# rustframe

📚 [Docs](https://magnus167.github.io/rustframe/) | 🐙 [GitHub](https://github.com/Magnus167/rustframe) | 🌐 [Gitea mirror](https://gitea.nulltech.uk/Magnus167/rustframe) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)

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---

## Rustframe: _A lightweight dataframe & math toolkit for Rust_

Rustframe provides intuitive dataframe, matrix, and series operations for data analysis and manipulation.

Rustframe keeps things simple, safe, and readable. It is handy for quick numeric experiments and small analytical tasks as well as for educational purposes. It is designed to be easy to use and understand, with a clean API implemented in 100% safe Rust.

Rustframe is an educational project, and is not intended for production use. It is **not** meant to compete with powerhouse crates like `polars` or `ndarray`. It is a work in progress, and the API is subject to change. There are no guarantees of stability or performance, and it is not optimized for large datasets or high-performance computing.

### What it offers

- **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
- **Math that reads like math** - element-wise `+`, `−`, `×`, `÷` on entire frames or scalars.
- **Frames** - Column major data structure for single-type data, with labeled columns and typed row indices.
- **Compute module** - Implements various statistical computations and machine learning models.

- **[Coming Soon]** _DataFrame_ - Multi-type data structure for heterogeneous data, with labeled columns and typed row indices.

- **Random number utils** - Built-in pseudo and cryptographically secure generators for simulations.

#### Matrix and Frame functionality

- **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
- **Frame operations** - Column manipulation, sorting, and more.

#### Compute Module

The `compute` module provides implementations for various statistical computations and machine learning models.

**Statistics, Data Analysis, and Machine Learning:**

- Correlation analysis
- Descriptive statistics
- Distributions
- Inferential statistics

- Dense Neural Networks
- Gaussian Naive Bayes
- K-Means Clustering
- Linear Regression
- Logistic Regression
- Principal Component Analysis

### Heads up

- **Not memory‑efficient (yet)** - footprint needs work.
- **The feature set is still limited** - expect missing pieces.

### Somewhere down the line

- Optional GPU acceleration (Vulkan or similar) for heavier workloads.
- Straightforward Python bindings using `pyo3`.

---

## Quick start

```rust
use chrono::NaiveDate;
use rustframe::{
    frame::{Frame, RowIndex},
    matrix::{BoolOps, Matrix, SeriesOps},
    utils::{DateFreq, BDatesList},
};

let n_periods = 4;

// Four business days starting 2024-01-02
let dates: Vec<NaiveDate> =
    BDatesList::from_n_periods("2024-01-02".to_string(), DateFreq::Daily, n_periods)
        .unwrap()
        .list().unwrap();

let col_names: Vec<String> = vec!["a".to_string(), "b".to_string()];

let ma: Matrix<f64> =
    Matrix::from_cols(vec![vec![1.0, 2.0, 3.0, 4.0], vec![5.0, 6.0, 7.0, 8.0]]);
let mb: Matrix<f64> =
    Matrix::from_cols(vec![vec![4.0, 3.0, 2.0, 1.0], vec![8.0, 7.0, 6.0, 5.0]]);

let fa: Frame<f64> = Frame::new(
    ma.clone(),
    col_names.clone(),
    Some(RowIndex::Date(dates.clone())),
);
let fb: Frame<f64> = Frame::new(mb, col_names, Some(RowIndex::Date(dates)));

// Math that reads like math
let result: Frame<f64> = &fa * &fb; // element‑wise multiply
let total: f64 = result.sum_vertical().iter().sum::<f64>();
assert_eq!(total, 184.0);

// broadcast & reduce
let result: Matrix<f64> = ma.clone() + 1.0; // add scalar
let result: Matrix<f64> = result + &ma - &ma; // add matrix
let result: Matrix<f64> = result - 1.0; // subtract scalar
let result: Matrix<f64> = result * 2.0; // multiply by scalar
let result: Matrix<f64> = result / 2.0; // divide by scalar

let check: bool = result.eq_elem(ma.clone()).all();
assert!(check);

// Alternatively:
let check: bool = (&(&(&(&ma + 1.0) - 1.0) * 2.0) / 2.0)
    .eq_elem(ma.clone())
    .all();
assert!(check);

// or even as:
let check: bool = ((((ma.clone() + 1.0) - 1.0) * 2.0) / 2.0)
    .eq_elem(ma.clone())
    .all();
assert!(check);

// Matrix multiplication
let mc: Matrix<f64> = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
let md: Matrix<f64> = Matrix::from_cols(vec![vec![5.0, 6.0], vec![7.0, 8.0]]);
let mul_result: Matrix<f64> = mc.matrix_mul(&md);
// Expected:
assert_eq!(mul_result.data(), &[23.0, 34.0, 31.0, 46.0]);

// Dot product (alias for matrix_mul for FloatMatrix)
let dot_result: Matrix<f64> = mc.dot(&md);
assert_eq!(dot_result, mul_result);

// Transpose
let original_matrix: Matrix<f64> = Matrix::from_cols(vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]]);
let transposed_matrix: Matrix<f64> = original_matrix.transpose();
assert_eq!(transposed_matrix.rows(), 2);
assert_eq!(transposed_matrix.cols(), 3);
assert_eq!(transposed_matrix.data(), &[1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);

// Map
let matrix = Matrix::from_cols(vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]]);
// Map function to double each value
let mapped_matrix = matrix.map(|x| x * 2.0);
assert_eq!(mapped_matrix.data(), &[2.0, 4.0, 6.0, 8.0, 10.0, 12.0]);

// Zip
let a = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]); // 2x2 matrix
let b = Matrix::from_cols(vec![vec![5.0, 6.0], vec![7.0, 8.0]]); // 2x2 matrix
                                                                   // Zip function to add corresponding elements
let zipped_matrix = a.zip(&b, |x, y| x + y);
assert_eq!(zipped_matrix.data(), &[6.0, 8.0, 10.0, 12.0]);
```

