def test_train_save_load_predict(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results( self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results( self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results( self.features) # using dis_y only del xs['dis_u'] del xs['dis_v'] del xys['dis_u'] del xys['dis_v'] model = MomentRandomForestTrainTestModel({ 'norm_type': 'normalize', 'random_state': 0 }) model.train(xys) model.to_file(self.model_filename) self.assertTrue(os.path.exists(self.model_filename)) loaded_model = TrainTestModel.from_file(self.model_filename) result = loaded_model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def _load_model(self, asset): if self.optional_dict is not None \ and 'model_filepath' in self.optional_dict \ and self.optional_dict['model_filepath'] is not None: model_filepath = self.optional_dict['model_filepath'] else: model_filepath = self.DEFAULT_MODEL_FILEPATH model = TrainTestModel.from_file(model_filepath, self.logger) return model
def _load_model(self): model_filepath = self.optional_dict['model_filepath'] \ if (self.optional_dict is not None and 'model_filepath' in self.optional_dict and self.optional_dict['model_filepath'] is not None ) \ else self.DEFAULT_MODEL_FILEPATH model = TrainTestModel.from_file(model_filepath, self.logger) return model
def _load_model(self): model_filepath = self.optional_dict['model_filepath'] \ if (self.optional_dict is not None and 'model_filepath' in self.optional_dict and self.optional_dict['model_filepath'] is not None ) \ else self.DEFAULT_MODEL_FILEPATH model = TrainTestModel.from_file(model_filepath, self.logger) return model
def _load_model(self, asset): if self.optional_dict is not None \ and 'model_filepath' in self.optional_dict \ and self.optional_dict['model_filepath'] is not None: model_filepath = self.optional_dict['model_filepath'] else: model_filepath = self.DEFAULT_MODEL_FILEPATH model = TrainTestModel.from_file(model_filepath, self.logger) return model
def test_train_save_load_predict(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features) # using dis_y only del xs['dis_u'] del xs['dis_v'] del xys['dis_u'] del xys['dis_v'] model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0}) model.train(xys) model.to_file(self.model_filename) self.assertTrue(os.path.exists(self.model_filename)) loaded_model = TrainTestModel.from_file(self.model_filename) result = loaded_model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)