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package knobloch
// Self-contained plot over the accumulated frequency_stats.csv; nothing else in
// the package depends on it.
import (
"encoding/csv"
"fmt"
"image/color"
"os"
"regexp"
"slices"
"strconv"
"gonum.org/v1/plot"
"gonum.org/v1/plot/plotter"
"gonum.org/v1/plot/vg"
"gonum.org/v1/plot/vg/draw"
)
// matches the alpha encoded in names like gpt2_50256_mi050_minipile
var modelPattern = regexp.MustCompile(`_(mi?)(\d{3})_`)
type shareRow struct {
variant string
alpha float64
types float64
tokens float64
}
func PlotMorphemeShare(name, out string) error {
rows, err := readShareRows(name)
if err != nil {
return err
}
if len(rows) == 0 {
return fmt.Errorf("no usable rows in %s", name)
}
p := plot.New()
p.X.Label.Text = "Morpheme weight α (%)"
p.Y.Label.Text = "Share of vocabulary / token mass (%)"
p.Add(plotter.NewGrid())
variants := make([]string, 0, 2)
for _, r := range rows {
if !slices.Contains(variants, r.variant) {
variants = append(variants, r.variant)
}
}
slices.Sort(variants)
blue := color.NRGBA{R: 108, G: 126, B: 179, A: 255}
salmon := color.NRGBA{R: 214, G: 96, B: 77, A: 255}
lo, hi := 100.0, 0.0
for _, variant := range variants {
var types, tokens plotter.XYs
for _, r := range rows {
if r.variant != variant {
continue
}
types = append(types, plotter.XY{X: r.alpha, Y: r.types * 100})
tokens = append(tokens, plotter.XY{X: r.alpha, Y: r.tokens * 100})
lo = min(lo, r.types*100, r.tokens*100)
hi = max(hi, r.types*100, r.tokens*100)
}
// a dashed line separates the mi variant from m at the same alpha
var dashes []vg.Length
if variant != "m" {
dashes = []vg.Length{vg.Points(4), vg.Points(3)}
}
if err := addShareSeries(p, types, blue, dashes, variant+" types"); err != nil {
return err
}
if err := addShareSeries(p, tokens, salmon, dashes, variant+" tokens"); err != nil {
return err
}
}
// keep the series off the frame and away from the legend
pad := max((hi-lo)*0.15, 2)
p.Y.Min = lo - pad
p.Y.Max = hi + pad
p.X.Min = -5
p.X.Max = 105
p.Legend.Padding = vg.Points(4)
return p.Save(7*vg.Inch, 5*vg.Inch, out)
}
func addShareSeries(p *plot.Plot, pts plotter.XYs, c color.NRGBA, dashes []vg.Length, label string) error {
slices.SortFunc(pts, func(a, b plotter.XY) int {
switch {
case a.X < b.X:
return -1
case a.X > b.X:
return 1
default:
return 0
}
})
line, points, err := plotter.NewLinePoints(pts)
if err != nil {
return err
}
line.Color = c
line.Width = vg.Points(1.5)
line.Dashes = dashes
points.Color = c
points.Radius = vg.Points(3)
points.Shape = draw.CircleGlyph{}
p.Add(line, points)
p.Legend.Add(label, line, points)
return nil
}
func readShareRows(name string) ([]shareRow, error) {
file, err := os.Open(name)
if err != nil {
return nil, err
}
defer file.Close()
records, err := csv.NewReader(file).ReadAll()
if err != nil {
return nil, err
}
if len(records) == 0 {
return nil, fmt.Errorf("%s is empty", name)
}
index := make(map[string]int)
for i, h := range records[0] {
index[h] = i
}
for _, h := range []string{"model", "type_share", "token_share"} {
if _, ok := index[h]; !ok {
return nil, fmt.Errorf("missing column %q in %s", h, name)
}
}
// the stats file is appended to, so a rerun can repeat a model
seen := make(map[string]shareRow)
var order []string
for _, record := range records[1:] {
model := record[index["model"]]
m := modelPattern.FindStringSubmatch(model)
if m == nil {
continue
}
alpha, err := strconv.ParseFloat(m[2], 64)
if err != nil {
return nil, err
}
types, err := strconv.ParseFloat(record[index["type_share"]], 64)
if err != nil {
return nil, err
}
tokens, err := strconv.ParseFloat(record[index["token_share"]], 64)
if err != nil {
return nil, err
}
if _, ok := seen[model]; !ok {
order = append(order, model)
}
seen[model] = shareRow{
variant: m[1],
alpha: alpha,
types: types,
tokens: tokens,
}
}
rows := make([]shareRow, 0, len(order))
for _, model := range order {
rows = append(rows, seen[model])
}
return rows, nil
}
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