def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveA')
# [start-snippet-1]
self.inputs = [("Data", Orange.data.Table, self.set_dataset),
("Learner", Orange.classification.Learner, self.set_learner,
Multiple + Default)]
# [end-snippet-1]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.loadSettings()
self.updateCurvePoints() # sets self.curvePoints, self.steps equidistant points from 1/self.steps to 1
# [start-snippet-2]
self.scoring = [("Classification Accuracy", Orange.evaluation.scoring.CA),
("AUC", Orange.evaluation.scoring.AUC),
("BrierScore", Orange.evaluation.scoring.Brier_score),
("Information Score", Orange.evaluation.scoring.IS),
("Sensitivity", Orange.evaluation.scoring.Sensitivity),
("Specificity", Orange.evaluation.scoring.Specificity)]
# [end-snippet-2]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve() if self.commitOnChange else None)
OWGUI.spin(box, self, 'steps', 2, 100, step=1,
label='Learning curve points: ',
callback=[self.updateCurvePoints,
lambda: self.computeCurve() if self.commitOnChange else None])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting",
callback=self.computeCurve, disabled=1)
OWGUI.rubber(self.controlArea)
# table widget
self.table = OWGUI.table(self.mainArea,
selectionMode=QTableWidget.NoSelection)
self.resize(500,200)
def __init__(self,parent=None, signalManager = None):
OWWidget.__init__(self, parent, signalManager, "Confusion Matrix", 1)
# inputs
self.inputs=[("Evaluation Results", orngTest.ExperimentResults, self.setTestResults, Default)]
self.outputs=[("Selected Data", ExampleTable, 8)]
self.selectedLearner = []
self.learnerNames = []
self.selectionDirty = 0
self.autoApply = True
self.appendPredictions = True
self.appendProbabilities = False
self.shownQuantity = 0
self.learnerList = OWGUI.listBox(self.controlArea, self, "selectedLearner", "learnerNames", box = "Learners", callback = self.learnerChanged)
self.learnerList.setMinimumHeight(100)
OWGUI.separator(self.controlArea)
OWGUI.comboBox(self.controlArea, self, "shownQuantity", items = self.quantities, box = "Show", callback=self.reprint)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Selection") #, addSpace=True)
OWGUI.button(box, self, "Correct", callback=self.selectCorrect)
OWGUI.button(box, self, "Misclassified", callback=self.selectWrong)
OWGUI.button(box, self, "None", callback=self.selectNone)
OWGUI.separator(self.controlArea)
self.outputBox = box = OWGUI.widgetBox(self.controlArea, "Output")
OWGUI.checkBox(box, self, "appendPredictions", "Append class predictions", callback = self.sendIf)
OWGUI.checkBox(box, self, "appendProbabilities", "Append predicted class probabilities", callback = self.sendIf)
applyButton = OWGUI.button(box, self, "Commit", callback = self.sendData, default=True)
autoApplyCB = OWGUI.checkBox(box, self, "autoApply", "Commit automatically")
OWGUI.setStopper(self, applyButton, autoApplyCB, "selectionDirty", self.sendData)
import sip
sip.delete(self.mainArea.layout())
self.layout = QGridLayout(self.mainArea)
self.layout.addWidget(OWGUI.widgetLabel(self.mainArea, "Prediction"), 0, 1, Qt.AlignCenter)
label = TransformedLabel("Correct Class")
self.layout.addWidget(label, 2, 0, Qt.AlignCenter)
# self.layout.addWidget(OWGUI.widgetLabel(self.mainArea, "Correct Class "), 2, 0, Qt.AlignCenter)
self.table = OWGUI.table(self.mainArea, rows = 0, columns = 0, selectionMode = QTableWidget.MultiSelection, addToLayout = 0)
self.layout.addWidget(self.table, 2, 1)
self.layout.setColumnStretch(1, 100)
self.layout.setRowStretch(2, 100)
self.connect(self.table, SIGNAL("itemSelectionChanged()"), self.sendIf)
self.res = None
self.matrix = None
self.selectedLearner = None
self.resize(700,450)
def add_tables(self, networks):
self.networks = networks
self.tables = []
if networks is None:
return
networks.sort(key=lambda net: net.repository)
for k,g in itertools.groupby(networks, key=lambda net: net.repository):
network_group = list(g)
if len(network_group) > 0:
self.network_list.layout().addWidget(QLabel("<h3>" + network_group[0].repository + "</h3>"))
table = OWGUI.table(self.network_list, rows=len(network_group), columns=5, selectionMode = -1, addToLayout = 1)
table.setHorizontalHeaderLabels(['Name', 'Type', 'Nodes', 'Edges', 'Description'])
f = table.font()
f.setPointSize(9)
table.setFont(f)
table.verticalHeader().hide()
table.setSelectionMode(QAbstractItemView.SingleSelection)
table.setSelectionBehavior(QAbstractItemView.SelectRows)
self.connect(table, SIGNAL('itemSelectionChanged()'), lambda table=table: self.select_network(table))
for i, net in enumerate(network_group):
lbl = QLabel("<a href='"+ net.link +"'>" + net.name + "</a>")
lbl.setOpenExternalLinks(True)
table.setCellWidget(i, 0, lbl)
OWGUI.tableItem(table, i, 1, net.type)
OWGUI.tableItem(table, i, 2, net.nodes)
OWGUI.tableItem(table, i, 3, net.edges)
OWGUI.tableItem(table, i, 4, net.description)
table.setFixedSize(712, 100)
table.setColumnWidth(0, 120)
table.setColumnWidth(1, 80)
table.setColumnWidth(2, 80)
table.setColumnWidth(3, 80)
table.setColumnWidth(4, 350)
table.resizeRowsToContents()
table.setFixedSize(712, sum(table.rowHeight(i) for i in range(len(networks))) + 27)
self.tables.append(table)
OWGUI.separator(self.network_list, 10, 10)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveA')
self.inputs = [("Data", ExampleTable, self.dataset),
("Learner", orange.Learner, self.learner, Multiple)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.loadSettings()
self.setCurvePoints() # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA), ("AUC", orngStat.AUC), ("BrierScore", orngStat.BrierScore), ("Information Score", orngStat.IS), ("Sensitivity", orngStat.sens), ("Specificity", orngStat.spec)]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames, callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1, label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box, self, 'steps', 2, 100, step=1, label='Learning curve points: ',
callback=[self.setCurvePoints, lambda: self.computeCurve(self.commitOnChange)])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting", callback=self.computeCurve, disabled=1)
# table widget
self.table = OWGUI.table(self.mainArea, selectionMode=QTableWidget.NoSelection)
self.resize(500,200)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveC')
self.inputs = [("Data", ExampleTable, self.dataset),
("Learner", orange.Learner, self.learner, Multiple)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.graphPointSize = 5 # size of points in the graphs
self.graphDrawLines = 1 # draw lines between points in the graph
self.graphShowGrid = 1 # show gridlines in the graph
self.selectedLearners = []
self.loadSettings()
warnings.filterwarnings("ignore", ".*builtin attribute.*",
orange.AttributeWarning)
self.setCurvePoints(
) # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA),
("AUC", orngStat.AUC),
("BrierScore", orngStat.BrierScore),
("Information Score", orngStat.IS),
("Sensitivity", orngStat.sens),
("Specificity", orngStat.spec)]
self.learners = [
] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [
] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
## class selection (classQLB)
OWGUI.separator(self.controlArea)
self.cbox = OWGUI.widgetBox(self.controlArea, "Learners")
self.llb = OWGUI.listBox(self.cbox,
self,
"selectedLearners",
selectionMode=QListWidget.MultiSelection,
callback=self.learnerSelectionChanged)
self.llb.setMinimumHeight(50)
self.blockSelectionChanges = 0
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box,
self,
"scoringF",
items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box,
self,
'folds',
2,
100,
step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box,
self,
'steps',
2,
100,
step=1,
label='Learning curve points: ',
callback=[
self.setCurvePoints,
lambda: self.computeCurve(self.commitOnChange)
])
OWGUI.checkBox(box, self, 'commitOnChange',
'Apply setting on any change')
self.commitBtn = OWGUI.button(box,
self,
"Apply Setting",
callback=self.computeCurve,
disabled=1)
# start of content (right) area
tabs = OWGUI.tabWidget(self.mainArea)
# graph widget
tab = OWGUI.createTabPage(tabs, "Graph")
self.graph = OWGraph(tab)
self.graph.setAxisAutoScale(QwtPlot.xBottom)
self.graph.setAxisAutoScale(QwtPlot.yLeft)
tab.layout().addWidget(self.graph)
self.setGraphGrid()
# table widget
tab = OWGUI.createTabPage(tabs, "Table")
self.table = OWGUI.table(tab, selectionMode=QTableWidget.NoSelection)
self.resize(550, 200)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'LearningCurveC')
self.inputs = [("Data", ExampleTable, self.dataset), ("Learner", orange.Learner, self.learner, 0)]
self.folds = 5 # cross validation folds
self.steps = 10 # points in the learning curve
self.scoringF = 0 # scoring function
self.commitOnChange = 1 # compute curve on any change of parameters
self.graphPointSize = 5 # size of points in the graphs
self.graphDrawLines = 1 # draw lines between points in the graph
self.graphShowGrid = 1 # show gridlines in the graph
self.selectedLearners = []
self.loadSettings()
warnings.filterwarnings("ignore", ".*builtin attribute.*", orange.AttributeWarning)
self.setCurvePoints() # sets self.curvePoints, self.steps equidistantpoints from 1/self.steps to 1
self.scoring = [("Classification Accuracy", orngStat.CA), ("AUC", orngStat.AUC), ("BrierScore", orngStat.BrierScore), ("Information Score", orngStat.IS), ("Sensitivity", orngStat.sens), ("Specificity", orngStat.spec)]
self.learners = [] # list of current learners from input channel, tuples (id, learner)
self.data = None # data on which to construct the learning curve
self.curves = [] # list of evaluation results (one per learning curve point)
self.scores = [] # list of current scores, learnerID:[learner scores]
# GUI
box = OWGUI.widgetBox(self.controlArea, "Info")
self.infoa = OWGUI.widgetLabel(box, 'No data on input.')
self.infob = OWGUI.widgetLabel(box, 'No learners.')
## class selection (classQLB)
OWGUI.separator(self.controlArea)
self.cbox = OWGUI.widgetBox(self.controlArea, "Learners")
self.llb = OWGUI.listBox(self.cbox, self, "selectedLearners", selectionMode=QListWidget.MultiSelection, callback=self.learnerSelectionChanged)
self.llb.setMinimumHeight(50)
self.blockSelectionChanges = 0
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Evaluation Scores")
scoringNames = [x[0] for x in self.scoring]
OWGUI.comboBox(box, self, "scoringF", items=scoringNames,
callback=self.computeScores)
OWGUI.separator(self.controlArea)
box = OWGUI.widgetBox(self.controlArea, "Options")
OWGUI.spin(box, self, 'folds', 2, 100, step=1,
label='Cross validation folds: ',
callback=lambda: self.computeCurve(self.commitOnChange))
OWGUI.spin(box, self, 'steps', 2, 100, step=1,
label='Learning curve points: ',
callback=[self.setCurvePoints, lambda: self.computeCurve(self.commitOnChange)])
OWGUI.checkBox(box, self, 'commitOnChange', 'Apply setting on any change')
self.commitBtn = OWGUI.button(box, self, "Apply Setting", callback=self.computeCurve, disabled=1)
# start of content (right) area
tabs = OWGUI.tabWidget(self.mainArea)
# graph widget
tab = OWGUI.createTabPage(tabs, "Graph")
self.graph = OWPlot(tab)
self.graph.set_axis_autoscale(xBottom)
self.graph.set_axis_autoscale(yLeft)
tab.layout().addWidget(self.graph)
self.setGraphGrid()
# table widget
tab = OWGUI.createTabPage(tabs, "Table")
self.table = OWGUI.table(tab, selectionMode=QTableWidget.NoSelection)
self.resize(550,200)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Confusion Matrix", 1)
# inputs
self.inputs = [("Evaluation Results", orngTest.ExperimentResults,
self.setTestResults, Default)]
self.outputs = [("Selected Examples", ExampleTable, 8)]
self.selectedLearner = []
self.learnerNames = []
self.selectionDirty = 0
self.autoApply = True
self.appendPredictions = True
self.appendProbabilities = False
self.shownQuantity = 0
self.learnerList = OWGUI.listBox(self.controlArea,
self,
"selectedLearner",
"learnerNames",
box="Learners",
callback=self.learnerChanged)
self.learnerList.setMinimumHeight(100)
OWGUI.separator(self.controlArea)
OWGUI.comboBox(self.controlArea,
self,
"shownQuantity",
items=self.quantities,
box="Show",
callback=self.reprint)
box = OWGUI.widgetBox(self.controlArea, "Selection", addSpace=True)
OWGUI.button(box, self, "Correct", callback=self.selectCorrect)
OWGUI.button(box, self, "Misclassified", callback=self.selectWrong)
OWGUI.button(box, self, "None", callback=self.selectNone)
box = OWGUI.widgetBox(self.controlArea, "Output")
OWGUI.checkBox(box,
self,
"appendPredictions",
"Append class predictions",
callback=self.sendIf)
OWGUI.checkBox(box,
self,
"appendProbabilities",
"Append predicted class probabilities",
callback=self.sendIf)
applyButton = OWGUI.button(box, self, "Commit", callback=self.sendData)
autoApplyCB = OWGUI.checkBox(box, self, "autoApply",
"Commit automatically")
OWGUI.setStopper(self, applyButton, autoApplyCB, "selectionDirty",
self.sendData)
import sip
sip.delete(self.mainArea.layout())
self.layout = QGridLayout(self.mainArea)
self.layout.addWidget(OWGUI.widgetLabel(self.mainArea, "Prediction"),
0, 1, Qt.AlignCenter)
self.layout.addWidget(
OWGUI.widgetLabel(self.mainArea, "Correct Class "), 2, 0,
Qt.AlignCenter)
self.table = OWGUI.table(self.mainArea,
rows=0,
columns=0,
selectionMode=QTableWidget.MultiSelection,
addToLayout=0)
self.layout.addWidget(self.table, 2, 1)
self.layout.setColumnStretch(1, 100)
self.layout.setRowStretch(2, 100)
self.connect(self.table, SIGNAL("itemSelectionChanged()"), self.sendIf)
self.resize(700, 450)
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!')
