"""
<name>Label Powerset</name>
<description>LabelPowerset Multi-label Learner</description>
<icon>icons/Unknown.png</icon>
<contact>Wencan Luo (wencanluo.cn(@at@)gmail.com)</contact>
<priority>100</priority>
"""
from OWWidget import *
import OWGUI
from exceptions import Exception
from orngWrap import PreprocessedLearner

import Orange

class OWLP(OWWidget):
    settingsList = ["name"]

    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 send_report(self):
        self.reportSettings("Learning parameters",
                            [("base_learner", self.baselearnerList[self.base_learner][0])])
        self.reportData(self.data)
            
    def set_data(self,data):  
        if data == None:
            return

        if not Orange.multilabel.is_multilabel(data):
            self.warning(0, "Multi-label data with class values 0 and 1 is "
                            "expected on the input.")
            return
        self.warning(0, None)
        
        self.data = data
        self.set_learner()

    def set_preprocessor(self, pp):
        self.preprocessor = pp
        self.set_learner()
        
    def set_base_learner(self,base_learner):
        self.base_learner = base_learner
        self.set_learner()
    
    def set_learner(self):
        self.learner = Orange.multilabel.LabelPowersetLearner(base_learner = self.base_learner)
        if self.preprocessor:
            self.learner = self.preprocessor.wrapLearner(self.learner)
        self.learner.name = self.name

        self.send("Learner", self.learner)

        self.learn()

    def learn(self):
        self.classifier = None
        if self.data and self.learner:
            try:
                self.classifier = self.learner(self.data)
                self.classifier.name = self.name
                #for i in range(10):
                #    c,p = self.classifier(self.data[i],Orange.classification.Classifier.GetBoth)
                #    print c,p
            except Exception, (errValue):
                self.classifier = None
                self.error(str(errValue))
        self.send("LabelPowerset Classifier", self.classifier)

##############################################################################
# Test the widget.
# Make sure that a sample data set (emotions.tab) is in the directory.

if __name__=="__main__":
    a=QApplication(sys.argv)
    ow=OWLP()

    dataset = Orange.data.Table('emotions.tab')
    ow.set_data(dataset)

    ow.show()
    a.exec_()
    ow.saveSettings()