---

## DataFrame Usage Example

```rust
use chrono::NaiveDate;
use rustframe::dataframe::DataFrame;
use rustframe::utils::{BDateFreq, BDatesList};
use std::any::TypeId;
use std::collections::HashMap;

// Helper for NaiveDate
fn d(y: i32, m: u32, d: u32) -> NaiveDate {
    NaiveDate::from_ymd_opt(y, m, d).unwrap()
}

// Create a new DataFrame
let mut df = DataFrame::new();

// Add columns of different types
df.add_column("col_int1", vec![1, 2, 3, 4, 5]);
df.add_column("col_float1", vec![1.1, 2.2, 3.3, 4.4, 5.5]);
df.add_column(
    "col_string",
    vec![
        "apple".to_string(),
        "banana".to_string(),
        "cherry".to_string(),
        "date".to_string(),
        "elderberry".to_string(),
    ],
);
df.add_column("col_bool", vec![true, false, true, false, true]);
// df.add_column("col_date", vec![d(2023,1,1), d(2023,1,2), d(2023,1,3), d(2023,1,4), d(2023,1,5)]);
df.add_column(
    "col_date",
    BDatesList::from_n_periods("2023-01-01".to_string(), BDateFreq::Daily, 5)
        .unwrap()
        .list()
        .unwrap(),
);

println!("DataFrame after initial column additions:\n{}", df);

// Demonstrate frame re-use when adding columns of existing types
let initial_frames_count = df.num_internal_frames();
println!(
    "\nInitial number of internal frames: {}",
    initial_frames_count
);

df.add_column("col_int2", vec![6, 7, 8, 9, 10]);
df.add_column("col_float2", vec![6.6, 7.7, 8.8, 9.9, 10.0]);

let frames_after_reuse = df.num_internal_frames();
println!(
    "Number of internal frames after adding more columns of existing types: {}",
    frames_after_reuse
);
assert_eq!(initial_frames_count, frames_after_reuse); // Should be equal, demonstrating re-use

println!(
    "\nDataFrame after adding more columns of existing types:\n{}",
    df
);

// Get number of rows and columns
println!("Rows: {}", df.rows()); // Output: Rows: 5
println!("Columns: {}", df.cols()); // Output: Columns: 5

// Get column names
println!("Column names: {:?}", df.get_column_names());
// Output: Column names: ["col_int", "col_float", "col_string", "col_bool", "col_date"]

// Get a specific column by name and type
let int_col = df.get_column::<i32>("col_int1").unwrap();
// Output: Integer column: [1, 2, 3, 4, 5]
println!("Integer column (col_int1): {:?}", int_col);

let int_col2 = df.get_column::<i32>("col_int2").unwrap();
// Output: Integer column: [6, 7, 8, 9, 10]
println!("Integer column (col_int2): {:?}", int_col2);

let float_col = df.get_column::<f64>("col_float1").unwrap();
// Output: Float column: [1.1, 2.2, 3.3, 4.4, 5.5]
println!("Float column (col_float1): {:?}", float_col);

// Attempt to get a column with incorrect type (returns None)
let wrong_type_col = df.get_column::<bool>("col_int1");
// Output: Wrong type column: None
println!("Wrong type column: {:?}", wrong_type_col);

// Get a row by index
let row_0 = df.get_row(0).unwrap();
println!("Row 0: {:?}", row_0);
// Output: Row 0: {"col_int1": "1", "col_float1": "1.1", "col_string": "apple", "col_bool": "true", "col_date": "2023-01-01", "col_int2": "6", "col_float2": "6.6"}

let row_2 = df.get_row(2).unwrap();
println!("Row 2: {:?}", row_2);
// Output: Row 2: {"col_int1": "3", "col_float1": "3.3", "col_string": "cherry", "col_bool": "true", "col_date": "2023-01-03", "col_int2": "8", "col_float2": "8.8"}

// Attempt to get an out-of-bounds row (returns None)
let row_out_of_bounds = df.get_row(10);
// Output: Row out of bounds: None
println!("Row out of bounds: {:?}", row_out_of_bounds);

// Drop a column
df.drop_column("col_bool");
println!("\nDataFrame after dropping 'col_bool':\n{}", df);

println!("Columns after drop: {}", df.cols());
println!("Column names after drop: {:?}", df.get_column_names());

// Drop another column, ensuring the underlying Frame is removed if empty
df.drop_column("col_float1");
println!("\nDataFrame after dropping 'col_float1':\n{}", df);

println!("Columns after second drop: {}", df.cols());
println!(
    "Column names after second drop: {:?}",
    df.get_column_names()
);

// Attempt to drop a non-existent column (will panic)
// df.drop_column("non_existent_col"); // Uncomment to see panic
```

### More examples

See the [examples](./examples/) directory for some demonstrations of Rustframe's syntax and functionality.

To run the examples, use:

```bash
cargo run --example <example_name>
```

E.g. to run the `game_of_life` example:

```bash
cargo run --example game_of_life
```

More demos:

```bash
cargo run --example linear_regression
cargo run --example logistic_regression
cargo run --example k_means
cargo run --example pca
cargo run --example stats_overview
cargo run --example descriptive_stats
cargo run --example correlation
cargo run --example inferential_stats
cargo run --example distributions
```

To simply list all available examples, you can run:

```bash
# this technically raises an error, but it will list all examples
cargo run --example
```

Each demo runs a couple of mini-scenarios showcasing the APIs.

### Running benchmarks

To run the benchmarks, use:

```bash
cargo bench --features "bench"
```