# Queue radio buttons
self.queueBox = OWGUI.radioButtonsInBox(self.controlArea,
self,
"outputSel",
box="Output mode",
btnLabels=self.outputModes,
callback=self.changeOutputMode)
# Queue radio buttons
self.queueBox = OWGUI.radioButtonsInBox(self.controlArea,
self,
"queueType",
box="Execution Mode",
btnLabels=self.queueTypes,
callback=None)
# create table with selectable ML methods
self.colNames = ["ML method", "Enabled"]
mainRight = OWGUI.widgetBox(self.controlArea, "ML methods selection")
mainRight.setSizePolicy(
QSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding))
self.mlTable = OWGUI.table(self.controlArea,
rows=0,
columns=0,
selectionMode=QTableWidget.MultiSelection,
addToLayout=1)
self.mlTable.verticalHeader().hide()
self.mlTable.setSelectionMode(QTableWidget.NoSelection)
self.mlTable.setColumnCount(len(self.colNames))
self.mlTable.setHorizontalHeaderLabels(self.colNames)
# Set location of statistics file
boxFile = OWGUI.widgetBox(self.controlArea,
"Path for saving the results",
addSpace=True,
orientation=0)
L1 = OWGUI.lineEdit(
boxFile,
self,
"statPath",
labelWidth=80,
orientation="horizontal",
tooltip="Please use full path to results file to be created.")
L1.setMinimumWidth(200)
button = OWGUI.button(boxFile,
self,
'...',
callback=self.setStatSaveDir,
disabled=0,
tooltip="Choose the dir where to save.")
button.setMaximumWidth(25)
# Apply the settings and send the model to the output channel
OWGUI.button(self.controlArea,
self,
"&Apply settings",
callback=self.Apply)
# Set location of model file
self.boxFile = OWGUI.widgetBox(self.controlArea,
"Path for saving Model",
addSpace=True,
orientation=0)
self.L1 = OWGUI.lineEdit(
self.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."
)
self.L1.setMinimumWidth(200)
self.button = OWGUI.button(
self.boxFile,
self,
'...',
callback=self.setModelSavePath,
disabled=0,
tooltip=
"Choose the dir where to save. After chosen, add a name for the model file!"
)
self.button.setMaximumWidth(25)
# Save the model
self.saveButton = OWGUI.button(self.controlArea,
self,
"&Save model",
callback=self.saveModel)
# Statistics show
statBox = OWGUI.widgetBox(self.mainArea,
"Statistics",
addSpace=True,
orientation=0)
self.statInfoBox = OWGUI.widgetLabel(statBox, '')
# Save the model
OWGUI.button(self.mainArea,
self,
"&Save statistics",
callback=self.saveStat)
self.fillMLtable()
self.changeOutputMode()
self.adjustSize()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Predictions")
self.callbackDeposit = []
self.inputs = [("Examples", ExampleTable, self.setData),
("Predictors", orange.Classifier, self.setPredictor,
Multiple)]
self.outputs = [("Predictions", ExampleTable)]
self.predictors = {}
# saveble settings
self.showProb = 1
self.showClass = 1
self.ShowAttributeMethod = 0
self.sendOnChange = 1
self.classes = []
self.selectedClasses = []
self.loadSettings()
self.datalabel = "N/A"
self.predictorlabel = "N/A"
self.tasklabel = "N/A"
self.precision = 2
self.outvar = None # current output variable (set by the first predictor/data set send in)
self.data = None
# GUI - Options
# Options - classification
ibox = OWGUI.widgetBox(self.controlArea, "Info")
OWGUI.label(ibox, self, "Data: %(datalabel)s")
OWGUI.label(ibox, self, "Predictors: %(predictorlabel)s")
OWGUI.label(ibox, self, "Task: %(tasklabel)s")
OWGUI.separator(self.controlArea)
self.copt = OWGUI.widgetBox(self.controlArea,
"Options (classification)")
self.copt.setDisabled(1)
OWGUI.checkBox(self.copt,
self,
'showProb',
"Show predicted probabilities",
callback=self.updateTableOutcomes)
# self.lbClasses = OWGUI.listBox(self.copt, self, selectionMode = QListWidget.MultiSelection, callback = self.updateTableOutcomes)
self.lbcls = OWGUI.listBox(
self.copt,
self,
"selectedClasses",
"classes",
callback=[self.updateTableOutcomes, self.checksendpredictions],
selectionMode=QListWidget.MultiSelection)
self.lbcls.setFixedHeight(50)
OWGUI.spin(self.copt,
self,
"precision",
1,
6,
label="No. of decimals: ",
orientation=0,
callback=self.updateTableOutcomes)
OWGUI.checkBox(
self.copt,
self,
'showClass',
"Show predicted class",
callback=[self.updateTableOutcomes, self.checksendpredictions])
# Options - regression
# self.ropt = QVButtonGroup("Options (regression)", self.controlArea)
# OWGUI.checkBox(self.ropt, self, 'showClass', "Show predicted class",
# callback=[self.updateTableOutcomes, self.checksendpredictions])
# self.ropt.hide()
OWGUI.separator(self.controlArea)
self.att = OWGUI.widgetBox(self.controlArea, "Data attributes")
OWGUI.radioButtonsInBox(self.att,
self,
'ShowAttributeMethod',
['Show all', 'Hide all'],
callback=self.updateAttributes)
self.att.setDisabled(1)
OWGUI.rubber(self.controlArea)
OWGUI.separator(self.controlArea)
self.outbox = OWGUI.widgetBox(self.controlArea, "Output")
self.commitBtn = OWGUI.button(self.outbox,
self,
"Send Predictions",
callback=self.sendpredictions)
OWGUI.checkBox(self.outbox, self, 'sendOnChange', 'Send automatically')
self.outbox.setDisabled(1)
# GUI - Table
self.table = OWGUI.table(self.mainArea,
selectionMode=QTableWidget.NoSelection)
self.table.setItemDelegate(OWGUI.TableBarItem(self))
self.header = self.table.horizontalHeader()
self.vheader = self.table.verticalHeader()
# manage sorting (not correct, does not handle real values)
self.connect(self.header, SIGNAL("sectionPressed(int)"), self.sort)
self.sortby = -1
self.resize(800, 600)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'k-Means Clustering')
self.inputs = [("Data", ExampleTable, self.setData)]
