### Import Rustlearn Prelude

Source: https://github.com/maciejkula/rustlearn/blob/master/readme.md

Import the prelude for all linear algebra primitives and common traits in rustlearn.

```rust
use rustlearn::prelude::*;
```

--------------------------------

### Configure Factorization Machines

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Shows how to train factorization machines for classification, including parallel training options and sparse data handling.

```rust
use rustlearn::prelude::*;
use rustlearn::factorization::factorization_machines::Hyperparameters;
use rustlearn::datasets::iris;
use rustlearn::metrics::accuracy_score;

let (X, y) = iris::load_data();

// Configure factorization machine
let mut model = Hyperparameters::new(X.cols(), 10)  // (dim, num_components)
    .learning_rate(0.05)    // Initial learning rate (adaptive)
    .l2_penalty(0.0)        // L2 regularization
    .l1_penalty(0.0)        // L1 regularization for sparsity
    .one_vs_rest();         // Multiclass classification

// Train for multiple epochs
for _ in 0..20 {
    model.fit(&X, &y).unwrap();
}

let prediction = model.predict(&X).unwrap();
println!("FM accuracy: {}", accuracy_score(&y, &prediction));

// Binary factorization machine with parallel training (Hogwild)
let mut binary_model = Hyperparameters::new(X.cols(), 5)
    .learning_rate(0.05)
    .build();

for _ in 0..20 {
    binary_model.fit_parallel(&X, &y, 4).unwrap();  // 4 threads
}

// Access model parameters
let coefficients = binary_model.get_coefficients();
let latent_factors = binary_model.get_latent_factors();

// Works well with sparse data
let sparse_X = SparseRowArray::from(&X);
model.fit(&sparse_X, &y).unwrap();
```

--------------------------------

### Serialize and Deserialize Models with Rustlearn

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Demonstrates saving and loading trained models using bincode for binary format and serde_json for human-readable JSON format.

```rust
use rustlearn::prelude::*;
use rustlearn::ensemble::random_forest::Hyperparameters as RFHyperparameters;
use rustlearn::trees::decision_tree;
use rustlearn::datasets::iris;
use rustlearn::multiclass::OneVsRestWrapper;
use rustlearn::ensemble::random_forest::RandomForest;

// Using bincode for binary serialization
extern crate bincode;

let (data, target) = iris::load_data();

// Train a model
let mut tree_params = decision_tree::Hyperparameters::new(data.cols());
tree_params.min_samples_split(10).max_features(4);

let mut model = RFHyperparameters::new(tree_params, 10).one_vs_rest();
model.fit(&data, &target).unwrap();

// Serialize to binary
let encoded: Vec<u8> = bincode::serialize(&model).unwrap();

// Deserialize
let decoded: OneVsRestWrapper<RandomForest> = bincode::deserialize(&encoded).unwrap();

// Predictions work on deserialized model
let prediction = decoded.predict(&data).unwrap();

// Using JSON for human-readable serialization
extern crate serde_json;

let json_encoded = serde_json::to_string(&model).unwrap();
let json_decoded: OneVsRestWrapper<RandomForest> = serde_json::from_str(&json_encoded).unwrap();
```

--------------------------------

### Implement Support Vector Classification (SVC)

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Demonstrates training SVC models using Linear, RBF, and Polynomial kernels, including support for sparse data structures.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::svm::libsvm::svc::{Hyperparameters, KernelType};
use rustlearn::metrics::accuracy_score;

let (X, y) = iris::load_data();

// Linear kernel SVM
let mut linear_model = Hyperparameters::new(X.cols(), KernelType::Linear, 3)  // (dim, kernel, num_classes)
    .C(0.3)           // Regularization parameter (smaller = more regularization)
    .build();

linear_model.fit(&X, &y).unwrap();
let prediction = linear_model.predict(&X).unwrap();
println!("Linear SVM accuracy: {}", accuracy_score(&y, &prediction));

// RBF kernel SVM
let mut rbf_model = Hyperparameters::new(X.cols(), KernelType::RBF, 3)
    .C(1.0)
    .gamma(0.5)       // RBF kernel parameter (default: 1/dim)
    .build();

rbf_model.fit(&X, &y).unwrap();

