[python-package] Allow to pass Arrow table with boolean columns to dataset (#6353)

include/LightGBM/arrow.h

@@ -84,6 +84,10 @@ class ArrowChunkedArray {
   const ArrowSchema* schema_;
   /* List of length `n + 1` for `n` chunks containing the offsets for each chunk. */
   std::vector<int64_t> chunk_offsets_;
+  /* Indicator whether this chunked array needs to call the arrays' release callbacks.
+     NOTE: This is MUST only be set to `true` if this chunked array is not part of a
+           `ArrowTable` as children arrays may not be released by the consumer (see below). */
+  const bool releases_arrow_;
 
   inline void construct_chunk_offsets() {
     chunk_offsets_.reserve(chunks_.size() + 1);
@@ -100,7 +104,8 @@ class ArrowChunkedArray {
    * @param chunks A list with the chunks.
    * @param schema The schema for all chunks.
    */
-  inline ArrowChunkedArray(std::vector<const ArrowArray*> chunks, const ArrowSchema* schema) {
+  inline ArrowChunkedArray(std::vector<const ArrowArray*> chunks, const ArrowSchema* schema)
+      : releases_arrow_(false) {
     chunks_ = chunks;
     schema_ = schema;
     construct_chunk_offsets();
@@ -113,9 +118,9 @@ class ArrowChunkedArray {
    * @param chunks A C-style array containing the chunks.
    * @param schema The schema for all chunks.
    */
-  inline ArrowChunkedArray(int64_t n_chunks,
-                           const struct ArrowArray* chunks,
-                           const struct ArrowSchema* schema) {
+  inline ArrowChunkedArray(int64_t n_chunks, const struct ArrowArray* chunks,
+                           const struct ArrowSchema* schema)
+      : releases_arrow_(true) {
     chunks_.reserve(n_chunks);
     for (auto k = 0; k < n_chunks; ++k) {
       if (chunks[k].length == 0) continue;
@@ -125,6 +130,21 @@ class ArrowChunkedArray {
     construct_chunk_offsets();
   }
 
+  ~ArrowChunkedArray() {
+    if (!releases_arrow_) {
+      return;
+    }
+    for (size_t i = 0; i < chunks_.size(); ++i) {
+      auto chunk = chunks_[i];
+      if (chunk->release) {
+        chunk->release(const_cast<ArrowArray*>(chunk));
+      }
+    }
+    if (schema_->release) {
+      schema_->release(const_cast<ArrowSchema*>(schema_));
+    }
+  }
+
   /**
    * @brief Get the length of the chunked array.
    * This method returns the cumulative length of all chunks.
@@ -219,7 +239,7 @@ class ArrowTable {
    * @param chunks A C-style array containing the chunks.
    * @param schema The schema for all chunks.
    */
-  inline ArrowTable(int64_t n_chunks, const ArrowArray *chunks, const ArrowSchema *schema)
+  inline ArrowTable(int64_t n_chunks, const ArrowArray* chunks, const ArrowSchema* schema)
       : n_chunks_(n_chunks), chunks_ptr_(chunks), schema_ptr_(schema) {
     columns_.reserve(schema->n_children);
     for (int64_t j = 0; j < schema->n_children; ++j) {
@@ -236,7 +256,8 @@ class ArrowTable {
   ~ArrowTable() {
     // As consumer of the Arrow array, the Arrow table must release all Arrow arrays it receives
     // as well as the schema. As per the specification, children arrays are released by the
-    // producer. See: https://arrow.apache.org/docs/format/CDataInterface.html#release-callback-semantics-for-consumers
+    // producer. See:
+    // https://arrow.apache.org/docs/format/CDataInterface.html#release-callback-semantics-for-consumers
     for (int64_t i = 0; i < n_chunks_; ++i) {
       auto chunk = &chunks_ptr_[i];
       if (chunk->release) {

include/LightGBM/arrow.tpp

@@ -31,8 +31,7 @@ inline ArrowChunkedArray::Iterator<T> ArrowChunkedArray::end() const {
 /* ---------------------------------- ITERATOR IMPLEMENTATION ---------------------------------- */
 
