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import pytest
import tiledb
from .common import DiskTestCase
class AggregateTest(DiskTestCase):
@pytest.mark.parametrize("sparse", [True, False])
@pytest.mark.parametrize(
"dtype",
[
np.uint8,
np.int8,
np.uint16,
np.int16,
np.uint32,
np.int32,
np.uint64,
np.int64,
np.float32,
np.float64,
],
)
def test_basic(self, sparse, dtype):
path = self.path("test_basic")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [tiledb.Attr(name="a", dtype=dtype)]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=sparse)
tiledb.Array.create(path, schema)
data = np.random.randint(1, 10, size=10)
with tiledb.open(path, "w") as A:
if sparse:
A[np.arange(0, 10)] = data
else:
A[:] = data
all_aggregates = ("count", "sum", "min", "max", "mean")
with tiledb.open(path, "r") as A:
# entire column
q = A.query()
expected = q[:]["a"]
with pytest.raises(tiledb.TileDBError):
q.agg("bad")[:]
with pytest.raises(tiledb.TileDBError):
q.agg("null_count")[:]
with pytest.raises(NotImplementedError):
q.agg("count").df[:]
assert q.agg("sum")[:] == sum(expected)
assert q.agg("min")[:] == min(expected)
assert q.agg("max")[:] == max(expected)
assert q.agg("mean")[:] == sum(expected) / len(expected)
assert q.agg("count")[:] == len(expected)
assert q.agg({"a": "sum"})[:] == sum(expected)
assert q.agg({"a": "min"})[:] == min(expected)
assert q.agg({"a": "max"})[:] == max(expected)
assert q.agg({"a": "mean"})[:] == sum(expected) / len(expected)
assert q.agg({"a": "count"})[:] == len(expected)
actual = q.agg(all_aggregates)[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
actual = q.agg({"a": all_aggregates})[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
# subarray
expected = A[4:7]["a"]
assert q.agg("sum")[4:7] == sum(expected)
assert q.agg("min")[4:7] == min(expected)
assert q.agg("max")[4:7] == max(expected)
assert q.agg("mean")[4:7] == sum(expected) / len(expected)
assert q.agg("count")[4:7] == len(expected)
assert q.agg({"a": "sum"})[4:7] == sum(expected)
assert q.agg({"a": "min"})[4:7] == min(expected)
assert q.agg({"a": "max"})[4:7] == max(expected)
assert q.agg({"a": "mean"})[4:7] == sum(expected) / len(expected)
assert q.agg({"a": "count"})[4:7] == len(expected)
actual = q.agg(all_aggregates)[4:7]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
actual = q.agg({"a": all_aggregates})[4:7]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
@pytest.mark.parametrize("sparse", [True, False])
@pytest.mark.parametrize(
"dtype",
[
np.uint8,
np.int8,
np.uint16,
np.int16,
np.uint32,
np.int32,
np.uint64,
np.int64,
np.float32,
np.float64,
],
)
def test_multi_index(self, sparse, dtype):
path = self.path("test_multi_index")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [tiledb.Attr(name="a", dtype=dtype)]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=sparse)
tiledb.Array.create(path, schema)
data = np.random.randint(1, 10, size=10)
with tiledb.open(path, "w") as A:
if sparse:
A[np.arange(0, 10)] = data
else:
A[:] = data
all_aggregates = ("count", "sum", "min", "max", "mean")
with tiledb.open(path, "r") as A:
# entire column
q = A.query()
expected = q.multi_index[:]["a"]
with pytest.raises(tiledb.TileDBError):
q.agg("bad")[:]
with pytest.raises(tiledb.TileDBError):
q.agg("null_count")[:]
assert q.agg("sum").multi_index[:] == sum(expected)
assert q.agg("min").multi_index[:] == min(expected)
assert q.agg("max").multi_index[:] == max(expected)
assert q.agg("mean").multi_index[:] == sum(expected) / len(expected)
assert q.agg("count").multi_index[:] == len(expected)
actual = q.agg(all_aggregates).multi_index[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
actual = q.agg({"a": all_aggregates}).multi_index[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
# subarray
expected = A.multi_index[4:7]["a"]
assert q.agg("sum").multi_index[4:7] == sum(expected)
assert q.agg("min").multi_index[4:7] == min(expected)
assert q.agg("max").multi_index[4:7] == max(expected)
