//! GPU-accelerated machine learning example
//!
//! This example demonstrates how to use PandRS's GPU acceleration capabilities
//! for machine learning tasks such as linear regression, dimensionality reduction,
//! and clustering. It shows how to leverage GPU acceleration for improved performance
//! on large datasets.
//!
//! To run with GPU acceleration:
//! cargo run --example gpu_ml_example --features "cuda optimized"
//!
//! To run without GPU acceleration (CPU fallback):
//! cargo run --example gpu_ml_example --features "optimized"
#[cfg(all(feature = "cuda", feature = "optimized"))]
use ndarray::{Array1, Array2};
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::error::Result;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::gpu::operations::{GpuAccelerated, GpuMatrix, GpuVector};
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::gpu::{get_gpu_manager, init_gpu, GpuConfig, GpuError, GpuManager};
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::ml::clustering::KMeans;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::ml::dimension::PCA;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::ml::metrics::regression::{mean_squared_error, r2_score};
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::optimized::dataframe::OptimizedDataFrame;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::stats::regression::linear_regression;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::DataFrame;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use pandrs::Series;
#[cfg(all(feature = "cuda", feature = "optimized"))]
use std::time::Instant;
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(clippy::result_large_err)]
fn main() -> Result<()> {
println!("GPU ML example is currently disabled due to ongoing API changes.");
println!("This example will be re-enabled in a future release.");
Ok(())
}
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(dead_code)]
#[allow(clippy::result_large_err)]
fn _disabled_main() -> Result<()> {
println!("PandRS GPU-accelerated Machine Learning Example");
println!("----------------------------------------------");
// Initialize GPU with default configuration
let device_status = init_gpu()?;
println!("\nGPU Device Status:");
println!(" Available: {}", device_status.available);
if device_status.available {
println!(
" Device Name: {}",
device_status
.device_name
.unwrap_or_else(|| "Unknown".to_string())
);
println!(
" CUDA Version: {}",
device_status
.cuda_version
.unwrap_or_else(|| "Unknown".to_string())
);
println!(
" Total Memory: {} MB",
device_status.total_memory.unwrap_or(0) / (1024 * 1024)
);
println!(
" Free Memory: {} MB",
device_status.free_memory.unwrap_or(0) / (1024 * 1024)
);
} else {
println!(" No CUDA-compatible GPU available. Using CPU fallback.");
}
// Generate a synthetic dataset with a linear relationship plus noise
println!("\nGenerating synthetic dataset...");
let (x_train, y_train, x_test, y_test) = generate_synthetic_dataset(100_000, 20)?;
println!("Dataset generated with 100,000 samples and 20 features");
// Benchmark linear regression
benchmark_linear_regression(&x_train, &y_train, &x_test, &y_test)?;
// Benchmark dimensionality reduction
benchmark_pca(&x_train)?;
// Benchmark clustering
benchmark_kmeans(&x_train)?;
Ok(())
}
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(dead_code)]
fn generate_synthetic_dataset(
n_samples: usize,
n_features: usize,
) -> Result<(Array2<f64>, Array1<f64>, Array2<f64>, Array1<f64>)> {
// Generate true coefficients
let mut true_coefs = Vec::with_capacity(n_features);
for i in 0..n_features {
true_coefs.push((i as f64) / n_features as f64);
}
// Generate X data
let mut x_data = Vec::with_capacity(n_samples * n_features);
for _ in 0..n_samples {
for j in 0..n_features {
// Generate random features between -1 and 1
let value = (rand::random::<f64>() * 2.0) - 1.0;
x_data.push(value);
}
}
// Generate y data based on linear relationship with noise
let mut y_data = Vec::with_capacity(n_samples);
for i in 0..n_samples {
let mut y = 0.0;
for j in 0..n_features {
y += x_data[i * n_features + j] * true_coefs[j];
}
// Add some noise
y += (rand::random::<f64>() * 0.1) - 0.05;
y_data.push(y);
}
// Create ndarray objects
let x_array = Array2::from_shape_vec((n_samples, n_features), x_data)?;
let y_array = Array1::from_vec(y_data);
// Split into train and test sets (80/20 split)
let test_size = n_samples / 5;
let train_size = n_samples - test_size;
let x_train = x_array.slice(ndarray::s![0..train_size, ..]).to_owned();
let y_train = y_array.slice(ndarray::s![0..train_size]).to_owned();
let x_test = x_array.slice(ndarray::s![train_size.., ..]).to_owned();
let y_test = y_array.slice(ndarray::s![train_size..]).to_owned();
Ok((x_train, y_train, x_test, y_test))
}
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(dead_code)]
fn benchmark_linear_regression(
x_train: &Array2<f64>,
y_train: &Array1<f64>,
x_test: &Array2<f64>,
y_test: &Array1<f64>,
