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https://github.com/sjwhitworth/golearn.git
synced 2025-04-26 13:49:14 +08:00
50 lines
1.2 KiB
Go
50 lines
1.2 KiB
Go
package meta
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import (
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"fmt"
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"github.com/sjwhitworth/golearn/base"
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"github.com/sjwhitworth/golearn/evaluation"
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"github.com/sjwhitworth/golearn/linear_models"
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. "github.com/smartystreets/goconvey/convey"
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"testing"
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)
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func TestOneVsAllModel(t *testing.T) {
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classifierFunc := func(c string) base.Classifier {
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m, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
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if err != nil {
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panic(err)
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}
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return m
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}
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Convey("Given data", t, func() {
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inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
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So(err, ShouldBeNil)
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X, Y := base.InstancesTrainTestSplit(inst, 0.4)
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m := NewOneVsAllModel(classifierFunc)
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m.Fit(X)
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Convey("The maximum class index should be 2", func() {
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So(m.maxClassVal, ShouldEqual, 2)
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})
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Convey("There should be three of everything...", func() {
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So(len(m.filters), ShouldEqual, 3)
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So(len(m.classifiers), ShouldEqual, 3)
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})
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Convey("Predictions should work...", func() {
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predictions, err := m.Predict(Y)
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So(err, ShouldEqual, nil)
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cf, err := evaluation.GetConfusionMatrix(Y, predictions)
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So(err, ShouldEqual, nil)
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fmt.Println(evaluation.GetAccuracy(cf))
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fmt.Println(evaluation.GetSummary(cf))
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})
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})
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}
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