Polys: Refine dup_clear_denoms function and update tests #26180

mohakmalviya · 2024-02-05T14:02:18Z

References to other Issues or PRs

Brief description of what is fixed or changed

Refined the dup_clear_denoms function within the polys module, focusing on enhancing its efficiency and reliability when dealing with exact domains. The adjustments ensure that the function more accurately clears denominators, especially in scenarios involving symbolic expressions and rational numbers.

Other comments

Updated tests reflect these changes by focusing on:

Ensuring the convert flag functions as intended across different scenarios.

Release Notes

polys
- Improved dup_clear_denoms function's efficiency in symbolic computations.

…module, focusing on enhancing its efficiency and reliability when dealing with exact domains. The adjustments ensure that the function more accurately clears denominators, especially in scenarios involving symbolic expressions and rational numbers. Changes made include: - Fine-tuning the logic for handling denominators to ensure robust performance across a variety of use cases. - Streamlining the conversion process to guarantee that expressions are correctly converted to the target domain when requested. The primary aim of these modifications was to address feedback and improve the function's handling of symbolic and rational expressions, thereby increasing the utility and reliability of polynomial operations within Sympy. Updated tests reflect these changes by focusing on: - Verifying the accurate clearing of denominators in exact domains. - Ensuring the `convert` flag functions as intended across different scenarios. While initial considerations included enhancements for inexact domain handling, this version concentrates on exact domains to solidify the foundation of `dup_clear_denoms`. Future work may revisit inexact domain improvements. Fixes issue: sympy#26176 (sympy#26176)

sympy-bot · 2024-02-05T14:02:24Z

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polys
- Improved dup_clear_denoms function's efficiency in symbolic computations. (#26180 by @mohakmalviya and @sylee957)

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Fixes: #26176

#### Brief description of what is fixed or changed

Refined the `dup_clear_denoms` function within the polys module, focusing on enhancing its efficiency and reliability when dealing with exact domains. The adjustments ensure that the function more accurately clears denominators, especially in scenarios involving symbolic expressions and rational numbers.

#### Other comments

Updated tests reflect these changes by focusing on:
- Ensuring the convert flag functions as intended across different scenarios.

#### Release Notes

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  * Improved `dup_clear_denoms` function's efficiency in symbolic computations.

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Update

The release notes on the wiki have been updated.

sympy/polys/densetools.py

github-actions · 2024-02-05T14:56:55Z

Benchmark results from GitHub Actions

Lower numbers are good, higher numbers are bad. A ratio less than 1
means a speed up and greater than 1 means a slowdown. Green lines
beginning with + are slowdowns (the PR is slower then master or
master is slower than the previous release). Red lines beginning
with - are speedups.

Significantly changed benchmark results (PR vs master)

Significantly changed benchmark results (master vs previous release)

