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new 2d3c1e6115 [SYSTEMDS-3742] Python API Builtin unique
2d3c1e6115 is described below
commit 2d3c1e61156a29e58e299f9832202b7ad89a4a0f
Author: e-strauss <[email protected]>
AuthorDate: Tue Sep 3 22:08:16 2024 +0200
[SYSTEMDS-3742] Python API Builtin unique
Closes #2089
---
src/main/python/systemds/operator/nodes/matrix.py | 17 ++++
src/main/python/tests/matrix/test_unique.py | 118 ++++++++++++++++++++++
2 files changed, 135 insertions(+)
diff --git a/src/main/python/systemds/operator/nodes/matrix.py
b/src/main/python/systemds/operator/nodes/matrix.py
index 07566ebfd0..4e010bf088 100644
--- a/src/main/python/systemds/operator/nodes/matrix.py
+++ b/src/main/python/systemds/operator/nodes/matrix.py
@@ -296,6 +296,23 @@ class Matrix(OperationNode):
"""
return Scalar(self.sds_context, 'trace', [self])
+ def unique(self, axis: int = None) -> 'Matrix':
+ """Returns the unique values for the complete matrix, for each row or
for each column.
+
+ :param axis: can be 0 or 1 to do either row or column uniques
+ :return: `Matrix` representing operation
+ """
+ if axis == 0:
+ named_input_nodes = {"dir": '"c"'}
+ return Matrix(self.sds_context, 'unique', [self],
named_input_nodes=named_input_nodes)
+ elif axis == 1:
+ named_input_nodes = {"dir": '"r"'}
+ return Matrix(self.sds_context, 'unique', [self],
named_input_nodes=named_input_nodes)
+ elif axis is None:
+ return Matrix(self.sds_context, 'unique', [self])
+ raise ValueError(
+ f"Axis has to be either 0, 1 or None, for column, row or complete
{self.operation}")
+
def abs(self) -> 'Matrix':
"""Calculate absolute.
diff --git a/src/main/python/tests/matrix/test_unique.py
b/src/main/python/tests/matrix/test_unique.py
new file mode 100644
index 0000000000..b84c3ae2f5
--- /dev/null
+++ b/src/main/python/tests/matrix/test_unique.py
@@ -0,0 +1,118 @@
+# -------------------------------------------------------------
+#
+# 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
+
+
+np.random.seed(7)
+
+
+# np's unique applied on an axis checks for unique vectors along that axis ->
on the other hand systemds' unique
+# returns the unique values along that axis for each vector on that axis
+def compute_expected(m, num_cols, axis):
+ def padded(row):
+ unique = np.unique(row)
+ row = np.pad(unique, (num_cols - len(unique), 0), "constant",
constant_values=0)
+ return row
+
+ if axis == 1:
+ return np.array([padded(r) for r in m])
+ else:
+ return np.array([padded(r) for r in m.T]).T
+
+
+class TestUNIQUE(unittest.TestCase):
+ def setUp(self):
+ self.sds = SystemDSContext()
+
+ def tearDown(self):
+ self.sds.close()
+
+ def test_unique_basic(self):
+ input_matrix = np.array(
+ [[1, -2, 3, 4], [0, -6, 7, 8], [0, -10, 11, -12], [0, -14, 15,
-16]]
+ )
+
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique().compute()
+ sds_result = np.sort(np.reshape(sds_result, (-1)))
+ np_result = np.unique(input_matrix)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_basic2(self):
+ input_matrix = np.array(
+ [[1, 1, 1, 1], [2, 2, 2, 2], [0, 10, 11, 12], [0, 14, 15, 16]]
+ )
+
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique(1).compute()
+ sds_result = np.sort(sds_result, 1)
+ num_cols = sds_result.shape[1]
+ np_result = compute_expected(input_matrix, num_cols, 1)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_basic3(self):
+ input_matrix = np.array(
+ [[0, 1, 1, 1], [0, 1, 1, 1], [0, 10, 11, 12], [0, 14, 15, 16]]
+ )
+
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique(0).compute()
+ sds_result = np.sort(sds_result, 0)
+ num_rows = sds_result.shape[0]
+ np_result = compute_expected(input_matrix, num_rows, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_random1(self):
+ input_matrix = np.random.random((10, 10)) * 200
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique().compute()
+ sds_result = np.sort(np.reshape(sds_result, (-1)))
+ np_result = np.unique(input_matrix)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_random2(self):
+ input_matrix = np.random.random((10, 10)) * 200
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique(1).compute()
+ sds_result = np.sort(sds_result, 1)
+ num_cols = sds_result.shape[1]
+ np_result = compute_expected(input_matrix, num_cols, 1)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_random3(self):
+ input_matrix = np.random.random((10, 10)) * 200
+ sds_input = self.sds.from_numpy(input_matrix)
+ sds_result = sds_input.unique(0).compute()
+ sds_result = np.sort(sds_result, 0)
+ num_rows = sds_result.shape[0]
+ np_result = compute_expected(input_matrix, num_rows, 0)
+ assert np.allclose(sds_result, np_result, 1e-9)
+
+ def test_unique_error(self):
+ with self.assertRaises(ValueError):
+ self.sds.from_numpy(np.array([[1, 2]])).unique(2)
+
+
+if __name__ == "__main__":
+ unittest.main()