 template <typename T>
-ArrowChunkedArray::Iterator<T>::Iterator(const ArrowChunkedArray& array,
-                                         getter_fn get,
+ArrowChunkedArray::Iterator<T>::Iterator(const ArrowChunkedArray& array, getter_fn get,
                                          int64_t ptr_chunk)
     : array_(array), get_(get), ptr_chunk_(ptr_chunk) {
   this->ptr_offset_ = 0;
@@ -41,7 +40,7 @@ ArrowChunkedArray::Iterator<T>::Iterator(const ArrowChunkedArray& array,
 template <typename T>
 T ArrowChunkedArray::Iterator<T>::operator*() const {
   auto chunk = array_.chunks_[ptr_chunk_];
-  return static_cast<T>(get_(chunk, ptr_offset_));
+  return get_(chunk, ptr_offset_);
 }
 
 template <typename T>
@@ -54,7 +53,7 @@ T ArrowChunkedArray::Iterator<T>::operator[](I idx) const {
   auto chunk = array_.chunks_[chunk_idx];
 
   auto ptr_offset = static_cast<int64_t>(idx) - array_.chunk_offsets_[chunk_idx];
-  return static_cast<T>(get_(chunk, ptr_offset));
+  return get_(chunk, ptr_offset);
 }
 
 template <typename T>
@@ -147,11 +146,28 @@ struct ArrayIndexAccessor {
     if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) {
       // In case the index is valid, we take it from the data buffer
       auto data = static_cast<const T*>(array->buffers[1]);
-      return static_cast<double>(data[buffer_idx]);
+      return static_cast<V>(data[buffer_idx]);
     }
 
     // In case the index is not valid, we return a default value
-    return arrow_primitive_missing_value<T>();
+    return arrow_primitive_missing_value<V>();
+  }
+};
+
+template <typename V>
+struct ArrayIndexAccessor<bool, V> {
+  V operator()(const ArrowArray* array, size_t idx) {
+    // Custom implementation for booleans as values are bit-packed:
+    // https://arrow.apache.org/docs/cpp/api/datatype.html#_CPPv4N5arrow4Type4type4BOOLE
+    auto buffer_idx = idx + array->offset;
+    auto validity = static_cast<const char*>(array->buffers[0]);
+    if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) {
+      // In case the index is valid, we have to take the appropriate bit from the buffer
+      auto data = static_cast<const char*>(array->buffers[1]);
+      auto value = (data[buffer_idx / 8] & (1 << (buffer_idx % 8))) >> (buffer_idx % 8);
+      return static_cast<V>(value);
+    }
+    return arrow_primitive_missing_value<V>();
   }
 };
 
@@ -180,6 +196,8 @@ std::function<T(const ArrowArray*, size_t)> get_index_accessor(const char* dtype
       return ArrayIndexAccessor<float, T>();
     case 'g':
       return ArrayIndexAccessor<double, T>();
+    case 'b':
+      return ArrayIndexAccessor<bool, T>();
     default:
       throw std::invalid_argument("unsupported Arrow datatype");
   }

python-package/lightgbm/basic.py

@@ -22,6 +22,7 @@ from .compat import (
     PANDAS_INSTALLED,
     PYARROW_INSTALLED,
     arrow_cffi,
+    arrow_is_boolean,
     arrow_is_floating,
     arrow_is_integer,
     concat,
@@ -1688,7 +1689,7 @@ class _InnerPredictor:
             raise LightGBMError("Cannot predict from Arrow without `pyarrow` installed.")
 
         # Check that the input is valid: we only handle numbers (for now)
-        if not all(arrow_is_integer(t) or arrow_is_floating(t) for t in table.schema.types):
+        if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types):
             raise ValueError("Arrow table may only have integer or floating point datatypes")
 
         # Prepare prediction output array
@@ -2435,7 +2436,7 @@ class Dataset:
             raise LightGBMError("Cannot init dataframe from Arrow without `pyarrow` installed.")
 
