def __init__(self, parent=None, signalManager=None, name = "WordNgram"):
OWWidget.__init__(self,parent,signalManager,name)
self.inputs = [("Example Table", ExampleTable, self.dataset)]
self.outputs = [("Example Table", ExampleTable)]
self.recentFiles=[]
self.fileIndex = 0
self.loadSettings()
self.stopwords = None
self.size = 0
self.measure = 0
self.threshold = 0
self.data = None
self.measureDict = {0: 'FREQ', 1: 'MI', 2: 'DICE', 3: 'CHI', 4: 'LL'}
#GUI
optionBox = OWGUI.widgetBox(self.controlArea, "", "horizontal") #QHGroupBox('', self.controlArea)
OWGUI.radioButtonsInBox(optionBox, self, "size", box = "No. of words", btnLabels = ["2", "3", "4", "Named entities"], addSpace = True, callback = self.radioChanged)
self.ambox = OWGUI.radioButtonsInBox(optionBox, self, "measure", box = "Association measure", btnLabels = ["Frequency", "Mutual information", "Dice coefficient", "Chi square", "Log likelihood"], addSpace = True)
self.ambox.setEnabled(self.size - 3)
box = OWGUI.widgetBox(optionBox, "") #QVGroupBox('', optionBox)
OWGUI.lineEdit(box, self, "threshold", orientation="horizontal", valueType=float, box="Threshold")
stopbox = OWGUI.widgetBox(box, "Stopwords File")
stophbox = OWGUI.widgetBox(stopbox, orientation="horizontal") #1)
self.filecombo = OWGUI.comboBox(stophbox, self, "fileIndex", callback = self.loadFile)
OWGUI.button(stophbox, self, '...', callback = self.browseFile)
OWGUI.button(self.controlArea, self, "Apply", self.apply)
self.lblFeatureNo = OWGUI.widgetLabel(self.controlArea, "\nNo. of features: ") #QLabel("\nNo. of features: ", self.controlArea)
OWGUI.rubber(self.controlArea)
self.adjustSize()
if self.recentFiles:
self.loadFile()
def __init__(self, parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, 'Majority', wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),("Classifier", orange.Classifier)]
self.name = 'Majority'
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)
OWGUI.button(self.controlArea, self, "&Apply", callback = self.setLearner, disabled=0)
OWGUI.rubber(self.controlArea)
self.learner = orange.MajorityLearner()
self.setLearner()
self.resize(100,100)
def __init__(self, parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, 'Majority', wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Data", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),("Classifier", orange.Classifier)]
self.name = 'Majority'
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)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.setLearner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.learner = orange.MajorityLearner()
self.setLearner()
self.resize(100,100)
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 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, 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 defineGUI(self):
# Set the number of hidden neurons throught the GUI
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', \
tooltip='Name to be used by other widgets to identify your learner/classifier.<br>This should be a unique name!')
# Apply the settings and send the learner to the output channel
OWGUI.button(self.controlArea, self,"&Apply settings", callback=self.applySettings)
# Get desired location of model file
boxFile = OWGUI.widgetBox(self.controlArea, "Path for saving Model", addSpace = True, orientation=0)
L1 = OWGUI.lineEdit(boxFile, self, "modelFile", labelWidth=80, orientation = "horizontal", \
tooltip = "Once a model is created (connect this widget with a data widget), \nit can be saved by giving a \
file name here and clicking the save button.")
L1.setMinimumWidth(200)
button = OWGUI.button(boxFile, self, '...', callback = self.browseFile, disabled=0,tooltip = "Choose the dir where to save. After chosen, add a name for the model file!")
button.setMaximumWidth(25)
# Save the model
OWGUI.button(self.controlArea, self,"&Save model to file", callback=self.saveModel)
# Status Label
#box = OWGUI.widgetBox(self.controlArea, "Info ")
self.info = OWGUI.widgetLabel(self.controlArea, '')
self.applySettings()
self.adjustSize()
def init_optional_metadata_page(self):
self.optionalMetaDataPage = page = QWizardPage()
page.setTitle("Optional Add-on Information")
page.setLayout(QVBoxLayout())
self.addPage( page )
p = OWGUI.widgetBox(page, "Optionally, enter the following information about your add-on", orientation="vertical")
self.preferredDir = ""
self.e_preferreddir = ePreferredDir = OWGUI.lineEdit(p, self, "preferredDir", "Preferred directory name (within add-ons directory; optional):", callback=self.preferred_dir_change_callback, callbackOnType=True)
self.e_homepage = eHomePage = OWGUI.lineEdit(p, self, None, "Add-on webpage (optional):")
self.preferred_dir_touched = False
h = OWGUI.widgetBox(p, orientation="horizontal")
self.e_tags = self.new_textedit("Tags (one per line, optional):", h)
self.e_aorganizations = self.new_textedit("Contributing organizations (one per line, optional):", h)
h = OWGUI.widgetBox(p, orientation="horizontal")
self.e_aauthors = self.new_textedit("Authors (one per line, optional):", h)
self.e_acontributors = self.new_textedit("Contributors (one per line, optional):", h)
for noWrapEdit in [self.e_tags, self.e_aauthors, self.e_acontributors, self.e_aorganizations]:
noWrapEdit.setLineWrapMode(QTextEdit.NoWrap)
def formatList(control, ev):
entries = control.parseEntries()
control.setPlainText("\n".join(entries))
QTextEdit.focusOutEvent(control, ev)
for listEdit in [self.e_tags, self.e_aauthors, self.e_acontributors, self.e_aorganizations]:
listEdit.parseEntries = lambda control=listEdit: [entry for entry in map(lambda x: x.strip(), unicode(control.toPlainText()).split("\n")) if entry]
listEdit.focusOutEvent = formatList
def __init__(self, parent=None, signalManager = None, name='Label Powerset'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.set_data),
("Preprocess", PreprocessedLearner, self.set_preprocessor),
("Binary Classification", Orange.classification.Learner, self.set_base_learner)
]
self.outputs = [("Learner", orange.Learner),("LabelPowerset Classifier", Orange.multilabel.LabelPowersetClassifier)]
# Settings
self.name = 'Label Powerset'
self.base_learner = Orange.core.BayesLearner;
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)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.set_learner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def init_optional_metadata_page(self):
self.optionalMetaDataPage = page = QWizardPage()
page.setTitle("Optional Add-on Information")
page.setLayout(QVBoxLayout())
self.addPage( page )
p = OWGUI.widgetBox(page, "Optionally, enter the following information about your add-on", orientation="vertical")
self.preferredDir = ""
self.e_preferreddir = ePreferredDir = OWGUI.lineEdit(p, self, "preferredDir", "Preferred directory name (within add-ons directory; optional):", callback=self.preferred_dir_change_callback, callbackOnType=True)
self.e_homepage = eHomePage = OWGUI.lineEdit(p, self, None, "Add-on webpage (optional):")
self.preferred_dir_touched = False
h = OWGUI.widgetBox(p, orientation="horizontal")
self.e_tags = self.new_textedit("Tags (one per line, optional):", h)
self.e_aorganizations = self.new_textedit("Contributing organizations (one per line, optional):", h)
h = OWGUI.widgetBox(p, orientation="horizontal")
self.e_aauthors = self.new_textedit("Authors (one per line, optional):", h)
self.e_acontributors = self.new_textedit("Contributors (one per line, optional):", h)
for noWrapEdit in [self.e_tags, self.e_aauthors, self.e_acontributors, self.e_aorganizations]:
noWrapEdit.setLineWrapMode(QTextEdit.NoWrap)
def formatList(control, ev):
entries = control.parseEntries()
control.setPlainText("\n".join(entries))
QTextEdit.focusOutEvent(control, ev)
for listEdit in [self.e_tags, self.e_aauthors, self.e_acontributors, self.e_aorganizations]:
listEdit.parseEntries = lambda control=listEdit: [entry for entry in map(lambda x: x.strip(), unicode(control.toPlainText()).splitlines()) if entry]
listEdit.focusOutEvent = formatList
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="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='Binary Relevance'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.set_data),
("Preprocess", PreprocessedLearner, self.set_preprocessor),
("Binary Classification", Orange.classification.Learner, self.set_base_learner)
]
self.outputs = [("Learner", orange.Learner),("Binary Relevance Classifier", Orange.multilabel.BinaryRelevanceClassifier)]
# Settings
self.name = 'Binary Relevance'
self.base_learner = Orange.core.BayesLearner;
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)
OWGUI.button(self.controlArea, self, "&Apply", callback=self.set_learner, disabled=0, default=True)
OWGUI.rubber(self.controlArea)
self.resize(100,250)
def init_metadata_page(self):
self.metaDataPage = page = QWizardPage()
page.setTitle("Add-on Information")
page.setLayout(QVBoxLayout())
self.addPage(page)
page.initializePage = self.load_metadata
p = OWGUI.widgetBox(
page,
"Enter the following information about your add-on",
orientation="vertical")
import os
self.e_id = eId = OWGUI.lineEdit(p, self, None, "Globally unique ID:")
eId.setReadOnly(True)
eId.setFocusPolicy(Qt.NoFocus)
h = OWGUI.widgetBox(p, orientation="horizontal")
self.name = ""
self.preferred_dir_touched = True
self.e_name = e_name = OWGUI.lineEdit(
h,
self,
"name",
"Name:",
callback=self.name_change_callback,
callbackOnType=True)
self.e_version = eVersion = OWGUI.lineEdit(h, self, None, "Version:")
def nonEmpty(editor):
return bool(
str(editor.text() if hasattr(editor, "text") else editor.
