feat: add width_bucket function for PySpark parity

daft/functions/__init__.py

@@ -209,6 +209,7 @@
     between,
     bin,
     conv,
+    width_bucket,
     is_nan,
     is_inf,
     not_nan,
@@ -612,5 +613,6 @@
     "week_of_year",
     "weekofyear",
     "when",
+    "width_bucket",
     "year",
 ]

daft/functions/numeric.py

@@ -306,6 +306,23 @@ def conv(expr: Expression, from_base: int, to_base: int) -> Expression:
     return Expression._call_builtin_scalar_fn("conv", expr, from_base, to_base)
 
 
+def width_bucket(
+    value: Expression,
+    min: Expression,
+    max: Expression,
+    num_bucket: Expression,
+) -> Expression:
+    """Returns the 1-indexed bucket of ``value`` in an equiwidth histogram over ``[min, max]``.
+
+    Returns ``0`` below the range and ``num_bucket + 1`` at or above; descending bounds
+    (``min > max``) flip the orientation. Non-integer ``num_bucket`` truncates toward zero.
+    Examples: ``width_bucket(5.3, 0.2, 10.6, 5) == 3``, ``width_bucket(-2.1, 1.3, 3.4, 3) == 0``.
+    Returns NULL when ``num_bucket <= 0``, ``min == max``, ``value`` is NaN, or
+    ``min``/``max`` is NaN/Infinite.
+    """
+    return Expression._call_builtin_scalar_fn("width_bucket", value, min, max, num_bucket)
+
+
 def is_nan(expr: Expression) -> Expression:
     """Checks if values are NaN (a special float value indicating not-a-number).
 

src/daft-functions/src/numeric/mod.rs

@@ -18,6 +18,7 @@ pub mod round;
 pub mod sign;
 pub mod sqrt;
 pub mod trigonometry;
+pub mod width_bucket;
 
 use abs::Abs;
 use bin::Bin;
@@ -44,6 +45,7 @@ use power::Power;
 use round::Round;
 use sign::{Negate, Sign};
 use sqrt::Sqrt;
+use width_bucket::WidthBucket;
 
 fn to_field_numeric(f: &dyn ScalarUDF, input: &Expr, schema: &Schema) -> DaftResult<Field> {
     let field = input.to_field(schema)?;
@@ -91,6 +93,7 @@ impl FunctionModule for NumericFunctions {
         parent.add_fn(Sign);
         parent.add_fn(Negate);
         parent.add_fn(Sqrt);
+        parent.add_fn(WidthBucket);
 
         // trig functions
         use trigonometry::*;