// Polynomial kernel SVM
let mut poly_model = Hyperparameters::new(X.cols(), KernelType::Polynomial, 3)
    .C(1.0)
    .degree(3)        // Polynomial degree
    .coef0(0.0)       // Constant term in kernel function
    .cache_size(100.0) // Cache size in MB
    .build();

poly_model.fit(&X, &y).unwrap();

// With sparse data
let sparse_X = SparseRowArray::from(&X);
let mut sparse_model = Hyperparameters::new(X.cols(), KernelType::Linear, 3)
    .build();

sparse_model.fit(&sparse_X, &y).unwrap();
let sparse_prediction = sparse_model.predict(&sparse_X).unwrap();
```

--------------------------------

### Load and Inspect Built-in Datasets

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Shows how to load the Iris dataset and inspect the dimensions of the feature matrix and label vector.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;

// Load the iris dataset
let (X, y) = iris::load_data();

println!("Features shape: {} rows x {} cols", X.rows(), X.cols());
println!("Labels shape: {} rows", y.rows());

// X contains 150 samples with 4 features each:
// sepal length, sepal width, petal length, petal width

// y contains class labels: 0.0, 1.0, 2.0 for three iris species
let unique_classes: Vec<f32> = vec![0.0, 1.0, 2.0];
```

--------------------------------

### Import Specific Models and Utilities

Source: https://github.com/maciejkula/rustlearn/blob/master/readme.md

Import individual models and utilities from rustlearn's submodules.

```rust
use rustlearn::prelude::*;

use rustlearn::linear_models::sgdclassifier::Hyperparameters;
// more imports
```

--------------------------------

### Perform Dense Array Operations in Rustlearn

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Demonstrates creation, reshaping, element access, iteration, and arithmetic operations for dense arrays.

```rust
use rustlearn::prelude::*;

// Create arrays of zeros and ones
let zeros = Array::zeros(20, 10);
let ones = Array::ones(10, 10);

// Create from a vector and reshape
let mut array = Array::from(vec![0.0, 1.0, 2.0, 3.0]);
array.reshape(2, 2);

// Create from nested vectors
let array = Array::from(&vec![vec![0.0, 1.0],
                              vec![2.0, 3.0]]);

// Getting and setting values
let mut array = Array::zeros(2, 2);
array.set(0, 1, 1.0);
*array.get_mut(1, 0) = 2.0;

// Iteration over rows and columns
for row in array.iter_rows() {
    for element in row.iter() {
        // process element
    }
}

// Elementwise operations
let result = array.add(1.0);        // Add scalar
let result = array.times(2.0);      // Multiply by scalar
let result = array.add(&array);     // Add arrays
array.add_inplace(1.0);             // In-place operations

// Matrix multiplication and transpose
let x = Array::from(vec![1.0, 2.0]);
let y = Array::from(vec![3.0, 4.0]);
let dot = x.dot(&y.T());

// Row indexing
let subset = array.get_rows(&vec![0, 1]);
let subset = array.get_rows(&(0..2));
```

--------------------------------

### Manage Sparse Array Types in Rustlearn

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Covers conversion, creation, and iteration for sparse row and column arrays, suitable for high-dimensional data.

```rust
use rustlearn::prelude::*;

// Convert dense array to sparse row array
let dense_data = Array::from(&vec![vec![1.0, 0.0, 2.0],
                                    vec![0.0, 3.0, 0.0]]);
let sparse = SparseRowArray::from(&dense_data);

// Create empty sparse array and set values
let mut sparse = SparseRowArray::zeros(100, 1000);
sparse.set(0, 5, 1.0);
sparse.set(0, 100, 0.5);

// Get dimensions and values
let rows = sparse.rows();
let cols = sparse.cols();
let value = sparse.get(0, 5);

// Iterate over non-zero elements
for row in sparse.iter_rows() {
    for (col_idx, value) in row.iter_nonzero() {
        println!("Column {}: {}", col_idx, value);
    }
}

// SparseColumnArray for column-major access (used by decision trees)
let sparse_col = SparseColumnArray::from(&dense_data);
```