toPlainText()).strip())
e_name.isComplete = lambda: nonEmpty(e_name)
def versionIsComplete():
try:
map(int, str(e_version.text()).strip().split("."))
return bool(str(e_version.text()).strip())
except:
return False
eVersion.isComplete = versionIsComplete
self.e_description = eDescription = self.new_textedit(
"Description:", p)
eDescription.isComplete = lambda: nonEmpty(eDescription)
def evalPage():
page.emit(SIGNAL("completeChanged()"))
for nonOptional in [e_name, eVersion, eDescription]:
QObject.connect(nonOptional,
SIGNAL("textChanged(const QString &)"), evalPage)
QObject.connect(nonOptional, SIGNAL("textChanged()"), evalPage)
page.isComplete = lambda page: e_name.isComplete(
) and eVersion.isComplete() and eDescription.isComplete()
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, 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):
OWWidget.__init__(self, parent, signalManager, "Regression Tree", wantMainArea = 0, resizingEnabled = 0)
self.Name="Regression Tree"
self.MinInstCheck=1
self.MinInstVal=5
self.MinNodeCheck=1
self.MinNodeVal=10
self.MaxMajCheck=1
self.MaxMajVal=70
self.PostMaj=1
self.PostMPCheck=1
self.PostMPVal=5
self.Bin=1
self.loadSettings()
self.data=None
self.preprocessor = None
self.inputs=[("Data",ExampleTable,self.dataset),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs=[("Learner", orange.Learner),
("Regressor", orange.Classifier),
("Regression Tree", Orange.regression.tree.TreeClassifier)]
##
#GUI
##
OWGUI.lineEdit(self.controlArea, self, "Name", box="Learner/Classifier name")
OWGUI.separator(self.controlArea)
OWGUI.checkBox(self.controlArea, self, "Bin", label="Binarization", box ="Tree structure")
OWGUI.separator(self.controlArea)
self.prePBox=OWGUI.widgetBox(self.controlArea, "Pre-Pruning")
#OWGUI.checkWithSpin(self.prePBox, self, "Min. instances in leaves: ", 1, 1000,
# "MinInstCheck", "MinInstVal")
OWGUI.checkWithSpin(self.prePBox, self, "Do not split nodes with less instances than", 1, 1000,
"MinNodeCheck", "MinNodeVal")
#OWGUI.checkWithSpin(self.prePBox, self, "Stop splitting nodes with ", 1, 100,
# "MaxMajCheck", "MaxMajVal", "% of majority class")
#OWGUI.checkBox(self.postPBox, self, 'PostMaj', 'Recursively merge leaves with same majority class')
OWGUI.separator(self.controlArea)
self.postPBox=OWGUI.widgetBox(self.controlArea, "Post-Pruning")
OWGUI.checkWithSpin(self.postPBox, self, "Pruning with m-estimate, m:", 0, 1000, 'PostMPCheck', 'PostMPVal')
OWGUI.button(self.controlArea, self, "&Apply settings", callback=self.setLearner, default=True)
OWGUI.rubber(self.controlArea)
self.setLearner()
self.resize(100,100)
def __init__(self, parent=None, signalManager=None, name='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='Outlier'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0)
self.inputs = [("Data", ExampleTable, self.cdata),("Distances", orange.SymMatrix, self.cdistance)]
self.outputs = [("Outliers", ExampleTable), ("Inliers", ExampleTable), ("Data with z-score", ExampleTable)]
# Settings
self.zscore = '4.0'
self.k = 1
self.metric = 0
self.loadSettings()
self.haveInput = 0
self.data = None # input data set
self.dataInput = None
self.distanceMatrix = None
kernelSizeValid = QDoubleValidator(self.controlArea)
self.metrics = [("Euclidean", orange.ExamplesDistanceConstructor_Euclidean),
("Manhattan", orange.ExamplesDistanceConstructor_Manhattan),
("Hamming", orange.ExamplesDistanceConstructor_Hamming),
("Relief", orange.ExamplesDistanceConstructor_Relief)]
self.ks = [("All",0), ("1", 1), ("2",2), ("3",3), ("5",5), ("10",10), ("15",15)]
items = [x[0] for x in self.metrics]
itemsk = [x[0] for x in self.ks]
OWGUI.comboBox(self.controlArea, self, "metric", box="Distance Metrics", items=items,
tooltip="Metrics to measure pairwise distance between data instances.",
callback=self.dataChange)
OWGUI.separator(self.controlArea)
OWGUI.comboBox(self.controlArea, self, "k", box="Nearest Neighbours", items=itemsk,
tooltip="Neighbours considered when computing the distance.",
callback=self.applySettings)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Outliers")
OWGUI.lineEdit(box, self, 'zscore',
label = 'Outlier Z:', labelWidth=80,
orientation='horizontal', # box=None,
validator = kernelSizeValid,
tooltip="Minimum Z-score of an outlier.",
callback=self.applySettings)
OWGUI.rubber(self.controlArea)
self.loadSettings()
self.resize(100,100)
self.applySettings()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "FeatureConstructor")
self.inputs = [("Data", orange.ExampleTable, self.setData)]
self.outputs = [("Data", ExampleTable)]
self.expression = self.attrname = ""
self.selected_def = []
self.def_labels = []
self.data = None
self.definitions = []
self.selected_features = 0
self.selectedFunc = 0
self.autosend = True
self.loadSettings()
db = OWGUI.widgetBox(self.controlArea, "Attribute definitions", addSpace=True)
hb = OWGUI.widgetBox(db, None, "horizontal")
hbv = OWGUI.widgetBox(hb)
self.leAttrName = OWGUI.lineEdit(hbv, self, "attrname", "New attribute")
OWGUI.rubber(hbv)
vb = OWGUI.widgetBox(hb, None, "vertical", addSpace=True)
self.leExpression = OWGUI.lineEdit(vb, self, "expression", "Expression")
hhb = OWGUI.widgetBox(vb, None, "horizontal")
self.cbAttrs = OWGUI.comboBox(
hhb, self, "selected_features", items=["(all attributes)"], callback=self.feature_list_selected
)
sortedFuncs = sorted(m for m in AttrComputer.FUNCTIONS.keys())
self.cbFuncs = OWGUI.comboBox(
hhb, self, "selectedFunc", items=["(all functions)"] + sortedFuncs, callback=self.funcListSelected
)
model = self.cbFuncs.model()
for i, func in enumerate(sortedFuncs):
model.item(i + 1).setToolTip(inspect.getdoc(AttrComputer.FUNCTIONS[func]))
hb = OWGUI.widgetBox(db, None, "horizontal", addSpace=True)
OWGUI.button(hb, self, "Add", callback=self.addAttr, autoDefault=True)
OWGUI.button(hb, self, "Update", callback=self.updateAttr)
OWGUI.button(hb, self, "Remove", callback=self.remove_feature)
OWGUI.button(hb, self, "Remove All", callback=self.remove_all_features)
self.lbDefinitions = OWGUI.listBox(db, self, "selected_def", "def_labels", callback=self.select_feature)