src/daft-functions/src/numeric/width_bucket.rs

@@ -0,0 +1,175 @@
+use common_error::{DaftError, DaftResult};
+use daft_core::{
+    datatypes::Int64Array,
+    prelude::{DataType, Field, Schema},
+    series::{IntoSeries, Series},
+};
+use daft_dsl::{
+    ExprRef,
+    functions::{FunctionArgs, ScalarUDF, scalar::ScalarFn},
+};
+use serde::{Deserialize, Serialize};
+
+#[derive(Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
+pub struct WidthBucket;
+
+#[derive(FunctionArgs)]
+struct WidthBucketArgs<T> {
+    value: T,
+    min: T,
+    max: T,
+    num_bucket: T,
+}
+
+#[typetag::serde]
+impl ScalarUDF for WidthBucket {
+    fn call(
+        &self,
+        inputs: FunctionArgs<Series>,
+        _ctx: &daft_dsl::functions::scalar::EvalContext,
+    ) -> DaftResult<Series> {
+        let WidthBucketArgs {
+            value,
+            min,
+            max,
+            num_bucket,
+        } = inputs.try_into()?;
+        width_bucket_impl(value, min, max, num_bucket)
+    }
+
+    fn name(&self) -> &'static str {
+        "width_bucket"
+    }
+
+    fn get_return_field(
+        &self,
+        inputs: FunctionArgs<ExprRef>,
+        schema: &Schema,
+    ) -> DaftResult<Field> {
+        let WidthBucketArgs {
+            value,
+            min,
+            max,
+            num_bucket,
+        } = inputs.try_into()?;
+        let value_field = value.to_field(schema)?;
+        let min_field = min.to_field(schema)?;
+        let max_field = max.to_field(schema)?;
+        let num_bucket_field = num_bucket.to_field(schema)?;
+        for (name, field) in [
+            ("value", &value_field),
+            ("min", &min_field),
+            ("max", &max_field),
+            ("num_bucket", &num_bucket_field),
+        ] {
+            if !field.dtype.is_numeric() {
+                return Err(DaftError::TypeError(format!(
+                    "Expected `{name}` of width_bucket to be numeric, got {}",
+                    field.dtype
+                )));
+            }
+        }
+        Ok(Field::new(value_field.name, DataType::Int64))
+    }
+
+    fn docstring(&self) -> &'static str {
+        "Returns the bucket number for `value` in an equiwidth histogram with \
+         `num_bucket` buckets in the range [min, max]. Returns 0 below the range and \
+         num_bucket+1 at or above. Supports descending bounds (min > max). Returns NULL \
+         if num_bucket <= 0, min == max, value/min/max is NaN, or min/max is infinite."
+    }
+}
+
+fn width_bucket_impl(
+    value: Series,
+    min: Series,
+    max: Series,
+    num_bucket: Series,
+) -> DaftResult<Series> {
+    let value = value.cast(&DataType::Float64)?;
+    let min = min.cast(&DataType::Float64)?;
+    let max = max.cast(&DataType::Float64)?;
+    let num_bucket = num_bucket.cast(&DataType::Int64)?;
+    let (value, min, max, num_bucket) = align_lengths(value, min, max, num_bucket)?;
+
+    let v_arr = value.f64().unwrap();
+    let mn_arr = min.f64().unwrap();
+    let mx_arr = max.f64().unwrap();
+    let nb_arr = num_bucket.i64().unwrap();
+
+    let iter = v_arr
+        .iter()
+        .zip(mn_arr.iter())
+        .zip(mx_arr.iter())
+        .zip(nb_arr.iter())
+        .map(|(((v, mn), mx), nb)| match (v, mn, mx, nb) {
+            (Some(v), Some(mn), Some(mx), Some(nb)) => compute_bucket(v, mn, mx, nb),
+            _ => None,
+        });
+    Ok(Int64Array::from_iter(Field::new(v_arr.name(), DataType::Int64), iter).into_series())
+}
+
+fn align_lengths(
+    a: Series,
+    b: Series,
+    c: Series,
+    d: Series,
+) -> DaftResult<(Series, Series, Series, Series)> {
+    let lens = [a.len(), b.len(), c.len(), d.len()];
+    let max_len = *lens.iter().max().unwrap();
+    for &l in &lens {
+        if l != 1 && l != max_len {
+            return Err(DaftError::ValueError(format!(
+                "Cannot apply width_bucket to arrays of different lengths: {} vs {} vs {} vs {}",
+                lens[0], lens[1], lens[2], lens[3]
+            )));
+        }
+    }
+    let bcast = |s: Series| -> DaftResult<Series> {
+        if s.len() == max_len {
+            Ok(s)
+        } else {
+            s.broadcast(max_len)
+        }
+    };
+    Ok((bcast(a)?, bcast(b)?, bcast(c)?, bcast(d)?))
+}
+
+/// Mirrors Spark's `WidthBucket` NULL and bucketing semantics for parity.
+fn compute_bucket(v: f64, mn: f64, mx: f64, nb: i64) -> Option<i64> {
+    if nb <= 0
+        || nb == i64::MAX
+        || v.is_nan()
+        || mn == mx
+        || mn.is_nan()
+        || mn.is_infinite()
+        || mx.is_nan()
+        || mx.is_infinite()
+    {
+        return None;
+    }
+    let bucket = if mn < mx {
+        if v < mn {
+            0
+        } else if v >= mx {
+            nb + 1
+        } else {
+            ((nb as f64) * (v - mn) / (mx - mn)) as i64 + 1
+        }
+    } else {
+        // Descending: roles of below/above flip so a smaller value sorts higher.
+        if v > mn {
+            0
+        } else if v <= mx {
+            nb + 1
+        } else {
+            ((nb as f64) * (mn - v) / (mn - mx)) as i64 + 1
+        }
+    };
+    Some(bucket)
+}
+
+#[must_use]
+pub fn width_bucket(value: ExprRef, min: ExprRef, max: ExprRef, num_bucket: ExprRef) -> ExprRef {
+    ScalarFn::builtin(WidthBucket, vec![value, min, max, num_bucket]).into()
+}