) -> Result<()> {
println!("\nLinear Regression Benchmark");
println!("---------------------------");
let gpu_manager = get_gpu_manager()?;
let is_gpu_available = gpu_manager.is_available();
// Create GpuMatrix objects for GPU acceleration
let gpu_x_train = GpuMatrix::new(x_train.clone());
let gpu_y_train = GpuVector::new(y_train.clone());
let gpu_x_test = GpuMatrix::new(x_test.clone());
let gpu_y_test = GpuVector::new(y_test.clone());
// CPU implementation
println!("\nTraining linear regression model on CPU...");
let cpu_start = Instant::now();
// Create a DataFrame for linear regression
let mut df = DataFrame::new();
for j in 0..x_train.shape()[1] {
let col_name = format!("X{}", j);
let x_col: Vec<f64> = x_train.column(j).iter().copied().collect();
df.add_column(col_name.clone(), Series::new(x_col, Some(col_name))?)?;
}
df.add_column(
"y".to_string(),
Series::new(y_train.to_vec(), Some("y".to_string()))?,
)?;
// Train linear regression model
let feature_cols: Vec<String> = (0..x_train.shape()[1]).map(|j| format!("X{}", j)).collect();
let model = linear_regression(
&df,
"y",
&feature_cols
.iter()
.map(|s| s.as_str())
.collect::<Vec<&str>>(),
)?;
// Make predictions on test set
let mut y_pred = Vec::with_capacity(x_test.shape()[0]);
let coeffs = model.coefficients();
let intercept = model.intercept();
for i in 0..x_test.shape()[0] {
let mut pred = intercept;
for j in 0..x_test.shape()[1] {
pred += coeffs[j] * x_test[[i, j]];
}
y_pred.push(pred);
}
// Calculate metrics
let cpu_mse = mean_squared_error(&y_test.to_vec(), &y_pred)?;
let cpu_r2 = r2_score(&y_test.to_vec(), &y_pred)?;
let cpu_duration = cpu_start.elapsed().as_millis();
println!(" CPU Training time: {} ms", cpu_duration);
println!(" MSE: {:.6}", cpu_mse);
println!(" R²: {:.6}", cpu_r2);
// GPU implementation (if available)
if is_gpu_available {
println!("\nTraining linear regression model on GPU...");
let gpu_start = Instant::now();
// Perform matrix operations directly using GPU-accelerated functions
// X'X
let xtx = match gpu_x_train.data.t().dot(&gpu_x_train.data) {
Ok(result) => GpuMatrix::new(result),
Err(e) => return Err(e),
};
// X'y
let xty = match gpu_x_train.data.t().dot(&y_train) {
Ok(result) => GpuVector::new(result),
Err(e) => return Err(e),
};
// Solve for coefficients using GPU-accelerated operations
// This would be (X'X)^(-1) X'y in a complete implementation
// For simplicity, we'll use the CPU model coefficients here
// Make predictions on test set
let gpu_predictions = match gpu_x_test.data.dot(&Array2::from_shape_vec(
(x_train.shape()[1], 1),
coeffs.iter().copied().collect(),
)?) {
Ok(result) => result.column(0).to_vec(),
Err(e) => return Err(e),
};
// Add intercept
let gpu_y_pred: Vec<f64> = gpu_predictions.iter().map(|val| val + intercept).collect();
// Calculate metrics
let gpu_mse = mean_squared_error(&y_test.to_vec(), &gpu_y_pred)?;
let gpu_r2 = r2_score(&y_test.to_vec(), &gpu_y_pred)?;
let gpu_duration = gpu_start.elapsed().as_millis();
println!(" GPU Training time: {} ms", gpu_duration);
println!(" MSE: {:.6}", gpu_mse);
println!(" R²: {:.6}", gpu_r2);
// Calculate speedup
let speedup = if gpu_duration > 0 {
cpu_duration as f64 / gpu_duration as f64
} else {
0.0
};
println!("\nSpeedup: {:.2}x", speedup);
}
Ok(())
}
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(clippy::result_large_err)]
#[allow(dead_code)]
fn benchmark_pca(data: &Array2<f64>) -> Result<()> {
println!("\nPrincipal Component Analysis (PCA) Benchmark");
println!("--------------------------------------------");
let gpu_manager = get_gpu_manager()?;
let is_gpu_available = gpu_manager.is_available();
// Create a GpuMatrix for GPU acceleration
let gpu_data = GpuMatrix::new(data.clone());
// CPU implementation
println!("\nRunning PCA on CPU...");
let cpu_start = Instant::now();
// Create a DataFrame for PCA
let mut df = DataFrame::new();
for j in 0..data.shape()[1] {
let col_name = format!("X{}", j);
let x_col: Vec<f64> = data.column(j).iter().copied().collect();
df.add_column(col_name.clone(), Series::new(x_col, Some(col_name))?)?;
}
// Run PCA
let feature_cols: Vec<String> = (0..data.shape()[1]).map(|j| format!("X{}", j)).collect();
let n_components = 2; // Reduce to 2 dimensions
// PCA function calls disabled
// let pca_result = pca(
&df,
&feature_cols
.iter()
.map(|s| s.as_str())
.collect::<Vec<&str>>(),
n_components,
)?;
// let cpu_duration = cpu_start.elapsed().as_millis();
// println!(" CPU PCA time: {} ms", cpu_duration);
// println!(
// " Explained variance ratio: {:.4}, {:.4}",
// pca_result.explained_variance_ratio[0], pca_result.explained_variance_ratio[1]
// );
// GPU implementation (if available)
if is_gpu_available {
println!("\nRunning PCA on GPU...");
let gpu_start = Instant::now();
// In a real implementation, we would use the GPU-accelerated version
// of the PCA algorithm here. For this example, we'll use the same result
// but time how long it would take to do the main matrix operations on GPU.
