Skip to main content

LineBuilder

esoc_chart::axes

Struct LineBuilder 

Source 
pub struct LineBuilder<'a> { /* private fields */ }
Expand description

Builder for a line series.

Implementations§

Source§

impl<'a> LineBuilder<'a>

Source

pub fn label(self, label: impl Into<String>) -> Self

Set the series label.

Examples found in repository?
examples/training_curves.rs (line 55)
10fn main() -> Result<()> {
11    // ── Simulate training curves ─────────────────────────────────────
12    let epochs: Vec<f64> = (1..=50).map(f64::from).collect();
13
14    // Training loss: exponential decay + noise
15    let mut rng = SimpleRng::new(42);
16    let train_loss: Vec<f64> = epochs
17        .iter()
18        .map(|&e| 2.0 * (-e / 15.0).exp() + 0.05 + rng.normal() * 0.02)
19        .collect();
20
21    // Validation loss: decays slower, starts overfitting around epoch 30
22    let val_loss: Vec<f64> = epochs
23        .iter()
24        .map(|&e| {
25            let base = 2.0 * (-e / 20.0).exp() + 0.1;
26            let overfit = if e > 30.0 { (e - 30.0) * 0.005 } else { 0.0 };
27            base + overfit + rng.normal() * 0.03
28        })
29        .collect();
30
31    // Training accuracy: rises from ~50% to ~98%
32    let train_acc: Vec<f64> = epochs
33        .iter()
34        .map(|&e| (0.98 - 0.48 * (-e / 12.0).exp()).min(1.0) + rng.normal() * 0.01)
35        .collect();
36
37    // Validation accuracy: rises but plateaus earlier
38    let val_acc: Vec<f64> = epochs
39        .iter()
40        .map(|&e| {
41            let base = 0.93 - 0.43 * (-e / 18.0).exp();
42            let overfit = if e > 30.0 { -(e - 30.0) * 0.002 } else { 0.0 };
43            (base + overfit + rng.normal() * 0.015).min(1.0)
44        })
45        .collect();
46
47    // ── Plot 1: Loss curves ──────────────────────────────────────────
48    let mut fig = Figure::new()
49        .size(750.0, 500.0)
50        .title("Training & Validation Loss");
51
52    let ax = fig.add_axes();
53    ax.x_label("Epoch").y_label("Loss");
54    ax.line(&epochs, &train_loss)
55        .label("Train Loss")
56        .color(Color::from_hex("#1f77b4").unwrap().into())
57        .width(2.0)
58        .done();
59    ax.line(&epochs, &val_loss)
60        .label("Val Loss")
61        .color(Color::from_hex("#ff7f0e").unwrap().into())
62        .width(2.0)
63        .dash(&[8.0, 4.0])
64        .done();
65
66    fig.save_svg("training_loss.svg")?;
67    println!("Saved training_loss.svg");
68
69    // ── Plot 2: Accuracy curves ──────────────────────────────────────
70    let mut fig2 = Figure::new()
71        .size(750.0, 500.0)
72        .title("Training & Validation Accuracy");
73
74    let ax2 = fig2.add_axes();
75    ax2.x_label("Epoch").y_label("Accuracy").y_range(0.4, 1.05);
76    ax2.line(&epochs, &train_acc)
77        .label("Train Accuracy")
78        .color(Color::from_hex("#2ca02c").unwrap().into())
79        .width(2.0)
80        .done();
81    ax2.line(&epochs, &val_acc)
82        .label("Val Accuracy")
83        .color(Color::from_hex("#d62728").unwrap().into())
84        .width(2.0)
85        .dash(&[8.0, 4.0])
86        .done();
87
88    fig2.save_svg("training_accuracy.svg")?;
89    println!("Saved training_accuracy.svg");
90
91    Ok(())
92}
Source

pub fn color(self, color: Color) -> Self

Set the line color.