--------------------------------

### Train Random Forest Model

Source: https://github.com/maciejkula/rustlearn/blob/master/readme.md

Loads the Iris dataset, configures decision tree hyperparameters, trains a Random Forest model using the one-vs-rest strategy, and makes predictions. Ensure you have the 'rustlearn' crate and its 'datasets' feature enabled.

```rust
use rustlearn::prelude::*;

use rustlearn::ensemble::random_forest::Hyperparameters;
use rustlearn::datasets::iris;
use rustlearn::trees::decision_tree;

let (data, target) = iris::load_data();

let mut tree_params = decision_tree::Hyperparameters::new(data.cols());
tree_params.min_samples_split(10)
    .max_features(4);

let mut model = Hyperparameters::new(tree_params, 10)
    .one_vs_rest();

model.fit(&data, &target).unwrap();

// Optionally serialize and deserialize the model

// let encoded = bincode::serialize(&model).unwrap();
// let decoded: OneVsRestWrapper<RandomForest> = bincode::deserialize(&encoded).unwrap();

let prediction = model.predict(&data).unwrap();
```

--------------------------------

### Evaluation Metrics in Rustlearn

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Calculates various performance metrics for classification, ranking, and regression tasks. Some metrics like ROC AUC require probability scores.

```rust
use rustlearn::prelude::*;
use rustlearn::metrics::{accuracy_score, mean_absolute_error, mean_squared_error};
use rustlearn::metrics::{roc_auc_score, dcg_score, ndcg_score};

// Classification accuracy
let y_true = Array::from(vec![1.0, 1.0, 0.0, 0.0]);
let y_pred = Array::from(vec![1.0, 0.0, 0.0, 0.0]);
let accuracy = accuracy_score(&y_true, &y_pred);
println!("Accuracy: {}", accuracy);  // 0.75

// ROC AUC for binary classification (requires probability scores)
let y_true = Array::from(vec![1.0, 1.0, 0.0, 0.0]);
let y_scores = Array::from(vec![0.9, 0.4, 0.3, 0.1]);  // Predicted probabilities
let auc = roc_auc_score(&y_true, &y_scores).unwrap();
println!("ROC AUC: {}", auc);

// Ranking metrics
let relevance = Array::from(vec![5.0, 3.0, 2.0]);
let scores = Array::from(vec![2.0, 1.0, 0.0]);
let dcg = dcg_score(&relevance, &scores, 10);   // k=10
let ndcg = ndcg_score(&relevance, &scores, 10);  // Normalized DCG
println!("DCG: {}, NDCG: {}", dcg, ndcg);

// Regression metrics
let y_true = Array::from(vec![3.0, -0.5, 2.0, 7.0]);
let y_pred = Array::from(vec![2.5, 0.0, 2.0, 8.0]);
let mae = mean_absolute_error(&y_true, &y_pred);
let mse = mean_squared_error(&y_true, &y_pred);
println!("MAE: {}, MSE: {}", mae, mse);
```

--------------------------------

### Decision Tree Classifier

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Implements the CART algorithm for classification. Supports configurable tree depth and split criteria for both dense and sparse data. Use `.one_vs_rest()` for multiclass or `.build()` for binary classification.

```rust
use rustlearn::prelude::*;
use rustlearn::trees::decision_tree::Hyperparameters;
use rustlearn::datasets::iris;
use rustlearn::metrics::accuracy_score;

let (X, y) = iris::load_data();

// Configure decision tree hyperparameters
let mut model = Hyperparameters::new(X.cols())
    .min_samples_split(5)    // Minimum samples required to split a node
    .max_features(4)         // Max features to consider per split (default: sqrt(n_features))
    .max_depth(40)           // Maximum tree depth
    .one_vs_rest();          // Multiclass classification

model.fit(&X, &y).unwrap();

let prediction = model.predict(&X).unwrap();
let train_accuracy = accuracy_score(&y, &prediction);
println!("Training accuracy: {}", train_accuracy);

// For sparse data (e.g., text classification)
let sparse_X = SparseColumnArray::from(&X);
let mut sparse_model = Hyperparameters::new(X.cols())
    .min_samples_split(5)
    .one_vs_rest();

sparse_model.fit(&sparse_X, &y).unwrap();
let sparse_prediction = sparse_model.predict(&sparse_X).unwrap();

// Binary decision tree (without one-vs-rest wrapper)
let mut binary_tree = Hyperparameters::new(X.cols())
    .min_samples_split(10)
    .build();