assert q.agg("mean").multi_index[4:7] == sum(expected) / len(expected)
assert q.agg("count").multi_index[4:7] == len(expected)
actual = q.agg(all_aggregates).multi_index[4:7]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
@pytest.mark.parametrize(
"dtype",
[
np.uint8,
np.int8,
np.uint16,
np.int16,
np.uint32,
np.int32,
np.uint64,
np.int64,
np.float32,
np.float64,
],
)
def test_with_query_condition(self, dtype):
path = self.path("test_with_query_condition")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [tiledb.Attr(name="a", dtype=dtype)]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=True)
tiledb.Array.create(path, schema)
with tiledb.open(path, "w") as A:
# hardcode the first value to be 1 to ensure that the a < 5
# query condition always returns a non-empty result
data = np.random.randint(1, 10, size=10)
data[0] = 1
A[np.arange(0, 10)] = data
all_aggregates = ("count", "sum", "min", "max", "mean")
with tiledb.open(path, "r") as A:
q = A.query(cond="a < 5")
expected = q[:]["a"]
actual = q.agg(all_aggregates)[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
expected = q.multi_index[:]["a"]
actual = q.agg(all_aggregates).multi_index[:]
assert actual["sum"] == sum(expected)
assert actual["min"] == min(expected)
assert actual["max"] == max(expected)
assert actual["mean"] == sum(expected) / len(expected)
assert actual["count"] == len(expected)
# no value matches query condition
q = A.query(cond="a > 10")
expected = q[:]
actual = q.agg(all_aggregates)[:]
assert actual["sum"] == 0
if dtype in (np.float32, np.float64):
assert np.isnan(actual["min"])
assert np.isnan(actual["max"])
else:
assert actual["min"] is None
assert actual["max"] is None
assert np.isnan(actual["mean"])
assert actual["count"] == 0
expected = q.multi_index[:]
actual = q.agg(all_aggregates).multi_index[:]
assert actual["sum"] == 0
if dtype in (np.float32, np.float64):
assert np.isnan(actual["min"])
assert np.isnan(actual["max"])
else:
assert actual["min"] is None
assert actual["max"] is None
assert np.isnan(actual["mean"])
assert actual["count"] == 0
@pytest.mark.parametrize("sparse", [True, False])
def test_nullable(self, sparse):
path = self.path("test_nullable")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [
tiledb.Attr(name="integer", nullable=True, dtype=int),
tiledb.Attr(name="float", nullable=True, dtype=float),
]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=sparse)
tiledb.Array.create(path, schema)
# set index 5 and 7 to be null
data = np.random.rand(10)
data[5], data[7] = np.nan, np.nan
# write data
with tiledb.open(path, "w") as A:
if sparse:
A[np.arange(0, 10)] = {"integer": data, "float": data}
else:
A[:] = {"integer": data, "float": data}
with tiledb.open(path, "r") as A:
agg = A.query().agg
result = agg("null_count")
assert result[0]["integer"]["null_count"] == 0
assert result[:6]["integer"]["null_count"] == 1
assert result[5:8]["integer"]["null_count"] == 2
assert result[5]["integer"]["null_count"] == 1
assert result[6:]["integer"]["null_count"] == 1
assert result[7]["integer"]["null_count"] == 1
assert result[:]["integer"]["null_count"] == 2
assert result[0]["float"]["null_count"] == 0
assert result[:6]["float"]["null_count"] == 1
assert result[5:8]["float"]["null_count"] == 2
assert result[5]["float"]["null_count"] == 1
assert result[6:]["float"]["null_count"] == 1
assert result[7]["float"]["null_count"] == 1
assert result[:]["float"]["null_count"] == 2
all_aggregates = ("count", "sum", "min", "max", "mean", "null_count")
actual = agg({"integer": all_aggregates, "float": all_aggregates})[:]
expected = A[:]["integer"]
expected_no_null = A[:]["integer"].compressed()
assert actual["integer"]["sum"] == sum(expected_no_null)
assert actual["integer"]["min"] == min(expected_no_null)
assert actual["integer"]["max"] == max(expected_no_null)
assert actual["integer"]["mean"] == sum(expected_no_null) / len(
expected_no_null
)
assert actual["integer"]["count"] == len(expected)