         # Check that the input is valid: we only handle numbers (for now)
-        if not all(arrow_is_integer(t) or arrow_is_floating(t) for t in table.schema.types):
+        if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types):
             raise ValueError("Arrow table may only have integer or floating point datatypes")
 
         # Export Arrow table to C

python-package/lightgbm/compat.py

@@ -222,6 +222,7 @@ try:
     from pyarrow import Table as pa_Table
     from pyarrow import chunked_array as pa_chunked_array
     from pyarrow.cffi import ffi as arrow_cffi
+    from pyarrow.types import is_boolean as arrow_is_boolean
     from pyarrow.types import is_floating as arrow_is_floating
     from pyarrow.types import is_integer as arrow_is_integer
 
@@ -265,6 +266,7 @@ except ImportError:
         equal = None
 
     pa_chunked_array = None
+    arrow_is_boolean = None
     arrow_is_integer = None
     arrow_is_floating = None
 

tests/cpp_tests/test_arrow.cpp

@@ -5,87 +5,151 @@
  * Author: Oliver Borchert
  */
 
-#include <gtest/gtest.h>
 #include <LightGBM/arrow.h>
+#include <gtest/gtest.h>
 
-#include <cstdlib>
 #include <cmath>
+#include <cstdlib>
 
 using LightGBM::ArrowChunkedArray;
 using LightGBM::ArrowTable;
 
+/* --------------------------------------------------------------------------------------------- */
+/*                                             UTILS                                             */
+/* --------------------------------------------------------------------------------------------- */
+// This code is copied and adapted from the official Arrow producer examples:
+// https://arrow.apache.org/docs/format/CDataInterface.html#exporting-a-struct-float32-utf8-array
+
+static void release_schema(struct ArrowSchema* schema) {
+  // Free children
+  if (schema->children) {
+    for (int64_t i = 0; i < schema->n_children; ++i) {
+      struct ArrowSchema* child = schema->children[i];
+      if (child->release) {
+        child->release(child);
+      }
+      free(child);
+    }
+    free(schema->children);
+  }
+
+  // Finalize
+  schema->release = nullptr;
+}
+
+static void release_array(struct ArrowArray* array) {
+  // Free children
+  if (array->children) {
+    for (int64_t i = 0; i < array->n_children; ++i) {
+      struct ArrowArray* child = array->children[i];
+      if (child->release) {
+        child->release(child);
+      }
+      free(child);
+    }
+    free(array->children);
+  }
+
+  // Free buffers
+  for (int64_t i = 0; i < array->n_buffers; ++i) {
+    if (array->buffers[i]) {
+      free(const_cast<void*>(array->buffers[i]));
+    }
+  }
+  free(array->buffers);
+
+  // Finalize
+  array->release = nullptr;
+}
+
+/* ------------------------------------------ PRODUCER ----------------------------------------- */
+
 class ArrowChunkedArrayTest : public testing::Test {
  protected:
   void SetUp() override {}
 
-  ArrowArray created_nested_array(const std::vector<ArrowArray*>& arrays) {
+  /* -------------------------------------- ARRAY CREATION ------------------------------------- */
+
+  char* build_validity_bitmap(int64_t size, std::vector<int64_t> null_indices = {}) {
+    if (null_indices.empty()) {
+      return nullptr;
+    }
+    auto num_bytes = (size + 7) / 8;
+    auto validity = static_cast<char*>(malloc(num_bytes * sizeof(char)));
+    memset(validity, 0xff, num_bytes * sizeof(char));
+    for (auto idx : null_indices) {
+      validity[idx / 8] &= ~(1 << (idx % 8));
+    }
+    return validity;
+  }
+
+  ArrowArray build_primitive_array(void* data, int64_t size, int64_t offset,
+                                   std::vector<int64_t> null_indices) {
+    const void** buffers = (const void**)malloc(sizeof(void*) * 2);
+    buffers[0] = build_validity_bitmap(size, null_indices);
+    buffers[1] = data;
+
     ArrowArray arr;
-    arr.buffers = nullptr;
-    arr.children = (ArrowArray**)arrays.data();  // NOLINT
+    arr.length = size - offset;
+    arr.null_count = static_cast<int64_t>(null_indices.size());
+    arr.offset = offset;
+    arr.n_buffers = 2;
+    arr.n_children = 0;
+    arr.buffers = buffers;
+    arr.children = nullptr;
     arr.dictionary = nullptr;
-    arr.length = arrays[0]->length;
-    arr.n_buffers = 0;
-    arr.n_children = arrays.size();
-    arr.null_count = 0;
-    arr.offset = 0;
+    arr.release = &release_array;
     arr.private_data = nullptr;
-    arr.release = nullptr;
     return arr;
   }
 