self.lbDefinitions.setFixedHeight(160)
hb = OWGUI.widgetBox(self.controlArea, "Apply", "horizontal")
OWGUI.button(hb, self, "Apply", callback=self.apply)
cb = OWGUI.checkBox(hb, self, "autosend", "Apply automatically", callback=self.enableAuto)
cb.setSizePolicy(QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed))
self.adjustSize()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Regression Tree", wantMainArea = 0, resizingEnabled = 0)
self.Name="Regression Tree"
self.MinInstCheck=1
self.MinInstVal=5
self.MinNodeCheck=1
self.MinNodeVal=10
self.MaxMajCheck=1
self.MaxMajVal=70
self.PostMaj=1
self.PostMPCheck=1
self.PostMPVal=5
self.Bin=1
self.loadSettings()
self.data=None
self.preprocessor = None
self.inputs=[("Example Table",ExampleTable,self.dataset), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs=[("Learner",orange.Learner),("Regressor",orange.Classifier),("Regression Tree",orange.TreeClassifier)]
##
#GUI
##
OWGUI.lineEdit(self.controlArea, self, "Name", box="Learner/Classifier name")
OWGUI.separator(self.controlArea)
OWGUI.checkBox(self.controlArea, self, "Bin", label="Binarization", box ="Tree structure")
OWGUI.separator(self.controlArea)
self.prePBox=OWGUI.widgetBox(self.controlArea, "Pre-Pruning")
#OWGUI.checkWithSpin(self.prePBox, self, "Min. instances in leaves: ", 1, 1000,
# "MinInstCheck", "MinInstVal")
OWGUI.checkWithSpin(self.prePBox, self, "Do not split nodes with less instances than", 1, 1000,
"MinNodeCheck", "MinNodeVal")
#OWGUI.checkWithSpin(self.prePBox, self, "Stop splitting nodes with ", 1, 100,
# "MaxMajCheck", "MaxMajVal", "% of majority class")
#OWGUI.checkBox(self.postPBox, self, 'PostMaj', 'Recursively merge leaves with same majority class')
OWGUI.separator(self.controlArea)
self.postPBox=OWGUI.widgetBox(self.controlArea, "Post-Pruning")
OWGUI.checkWithSpin(self.postPBox, self, "Pruning with m-estimate, m:", 0, 1000, 'PostMPCheck', 'PostMPVal')
OWGUI.button(self.controlArea, self, "&Apply settings",callback=self.setLearner)
OWGUI.rubber(self.controlArea)
self.setLearner()
self.resize(100,100)
def __init__(self, parent=None, signalManager = None, name='Random Forest'):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea=False, resizingEnabled=False)
self.inputs = [("Data", ExampleTable, self.setData),
("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner),
("Random Forest Classifier", orange.Classifier)]
self.name = 'Random Forest'
self.trees = 10
self.attributes = 0
self.attributesP = 5
self.preNodeInst = 1
self.preNodeInstP = 5
self.limitDepth = 0
self.limitDepthP = 3
self.rseed = 0
self.maxTrees = 10000
self.loadSettings()
self.data = None
self.preprocessor = None
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
self.bBox = OWGUI.widgetBox(self.controlArea, 'Basic Properties')
self.treesBox = OWGUI.spin(self.bBox, self, "trees", 1, self.maxTrees, orientation="horizontal", label="Number of trees in forest")
self.attributesBox, self.attributesPBox = OWGUI.checkWithSpin(self.bBox, self, "Consider exactly", 1, 10000, "attributes", "attributesP", " "+"random attributes at each split.")
self.rseedBox = OWGUI.spin(self.bBox, self, "rseed", 0, 100000, orientation="horizontal", label="Seed for random generator ")
OWGUI.separator(self.controlArea)
self.pBox = OWGUI.widgetBox(self.controlArea, 'Growth Control')
self.limitDepthBox, self.limitDepthPBox = OWGUI.checkWithSpin(self.pBox, self, "Maximal depth of individual trees", 1, 1000, "limitDepth", "limitDepthP", "")
self.preNodeInstBox, self.preNodeInstPBox = OWGUI.checkWithSpin(self.pBox, self, "Stop splitting nodes with ", 1, 1000, "preNodeInst", "preNodeInstP", " or fewer instances")
OWGUI.separator(self.controlArea)
OWGUI.separator(self.controlArea)
self.btnApply = OWGUI.button(self.controlArea, self, "&Apply Changes", callback = self.doBoth, disabled=0, default=True)
self.resize(100,200)
self.setLearner()
def __init__ (self, parent=None, signalManager = None, name = "Logistic regression"):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0, resizingEnabled = 0)
self.inputs = [("Data", ExampleTable, self.setData), ("Preprocess", PreprocessedLearner, self.setPreprocessor)]
self.outputs = [("Learner", orange.Learner), ("Classifier", orange.Classifier), ("Features", list)]
from orngTree import TreeLearner
imputeByModel = orange.ImputerConstructor_model()
imputeByModel.learnerDiscrete = TreeLearner(measure = "infoGain", minSubset = 50)
imputeByModel.learnerContinuous = TreeLearner(measure = "retis", minSubset = 50)
self.imputationMethods = [imputeByModel, orange.ImputerConstructor_average(), orange.ImputerConstructor_minimal(), orange.ImputerConstructor_maximal(), None]
self.imputationMethodsStr = ["Classification/Regression trees", "Average values", "Minimal value", "Maximal value", "None (skip examples)"]
self.name = "Logistic regression"
self.univariate = 0
self.stepwiseLR = 0
self.addCrit = 10
self.removeCrit = 10
self.numAttr = 10
self.limitNumAttr = False
self.zeroPoint = 0
self.imputation = 1
self.data = None
self.preprocessor = None
self.loadSettings()
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', tooltip='Name to be used by other widgets to identify your learner/classifier.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Attribute selection")
stepwiseCb = OWGUI.checkBox(box, self, "stepwiseLR", "Stepwise attribute selection")
ibox = OWGUI.indentedBox(box, sep=OWGUI.checkButtonOffsetHint(stepwiseCb))
addCritSpin = OWGUI.spin(ibox, self, "addCrit", 1, 50, label="Add threshold [%]", labelWidth=155, tooltip="Requested significance for adding an attribute")
remCritSpin = OWGUI.spin(ibox, self, "removeCrit", 1, 50, label="Remove threshold [%]", labelWidth=155, tooltip="Requested significance for removing an attribute")
limitAttSpin = OWGUI.checkWithSpin(ibox, self, "Limit number of attributes to ", 1, 100, "limitNumAttr", "numAttr", step=1, labelWidth=155, tooltip="Maximum number of attributes. Algorithm stops when it selects specified number of attributes.")
