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[Datasets] Fix boundary sampling concatenation. #20784

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Nov 30, 2021
Merged

[Datasets] Fix boundary sampling concatenation. #20784

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2050d52
Fix boundary sampling concatenation.
clarkzinzow Nov 30, 2021
eb06d9d
Preserve to_numpy_refs() semantics for Arrow tables.
clarkzinzow Nov 30, 2021
80e2dba
Revert back to chunk combining/selecting.
clarkzinzow Nov 30, 2021
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26 changes: 12 additions & 14 deletions python/ray/data/impl/arrow_block.py
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Original file line number Diff line number Diff line change
Expand Up @@ -224,20 +224,18 @@ def to_pandas(self) -> "pandas.DataFrame":
return self._table.to_pandas()

def to_numpy(self, column: str = None) -> np.ndarray:
if not column:
raise ValueError(
"`column` must be specified when calling .to_numpy() "
"on Arrow blocks.")
if column not in self._table.column_names:
raise ValueError(
"Cannot find column {}, available columns: {}".format(
column, self._table.column_names))
array = self._table[column]
if array.num_chunks > 1:
# TODO(ekl) combine fails since we can't concat ArrowTensorType?
array = array.combine_chunks()
assert array.num_chunks == 1, array
return self._table[column].chunk(0).to_numpy()
if column is None:
columns = self._table.column_names
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else:
if column not in self._table.column_names:
raise ValueError(
"Cannot find column {}, available columns: {}".format(
column, self._table.column_names))
columns = [column]
arrays = [self._table[col].to_numpy() for col in columns]
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if len(arrays) == 1:
return arrays[0]
return np.column_stack(arrays)

def to_arrow(self) -> "pyarrow.Table":
return self._table
Expand Down
11 changes: 8 additions & 3 deletions python/ray/data/impl/sort.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
import ray
from ray.types import ObjectRef
from ray.data.block import Block, BlockAccessor
from ray.data.impl.arrow_block import DelegatingArrowBlockBuilder
from ray.data.impl.block_list import BlockList
from ray.data.impl.progress_bar import ProgressBar
from ray.data.impl.remote_fn import cached_remote_fn
Expand Down Expand Up @@ -56,11 +57,15 @@ def sample_boundaries(blocks: List[ObjectRef[Block]], key: SortKeyT,
# The dataset is empty
if len(samples) == 0:
return [None] * (num_reducers - 1)
sample_items = np.concatenate(samples)
sample_items.sort()
builder = DelegatingArrowBlockBuilder()
for sample in samples:
builder.add_block(sample)
samples = builder.build()
sample_items = BlockAccessor.for_block(samples).to_numpy()
sample_items = np.sort(sample_items)
ret = [
np.quantile(sample_items, q, interpolation="nearest")
for q in np.arange(0, 1, 1 / num_reducers)
for q in np.linspace(0, 1, num_reducers)
]
return ret[1:]

Expand Down
34 changes: 17 additions & 17 deletions python/ray/data/tests/test_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2931,7 +2931,7 @@ def test_groupby_arrow(ray_start_regular_shared):
assert agg_ds.count() == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_agg_name_conflict(ray_start_regular_shared, num_parts):
# Test aggregation name conflict.
xs = list(range(100))
Expand Down Expand Up @@ -2959,7 +2959,7 @@ def test_groupby_agg_name_conflict(ray_start_regular_shared, num_parts):
{"A": 2, "foo": 50.0, "foo_2": 50.0}]


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_count(ray_start_regular_shared, num_parts):
# Test built-in count aggregation
seed = int(time.time())
Expand All @@ -2977,7 +2977,7 @@ def test_groupby_arrow_count(ray_start_regular_shared, num_parts):
{"A": 2, "count()": 33}]


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_sum(ray_start_regular_shared, num_parts):
# Test built-in sum aggregation
seed = int(time.time())
Expand All @@ -3001,7 +3001,7 @@ def test_groupby_arrow_sum(ray_start_regular_shared, num_parts):
"value") == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_min(ray_start_regular_shared, num_parts):
# Test built-in min aggregation
seed = int(time.time())
Expand All @@ -3025,7 +3025,7 @@ def test_groupby_arrow_min(ray_start_regular_shared, num_parts):
ray.data.range_arrow(10).filter(lambda r: r["value"] > 10).min("value")