Examples found in repository?
examples/training_curves.rs (line 56)
10fn main() -> Result<()> {
11    // ── Simulate training curves ─────────────────────────────────────
12    let epochs: Vec<f64> = (1..=50).map(f64::from).collect();
13
14    // Training loss: exponential decay + noise
15    let mut rng = SimpleRng::new(42);
16    let train_loss: Vec<f64> = epochs
17        .iter()
18        .map(|&e| 2.0 * (-e / 15.0).exp() + 0.05 + rng.normal() * 0.02)
19        .collect();
20
21    // Validation loss: decays slower, starts overfitting around epoch 30
22    let val_loss: Vec<f64> = epochs
23        .iter()
24        .map(|&e| {
25            let base = 2.0 * (-e / 20.0).exp() + 0.1;
26            let overfit = if e > 30.0 { (e - 30.0) * 0.005 } else { 0.0 };
27            base + overfit + rng.normal() * 0.03
28        })
29        .collect();
30
31    // Training accuracy: rises from ~50% to ~98%
32    let train_acc: Vec<f64> = epochs
33        .iter()
34        .map(|&e| (0.98 - 0.48 * (-e / 12.0).exp()).min(1.0) + rng.normal() * 0.01)
35        .collect();
36
37    // Validation accuracy: rises but plateaus earlier
38    let val_acc: Vec<f64> = epochs
39        .iter()
40        .map(|&e| {
41            let base = 0.93 - 0.43 * (-e / 18.0).exp();
42            let overfit = if e > 30.0 { -(e - 30.0) * 0.002 } else { 0.0 };
43            (base + overfit + rng.normal() * 0.015).min(1.0)
44        })
45        .collect();
46
47    // ── Plot 1: Loss curves ──────────────────────────────────────────
48    let mut fig = Figure::new()
49        .size(750.0, 500.0)
50        .title("Training & Validation Loss");
51
52    let ax = fig.add_axes();
53    ax.x_label("Epoch").y_label("Loss");
54    ax.line(&epochs, &train_loss)
55        .label("Train Loss")
56        .color(Color::from_hex("#1f77b4").unwrap().into())
57        .width(2.0)
58        .done();
59    ax.line(&epochs, &val_loss)
60        .label("Val Loss")
61        .color(Color::from_hex("#ff7f0e").unwrap().into())
62        .width(2.0)
63        .dash(&[8.0, 4.0])
64        .done();
65
66    fig.save_svg("training_loss.svg")?;
67    println!("Saved training_loss.svg");
68
69    // ── Plot 2: Accuracy curves ──────────────────────────────────────
70    let mut fig2 = Figure::new()
71        .size(750.0, 500.0)
72        .title("Training & Validation Accuracy");
73
74    let ax2 = fig2.add_axes();
75    ax2.x_label("Epoch").y_label("Accuracy").y_range(0.4, 1.05);
76    ax2.line(&epochs, &train_acc)
77        .label("Train Accuracy")
78        .color(Color::from_hex("#2ca02c").unwrap().into())
79        .width(2.0)
80        .done();
81    ax2.line(&epochs, &val_acc)
82        .label("Val Accuracy")
83        .color(Color::from_hex("#d62728").unwrap().into())
84        .width(2.0)
85        .dash(&[8.0, 4.0])
86        .done();
87
88    fig2.save_svg("training_accuracy.svg")?;
89    println!("Saved training_accuracy.svg");
90
91    Ok(())
92}
Source

pub fn width(self, width: f64) -> Self

Set the line width.

Examples found in repository?
examples/training_curves.rs (line 57)
10fn main() -> Result<()> {
11    // ── Simulate training curves ─────────────────────────────────────
12    let epochs: Vec<f64> = (1..=50).map(f64::from).collect();
13
14    // Training loss: exponential decay + noise
15    let mut rng = SimpleRng::new(42);
16    let train_loss: Vec<f64> = epochs
17        .iter()
18        .map(|&e| 2.0 * (-e / 15.0).exp() + 0.05 + rng.normal() * 0.02)
19        .collect();
20
21    // Validation loss: decays slower, starts overfitting around epoch 30
22    let val_loss: Vec<f64> = epochs
23        .iter()
24        .map(|&e| {
25            let base = 2.0 * (-e / 20.0).exp() + 0.1;
26            let overfit = if e > 30.0 { (e - 30.0) * 0.005 } else { 0.0 };
27            base + overfit + rng.normal() * 0.03
28        })
29        .collect();
30
31    // Training accuracy: rises from ~50% to ~98%
32    let train_acc: Vec<f64> = epochs
33        .iter()
34        .map(|&e| (0.98 - 0.48 * (-e / 12.0).exp()).min(1.0) + rng.normal() * 0.01)
35        .collect();
36
37    // Validation accuracy: rises but plateaus earlier
38    let val_acc: Vec<f64> = epochs
39        .iter()
40        .map(|&e| {
41            let base = 0.93 - 0.43 * (-e / 18.0).exp();
42            let overfit = if e > 30.0 { -(e - 30.0) * 0.002 } else { 0.0 };
43            (base + overfit + rng.normal() * 0.015).min(1.0)
44        })
45        .collect();
46
47    // ── Plot 1: Loss curves ──────────────────────────────────────────
48    let mut fig = Figure::new()
49        .size(750.0, 500.0)
50        .title("Training & Validation Loss");
51
52    let ax = fig.add_axes();
53    ax.x_label("Epoch").y_label("Loss");
54    ax.line(&epochs, &train_loss)
55        .label("Train Loss")
56        .color(Color::from_hex("#1f77b4").unwrap().into())
57        .width(2.0)
58        .done();
59    ax.line(&epochs, &val_loss)
60        .label("Val Loss")
61        .color(Color::from_hex("#ff7f0e").unwrap().into())
62        .width(2.0)
63        .dash(&[8.0, 4.0])
64        .done();
65
66    fig.save_svg("training_loss.svg")?;
67    println!("Saved training_loss.svg");
68
69    // ── Plot 2: Accuracy curves ──────────────────────────────────────
70    let mut fig2 = Figure::new()
71        .size(750.0, 500.0)
72        .title("Training & Validation Accuracy");
73
74    let ax2 = fig2.add_axes();
75    ax2.x_label("Epoch").y_label("Accuracy").y_range(0.4, 1.05);
76    ax2.line(&epochs, &train_acc)
77        .label("Train Accuracy")
78        .color(Color::from_hex("#2ca02c").unwrap().into())
79        .width(2.0)
80        .done();
81    ax2.line(&epochs, &val_acc)
82        .label("Val Accuracy")
83        .color(Color::from_hex("#d62728").unwrap().into())
84        .width(2.0)
85        .dash(&[8.0, 4.0])
86        .done();
87
88    fig2.save_svg("training_accuracy.svg")?;
89    println!("Saved training_accuracy.svg");
90
91    Ok(())
92}
Source