```

--------------------------------

### SGDClassifier for Logistic Regression

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Implements logistic regression using stochastic gradient descent with adaptive learning rates (Adagrad). Supports L1 and L2 regularization and multiclass classification via one-vs-rest. Configure hyperparameters like learning rate and regularization strength.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::CrossValidation;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;

let (X, y) = iris::load_data();

let num_splits = 10;
let num_epochs = 5;

let mut accuracy = 0.0;

for (train_idx, test_idx) in CrossValidation::new(X.rows(), num_splits) {
    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    // Configure model with hyperparameters
    let mut model = Hyperparameters::new(X.cols())
        .learning_rate(0.5)      // Initial learning rate (adaptive via Adagrad)
        .l2_penalty(0.0)         // L2 regularization strength
        .l1_penalty(0.0)         // L1 regularization for sparsity
        .one_vs_rest();          // Multiclass via one-vs-rest

    // Multiple epochs by calling fit repeatedly
    for _ in 0..num_epochs {
        model.fit(&X_train, &y_train).unwrap();
    }

    let prediction = model.predict(&X_test).unwrap();
    accuracy += accuracy_score(&y_test, &prediction);
}

accuracy /= num_splits as f32;
println!("Cross-validation accuracy: {}", accuracy);

// For binary classification, use .build() instead of .one_vs_rest()
let mut binary_model = Hyperparameters::new(X.cols())
    .learning_rate(1.0)
    .l2_penalty(0.5)
    .build();

// Access coefficients after training
binary_model.fit(&X, &y).unwrap();
let coefficients = binary_model.get_coefficients();

```

--------------------------------

### Shuffle Split Cross-Validation in Rust

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Performs repeated random train/test splits for model validation. Ensure reproducibility by setting a random seed.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::ShuffleSplit;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;
use rand::{SeedableRng, StdRng};

let (X, y) = iris::load_data();

let num_splits = 10;
let test_percentage = 0.2;  // 20% test, 80% train

let mut split = ShuffleSplit::new(X.rows(), num_splits, test_percentage);
split.set_rng(StdRng::from_seed(&[1, 2, 3, 4]));  // For reproducibility

let mut test_accuracy = 0.0;

for (train_idx, test_idx) in split {
    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    let mut model = Hyperparameters::new(X.cols())
        .learning_rate(0.5)
        .one_vs_rest();

    for _ in 0..20 {
        model.fit(&X_train, &y_train).unwrap();
    }

    let y_hat = model.predict(&X_test).unwrap();
    test_accuracy += accuracy_score(&y_test, &y_hat);
}

test_accuracy /= num_splits as f32;
println!("Shuffle split accuracy: {}", test_accuracy);
```

--------------------------------

### Random Forest Classifier

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Fits an ensemble of decision trees using bootstrap samples for improved generalization. Supports parallel training and prediction. Configure base decision tree parameters and the number of trees.

```rust
use rustlearn::prelude::*;
use rustlearn::ensemble::random_forest::Hyperparameters;
use rustlearn::trees::decision_tree;
use rustlearn::datasets::iris;
use rustlearn::metrics::accuracy_score;

let (data, target) = iris::load_data();

// First configure the base decision tree parameters
let mut tree_params = decision_tree::Hyperparameters::new(data.cols());
tree_params
    .min_samples_split(10)
    .max_features(4);

// Build the random forest with 10 trees
let mut model = Hyperparameters::new(tree_params, 10)  // (tree_params, num_trees)
    .one_vs_rest();

model.fit(&data, &target).unwrap();

let prediction = model.predict(&data).unwrap();
let accuracy = accuracy_score(&target, &prediction);
println!("Random Forest accuracy: {}", accuracy);

// Parallel training and prediction
model.fit_parallel(&data, &target, 4).unwrap();  // 4 threads
let parallel_prediction = model.predict_parallel(&data, 4).unwrap();

// Access individual trees
let trees = model.trees();
println!("Number of trees: {}", trees.len());

// With sparse data
let sparse_data = SparseRowArray::from(&data);
let mut sparse_model = Hyperparameters::new(tree_params.clone(), 20)
    .one_vs_rest();

sparse_model.fit(&sparse_data, &target).unwrap();

