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128 lines
3.5 KiB
Go
128 lines
3.5 KiB
Go
// Copyright ©2016 The Gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package stat_test
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import (
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"fmt"
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"math"
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"gonum.org/v1/gonum/floats"
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"gonum.org/v1/gonum/integrate"
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"gonum.org/v1/gonum/stat"
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)
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func ExampleROC_weighted() {
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y := []float64{0, 3, 5, 6, 7.5, 8}
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classes := []bool{false, true, false, true, true, true}
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weights := []float64{4, 1, 6, 3, 2, 2}
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tpr, fpr, _ := stat.ROC(nil, y, classes, weights)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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// Output:
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// true positive rate: [0 0.25 0.5 0.875 0.875 1 1]
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// false positive rate: [0 0 0 0 0.6 0.6 1]
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}
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func ExampleROC_unweighted() {
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y := []float64{0, 3, 5, 6, 7.5, 8}
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classes := []bool{false, true, false, true, true, true}
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tpr, fpr, _ := stat.ROC(nil, y, classes, nil)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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// Output:
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// true positive rate: [0 0.25 0.5 0.75 0.75 1 1]
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// false positive rate: [0 0 0 0 0.5 0.5 1]
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}
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func ExampleROC_threshold() {
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y := []float64{0.1, 0.4, 0.35, 0.8}
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classes := []bool{false, false, true, true}
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stat.SortWeightedLabeled(y, classes, nil)
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tpr, fpr, thresh := stat.ROC(nil, y, classes, nil)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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fmt.Printf("cutoff thresholds: %v\n", thresh)
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// Output:
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// true positive rate: [0 0.5 0.5 1 1]
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// false positive rate: [0 0 0.5 0.5 1]
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// cutoff thresholds: [+Inf 0.8 0.4 0.35 0.1]
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}
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func ExampleROC_unsorted() {
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y := []float64{8, 7.5, 6, 5, 3, 0}
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classes := []bool{true, true, true, false, true, false}
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weights := []float64{2, 2, 3, 6, 1, 4}
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stat.SortWeightedLabeled(y, classes, weights)
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tpr, fpr, _ := stat.ROC(nil, y, classes, weights)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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// Output:
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// true positive rate: [0 0.25 0.5 0.875 0.875 1 1]
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// false positive rate: [0 0 0 0 0.6 0.6 1]
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}
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func ExampleROC_knownCutoffs() {
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y := []float64{8, 7.5, 6, 5, 3, 0}
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classes := []bool{true, true, true, false, true, false}
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weights := []float64{2, 2, 3, 6, 1, 4}
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cutoffs := []float64{-1, 3, 4}
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stat.SortWeightedLabeled(y, classes, weights)
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tpr, fpr, _ := stat.ROC(cutoffs, y, classes, weights)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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// Output:
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// true positive rate: [0.875 0.875 1]
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// false positive rate: [0.6 0.6 1]
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}
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func ExampleROC_equallySpacedCutoffs() {
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y := []float64{8, 7.5, 6, 5, 3, 0}
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classes := []bool{true, true, true, false, true, true}
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weights := []float64{2, 2, 3, 6, 1, 4}
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n := 9
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stat.SortWeightedLabeled(y, classes, weights)
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cutoffs := make([]float64, n)
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floats.Span(cutoffs, math.Nextafter(y[0], y[0]-1), y[len(y)-1])
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tpr, fpr, _ := stat.ROC(cutoffs, y, classes, weights)
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fmt.Printf("true positive rate: %.3v\n", tpr)
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fmt.Printf("false positive rate: %.3v\n", fpr)
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// Output:
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// true positive rate: [0 0.333 0.333 0.583 0.583 0.583 0.667 0.667 1]
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// false positive rate: [0 0 0 0 1 1 1 1 1]
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}
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func ExampleROC_aUC() {
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y := []float64{0.1, 0.35, 0.4, 0.8}
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classes := []bool{true, false, true, false}
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tpr, fpr, _ := stat.ROC(nil, y, classes, nil)
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// Compute Area Under Curve.
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auc := integrate.Trapezoidal(fpr, tpr)
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fmt.Printf("true positive rate: %v\n", tpr)
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fmt.Printf("false positive rate: %v\n", fpr)
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fmt.Printf("auc: %v\n", auc)
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// Output:
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// true positive rate: [0 0 0.5 0.5 1]
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// false positive rate: [0 0.5 0.5 1 1]
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// auc: 0.25
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}
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