Error in py_call_impl(callable, dots$args, dots$keywords): AttributeError: Module 'scipy' has no attribute 'random'

[royfrancis]

I have installed a conda env with mofapy.

> reticulate::py_config()
python:         /home/roy/miniconda3/envs/mofa/bin/python
libpython:      /home/roy/miniconda3/envs/mofa/lib/libpython3.10.so
pythonhome:     /home/roy/miniconda3/envs/mofa:/home/roy/miniconda3/envs/mofa
version:        3.10.6 (main, Oct 24 2022, 16:07:47) [GCC 11.2.0]
numpy:          /home/roy/miniconda3/envs/mofa/lib/python3.10/site-packages/numpy
numpy_version:  1.23.4
mofapy2:        /home/roy/miniconda3/envs/mofa/lib/python3.10/site-packages/mofapy2

But, I can't get run_mofa to work.

run_mofa(mo,outfile,use_basilisk=FALSE)
Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)... 
    Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
    If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'

Warning: The latest mofapy2 version is 0.6.4, you are using 0.6.7. Please upgrade with 'pip install mofapy2'Warning: mofapy2_0.6.4 is not detected in the specified python binary, see reticulate::py_config(). Setting use_basilisk = TRUE...Connecting to the mofapy2 package using basilisk. 

Then , I reinstalled mofapy to an older version: 0.6.4. This worked a little bit more and crashed at this error:

Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)... 
    Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
    If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'

        #########################################################
        ###           __  __  ____  ______                    ### 
        ###          |  \/  |/ __ \|  ____/\    _             ### 
        ###          | \  / | |  | | |__ /  \ _| |_           ### 
        ###          | |\/| | |  | |  __/ /\ \_   _|          ###
        ###          | |  | | |__| | | / ____ \|_|            ###
        ###          |_|  |_|\____/|_|/_/    \_\              ###
        ###                                                   ### 
        ######################################################### 

Successfully loaded view='view_1' group='group1' with N=200 samples and D=1000 features...
Successfully loaded view='view_2' group='group1' with N=200 samples and D=1000 features...

Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (view_1): gaussian
- View 1 (view_2): gaussian

 Error in py_call_impl(callable, dots$args, dots$keywords) : 
AttributeError: Module 'scipy' has no attribute 'random'

Here is a complete reprex:

library(MOFA2)
#> 
#> Attaching package: 'MOFA2'
#> The following object is masked from 'package:stats':
#> 
#>     predict

reticulate::use_condaenv("mofa")

data <- make_example_data(
  n_views = 2, 
  n_samples = 200, 
  n_features = 1000, 
  n_factors = 10
)[[1]]

m <- create_mofa(data)
#> Creating MOFA object from a list of matrices (features as rows, sample as columns)...
data_opts <- get_default_data_options(m)
model_opts <- get_default_model_options(m)
train_opts <- get_default_training_options(m)

mo <- prepare_mofa(
  object = m,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
)
#> Checking data options...
#> Checking training options...
#> Checking model options...

outfile <- file.path(getwd(),"mofa.hdf5")
mot <- run_mofa(mo,outfile,use_basilisk=FALSE)
#> Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)... 
#>     Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
#>     If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'
#> Error in py_call_impl(callable, dots$args, dots$keywords): AttributeError: Module 'scipy' has no attribute 'random'

