUpdate = OWGUI.button(boxSettings, self, "Send", self.sendData, default=True)
OWGUI.setStopper(self, btnUpdate, cbSendAuto, "dataChanged", self.sendData)
self.rules = None
def __init__(self, parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Predictions")
self.callbackDeposit = []
self.inputs = [("Data", ExampleTable, self.setData), ("Predictors", orange.Classifier, self.setPredictor, Multiple)]
self.outputs = [("Predictions", ExampleTable)]
self.predictors = {}
# saveble settings
self.showProb = 1;
self.showClass = 1
self.ShowAttributeMethod = 0
self.sendOnChange = 1
self.classes = []
self.selectedClasses = []
self.loadSettings()
self.datalabel = "N/A"
self.predictorlabel = "N/A"
self.tasklabel = "N/A"
self.precision = 2
self.doPrediction = False
self.outvar = None # current output variable (set by the first predictor/data set send in)
self.data = None
self.changedFlag = False
self.loadSettings()
# GUI - Options
# Options - classification
ibox = OWGUI.widgetBox(self.controlArea, "Info")
OWGUI.label(ibox, self, "Data: %(datalabel)s")
OWGUI.label(ibox, self, "Predictors: %(predictorlabel)s")
OWGUI.label(ibox, self, "Task: %(tasklabel)s")
OWGUI.separator(self.controlArea)
self.copt = OWGUI.widgetBox(self.controlArea, "Options (classification)")
self.copt.setDisabled(1)
cb = OWGUI.checkBox(self.copt, self, 'showProb', "Show predicted probabilities", callback=self.setPredictionDelegate)#self.updateTableOutcomes)
# self.lbClasses = OWGUI.listBox(self.copt, self, selectionMode = QListWidget.MultiSelection, callback = self.updateTableOutcomes)
ibox = OWGUI.indentedBox(self.copt, sep=OWGUI.checkButtonOffsetHint(cb))
self.lbcls = OWGUI.listBox(ibox, self, "selectedClasses", "classes",
callback=[self.setPredictionDelegate, self.checksendpredictions],
# callback=[self.updateTableOutcomes, self.checksendpredictions],
selectionMode=QListWidget.MultiSelection)
self.lbcls.setFixedHeight(50)
OWGUI.spin(ibox, self, "precision", 1, 6, label="No. of decimals: ",
orientation=0, callback=self.setPredictionDelegate) #self.updateTableOutcomes)
cb.disables.append(ibox)
ibox.setEnabled(bool(self.showProb))
OWGUI.checkBox(self.copt, self, 'showClass', "Show predicted class",
callback=[self.setPredictionDelegate, self.checksendpredictions])
# callback=[self.updateTableOutcomes, self.checksendpredictions])
OWGUI.separator(self.controlArea)
self.att = OWGUI.widgetBox(self.controlArea, "Data attributes")
OWGUI.radioButtonsInBox(self.att, self, 'ShowAttributeMethod', ['Show all', 'Hide all'], callback=lambda :self.setDataModel(self.data)) #self.updateAttributes)
self.att.setDisabled(1)
OWGUI.rubber(self.controlArea)
OWGUI.separator(self.controlArea)
self.outbox = OWGUI.widgetBox(self.controlArea, "Output")
b = self.commitBtn = OWGUI.button(self.outbox, self, "Send Predictions", callback=self.sendpredictions, default=True)
cb = OWGUI.checkBox(self.outbox, self, 'sendOnChange', 'Send automatically')
OWGUI.setStopper(self, b, cb, "changedFlag", callback=self.sendpredictions)
OWGUI.checkBox(self.outbox, self, "doPrediction", "Replace/add predicted class",
tooltip="Apply the first predictor to input examples and replace/add the predicted value as the new class variable.",
callback=self.checksendpredictions)
self.outbox.setDisabled(1)
## GUI table
self.splitter = splitter = QSplitter(Qt.Horizontal, self.mainArea)
self.dataView = QTableView()
self.predictionsView = QTableView()
self.dataView.verticalHeader().setDefaultSectionSize(22)
self.dataView.setHorizontalScrollMode(QTableWidget.ScrollPerPixel)
self.dataView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.dataView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
self.predictionsView.verticalHeader().setDefaultSectionSize(22)
self.predictionsView.setHorizontalScrollMode(QTableWidget.ScrollPerPixel)
self.predictionsView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
self.predictionsView.verticalHeader().hide()
# def syncVertical(value):
# """ sync vertical scroll positions of the two views
# """
# v1 = self.predictionsView.verticalScrollBar().value()
# if v1 != value:
# self.predictionsView.verticalScrollBar().setValue(value)
# v2 = self.dataView.verticalScrollBar().value()
# if v2 != value:
# self.dataView.verticalScrollBar().setValue(v1)
self.connect(self.dataView.verticalScrollBar(), SIGNAL("valueChanged(int)"), self.syncVertical)
self.connect(self.predictionsView.verticalScrollBar(), SIGNAL("valueChanged(int)"), self.syncVertical)
splitter.addWidget(self.dataView)
