knn: tests now passing

base/error.go

@@ -40,8 +40,20 @@ func (g *GoLearnError) attachFormattedStack() {
 	stackFrames := strings.Split(stackString, "\n")
 
 	stackFmt := make([]string, 0)
-	for i := 3; i < len(stackFrames); i++ {
+	for i := 0; i < len(stackFrames); i++ {
 		if strings.Contains(stackFrames[i], "golearn") {
+			if strings.Contains(stackFrames[i], "golearn/base/error.go") {
+				continue
+			}
+			if strings.Contains(stackFrames[i], "base.WrapError") {
+				continue
+			}
+			if strings.Contains(stackFrames[i], "base.DescribeError") {
+				continue
+			}
+			if strings.Contains(stackFrames[i], "golearn/base.(*GoLearnError).attachFormattedStack") {
+				continue
+			}
 			stackFmt = append(stackFmt, stackFrames[i])
 		}
 	}

base/serialize_instances.go

@@ -114,7 +114,7 @@ func DeserializeInstancesFromTarReader(tr *FunctionalTarReader, prefix string) (
 	if err != nil {
 		return nil, DescribeError("Class Attribute deserialization error", err)
 	}
-	attrBytes, err = tr.GetNamedFile("ATTRS")
+	attrBytes, err = tr.GetNamedFile(p("ATTRS"))
 	if err != nil {
 		return nil, DescribeError("Unable to read ATTRS", err)
 	}

knn/knn.go

@@ -326,6 +326,96 @@ func (KNN *KNNClassifier) weightedVote(maxmap map[string]float64, values []int,
 	return maxClass
 }
 
+// GetMetadata returns required serialization information for this classifier
+func (KNN *KNNClassifier) GetMetadata() base.ClassifierMetadataV1 {
+
+	classifierParams := make(map[string]interface{})
+	classifierParams["distance_func"] = KNN.DistanceFunc
+	classifierParams["algorithm"] = KNN.Algorithm
+	classifierParams["neighbours"] = KNN.NearestNeighbours
+	classifierParams["weighted"] = KNN.Weighted
+	classifierParams["allow_optimizations"] = KNN.AllowOptimisations
+
+	return base.ClassifierMetadataV1{
+		FormatVersion: 1,
+		ClassifierName: "KNN",
+		ClassifierVersion: "1.0",
+		ClassifierMetadata: classifierParams,
+	}
+}
+
+// Save outputs a given KNN classifier.
+func (KNN *KNNClassifier) Save(filePath string) error {
+	writer, err := base.CreateSerializedClassifierStub(filePath, KNN.GetMetadata())
+	if err != nil {
+		return err
+	}
+	fmt.Printf("writer: %v", writer)
+	return KNN.SaveWithPrefix(writer, "")
+}
+
+// SaveWithPrefix outputs KNN as part of another file.
+func (KNN *KNNClassifier) SaveWithPrefix(writer *base.ClassifierSerializer, prefix string) error {
+	fmt.Printf("writer: %v", writer)
+	err := writer.WriteInstancesForKey(writer.Prefix(prefix, "TrainingInstances"), KNN.TrainingData, true)
+	if err != nil {
+		return err
+	}
+	err = writer.Close()
+	return err
+}
+
+// Load reloads a given KNN classifier when it's the only thing in the output file.
+func (KNN *KNNClassifier) Load(filePath string) error {
+	reader, err := base.ReadSerializedClassifierStub(filePath)
+	if err != nil {
+		return err
+	}
+
+	return KNN.LoadWithPrefix(reader, "")
+}
+
+// LoadWithPrefix reloads a given KNN classifier when it's part of another file.
+func (KNN *KNNClassifier) LoadWithPrefix(reader *base.ClassifierDeserializer, prefix string) error {
+
+	clsMetadata, err := reader.ReadMetadataAtPrefix(prefix)
+	if err != nil {
+		return err
+	}
+
+	if clsMetadata.ClassifierName != "KNN" {
+		return fmt.Errorf("This file doesn't contain a KNN classifier")
+	}
+	if clsMetadata.ClassifierVersion != "1.0" {
+		return fmt.Errorf("Can't understand this file format")
+	}
+
+	metadata := clsMetadata.ClassifierMetadata
+	KNN.DistanceFunc = metadata["distance_func"].(string)
+	KNN.Algorithm = metadata["algorithm"].(string)
+	//KNN.NearestNeighbours = metadata["neighbours"].(int)
+	KNN.Weighted = metadata["weighted"].(bool)
+	KNN.AllowOptimisations = metadata["allow_optimizations"].(bool)
+
+	// 101 on why JSON is a bad serialization format
+	floatNeighbours := metadata["neighbours"].(float64)
+	KNN.NearestNeighbours = int(floatNeighbours)
+
+	KNN.TrainingData, err = reader.GetInstancesForKey(reader.Prefix(prefix, "TrainingInstances"))
+
+	return err
+}
+
+// ReloadKNNClassifier reloads a KNNClassifier when it's the only thing in an output file.
+func ReloadKNNClassifier(filePath string) (*KNNClassifier, error) {
+	stub := &KNNClassifier{}
+	err := stub.Load(filePath)
+	if err != nil {
+		return nil, err
+	}
+	return stub, nil
+}
+
 // A KNNRegressor consists of a data matrix, associated result variables in the same order as the matrix, and a name.
 type KNNRegressor struct {
 	base.BaseEstimator
@@ -384,4 +474,4 @@ func (KNN *KNNRegressor) Predict(vector *mat64.Dense, K int) float64 {
 
 	average := sum / float64(K)
 	return average
-}
+}

knn/knn_test.go

@@ -5,6 +5,7 @@ import (
 
 	"github.com/sjwhitworth/golearn/base"
 	. "github.com/smartystreets/goconvey/convey"
+	"fmt"
 )
 
 func TestKnnClassifierWithoutOptimisations(t *testing.T) {
@@ -38,6 +39,36 @@ func TestKnnClassifierWithoutOptimisations(t *testing.T) {
 	})
 }
 
+func TestKnnSaveAndReload(t *testing.T) {
+	Convey("Given labels, a classifier and data", t, func() {
+		trainingData, err := base.ParseCSVToInstances("knn_train_1.csv", false)
+		So(err, ShouldBeNil)
+
+		testingData, err := base.ParseCSVToInstances("knn_test_1.csv", false)
+		So(err, ShouldBeNil)
+
+		cls := NewKnnClassifier("euclidean", "linear", 2)
+		cls.AllowOptimisations = false
+		cls.Fit(trainingData)
+		predictions, err := cls.Predict(testingData)
+		So(err, ShouldBeNil)
+		So(predictions, ShouldNotEqual, nil)
+
+		Convey("So saving the classifier should work...", func(){
+			err := cls.Save("temp.cls")
+			So(err, ShouldBeNil)
+			Convey("So loading the classifier should work...", func(){
+				clsR, err := ReloadKNNClassifier("temp.cls")
+				So(err, ShouldBeNil)
+				So(cls.String(), ShouldEqual, clsR.String())
+				predictionsR, err := clsR.Predict(testingData)
+				So(err, ShouldBeNil)
+				So(fmt.Sprintf("%v", predictionsR), ShouldEqual, fmt.Sprintf("%v", predictions))
+			})
+		})
+	})
+}
+
 func TestKnnClassifierWithOptimisations(t *testing.T) {
 	Convey("Given labels, a classifier and data", t, func() {
 		trainingData, err := base.ParseCSVToInstances("knn_train_1.csv", false)