Files
lancedb/python/src/permutation.rs
T
Jack Ye 1564ec10c8 feat: add Bootstrap split strategy for sampling with replacement (#4597)
## Context

`StreamingDataset`'s shipped split strategies (`split_random`,
`split_sequential`, `split_hash`, `split_calculated`) all share one
trait: every row in the base table is assigned to exactly one split,
exactly once. They only differ in *how* that one-to-one assignment is
chosen -- none of them can express "give me a resampled stream of N
rows, allowing repeats."

That's a distinct, common need in ML training: **the bootstrap** -- the
statistical technique (Efron, 1979) of estimating a dataset's properties
by resampling it *with replacement*. It's the same sense scikit-learn's
bagging classifiers mean by `bootstrap=True`. `SplitStrategy::Bootstrap`
adds exactly this to the data loader: sample rows uniformly with
replacement from the (optionally filtered) base table, independently per
split.

## Main use cases

- **Bagging / ensemble training** (e.g. random forests): each split
trains on an independent bootstrap resample of the training set.
- **Oversampling a rare class**: filter to the minority class, then
sample it up to whatever count balances it against the majority, instead
of exhausting its small set once per epoch.
- **Fixed-size training steps independent of dataset size**: want
exactly N batches per epoch regardless of whether the table has 50 rows
or 50 million? Set `num_samples` directly instead of it being implicitly
`len(dataset)`.
- **Weighted sampling without a weight column**: filter to "eligible"
rows (e.g. a windowed-sampling workload filtering to valid window-start
positions), then bootstrap the filtered set -- an item with more
eligible rows is naturally drawn proportionally more often, with no
explicit per-row weight needed.

## Example (an actual run, not a hypothetical one)

A 5-row base table (`row_id` 0..4), sampled via `Bootstrap {
num_samples: 8, sizes: Fixed(2), seed: Some(42) }`:

```
row_id  split_id
     0         0
     2         0
     4         0
     4         0      <- row 4 repeated within split 0
     4         1      <- row 4 also drawn in split 1 (independent draw)
     4         1
     1         1
     2         1
```

8 output rows from a 5-row table -- not 5. Row 4 drawn 4 times total,
row 3 never drawn. That's with-replacement sampling actually behaving
like with-replacement sampling, not an even round-robin dressed up with
a new name.

## What changed

Adds `SplitStrategy::Bootstrap` (uniform sampling with replacement,
optionally filtered, independently seeded per split -- a given split's
content doesn't depend on how many other splits there are, the same
elastic-determinism property documented for the loader's other
strategies). No new `PermutationBuilder` Rust method was needed:
`with_split_strategy` already accepts any `SplitStrategy` value
generically. Python binding:
`PermutationBuilder.bootstrap_sample(num_samples, *, ratios=None,
counts=None, fixed=None, seed=None, split_names=None)`, mirroring
`split_random`'s existing shape.

Also adds a generic `split_strategy` parameter to `StreamingDataset`'s
constructor (a callable taking/returning a `PermutationBuilder`), since
it previously could only reach `split_random`/`split_sequential` even
though `split_hash`/`split_calculated` already shipped. This unblocks
`Bootstrap` and any future strategy without further constructor changes.
2026-10-21 23:18:30 -07:00

