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use crate::{GreenersError, formula::Formula};
use ndarray::{Array1, Array2};
use std::collections::HashMap;
use std::path::Path;
/// A simple DataFrame-like structure for storing column-oriented data.
///
/// This provides a minimal interface similar to pandas/polars for working with
/// tabular data and formulas.
#[derive(Debug, Clone)]
pub struct DataFrame {
/// Column data stored as f64 arrays
columns: HashMap<String, Array1<f64>>,
/// Number of rows (all columns must have the same length)
n_rows: usize,
}
impl DataFrame {
/// Create a new DataFrame from a HashMap of column names to data arrays.
///
/// # Examples
/// ```
/// use greeners::dataframe::DataFrame;
/// use ndarray::Array1;
/// use std::collections::HashMap;
///
/// let mut data = HashMap::new();
/// data.insert("x".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
/// data.insert("y".to_string(), Array1::from(vec![2.0, 4.0, 6.0]));
///
/// let df = DataFrame::new(data).unwrap();
/// assert_eq!(df.n_rows(), 3);
/// ```
pub fn new(columns: HashMap<String, Array1<f64>>) -> Result<Self, GreenersError> {
if columns.is_empty() {
return Ok(DataFrame {
columns,
n_rows: 0,
});
}
// Check that all columns have the same length
let first_len = columns.values().next().unwrap().len();
for (name, col) in &columns {
if col.len() != first_len {
return Err(GreenersError::ShapeMismatch(
format!("Column '{}' has length {}, expected {}", name, col.len(), first_len)
));
}
}
Ok(DataFrame {
columns,
n_rows: first_len,
})
}
/// Get the number of rows in the DataFrame
pub fn n_rows(&self) -> usize {
self.n_rows
}
/// Get the number of columns in the DataFrame
pub fn n_cols(&self) -> usize {
self.columns.len()
}
/// Get a column by name, returning a reference to the Array1
pub fn get(&self, name: &str) -> Result<&Array1<f64>, GreenersError> {
self.columns.get(name).ok_or_else(|| {
GreenersError::VariableNotFound(format!("Column '{}' not found", name))
})
}
/// Get a mutable column by name
pub fn get_mut(&mut self, name: &str) -> Result<&mut Array1<f64>, GreenersError> {
self.columns.get_mut(name).ok_or_else(|| {
GreenersError::VariableNotFound(format!("Column '{}' not found", name))
})
}
/// Check if a column exists
pub fn has_column(&self, name: &str) -> bool {
self.columns.contains_key(name)
}
/// Get all column names
pub fn column_names(&self) -> Vec<String> {
self.columns.keys().cloned().collect()
}
/// Count the actual number of columns in the design matrix
/// (accounting for categorical variable expansion)
fn count_design_matrix_cols(&self, formula: &Formula) -> Result<usize, GreenersError> {
let mut count = 0;
// Intercept
if formula.intercept {
count += 1;
}
// Independent variables
for var_name in &formula.independents {
if var_name.starts_with("C(") && var_name.ends_with(')') {
// Categorical: count unique values - 1 (drop first)
let var = var_name[2..var_name.len()-1].trim();
let col_data = self.get(var)?;
use std::collections::BTreeSet;
let unique_vals: BTreeSet<i32> = col_data.iter()
.map(|&v| v.round() as i32)
.collect();
let n_categories = unique_vals.len();
if n_categories < 2 {
return Err(GreenersError::FormulaError(
format!("Categorical variable '{}' must have at least 2 categories", var)
));
}
count += n_categories - 1; // Drop first category
} else {
// Regular, interaction, or polynomial: 1 column each
count += 1;
}
}
Ok(count)
}
/// Build design matrix (X) and response vector (y) from a Formula.