pub fn dash(self, dashes: &[f64]) -> Self

Set a dash pattern.

Examples found in repository?
examples/training_curves.rs (line 63)
10fn main() -> Result<()> {
11    // ── Simulate training curves ─────────────────────────────────────
12    let epochs: Vec<f64> = (1..=50).map(f64::from).collect();
13
14    // Training loss: exponential decay + noise
15    let mut rng = SimpleRng::new(42);
16    let train_loss: Vec<f64> = epochs
17        .iter()
18        .map(|&e| 2.0 * (-e / 15.0).exp() + 0.05 + rng.normal() * 0.02)
19        .collect();
20
21    // Validation loss: decays slower, starts overfitting around epoch 30
22    let val_loss: Vec<f64> = epochs
23        .iter()
24        .map(|&e| {
25            let base = 2.0 * (-e / 20.0).exp() + 0.1;
26            let overfit = if e > 30.0 { (e - 30.0) * 0.005 } else { 0.0 };
27            base + overfit + rng.normal() * 0.03
28        })
29        .collect();
30
31    // Training accuracy: rises from ~50% to ~98%
32    let train_acc: Vec<f64> = epochs
33        .iter()
34        .map(|&e| (0.98 - 0.48 * (-e / 12.0).exp()).min(1.0) + rng.normal() * 0.01)
35        .collect();
36
37    // Validation accuracy: rises but plateaus earlier
38    let val_acc: Vec<f64> = epochs
39        .iter()
40        .map(|&e| {
41            let base = 0.93 - 0.43 * (-e / 18.0).exp();
42            let overfit = if e > 30.0 { -(e - 30.0) * 0.002 } else { 0.0 };
43            (base + overfit + rng.normal() * 0.015).min(1.0)
44        })
45        .collect();
46
47    // ── Plot 1: Loss curves ──────────────────────────────────────────
48    let mut fig = Figure::new()
49        .size(750.0, 500.0)
50        .title("Training & Validation Loss");
51
52    let ax = fig.add_axes();
53    ax.x_label("Epoch").y_label("Loss");
54    ax.line(&epochs, &train_loss)
55        .label("Train Loss")
56        .color(Color::from_hex("#1f77b4").unwrap().into())
57        .width(2.0)
58        .done();
59    ax.line(&epochs, &val_loss)
60        .label("Val Loss")
61        .color(Color::from_hex("#ff7f0e").unwrap().into())
62        .width(2.0)
63        .dash(&[8.0, 4.0])
64        .done();
65
66    fig.save_svg("training_loss.svg")?;
67    println!("Saved training_loss.svg");
68
69    // ── Plot 2: Accuracy curves ──────────────────────────────────────
70    let mut fig2 = Figure::new()
71        .size(750.0, 500.0)
72        .title("Training & Validation Accuracy");
73
74    let ax2 = fig2.add_axes();
75    ax2.x_label("Epoch").y_label("Accuracy").y_range(0.4, 1.05);
76    ax2.line(&epochs, &train_acc)
77        .label("Train Accuracy")
78        .color(Color::from_hex("#2ca02c").unwrap().into())
79        .width(2.0)
80        .done();
81    ax2.line(&epochs, &val_acc)
82        .label("Val Accuracy")
83        .color(Color::from_hex("#d62728").unwrap().into())
84        .width(2.0)
85        .dash(&[8.0, 4.0])
86        .done();
87
88    fig2.save_svg("training_accuracy.svg")?;
89    println!("Saved training_accuracy.svg");
90
91    Ok(())
92}
Source

pub fn done(self) -> &'a mut Axes

Finish and add the series.