   template <typename T>
-  ArrowArray create_primitive_array(const std::vector<T>& values,
-                                    int64_t offset = 0,
+  ArrowArray create_primitive_array(const std::vector<T>& values, int64_t offset = 0,
                                     std::vector<int64_t> null_indices = {}) {
     // NOTE: Arrow arrays have 64-bit alignment but we can safely ignore this in tests
-    // 1) Create validity bitmap
-    char* validity = nullptr;
-    if (!null_indices.empty()) {
-      auto num_bytes = (values.size() + 7) / 8;
-      validity = static_cast<char*>(calloc(num_bytes, sizeof(char)));
-      memset(validity, 0xff, num_bytes * sizeof(char));
-      for (size_t i = 0; i < values.size(); ++i) {
-        if (std::find(null_indices.begin(), null_indices.end(), i) != null_indices.end()) {
-          validity[i / 8] &= ~(1 << (i % 8));
-        }
+    auto buffer = static_cast<T*>(malloc(sizeof(T) * values.size()));
+    for (size_t i = 0; i < values.size(); ++i) {
+      buffer[i] = values[i];
+    }
+    return build_primitive_array(buffer, values.size(), offset, null_indices);
+  }
+
+  ArrowArray create_primitive_array(const std::vector<bool>& values, int64_t offset = 0,
+                                    std::vector<int64_t> null_indices = {}) {
+    auto num_bytes = (values.size() + 7) / 8;
+    auto buffer = static_cast<char*>(calloc(sizeof(char), num_bytes));
+    for (size_t i = 0; i < values.size(); ++i) {
+      // By using `calloc` above, we only need to set 'true' values
+      if (values[i]) {
+        buffer[i / 8] |= (1 << (i % 8));
       }
     }
+    return build_primitive_array(buffer, values.size(), offset, null_indices);
+  }
 
-    // 2) Create buffers
-    const void** buffers = (const void**)malloc(sizeof(void*) * 2);
-    buffers[0] = validity;
-    buffers[1] = values.data() + offset;
+  ArrowArray created_nested_array(const std::vector<ArrowArray*>& arrays) {
+    auto children = static_cast<ArrowArray**>(malloc(sizeof(ArrowArray*) * arrays.size()));
+    for (size_t i = 0; i < arrays.size(); ++i) {
+      auto child = static_cast<ArrowArray*>(malloc(sizeof(ArrowArray)));
+      *child = *arrays[i];
+      children[i] = child;
+    }
 
-    // Create arrow array
     ArrowArray arr;
-    arr.buffers = buffers;
-    arr.children = nullptr;
-    arr.dictionary = nullptr;
-    arr.length = values.size() - offset;
+    arr.length = children[0]->length;
     arr.null_count = 0;
     arr.offset = 0;
+    arr.n_buffers = 0;
+    arr.n_children = static_cast<int64_t>(arrays.size());
+    arr.buffers = nullptr;
+    arr.children = children;
+    arr.dictionary = nullptr;
+    arr.release = &release_array;
     arr.private_data = nullptr;
-    arr.release = [](ArrowArray* arr) {
-      if (arr->buffers[0] != nullptr)
-        free((void*)(arr->buffers[0]));  // NOLINT
-      free((void*)(arr->buffers));  // NOLINT
-    };
     return arr;
   }
 
