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//! Learning curve for model performance analysis
//!
//! This module provides the LearningCurve data structure for visualizing model
//! performance across different training set sizes, comparing training and
//! validation metrics.
use crate::error::{NeuralError, Result};
use crate::utils::colors::{colorize, stylize, Color, ColorOptions, Style};
use crate::utils::evaluation::helpers::draw_line_with_coords;
use scirs2_core::ndarray::{Array1, Array2, Axis};
use scirs2_core::numeric::{Float, FromPrimitive};
use std::fmt::{Debug, Display};
// Removed problematic type alias - use trait bounds directly in implementations
/// Learning curve data structure for visualizing model performance
///
/// This structure represents learning curves that show how model performance
/// changes as the training set size increases, comparing training and validation
/// metrics to help diagnose overfitting, underfitting, and other training issues.
pub struct LearningCurve<F: Float + Debug + Display> {
/// Training set sizes used for evaluation
pub train_sizes: Array1<usize>,
/// Training scores for each size and fold (rows=sizes, cols=folds)
pub train_scores: Array2<F>,
/// Validation scores for each size and fold (rows=sizes, cols=folds)
pub val_scores: Array2<F>,
/// Mean training scores across folds
pub train_mean: Array1<F>,
/// Standard deviation of training scores
pub train_std: Array1<F>,
/// Mean validation scores across folds
pub val_mean: Array1<F>,
/// Standard deviation of validation scores
pub val_std: Array1<F>,
}
impl<F: Float + Debug + Display + FromPrimitive> LearningCurve<F> {
/// Create a new learning curve from training and validation scores
///
/// # Arguments
/// * `train_sizes` - Array of training set sizes
/// * `train_scores` - 2D array of training scores (rows=sizes, cols=cv folds)
/// * `val_scores` - 2D array of validation scores (rows=sizes, cols=cv folds)
/// # Returns
/// * `Result<LearningCurve<F>>` - Learning curve data
/// # Example
/// ```
/// use scirs2_core::ndarray::{Array1, Array2};
/// use scirs2_neural::utils::evaluation::LearningCurve;
/// // Create sample data
/// let train_sizes = Array1::from_vec(vec![100, 200, 300, 400, 500]);
/// let train_scores = Array2::from_shape_vec((5, 3), vec![
/// 0.6, 0.62, 0.58, // 100 samples, 3 folds
/// 0.7, 0.72, 0.68, // 200 samples, 3 folds
/// 0.8, 0.78, 0.79, // 300 samples, 3 folds
/// 0.85, 0.83, 0.84, // 400 samples, 3 folds
/// 0.87, 0.88, 0.86, // 500 samples, 3 folds
/// ]).expect("Operation failed");
/// let val_scores = Array2::from_shape_vec((5, 3), vec![
/// 0.55, 0.53, 0.54, // 100 samples, 3 folds
/// 0.65, 0.63, 0.64, // 200 samples, 3 folds
/// 0.75, 0.73, 0.74, // 300 samples, 3 folds
/// 0.76, 0.74, 0.75, // 400 samples, 3 folds
/// 0.77, 0.76, 0.76, // 500 samples, 3 folds
/// ]).expect("Operation failed");
/// // Create learning curve
/// let curve = LearningCurve::<f64>::new(train_sizes, train_scores, val_scores).expect("Operation failed");
/// ```
pub fn new(
train_sizes: Array1<usize>,
train_scores: Array2<F>,
val_scores: Array2<F>,
) -> Result<Self> {
let n_sizes = train_sizes.len();
if train_scores.shape()[0] != n_sizes || val_scores.shape()[0] != n_sizes {
return Err(NeuralError::ValidationError(
"Number of _scores must match number of training _sizes".to_string(),
));
}
if train_scores.shape()[1] != val_scores.shape()[1] {
return Err(NeuralError::ValidationError(
"Training and validation _scores must have the same number of CV folds".to_string(),
));
}
// Compute means and standard deviations
let train_mean = train_scores.mean_axis(Axis(1)).expect("Operation failed");
let val_mean = val_scores.mean_axis(Axis(1)).expect("Operation failed");
// Compute standard deviations using helper function
let train_std = compute_std(&train_scores, &train_mean, n_sizes);
let val_std = compute_std(&val_scores, &val_mean, n_sizes);
Ok(LearningCurve {
train_sizes,
train_scores,
val_scores,
train_mean,
train_std,
val_mean,
val_std,
})
}
/// Create an ASCII line plot of the learning curve
/// * `title` - Optional title for the plot
/// * `width` - Width of the plot
/// * `height` - Height of the plot
/// * `metric_name` - Name of the metric (e.g., "Accuracy")
/// * `String` - ASCII line plot
pub fn to_ascii(
&self,
title: Option<&str>,
width: usize,
height: usize,
metric_name: &str,
) -> String {
self.to_ascii_with_options(title, width, height, metric_name, &ColorOptions::default())
}
/// Create an ASCII line plot of the learning curve with customizable colors
/// This method allows fine-grained control over the color scheme using the
/// provided ColorOptions parameter.
