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//! GPU-accelerated reduction operations.
//!
//! Provides reduce, scan (prefix-sum), and segmented reduce for [`Column`]s.
//!
//! # Examples
//!
//! ```rust,no_run
//! use cudf::Column;
//! use cudf::reduction::ReduceOp;
//! use cudf::types::DataType;
//! use cudf::TypeId;
//!
//! let col = Column::from_slice(&[1i32, 2, 3, 4]).unwrap();
//! let sum = col.reduce(ReduceOp::Sum, DataType::new(TypeId::Int64)).unwrap();
//! assert!(sum.is_valid());
//! ```
use crate::column::Column;
use crate::error::{CudfError, Result};
use crate::scalar::Scalar;
use crate::types::DataType;
/// Aggregation operations for column reduction.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ReduceOp {
/// Sum of all values.
Sum = 0,
/// Product of all values.
Product = 1,
/// Minimum value.
Min = 2,
/// Maximum value.
Max = 3,
/// Sum of squares of all values.
SumOfSquares = 4,
/// Arithmetic mean.
Mean = 5,
/// Variance.
Variance = 6,
/// Standard deviation.
Std = 7,
/// Logical OR (true if any value is true).
Any = 8,
/// Logical AND (true if all values are true).
All = 9,
/// Median value.
Median = 10,
}
/// Aggregation operations for prefix scan.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ScanOp {
/// Cumulative sum.
Sum = 0,
/// Cumulative product.
Product = 1,
/// Cumulative minimum.
Min = 2,
/// Cumulative maximum.
Max = 3,
}
/// Result of a [`Column::minmax`] operation.
pub struct MinMaxResult {
/// The minimum value.
pub min: Scalar,
/// The maximum value.
pub max: Scalar,
}
impl Column {
/// Compute the minimum and maximum values simultaneously.
///
/// This is more efficient than calling `reduce(Min)` and `reduce(Max)`
/// separately, as it requires only a single pass over the data.
///
/// # Errors
///
/// Returns an error if the column type does not support comparison
/// or if a GPU error occurs.
pub fn minmax(&self) -> Result<MinMaxResult> {
let mut raw = cudf_cxx::reduction::ffi::minmax(&self.inner).map_err(CudfError::from_cxx)?;
let min_raw = cudf_cxx::reduction::ffi::minmax_take_min(raw.pin_mut())
.map_err(CudfError::from_cxx)?;
let max_raw = cudf_cxx::reduction::ffi::minmax_take_max(raw.pin_mut())
.map_err(CudfError::from_cxx)?;
Ok(MinMaxResult {
min: Scalar { inner: min_raw },
max: Scalar { inner: max_raw },
})
}
/// Reduce the entire column to a single scalar value.
///
/// # Arguments
///
/// * `op` - The reduction operation to apply.
/// * `output_type` - The desired output scalar data type.
///
/// # Errors
///
/// Returns an error if the operation is unsupported for the column type
/// or if a GPU error occurs.
pub fn reduce(&self, op: ReduceOp, output_type: DataType) -> Result<Scalar> {
let raw = cudf_cxx::reduction::ffi::reduce(&self.inner, op as i32, output_type.id() as i32)
.map_err(CudfError::from_cxx)?;
Ok(Scalar { inner: raw })
}
/// Compute a prefix scan (cumulative operation) over this column.
///
/// # Arguments
///
/// * `op` - The scan operation to apply.
/// * `inclusive` - If true, element i includes itself in the result.
/// If false (exclusive), element i includes only elements before it.
///
/// # Errors
///
/// Returns an error if the operation is unsupported for the column type
/// or if a GPU error occurs.
pub fn scan(&self, op: ScanOp, inclusive: bool) -> Result<Column> {
let raw = cudf_cxx::reduction::ffi::scan(&self.inner, op as i32, inclusive)
.map_err(CudfError::from_cxx)?;
Ok(Column { inner: raw })
}
/// Reduce within segments defined by an offsets column.
///
/// The offsets column contains `n+1` values defining `n` segments:
/// segment `i` covers elements `[offsets[i], offsets[i+1])`.
///
/// # Arguments
///
/// * `offsets` - Column of segment boundaries (int32).
/// * `op` - The reduction operation to apply within each segment.
/// * `output_type` - The desired output column data type.
/// * `include_nulls` - Whether to include null values in the reduction.
///
/// # Errors
///
/// Returns an error if the arguments are invalid or if a GPU error occurs.
pub fn segmented_reduce(
&self,
offsets: &Column,
op: ReduceOp,
output_type: DataType,
include_nulls: bool,
) -> Result<Column> {
let raw = cudf_cxx::reduction::ffi::segmented_reduce(
&self.inner,
&offsets.inner,
op as i32,
output_type.id() as i32,
include_nulls,
)
.map_err(CudfError::from_cxx)?;
Ok(Column { inner: raw })
}
/// Reduce with an initial value.
///
/// Only `sum`, `product`, `min`, `max`, `any`, `all` reductions support
/// an initial value. The initial value is included in the reduction.
pub fn reduce_with_init(
&self,
op: ReduceOp,
output_type: DataType,
init: &Scalar,
) -> Result<Scalar> {
let raw = cudf_cxx::reduction::ffi::reduce_with_init(
&self.inner,
op as i32,
output_type.id() as i32,
&init.inner,
)
.map_err(CudfError::from_cxx)?;
Ok(Scalar { inner: raw })
}
/// Reduce variance with a specific degrees of freedom correction (ddof).
///
/// Unlike the generic which uses ddof=1,
/// this allows specifying any ddof value.
pub fn reduce_var_with_ddof(&self, ddof: i32, output_type: DataType) -> Result<Scalar> {
let raw = cudf_cxx::reduction::ffi::reduce_var_with_ddof(
&self.inner,
ddof,
output_type.id() as i32,
)
.map_err(CudfError::from_cxx)?;
Ok(Scalar { inner: raw })
}
/// Reduce standard deviation with a specific degrees of freedom correction (ddof).
///
/// Unlike the generic which uses ddof=1,
/// this allows specifying any ddof value.
pub fn reduce_std_with_ddof(&self, ddof: i32, output_type: DataType) -> Result<Scalar> {
let raw = cudf_cxx::reduction::ffi::reduce_std_with_ddof(
&self.inner,
ddof,
output_type.id() as i32,
)
.map_err(CudfError::from_cxx)?;
Ok(Scalar { inner: raw })
}
}
/// Check if a reduction aggregation is valid for a given source data type.
pub fn is_valid_reduction_aggregation(source_type: DataType, op: ReduceOp) -> Result<bool> {
cudf_cxx::reduction::ffi::is_valid_reduction_aggregation(source_type.id() as i32, op as i32)
.map_err(CudfError::from_cxx)
}