-  ArrowSchema create_nested_schema(const std::vector<ArrowSchema*>& arrays) {
-    ArrowSchema schema;
-    schema.format = "+s";
-    schema.name = nullptr;
-    schema.metadata = nullptr;
-    schema.flags = 0;
-    schema.n_children = arrays.size();
-    schema.children = (ArrowSchema**)arrays.data();  // NOLINT
-    schema.dictionary = nullptr;
-    schema.private_data = nullptr;
-    schema.release = nullptr;
-    return schema;
-  }
+  /* ------------------------------------- SCHEMA CREATION ------------------------------------- */
 
   template <typename T>
   ArrowSchema create_primitive_schema() {
@@ -102,27 +166,71 @@ class ArrowChunkedArrayTest : public testing::Test {
     schema.n_children = 0;
     schema.children = nullptr;
     schema.dictionary = nullptr;
+    schema.release = nullptr;
     schema.private_data = nullptr;
+    return schema;
+  }
+
+  template <>
+  ArrowSchema create_primitive_schema<bool>() {
+    ArrowSchema schema;
+    schema.format = "b";
+    schema.name = nullptr;
+    schema.metadata = nullptr;
+    schema.flags = 0;
+    schema.n_children = 0;
+    schema.children = nullptr;
+    schema.dictionary = nullptr;
     schema.release = nullptr;
+    schema.private_data = nullptr;
+    return schema;
+  }
+
+  ArrowSchema create_nested_schema(const std::vector<ArrowSchema*>& arrays) {
+    auto children = static_cast<ArrowSchema**>(malloc(sizeof(ArrowSchema*) * arrays.size()));
+    for (size_t i = 0; i < arrays.size(); ++i) {
+      auto child = static_cast<ArrowSchema*>(malloc(sizeof(ArrowSchema)));
+      *child = *arrays[i];
+      children[i] = child;
+    }
+
+    ArrowSchema schema;
+    schema.format = "+s";
+    schema.name = nullptr;
+    schema.metadata = nullptr;
+    schema.flags = 0;
+    schema.n_children = static_cast<int64_t>(arrays.size());
+    schema.children = children;
+    schema.dictionary = nullptr;
+    schema.release = &release_schema;
+    schema.private_data = nullptr;
     return schema;
   }
 };
 
+/* --------------------------------------------------------------------------------------------- */
+/*                                             TESTS                                             */
+/* --------------------------------------------------------------------------------------------- */
+
 TEST_F(ArrowChunkedArrayTest, GetLength) {
+  auto schema = create_primitive_schema<float>();
+
   std::vector<float> dat1 = {1, 2};
   auto arr1 = create_primitive_array(dat1);
-
-  ArrowChunkedArray ca1(1, &arr1, nullptr);
+  ArrowChunkedArray ca1(1, &arr1, &schema);
   ASSERT_EQ(ca1.get_length(), 2);
 
   std::vector<float> dat2 = {3, 4, 5, 6};
-  auto arr2 = create_primitive_array<float>(dat2);
-  ArrowArray arrs[2] = {arr1, arr2};
-  ArrowChunkedArray ca2(2, arrs, nullptr);
+  auto arr2 = create_primitive_array(dat1);
+  auto arr3 = create_primitive_array(dat2);
+  ArrowArray arrs[2] = {arr2, arr3};
+  ArrowChunkedArray ca2(2, arrs, &schema);
   ASSERT_EQ(ca2.get_length(), 6);
 
-  arr1.release(&arr1);
-  arr2.release(&arr2);
+  std::vector<bool> dat3 = {true, false, true, true};
+  auto arr4 = create_primitive_array(dat3, 1);
+  ArrowChunkedArray ca3(1, &arr4, &schema);
+  ASSERT_EQ(ca3.get_length(), 3);
 }
 
 TEST_F(ArrowChunkedArrayTest, GetColumns) {
@@ -149,18 +257,15 @@ TEST_F(ArrowChunkedArrayTest, GetColumns) {
   auto ca2 = table.get_column(1);
   ASSERT_EQ(ca2.get_length(), 3);
   ASSERT_EQ(*ca2.begin<int32_t>(), 4);
-
-  arr1.release(&arr1);
-  arr2.release(&arr2);
 }
 
 TEST_F(ArrowChunkedArrayTest, IteratorArithmetic) {
   std::vector<float> dat1 = {1, 2};
-  auto arr1 = create_primitive_array<float>(dat1);
+  auto arr1 = create_primitive_array(dat1);
   std::vector<float> dat2 = {3, 4, 5, 6};
-  auto arr2 = create_primitive_array<float>(dat2);
+  auto arr2 = create_primitive_array(dat2);
   std::vector<float> dat3 = {7};
-  auto arr3 = create_primitive_array<float>(dat3);
+  auto arr3 = create_primitive_array(dat3);
   auto schema = create_primitive_schema<float>();
 