stepwiseCb.disables += [addCritSpin, remCritSpin, limitAttSpin]
stepwiseCb.makeConsistent()
OWGUI.separator(self.controlArea)
self.imputationCombo = OWGUI.comboBox(self.controlArea, self, "imputation", box="Imputation of unknown values", items=self.imputationMethodsStr)
OWGUI.separator(self.controlArea)
applyButton = OWGUI.button(self.controlArea, self, "&Apply", callback=self.applyLearner, default=True)
OWGUI.rubber(self.controlArea)
#self.adjustSize()
self.applyLearner()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "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='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,
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 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, 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 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, 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)]
self.regularizations = [ Orange.classification.logreg.LibLinearLogRegLearner.L2R_LR, Orange.classification.logreg.LibLinearLogRegLearner.L1R_LR ]
self.regularizationsStr = [ "L2 (squared weights)", "L1 (absolute weights)" ]
self.name = "Logistic regression"
self.normalization = True
self.C = 1.
self.regularization = 0
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, "Regularization")
self.regularizationCombo = OWGUI.comboBox(box, self, "regularization", items=self.regularizationsStr)
cset = OWGUI.doubleSpin(box, self, "C", 0.01, 512.0, 0.1,
decimals=2,
addToLayout=True,
label="Training error cost (C)",
alignment=Qt.AlignRight,
tooltip= "Weight of log-loss term (higher C means better fit on the training data)."),
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Preprocessing")
OWGUI.checkBox(box, self, "normalization",
label="Normalize data",
tooltip="Normalize data before learning.")
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, title="Suffle"):
OWWidget.__init__(self, parent, signalManager, title,
wantMainArea=False)
self.inputs = [("Data", Orange.data.Table, self.set_data)]
self.outputs = [("Shuffled Data", Orange.data.Table)]
self.seed = 0
OWGUI.lineEdit(self.controlArea, self, "seed", box="Seed",
tooltip="Random seed",
callback=self.run,
valueType=int,
validator=QIntValidator()
)
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "FeatureConstructor")
self.inputs = [("Examples", orange.ExampleTable, self.setData)]
self.outputs = [("Examples", ExampleTable)]
self.expression = self.attrname = ""
self.selectedDef = []
self.defLabels = []
self.data = None
self.definitions = []
self.selectedAttr = 0
self.selectedFunc = 0
self.autosend = True
self.loadSettings()
db = OWGUI.widgetBox(self.controlArea, "Attribute definitions", addSpace = True)
hb = OWGUI.widgetBox(db, None, "horizontal")
hbv = OWGUI.widgetBox(hb)
self.leAttrName = OWGUI.lineEdit(hbv, self, "attrname", "New attribute")
OWGUI.rubber(hbv)
vb = OWGUI.widgetBox(hb, None, "vertical", addSpace=True)
self.leExpression = OWGUI.lineEdit(vb, self, "expression", "Expression")
hhb = OWGUI.widgetBox(vb, None, "horizontal")
self.cbAttrs = OWGUI.comboBox(hhb, self, "selectedAttr", items = ["(all attributes)"], callback = self.attrListSelected)
sortedFuncs = sorted(m for m in list(AttrComputer.FUNCTIONS.keys()))
self.cbFuncs = OWGUI.comboBox(hhb, self, "selectedFunc", items = ["(all functions)"] + sortedFuncs, callback = self.funcListSelected)
model = self.cbFuncs.model()
for i, func in enumerate(sortedFuncs):
model.item(i + 1).setToolTip(AttrComputer.FUNCTIONS[func].__doc__)
hb = OWGUI.widgetBox(db, None, "horizontal", addSpace=True)
OWGUI.button(hb, self, "Add", callback = self.addAttr)
OWGUI.button(hb, self, "Update", callback = self.updateAttr)
OWGUI.button(hb, self, "Remove", callback = self.removeAttr)
OWGUI.button(hb, self, "Remove All", callback = self.removeAllAttr)
self.lbDefinitions = OWGUI.listBox(db, self, "selectedDef", "defLabels", callback=self.selectAttr)
self.lbDefinitions.setFixedHeight(160)
hb = OWGUI.widgetBox(self.controlArea, "Apply", "horizontal")
OWGUI.button(hb, self, "Apply", callback = self.apply)
cb = OWGUI.checkBox(hb, self, "autosend", "Apply automatically", callback=self.enableAuto)
cb.setSizePolicy(QSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed))
self.adjustSize()
def defineGUI(self):
# methods Selection
self.methodsListBox = OWGUI.listBox(
self.controlArea,
self,
"selectedMethods",
"methodsList",
box="Select Chemical Similarity Methods",
selectionMode=QListWidget.ExtendedSelection,
)
# Set location of model file
boxFile = OWGUI.widgetBox(self.controlArea, "Path for loading Actives", addSpace=True, orientation=0)
L1 = OWGUI.lineEdit(
boxFile,
self,
"activesFile",
labelWidth=80,
orientation="horizontal",
tooltip="Actives must be specified in a .smi file",
)
L1.setMinimumWidth(200)
button = OWGUI.button(
boxFile, self, "...", callback=self.browseFile, disabled=0, tooltip="Browse for a file to load the Actives"
)
button.setMaximumWidth(25)
# Load Actives
OWGUI.button(boxFile, self, "Load Actives", callback=self.loadActives)
# Start descriptors calculation
OWGUI.button(self.controlArea, self, "Calculate descriptors", callback=self.calcDesc)
self.adjustSize()
def init_addon_selection_page(self):
self.addon_selection_page = page = QWizardPage()
page.setTitle("Add-on Selection")
page.setLayout(QVBoxLayout())
self.addPage(page)
import os
p = OWGUI.widgetBox(page,
"Select a registered add-on to pack",
orientation="horizontal")
self.aolist = OWGUI.listBox(p,
self,
callback=self.list_selection_callback)
self.registered_addons = Orange.misc.addons.registered_addons
for ao in self.registered_addons:
self.aolist.addItem(ao.name)
pbtn = OWGUI.widgetBox(p, orientation="vertical")
btn_custom_location = OWGUI.button(
pbtn,
self,
"Custom Location...",
callback=self.select_custom_location)
pbtn.layout().addStretch(1)
self.directory = ""
self.e_directory = OWGUI.lineEdit(
page,
self,
"directory",
label="Add-on directory: ",
callback=self.directory_change_callback,
callbackOnType=True)
page.isComplete = lambda page: os.path.isdir(
os.path.join(self.directory, "widgets"))
def initDestinationPage(self):
self.optionalMetaDataPage = page = QWizardPage()
page.setTitle("Destination")
page.setLayout(QVBoxLayout())
self.addPage(page)
self.prepare_only = False
chk_prepare_only = OWGUI.checkBox(
page,
self,
"prepare_only",
"Do not pack, only prepare addon.xml and arrange documentation",
callback=self.callbackPrepareOnlyChange)
p = OWGUI.widgetBox(page, "Filesystem", orientation="horizontal")
self.oaofilename = ""
self.e_oaofilename = OWGUI.lineEdit(p, self, "oaofilename")
button = OWGUI.button(p,
self,
'...',
callback=self.browseDestinationFile,
disabled=0)
button.setIcon(self.style().standardIcon(QStyle.SP_DirOpenIcon))
button.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Fixed)
def initOao(page):
if not self.oaofilename:
self.e_oaofilename.setText(self.directory + ".oao")
page.initializePage = initOao
p = OWGUI.widgetBox(page, "Repository", orientation="vertical")
OWGUI.label(
p, self,
"Uploading into repositories is not yet implemented, sorry.")
