def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Split selection"] = self.scores[self.attribute_score][0] items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None" return items
def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Split selection"] = self.scores[self.attribute_score][0] items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None" return items
def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None" items["Binary trees"] = ("No", "Yes")[self.binary_trees] return items
def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth) ) if c) or "None" items["Binary trees"] = ("No", "Yes")[self.binary_trees] return items
def send_report(self): from Orange.canvas.report import plural_w self.report_items((("Name", self.model_name), )) self.report_items( "Model parameters", [("Split selection", self.scores[self.attribute_score][0]), ("Pruning", ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None")]) if self.data: self.report_data("Data", self.data)
def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None" if self.limit_majority: items["Splitting"] = "Stop splitting when majority reaches %d%% " \ "(classification only)" % \ self.sufficient_majority items["Binary trees"] = ("No", "Yes")[self.binary_trees] return items
def send_report(self): from Orange.canvas.report import plural_w self.report_items((("Name", self.model_name),)) self.report_items( "Model parameters", [("Split selection", self.scores[self.attribute_score][0]), ("Pruning", ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth)) if c) or "None")]) if self.data: self.report_data("Data", self.data)
def get_learner_parameters(self): from Orange.canvas.report import plural_w items = OrderedDict() items["Pruning"] = ", ".join(s for s, c in ( (plural_w("at least {number} instance{s} in leaves", self.min_leaf), self.limit_min_leaf), (plural_w("at least {number} instance{s} in internal nodes", self.min_internal), self.limit_min_internal), ("maximum depth {}".format(self.max_depth), self.limit_depth) ) if c) or "None" if self.limit_majority: items["Splitting"] = "Stop splitting when majority reaches %d%% " \ "(classification only)" % \ self.sufficient_majority items["Binary trees"] = ("No", "Yes")[self.binary_trees] return items