   ArrowArray arrs[3] = {arr1, arr2, arr3};
@@ -190,15 +295,39 @@ TEST_F(ArrowChunkedArrayTest, IteratorArithmetic) {
   auto end = ca.end<int32_t>();
   ASSERT_EQ(end - it, 2);
   ASSERT_EQ(end - ca.begin<int32_t>(), 7);
+}
+
+TEST_F(ArrowChunkedArrayTest, BooleanIterator) {
+  std::vector<bool> dat1 = {false, true, false};
+  auto arr1 = create_primitive_array(dat1, 0, {2});
+  std::vector<bool> dat2 = {false, false, false, false, true, true, true, true, false, true};
+  auto arr2 = create_primitive_array(dat2, 1);
+  auto schema = create_primitive_schema<bool>();
+
+  ArrowArray arrs[2] = {arr1, arr2};
+  ArrowChunkedArray ca(2, arrs, &schema);
+
+  // Check for values in first chunk
+  auto it = ca.begin<float>();
+  ASSERT_EQ(*it, 0);
+  ASSERT_EQ(*(++it), 1);
+  ASSERT_TRUE(std::isnan(*(++it)));
+
+  // Check for some values in second chunk
+  ASSERT_EQ(*(++it), 0);
+  it += 3;
+  ASSERT_EQ(*it, 1);
+  it += 4;
+  ASSERT_EQ(*it, 0);
+  ASSERT_EQ(*(++it), 1);
 
-  arr1.release(&arr1);
-  arr2.release(&arr2);
-  arr2.release(&arr3);
+  // Check end
+  ASSERT_EQ(++it, ca.end<float>());
 }
 
 TEST_F(ArrowChunkedArrayTest, OffsetAndValidity) {
   std::vector<float> dat = {0, 1, 2, 3, 4, 5, 6};
-  auto arr = create_primitive_array(dat, 2, {0, 1});
+  auto arr = create_primitive_array(dat, 2, {2, 3});
   auto schema = create_primitive_schema<float>();
   ArrowChunkedArray ca(1, &arr, &schema);
 

tests/python_package_test/test_arrow.py

 # coding: utf-8
 import filecmp
+from pathlib import Path
 from typing import Any, Dict, Optional
 
 import numpy as np
@@ -43,16 +44,17 @@ def generate_simple_arrow_table(empty_chunks: bool = False) -> pa.Table:
         pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.int64()),
         pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.float32()),
         pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.float64()),
+        pa.chunked_array(c + [[True, True, False]] + c + [[False, True]] + c, type=pa.bool_()),
     ]
     return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))])
 
 
-def generate_nullable_arrow_table() -> pa.Table:
+def generate_nullable_arrow_table(dtype: Any) -> pa.Table:
     columns = [
-        pa.chunked_array([[1, None, 3, 4, 5]], type=pa.float32()),
-        pa.chunked_array([[None, 2, 3, 4, 5]], type=pa.float32()),
-        pa.chunked_array([[1, 2, 3, 4, None]], type=pa.float32()),
-        pa.chunked_array([[None, None, None, None, None]], type=pa.float32()),
+        pa.chunked_array([[1, None, 3, 4, 5]], type=dtype),
+        pa.chunked_array([[None, 2, 3, 4, 5]], type=dtype),
+        pa.chunked_array([[1, 2, 3, 4, None]], type=dtype),
+        pa.chunked_array([[None, None, None, None, None]], type=dtype),
     ]
     return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))])
 
@@ -120,13 +122,20 @@ def dummy_dataset_params() -> Dict[str, Any]:
 # ------------------------------------------- DATASET ------------------------------------------- #
 
