-
Notifications
You must be signed in to change notification settings - Fork 0
/
diags_test.go
178 lines (144 loc) · 3.76 KB
/
diags_test.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
package seafan
import (
"fmt"
"os"
"testing"
"github.com/stretchr/testify/assert"
)
func TestCoalesce(t *testing.T) {
obs := []float64{0, 0, 1,
0, 0, 1,
0, 1, 0,
1, 0, 0}
fit := []float64{.2, .3, .5,
.2, .5, .3,
.2, .4, .4,
.5, .3, .2}
nCat := 3
expObs := []float64{1, 1, 0, 0}
expFit := []float64{.5, .3, .4, .2}
targ := []int{2}
fitTest, e := Coalesce(fit, nCat, targ, false, false, nil)
assert.Nil(t, e)
obsTest, e := Coalesce(obs, nCat, targ, true, false, nil)
assert.Nil(t, e)
assert.ElementsMatch(t, obsTest, expObs)
assert.ElementsMatch(t, fitTest, expFit)
targ = []int{1, 2}
expObs = []float64{1, 1, 1, 0}
expFit = []float64{.8, .8, .8, .5}
fitTest, e = Coalesce(fit, nCat, targ, false, false, nil)
assert.Nil(t, e)
obsTest, e = Coalesce(obs, nCat, targ, true, false, nil)
assert.Nil(t, e)
assert.Nil(t, e)
assert.ElementsMatch(t, obsTest, expObs)
assert.ElementsMatch(t, fitTest, expFit)
}
func TestKS(t *testing.T) {
y := make([]float64, 0)
p := make([]float64, 0)
n := 1000
cnt := 0
for k := 0; k < n; k++ {
y = append(y, 0, 1)
px := float64(k) / float64(n)
p = append(p, 1.0-px, px)
y = append(y, 1, 0)
p = append(p, 1-px*px, px*px)
if px*px <= 0.25 {
cnt++
}
}
fit, e := Coalesce(p, 2, []int{1}, false, false, nil)
assert.Nil(t, e)
obs, e := Coalesce(y, 2, []int{1}, true, false, nil)
assert.Nil(t, e)
xy, e := NewXY(fit, obs)
assert.Nil(t, e)
ks, _, _, e := KS(xy, nil)
assert.Nil(t, e)
assert.InEpsilon(t, ks, 25.0, .01)
}
func ExampleSlice_Iter() {
// An example of slicing through the data to generate diagnostics on subsets.
// The code here will generate a decile plot for each of the 20 levels of x4.
Verbose = false
bSize := 100
// generate a Pipeline of type *ChData that reads test.csv in the data directory
pipe := chPipe(bSize, "test1.csv")
// The feature x4 takes on values 0,1,2,...19. chPipe treats this a continuous feature.
// Let's override that and re-initialize the pipeline.
WithCats("x4")(pipe)
WithOneHot("x4oh", "x4")(pipe)
if e := pipe.Init(); e != nil {
panic(e)
}
mod := ModSpec{
"Input(x1+x2+x3+x4oh)",
"FC(size:2, activation:softmax)",
"Target(yoh)",
}
nn, e := NewNNModel(mod, pipe, true)
if e != nil {
panic(e)
}
WithCostFn(CrossEntropy)(nn)
ft := NewFit(nn, 100, pipe)
if e = ft.Do(); e != nil {
panic(e)
}
sf := os.TempDir() + "/nnTest"
e = nn.Save(sf)
if e != nil {
panic(e)
}
WithBatchSize(8500)(pipe)
pred, e := PredictNN(sf, pipe, false)
if e != nil {
panic(e)
}
if e = AddFitted(pipe, sf, []int{1}, "fit", nil, false, nil); e != nil {
panic(e)
}
_ = os.Remove(sf + "P.nn")
_ = os.Remove(sf + "S.nn")
s, e := NewSlice("x4", 0, pipe, nil)
if e != nil {
panic(e)
}
fit, e := Coalesce(pred.FitSlice(), 2, []int{1}, false, false, nil)
if e != nil {
panic(e)
}
desc, e := NewDesc(nil, "Descriptive Statistics")
for s.Iter() {
slicer := s.MakeSlicer()
if e != nil {
panic(e)
}
desc.Populate(fit, true, slicer)
fmt.Printf("Slice x4=%v has %d observations\n", s.Value(), desc.N)
}
// Output:
// Slice x4=0 has 391 observations
// Slice x4=1 has 408 observations
// Slice x4=2 has 436 observations
// Slice x4=3 has 428 observations
// Slice x4=4 has 417 observations
// Slice x4=5 has 472 observations
// Slice x4=6 has 424 observations
// Slice x4=7 has 455 observations
// Slice x4=8 has 431 observations
// Slice x4=9 has 442 observations
// Slice x4=10 has 411 observations
// Slice x4=11 has 413 observations
// Slice x4=12 has 433 observations
// Slice x4=13 has 416 observations
// Slice x4=14 has 434 observations
// Slice x4=15 has 367 observations
// Slice x4=16 has 437 observations
// Slice x4=17 has 433 observations
// Slice x4=18 has 429 observations
// Slice x4=19 has 423 observations
}