^{Created on 2022-10-26 with reprex v2.0.2}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.1.0 (2021-05-18) #> os Ubuntu 20.04.5 LTS #> system x86_64, linux-gnu #> ui X11 #> language en_GB:en #> collate en_GB.UTF-8 #> ctype en_GB.UTF-8 #> date 2022-10-26 #> pandoc 2.18 @ /usr/lib/rstudio/bin/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0) #> basilisk 1.4.0 2021-05-19 [1] Bioconductor #> basilisk.utils 1.4.0 2021-05-19 [1] Bioconductor #> BiocGenerics 0.40.0 2021-10-26 [1] Bioconductor #> cli 3.4.1 2022-09-23 [1] CRAN (R 4.1.0) #> colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.0) #> corrplot 0.92 2021-11-18 [1] CRAN (R 4.1.0) #> cowplot 1.1.1 2020-12-30 [1] CRAN (R 4.1.0) #> DBI 1.1.3 2022-06-18 [1] CRAN (R 4.1.0) #> DelayedArray 0.20.0 2021-10-26 [1] Bioconductor #> digest 0.6.30 2022-10-18 [1] CRAN (R 4.1.0) #> dir.expiry 1.0.0 2021-05-19 [1] Bioconductor #> dplyr 1.0.10 2022-09-01 [1] CRAN (R 4.1.0) #> evaluate 0.17 2022-10-07 [1] CRAN (R 4.1.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.1.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.0) #> filelock 1.0.2 2018-10-05 [1] CRAN (R 4.1.0) #> forcats 0.5.2 2022-08-19 [1] CRAN (R 4.1.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.1.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.1.0) #> ggplot2 3.3.6 2022-05-03 [1] CRAN (R 4.1.0) #> ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.1.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.0) #> gtable 0.3.1 2022-09-01 [1] CRAN (R 4.1.0) #> HDF5Array 1.22.1 2021-11-14 [1] Bioconductor #> here 1.0.1 2020-12-13 [1] CRAN (R 4.1.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.1.0) #> IRanges 2.28.0 2021-10-26 [1] Bioconductor #> jsonlite 1.8.3 2022-10-21 [1] CRAN (R 4.1.0) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.1.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.1.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.1.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.1.0) #> Matrix 1.5-1 2022-09-13 [1] CRAN (R 4.1.0) #> MatrixGenerics 1.6.0 2021-10-26 [1] Bioconductor #> matrixStats 0.62.0 2022-04-19 [1] CRAN (R 4.1.0) #> MOFA2 * 1.4.0 2021-10-26 [1] Bioconductor #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0) #> pheatmap 1.0.12 2019-01-04 [1] CRAN (R 4.1.0) #> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.1.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0) #> plyr 1.8.7 2022-03-24 [1] CRAN (R 4.1.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0) #> purrr 0.3.5 2022-10-06 [1] CRAN (R 4.1.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.1.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.1.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.1.0) #> R.utils 2.12.0 2022-06-28 [1] CRAN (R 4.1.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0) #> rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.1.0) #> RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.1.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.1.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.1.0) #> reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.1.0) #> reticulate 1.26 2022-08-31 [1] CRAN (R 4.1.0) #> rhdf5 2.38.1 2022-03-10 [1] Bioconductor #> rhdf5filters 1.6.0 2021-10-26 [1] Bioconductor #> Rhdf5lib 1.16.0 2021-10-26 [1] Bioconductor #> rlang 1.0.6 2022-09-24 [1] CRAN (R 4.1.0) #> rmarkdown 2.17 2022-10-07 [1] CRAN (R 4.1.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.1.0) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.1.0) #> Rtsne 0.16 2022-04-17 [1] CRAN (R 4.1.0) #> S4Vectors 0.32.4 2022-03-24 [1] Bioconductor #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.1.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0) #> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.1.0) #> stringr 1.4.1 2022-08-20 [1] CRAN (R 4.1.0) #> styler 1.8.0 2022-10-22 [1] CRAN (R 4.1.0) #> tibble 3.1.8 2022-07-22 [1] CRAN (R 4.1.0) #> tidyr 1.2.1 2022-09-08 [1] CRAN (R 4.1.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.1.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0) #> uwot 0.1.14 2022-08-22 [1] CRAN (R 4.1.0) #> vctrs 0.5.0 2022-10-22 [1] CRAN (R 4.1.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.0) #> xfun 0.34 2022-10-18 [1] CRAN (R 4.1.0) #> yaml 2.3.6 2022-10-18 [1] CRAN (R 4.1.0) #> #> [1] /home/roy/miniconda3/envs/r-4.1/lib/R/library #> #> ─ Python configuration ─────────────────────────────────────────────────────── #> python: /home/roy/miniconda3/envs/mofa/bin/python #> libpython: /home/roy/miniconda3/envs/mofa/lib/libpython3.10.so #> pythonhome: /home/roy/miniconda3/envs/mofa:/home/roy/miniconda3/envs/mofa #> version: 3.10.6 (main, Oct 24 2022, 16:07:47) [GCC 11.2.0] #> numpy: /home/roy/miniconda3/envs/mofa/lib/python3.10/site-packages/numpy #> numpy_version: 1.23.4 #> mofapy2: /home/roy/miniconda3/envs/mofa/lib/python3.10/site-packages/mofapy2 #> #> NOTE: Python version was forced by use_python function #> #> ────────────────────────────────────────────────────────────────────────────── ```