 
+def assert_datasets_equal(tmp_path: Path, lhs: lgb.Dataset, rhs: lgb.Dataset):
+    lhs._dump_text(tmp_path / "arrow.txt")
+    rhs._dump_text(tmp_path / "pandas.txt")
+    assert filecmp.cmp(tmp_path / "arrow.txt", tmp_path / "pandas.txt")
+
+
 @pytest.mark.parametrize(
     ("arrow_table_fn", "dataset_params"),
     [  # Use lambda functions here to minimize memory consumption
         (lambda: generate_simple_arrow_table(), dummy_dataset_params()),
         (lambda: generate_simple_arrow_table(empty_chunks=True), dummy_dataset_params()),
         (lambda: generate_dummy_arrow_table(), dummy_dataset_params()),
-        (lambda: generate_nullable_arrow_table(), dummy_dataset_params()),
+        (lambda: generate_nullable_arrow_table(pa.float32()), dummy_dataset_params()),
+        (lambda: generate_nullable_arrow_table(pa.int32()), dummy_dataset_params()),
         (lambda: generate_random_arrow_table(3, 1000, 42), {}),
         (lambda: generate_random_arrow_table(100, 10000, 43), {}),
     ],
@@ -140,9 +149,22 @@ def test_dataset_construct_fuzzy(tmp_path, arrow_table_fn, dataset_params):
     pandas_dataset = lgb.Dataset(arrow_table.to_pandas(), params=dataset_params)
     pandas_dataset.construct()
 
-    arrow_dataset._dump_text(tmp_path / "arrow.txt")
-    pandas_dataset._dump_text(tmp_path / "pandas.txt")
-    assert filecmp.cmp(tmp_path / "arrow.txt", tmp_path / "pandas.txt")
+    assert_datasets_equal(tmp_path, arrow_dataset, pandas_dataset)
+
+
+def test_dataset_construct_fuzzy_boolean(tmp_path):
+    boolean_data = generate_random_arrow_table(10, 10000, 42, generate_nulls=False, values=np.array([True, False]))
+
+    float_schema = pa.schema([pa.field(f"col_{i}", pa.float32()) for i in range(len(boolean_data.columns))])
+    float_data = boolean_data.cast(float_schema)
+
+    arrow_dataset = lgb.Dataset(boolean_data)
+    arrow_dataset.construct()
+
+    pandas_dataset = lgb.Dataset(float_data.to_pandas())
+    pandas_dataset.construct()
+
+    assert_datasets_equal(tmp_path, arrow_dataset, pandas_dataset)
 
 
 # -------------------------------------------- FIELDS ------------------------------------------- #
@@ -195,6 +217,25 @@ def test_dataset_construct_labels(array_type, label_data, arrow_type):
     np_assert_array_equal(expected, dataset.get_label(), strict=True)
 
 
+@pytest.mark.parametrize(
+    ["array_type", "label_data"],
+    [
+        (pa.array, [False, True, False, False, True]),
+        (pa.chunked_array, [[False], [True, False, False, True]]),
+        (pa.chunked_array, [[], [False], [True, False, False, True]]),
+        (pa.chunked_array, [[False], [], [True, False], [], [], [False, True], []]),
+    ],
+)
+def test_dataset_construct_labels_boolean(array_type, label_data):
+    data = generate_dummy_arrow_table()
+    labels = array_type(label_data, type=pa.bool_())
+    dataset = lgb.Dataset(data, label=labels, params=dummy_dataset_params())
+    dataset.construct()
+
+    expected = np.array([0, 1, 0, 0, 1], dtype=np.float32)
+    np_assert_array_equal(expected, dataset.get_label(), strict=True)
+
+
 # ------------------------------------------- WEIGHTS ------------------------------------------- #
 
 
@@ -317,7 +358,10 @@ def assert_equal_predict_arrow_pandas(booster: lgb.Booster, data: pa.Table):
 
 
 def test_predict_regression():
-    data = generate_random_arrow_table(10, 10000, 42)
+    data_float = generate_random_arrow_table(10, 10000, 42)
+    data_bool = generate_random_arrow_table(1, 10000, 42, generate_nulls=False, values=np.array([True, False]))
+    data = pa.Table.from_arrays(data_float.columns + data_bool.columns, names=data_float.schema.names + ["col_bool"])
+
     dataset = lgb.Dataset(
         data,
         label=generate_random_arrow_array(10000, 43, generate_nulls=False),