self.outputs = [("Data", ExampleTable), ("Centroids", ExampleTable)]
#set default settings
self.K = 2
self.optimized = True
self.optimizationFrom = 2
self.optimizationTo = 5
self.scoring = 0
self.distanceMeasure = 0
self.initializationType = 0
self.restarts = 1
self.classifySelected = 1
self.addIdAs = 0
self.runAnyChange = 1
self.classifyName = "Cluster"
self.settingsChanged = False
self.loadSettings()
self.data = None # holds input data
self.km = None # holds clustering object
# GUI definition
# settings
box = OWGUI.widgetBox(self.controlArea, "Clusters (k)",
addSpace=True, spacing=0)
# left, top, right, bottom = box.getContentsMargins()
# box.setContentsMargins(left, 0, right, 0)
bg = OWGUI.radioButtonsInBox(box, self, "optimized", [],
callback=self.setOptimization)
fixedBox = OWGUI.widgetBox(box, orientation="horizontal",
margin=0, spacing=bg.layout().spacing())
button = OWGUI.appendRadioButton(bg, self, "optimized", "Fixed",
insertInto=fixedBox,
tooltip="Fixed number of clusters")
button.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)
fixedBox.layout().setAlignment(button, Qt.AlignLeft)
self.fixedSpinBox = OWGUI.spin(OWGUI.widgetBox(fixedBox), self, "K",
min=2, max=30,
tooltip="Fixed number of clusters",
callback=self.update,
callbackOnReturn=True)
optimizedBox = OWGUI.widgetBox(box, margin=0,
spacing=bg.layout().spacing())
button = OWGUI.appendRadioButton(bg, self, "optimized", "Optimized",
insertInto=optimizedBox)
box = OWGUI.indentedBox(optimizedBox,
sep=OWGUI.checkButtonOffsetHint(button))
box.layout().setSpacing(0)
self.optimizationBox = box
OWGUI.spin(box, self, "optimizationFrom", label="From",
min=2, max=99,
tooltip="Minimum number of clusters to try",
callback=self.updateOptimizationFrom,
callbackOnReturn=True)
OWGUI.spin(box, self, "optimizationTo", label="To",
min=3, max=100,
tooltip="Maximum number of clusters to try",
callback=self.updateOptimizationTo,
callbackOnReturn=True)
OWGUI.comboBox(box, self, "scoring", label="Scoring",
orientation="horizontal",
items=[m[0] for m in self.scoringMethods],
callback=self.update)
box = OWGUI.widgetBox(self.controlArea, "Settings", addSpace=True)
OWGUI.comboBox(box, self, "distanceMeasure", label="Distance measures",
items=[name for name, _ in self.distanceMeasures],
tooltip=None,
indent=20,
callback=self.update)
cb = OWGUI.comboBox(box, self, "initializationType",
label="Initialization",
items=[name for name, _ in self.initializations],
tooltip=None,
indent=20,
callback=self.update)
OWGUI.spin(cb.box, self, "restarts", label="Restarts",
orientation="horizontal",
min=1,
max=100 if not orngDebugging.orngDebuggingEnabled else 5,
callback=self.update,
callbackOnReturn=True)
box = OWGUI.widgetBox(self.controlArea, "Cluster IDs", addSpace=True)
cb = OWGUI.checkBox(box, self, "classifySelected",
"Append cluster indices")
box = OWGUI.indentedBox(box, sep=OWGUI.checkButtonOffsetHint(cb))
form = QWidget()
le = OWGUI.lineEdit(form, self, "classifyName", None,
orientation="horizontal",
valueType=str)
cc = OWGUI.comboBox(form, self, "addIdAs", label=" ",
orientation="horizontal",
items=["Class attribute",
"Attribute",
"Meta attribute"])
layout = QFormLayout()
layout.setSpacing(8)
layout.setFieldGrowthPolicy(QFormLayout.AllNonFixedFieldsGrow)
layout.setLabelAlignment(Qt.AlignLeft | Qt.AlignJustify)
layout.addRow("Name ", le)
layout.addRow("Place ", cc)
form.setLayout(layout)
box.layout().addWidget(form)
left, top, right, bottom = layout.getContentsMargins()
layout.setContentsMargins(0, top, right, bottom)
cb.disables.append(box)
cb.makeConsistent()
box = OWGUI.widgetBox(self.controlArea, "Run")
cb = OWGUI.checkBox(box, self, "runAnyChange", "Run after any change")
self.runButton = b = OWGUI.button(box, self, "Run Clustering",
callback=self.run)
OWGUI.setStopper(self, b, cb, "settingsChanged", callback=self.run)
OWGUI.rubber(self.controlArea)
# display of clustering results
self.optimizationReportBox = OWGUI.widgetBox(self.mainArea)
self.tableBox = OWGUI.widgetBox(self.optimizationReportBox,
"Optimization Report")
self.table = OWGUI.table(self.tableBox,
selectionMode=QTableWidget.SingleSelection)
self.table.setHorizontalScrollMode(QTableWidget.ScrollPerPixel)
self.table.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.table.setSelectionBehavior(QAbstractItemView.SelectRows)
self.table.setColumnCount(3)
self.table.setHorizontalHeaderLabels(["k", "Best", "Score"])
self.table.verticalHeader().hide()
self.table.horizontalHeader().setStretchLastSection(True)
self.table.setItemDelegateForColumn(
2, OWGUI.TableBarItem(self, self.table))
self.table.setItemDelegateForColumn(
1, OWGUI.IndicatorItemDelegate(self))
self.table.setSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding)
self.connect(self.table,
SIGNAL("itemSelectionChanged()"),
self.tableItemSelected)
self.setSizePolicy(QSizePolicy.Preferred,
QSizePolicy.Preferred)
self.mainArea.setSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding)
OWGUI.rubber(self.topWidgetPart)
self.updateOptimizationGui()
def defineGUI(self):
self.sBox = OWGUI.widgetBox(self.controlArea, "Execution environment")
itms = [e[0] for e in self.execEnvs]
OWGUI.radioButtonsInBox(self.sBox, self, "execEnv", btnLabels=itms)
boxGrid = OWGUI.widgetBox(self.controlArea,'Initial Point for the Optimizer')
OWGUI.checkBox(boxGrid, self, 'UseGridSearch','Use Grid-Search', tooltip='Use Grid-Search to find the best initial point to start the optimization.<br>If not checked, the midrange point will be used as optimization initial point.')