splitter.addWidget(self.predictionsView)
splitter.setHandleWidth(3)
splitter.setChildrenCollapsible(False)
self.mainArea.layout().addWidget(splitter)
self.spliter_restore_state = -1, 0
self.dataModel = None
self.predictionsModel = None
self.resize(800, 600)
self.handledAllSignalsFlag = False
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):
OWWidget.__init__(self, parent, signalManager,
"Association rules viewer")
self.inputs = [("Association Rules", orange.AssociationRules,
self.arules)]
self.outputs = [("Association Rules", orange.AssociationRules),
("Covered Data", ExampleTable),
("Matching Data", ExampleTable),
("Mismatched Data", ExampleTable)]
self.showWholeRules = 1
self.treeDepth = 2
self.autoSend = True
self.dataChanged = False
self.purgeAttributes = True
self.purgeClasses = True
self.nRules = self.nSelectedRules = self.nSelectedExamples = self.nMatchingExamples = self.nMismatchingExamples = ""
self.showsupport = self.showconfidence = 1
self.showlift = self.showleverage = self.showstrength = self.showcoverage = 0
self.loadSettings()
# self.grid = QGridLayout()
# rightUpRight = OWGUI.widgetBox(self.mainArea, box="Shown measures", orientation = self.grid)
# self.cbMeasures = [OWGUI.checkBox(rightUpRight, self, "show"+attr, long, callback = self.showHideColumn, addToLayout = 0) for long, short, attr in self.measures]
# for i, cb in enumerate(self.cbMeasures):
# self.grid.addWidget(cb, i % 2, i / 2)
box = OWGUI.widgetBox(self.mainArea, orientation=0)
OWGUI.widgetLabel(box, "Shown measures: ")
self.cbMeasures = [
OWGUI.checkBox(box,
self,
"show" + attr,
long + " ",
callback=self.showHideColumn)
for long, short, attr in self.measures
]
OWGUI.rubber(box)
self.treeRules = QTreeWidget(self.mainArea)
self.mainArea.layout().addWidget(self.treeRules)
self.treeRules.setSelectionMode(QTreeWidget.ExtendedSelection)
self.treeRules.setHeaderLabels(["Rules"] +
[m[1] for m in self.measures])
self.treeRules.setAllColumnsShowFocus(1)
self.treeRules.setAlternatingRowColors(1)
self.showHideColumn()
self.connect(self.treeRules, SIGNAL("itemSelectionChanged()"),
self.selectionChanged)
box = OWGUI.widgetBox(self.controlArea, "Info", addSpace=True)
OWGUI.label(box, self, "Number of rules: %(nRules)s")
OWGUI.label(box, self, "Selected rules: %(nSelectedRules)s")
OWGUI.label(box, self, "Selected examples: %(nSelectedExamples)s")
ibox = OWGUI.indentedBox(box)
OWGUI.label(ibox, self, "... matching: %(nMatchingExamples)s")
OWGUI.label(ibox, self, "... mismatching: %(nMismatchingExamples)s")
box = OWGUI.widgetBox(self.controlArea, "Options", addSpace=True)
OWGUI.spin(box,
self,
"treeDepth",
label="Tree depth",
min=0,
max=10,
step=1,
callback=self.displayRules,
callbackOnReturn=True)
OWGUI.separator(box)
OWGUI.checkBox(box,
self,
"showWholeRules",
"Display whole rules",
callback=self.setWholeRules)
OWGUI.rubber(self.controlArea)
boxSettings = OWGUI.widgetBox(self.controlArea, 'Send selection')
OWGUI.checkBox(boxSettings,
self,
"purgeAttributes",
"Purge attribute values/attributes",
box=None,
callback=self.purgeChanged)
self.purgeClassesCB = OWGUI.checkBox(OWGUI.indentedBox(boxSettings),
self,
"purgeClasses",
"Purge class attribute",
callback=self.purgeChanged)
if not self.purgeAttributes:
self.purgeClassesCB.setEnabled(False)
self.oldPurgeClasses = False
cbSendAuto = OWGUI.checkBox(boxSettings,
self,
"autoSend",
"Send immediately",
box=None)
btnUpdate = OWGUI.button(boxSettings,
self,
"Send",
self.sendData,
default=True)
OWGUI.setStopper(self, btnUpdate, cbSendAuto, "dataChanged",
self.sendData)
self.rules = None
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Predictions")
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.setData),
("Predictors", orange.Classifier, self.setPredictor,
Multiple)]
self.outputs = [("Predictions", ExampleTable)]
self.predictors = {}
# saveble settings
self.showProb = 1
self.showClass = 1
self.ShowAttributeMethod = 0
self.sendOnChange = 1
self.classes = []
self.selectedClasses = []
self.loadSettings()
self.datalabel = "N/A"
self.predictorlabel = "N/A"
self.tasklabel = "N/A"
self.precision = 2
self.outvar = None # current output variable (set by the first predictor/data set send in)
self.data = None
# GUI - Options
# Options - classification
ibox = OWGUI.widgetBox(self.controlArea, "Info")
OWGUI.label(ibox, self, "Data: %(datalabel)s")
OWGUI.label(ibox, self, "Predictors: %(predictorlabel)s")
OWGUI.label(ibox, self, "Task: %(tasklabel)s")
OWGUI.separator(self.controlArea)
