github.com/jingcheng-WU/gonum@v0.9.1-0.20210323123734-f1a2a11a8f7b/stat/roc_example_test.go (about)

     1  // Copyright ©2016 The Gonum Authors. All rights reserved.
     2  // Use of this source code is governed by a BSD-style
     3  // license that can be found in the LICENSE file.
     4  
     5  package stat_test
     6  
     7  import (
     8  	"fmt"
     9  	"math"
    10  
    11  	"github.com/jingcheng-WU/gonum/floats"
    12  	"github.com/jingcheng-WU/gonum/integrate"
    13  	"github.com/jingcheng-WU/gonum/stat"
    14  )
    15  
    16  func ExampleROC_weighted() {
    17  	y := []float64{0, 3, 5, 6, 7.5, 8}
    18  	classes := []bool{false, true, false, true, true, true}
    19  	weights := []float64{4, 1, 6, 3, 2, 2}
    20  
    21  	tpr, fpr, _ := stat.ROC(nil, y, classes, weights)
    22  	fmt.Printf("true  positive rate: %v\n", tpr)
    23  	fmt.Printf("false positive rate: %v\n", fpr)
    24  
    25  	// Output:
    26  	// true  positive rate: [0 0.25 0.5 0.875 0.875 1 1]
    27  	// false positive rate: [0 0 0 0 0.6 0.6 1]
    28  }
    29  
    30  func ExampleROC_unweighted() {
    31  	y := []float64{0, 3, 5, 6, 7.5, 8}
    32  	classes := []bool{false, true, false, true, true, true}
    33  
    34  	tpr, fpr, _ := stat.ROC(nil, y, classes, nil)
    35  	fmt.Printf("true  positive rate: %v\n", tpr)
    36  	fmt.Printf("false positive rate: %v\n", fpr)
    37  
    38  	// Output:
    39  	// true  positive rate: [0 0.25 0.5 0.75 0.75 1 1]
    40  	// false positive rate: [0 0 0 0 0.5 0.5 1]
    41  }
    42  
    43  func ExampleROC_threshold() {
    44  	y := []float64{0.1, 0.4, 0.35, 0.8}
    45  	classes := []bool{false, false, true, true}
    46  	stat.SortWeightedLabeled(y, classes, nil)
    47  
    48  	tpr, fpr, thresh := stat.ROC(nil, y, classes, nil)
    49  	fmt.Printf("true  positive rate: %v\n", tpr)
    50  	fmt.Printf("false positive rate: %v\n", fpr)
    51  	fmt.Printf("cutoff thresholds: %v\n", thresh)
    52  
    53  	// Output:
    54  	// true  positive rate: [0 0.5 0.5 1 1]
    55  	// false positive rate: [0 0 0.5 0.5 1]
    56  	// cutoff thresholds: [+Inf 0.8 0.4 0.35 0.1]
    57  }
    58  
    59  func ExampleROC_unsorted() {
    60  	y := []float64{8, 7.5, 6, 5, 3, 0}
    61  	classes := []bool{true, true, true, false, true, false}
    62  	weights := []float64{2, 2, 3, 6, 1, 4}
    63  
    64  	stat.SortWeightedLabeled(y, classes, weights)
    65  
    66  	tpr, fpr, _ := stat.ROC(nil, y, classes, weights)
    67  	fmt.Printf("true  positive rate: %v\n", tpr)
    68  	fmt.Printf("false positive rate: %v\n", fpr)
    69  
    70  	// Output:
    71  	// true  positive rate: [0 0.25 0.5 0.875 0.875 1 1]
    72  	// false positive rate: [0 0 0 0 0.6 0.6 1]
    73  }
    74  
    75  func ExampleROC_knownCutoffs() {
    76  	y := []float64{8, 7.5, 6, 5, 3, 0}
    77  	classes := []bool{true, true, true, false, true, false}
    78  	weights := []float64{2, 2, 3, 6, 1, 4}
    79  	cutoffs := []float64{-1, 3, 4}
    80  
    81  	stat.SortWeightedLabeled(y, classes, weights)
    82  
    83  	tpr, fpr, _ := stat.ROC(cutoffs, y, classes, weights)
    84  	fmt.Printf("true  positive rate: %v\n", tpr)
    85  	fmt.Printf("false positive rate: %v\n", fpr)
    86  
    87  	// Output:
    88  	// true  positive rate: [0.875 1 1]
    89  	// false positive rate: [0.6 0.6 1]
    90  }
    91  
    92  func ExampleROC_equallySpacedCutoffs() {
    93  	y := []float64{8, 7.5, 6, 5, 3, 0}
    94  	classes := []bool{true, true, true, false, true, true}
    95  	weights := []float64{2, 2, 3, 6, 1, 4}
    96  	n := 9
    97  
    98  	stat.SortWeightedLabeled(y, classes, weights)
    99  	cutoffs := make([]float64, n)
   100  	floats.Span(cutoffs, math.Nextafter(y[0], y[0]-1), y[len(y)-1])
   101  
   102  	tpr, fpr, _ := stat.ROC(cutoffs, y, classes, weights)
   103  	fmt.Printf("true  positive rate: %.3v\n", tpr)
   104  	fmt.Printf("false positive rate: %.3v\n", fpr)
   105  
   106  	// Output:
   107  	// true  positive rate: [0.167 0.333 0.583 0.583 0.583 0.667 0.667 0.667 1]
   108  	// false positive rate: [0 0 0 1 1 1 1 1 1]
   109  }
   110  
   111  func ExampleROC_aUC() {
   112  	y := []float64{0.1, 0.35, 0.4, 0.8}
   113  	classes := []bool{true, false, true, false}
   114  
   115  	tpr, fpr, _ := stat.ROC(nil, y, classes, nil)
   116  
   117  	// Compute Area Under Curve.
   118  	auc := integrate.Trapezoidal(fpr, tpr)
   119  	fmt.Printf("true  positive rate: %v\n", tpr)
   120  	fmt.Printf("false positive rate: %v\n", fpr)
   121  	fmt.Printf("auc: %v\n", auc)
   122  
   123  	// Output:
   124  	// true  positive rate: [0 0 0.5 0.5 1]
   125  	// false positive rate: [0 0.5 0.5 1 1]
   126  	// auc: 0.25
   127  }