def __init__(self,parent=None, signalManager = None, name = "Impute"):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea = 0)
self.inputs = [("Examples", ExampleTable, self.setData, Default), ("Learner for Imputation", orange.Learner, self.setModel)]
self.outputs = [("Examples", ExampleTable), ("Imputer", orange.ImputerConstructor)]
self.attrIcons = self.createAttributeIconDict()
self.defaultMethod = 0
self.selectedAttr = 0
self.indiType = 0
self.imputeClass = 0
self.autosend = 1
self.methods = {}
self.dataChanged = False
self.model = self.data = None
self.indiValue = ""
self.indiValCom = 0
self.loadSettings()
self.controlArea.layout().setSpacing(8)
bgTreat = OWGUI.radioButtonsInBox(self.controlArea, self, "defaultMethod", self.defaultMethods, "Default imputation method", callback=self.sendIf)
self.indibox = OWGUI.widgetBox(self.controlArea, "Individual attribute settings", "horizontal")
attrListBox = OWGUI.widgetBox(self.indibox)
self.attrList = OWGUI.listBox(attrListBox, self, callback = self.individualSelected)
self.attrList.setMinimumWidth(220)
self.attrList.setItemDelegate(ImputeListItemDelegate(self, self.attrList))
indiMethBox = OWGUI.widgetBox(self.indibox)
indiMethBox.setFixedWidth(160)
self.indiButtons = OWGUI.radioButtonsInBox(indiMethBox, self, "indiType", ["Default (above)", "Don't impute", "Avg/Most frequent", "Model-based", "Random", "Remove examples", "Value"], 1, callback=self.indiMethodChanged)
self.indiValueCtrlBox = OWGUI.indentedBox(self.indiButtons)
self.indiValueLineEdit = OWGUI.lineEdit(self.indiValueCtrlBox, self, "indiValue", callback = self.lineEditChanged)
#self.indiValueLineEdit.hide()
valid = QDoubleValidator(self)
valid.setRange(-1e30, 1e30, 10)
self.indiValueLineEdit.setValidator(valid)
self.indiValueComboBox = OWGUI.comboBox(self.indiValueCtrlBox, self, "indiValCom", callback = self.valueComboChanged)
self.indiValueComboBox.hide()
OWGUI.rubber(indiMethBox)
self.btAllToDefault = OWGUI.button(indiMethBox, self, "Set All to Default", callback = self.allToDefault)
box = OWGUI.widgetBox(self.controlArea, "Class Imputation")
self.cbImputeClass = OWGUI.checkBox(box, self, "imputeClass", "Impute class values", callback=self.sendIf)
snbox = OWGUI.widgetBox(self.controlArea, self, "Send data and imputer")
self.btApply = OWGUI.button(snbox, self, "Apply", callback=self.sendDataAndImputer)
OWGUI.checkBox(snbox, self, "autosend", "Send automatically", callback=self.enableAuto, disables = [(-1, self.btApply)])
self.individualSelected(self.selectedAttr)
self.btApply.setDisabled(self.autosend)
self.setBtAllToDefault()
self.resize(200,200)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self,parent,signalManager,"FeatureSelection")
self.inputs = [("Example Table", ExampleTable, self.dataset)]
self.outputs = [("Example Table", ExampleTable)]
warnings.filterwarnings("ignore", "", orange.AttributeWarning)
self.data = None
self.chosenMeasure = [0]
self.measures = ['Term frequency', 'Random', 'Term document frequency', 'Word frequency', 'Number of features']
self.chosenOp = [0]
self.measureDict = {0: 'TF', 1: 'RAND', 2: 'TDF', 3: 'WF', 4: 'NF'}
self.operators = ['MIN', 'MAX']
self.tmpData = None
self.perc = 1
self.threshold = 90
self.selections = []
#GUI
#ca=QFrame(self.controlArea)
#gl=QGridLayout(ca)
selectionbox = OWGUI.widgetBox(self.controlArea, "Feature selection", "horizontal") #OWGUI.QHGroupBox('Feature selection', self.controlArea)
OWGUI.listBox(selectionbox, self, 'chosenMeasure', 'measures', box = 'Select measure', callback = self.selectionChanged)
OWGUI.listBox(selectionbox, self, 'chosenOp', 'operators', box = 'Select operator', callback = self.selectionChanged)
boxAttrStat = OWGUI.widgetBox(self.controlArea, "Statistics for features") #QVGroupBox("Statistics for features", self.controlArea)
self.lblFeatNo = OWGUI.widgetLabel(boxAttrStat, "No. of features: ") #QLabel("No. of features: ", boxAttrStat)
self.lblMin = OWGUI.widgetLabel(boxAttrStat, "Min: ") #QLabel("Min: ", boxAttrStat)
self.lblAvg = OWGUI.widgetLabel(boxAttrStat, "Avg: ") #QLabel("Avg: ", boxAttrStat)
self.lblMax = OWGUI.widgetLabel(boxAttrStat, "Max: ") #QLabel("Max: ", boxAttrStat)
boxDocStat = OWGUI.widgetBox(self.controlArea, "Statistics for documents") #QVGroupBox("Statistics for documents", self.controlArea)
self.lblDocNo = OWGUI.widgetLabel(boxDocStat, "No. of documents: ") #QLabel("No. of documents: ", boxDocStat)
self.lblDocAvg = OWGUI.widgetLabel(boxDocStat, "Avg: ") #QLabel("Avg: ", boxDocStat)
self.lblDocMax = OWGUI.widgetLabel(boxDocStat, "Max: ") #QLabel("Max: ", boxDocStat)
self.lblDocMin = OWGUI.widgetLabel(boxDocStat, "Min: ") #QLabel("Min: ", boxDocStat)
optionBox = OWGUI.widgetBox(selectionbox, "") #OWGUI.QVGroupBox('', selectionbox)
self.applyButton = OWGUI.button(optionBox, self, "Apply", self.apply)
self.applyButton.setDisabled(1)
OWGUI.checkBox(optionBox, self, "perc", "percentage", callback = self.selectionChanged)
#OWGUI.spin(optionBox, self, "threshold", 0, 10000, label="Threshold:", callback = None)
OWGUI.lineEdit(optionBox, self, "threshold", orientation="horizontal", valueType=float, box="Threshold", callback = self.selectionChanged)
OWGUI.rubber(self.controlArea)
self.controlArea.adjustSize()
def __init__(self, graph, extraButtons = [], defaultName="graph", parent=None, saveMatplotlib=None):
OWBaseWidget.__init__(self, parent, None, "Image settings", modal = TRUE, resizingEnabled = 0)
self.graph = graph
self.selectedSize = 0
self.customX = 400
self.customY = 400
self.saveAllSizes = 0
self.penWidthFactor = 1
self.lastSaveDirName = "./"
self.defaultName = defaultName
self.loadSettings()
self.setLayout(QVBoxLayout(self))
self.space = OWGUI.widgetBox(self)
self.layout().setMargin(8)
#self.layout().addWidget(self.space)
box = OWGUI.widgetBox(self.space, "Image Size")
global _have_qwt
if _have_qwt and isinstance(graph, QwtPlot):
size = OWGUI.radioButtonsInBox(box, self, "selectedSize", ["Current size", "400 x 400", "600 x 600", "800 x 800", "Custom:"], callback = self.updateGUI)
self.customXEdit = OWGUI.lineEdit(OWGUI.indentedBox(box), self, "customX", "Width: ", orientation = "horizontal", valueType = int)
self.customYEdit = OWGUI.lineEdit(OWGUI.indentedBox(box), self, "customY", "Height:", orientation = "horizontal", valueType = int)
OWGUI.comboBoxWithCaption(self.space, self, "penWidthFactor", label = 'Factor: ', box = " Pen width multiplication factor ", tooltip = "Set the pen width factor for all curves in the plot\n(Useful for example when the lines in the plot look to thin)\nDefault: 1", sendSelectedValue = 1, valueType = int, items = range(1,20))
elif isinstance(graph, QGraphicsScene) or isinstance(graph, QGraphicsView) or (_have_gl and isinstance(graph, QGLWidget)):
OWGUI.widgetLabel(box, "Image size will be set automatically.")