self.copt = OWGUI.widgetBox(self.controlArea,
"Options (classification)")
self.copt.setDisabled(1)
OWGUI.checkBox(self.copt,
self,
'showProb',
"Show predicted probabilities",
callback=self.updateTableOutcomes)
# self.lbClasses = OWGUI.listBox(self.copt, self, selectionMode = QListWidget.MultiSelection, callback = self.updateTableOutcomes)
self.lbcls = OWGUI.listBox(
self.copt,
self,
"selectedClasses",
"classes",
callback=[self.updateTableOutcomes, self.checksendpredictions],
selectionMode=QListWidget.MultiSelection)
self.lbcls.setFixedHeight(50)
OWGUI.spin(self.copt,
self,
"precision",
1,
6,
label="No. of decimals: ",
orientation=0,
callback=self.updateTableOutcomes)
OWGUI.checkBox(
self.copt,
self,
'showClass',
"Show predicted class",
callback=[self.updateTableOutcomes, self.checksendpredictions])
# Options - regression
# self.ropt = QVButtonGroup("Options (regression)", self.controlArea)
# OWGUI.checkBox(self.ropt, self, 'showClass', "Show predicted class",
# callback=[self.updateTableOutcomes, self.checksendpredictions])
# self.ropt.hide()
OWGUI.separator(self.controlArea)
self.att = OWGUI.widgetBox(self.controlArea, "Data attributes")
OWGUI.radioButtonsInBox(self.att,
self,
'ShowAttributeMethod',
['Show all', 'Hide all'],
callback=self.updateAttributes)
self.att.setDisabled(1)
OWGUI.rubber(self.controlArea)
OWGUI.separator(self.controlArea)
self.outbox = OWGUI.widgetBox(self.controlArea, "Output")
self.commitBtn = OWGUI.button(self.outbox,
self,
"Send Predictions",
callback=self.sendpredictions)
OWGUI.checkBox(self.outbox, self, 'sendOnChange', 'Send automatically')
self.outbox.setDisabled(1)
# GUI - Table
self.table = OWGUI.table(self.mainArea,
selectionMode=QTableWidget.NoSelection)
self.table.setItemDelegate(OWGUI.TableBarItem(self))
self.header = self.table.horizontalHeader()
self.vheader = self.table.verticalHeader()
# manage sorting (not correct, does not handle real values)
self.connect(self.header, SIGNAL("sectionPressed(int)"), self.sort)
self.sortby = -1
self.resize(800, 600)
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", 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=list(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)
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):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveC')
self.inputs = [("Data", ExampleTable, self.dataset),
("Learner", orange.Learner, self.learner, Multiple)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.graphPointSize = 5 # size of points in the graphs
self.graphDrawLines = 1 # draw lines between points in the graph
self.graphShowGrid = 1 # show gridlines in the graph
self.selectedLearners = []
self.loadSettings()
warnings.filterwarnings("ignore", ".*builtin attribute.*",
orange.AttributeWarning)
self.setCurvePoints(
) # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA),
("AUC", orngStat.AUC),
("BrierScore", orngStat.BrierScore),
("Information Score", orngStat.IS),
("Sensitivity", orngStat.sens),
("Specificity", orngStat.spec)]
self.learners = [
] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [
] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
## class selection (classQLB)
OWGUI.separator(self.controlArea)
self.cbox = OWGUI.widgetBox(self.controlArea, "Learners")
self.llb = OWGUI.listBox(self.cbox,
self,
"selectedLearners",
selectionMode=QListWidget.MultiSelection,
callback=self.learnerSelectionChanged)
self.llb.setMinimumHeight(50)
self.blockSelectionChanges = 0
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box,
self,
"scoringF",
items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box,
self,
'folds',
2,
100,
step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box,
self,
'steps',
2,
100,
step=1,
label='Learning curve points: ',
callback=[
self.setCurvePoints,
lambda: self.computeCurve(self.commitOnChange)
])
OWGUI.checkBox(box, self, 'commitOnChange',
'Apply setting on any change')
self.commitBtn = OWGUI.button(box,
self,
"Apply Setting",
callback=self.computeCurve,
disabled=1)
# start of content (right) area
tabs = OWGUI.tabWidget(self.mainArea)
# graph widget
tab = OWGUI.createTabPage(tabs, "Graph")
self.graph = OWGraph(tab)
self.graph.setAxisAutoScale(QwtPlot.xBottom)
self.graph.setAxisAutoScale(QwtPlot.yLeft)
tab.layout().addWidget(self.graph)
self.setGraphGrid()
# table widget
tab = OWGUI.createTabPage(tabs, "Table")
self.table = OWGUI.table(tab, selectionMode=QTableWidget.NoSelection)
self.resize(550, 200)
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
