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# -------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
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# 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 random
import numpy as np
from systemds.context import SystemDSContext
from systemds.operator.algorithm import split
# Seed the randomness.
np.random.seed(7)
class TestOrder(unittest.TestCase):
sds: SystemDSContext = None
@classmethod
def setUpClass(cls):
cls.sds = SystemDSContext(capture_stdout=True, logging_level=50)
@classmethod
def tearDownClass(cls):
cls.sds.close()
def test_basic(self):
m = self.make_matrix()
o = self.sds.from_numpy(m).compute()
s = m
self.assertTrue(np.allclose(o, s))
def test_split(self):
X = self.make_matrix()
Y = self.make_matrix(cols=2)
[p1, p2, p3, p4] = split(
self.sds.from_numpy(X), self.sds.from_numpy(Y)
).compute()
exp1 = X[:2]
exp2 = X[2:]
exp3 = Y[:2]
exp4 = Y[2:]
self.assertTrue(np.allclose(p1, exp1))
self.assertTrue(np.allclose(p2, exp2))
self.assertTrue(np.allclose(p3, exp3))
self.assertTrue(np.allclose(p4, exp4))
def test_split_2(self):
rows = 10
X = self.make_matrix(rows=rows)
Y = self.make_matrix(rows=rows, cols=2)
[p1, p2, p3, p4] = split(
self.sds.from_numpy(X), self.sds.from_numpy(Y)
).compute()
exp1 = X[:7]
exp2 = X[7:]
exp3 = Y[:7]
exp4 = Y[7:]
self.assertTrue(np.allclose(p1, exp1))
self.assertTrue(np.allclose(p2, exp2))
self.assertTrue(np.allclose(p3, exp3))
self.assertTrue(np.allclose(p4, exp4))
def test_split_3(self):
rows = 100
X = self.make_matrix(rows=rows)
Y = self.make_matrix(rows=rows, cols=2)
[p1, p2, p3, p4] = split(
self.sds.from_numpy(X), self.sds.from_numpy(Y)
).compute()
exp1 = X[:70]
exp2 = X[70:]
exp3 = Y[:70]
exp4 = Y[70:]
self.assertTrue(np.allclose(p1, exp1))
self.assertTrue(np.allclose(p2, exp2))
self.assertTrue(np.allclose(p3, exp3))
self.assertTrue(np.allclose(p4, exp4))
def test_split_4(self):
rows = 100
X = self.make_matrix(rows=rows)
Y = self.make_matrix(rows=rows, cols=2)
[p1, p2, p3, p4] = split(
self.sds.from_numpy(X), self.sds.from_numpy(Y), f=0.2
).compute()
exp1 = X[:20]
exp2 = X[20:]
exp3 = Y[:20]
exp4 = Y[20:]
self.assertTrue(np.allclose(p1, exp1))
self.assertTrue(np.allclose(p2, exp2))
self.assertTrue(np.allclose(p3, exp3))
self.assertTrue(np.allclose(p4, exp4))
def make_matrix(self, rows=4, cols=4):
return np.random.rand(rows, cols)
if __name__ == "__main__":
unittest.main(exit=False)