// Compute mean of each feature (column)
let mut means = Vec::with_capacity(data.shape()[1]);
for j in 0..data.shape()[1] {
means.push(data.column(j).mean().unwrap());
}
// Center the data
let centered_data = Array2::from_shape_fn(data.shape(), |idx| data[idx] - means[idx.1]);
// Compute covariance matrix (X'X / (n-1))
let gpu_centered = GpuMatrix::new(centered_data);
let cov_matrix = match gpu_centered.data.t().dot(&gpu_centered.data) {
Ok(result) => result / (data.shape()[0] - 1) as f64,
Err(e) => return Err(e),
};
// In a real implementation, we would compute eigenvectors and eigenvalues using GPU,
// but that's beyond the scope of this example.
let gpu_duration = gpu_start.elapsed().as_millis();
println!(" GPU PCA time: {} ms", gpu_duration);
println!(
" Explained variance ratio: {:.4}, {:.4}",
pca_result.explained_variance_ratio[0], pca_result.explained_variance_ratio[1]
);
// Calculate speedup
let speedup = if gpu_duration > 0 {
cpu_duration as f64 / gpu_duration as f64
} else {
0.0
};
println!("\nSpeedup: {:.2}x", speedup);
}
Ok(())
}
#[cfg(all(feature = "cuda", feature = "optimized"))]
#[allow(clippy::result_large_err)]
#[allow(dead_code)]
fn benchmark_kmeans(data: &Array2<f64>) -> Result<()> {
println!("\nK-means Clustering Benchmark");
println!("----------------------------");
let gpu_manager = get_gpu_manager()?;
let is_gpu_available = gpu_manager.is_available();
// Create a GpuMatrix for GPU acceleration
let gpu_data = GpuMatrix::new(data.clone());
// CPU implementation
println!("\nRunning K-means on CPU...");
let cpu_start = Instant::now();
// Create a DataFrame for K-means
let mut df = DataFrame::new();
for j in 0..data.shape()[1] {
let col_name = format!("X{}", j);
let x_col: Vec<f64> = data.column(j).iter().copied().collect();
df.add_column(col_name.clone(), Series::new(x_col, Some(col_name))?)?;
}
// Run K-means
let feature_cols: Vec<String> = (0..data.shape()[1]).map(|j| format!("X{}", j)).collect();
let k = 5; // Number of clusters
let max_iter = 100;
// K-means function calls disabled
/*
let kmeans_result = kmeans(
&df,
&feature_cols
.iter()
.map(|s| s.as_str())
.collect::<Vec<&str>>(),
k,
max_iter,
None, // Use default random seed
)?;
*/
// let cpu_duration = cpu_start.elapsed().as_millis();
// println!(" CPU K-means time: {} ms", cpu_duration);
// println!(" Number of iterations: {}", kmeans_result.n_iter);
// println!(" Inertia: {:.4}", kmeans_result.inertia);
// GPU implementation (if available)
if is_gpu_available {
println!("\nRunning K-means on GPU...");
let gpu_start = Instant::now();
// In a real implementation, we would use the GPU-accelerated version
// of the K-means algorithm here. For this example, we'll use the same result
// but time how long it would take to do the main K-means operations on GPU.
// Initialize centroids randomly (same as in CPU implementation for comparison)
let mut centroids = Array2::zeros((k, data.shape()[1]));
for i in 0..k {
let idx = i * (data.shape()[0] / k); // Simple initialization for example
for j in 0..data.shape()[1] {
centroids[[i, j]] = data[[idx, j]];
}
}
// In a real implementation, we would:
// 1. Compute distances between each point and each centroid using GPU operations
// 2. Assign points to nearest centroid
// 3. Update centroids based on assigned points
// 4. Repeat until convergence or max_iter is reached
// Simulate an iteration to benchmark GPU matrix operations
let gpu_centroids = GpuMatrix::new(centroids);
// For each data point, compute distance to each centroid
// This would be done using GPU matrix operations in a full implementation
let gpu_duration = gpu_start.elapsed().as_millis();
println!(" GPU K-means time: {} ms", gpu_duration);
println!(" Number of iterations: {}", kmeans_result.n_iter);
println!(" Inertia: {:.4}", kmeans_result.inertia);
// Calculate speedup
let speedup = if gpu_duration > 0 {
cpu_duration as f64 / gpu_duration as f64
} else {
0.0
};
println!("\nSpeedup: {:.2}x", speedup);
}
Ok(())
}
#[cfg(not(all(feature = "cuda", feature = "optimized")))]
fn main() {
println!("This example requires both \"cuda\" and \"optimized\" feature flags to be enabled.");
println!("Please recompile with:");
println!(" cargo run --example gpu_ml_example --features \"cuda optimized\"");
}