box = OWGUI.widgetBox(self.space, 1)
#self.printButton = OWGUI.button(self.space, self, "Print", callback = self.printPic)
self.saveImageButton = OWGUI.button(box, self, "Save Image", callback = self.saveImage)
# If None we try to determine if save can succeed automatically
if saveMatplotlib is None:
saveMatplotlib = self.canSaveToMatplotlib(graph)
if saveMatplotlib and not (_have_gl and isinstance(graph, QGLWidget)):
self.saveMatplotlibButton = OWGUI.button(box, self, "Save Graph as matplotlib Script", callback = self.saveToMatplotlib)
for (text, funct) in extraButtons:
butt = OWGUI.button(box, self, text, callback = funct)
self.connect(butt, SIGNAL("clicked()"), self.accept) # also connect the button to accept so that we close the dialog
OWGUI.button(box, self, "Cancel", callback = self.reject)
self.resize(250,300)
self.updateGUI()
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):
OWWidget.__init__(self,
parent,
signalManager,
"FeatureConstructor",
wantMainArea=0)
self.inputs = [("Examples", ExampleTable, self.setData)]
self.outputs = [("Examples", ExampleTable)]
self.newName = "NEW_CLASS"
self.value = "?"
self.values = "[]"
self.data = None
box = OWGUI.widgetBox(self.controlArea, "Add discrete class")
newNameBox = OWGUI.lineEdit(
box,
self,
"newName",
"Class name: ",
labelWidth=70,
orientation="horizontal",
tooltip="Name of the new Class attribute to be added")
valueBox = OWGUI.lineEdit(
box,
self,
"value",
"Value: ",
labelWidth=70,
orientation="horizontal",
tooltip="Class value to be assigned to each example")
#valuesBox = OWGUI.lineEdit(box, self, "values", "Class values: ", labelWidth=70, orientation="horizontal", tooltip='Optional, list of possible values of new discrete class attribute ex: ["POS", "NEG"]')
self.BT = OWGUI.button(self.controlArea,
self,
"&Apply",
callback=self.apply,
disabled=0)
infoBox = OWGUI.widgetBox(self, "Info")
OWGUI.widgetLabel(
infoBox,
'Adds a new discrete response variable\nto the data and gives all examples the same value.'
)
self.adjustSize()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "FeatureConstructor")
self.inputs = [("Primary Table", orange.ExampleTable, self.setData),
("Additional Tables", orange.ExampleTable,
self.setMoreData, Multiple)]
self.outputs = [("Examples", ExampleTable)]
self.mergeAttributes = 0
self.dataSourceSelected = 1
self.addIdAs = 0
self.dataSourceName = "clusterId"
self.primary = None
self.additional = {}
self.loadSettings()
bg = self.bgMerge = OWGUI.radioButtonsInBox(self.controlArea,
self,
"mergeAttributes", [],
"Domains merging",
callback=self.apply)
OWGUI.widgetLabel(
bg, "When there is no primary table, the domain should be")
OWGUI.appendRadioButton(bg, self, "mergeAttributes",
"Union of attributes appearing in all tables")
OWGUI.appendRadioButton(bg, self, "mergeAttributes",
"Intersection of attributes in all tables")
OWGUI.widgetLabel(
bg,
"The resulting table will have class only if there is no conflict betwen input classes."
)
box = OWGUI.widgetBox(self.controlArea, "Data source IDs")
cb = OWGUI.checkBox(box, self, "dataSourceSelected",
"Append data source IDs")
self.classificationBox = ib = OWGUI.widgetBox(box)
le = OWGUI.lineEdit(ib,
self,
"dataSourceName",
"Name" + " ",
orientation='horizontal',
valueType=str)
OWGUI.separator(ib, height=4)
aa = OWGUI.comboBox(
ib,
self,
"addIdAs",
label="Place" + " ",
orientation='horizontal',
items=["Class attribute", "Attribute", "Meta attribute"])
cb.disables.append(ib)
cb.makeConsistent()
OWGUI.separator(box)
OWGUI.button(box, self, "Apply Changes", callback=self.apply)
self.adjustSize()
def defineGUI(self):
OWGUI.lineEdit(self.controlArea, self, 'name', box='Name', \
tooltip='Name to be used by other widgets to identify your classifier.<br>This should be a unique name!')
# Apply the settings and send the learner to the output channel
OWGUI.button(self.controlArea,
self,
"&Apply settings",
callback=self.createClassifier)
# Set location of model file
boxFile = OWGUI.widgetBox(self.controlArea,
"Path for saving Model",
addSpace=True,
orientation=0)
L1 = OWGUI.lineEdit(
boxFile,
self,
"modelFile",
labelWidth=80,
orientation="horizontal",
tooltip=
"Once a model is created (connect this widget with a data widget), \nit can be saved by giving a file name here and clicking the save button."
)
L1.setMinimumWidth(200)
button = OWGUI.button(
boxFile,
self,
'...',
callback=self.browseFile,
disabled=0,
tooltip=
"Choose the dir where to save. After chosen, add a name for the model file!"