/// * `color_options` - Color options for visualization
/// * `String` - ASCII line plot with colors
pub fn to_ascii_with_options(
&self,
title: Option<&str>,
width: usize,
height: usize,
metric_name: &str,
color_options: &ColorOptions,
) -> String {
// Pre-allocate result string with estimated capacity
let mut result = String::with_capacity(width * height * 2);
// Add title with styling if provided
if let Some(titletext) = title {
if color_options.enabled {
let styled_title = stylize(titletext, Style::Bold);
result.push_str(&format!("{styled_title}\n\n"));
} else {
result.push_str(&format!("{titletext}\n\n"));
}
} else if color_options.enabled {
let styled_metric = stylize(metric_name, Style::Bold);
let title = format!("Learning Curve ({styled_metric})");
let styled_title = stylize(title, Style::Bold);
result.push_str(&format!("{styled_title}\n\n"));
} else {
result.push_str(&format!("Learning Curve ({metric_name})\n\n"));
}
// Find min and max values for y-axis scaling
let min_score = self
.val_mean
.iter()
.fold(F::infinity(), |acc, &v| if v < acc { v } else { acc });
let max_score =
self.train_mean
.iter()
.fold(F::neg_infinity(), |acc, &v| if v > acc { v } else { acc });
// Add a small margin to the y-range
let y_margin =
F::from(0.1).expect("Failed to convert constant to float") * (max_score - min_score);
let y_min = min_score - y_margin;
let y_max = max_score + y_margin;
// Create a 2D grid for the plot
let mut grid = vec![vec![' '; width]; height];
let mut grid_markers = vec![vec![(false, false); width]; height]; // Track which points are training vs. validation
// Function to map a value to a y-coordinate
let y_coord = |value: F| -> usize {
let norm = (value - y_min) / (y_max - y_min);
let y = height
- 1
- (norm.to_f64().expect("Operation failed") * (height - 1) as f64).round() as usize;
std::cmp::min(y, height - 1)
};
// Function to map a training size to an x-coordinate
let x_coord = |size_idx: usize| -> usize {
((size_idx as f64) / ((self.train_sizes.len() - 1) as f64) * (width - 1) as f64).round()
as usize
};
// Draw training curve and mark as training points
for i in 0..self.train_sizes.len() - 1 {
let x1 = x_coord(i);
let y1 = y_coord(self.train_mean[i]);
let x2 = x_coord(i + 1);
let y2 = y_coord(self.train_mean[i + 1]);
// Draw a line between points and mark as training points
for (x, y) in draw_line_with_coords(x1, y1, x2, y2, Some(width), Some(height)) {
grid[y][x] = '●';
grid_markers[y][x].0 = true; // Mark as training point
}
}
// Draw validation curve and mark as validation points
for i in 0..self.train_sizes.len() - 1 {
let x1 = x_coord(i);
let y1 = y_coord(self.val_mean[i]);
let x2 = x_coord(i + 1);
let y2 = y_coord(self.val_mean[i + 1]);
// Draw a line between points and mark as validation points
for (x, y) in draw_line_with_coords(x1, y1, x2, y2, Some(width), Some(height)) {
grid[y][x] = '○';
grid_markers[y][x].1 = true; // Mark as validation point
}
}
// Draw the grid
for y in 0..height {
// Y-axis labels with styling
