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new d3bdb8ad3e [SYSTEMDS-3744-48] Python API cumulative aggregate
d3bdb8ad3e is described below
commit d3bdb8ad3e5188d6ccab9a383ee8d501299200a4
Author: e-strauss <lathan...@gmx.de>
AuthorDate: Wed Sep 4 21:51:33 2024 +0200
[SYSTEMDS-3744-48] Python API cumulative aggregate
This commit adds the cumulative column aggregates to
the python API:
cumsum, cumprod, cumsumprod, cummin, cummax
Closes #2093
---
src/main/python/systemds/operator/nodes/matrix.py | 40 ++++++-
src/main/python/tests/matrix/test_cumulative.py | 130 ++++++++++++++++++++++
2 files changed, 169 insertions(+), 1 deletion(-)
diff --git a/src/main/python/systemds/operator/nodes/matrix.py
b/src/main/python/systemds/operator/nodes/matrix.py
index 672c40c796..a843f4ac78 100644
--- a/src/main/python/systemds/operator/nodes/matrix.py
+++ b/src/main/python/systemds/operator/nodes/matrix.py
@@ -688,11 +688,49 @@ class Matrix(OperationNode):
)
def inv(self) -> "Matrix":
- """ Computes the inverse of a squared matrix.
+ """Computes the inverse of a squared matrix.
:return: The Matrix representing the result of this operation
"""
return Matrix(self.sds_context, "inv", [self])
+ def cumsum(self) -> "Matrix":
+ """Column prefix-sum. (For row-prefix sum, use X.t().cumsum().t())
+
+ :return: The Matrix representing the result of this operation
+ """
+ return Matrix(self.sds_context, "cumsum", [self])
+
+ def cumprod(self) -> "Matrix":
+ """Column prefix-product. (For row-prefix prod, use
X.t().cumprod().t())
+
+ :return: The Matrix representing the result of this operation
+ """
+ return Matrix(self.sds_context, "cumprod", [self])
+
+ def cumsumprod(self) -> "Matrix":
+ """Column prefix-sumprod of an 2-column matrix:
+ Y = X.comsumprod(),
+ where Y[i,1] = X[i,1] + X[i,2]*Y[i-1,1] for i in [1,2, .., nrow(X)]
+ The aggregator is initialized with 0 (Y[0,1] = 0)
+
+ :return: The Matrix representing the result of this operation
+ """
+ return Matrix(self.sds_context, "cumsumprod", [self])
+
+ def cummin(self) -> "Matrix":
+ """Column prefix-min. (For row-prefix min, use X.t().cummin().t())
+
+ :return: The Matrix representing the result of this operation
+ """
+ return Matrix(self.sds_context, "cummin", [self])
+
+ def cummax(self) -> "Matrix":
+ """Column prefix-max. (For row-prefix max, use X.t().cummax().t())
+
+ :return: The Matrix representing the result of this operation
+ """
+ return Matrix(self.sds_context, "cummax", [self])
+
def __str__(self):
return "MatrixNode"
diff --git a/src/main/python/tests/matrix/test_cumulative.py
b/src/main/python/tests/matrix/test_cumulative.py
new file mode 100644
index 0000000000..963f101417
--- /dev/null
+++ b/src/main/python/tests/matrix/test_cumulative.py
@@ -0,0 +1,130 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+import unittest
+import numpy as np
+from systemds.context import SystemDSContext
+
+m1 = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]])
+m2 = np.random.randint(10, size=(10, 10))
+m3 = np.random.random((10, 10))
+
+
+def comsumprod(m):
+ s = 0
+ out = []
+ for i in m:
+ s = i[0] + i[1] * s
+ out.append(s)
+ return np.array(out).reshape(-1, 1)
+
+
+class TestCUMBASE(unittest.TestCase):
+ def setUp(self):
+ self.sds = SystemDSContext()
+
+ def tearDown(self):
+ self.sds.close()
+
+ def test_cumsum_basic(self):
+ sds_input = self.sds.from_numpy(m1)
+ sds_result = sds_input.cumsum().compute()
+ np_result = np.cumsum(m1, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumsum_random1(self):
+ sds_input = self.sds.from_numpy(m2)
+ sds_result = sds_input.cumsum().compute()
+ np_result = np.cumsum(m2, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumsum_random2(self):
+ sds_input = self.sds.from_numpy(m3)
+ sds_result = sds_input.cumsum().compute()
+ np_result = np.cumsum(m3, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumprod_basic(self):
+ sds_input = self.sds.from_numpy(m1)
+ sds_result = sds_input.cumprod().compute()
+ np_result = np.cumprod(m1, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumprod_random1(self):
+ sds_input = self.sds.from_numpy(m2)
+ sds_result = sds_input.cumprod().compute()
+ np_result = np.cumprod(m2, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumprod_random2(self):
+ sds_input = self.sds.from_numpy(m3)
+ sds_result = sds_input.cumprod().compute()
+ np_result = np.cumprod(m3, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cumsumprod_basic(self):
+ m = m1[:, :2] # 2-col matrix
+ sds_input = self.sds.from_numpy(m)
+ sds_result = sds_input.cumsumprod().compute()
+ exp_result = comsumprod(m)
+ self.assertTrue(np.allclose(sds_result, exp_result, 1e-9))
+
+ def test_cumsumprod_random1(self):
+ m = m2[:, :2]
+ sds_input = self.sds.from_numpy(m)
+ sds_result = sds_input.cumsumprod().compute()
+ exp_result = comsumprod(m)
+ self.assertTrue(np.allclose(sds_result, exp_result, 1e-9))
+
+ def test_cumsumprod_random2(self):
+ m = m3[:, :2]
+ sds_input = self.sds.from_numpy(m)
+ sds_result = sds_input.cumsumprod().compute()
+ exp_result = comsumprod(m)
+ self.assertTrue(np.allclose(sds_result, exp_result, 1e-9))
+
+ def test_cummin_random1(self):
+ sds_input = self.sds.from_numpy(m2)
+ sds_result = sds_input.cummin().compute()
+ np_result = np.minimum.accumulate(m2, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cummin_random2(self):
+ sds_input = self.sds.from_numpy(m3)
+ sds_result = sds_input.cummin().compute()
+ np_result = np.minimum.accumulate(m3, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cummax_random1(self):
+ sds_input = self.sds.from_numpy(m2)
+ sds_result = sds_input.cummax().compute()
+ np_result = np.maximum.accumulate(m2, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_cummax_random2(self):
+ sds_input = self.sds.from_numpy(m3)
+ sds_result = sds_input.cummax().compute()
+ np_result = np.maximum.accumulate(m3, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+
+if __name__ == "__main__":
+ unittest.main()