[rargelaguet]

Hi @royfrancis, can you describe how did you install the MOFA2 R pacakge and the mofapy2 package? The latest stable version of MOFA2 in Bioconductor 3.14 should be 1.6.0 (https://bioconductor.org/packages/3.14/bioc/html/MOFA2.html). I noticed that you have 1.4.0. Maybe this is the issue?

Also, if you have issues with the R/python connection I would suggest trying the basilisk function which will install the conda environment from scratch

mot <- run_mofa(mo,outfile, use_basilisk=TRUE)

[royfrancis]

Hi @rargelaguet, yes, it works when using use_basilisk=TRUE.

But anyway, just to describe my setup, I have MOFA2 (installed from Bioconductor) in a conda env which I use for all R stuff. And I installed mofapy2 and all it's dependencies as a separate conda env.

[pettyalex]

This specific error Module 'scipy' has no attribute 'random' is likely happening because older releases of scipy used to export numpy functions, but scipy no longer does.

You've installed a newer version of scipy than MOFA2 can work with, so the recommendations here are to install an older, compatible version of scipy.

[rargelaguet]

@royfrancis and @pettyalex could you post the list of python packages in your environment? (particularly for scipy). A few months ago I removed all the numpy functions called via scipy. MOFA should now work with the latest version of scipy.

[royfrancis]

Here is a list of all packages in the conda env.

