Python TrainTestModel.from_file Examples

Example #1

File: train_test_model_test.py Project: taosenbai/vmaf

    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)

Example #2

File: quality_runner.py Project: tdaede/vmaf

 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

Example #3

File: quality_runner.py Project: wanjinchang/vmaf

 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

Example #4

File: quality_runner.py Project: Alex-Song/vmaf

 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

Example #5

File: quality_runner.py Project: Netflix/vmaf

 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

Example #6

File: train_test_model_test.py Project: Netflix/vmaf

    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)