def tests_no_metrics_to_protobuf_regression(): mod_met = ModelMetrics(model_type=ModelType.REGRESSION) assert mod_met.model_type == ModelType.REGRESSION message = mod_met.to_protobuf() model_metrics = ModelMetrics.from_protobuf(message) assert model_metrics.model_type == ModelType.REGRESSION
def tests_no_metrics_to_protobuf_classification(): mod_met = ModelMetrics(model_type=ModelType.CLASSIFICATION) assert mod_met.model_type == ModelType.CLASSIFICATION message = mod_met.to_protobuf() model_metrics = ModelMetrics.from_protobuf(message) assert model_metrics.model_type == ModelType.CLASSIFICATION
def tests_model_metrics_to_protobuf_regression(): regression_model = ModelMetrics(model_type=ModelType.REGRESSION) targets_1 = [0.1, 0.3, 0.4] predictions_1 = [0.5, 0.5, 0.5] regression_model.compute_regression_metrics(predictions_1, targets_1) regression_message = regression_model.to_protobuf() model_metrics_from_message = ModelMetrics.from_protobuf(regression_message) assert model_metrics_from_message.model_type == ModelType.REGRESSION
def tests_model_metrics_to_protobuf_classification(): mod_met = ModelMetrics(model_type=ModelType.CLASSIFICATION) targets_1 = ["cat", "dog", "pig"] predictions_1 = ["cat", "dog", "dog"] scores_1 = [0.1, 0.2, 0.4] mod_met.compute_confusion_matrix(predictions_1, targets_1, scores_1) message = mod_met.to_protobuf() model_metrics = ModelMetrics.from_protobuf(message) assert model_metrics.model_type == ModelType.CLASSIFICATION assert model_metrics.confusion_matrix.labels == ["cat", "dog", "pig"]
def tests_model_metrics_to_protobuf(): mod_met = ModelMetrics() targets_1 = ["cat", "dog", "pig"] predictions_1 = ["cat", "dog", "dog"] scores_1 = [0.1, 0.2, 0.4] expected_1 = [[1, 0, 0], [0, 1, 1], [0, 0, 0]] mod_met.compute_confusion_matrix(predictions_1, targets_1, scores_1) message = mod_met.to_protobuf() ModelMetrics.from_protobuf(message)