397 lines
13 KiB
Rust

// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use std::sync::{Arc, Mutex};
use crate::{
arrow::RecordBatchStream, error::PythonErrorExt, runtime::future_into_py, table::Table,
};
use arrow::pyarrow::{PyArrowType, ToPyArrow};
use lancedb::{
dataloader::permutation::{
builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
reader::PermutationReader,
split::{SplitSizes, SplitStrategy},
},
query::Select,
};
use pyo3::{
Bound, PyAny, PyRef, PyRefMut, PyResult, Python,
exceptions::PyRuntimeError,
pyclass, pymethods,
types::{PyAnyMethods, PyDict, PyDictMethods, PyType},
};
fn table_from_py<'a>(table: Bound<'a, PyAny>) -> PyResult<Bound<'a, Table>> {
if table.hasattr("_inner")? {
Ok(table.getattr("_inner")?.cast_into::<Table>()?)
} else if table.hasattr("_table")? {
Ok(table
.getattr("_table")?
.getattr("_inner")?
.cast_into::<Table>()?)
} else {
Err(PyRuntimeError::new_err(
"Provided table does not appear to be a Table or RemoteTable instance",
))
}
}
/// Create a permutation builder for the given table
#[pyo3::pyfunction]
pub fn async_permutation_builder(table: Bound<'_, PyAny>) -> PyResult<PyAsyncPermutationBuilder> {
let table = table_from_py(table)?;
let inner_table = table.borrow().inner_ref()?.clone();
let inner_builder = LancePermutationBuilder::new(inner_table);
Ok(PyAsyncPermutationBuilder {
state: Arc::new(Mutex::new(PyAsyncPermutationBuilderState {
builder: Some(inner_builder),
})),
})
}
struct PyAsyncPermutationBuilderState {
builder: Option<LancePermutationBuilder>,
}
#[pyclass(name = "AsyncPermutationBuilder")]
pub struct PyAsyncPermutationBuilder {
state: Arc<Mutex<PyAsyncPermutationBuilderState>>,
}
impl PyAsyncPermutationBuilder {
fn modify(
&self,
func: impl FnOnce(LancePermutationBuilder) -> LancePermutationBuilder,
) -> PyResult<Self> {
let mut state = self.state.lock().unwrap();
let builder = state
.builder
.take()
.ok_or_else(|| PyRuntimeError::new_err("Builder already consumed"))?;
state.builder = Some(func(builder));
Ok(Self {
state: self.state.clone(),
})
}
}
#[pymethods]
impl PyAsyncPermutationBuilder {
#[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None, clump_size=None, split_names=None))]
pub fn split_random(
slf: PyRefMut<'_, Self>,
ratios: Option<Vec<f64>>,
counts: Option<Vec<u64>>,
fixed: Option<u64>,
seed: Option<u64>,
clump_size: Option<u64>,
split_names: Option<Vec<String>>,
) -> PyResult<Self> {
// Check that exactly one split type is provided
let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
.iter()
.filter(|&&x| x)
.count();
if split_args_count != 1 {
return Err(pyo3::exceptions::PyValueError::new_err(
"Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
));
}
let sizes = if let Some(ratios) = ratios {
SplitSizes::Percentages(ratios)
} else if let Some(counts) = counts {
SplitSizes::Counts(counts)
} else if let Some(fixed) = fixed {
SplitSizes::Fixed(fixed)
} else {
unreachable!("One of the split arguments must be provided");
};
slf.modify(|builder| {
builder.with_split_strategy(
SplitStrategy::Random {
seed,
sizes,
clump_size,
},
split_names,
)
})
}
#[pyo3(signature = (columns, split_weights, *, discard_weight=0, split_names=None))]
pub fn split_hash(
slf: PyRefMut<'_, Self>,
columns: Vec<String>,
split_weights: Vec<u64>,
discard_weight: u64,
split_names: Option<Vec<String>>,
) -> PyResult<Self> {
slf.modify(|builder| {
builder.with_split_strategy(
SplitStrategy::Hash {
columns,
split_weights,
discard_weight,
},
split_names,
)
})
}
#[pyo3(signature = (*, ratios=None, counts=None, fixed=None, split_names=None))]
pub fn split_sequential(
slf: PyRefMut<'_, Self>,
ratios: Option<Vec<f64>>,
counts: Option<Vec<u64>>,
fixed: Option<u64>,
split_names: Option<Vec<String>>,
) -> PyResult<Self> {
// Check that exactly one split type is provided
let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
.iter()
.filter(|&&x| x)
.count();
if split_args_count != 1 {
return Err(pyo3::exceptions::PyValueError::new_err(
"Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
));
}
let sizes = if let Some(ratios) = ratios {
SplitSizes::Percentages(ratios)
} else if let Some(counts) = counts {
SplitSizes::Counts(counts)
} else if let Some(fixed) = fixed {
SplitSizes::Fixed(fixed)
} else {
unreachable!("One of the split arguments must be provided");
};
slf.modify(|builder| {
builder.with_split_strategy(SplitStrategy::Sequential { sizes }, split_names)
})
}
#[pyo3(signature = (num_samples, *, ratios=None, counts=None, fixed=None, seed=None, split_names=None))]
pub fn bootstrap_sample(
slf: PyRefMut<'_, Self>,
num_samples: u64,
ratios: Option<Vec<f64>>,
counts: Option<Vec<u64>>,
fixed: Option<u64>,
seed: Option<u64>,
split_names: Option<Vec<String>>,
) -> PyResult<Self> {
// Check that exactly one split type is provided
let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
.iter()
.filter(|&&x| x)
.count();
if split_args_count != 1 {
return Err(pyo3::exceptions::PyValueError::new_err(
"Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
));