| Change   | Before [a00718ba]    | After [d5563740]    |   Ratio | Benchmark (Parameter)                                                |
|----------|----------------------|---------------------|---------|----------------------------------------------------------------------|
| -        | 68.3±0.6ms           | 44.0±0.3ms          |    0.64 | integrate.TimeIntegrationRisch02.time_doit(10)                       |
| -        | 68.6±1ms             | 43.2±0.2ms          |    0.63 | integrate.TimeIntegrationRisch02.time_doit_risch(10)                 |
| +        | 18.6±0.4μs           | 29.7±0.4μs          |    1.6  | integrate.TimeIntegrationRisch03.time_doit(1)                        |
| -        | 5.38±0.04ms          | 2.84±0.03ms         |    0.53 | logic.LogicSuite.time_load_file                                      |
| -        | 73.5±0.4ms           | 28.3±0.4ms          |    0.38 | polys.TimeGCD_GaussInt.time_op(1, 'dense')                           |
| -        | 25.8±0.04ms          | 16.7±0.06ms         |    0.65 | polys.TimeGCD_GaussInt.time_op(1, 'expr')                            |
| -        | 73.6±0.2ms           | 28.6±0.2ms          |    0.39 | polys.TimeGCD_GaussInt.time_op(1, 'sparse')                          |
| -        | 254±2ms              | 123±1ms             |    0.48 | polys.TimeGCD_GaussInt.time_op(2, 'dense')                           |
| -        | 254±1ms              | 123±1ms             |    0.48 | polys.TimeGCD_GaussInt.time_op(2, 'sparse')                          |
| -        | 647±3ms              | 366±0.9ms           |    0.57 | polys.TimeGCD_GaussInt.time_op(3, 'dense')                           |
| -        | 653±3ms              | 367±2ms             |    0.56 | polys.TimeGCD_GaussInt.time_op(3, 'sparse')                          |
| -        | 497±2μs              | 282±2μs             |    0.57 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(1, 'dense')            |
| -        | 1.81±0.01ms          | 1.05±0.01ms         |    0.58 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(2, 'dense')            |
| -        | 5.83±0.02ms          | 3.09±0.02ms         |    0.53 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(3, 'dense')            |
| -        | 449±2μs              | 227±2μs             |    0.51 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(1, 'dense')               |
| -        | 1.48±0.01ms          | 669±3μs             |    0.45 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(2, 'dense')               |
| -        | 4.85±0.02ms          | 1.66±0ms            |    0.34 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(3, 'dense')               |
| -        | 381±2μs              | 205±0.7μs           |    0.54 | polys.TimeGCD_SparseGCDHighDegree.time_op(1, 'dense')                |
| -        | 2.45±0.03ms          | 1.24±0ms            |    0.5  | polys.TimeGCD_SparseGCDHighDegree.time_op(3, 'dense')                |
| -        | 10.0±0.05ms          | 4.33±0.04ms         |    0.43 | polys.TimeGCD_SparseGCDHighDegree.time_op(5, 'dense')                |
| -        | 362±1μs              | 171±1μs             |    0.47 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(1, 'dense')            |
| -        | 2.48±0.01ms          | 884±10μs            |    0.36 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(3, 'dense')            |
| -        | 9.59±0.05ms          | 2.68±0.01ms         |    0.28 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(5, 'dense')            |
| -        | 1.04±0ms             | 427±6μs             |    0.41 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'dense')           |
| -        | 1.73±0.01ms          | 503±2μs             |    0.29 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'sparse')          |
| -        | 5.88±0.03ms          | 1.79±0.02ms         |    0.3  | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'dense')           |
| -        | 8.44±0.02ms          | 1.48±0.01ms         |    0.18 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'sparse')          |
| -        | 284±0.8μs            | 64.3±0.2μs          |    0.23 | polys.TimePREM_QuadraticNonMonicGCD.time_op(1, 'sparse')             |
| -        | 3.38±0.04ms          | 389±2μs             |    0.11 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'dense')              |
| -        | 4.00±0.04ms          | 279±1μs             |    0.07 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'sparse')             |
| -        | 7.15±0.07ms          | 1.24±0.01ms         |    0.17 | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'dense')              |
| -        | 8.75±0.03ms          | 835±3μs             |    0.1  | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'sparse')             |
| -        | 5.06±0.01ms          | 2.97±0.01ms         |    0.59 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(2, 'sparse') |
| -        | 12.0±0.05ms          | 6.54±0.02ms         |    0.54 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'dense')  |
| -        | 22.7±0.1ms           | 8.99±0.04ms         |    0.4  | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'sparse') |
| -        | 5.26±0.05ms          | 864±9μs             |    0.16 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(1, 'sparse')    |
| -        | 12.8±0.08ms          | 7.09±0.03ms         |    0.56 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(2, 'sparse')    |
| -        | 102±0.7ms            | 25.4±0.1ms          |    0.25 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'dense')     |
| -        | 169±0.3ms            | 53.3±0.2ms          |    0.31 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'sparse')    |
| -        | 175±0.5μs            | 112±0.9μs           |    0.64 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(1, 'dense')      |
| -        | 359±1μs              | 213±1μs             |    0.59 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(1, 'sparse')     |
| -        | 4.31±0.01ms          | 829±6μs             |    0.19 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'dense')      |
| -        | 5.25±0.02ms          | 381±0.8μs           |    0.07 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'sparse')     |
| -        | 19.9±0.1ms           | 2.81±0.02ms         |    0.14 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'dense')      |
| -        | 22.7±0.2ms           | 623±3μs             |    0.03 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'sparse')     |
| -        | 474±3μs              | 134±1μs             |    0.28 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(1, 'sparse') |
| -        | 4.75±0.02ms          | 619±5μs             |    0.13 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'dense')  |
| -        | 5.24±0.03ms          | 140±2μs             |    0.03 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'sparse') |
| -        | 13.3±0.1ms           | 1.29±0ms            |    0.1  | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'dense')  |
| -        | 14.0±0.1ms           | 139±0.5μs           |    0.01 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'sparse') |
| -        | 132±0.5μs            | 73.7±0.6μs          |    0.56 | solve.TimeMatrixOperations.time_rref(3, 0)                           |
| -        | 247±0.4μs            | 87.2±0.5μs          |    0.35 | solve.TimeMatrixOperations.time_rref(4, 0)                           |
| -        | 24.4±0.1ms           | 10.1±0.03ms         |    0.42 | solve.TimeSolveLinSys189x49.time_solve_lin_sys                       |
| -        | 28.5±0.3ms           | 15.5±0.2ms          |    0.54 | solve.TimeSparseSystem.time_linsolve_Aaug(20)                        |
| -        | 54.6±0.5ms           | 24.8±0.09ms         |    0.45 | solve.TimeSparseSystem.time_linsolve_Aaug(30)                        |
| -        | 28.1±0.2ms           | 15.4±0.1ms          |    0.55 | solve.TimeSparseSystem.time_linsolve_Ab(20)                          |
| -        | 54.2±0.3ms           | 24.6±0.1ms          |    0.45 | solve.TimeSparseSystem.time_linsolve_Ab(30)                          |