```

--------------------------------

### Logistic Regression with Cross-Validation

Source: https://github.com/maciejkula/rustlearn/blob/master/readme.md

Performs k-fold cross-validation for a logistic regression model using the iris dataset. Fits the model multiple times and calculates average accuracy.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::CrossValidation;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;


let (X, y) = iris::load_data();

let num_splits = 10;
let num_epochs = 5;

let mut accuracy = 0.0;

for (train_idx, test_idx) in CrossValidation::new(X.rows(), num_splits) {

    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    let mut model = Hyperparameters::new(X.cols())
                                    .learning_rate(0.5)
                                    .l2_penalty(0.0)
                                    .l1_penalty(0.0)
                                    .one_vs_rest();

    for _ in 0..num_epochs {
        model.fit(&X_train, &y_train).unwrap();
    }

    let prediction = model.predict(&X_test).unwrap();
    accuracy += accuracy_score(&y_test, &prediction);
}

accuracy /= num_splits as f32;

```

--------------------------------

### Feature Extraction with DictVectorizer in Rust

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Transforms named features into sparse arrays using one-hot encoding. Useful for categorical and text data. Can handle weighted features.

```rust
use rustlearn::prelude::*;
use rustlearn::feature_extraction::DictVectorizer;

// Example: converting categorical features to sparse matrix
let features = vec![
    vec!["color:red", "size:large"],
    vec!["color:blue", "size:small"],
    vec!["color:red", "size:medium"],
];

let mut vectorizer = DictVectorizer::new();

for (row_idx, row) in features.iter().enumerate() {
    for feature in row.iter() {
        vectorizer.partial_fit(row_idx, feature, 1.0);  // (row, feature_name, value)
    }
}

// Transform to sparse array
let X = vectorizer.transform();
println!("Shape: {} rows x {} columns", X.rows(), X.cols());

// Access the feature dictionary
let dictionary = vectorizer.dictionary();
for (feature_name, (col_idx, count)) in dictionary {
    println!("{}: column {}, count {}", feature_name, col_idx, count);
}

// Use with weighted features
let mut weighted_vectorizer = DictVectorizer::new();
weighted_vectorizer.partial_fit(0, "word:hello", 0.5);  // TF-IDF weights
weighted_vectorizer.partial_fit(0, "word:world", 0.3);
weighted_vectorizer.partial_fit(1, "word:hello", 0.2);
let weighted_X = weighted_vectorizer.transform();
```

--------------------------------

### Perform K-Fold Cross-Validation

Source: https://context7.com/maciejkula/rustlearn/llms.txt

Uses the CrossValidation iterator to split data and evaluate model performance across multiple folds.

```rust
use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::CrossValidation;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;
use rand::{SeedableRng, StdRng};

let (X, y) = iris::load_data();

let num_splits = 10;
let mut test_accuracy = 0.0;

// Create cross-validation iterator
let mut cv = CrossValidation::new(X.rows(), num_splits);

// Set random seed for reproducibility
cv.set_rng(StdRng::from_seed(&[100]));

for (train_idx, test_idx) in cv {
    // train_idx and test_idx are Vec<usize>
    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    let mut model = Hyperparameters::new(X.cols())
        .learning_rate(0.5)
        .one_vs_rest();

    for _ in 0..20 {
        model.fit(&X_train, &y_train).unwrap();
    }

    let y_hat = model.predict(&X_test).unwrap();
    test_accuracy += accuracy_score(&y_test, &y_hat);
}

test_accuracy /= num_splits as f32;
println!("10-fold CV accuracy: {}", test_accuracy);
```

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