91 lines
1.7 KiB
Go
91 lines
1.7 KiB
Go
package main
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import (
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"fmt"
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"log"
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"os"
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"github.com/sjwhitworth/golearn/base"
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"github.com/sjwhitworth/golearn/trees"
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)
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var (
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fname = "data.csv"
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outliers = 15
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randomDataSize = 10001
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)
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func main() {
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data, err := loadData()
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if err != nil {
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log.Fatalln(err)
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}
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log.Printf(
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"Train Isolation Forest model (nTrees: %d, maxDepth: %d, subSpace: %d) on the data\n",
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1000, 1000, 7500,
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)
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predictions := train(data)
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log.Println("Calculate avg/min scores")
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avg := 0.0
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minScore := 1.0
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for i := 0; i < randomDataSize; i++ {
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tmp := predictions[i]
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avg += tmp
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if tmp < minScore {
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minScore = tmp
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}
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}
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fmt.Fprintf(os.Stderr, "\tAverage anomaly score for normal data: %f\n",
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avg/float64(randomDataSize),
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)
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fmt.Fprintf(os.Stderr, "\tMinimum anomaly score for normal data: %f\n",
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minScore,
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)
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// these values should be much higher as comapred to the scores for normal
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// data.
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fmt.Fprintln(os.Stderr, "\tAnomaly scores for outliers are:")
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for i := randomDataSize; i < (randomDataSize + outliers); i++ {
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fmt.Fprint(os.Stderr, "\t")
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fmt.Fprintln(os.Stderr, predictions[i])
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}
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log.Println("we're done")
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}
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func train(data *base.DenseInstances) []float64 {
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// get a new Isolation Forest with 700 trees, max depth 700 and each tree
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// using 7500 datapoints.
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forest := trees.NewIsolationForest(1000, 1000, 7500)
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// fit the isolation forest to the data.
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forest.Fit(data)
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// return predictions.
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return forest.Predict(data)
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}
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func loadData() (*base.DenseInstances, error) {
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// generate and save random data, along with known outlier values.
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err := prepData(fname)
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if err != nil {
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return nil, err
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}
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data, err := base.ParseCSVToInstances(fname, false)
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if err != nil {
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return nil, err
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}
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return data, nil
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}
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