OWGUI.spin(boxGrid, self, 'nInnerPoints', 1, 100, step=1, label=' Number of inner points:', tooltip='Number of points to break down each variable to evaluate the initial point.<br>It will evaluate nInnerPoints^nOptimizedVars')
OWGUI.separator(self.controlArea)
self.sBox = OWGUI.widgetBox(self.controlArea, "Sampling Method")
itms = [e[0] for e in self.SMethods]
self.comboDistItems = ["Continuous","Power2","By Step","Specific Values"]
OWGUI.radioButtonsInBox(self.sBox, self, "SMethod", btnLabels=itms)
hBox = OWGUI.widgetBox(OWGUI.indentedBox(self.sBox))
#QWidget(hBox).setFixedSize(19, 8)
OWGUI.spin(hBox, self, 'nFolds', 2, 100, step=1, label='Number of Folds: ')
OWGUI.separator(self.controlArea)
box2 = OWGUI.widgetBox(self.controlArea,'Evaluation Method')
width = 150
itms = [e[0] for e in self.CMethods]
OWGUI.comboBox(box2, self, 'CMethod', items=itms, label='For Classifiers:', labelWidth=width, orientation='horizontal',
tooltip='Method used for evaluation in case of classifiers.')
itms = [e[0] for e in self.RMethods]
OWGUI.comboBox(box2, self, 'RMethod', items=itms, label='For Regressors:', labelWidth=width,
orientation='horizontal', tooltip='Method used for evaluation in case of regressors.')
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self,"&Reload Defaults ", callback=self.reloadDefaults)
OWGUI.separator(self.controlArea)
#OWGUI.separator(self.controlArea, height=24)
infoBox = OWGUI.widgetBox(self.controlArea, "Optimizer status")
self.infoStatus = OWGUI.label(infoBox,self,'Waiting for inputs...')
self.infoPars = OWGUI.label(infoBox,self,'')
self.infoRes = OWGUI.label(infoBox,self,'')
OWGUI.label(infoBox,self,'')
self.infoErr = OWGUI.label(infoBox,self,'')
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea, self,"&Apply Settings ", callback=self.optimizeParameters)
# Main area GUI
import sip
sip.delete(self.mainArea.layout())
self.mainLayout = QGridLayout(self.mainArea)
mainRight = OWGUI.widgetBox(self.mainArea, "Parameters Configuration")
mainRight.setSizePolicy(QSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding))
self.paramsTable = OWGUI.table(self.mainArea, rows = 0, columns = 0, selectionMode = QTableWidget.MultiSelection, addToLayout = 0)
#self.paramsTable.setLeftMargin(0)
self.paramsTable.verticalHeader().hide()
self.paramsTable.setSelectionMode(QTableWidget.NoSelection)
self.paramsTable.setColumnCount(len(self.paramsNames))
#for i, m in enumerate(self.paramsNames):
# self.paramsTable.setColumnStretchable(i, 0)
# header.setLabel(i, m)
self.paramsTable.setHorizontalHeaderLabels(self.paramsNames)
#self.mainLayout.setColumnStretch(1, 100)
#self.mainLayout.setRowStretch(2, 100)
self.mainLayout.addWidget(self.paramsTable, 0, 0, 1,2)
self.mainLayout.addWidget(OWGUI.label(mainRight,self,'Red - Parameter selected to be optimized\r\nGreen - Parameter optimized\r\nBlack - Parameter will not be optimized'),1,0)
self.mainLayout.addWidget(OWGUI.label(mainRight,self,'N_EX - Number of Examples in dataset\r\nN_ATTR - Number of attributes in dataset'),1,1)
self.adjustSize()
self.create()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "Confusion Matrix", 1)
# inputs
self.inputs = [("Evaluation Results", orngTest.ExperimentResults,
self.setTestResults, Default)]
self.outputs = [("Selected Examples", ExampleTable, 8)]
self.selectedLearner = []
self.learnerNames = []
self.selectionDirty = 0
self.autoApply = True
self.appendPredictions = True
self.appendProbabilities = False
self.shownQuantity = 0
self.learnerList = OWGUI.listBox(self.controlArea,
self,
"selectedLearner",
"learnerNames",
box="Learners",
callback=self.learnerChanged)
self.learnerList.setMinimumHeight(100)
OWGUI.separator(self.controlArea)
OWGUI.comboBox(self.controlArea,
self,
"shownQuantity",
items=self.quantities,
box="Show",
callback=self.reprint)
box = OWGUI.widgetBox(self.controlArea, "Selection", addSpace=True)
OWGUI.button(box, self, "Correct", callback=self.selectCorrect)
OWGUI.button(box, self, "Misclassified", callback=self.selectWrong)
OWGUI.button(box, self, "None", callback=self.selectNone)
box = OWGUI.widgetBox(self.controlArea, "Output")
OWGUI.checkBox(box,
self,
"appendPredictions",
"Append class predictions",
callback=self.sendIf)
OWGUI.checkBox(box,
self,
"appendProbabilities",
"Append predicted class probabilities",
callback=self.sendIf)
applyButton = OWGUI.button(box, self, "Commit", callback=self.sendData)
autoApplyCB = OWGUI.checkBox(box, self, "autoApply",
"Commit automatically")
OWGUI.setStopper(self, applyButton, autoApplyCB, "selectionDirty",
self.sendData)
import sip
sip.delete(self.mainArea.layout())
self.layout = QGridLayout(self.mainArea)
self.layout.addWidget(OWGUI.widgetLabel(self.mainArea, "Prediction"),
0, 1, Qt.AlignCenter)
self.layout.addWidget(
OWGUI.widgetLabel(self.mainArea, "Correct Class "), 2, 0,
Qt.AlignCenter)
self.table = OWGUI.table(self.mainArea,
rows=0,
columns=0,
selectionMode=QTableWidget.MultiSelection,
addToLayout=0)
self.layout.addWidget(self.table, 2, 1)
self.layout.setColumnStretch(1, 100)
self.layout.setRowStretch(2, 100)
self.connect(self.table, SIGNAL("itemSelectionChanged()"), self.sendIf)
##scPA
# Get location of model file
self.filePath = os.path.join(os.getcwd(), "ConfusionMat.txt")
boxFile = OWGUI.widgetBox(self.controlArea,
"File for saving results",
addSpace=True,
orientation=0)
L1 = OWGUI.lineEdit(
boxFile,
self,
"filePath",
labelWidth=80,
orientation="horizontal",
tooltip=
"Enter full file path to save the displayed results to a tab separated file."