}
let sizes = if let Some(ratios) = ratios {
SplitSizes::Percentages(ratios)
} else if let Some(counts) = counts {
SplitSizes::Counts(counts)
} else if let Some(fixed) = fixed {
SplitSizes::Fixed(fixed)
} else {
unreachable!("One of the split arguments must be provided");
};
slf.modify(|builder| {
builder.with_split_strategy(
SplitStrategy::Bootstrap {
num_samples,
sizes,
seed,
},
split_names,
)
})
}
pub fn split_calculated(
slf: PyRefMut<'_, Self>,
calculation: String,
split_names: Option<Vec<String>>,
) -> PyResult<Self> {
slf.modify(|builder| {
builder.with_split_strategy(SplitStrategy::Calculated { calculation }, split_names)
})
}
pub fn shuffle(
slf: PyRefMut<'_, Self>,
seed: Option<u64>,
clump_size: Option<u64>,
) -> PyResult<Self> {
slf.modify(|builder| {
builder.with_shuffle_strategy(ShuffleStrategy::Random { seed, clump_size })
})
}
pub fn filter(slf: PyRefMut<'_, Self>, filter: String) -> PyResult<Self> {
slf.modify(|builder| builder.with_filter(filter))
}
pub fn execute(slf: PyRefMut<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
let mut state = slf.state.lock().unwrap();
let builder = state
.builder
.take()
.ok_or_else(|| PyRuntimeError::new_err("Builder already consumed"))?;
future_into_py(slf.py(), async move {
let table = builder.build().await.infer_error()?;
Ok(Table::new(table))
})
}
}
#[pyclass(name = "PermutationReader")]
pub struct PyPermutationReader {
reader: Arc<PermutationReader>,
}
impl PyPermutationReader {
fn from_reader(reader: PermutationReader) -> Self {
Self {
reader: Arc::new(reader),
}
}
fn parse_selection(selection: Option<Bound<'_, PyAny>>) -> PyResult<Select> {
let Some(selection) = selection else {
return Ok(Select::All);
};
let selection = selection.cast_into::<PyDict>()?;
let selection = selection
.iter()
.map(|(key, value)| {
let key = key.extract::<String>()?;
let value = value.extract::<String>()?;
Ok((key, value))
})
.collect::<PyResult<Vec<_>>>()?;
Ok(Select::dynamic(&selection))
}
}
#[pymethods]
impl PyPermutationReader {
#[classmethod]
pub fn from_tables<'py>(
cls: &Bound<'py, PyType>,
base_table: Bound<'py, PyAny>,
permutation_table: Option<Bound<'py, PyAny>>,
split: u64,
) -> PyResult<Bound<'py, PyAny>> {
let base_table = table_from_py(base_table)?;
let permutation_table = permutation_table.map(table_from_py).transpose()?;
let base_table = base_table.borrow().inner_ref()?.base_table().clone();
let permutation_table = permutation_table
.map(|p| PyResult::Ok(p.borrow().inner_ref()?.base_table().clone()))
.transpose()?;
future_into_py(cls.py(), async move {
let reader = if let Some(permutation_table) = permutation_table {
PermutationReader::try_from_tables(base_table, permutation_table, split)
.await
.infer_error()?
} else {
PermutationReader::identity(base_table)
.await
.infer_error()?
};
Ok(Self::from_reader(reader))
})
}
#[pyo3(signature = (selection=None))]
pub fn output_schema<'py>(
slf: PyRef<'py, Self>,
selection: Option<Bound<'py, PyAny>>,
) -> PyResult<Bound<'py, PyAny>> {
let selection = Self::parse_selection(selection)?;
let reader = slf.reader.clone();
future_into_py(slf.py(), async move {
let schema = reader.output_schema(selection).await.infer_error()?;
Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
})
}
#[pyo3(signature = ())]
pub fn count_rows<'py>(slf: PyRef<'py, Self>) -> u64 {
slf.reader.count_rows()
}
#[pyo3(signature = (offset))]
pub fn with_offset<'py>(slf: PyRef<'py, Self>, offset: u64) -> PyResult<Bound<'py, PyAny>> {
let reader = slf.reader.as_ref().clone();
future_into_py(slf.py(), async move {
let reader = reader.with_offset(offset).await.infer_error()?;
Ok(Self::from_reader(reader))
})
}
#[pyo3(signature = (limit))]
pub fn with_limit<'py>(slf: PyRef<'py, Self>, limit: u64) -> PyResult<Bound<'py, PyAny>> {
let reader = slf.reader.as_ref().clone();
future_into_py(slf.py(), async move {
let reader = reader.with_limit(limit).await.infer_error()?;
Ok(Self::from_reader(reader))
})
}
#[pyo3(signature = (selection=None, *, batch_size=None))]
pub fn read<'py>(
slf: PyRef<'py, Self>,
selection: Option<Bound<'py, PyAny>>,
batch_size: Option<u32>,
) -> PyResult<Bound<'py, PyAny>> {
let selection = Self::parse_selection(selection)?;
let reader = slf.reader.clone();
let batch_size = batch_size.unwrap_or(1024);
future_into_py(slf.py(), async move {
use lancedb::query::QueryExecutionOptions;
let mut execution_options = QueryExecutionOptions::default();
execution_options.max_batch_length = batch_size;
let stream = reader
.read(selection, execution_options)
.await
.infer_error()?;
Ok(RecordBatchStream::new(stream))
})
}
#[pyo3(signature = (indices, *, selection=None))]
pub fn take_offsets<'py>(
slf: PyRef<'py, Self>,
indices: Vec<u64>,
selection: Option<Bound<'py, PyAny>>,
) -> PyResult<Bound<'py, PyAny>> {
let selection = Self::parse_selection(selection)?;
let reader = slf.reader.clone();
future_into_py(slf.py(), async move {
let batch = reader
.take_offsets(&indices, selection)
.await
.infer_error()?;
Ok(PyArrowType(batch))
})
}
}