///
/// # Examples
/// ```
/// use greeners::dataframe::DataFrame;
/// use greeners::formula::Formula;
/// use ndarray::Array1;
/// use std::collections::HashMap;
///
/// let mut data = HashMap::new();
/// data.insert("y".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
/// data.insert("x1".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
/// data.insert("x2".to_string(), Array1::from(vec![2.0, 3.0, 4.0]));
///
/// let df = DataFrame::new(data).unwrap();
/// let formula = Formula::parse("y ~ x1 + x2").unwrap();
///
/// let (y, x) = df.to_design_matrix(&formula).unwrap();
/// assert_eq!(y.len(), 3);
/// assert_eq!(x.shape(), &[3, 3]); // 3 rows, 3 cols (intercept + x1 + x2)
/// ```
pub fn to_design_matrix(&self, formula: &Formula) -> Result<(Array1<f64>, Array2<f64>), GreenersError> {
// Extract y (dependent variable)
let y = self.get(&formula.dependent)?.clone();
// Build X matrix
let n_rows = self.n_rows;
// Count actual number of columns (accounting for categorical expansion)
let actual_n_cols = self.count_design_matrix_cols(formula)?;
let mut x_mat = Array2::<f64>::zeros((n_rows, actual_n_cols));
let mut col_idx = 0;
// Add intercept if requested
if formula.intercept {
for i in 0..n_rows {
x_mat[[i, col_idx]] = 1.0;
}
col_idx += 1;
}
// Add independent variables (including interactions, categorical, polynomial)
for var_name in &formula.independents {
// Check for categorical variable: C(var)
if var_name.starts_with("C(") && var_name.ends_with(')') {
// Extract variable name from C(var)
let var = var_name[2..var_name.len()-1].trim();
let col_data = self.get(var)?;
// Get unique values (categories)
use std::collections::BTreeSet;
let unique_vals: BTreeSet<i32> = col_data.iter()
.map(|&v| v.round() as i32)
.collect();
let mut categories: Vec<i32> = unique_vals.into_iter().collect();
categories.sort();
// Create dummies (drop first category for identification)
// e.g., if categories are [0, 1, 2], create dummies for 1 and 2
if categories.len() < 2 {
return Err(GreenersError::FormulaError(
format!("Categorical variable '{}' must have at least 2 categories", var)
));
}
// Skip first category (reference level)
for &cat_val in categories.iter().skip(1) {
for i in 0..n_rows {
x_mat[[i, col_idx]] = if (col_data[i].round() as i32) == cat_val { 1.0 } else { 0.0 };
}
col_idx += 1;
}
continue; // Skip col_idx increment at end since we handled it
}
// Check for polynomial term: I(expr)
if var_name.starts_with("I(") && var_name.ends_with(')') {
// Extract expression from I(...)
let expr = var_name[2..var_name.len()-1].trim();
// Parse simple expressions: var^power or var**power
if expr.find('^').or_else(|| expr.find("**")).is_some() {
let var_part;
let power_part;
if expr.contains("**") {
let parts: Vec<&str> = expr.split("**").collect();
if parts.len() != 2 {
return Err(GreenersError::FormulaError(
format!("Invalid polynomial expression '{}'", expr)
));
}
var_part = parts[0].trim();
power_part = parts[1].trim();
} else {
let parts: Vec<&str> = expr.split('^').collect();
if parts.len() != 2 {
return Err(GreenersError::FormulaError(
format!("Invalid polynomial expression '{}'", expr)
));
}
var_part = parts[0].trim();
power_part = parts[1].trim();
}
let col_data = self.get(var_part)?;
let power: i32 = power_part.parse().map_err(|_| {
GreenersError::FormulaError(
format!("Invalid power in expression '{}'", expr)
)
})?;
// Compute x^power
for i in 0..n_rows {
x_mat[[i, col_idx]] = col_data[i].powi(power);
}
} else {
return Err(GreenersError::FormulaError(
format!("I() expression must contain ^ or **: '{}'", expr)
));
}
}
// Check if this is an interaction term (contains ':')
else if var_name.contains(':') {
// Parse interaction: "x1:x2"
let parts: Vec<&str> = var_name.split(':').collect();
if parts.len() != 2 {
return Err(GreenersError::FormulaError(
format!("Invalid interaction term '{}'", var_name)
));
}
let var1 = self.get(parts[0].trim())?;
let var2 = self.get(parts[1].trim())?;
// Compute interaction: elementwise multiplication
for i in 0..n_rows {
x_mat[[i, col_idx]] = var1[i] * var2[i];
}
} else {
// Regular variable
let col_data = self.get(var_name)?;
for i in 0..n_rows {
x_mat[[i, col_idx]] = col_data[i];
}
}
col_idx += 1;
}
Ok((y, x_mat))
}
/// Insert or update a column
pub fn insert(&mut self, name: String, data: Array1<f64>) -> Result<(), GreenersError> {
if !self.columns.is_empty() && data.len() != self.n_rows {
return Err(GreenersError::ShapeMismatch(
format!("New column '{}' has length {}, expected {}", name, data.len(), self.n_rows)
));
}
if self.columns.is_empty() {
self.n_rows = data.len();
}
self.columns.insert(name, data);
Ok(())
}
/// Read a DataFrame from a CSV file with headers.