tests/recordbatch/numeric/test_numeric.py

@@ -1186,3 +1186,99 @@ def test_pmod_bad_input() -> None:
     table = MicroPartition.from_pydict({"a": ["x", "y"], "b": [1, 2]})
     with pytest.raises(ValueError, match="Expected inputs to pmod to be numeric"):
         table.eval_expression_list([pmod(col("a"), col("b"))])
+
+
+def test_width_bucket_basic() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict(
+        {
+            "v": [5.3, -2.1, 8.1, -0.9],
+            "mn": [0.2, 1.3, 0.0, 5.2],
+            "mx": [10.6, 3.4, 5.7, 0.5],
+            "nb": [5, 3, 4, 2],
+        }
+    )
+    result = table.eval_expression_list([width_bucket(col("v"), col("mn"), col("mx"), col("nb")).alias("bucket")])
+    assert result.get_column_by_name("bucket").to_pylist() == [3, 0, 5, 3]
+
+
+def test_width_bucket_descending() -> None:
+    """Min > max flips orientation; v > min underflows, v <= max overflows."""
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": [11.0, 5.0, 0.0, -1.0]})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(10.0), lit(0.0), lit(5)).alias("bucket")])
+    # min=10, max=0, nb=5: v=11 underflows -> 0; v=5 mid -> floor(5*(10-5)/10)+1=3;
+    # v=0 == max -> nb+1=6; v=-1 < max -> nb+1=6.
+    assert result.get_column_by_name("bucket").to_pylist() == [0, 3, 6, 6]
+
+
+def test_width_bucket_integer_inputs() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": pa.array([3, 5, 7], type=pa.int32())})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(0), lit(10), lit(5)).alias("bucket")])
+    assert result.get_column_by_name("bucket").to_pylist() == [2, 3, 4]
+
+
+def test_width_bucket_below_and_above_range() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": [-0.1, 0.0, 5.0, 10.0, 10.1]})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(0.0), lit(10.0), lit(5)).alias("bucket")])
+    # v=0 is at min so bucket 1; v=10 hits the v>=max branch so nb+1=6.
+    assert result.get_column_by_name("bucket").to_pylist() == [0, 1, 3, 6, 6]
+
+
+def test_width_bucket_null_returning() -> None:
+    from daft.functions import width_bucket
+
+    i64_max = 2**63 - 1
+    table = MicroPartition.from_pydict(
+        {
+            "v": [1.0, 1.0, 1.0, 1.0, math.nan, 1.0, 1.0, 1.0],
+            "mn": [0.0, 0.0, 0.0, 5.0, 0.0, math.nan, 0.0, 0.0],
+            "mx": [10.0, 10.0, 10.0, 5.0, 10.0, 10.0, math.inf, 10.0],
+            "nb": [0, -3, i64_max, 4, 4, 4, 4, 4],
+        }
+    )
+    result = table.eval_expression_list([width_bucket(col("v"), col("mn"), col("mx"), col("nb")).alias("bucket")])
+    values = result.get_column_by_name("bucket").to_pylist()
+    # Only the last row (all valid) should be non-NULL.
+    assert values[:-1] == [None] * 7
+    assert values[-1] is not None
+
+
+def test_width_bucket_null_propagation() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": [1.0, None, 5.0]})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(0.0), lit(10.0), lit(5)).alias("bucket")])
+    assert result.get_column_by_name("bucket").to_pylist() == [1, None, 3]
+
+
+def test_width_bucket_scalar_broadcast() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": [-1.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 11.0]})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(0.0), lit(10.0), lit(5)).alias("bucket")])
+    assert result.get_column_by_name("bucket").to_pylist() == [0, 1, 2, 3, 4, 5, 6, 6]
+
+
+def test_width_bucket_fractional_num_bucket_truncates() -> None:
+    """Daft promotes any numeric num_bucket to Int64 by truncation."""
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": [3.0]})
+    result = table.eval_expression_list([width_bucket(col("v"), lit(0.0), lit(10.0), lit(5.7)).alias("bucket")])
+    # 5.7 truncates to 5; floor(5 * 3 / 10) + 1 = 2.
+    assert result.get_column_by_name("bucket").to_pylist() == [2]
+
+
+def test_width_bucket_bad_input() -> None:
+    from daft.functions import width_bucket
+
+    table = MicroPartition.from_pydict({"v": ["a", "b"]})
+    with pytest.raises(ValueError, match="Expected `value` of width_bucket to be numeric"):
+        table.eval_expression_list([width_bucket(col("v"), lit(0.0), lit(10.0), lit(5))])