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Test Failed on CICD Pipeline with varied LinearRegression predictions #23970

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jyotirmay123 opened this issue Jul 21, 2022 · 4 comments
Open

Test Failed on CICD Pipeline with varied LinearRegression predictions #23970

jyotirmay123 opened this issue Jul 21, 2022 · 4 comments
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module:linear_model Needs Investigation Issue requires investigation

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@jyotirmay123
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We have a very simple use case, when we take some data X, y and fir it to LinearRegression() like below. and try to predict some more value.

We have set the seed to some non-zero number again mentioned below.

  np.random.seed(42)
  regr = LinearRegression()
  regr.fit(x, y)
  
  pred = regr.predict(pred_set)[0]

The above prediction where as always same in one machine, changes when ran from different machine, specifically when we ran it in some CICD test pipeline on cloud environment.

As per my understanding, setting seed to a specific number should confirm the reproducibility, but is there anything I am missing here. Or If I need to set seed in some other way.

Kindly, let me know.
@github-actions github-actions bot added the Needs Triage Issue requires triage label Jul 21, 2022
@thomasjpfan
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There are many factors that can influence how an estimator behaves on different machines. For LinearRegression, can you provide more details such as:

  1. Data you trained on
  2. What the predictions were
  3. How different were the predictions
  4. Details on the machines you ran your script on.
  5. What is the scikit-learn configuration: python -c 'import sklearn; sklearn.show_versions()'
  6. Since LinearRegression uses lstsq, it could be different LAPACKs. To find out run: python -c 'import scipy; scipy.__config__.show()'

@thomasjpfan thomasjpfan added Needs Investigation Issue requires investigation and removed Needs Triage Issue requires triage labels Jul 21, 2022
@jyotirmay123
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jyotirmay123 commented Jul 21, 2022 •
edited by thomasjpfan

Here is details of two different machine I tried to run on: Please Note, one is local machine and another one is from docker container. Please find all the data asked for below.

1: DATA TRAINED ON:
balanceDay and balanceIn are the input and trying to predict some future output for future balanceDay with value in balanceOut.

Data trained on 
 [
    {
      "balanceIn": 0.0,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-21"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-22"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-23"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-24"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-25"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-26"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-27"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-28"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-29"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-30"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-07-31"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-08-01"
    },
    {
      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-08-02"
    },
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      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-08-03"
    },
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      "balanceIn": -3799.2,
      "balanceOut": -3799.2,
      "balanceDay": "2020-08-04"
    },
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      "balanceDay": "2020-08-05"
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      "balanceDay": "2020-08-06"
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      "balanceDay": "2020-08-07"
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      "balanceDay": "2020-08-08"
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Predictions 
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            "balanceOut": 926265.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-18",
            "balanceOut": 892020.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-19",
            "balanceOut": 870928.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-20",
            "balanceOut": 838316.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-21",
            "balanceOut": 830008.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-22",
            "balanceOut": 817232.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-23",
            "balanceOut": 830228.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-24",
            "balanceOut": 840496.6146029874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-25",
            "balanceOut": 875370.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-26",
            "balanceOut": 904028.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-27",
            "balanceOut": 949736.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-28",
            "balanceOut": 984080.0364779874,
            "breachAmount": 0.0
        },
        {
            "balanceDay": "2021-11-29",
            "balanceOut": 1027216.0364779874,
            "breachAmount": 0.0
        }
    ]

3: Predictions are some future value of current fit. Mostly numbers and same as input X.
5: SCIKIT Configuration:
LOCAL MACHINE:

Local Machine 
System:
    python: 3.9.10 (tags/v3.9.10:f2f3f53, Jan 17 2022, 15:14:21) [MSC v.1929 64 bit (AMD64)]  
executable: C:\Users\JyotirmaySenapati\Desktop\Work\ds_rnd\openapi-time-series-forecasting.ds\.venv\Scripts\python.exe
   machine: Windows-10-10.0.19044-SP0

