import numpy as np 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"] )