Update shuffle_poscar feature in run.py #1730

ztzkz · 2025-03-22T16:20:44Z

numpy.random.Generator.shuffle() performs the intended operation of shuffling the order of atoms. numpy.random.Generator.permuted() permutes the elements along the given axis, which mixes the coordinates of all atoms
shuffle

permuted

Summary by CodeRabbit

Refactor
- Improved the process that randomizes system coordinates when a specific option is enabled, enhancing internal data handling and consistency.

numpy.random.Generator.shuffle() performs the intended operation of shuffling the order of atoms. numpy.random.Generator.permuted() permutes the elements along the given axis, which mixes the coordinates of all atoms Signed-off-by: ztzkz <95387041+ztzkz@users.noreply.github.com>

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coderabbitai · 2025-03-22T16:21:41Z

📝 Walkthrough

Walkthrough

The change updates the make_model_devi function in the coordinate generation process. When the shuffle_poscar flag is set to True, the function now uses rng.shuffle to shuffle the system coordinates in place instead of generating a permuted copy with rng.permuted. This update modifies the internal handling of the coordinates during the model generation process.

Changes

File	Change Summary
dpgen/generator/run.py	In the `make_model_devi` function, replaced `rng.permuted(system.data["coords"], axis=1)` with `rng.shuffle(system.data["coords"], axis=1)` to shuffle coordinates in place.

Sequence Diagram(s)

sequenceDiagram
    participant Caller
    participant make_model_devi
    participant RNG

    Caller->>make_model_devi: Call with shuffle_poscar=True
    make_model_devi->>RNG: rng.shuffle(system.data["coords"], axis=1)
    RNG-->>make_model_devi: Return shuffled coordinates
    make_model_devi-->>Caller: Return modified system

Possibly related PRs

fix: support shuffle_poscar for other formats #1610: Alters the shuffling method in the make_model_devi function and removes the poscar_shuffle function, representing a code-level modification closely tied to this change.

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Reviewing files that changed from the base of the PR and between 9b5fab4 and 752ed21.

📒 Files selected for processing (1)

dpgen/generator/run.py (1 hunks)

🔇 Additional comments (1)

dpgen/generator/run.py (1)

1308-1308: Good change: Using shuffle instead of permuted for atomic coordinates

This change correctly modifies the behavior to shuffle atom order rather than mixing atom coordinates. Using rng.shuffle() in-place instead of rng.permuted() ensures that whole atoms (with their x, y, z coordinates intact) are shuffled in their positions, rather than permuting individual coordinate values across atoms.

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codecov · 2025-03-22T16:51:23Z

Codecov Report

❌ Patch coverage is 0% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 49.40%. Comparing base (9b5fab4) to head (752ed21).
⚠️ Report is 28 commits behind head on devel.

Files with missing lines	Patch %	Lines
dpgen/generator/run.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##            devel    #1730   +/-   ##
=======================================
  Coverage   49.40%   49.40%           
=======================================
  Files          83       83           
  Lines       14770    14770           
=======================================
  Hits         7297     7297           
  Misses       7473     7473

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njzjz

Hi, I can't understand what this PR changes. Please explain more about it.

Copilot

Pull Request Overview

This PR fixes a bug in the shuffle_poscar feature where the wrong NumPy random method was being used. The change corrects the atom coordinate shuffling logic to use the proper in-place shuffle operation instead of a permutation that incorrectly mixed coordinates across all atoms.

Replaced rng.permuted() with rng.shuffle() for proper atom coordinate shuffling
Fixed the shuffling behavior to preserve atomic coordinate integrity

dpgen/generator/run.py

ztzkz · 2025-08-06T16:28:40Z

Hi, I can't understand what this PR changes. Please explain more about it.

The original code uses numpy.random.Generator.permuted function, which according to manual

Unlike shuffle, each slice along the given axis is shuffled independently of the others.

The intended function should be numpy.random.Generator.shuffle, where

The order of sub-arrays is changed but their contents remains the same.

So permute randomizes the x, y, z coordinates of each atom independently, so it creates coordinates entirely unphysical, as shown in the picture, leading to non-convergence in DFT.
Shuffle essentially reassigns atom ID, such that in a composite system atoms of different types can be swapped, but has no real effect for a pure material.

I believe shuffle is the intended logic for this functionality.

njzjz · 2025-08-06T16:59:28Z

I create an example to test, and shuffle and permutation give the same result. Thus, I cannot get your point.

import numpy as np
x = np.arange(0, 30).reshape(2,5,3)
print(f"{x=}")
rng = np.random.default_rng(123)
print(f"{rng.permutation(x, axis=1)=}")
rng = np.random.default_rng(123)
rng.shuffle(x, axis=1)
print(f"{x=}")

x=array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [12, 13, 14]],

       [[15, 16, 17],
        [18, 19, 20],
        [21, 22, 23],
        [24, 25, 26],
        [27, 28, 29]]])
rng.permutation(x, axis=1)=array([[[12, 13, 14],
        [ 0,  1,  2],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [ 3,  4,  5]],

       [[27, 28, 29],
        [15, 16, 17],
        [21, 22, 23],
        [24, 25, 26],
        [18, 19, 20]]])
x=array([[[12, 13, 14],
        [ 0,  1,  2],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [ 3,  4,  5]],

       [[27, 28, 29],
        [15, 16, 17],
        [21, 22, 23],
        [24, 25, 26],
        [18, 19, 20]]])

ztzkz · 2025-08-06T17:11:34Z

I see the problem. It seems permutation and shuffle behave the same in this case, but the current code uses permuted, which gives the unintended behavior.

import numpy as np
x = np.arange(0, 30).reshape(2,5,3)
print(f"{x=}")
rng = np.random.default_rng(123)
print(f"{rng.permuted(x, axis=1)=}")
rng = np.random.default_rng(123)
rng.shuffle(x, axis=1)
print(f"{x=}")

x=array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [12, 13, 14]],

       [[15, 16, 17],
        [18, 19, 20],
        [21, 22, 23],
        [24, 25, 26],
        [27, 28, 29]]])
rng.permuted(x, axis=1)=array([[[12, 10, 14],
        [ 0, 13,  8],
        [ 6,  1,  5],
        [ 9,  7,  2],
        [ 3,  4, 11]],

       [[27, 25, 23],
        [21, 22, 29],
        [24, 16, 17],
        [15, 19, 20],
        [18, 28, 26]]])
x=array([[[12, 13, 14],
        [ 0,  1,  2],
        [ 6,  7,  8],
        [ 9, 10, 11],
        [ 3,  4,  5]],

       [[27, 28, 29],
        [15, 16, 17],
        [21, 22, 23],
        [24, 25, 26],
        [18, 19, 20]]])

njzjz · 2025-08-07T07:13:29Z

Thank you for reminding me. I didn't realize they are different.

ztzkz and others added 2 commits March 22, 2025 09:15

[pre-commit.ci] auto fixes from pre-commit.com hooks

752ed21

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njzjz changed the base branch from master to devel March 22, 2025 16:49

njzjz reviewed Aug 6, 2025

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njzjz requested a review from Copilot August 6, 2025 09:10

Copilot AI reviewed Aug 6, 2025

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njzjz approved these changes Aug 7, 2025

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njzjz merged commit 41402ad into deepmodeling:devel Aug 7, 2025
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merge devel to master and release v0.13.2 #1789

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