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golearn/meta/one_v_all_test.go
2017-09-10 20:35:34 +01:00

71 lines
1.8 KiB
Go

package meta
import (
"fmt"
"github.com/sjwhitworth/golearn/base"
"github.com/sjwhitworth/golearn/evaluation"
"github.com/sjwhitworth/golearn/linear_models"
. "github.com/smartystreets/goconvey/convey"
"io/ioutil"
"os"
"testing"
)
func TestOneVsAllModel(t *testing.T) {
classifierFunc := func(c string) base.Classifier {
m, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
if err != nil {
panic(err)
}
return m
}
Convey("Given data", t, func() {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
So(err, ShouldBeNil)
X, Y := base.InstancesTrainTestSplit(inst, 0.4)
m := NewOneVsAllModel(classifierFunc)
m.Fit(X)
Convey("The maximum class index should be 2", func() {
So(m.maxClassVal, ShouldEqual, 2)
})
Convey("There should be three of everything...", func() {
So(len(m.filters), ShouldEqual, 3)
So(len(m.classifiers), ShouldEqual, 3)
})
Convey("Predictions should work...", func() {
predictions, err := m.Predict(Y)
So(err, ShouldEqual, nil)
cf, err := evaluation.GetConfusionMatrix(Y, predictions)
So(err, ShouldEqual, nil)
fmt.Println(evaluation.GetAccuracy(cf))
fmt.Println(evaluation.GetSummary(cf))
})
Convey("Saving and reloading should work...", func() {
predictions, err := m.Predict(Y)
So(err, ShouldEqual, nil)
f, err := ioutil.TempFile(os.TempDir(), "tmpCls")
defer func() {
f.Close()
}()
err = m.Save(f.Name())
So(err, ShouldBeNil)
Convey("Reloaded classifier should output the same predictions", func() {
m := NewOneVsAllModel(classifierFunc)
err := m.Load(f.Name())
So(err, ShouldBeNil)
newPredictions, err := m.Predict(Y)
So(err, ShouldBeNil)
So(base.InstancesAreEqual(predictions, newPredictions), ShouldBeTrue)
})
})
})
}