)
L1.setMinimumWidth(200)
button = OWGUI.button(boxFile,
self,
'...',
callback=self.browseFile,
disabled=0,
tooltip="Browse for a location...")
button.setMaximumWidth(25)
# Save the model
OWGUI.button(self.controlArea,
self,
"&Save Results",
callback=self.saveRes)
##ecPA
self.resize(700, 450)
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, "TestLearners")
self.inputs = [("Data", ExampleTable, self.setData, Default),
("Separate Test Data", ExampleTable, self.setTestData),
("Learner", orange.Learner, self.setLearner, Multiple),
("Preprocess", PreprocessedLearner,
self.setPreprocessor)]
self.outputs = [("Evaluation Results", orngTest.ExperimentResults)]
# Settings
self.resampling = 0 # cross-validation
self.nFolds = 5 # cross validation folds
self.pLearning = 70 # size of learning set when sampling [%]
self.pRepeat = 10
self.precision = 4
self.applyOnAnyChange = True
self.selectedCScores = [
i for (i, s) in enumerate(self.cStatistics) if s.show
]
self.selectedRScores = [
i for (i, s) in enumerate(self.rStatistics) if s.show
]
self.targetClass = 0
self.loadSettings()
self.resampling = 0 # cross-validation
self.stat = self.cStatistics
self.data = None # input data set
self.testdata = None # separate test data set
self.learners = {} # set of learners (input)
self.results = None # from orngTest
self.preprocessor = None
self.controlArea.layout().setSpacing(8)
# GUI
self.sBtns = OWGUI.radioButtonsInBox(
self.controlArea,
self,
"resampling",
box="Sampling",
btnLabels=self.resamplingMethods[:1],
callback=self.newsampling)
indent = OWGUI.checkButtonOffsetHint(self.sBtns.buttons[-1])
ibox = OWGUI.widgetBox(OWGUI.indentedBox(self.sBtns, sep=indent))
OWGUI.spin(ibox,
self,
'nFolds',
2,
100,
step=1,
label='Number of folds:',
callback=lambda p=0: self.conditionalRecompute(p))
OWGUI.separator(self.sBtns, height=3)
OWGUI.appendRadioButton(self.sBtns, self, "resampling",
self.resamplingMethods[1]) # leave one out
OWGUI.separator(self.sBtns, height=3)
OWGUI.appendRadioButton(self.sBtns, self, "resampling",
self.resamplingMethods[2]) # random sampling
ibox = OWGUI.widgetBox(OWGUI.indentedBox(self.sBtns, sep=indent))
OWGUI.spin(ibox,
self,
'pRepeat',
1,
100,
step=1,
label='Repeat train/test:',
callback=lambda p=2: self.conditionalRecompute(p))
OWGUI.widgetLabel(ibox, "Relative training set size:")
OWGUI.hSlider(ibox,
self,
'pLearning',
minValue=10,
maxValue=100,
step=1,
ticks=10,
labelFormat=" %d%%",
callback=lambda p=2: self.conditionalRecompute(p))
OWGUI.separator(self.sBtns, height=3)
OWGUI.appendRadioButton(self.sBtns, self, "resampling",
self.resamplingMethods[3]) # test on train
OWGUI.separator(self.sBtns, height=3)
OWGUI.appendRadioButton(self.sBtns, self, "resampling",
self.resamplingMethods[4]) # test on test
self.trainDataBtn = self.sBtns.buttons[-2]
self.testDataBtn = self.sBtns.buttons[-1]
self.testDataBtn.setDisabled(True)
# box = OWGUI.widgetBox(self.sBtns, orientation='vertical', addSpace=False)
# OWGUI.separator(box)
OWGUI.separator(self.sBtns)
OWGUI.checkBox(self.sBtns,
self,
'applyOnAnyChange',
label="Apply on any change",
callback=self.applyChange)
self.applyBtn = OWGUI.button(self.sBtns,
self,
"&Apply",
callback=lambda f=True: self.recompute(f))
self.applyBtn.setDisabled(True)
if self.resampling == 4:
self.resampling = 3
# OWGUI.separator(self.controlArea)
# statistics
self.statLayout = QStackedLayout()
# self.cbox = OWGUI.widgetBox(self.controlArea, spacing=8, margin=0)
# self.cbox.layout().setSpacing(8)
self.cbox = OWGUI.widgetBox(self.controlArea, addToLayout=False)
self.cStatLabels = [s.name for s in self.cStatistics]
self.cstatLB = OWGUI.listBox(self.cbox,
self,
'selectedCScores',
'cStatLabels',
box="Performance scores",
selectionMode=QListWidget.MultiSelection,
callback=self.newscoreselection)
# OWGUI.separator(self.cbox)
self.cbox.layout().addSpacing(8)
self.targetCombo = OWGUI.comboBox(self.cbox,
self,
"targetClass",
orientation=0,
callback=[self.changedTarget],
box="Target class")
self.rStatLabels = [s.name for s in self.rStatistics]
self.rbox = OWGUI.widgetBox(self.controlArea,
"Performance scores",
addToLayout=False)
self.rstatLB = OWGUI.listBox(self.rbox,
self,
'selectedRScores',
'rStatLabels',
selectionMode=QListWidget.MultiSelection,
callback=self.newscoreselection)
self.statLayout.addWidget(self.cbox)
self.statLayout.addWidget(self.rbox)
self.controlArea.layout().addLayout(self.statLayout)
self.statLayout.setCurrentWidget(self.cbox)
# self.rstatLB.box.hide()
# score table
# table with results
self.g = OWGUI.widgetBox(self.mainArea, 'Evaluation Results')
self.tab = OWGUI.table(self.g, selectionMode=QTableWidget.NoSelection)
#self.lab = QLabel(self.g)
self.resize(680, 470)
def defineGUI(self):
self.sBox = OWGUI.widgetBox(self.controlArea, "Execution environment")
itms = [e[0] for e in self.execEnvs]
OWGUI.radioButtonsInBox(self.sBox, self, "execEnv", btnLabels=itms)
boxGrid = OWGUI.widgetBox(self.controlArea,
'Initial Point for the Optimizer')
OWGUI.checkBox(
boxGrid,
self,
'UseGridSearch',
'Use Grid-Search',
tooltip=
'Use Grid-Search to find the best initial point to start the optimization.<br>If not checked, the midrange point will be used as optimization initial point.'