Python dependencies:
      sklearn: 1.1.1
          pip: 22.1.2
   setuptools: 62.6.0
        numpy: 1.22.3
        scipy: 1.8.1
       Cython: 0.29.30
       pandas: 1.4.2
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: vcomp
       filepath: C:\Users\JyotirmaySenapati\Desktop\Work\ds_rnd\openapi-time-series-forecasting.ds\.venv\Lib\site-packages\sklearn\.libs\vcomp140.dll
        version: None
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: C:\Users\JyotirmaySenapati\Desktop\Work\ds_rnd\openapi-time-series-forecasting.ds\.venv\Lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
        version: 0.3.18
threading_layer: pthreads
   architecture: Haswell
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: C:\Users\JyotirmaySenapati\Desktop\Work\ds_rnd\openapi-time-series-forecasting.ds\.venv\Lib\site-packages\scipy\.libs\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll
        version: 0.3.17
threading_layer: pthreads
   architecture: Haswell
    num_threads: 8
Docker 
    python: 3.9.13 (main, Jun 23 2022, 11:33:37)  [GCC 8.3.0]
executable: /usr/local/bin/python
   machine: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.28

Python dependencies:
      sklearn: 1.1.1
          pip: 22.0.4
   setuptools: 62.6.0
        numpy: 1.22.3
        scipy: 1.8.1
       Cython: 0.29.30
       pandas: 1.4.2
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /usr/local/lib/python3.9/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /usr/local/lib/python3.9/site-packages/numpy.libs/libopenblas64_p-r0-2f7c42d4.3.18.so
        version: 0.3.18
threading_layer: pthreads
   architecture: Haswell
    num_threads: 8

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /usr/local/lib/python3.9/site-packages/scipy.libs/libopenblasp-r0-8b9e111f.3.17.so
        version: 0.3.17
threading_layer: pthreads
   architecture: Haswell
    num_threads: 8

6: SciPy config

Local Machine 
lapack_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
    library_dirs = ['C:\\projects\\scipy-wheels\\scipy\\build\\openblas_lapack_info']
    libraries = ['openblas_lapack_info']
    language = f77
    define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
    library_dirs = ['C:\\projects\\scipy-wheels\\scipy\\build\\openblas_lapack_info']
    libraries = ['openblas_lapack_info']
    language = f77
    define_macros = [('HAVE_CBLAS', None)]
blas_mkl_info:
  NOT AVAILABLE
blis_info:
  NOT AVAILABLE
openblas_info:
    library_dirs = ['C:\\projects\\scipy-wheels\\scipy\\build\\openblas_info']       
    libraries = ['openblas_info']
    language = f77
    define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
    library_dirs = ['C:\\projects\\scipy-wheels\\scipy\\build\\openblas_info']       
    libraries = ['openblas_info']
    language = f77
    define_macros = [('HAVE_CBLAS', None)]
Docker

DOCKER:

lapack_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
    runtime_library_dirs = ['/usr/local/lib']
lapack_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
    runtime_library_dirs = ['/usr/local/lib']
blas_mkl_info:
  NOT AVAILABLE
blis_info:
  NOT AVAILABLE
openblas_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
    runtime_library_dirs = ['/usr/local/lib']
blas_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/local/lib']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
    runtime_library_dirs = ['/usr/local/lib']

@thomasjpfan
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thomasjpfan commented Jul 21, 2022 •
edited

  1. Can you show the predictions locally and the docker machines as a NumPy array? Specifically, something like this:
# local machine predictions
[959.03, 842.213, 3213, ...]

# docker predictions
[949.03, 843.213, 32132, ...]

I want to see how different the predictions are. If there is only a slightly difference, than it is okay. It could be that different lapacks give results. (The SciPy config for lapack_opt_info is different for you locally vs on docker.)

  1. What are the model's coefs on different machines?
regr = LinearRegression()
regr.fit(x, y)
print(regr.coef_)

@thomasjpfan
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thomasjpfan commented Jul 21, 2022 •
edited

When I ask for the training data, I was referring to the NumPy array passed in. scikit-learn's repo has many issues to look through and anything you can do to make it easier for maintainers to understand your issue would be helpful.

Specifically, I am trying to see if this a bug in scikit-learn, another library, user error, or an expected behavior.

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