if y == 0 {
if color_options.enabled {
let value = format!("{y_max:.2}");
result.push_str(&format!("{} |", colorize(value, Color::BrightCyan)));
} else {
result.push_str(&format!("{y_max:.2} |"));
}
} else if y == height - 1 {
if color_options.enabled {
let value = format!("{y_min:.2}");
result.push_str(&format!("{} |", colorize(value, Color::BrightCyan)));
} else {
result.push_str(&format!("{y_min:.2} |"));
}
} else if y == height / 2 {
let mid = y_min
+ (y_max - y_min) * F::from(0.5).expect("Failed to convert constant to float");
if color_options.enabled {
let value = format!("{mid:.2}");
result.push_str(&format!("{} |", colorize(value, Color::BrightCyan)));
} else {
result.push_str(&format!("{mid:.2} |"));
}
} else {
result.push_str(" |");
}
// Grid content with coloring
for x in 0..width {
let char = grid[y][x];
let (is_train, is_val) = grid_markers[y][x];
if color_options.enabled {
if is_train {
// Training point
result.push_str(&colorize("●", Color::BrightBlue));
} else if is_val {
// Validation point
result.push_str(&colorize("○", Color::BrightGreen));
} else {
result.push(char);
}
} else {
result.push(char);
}
}
result.push('\n');
}
// X-axis
result.push_str(" +");
result.push_str(&"-".repeat(width));
result.push('\n');
// X-axis labels with styling
result.push_str(" ");
// Put a few size labels along the x-axis
let n_labels = std::cmp::min(5, self.train_sizes.len());
for i in 0..n_labels {
let idx = i * (self.train_sizes.len() - 1) / (n_labels - 1);
let size = self.train_sizes[idx];
let label = format!("{size}");
let x = x_coord(idx);
// Position the label with styling
if i == 0 {
if color_options.enabled {
result.push_str(&colorize(label, Color::BrightCyan));
} else {
result.push_str(&label);
}
} else {
let prev_end = result.len();
let spaces = x.saturating_sub(prev_end - 7);
result.push_str(&" ".repeat(spaces));
if color_options.enabled {
result.push_str(&colorize(label, Color::BrightCyan));
} else {
result.push_str(&label);
}
}
}
result.push('\n');
// X-axis title with styling
if color_options.enabled {
result.push_str(&format!(
" {}\n\n",
stylize("Training Set Size", Style::Bold)
));
} else {
result.push_str(" Training Set Size\n\n");
}
// Add legend with colors
if color_options.enabled {
result.push_str(&format!(
" {} Training score {} Validation score\n",
colorize("●", Color::BrightBlue),
colorize("○", Color::BrightGreen)
));
} else {
result.push_str(" ● Training score ○ Validation score\n");
}
result
}
}
/// Helper function to compute standard deviation for scores
#[allow(dead_code)]
fn compute_std<F: Float + Debug + Display + FromPrimitive>(
scores: &Array2<F>,
mean: &Array1<F>,
n_sizes: usize,
) -> Array1<F> {
let mut std_arr = Array1::zeros(n_sizes);
let n_folds = scores.shape()[1];
for i in 0..n_sizes {
let mut sum_sq_diff = F::zero();
for j in 0..n_folds {
let diff = scores[[i, j]] - mean[i];
sum_sq_diff = sum_sq_diff + diff * diff;
}
std_arr[i] = (sum_sq_diff / F::from(n_folds).expect("Failed to convert to float")).sqrt();
}
std_arr
}