Full benchmark results can be found as artifacts in GitHub Actions
(click on checks at the top of the PR).

oscarbenjamin · 2024-02-05T15:00:00Z

sympy/polys/tests/test_densetools.py

-    assert dup_clear_denoms(
-        [QQ(3), QQ(1), QQ(0)], QQ, ZZ) == (ZZ(1), [QQ(3), QQ(1), QQ(0)])
-    assert dup_clear_denoms(
-        [QQ(1), QQ(1, 2), QQ(0)], QQ, ZZ) == (ZZ(2), [QQ(2), QQ(1), QQ(0)])
-
-    assert dup_clear_denoms([QQ(3), QQ(
-        1), QQ(0)], QQ, ZZ, convert=True) == (ZZ(1), [ZZ(3), ZZ(1), ZZ(0)])
-    assert dup_clear_denoms([QQ(1), QQ(
-        1, 2), QQ(0)], QQ, ZZ, convert=True) == (ZZ(2), [ZZ(2), ZZ(1), ZZ(0)])
+    assert dup_clear_denoms([QQ(3), QQ(1), QQ(0)], QQ, ZZ) == (ZZ(1), [QQ(3), QQ(1), QQ(0)])
+    assert dup_clear_denoms([QQ(1), QQ(1, 2), QQ(0)], QQ, ZZ) == (ZZ(2), [QQ(2), QQ(1), QQ(0)])

-    assert dup_clear_denoms(
-        [EX(S(3)/2), EX(S(9)/4)], EX) == (EX(4), [EX(6), EX(9)])
+    assert dup_clear_denoms([QQ(3), QQ(1), QQ(0)], QQ, ZZ, convert=True) == (ZZ(1), [ZZ(3), ZZ(1), ZZ(0)])
+    assert dup_clear_denoms([QQ(1), QQ(1, 2), QQ(0)], QQ, ZZ, convert=True) == (ZZ(2), [ZZ(2), ZZ(1), ZZ(0)])


Why have these tests been changed?

sorry sir it seems like i haven't pushed the updated code because i have written the tests for RR.frac_field(x) but i can't see it in this code....sorry for this

oscarbenjamin · 2024-02-05T15:00:32Z

sympy/polys/tests/test_densetools.py


+    assert dup_clear_denoms([EX(S(3)/2), EX(S(9)/4)], EX) == (EX(4), [EX(6), EX(9)])


Does this example fail without the fix?

The problem in the issue was with the domain RR.frac_field(x).

oscarbenjamin · 2024-02-05T17:50:04Z

sympy/polys/tests/test_densetools.py

    assert dup_clear_denoms([EX(7)], EX) == (EX(1), [EX(7)])
    assert dup_clear_denoms([EX(sin(x)/x), EX(0)], EX) == (EX(x), [EX(sin(x)), EX(0)])

+    # This test is for inexact domain handling
+    R, x = ring("x", RR)
+    f = RR(0.5)*x + RR(0.333)
+    common, cleared_f = dup_clear_denoms(f, RR)
+    assert cleared_f == f, "Function should not modify input for inexact domains when no clear action is needed"


Look at the rest of the code in this file for examples of how to write the test code.

Look at the rest of the code in this file for examples of how to write the test code.

sir i have used R.to_domain() with dup_decompose function but it does not seem to work

oscarbenjamin · 2024-02-05T17:55:21Z

The operation to be tested is:

In [18]: F = RR.frac_field(x)

In [19]: dup_clear_denoms([F(8.48717/(8.0089*x + 2.83)), F(0.0)], F)
Out[19]: (x + 0.353356890459364, [(8.48717*x + 2.999)/(8.0089*x + 2.83), 0.0])

It is awkward to test output with floats but a string test is fine like `assert str(result) == "(x + ...".

mohakmalviya · 2024-02-06T18:05:36Z

The operation to be tested is:
In [18]: F = RR.frac_field(x)

In [19]: dup_clear_denoms([F(8.48717/(8.0089*x + 2.83)), F(0.0)], F)
Out[19]: (x + 0.353356890459364, [(8.48717*x + 2.999)/(8.0089*x + 2.83), 0.0])
It is awkward to test output with floats but a string test is fine like `assert str(result) == "(x + ...".

sir so do i have to add the test for specificaly this output? or it should be generalised for these type of polynomial expressions?