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_max(ray_start_regular_shared, num_parts):
# Test built-in max aggregation
seed = int(time.time())
Expand All @@ -3049,7 +3049,7 @@ def test_groupby_arrow_max(ray_start_regular_shared, num_parts):
ray.data.range_arrow(10).filter(lambda r: r["value"] > 10).max("value")


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_mean(ray_start_regular_shared, num_parts):
# Test built-in mean aggregation
seed = int(time.time())
Expand All @@ -3074,7 +3074,7 @@ def test_groupby_arrow_mean(ray_start_regular_shared, num_parts):
"value")


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_std(ray_start_regular_shared, num_parts):
# Test built-in std aggregation
seed = int(time.time())
Expand Down Expand Up @@ -3111,7 +3111,7 @@ def test_groupby_arrow_std(ray_start_regular_shared, num_parts):
assert ray.data.from_pandas(pd.DataFrame({"A": [3]})).std("A") == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_multicolumn(ray_start_regular_shared, num_parts):
# Test built-in mean aggregation on multiple columns
seed = int(time.time())
Expand Down Expand Up @@ -3192,7 +3192,7 @@ def test_groupby_agg_bad_on(ray_start_regular_shared):
ray.data.from_items(xs).mean("A")


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_arrow_multi_agg(ray_start_regular_shared, num_parts):
seed = int(time.time())
print(f"Seeding RNG for test_groupby_arrow_multi_agg with: {seed}")
Expand Down Expand Up @@ -3288,7 +3288,7 @@ def test_groupby_simple(ray_start_regular_shared):
assert agg_ds.count() == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_count(ray_start_regular_shared, num_parts):
# Test built-in count aggregation
seed = int(time.time())
Expand All @@ -3303,7 +3303,7 @@ def test_groupby_simple_count(ray_start_regular_shared, num_parts):
33)]


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_sum(ray_start_regular_shared, num_parts):
# Test built-in sum aggregation
seed = int(time.time())
Expand All @@ -3321,7 +3321,7 @@ def test_groupby_simple_sum(ray_start_regular_shared, num_parts):
assert ray.data.range(10).filter(lambda r: r > 10).sum() == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_min(ray_start_regular_shared, num_parts):
# Test built-in min aggregation
seed = int(time.time())
Expand All @@ -3339,7 +3339,7 @@ def test_groupby_simple_min(ray_start_regular_shared, num_parts):
ray.data.range(10).filter(lambda r: r > 10).min()


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_max(ray_start_regular_shared, num_parts):
# Test built-in max aggregation
seed = int(time.time())
Expand All @@ -3358,7 +3358,7 @@ def test_groupby_simple_max(ray_start_regular_shared, num_parts):
ray.data.range(10).filter(lambda r: r > 10).max()


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_mean(ray_start_regular_shared, num_parts):
# Test built-in mean aggregation
seed = int(time.time())
Expand All @@ -3377,7 +3377,7 @@ def test_groupby_simple_mean(ray_start_regular_shared, num_parts):
ray.data.range(10).filter(lambda r: r > 10).mean()


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_std(ray_start_regular_shared, num_parts):
# Test built-in std aggregation
seed = int(time.time())
Expand Down Expand Up @@ -3420,7 +3420,7 @@ def test_groupby_simple_std(ray_start_regular_shared, num_parts):
assert ray.data.from_items([3]).std() == 0


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_multilambda(ray_start_regular_shared, num_parts):
# Test built-in mean aggregation
seed = int(time.time())
Expand All @@ -3446,7 +3446,7 @@ def test_groupby_simple_multilambda(ray_start_regular_shared, num_parts):
.mean([lambda x: x[0], lambda x: x[1]])


@pytest.mark.parametrize("num_parts", [1, 10, 100])
@pytest.mark.parametrize("num_parts", [1, 15, 100])
def test_groupby_simple_multi_agg(ray_start_regular_shared, num_parts):
seed = int(time.time())
print(f"Seeding RNG for test_groupby_simple_multi_agg with: {seed}")
Expand Down