assert actual["integer"]["null_count"] == np.count_nonzero(expected.mask)
expected = A[:]["float"]
expected_no_null = A[:]["float"].compressed()
assert actual["float"]["sum"] == sum(expected_no_null)
assert actual["float"]["min"] == min(expected_no_null)
assert actual["float"]["max"] == max(expected_no_null)
assert actual["float"]["mean"] == sum(expected_no_null) / len(
expected_no_null
)
assert actual["float"]["count"] == len(expected)
assert actual["float"]["null_count"] == np.count_nonzero(expected.mask)
# no valid values
actual = agg({"integer": all_aggregates, "float": all_aggregates})[5]
assert actual["integer"]["sum"] is None
assert actual["integer"]["min"] is None
assert actual["integer"]["max"] is None
assert actual["integer"]["mean"] is None
assert actual["integer"]["count"] == 1
assert actual["integer"]["null_count"] == 1
assert np.isnan(actual["float"]["sum"])
assert np.isnan(actual["float"]["min"])
assert np.isnan(actual["float"]["max"])
assert np.isnan(actual["float"]["mean"])
assert actual["float"]["count"] == 1
assert actual["float"]["null_count"] == 1
@pytest.mark.parametrize("sparse", [True, False])
def test_empty(self, sparse):
path = self.path("test_empty_sparse")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [
tiledb.Attr(name="integer", nullable=True, dtype=int),
tiledb.Attr(name="float", nullable=True, dtype=float),
]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=sparse)
tiledb.Array.create(path, schema)
data = np.random.rand(5)
# write data
with tiledb.open(path, "w") as A:
if sparse:
A[np.arange(0, 5)] = {"integer": data, "float": data}
else:
A[:5] = {"integer": data, "float": data}
with tiledb.open(path, "r") as A:
invalid_aggregates = ("sum", "min", "max", "mean")
actual = A.query().agg(invalid_aggregates)[6:]
assert actual["integer"]["sum"] is None
assert actual["integer"]["min"] is None
assert actual["integer"]["max"] is None
assert actual["integer"]["mean"] is None
assert np.isnan(actual["float"]["sum"])
assert np.isnan(actual["float"]["min"])
assert np.isnan(actual["float"]["max"])
assert np.isnan(actual["float"]["mean"])
def test_multiple_attrs(self):
path = self.path("test_multiple_attrs")
dom = tiledb.Domain(tiledb.Dim(name="d", domain=(0, 9), dtype=np.int32))
attrs = [
tiledb.Attr(name="integer", dtype=int),
tiledb.Attr(name="float", dtype=float),
tiledb.Attr(name="string", dtype=str),
]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=True)
tiledb.Array.create(path, schema)
with tiledb.open(path, "w") as A:
A[np.arange(0, 10)] = {
"integer": np.random.randint(1, 10, size=10),
"float": np.random.randint(1, 10, size=10),
"string": np.random.randint(1, 10, size=10).astype(str),
}
with tiledb.open(path, "r") as A:
actual = A.query()[:]
agg = A.query().agg
assert agg({"string": "count"})[:] == len(actual["string"])
invalid_aggregates = ("sum", "min", "max", "mean")
for invalid_agg in invalid_aggregates:
with pytest.raises(tiledb.TileDBError):
agg({"string": invalid_agg})[:]
result = agg("count")[:]
assert result["integer"]["count"] == len(actual["integer"])
assert result["float"]["count"] == len(actual["float"])
assert result["string"]["count"] == len(actual["string"])
with pytest.raises(tiledb.TileDBError):
agg("sum")[:]
result = agg({"integer": "sum", "float": "sum"})[:]
assert "string" not in result
assert result["integer"]["sum"] == sum(actual["integer"])
assert result["float"]["sum"] == sum(actual["float"])
result = agg(
{
"string": ("count",),
"integer": "sum",
"float": ["max", "min", "sum", "mean"],
}
)[:]
assert result["string"]["count"] == len(actual["string"])
assert result["integer"]["sum"] == sum(actual["integer"])
assert result["float"]["max"] == max(actual["float"])
assert result["float"]["min"] == min(actual["float"])
assert result["float"]["sum"] == sum(actual["float"])
assert result["float"]["mean"] == sum(actual["float"]) / len(
actual["float"]
)
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