Examples found in repository?
examples/training_curves.rs (line 58)
10fn main() -> Result<()> {
11    // ── Simulate training curves ─────────────────────────────────────
12    let epochs: Vec<f64> = (1..=50).map(f64::from).collect();
13
14    // Training loss: exponential decay + noise
15    let mut rng = SimpleRng::new(42);
16    let train_loss: Vec<f64> = epochs
17        .iter()
18        .map(|&e| 2.0 * (-e / 15.0).exp() + 0.05 + rng.normal() * 0.02)
19        .collect();
20
21    // Validation loss: decays slower, starts overfitting around epoch 30
22    let val_loss: Vec<f64> = epochs
23        .iter()
24        .map(|&e| {
25            let base = 2.0 * (-e / 20.0).exp() + 0.1;
26            let overfit = if e > 30.0 { (e - 30.0) * 0.005 } else { 0.0 };
27            base + overfit + rng.normal() * 0.03
28        })
29        .collect();
30
31    // Training accuracy: rises from ~50% to ~98%
32    let train_acc: Vec<f64> = epochs
33        .iter()
34        .map(|&e| (0.98 - 0.48 * (-e / 12.0).exp()).min(1.0) + rng.normal() * 0.01)
35        .collect();
36
37    // Validation accuracy: rises but plateaus earlier
38    let val_acc: Vec<f64> = epochs
39        .iter()
40        .map(|&e| {
41            let base = 0.93 - 0.43 * (-e / 18.0).exp();
42            let overfit = if e > 30.0 { -(e - 30.0) * 0.002 } else { 0.0 };
43            (base + overfit + rng.normal() * 0.015).min(1.0)
44        })
45        .collect();
46
47    // ── Plot 1: Loss curves ──────────────────────────────────────────
48    let mut fig = Figure::new()
49        .size(750.0, 500.0)
50        .title("Training & Validation Loss");
51
52    let ax = fig.add_axes();
53    ax.x_label("Epoch").y_label("Loss");
54    ax.line(&epochs, &train_loss)
55        .label("Train Loss")
56        .color(Color::from_hex("#1f77b4").unwrap().into())
57        .width(2.0)
58        .done();
59    ax.line(&epochs, &val_loss)
60        .label("Val Loss")
61        .color(Color::from_hex("#ff7f0e").unwrap().into())
62        .width(2.0)
63        .dash(&[8.0, 4.0])
64        .done();
65
66    fig.save_svg("training_loss.svg")?;
67    println!("Saved training_loss.svg");
68
69    // ── Plot 2: Accuracy curves ──────────────────────────────────────
70    let mut fig2 = Figure::new()
71        .size(750.0, 500.0)
72        .title("Training & Validation Accuracy");
73
74    let ax2 = fig2.add_axes();
75    ax2.x_label("Epoch").y_label("Accuracy").y_range(0.4, 1.05);
76    ax2.line(&epochs, &train_acc)
77        .label("Train Accuracy")
78        .color(Color::from_hex("#2ca02c").unwrap().into())
79        .width(2.0)
80        .done();
81    ax2.line(&epochs, &val_acc)
82        .label("Val Accuracy")
83        .color(Color::from_hex("#d62728").unwrap().into())
84        .width(2.0)
85        .dash(&[8.0, 4.0])
86        .done();
87
88    fig2.save_svg("training_accuracy.svg")?;
89    println!("Saved training_accuracy.svg");
90
91    Ok(())
92}

Auto Trait Implementations§

§

impl<'a> Freeze for LineBuilder<'a>

§

impl<'a> !RefUnwindSafe for LineBuilder<'a>

§

impl<'a> !Send for LineBuilder<'a>

§

impl<'a> !Sync for LineBuilder<'a>

§

impl<'a> Unpin for LineBuilder<'a>

§

impl<'a> UnsafeUnpin for LineBuilder<'a>

§

impl<'a> !UnwindSafe for LineBuilder<'a>

Blanket Implementations§

Source§

impl<T> Any for T
where T: 'static + ?Sized,

Source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
Source§

impl<T> Borrow<T> for T
where T: ?Sized,

Source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
Source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

Source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
Source§

impl<T> From<T> for T

Source§

fn from(t: T) -> T

Returns the argument unchanged.

Source§

impl<T, U> Into<U> for T
where U: From<T>,

Source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

Source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

Source§

type Error = Infallible

The type returned in the event of a conversion error.
Source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
Source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

Source§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
Source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.