def test_divergence(self, *args):
learner = UserItemBaselineLearner()
super().test_divergence(learner, filename=__dataset__)
def test_input_data_continuous(self, *args):
learner = UserItemBaselineLearner(verbose=True)
super().test_input_data_continuous(learner, filename=__dataset__)
def test_warnings(self, *args):
learner = UserItemBaselineLearner()
super().test_warnings(learner, filename=__dataset__)
def test_swap_columns(self, *args):
learner = UserItemBaselineLearner()
super().test_swap_columns(learner,
filename1='ratings_dis.tab',
filename2='ratings_dis_swap.tab')
def test_predict_items(self, *args):
learner = UserItemBaselineLearner()
super().test_predict_items(learner, filename=__dataset__)
def test_input_data_discrete(self, *args):
learner = UserItemBaselineLearner()
super().test_input_data_discrete(learner, filename='ratings_dis.tab')
from PyQt4.QtGui import QApplication
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from orangecontrib.recommendation import UserItemBaselineLearner
class OWUserItemBaseline(OWBaseLearner):
# Widget needs a name, or it is considered an abstract widget
# and not shown in the menu.
name = "User-Item Baseline"
description = 'This model takes the bias of users and items plus the ' \
'global average to make predictions.'
icon = "icons/user-item-baseline.svg"
priority = 80
LEARNER = UserItemBaselineLearner
if __name__ == '__main__':
app = QApplication([])
widget = UserItemBaselineLearner()
widget.show()
app.exec()