)
OWGUI.spin(
boxGrid,
self,
'nInnerPoints',
1,
100,
step=1,
label=' Number of inner points:',
tooltip=
'Number of points to break down each variable to evaluate the initial point.<br>It will evaluate nInnerPoints^nOptimizedVars'
)
OWGUI.separator(self.controlArea)
self.sBox = OWGUI.widgetBox(self.controlArea, "Sampling Method")
itms = [e[0] for e in self.SMethods]
self.comboDistItems = [
"Continuous", "Power2", "By Step", "Specific Values"
]
OWGUI.radioButtonsInBox(self.sBox, self, "SMethod", btnLabels=itms)
hBox = OWGUI.widgetBox(OWGUI.indentedBox(self.sBox))
#QWidget(hBox).setFixedSize(19, 8)
OWGUI.spin(hBox,
self,
'nFolds',
2,
100,
step=1,
label='Number of Folds: ')
OWGUI.separator(self.controlArea)
box2 = OWGUI.widgetBox(self.controlArea, 'Evaluation Method')
width = 150
itms = [e[0] for e in self.CMethods]
OWGUI.comboBox(
box2,
self,
'CMethod',
items=itms,
label='For Classifiers:',
labelWidth=width,
orientation='horizontal',
tooltip='Method used for evaluation in case of classifiers.')
itms = [e[0] for e in self.RMethods]
OWGUI.comboBox(
box2,
self,
'RMethod',
items=itms,
label='For Regressors:',
labelWidth=width,
orientation='horizontal',
tooltip='Method used for evaluation in case of regressors.')
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea,
self,
"&Reload Defaults ",
callback=self.reloadDefaults)
OWGUI.separator(self.controlArea)
#OWGUI.separator(self.controlArea, height=24)
infoBox = OWGUI.widgetBox(self.controlArea, "Optimizer status")
self.infoStatus = OWGUI.label(infoBox, self, 'Waiting for inputs...')
self.infoPars = OWGUI.label(infoBox, self, '')
self.infoRes = OWGUI.label(infoBox, self, '')
OWGUI.label(infoBox, self, '')
self.infoErr = OWGUI.label(infoBox, self, '')
OWGUI.separator(self.controlArea)
OWGUI.button(self.controlArea,
self,
"&Apply Settings ",
callback=self.optimizeParameters)
# Main area GUI
import sip
sip.delete(self.mainArea.layout())
self.mainLayout = QGridLayout(self.mainArea)
mainRight = OWGUI.widgetBox(self.mainArea, "Parameters Configuration")
mainRight.setSizePolicy(
QSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding))
self.paramsTable = OWGUI.table(
self.mainArea,
rows=0,
columns=0,
selectionMode=QTableWidget.MultiSelection,
addToLayout=0)
#self.paramsTable.setLeftMargin(0)
self.paramsTable.verticalHeader().hide()
self.paramsTable.setSelectionMode(QTableWidget.NoSelection)
self.paramsTable.setColumnCount(len(self.paramsNames))
#for i, m in enumerate(self.paramsNames):
# self.paramsTable.setColumnStretchable(i, 0)
# header.setLabel(i, m)
self.paramsTable.setHorizontalHeaderLabels(self.paramsNames)
#self.mainLayout.setColumnStretch(1, 100)
#self.mainLayout.setRowStretch(2, 100)
self.mainLayout.addWidget(self.paramsTable, 0, 0, 1, 2)
self.mainLayout.addWidget(
OWGUI.label(
mainRight, self,
'Red - Parameter selected to be optimized\r\nGreen - Parameter optimized\r\nBlack - Parameter will not be optimized'
), 1, 0)
self.mainLayout.addWidget(
OWGUI.label(
mainRight, self,
'N_EX - Number of Examples in dataset\r\nN_ATTR - Number of attributes in dataset'
), 1, 1)
self.adjustSize()
self.create()
def __init__(self, parent=None, signalManager=None):
OWWidget.__init__(self, parent, signalManager, 'k-Means Clustering')
self.inputs = [("Examples", ExampleTable, self.setData)]
self.outputs = [("Examples", ExampleTable),
("Centroids", ExampleTable)]
#set default settings
self.K = 2
self.optimized = True
self.optimizationFrom = 2
self.optimizationTo = 5
self.scoring = 0
self.distanceMeasure = 0
self.initializationType = 0
self.restarts = 1
self.classifySelected = 1
self.addIdAs = 0
self.runAnyChange = 1
self.classifyName = "Cluster"
self.settingsChanged = False
self.loadSettings()
self.data = None # holds input data
self.km = None # holds clustering object
# GUI definition
# settings
box = OWGUI.widgetBox(self.controlArea, "Clusters (k)")
bg = OWGUI.radioButtonsInBox(box,
self,
"optimized", [],
callback=self.setOptimization)
fixedBox = OWGUI.widgetBox(box, orientation="horizontal")
button = OWGUI.appendRadioButton(bg,
self,
"optimized",
"Fixed",
insertInto=fixedBox,
tooltip="Fixed number of clusters")
self.fixedSpinBox = OWGUI.spin(OWGUI.widgetBox(fixedBox),
self,
"K",
min=2,
max=30,
tooltip="Fixed number of clusters",
callback=self.update,
callbackOnReturn=True)
OWGUI.rubber(fixedBox)
optimizedBox = OWGUI.widgetBox(box)
button = OWGUI.appendRadioButton(bg,
self,
"optimized",
"Optimized",
insertInto=optimizedBox)
option = QStyleOptionButton()
option.initFrom(button)
box = OWGUI.indentedBox(
optimizedBox,
qApp.style().subElementRect(QStyle.SE_CheckBoxIndicator, option,
button).width() - 3)
self.optimizationBox = box
OWGUI.spin(box,
self,
"optimizationFrom",
label="From",
min=2,
max=99,
tooltip="Minimum number of clusters to try",
callback=self.updateOptimizationFrom,
callbackOnReturn=True)
b = OWGUI.spin(box,
self,
"optimizationTo",
label="To",
min=3,
max=100,
tooltip="Maximum number of clusters to try",
callback=self.updateOptimizationTo,
callbackOnReturn=True)
# b.control.setLineEdit(OWGUI.LineEditWFocusOut(b))
OWGUI.comboBox(box,
self,
"scoring",
label="Scoring",
orientation="horizontal",
items=[m[0] for m in self.scoringMethods],
callback=self.update)
box = OWGUI.widgetBox(self.controlArea, "Settings", addSpace=True)
# OWGUI.spin(box, self, "K", label="Number of clusters"+" ", min=1, max=30, step=1,
# callback = self.initializeClustering)
OWGUI.comboBox(box,
