[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

@@ -688,11 +688,49 @@ def replace(
         )
 
     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"

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()