[+] NOTES.md & README.md

NOTES.md

@@ -0,0 +1,28 @@
+## 16.03.2019
+
+Transformation functions are introduced. Before this step `leaves` was able to output only raw predictions. Here is new bool option named `loadTransformation` adedd to all model load functions: `XGEnsembleFromReader`, `XGEnsembleFromFile`, `XGBLinearFromReader`, `XGBLinearFromFile`, `SKEnsembleFromReader`, `SKEnsembleFromFile`, `LGEnsembleFromJSON`, `LGEnsembleFromReader`, `LGEnsembleFromFile`.
+
+For example, line:
+```go
+model, err := leaves.LGEnsembleFromFile("lg_breast_cancer.model")
+```
+
+Should be changed to:
+```go
+model, err := leaves.LGEnsembleFromFile("lg_breast_cancer.model", false)
+```
+
+if one wants to leave old behaviour.
+
+
+Also, `NClasses` `Ensemble` method will be renamed to `NRawOutputGroups` while keeping the same meaning - number of values that model provides for every object in raw predictions. There is also added `NOutputGroups` - number of values that model provides for every object after applying transformation function. Generally, that means that transformation function can change outputs dimensionality. Please note, if current transformation funciton is `raw`:
+
+```go
+model.Transformation().Name() == "raw"
+```
+
+then
+
+```go
+model.RawOutputGroups() == model.NOutputGroups()
+```

README.md

@@ -14,6 +14,7 @@ _leaves_ is a library implementing prediction code for GBRT (Gradient Boosting R
 ## Features
   * General Features:
     * support parallel predictions for batches
+    * support sigmoid, softmax transformation functions
   * Support LightGBM ([repo](https://github.com/Microsoft/LightGBM)) models:
     * read models from `text` format and from `JSON` format
     * support `gbdt`, `rf` (random forest) and `dart` models
@@ -51,7 +52,8 @@ import (
 
 func main() {
 	// 1. Read model
-	model, err := leaves.LGEnsembleFromFile("lightgbm_model.txt")
+	useTransformation := true
+	model, err := leaves.LGEnsembleFromFile("lightgbm_model.txt", useTransformation)
 	if err != nil {
 		panic(err)
 	}
@@ -69,6 +71,8 @@ In order to use XGBoost model, just change `leaves.LGEnsembleFromFile`, to `leav
 
 Documentation is hosted on godoc ([link](https://godoc.org/github.com/dmitryikh/leaves)). Documentation contains complex usage examples and full API reference. Some additional information about usage examples can be found in [leaves_test.go](leaves_test.go).
 
+Some additional information on new features and backward compatibility can be found in [NOTES.md](NOTES.md).
+
 ## Benchmark
 
 Below are comparisons of prediction speed on batches (~1000 objects in 1 API
@@ -104,9 +108,9 @@ Single thread:
 ## Limitations
 
   * LightGBM models:
-    * no support transformations functions (sigmoid, lambdarank, etc). Output scores is _raw scores_
+    * limited support of transformation functions (support only sigmoid, softmax)
   * XGBoost models:
-    * no support transformations functions. Output scores is _raw scores_
+    * limited support of transformation functions (support only sigmoid, softmax)
     * could be slight divergence between C API predictions vs. _leaves_ because of floating point convertions and comparisons tolerances
   * scikit-learn tree models:
     * no support transformations functions. Output scores is _raw scores_ (as from `GradientBoostingClassifier.decision_function`)