///
/// # Arguments
/// * `path` - Path to the CSV file
///
/// # Examples
/// ```no_run
/// use greeners::DataFrame;
///
/// let df = DataFrame::from_csv("data.csv").unwrap();
/// println!("Loaded {} rows and {} columns", df.n_rows(), df.n_cols());
/// println!("Columns: {:?}", df.column_names());
/// ```
pub fn from_csv<P: AsRef<Path>>(path: P) -> Result<Self, GreenersError> {
use csv::ReaderBuilder;
let mut reader = ReaderBuilder::new()
.has_headers(true)
.from_path(path)
.map_err(|e| GreenersError::FormulaError(format!("Failed to read CSV: {}", e)))?;
// Get headers
let headers = reader.headers()
.map_err(|e| GreenersError::FormulaError(format!("Failed to read headers: {}", e)))?
.clone();
// Initialize column vectors
let mut columns: HashMap<String, Vec<f64>> = HashMap::new();
for header in headers.iter() {
columns.insert(header.to_string(), Vec::new());
}
// Read all records
for result in reader.records() {
let record = result
.map_err(|e| GreenersError::FormulaError(format!("Failed to read record: {}", e)))?;
for (i, field) in record.iter().enumerate() {
let header = &headers[i];
let value: f64 = field.trim().parse()
.map_err(|_| GreenersError::FormulaError(
format!("Failed to parse '{}' as f64 in column '{}'", field, header)
))?;
columns.get_mut(header).unwrap().push(value);
}
}
// Convert Vec<f64> to Array1<f64>
let mut data = HashMap::new();
for (name, values) in columns {
data.insert(name, Array1::from(values));
}
DataFrame::new(data)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_dataframe_creation() {
let mut data = HashMap::new();
data.insert("x".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
data.insert("y".to_string(), Array1::from(vec![2.0, 4.0, 6.0]));
let df = DataFrame::new(data).unwrap();
assert_eq!(df.n_rows(), 3);
assert_eq!(df.n_cols(), 2);
}
#[test]
fn test_dataframe_get() {
let mut data = HashMap::new();
data.insert("x".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
let df = DataFrame::new(data).unwrap();
let col = df.get("x").unwrap();
assert_eq!(col[0], 1.0);
assert_eq!(col[1], 2.0);
assert_eq!(col[2], 3.0);
}
#[test]
fn test_to_design_matrix() {
let mut data = HashMap::new();
data.insert("y".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
data.insert("x1".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
data.insert("x2".to_string(), Array1::from(vec![2.0, 3.0, 4.0]));
let df = DataFrame::new(data).unwrap();
let formula = Formula::parse("y ~ x1 + x2").unwrap();
let (y, x) = df.to_design_matrix(&formula).unwrap();
assert_eq!(y.len(), 3);
assert_eq!(x.shape(), &[3, 3]); // intercept + x1 + x2
// Check intercept column
assert_eq!(x[[0, 0]], 1.0);
assert_eq!(x[[1, 0]], 1.0);
assert_eq!(x[[2, 0]], 1.0);
// Check x1 column
assert_eq!(x[[0, 1]], 1.0);
assert_eq!(x[[1, 1]], 2.0);
assert_eq!(x[[2, 1]], 3.0);
}
#[test]
fn test_design_matrix_no_intercept() {
let mut data = HashMap::new();
data.insert("y".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
data.insert("x1".to_string(), Array1::from(vec![1.0, 2.0, 3.0]));
let df = DataFrame::new(data).unwrap();
let formula = Formula::parse("y ~ x1 - 1").unwrap();
let (y, x) = df.to_design_matrix(&formula).unwrap();
assert_eq!(y.len(), 3);
assert_eq!(x.shape(), &[3, 1]); // only x1, no intercept
}
}