[*] adopt test to transformation functions

doc.go

@@ -28,11 +28,12 @@ build_breast_cancer_model.py:
 	    'objective': 'binary',
 	}
 	clf = lgb.train(params, d_train, n_estimators)
-	# note raw_score=True used here because `leaves` output only raw scores
-	y_pred = clf.predict(X_test, raw_score=True)
+	y_pred = clf.predict(X_test)
+	y_pred_raw = clf.predict(X_test, raw_score=True)
 
 	clf.save_model('lg_breast_cancer.model')  # save the model in txt format
 	np.savetxt('lg_breast_cancer_true_predictions.txt', y_pred)
+	np.savetxt('lg_breast_cancer_true_predictions_raw.txt', y_pred_raw)
 	np.savetxt('breast_cancer_test.tsv', X_test, delimiter='\t')
 
 predict_breast_cancer_model.go:
@@ -55,39 +56,52 @@ predict_breast_cancer_model.go:
 		}
 
 		// loading model
-		model, err := leaves.LGEnsembleFromFile("lg_breast_cancer.model", false)
+		model, err := leaves.LGEnsembleFromFile("lg_breast_cancer.model", true)
 		if err != nil {
 			panic(err)
 		}
 		fmt.Printf("Name: %s\n", model.Name())
 		fmt.Printf("NFeatures: %d\n", model.NFeatures())
-		fmt.Printf("NRawOutputGroups: %d\n", model.NRawOutputGroups())
+		fmt.Printf("NOutputGroups: %d\n", model.NOutputGroups())
 		fmt.Printf("NEstimators: %d\n", model.NEstimators())
+		fmt.Printf("Transformation: %s\n", model.Transformation().Name())
 
 		// loading true predictions as DenseMat
 		truePredictions, err := mat.DenseMatFromCsvFile("lg_breast_cancer_true_predictions.txt", 0, false, "\t", 0.0)
 		if err != nil {
 			panic(err)
 		}
+		truePredictionsRaw, err := mat.DenseMatFromCsvFile("lg_breast_cancer_true_predictions_raw.txt", 0, false, "\t", 0.0)
+		if err != nil {
+			panic(err)
+		}
 
 		// preallocate slice to store model predictions
-		predictions := make([]float64, test.Rows*model.NRawOutputGroups())
+		predictions := make([]float64, test.Rows*model.NOutputGroups())
 		// do predictions
 		model.PredictDense(test.Values, test.Rows, test.Cols, predictions, 0, 1)
 		// compare results
 		const tolerance = 1e-6
 		if err := util.AlmostEqualFloat64Slices(truePredictions.Values, predictions, tolerance); err != nil {
 			panic(fmt.Errorf("different predictions: %s", err.Error()))
 		}
+
+		// compare raw predictions (before transformation function)
+		rawModel := model.EnsembleWithRawPredictions()
+		rawModel.PredictDense(test.Values, test.Rows, test.Cols, predictions, 0, 1)
+		if err := util.AlmostEqualFloat64Slices(truePredictionsRaw.Values, predictions, tolerance); err != nil {
+			panic(fmt.Errorf("different raw predictions: %s", err.Error()))
+		}
 		fmt.Println("Predictions the same!")
 	}
 
 Output:
 
 	Name: lightgbm.gbdt
 	NFeatures: 30
-	NRawOutputGroups: 1
+	NOutputGroups: 1
 	NEstimators: 30
+	Transformation: logistic
 	Predictions the same!
 
 XGBoost Model
@@ -146,7 +160,7 @@ predict_iris_model.go:
 		}
 		fmt.Printf("Name: %s\n", model.Name())
 		fmt.Printf("NFeatures: %d\n", model.NFeatures())
-		fmt.Printf("NRawOutputGroups: %d\n", model.NRawOutputGroups())
+		fmt.Printf("NOutputGroups: %d\n", model.NOutputGroups())
 		fmt.Printf("NEstimators: %d\n", model.NEstimators())
 
 		// loading true predictions as DenseMat
@@ -156,7 +170,7 @@ predict_iris_model.go:
 		}
 
 		// preallocate slice to store model predictions
-		predictions := make([]float64, csr.Rows()*model.NRawOutputGroups())
+		predictions := make([]float64, csr.Rows()*model.NOutputGroups())
 		// do predictions
 		model.PredictCSR(csr.RowHeaders, csr.ColIndexes, csr.Values, predictions, 0, 1)
 		// compare results
@@ -177,7 +191,7 @@ Output:
 
 	Name: xgboost.gbtree
 	NFeatures: 4
-	NRawOutputGroups: 3
+	NOutputGroups: 3
 	NEstimators: 5
 	Predictions the same! (mismatch = 0)