self,
"distanceMeasure",
label="Distance measures",
items=[name for name, _ in self.distanceMeasures],
tooltip=None,
indent=20,
callback=self.update)
cb = OWGUI.comboBox(box,
self,
"initializationType",
label="Initialization",
items=[name for name, _ in self.initializations],
tooltip=None,
indent=20,
callback=self.update)
OWGUI.spin(cb.box,
self,
"restarts",
label="Restarts",
orientation="horizontal",
min=1,
max=100,
callback=self.update,
callbackOnReturn=True)
box = OWGUI.widgetBox(self.controlArea, "Cluster IDs")
cb = OWGUI.checkBox(box, self, "classifySelected",
"Append cluster indices")
box = OWGUI.indentedBox(box)
form = QWidget()
le = OWGUI.lineEdit(
form,
self,
"classifyName",
None, #"Name" + " ",
orientation="horizontal", #controlWidth=100,
valueType=str,
# callback=self.sendData,
# callbackOnReturn=True
)
cc = OWGUI.comboBox(
form,
self,
"addIdAs",
label=" ", #"Place" + " ",
orientation="horizontal",
items=["Class attribute", "Attribute", "Meta attribute"],
)
layout = QFormLayout()
layout.setSpacing(8)
layout.setFieldGrowthPolicy(QFormLayout.AllNonFixedFieldsGrow)
layout.setLabelAlignment(Qt.AlignLeft | Qt.AlignJustify)
layout.addRow("Name ", le)
layout.addRow("Place ", cc)
# le.setFixedWidth(cc.sizeHint().width())
form.setLayout(layout)
box.layout().addWidget(form)
cb.disables.append(box)
# cb.disables.append(cc.box)
cb.makeConsistent()
# OWGUI.separator(box)
box = OWGUI.widgetBox(self.controlArea, "Run", addSpace=True)
cb = OWGUI.checkBox(box, self, "runAnyChange", "Run after any change")
self.runButton = b = OWGUI.button(box,
self,
"Run Clustering",
callback=self.run)
OWGUI.setStopper(self, b, cb, "settingsChanged", callback=self.run)
OWGUI.rubber(self.controlArea)
# display of clustering results
self.optimizationReportBox = OWGUI.widgetBox(self.mainArea)
tableBox = OWGUI.widgetBox(self.optimizationReportBox,
"Optimization Report")
self.table = OWGUI.table(tableBox,
selectionMode=QTableWidget.SingleSelection)
self.table.setHorizontalScrollMode(QTableWidget.ScrollPerPixel)
self.table.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.table.setSelectionBehavior(QAbstractItemView.SelectRows)
self.table.setColumnCount(3)
self.table.setHorizontalHeaderLabels(["k", "Best", "Score"])
self.table.verticalHeader().hide()
self.table.horizontalHeader().setStretchLastSection(True)
self.table.setItemDelegateForColumn(
2, OWGUI.TableBarItem(self, self.table))
self.table.setItemDelegateForColumn(1,
OWGUI.IndicatorItemDelegate(self))
self.table.setSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding)
self.table.hide()
self.connect(self.table, SIGNAL("itemSelectionChanged()"),
self.tableItemSelected)
self.setSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding)
self.mainArea.setSizePolicy(QSizePolicy.MinimumExpanding,
QSizePolicy.MinimumExpanding)
OWGUI.rubber(self.topWidgetPart)
self.updateOptimizationGui()
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!",
)
# Queue radio buttons
self.queueBox = OWGUI.radioButtonsInBox(
self.controlArea,
self,
"outputSel",
box="Output mode",
btnLabels=self.outputModes,
callback=self.changeOutputMode,
)
# Queue radio buttons
self.queueBox = OWGUI.radioButtonsInBox(
self.controlArea, self, "queueType", box="Execution Mode", btnLabels=self.queueTypes, callback=None
)
# create table with selectable ML methods
self.colNames = ["ML method", "Enabled"]
mainRight = OWGUI.widgetBox(self.controlArea, "ML methods selection")
mainRight.setSizePolicy(QSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding))
self.mlTable = OWGUI.table(
self.controlArea, rows=0, columns=0, selectionMode=QTableWidget.MultiSelection, addToLayout=1
)
self.mlTable.verticalHeader().hide()
self.mlTable.setSelectionMode(QTableWidget.NoSelection)
self.mlTable.setColumnCount(len(self.colNames))
self.mlTable.setHorizontalHeaderLabels(self.colNames)
# Set location of statistics file
boxFile = OWGUI.widgetBox(self.controlArea, "Path for saving the results", addSpace=True, orientation=0)
L1 = OWGUI.lineEdit(
boxFile,
self,
"statPath",
labelWidth=80,
orientation="horizontal",
tooltip="Please use full path to results file to be created.",
)
L1.setMinimumWidth(200)
button = OWGUI.button(
boxFile, self, "...", callback=self.setStatSaveDir, disabled=0, tooltip="Choose the dir where to save."
)
button.setMaximumWidth(25)
# Apply the settings and send the model to the output channel
OWGUI.button(self.controlArea, self, "&Apply settings", callback=self.Apply)
# Set location of model file
self.boxFile = OWGUI.widgetBox(self.controlArea, "Path for saving Model", addSpace=True, orientation=0)
self.L1 = OWGUI.lineEdit(
self.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.",
)
self.L1.setMinimumWidth(200)
self.button = OWGUI.button(
self.boxFile,
self,
"...",
callback=self.setModelSavePath,
disabled=0,
tooltip="Choose the dir where to save. After chosen, add a name for the model file!",
)
self.button.setMaximumWidth(25)
# Save the model
self.saveButton = OWGUI.button(self.controlArea, self, "&Save model", callback=self.saveModel)
# Statistics show
statBox = OWGUI.widgetBox(self.mainArea, "Statistics", addSpace=True, orientation=0)
self.statInfoBox = OWGUI.widgetLabel(statBox, "")
# Save the model
OWGUI.button(self.mainArea, self, "&Save statistics", callback=self.saveStat)
self.fillMLtable()
self.changeOutputMode()
self.adjustSize()