)
button.setMaximumWidth(25)
# Save the model
OWGUI.button(self.controlArea,
self,
"&Save model",
callback=self.saveModel)
self.adjustSize()
def __init__(self, graph, extraButtons = [], defaultName="graph", parent=None):
OWBaseWidget.__init__(self, parent, None, "Image settings", modal = TRUE, resizingEnabled = 0)
self.graph = graph
self.selectedSize = 0
self.customX = 400
self.customY = 400
self.saveAllSizes = 0
self.penWidthFactor = 1
self.lastSaveDirName = "./"
self.defaultName = defaultName
self.loadSettings()
self.setLayout(QVBoxLayout(self))
self.space = OWGUI.widgetBox(self)
self.layout().setMargin(8)
#self.layout().addWidget(self.space)
box = OWGUI.widgetBox(self.space, "Image Size")
global _have_qwt
if _have_qwt and isinstance(graph, QwtPlot):
size = OWGUI.radioButtonsInBox(box, self, "selectedSize", ["Current size", "400 x 400", "600 x 600", "800 x 800", "Custom:"], callback = self.updateGUI)
self.customXEdit = OWGUI.lineEdit(OWGUI.indentedBox(box), self, "customX", "Width: ", orientation = "horizontal", valueType = int)
self.customYEdit = OWGUI.lineEdit(OWGUI.indentedBox(box), self, "customY", "Height:", orientation = "horizontal", valueType = int)
OWGUI.comboBoxWithCaption(self.space, self, "penWidthFactor", label = 'Factor: ', box = " Pen width multiplication factor ", tooltip = "Set the pen width factor for all curves in the plot\n(Useful for example when the lines in the plot look to thin)\nDefault: 1", sendSelectedValue = 1, valueType = int, items = range(1,20))
elif isinstance(graph, QGraphicsScene) or isinstance(graph, QGraphicsView) or (_have_gl and isinstance(graph, QGLWidget)):
OWGUI.widgetLabel(box, "Image size will be set automatically.")
box = OWGUI.widgetBox(self.space, 1)
#self.printButton = OWGUI.button(self.space, self, "Print", callback = self.printPic)
self.saveImageButton = OWGUI.button(box, self, "Save Image", callback = self.saveImage)
if not (_have_gl and isinstance(graph, QGLWidget)):
self.saveMatplotlibButton = OWGUI.button(box, self, "Save Graph as matplotlib Script", callback = self.saveToMatplotlib)
for (text, funct) in extraButtons:
butt = OWGUI.button(box, self, text, callback = funct)
self.connect(butt, SIGNAL("clicked()"), self.accept) # also connect the button to accept so that we close the dialog
OWGUI.button(box, self, "Cancel", callback = self.reject)
self.resize(250,300)
self.updateGUI()
def __init__(self, parent=None, signalManager=None, title="Suffle"):
OWWidget.__init__(self,
parent,
signalManager,
title,
wantMainArea=False)
self.inputs = [("Data", Orange.data.Table, self.set_data)]
self.outputs = [("Shuffled Data", Orange.data.Table)]
self.seed = 0
OWGUI.lineEdit(self.controlArea,
self,
"seed",
box="Seed",
tooltip="Random seed",
callback=self.run,
valueType=int,
validator=QIntValidator())
def __init__(self, parent=None, signalManager=None, name="Molecule Match"):
OWWidget.__init__(self, parent, signalManager, name, wantMainArea=False)
self.inputs = [("Molecules", ExampleTable, self.SetMoleculeTable)]
self.outputs = [("Selected molecules", ExampleTable), ("All molecules", ExampleTable)]
self.fragment = ""
self.recentFragments = []
self.comboSelection = 0
self.smilesAttr = 0
self.smilesAttrList = []
self.data = None
self.loadSettings()
##GUI
self.lineEdit = OWGUI.lineEdit(
self.controlArea,
self,
"fragment",
box="Pattern",
tooltip="A SMARTS pattern to filter the examples with.",
callback=self.LineEditSelect,
)
OWGUI.separator(self.controlArea)
self.fragmentComboBox = OWGUI.comboBox(
self.controlArea,
self,
"comboSelection",
"Recent Patterns",
tooltip="Select a recently used pattern.",
items=self.recentFragments,
callback=self.RecentSelect,
)
OWGUI.separator(self.controlArea)
self.smilesAttrCombo = OWGUI.comboBox(
self.controlArea,
self,
"smilesAttr",
"Examples SMILES attribute",
tooltip="Select the attribute in the input example \
table that stores the SMILES molecule description",
callback=self.Process,
)
OWGUI.rubber(self.controlArea)
self.resize(100, 100)
if not HAVE_PYBEL:
self.error(10, "This widget requires pybel module (part of openbabel python extension).")
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "FeatureConstructor",wantMainArea = 0)
self.inputs = [("Examples", ExampleTable, self.setData)]
self.outputs = [("Examples", ExampleTable)]
self.newName = "NEW_CLASS"
self.value = "?"
self.values = "[]"
self.data = None
box = OWGUI.widgetBox(self.controlArea,"Add discrete class")
newNameBox = OWGUI.lineEdit(box, self, "newName", "Class name: ", labelWidth=70, orientation="horizontal", tooltip="Name of the new Class attribute to be added")
valueBox = OWGUI.lineEdit(box, self, "value", "Value: ", labelWidth=70, orientation="horizontal", tooltip="Class value to be assigned to each example")
#valuesBox = OWGUI.lineEdit(box, self, "values", "Class values: ", labelWidth=70, orientation="horizontal", tooltip='Optional, list of possible values of new discrete class attribute ex: ["POS", "NEG"]')
self.BT = OWGUI.button(self.controlArea, self, "&Apply", callback = self.apply, disabled=0)
infoBox = OWGUI.widgetBox(self,"Info")
OWGUI.widgetLabel(infoBox, 'Adds a new discrete response variable\nto the data and gives all examples the same value.')
self.adjustSize()
def defineGUI(self):
# Set the number of hidden neurons throught the GUI
OWGUI.lineEdit(self.controlArea, self, 'name', box='Learner/Classifier Name', \
tooltip='Name to be used by other widgets to identify your learner/classifier.<br>This should be a unique name!')
OWGUI.lineEdit(self.controlArea, self, 'nHiddenGUI', box='The number of hidden neurons', \
tooltip='The number of neurons in each hidden layer should be given as a comma separated string. \n\
For example, 2,3,4 will create 3 hidden layers (5 layers in total, including in and out),\n\
with 2, 3 and 4 neurons respectively. If no value is given, the default, which is one layer of 5 neurons, \n\
will be used. ')
# Set the training function throught the GUI
# CVANNOPTALGDICT = {"FANN_TRAIN_BACKPROP":0, "FANN_TRAIN_RPROP":1}
self.trainingBox=b=OWGUI.widgetBox(self.controlArea,"Training function")
self.trainingRadio = OWGUI.radioButtonsInBox(b, self, "optAlgGUI", btnLabels= \
["TRAIN_BACKPROP - Backpropagation", \
"TRAIN_RPROP - iRPROP algorithm (Recommended)"],callback = self.changedOptAlg)
#CVANNSTOPCRITDICT = {"ITER":1, "EPS":2}
sc = OWGUI.widgetBox(self.controlArea,"Stop criteria")
self.trainingRadio = OWGUI.radioButtonsInBox(sc, self, "stopCritGUI", btnLabels= \
["Max number of Iterations", \
"Epsilon"], callback = self.OnChangedStopCrit)
OWGUI.lineEdit(OWGUI.indentedBox(sc), self, 'stopValueGUI', 'Value:', orientation="horizontal", tooltip="Value to use as stop Criteria")
self.priorBox = OWGUI.lineEdit(b, self, 'priorsGUI', box='Class weights', \
tooltip='Ex: POS:2 , NEG:1')
b=OWGUI.widgetBox(self.controlArea, "Scaling", addSpace = True)
self.scaleDataBox=OWGUI.checkBox(b, self, "scaleDataGUI", "Scale Data", tooltip="Scales the training data, and the input examples",callback = self.setLearnerVars)
self.scaleClassBox=OWGUI.checkBox(b, self, "scaleClassGUI", "Scale Class variable", tooltip="Scales the class variable of training data, and the output predictions",callback = self.setLearnerVars)
# Apply the settings and send the learner to the output channel
OWGUI.button(self.controlArea, self,"&Apply settings", callback=self.applySettings)
# Get desired location of model file
boxFile = OWGUI.widgetBox(self.controlArea, "Path for saving Model", addSpace = True, orientation=0)
L1 = OWGUI.lineEdit(boxFile, self, "modelFile", labelWidth=80, orientation = "horizontal", \
tooltip = "Once a model is created (connect this widget with a data widget), \nit can be saved by giving a \
file name here and clicking the save button.\nPlease observe that model names should not contain an extention.")
