def set_results(self, results): """Set the input evaluation results.""" self.clear() self.results = check_results_adequacy(results, self.Error) if self.results is not None: self._initialize(results) self._setup_plot()
def set_results(self, results): self.closeContext() self.clear() self.results = check_results_adequacy(results, self.Error) if self.results is not None: self._initialize(results) self.openContext(self.results.domain.class_var, self.classifier_names) self._setup_plot()
def set_results(self, results): self.clear() results = check_results_adequacy(results, self.Error) if results is not None and not results.actual.size: self.Warning.empty_input() else: self.Warning.empty_input.clear() self.results = results if self.results is not None: self._initialize(results) self._replot()
def set_results(self, results): """Set the input evaluation results.""" self.closeContext() self.clear() self.results = check_results_adequacy(results, self.Error) if self.results is not None: self._initialize(self.results) self.openContext(self.results.domain.class_var, self.classifier_names) self._setup_plot() else: self.warning()
def set_results(self, results): self.clear() self.results = check_results_adequacy(results, self.Error) if self.results is not None: self._initialize(results) self._replot()