mohakmalviya · 2024-02-06T19:35:52Z

Out[19]: (x + 0.353356890459364, [(8.48717x + 2.999)/(8.0089x + 2.83), 0.0])

when i am running tests for the output - Out[19]: (x + 0.353356890459364, [(8.48717*x + 2.999)/(8.0089*x + 2.83), 0.0]) they are failing because dup_clear_denoms does not give output in fractional form, it should be converted first into simplified form

oscarbenjamin · 2024-02-06T21:18:14Z

sympy/polys/tests/test_densetools.py

+    result_common_denom, result_transformed_elements = dup_clear_denoms([F(8.48717/(8.0089*x + 2.83)), F(0.0)], F)
+    result_str = str((result_common_denom, result_transformed_elements))
+    expected_output_str = "(x + 0.353356890459364, [1.05971731448763, 0.0])"
+    assert result_str == expected_output_str


This is unnecessarily complicated:

result = dup_clear_denoms([F(8.48717/(8.0089*x + 2.83)), F(0.0)], F) assert str(result) == "(x + 0.353356890459364, [1.05971731448763, 0.0])"

sir i have updated it

sympy/polys/tests/test_densetools.py

oscarbenjamin · 2024-02-07T13:26:54Z

Can you squash the commits?

mohakmalviya · 2024-02-07T21:17:40Z

Can you squash the commits?

done sir

oscarbenjamin · 2024-02-07T21:45:09Z

The commits are not squashed. There are 12 commits but there should only be one.

mohakmalviya · 2024-02-08T10:27:24Z

The commits are not squashed. There are 12 commits but there should only be one.

sir i am squashing the commits but it is still showing 12 on the commits tab
Edit : completed

…module, focusing on enhancing its efficiency and reliability when dealing with exact domains. The adjustments ensure that the function more accurately clears denominators, especially in scenarios involving symbolic expressions and rational numbers. Changes made include: - Fine-tuning the logic for handling denominators to ensure robust performance across a variety of use cases. - Streamlining the conversion process to guarantee that expressions are correctly converted to the target domain when requested. The primary aim of these modifications was to address feedback and improve the function's handling of symbolic and rational expressions, thereby increasing the utility and reliability of polynomial operations within Sympy. Updated tests reflect these changes by focusing on: - Verifying the accurate clearing of denominators in exact domains. - Ensuring the `convert` flag functions as intended across different scenarios. While initial considerations included enhancements for inexact domain handling, this version concentrates on exact domains to solidify the foundation of `dup_clear_denoms`. Future work may revisit inexact domain improvements. Fixes issue: sympy#26176 (sympy#26176) improved code quality Updated PR with comments Updated code quality Updated test cases for domain handling Updated code quality Added test with simplified expression Updated test Improved code quality Improved code quality improved code quality Updated PR with comments Updated code quality Updated test cases for domain handling Updated code quality Added test with simplified expression Updated test Improved code quality Improved code quality Squashed commits Updated PR with comments Updated code quality Updated test cases for domain handling Updated code quality Added test with simplified expression Updated test Improved code quality Improved code quality

sympy/polys/tests/test_densetools.py

sympy/polys/densetools.py

sylee957 · 2024-02-09T10:15:53Z

sympy/polys/tests/test_densetools.py

@@ -637,6 +635,9 @@ def test_dup_clear_denoms():
    assert dup_clear_denoms([EX(7)], EX) == (EX(1), [EX(7)])
    assert dup_clear_denoms([EX(sin(x)/x), EX(0)], EX) == (EX(x), [EX(sin(x)), EX(0)])

+    F = RR.frac_field(x)
+    result = dup_clear_denoms([F(8.48717/(8.0089*x + 2.83)), F(0.0)], F)
+    assert str(result) == "(x + 0.353356890459364, [1.05971731448763, 0.0])"


I'm not sure if it is generally a robust approach to use the str(result).
As far as I know, using exact value of float, can give fragile tests with
CPU vendors, math libraries, which may give slightly different numbers due to the implementation.

The floats used here are mpmath floats which are based on only integer arithmetic and should be the same on all hardware. There isn't any good function currently for comparing polyelement approximately over RR.

improved code quality

541204b

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mohakmalviya force-pushed the fix-coercion-integrals branch from edd8249 to 03df4d9 Compare February 8, 2024 10:47

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sylee957 merged commit 202c340 into sympy:master Feb 9, 2024
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Polys: Refine dup_clear_denoms function and update tests #26180

Polys: Refine dup_clear_denoms function and update tests #26180

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oscarbenjamin Feb 9, 2024


		assert dup_clear_denoms([EX(S(3)/2), EX(S(9)/4)], EX) == (EX(4), [EX(6), EX(9)])

Polys: Refine dup_clear_denoms function and update tests #26180

Polys: Refine dup_clear_denoms function and update tests #26180

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