L1.setMinimumWidth(200)
button = OWGUI.button(boxFile, self, '...', callback = self.browseFile, disabled=0,tooltip = "Choose the dir where to save. After chosen, add a name for the model file!")
button.setMaximumWidth(25)
# Save the model
OWGUI.button(self.controlArea, self,"&Save model to file", callback=self.saveModel)
self.setLearnerVars()
self.applySettings()
self.adjustSize()
def __init__(self, parent=None, signalManager = None, name='Loaded model'):
OWWidget.__init__(self, parent, signalManager, name)
self.resize(400,200)
# Define the input and output channels
self.inputs = []
self.outputs = [("Classifier", orange.Classifier)]
self.name = name
self.modelFile = os.getcwd()
self.modelType = None
#GUI
OWGUI.lineEdit(self.controlArea, self, 'name', box='Classifier Name', \
tooltip='Name to be used by other widgets to identify your classifier.<br>This should be a unique name!')
# Define the model type. SVM, SVM_Jakulin or ANN.
#self.kernelBox=b=OWGUI.widgetBox(self.controlArea, "Select the model type you wish to load")
#self.kernelradio = OWGUI.radioButtonsInBox(b, self, "modelType", btnLabels=["SVM", \
# "ANN ", \
# "Random Forest","PLS"])
# Get location of model file
boxFile = OWGUI.widgetBox(self.controlArea, "Model file location", addSpace = True, orientation=0)
L1 = OWGUI.lineEdit(boxFile, self, "modelFile", labelWidth=80, orientation = "horizontal", tooltip = "Enter full path to the model file location")
L1.setMinimumWidth(200)
button = OWGUI.button(boxFile, self, '...', callback = self.browse, disabled=0,tooltip = "Choose the dir where to save. After chosen, add a name for the model file!")
button.setMaximumWidth(25)
# Load the model
OWGUI.button(self.controlArea, self,"&Load model from file", callback=self.loadModel)
box = OWGUI.widgetBox(self.controlArea, "Info")
self.info = OWGUI.widgetLabel(box, 'No model loaded.')
self.adjustSize()
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Concatenate",
wantMainArea=False, resizingEnabled=False)
self.inputs = [("Primary Data", orange.ExampleTable, self.setData),
("Additional Data", orange.ExampleTable, self.setMoreData, Multiple)]
self.outputs = [("Data", ExampleTable)]
self.mergeAttributes = 0
self.dataSourceSelected = 1
self.addIdAs = 0
self.dataSourceName = "clusterId"
self.primary = None
self.additional = {}
self.loadSettings()
bg = self.bgMerge = OWGUI.radioButtonsInBox(self.controlArea, self, "mergeAttributes", [], "Domains merging", callback = self.apply)
OWGUI.widgetLabel(bg, "When there is no primary table, the domain should be")
OWGUI.appendRadioButton(bg, self, "mergeAttributes", "Union of attributes appearing in all tables")
OWGUI.appendRadioButton(bg, self, "mergeAttributes", "Intersection of attributes in all tables")
bg.layout().addSpacing(6)
label = OWGUI.widgetLabel(bg, "The resulting table will have class only if there is no conflict between input classes.")
label.setWordWrap(True)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Data source IDs", addSpace=True)
cb = OWGUI.checkBox(box, self, "dataSourceSelected", "Append data source IDs")
self.classificationBox = ib = OWGUI.indentedBox(box, sep=OWGUI.checkButtonOffsetHint(cb))
form = QFormLayout(
spacing=8, labelAlignment=Qt.AlignLeft, formAlignment=Qt.AlignLeft,
fieldGrowthPolicy=QFormLayout.AllNonFixedFieldsGrow
)
ib.layout().addLayout(form)
form.addRow("Name",
OWGUI.lineEdit(ib, self, "dataSourceName", valueType=str))
aa = OWGUI.comboBox(ib, self, "addIdAs", items=["Class attribute", "Attribute", "Meta attribute"])
cb.disables.append(ib)
cb.makeConsistent()
form.addRow("Place", aa)
OWGUI.button(self.controlArea, self, "Apply Changes", callback = self.apply, default=True)
OWGUI.rubber(self.controlArea)
self.adjustSize()
self.dataReport = None
def __init__(self,parent = None, signalManager = None):
OWClassificationTreeViewer.__init__(self, parent, signalManager, 'I&nteractive Tree Builder')
self.inputs = [("Data", ExampleTable, self.setData),
("Tree Learner", orange.Learner, self.setLearner)]
self.outputs = [("Data", ExampleTable),
("Classifier", Orange.classification.tree.TreeClassifier),
("Tree Learner", orange.Learner)]
self.attridx = 0
self.cutoffPoint = 0.0
self.targetClass = 0
self.loadSettings()
self.data = None
self.treeLearner = None
self.tree = None
self.learner = None
new_controlArea = OWGUI.widgetBox(self.leftWidgetPart, orientation="vertical", margin=4, addToLayout=False)
self.leftWidgetPart.layout().insertWidget(0, new_controlArea)
self.leftWidgetPart.layout().removeWidget(self.controlArea)
tabWidget = OWGUI.tabWidget(new_controlArea)
buildTab = OWGUI.createTabPage(tabWidget, "Build")
# new_controlArea.layout().addWidget(self.controlArea)
self.old_controlArea = self.controlArea
displayTab = OWGUI.createTabPage(tabWidget, "Display", self.controlArea)
self.controlArea = new_controlArea
self.old_controlArea.layout().removeWidget(self.infBox)
buildTab.layout().insertWidget(0, self.infBox)
OWGUI.separator(buildTab)
box = OWGUI.widgetBox(buildTab, "Split selection")
# OWGUI.widgetLabel(box, "Split By:")
self.attrsCombo = OWGUI.comboBox(box, self, 'attridx', orientation="horizontal", callback=self.cbAttributeSelected)
self.cutoffEdit = OWGUI.lineEdit(box, self, 'cutoffPoint', label = 'Cut off point: ', orientation='horizontal', validator=QDoubleValidator(self))
OWGUI.button(box, self, "Split", callback=self.btnSplitClicked)
OWGUI.separator(buildTab)
box = OWGUI.widgetBox(buildTab, "Prune or grow tree")
self.btnPrune = OWGUI.button(box, self, "Cut", callback = self.btnPruneClicked, disabled = 1)
self.btnBuild = OWGUI.button(box, self, "Build", callback = self.btnBuildClicked)
OWGUI.rubber(buildTab)
self.activateLoadedSettings()
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):
OWWidget.__init__(self, parent, signalManager, "FeatureConstructor",wantMainArea = 0)
self.inputs = [("Examples", ExampleTable, self.setData)]
self.outputs = [("Examples", ExampleTable)]
self.newName = "CLASS"
self.data = None
box = OWGUI.widgetBox(self.controlArea, "Rename class")
newNameBox = OWGUI.lineEdit(box, self, "newName", "New Class name: ", labelWidth=100, orientation="horizontal", tooltip="New name for the Class attribute")
self.BT = OWGUI.button(self.controlArea, self, "&Apply", callback = self.apply, disabled=0)
infoBox = OWGUI.widgetBox(self,"Info")
self.info = OWGUI.widgetLabel(infoBox, 'Renames the class attribute of the data.')
self.adjustSize()