conda list

``` # Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 1_gnu conda-forge _r-mutex 1.0.1 anacondar_1 conda-forge abseil-cpp 20210324.2 h9c3ff4c_0 conda-forge absl-py 0.15.0 pyhd8ed1ab_0 conda-forge aiohttp 3.8.1 py39h3811e60_0 conda-forge aiosignal 1.2.0 pyhd8ed1ab_0 conda-forge aniso8601 9.0.1 pypi_0 pypi anndata 0.8.0 pypi_0 pypi annoy 1.17.0 pypi_0 pypi argon2-cffi 20.1.0 py39he8ac12f_1 arpack 3.7.0 hdefa2d7_2 conda-forge asttokens 2.0.5 pyhd3eb1b0_0 astunparse 1.6.3 pyhd8ed1ab_0 conda-forge async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge async_generator 1.10 pyhd3eb1b0_0 atk-1.0 2.36.0 h3371d22_4 conda-forge attrs 21.2.0 pyhd3eb1b0_0 backcall 0.2.0 pyhd3eb1b0_0 binutils_impl_linux-64 2.35.1 h193b22a_2 conda-forge binutils_linux-64 2.35 h67ddf6f_30 conda-forge bioconductor-biocgenerics 0.40.0 r41hdfd78af_0 bioconda bioconductor-s4vectors 0.32.3 r41h5c21468_0 bioconda bleach 3.3.0 pyhd3eb1b0_0 blinker 1.4 py_1 conda-forge boost-cpp 1.74.0 h312852a_4 conda-forge boto3 1.24.54 pypi_0 pypi botocore 1.27.54 pypi_0 pypi brotli 1.0.9 pypi_0 pypi brotlipy 0.7.0 py39h3811e60_1003 conda-forge bwidget 1.9.14 0 conda-forge bzip2 1.0.8 h7b6447c_0 c-ares 1.18.1 h7f98852_0 conda-forge ca-certificates 2022.10.11 h06a4308_0 cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cachetools 5.0.0 pyhd8ed1ab_0 conda-forge cairo 1.16.0 hf32fb01_1 cellxgene 1.0.1 pypi_0 pypi certifi 2022.9.24 py39h06a4308_0 cffi 1.14.5 py39h261ae71_0 cfitsio 3.470 hb418390_7 conda-forge charset-normalizer 2.0.12 pyhd8ed1ab_0 conda-forge click 8.0.4 py39hf3d152e_0 conda-forge cryptography 36.0.2 py39hd97740a_0 conda-forge cudatoolkit 11.1.1 h6406543_10 conda-forge cudnn 8.2.1.32 h86fa8c9_0 conda-forge curl 7.82.0 h7bff187_0 conda-forge cycler 0.11.0 pyhd8ed1ab_0 conda-forge decorator 5.0.9 pyhd3eb1b0_0 defusedxml 0.7.1 pyhd3eb1b0_0 entrypoints 0.3 py39h06a4308_0 executing 0.8.3 pyhd3eb1b0_0 expat 2.4.1 h9c3ff4c_0 conda-forge fbpca 1.0 pypi_0 pypi fftw 3.3.9 nompi_h74d3f13_101 conda-forge flask 2.0.3 pypi_0 pypi flask-compress 1.12 pypi_0 pypi flask-cors 3.0.10 pypi_0 pypi flask-restful 0.3.9 pypi_0 pypi flask-server-timing 0.1.2 pypi_0 pypi flask-talisman 1.0.0 pypi_0 pypi flatten-dict 0.4.2 pypi_0 pypi font-ttf-dejavu-sans-mono 2.37 h6964260_0 font-ttf-inconsolata 3.000 h77eed37_0 conda-forge font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge font-ttf-ubuntu 0.83 h8b1ccd4_0 fontconfig 2.13.1 hba837de_1005 conda-forge fonts-conda-ecosystem 1 0 conda-forge fonts-conda-forge 1 0 conda-forge freetype 2.10.4 h5ab3b9f_0 freexl 1.0.6 h7f98852_0 conda-forge fribidi 1.0.10 h7b6447c_0 frozenlist 1.3.0 py39h3811e60_0 conda-forge fsspec 0.7.4 pypi_0 pypi gast 0.4.0 pyh9f0ad1d_0 conda-forge gcc_impl_linux-64 9.3.0 h70c0ae5_19 conda-forge gcc_linux-64 9.3.0 hf25ea35_30 conda-forge gdk-pixbuf 2.42.6 h04a7f16_0 conda-forge geos 3.9.1 h9c3ff4c_2 conda-forge geosketch 1.2 pypi_0 pypi geotiff 1.6.0 h4f31c25_6 conda-forge gettext 0.21.0 hf68c758_0 gfortran_impl_linux-64 9.3.0 hc4a2995_19 conda-forge gfortran_linux-64 9.3.0 hdc58fab_30 conda-forge ghostscript 9.54.0 h9c3ff4c_1 conda-forge giflib 5.2.1 h36c2ea0_2 conda-forge glib 2.68.4 h9c3ff4c_0 conda-forge glib-tools 2.68.4 h9c3ff4c_0 conda-forge glpk 4.65 h9202a9a_1004 conda-forge gmp 6.2.1 h58526e2_0 conda-forge google-auth 2.6.2 pyh6c4a22f_0 conda-forge google-auth-oauthlib 0.4.6 pyhd8ed1ab_0 conda-forge google-pasta 0.2.0 pyh8c360ce_0 conda-forge graphite2 1.3.14 h23475e2_0 graphviz 2.49.0 h85b4f2f_0 conda-forge grpc-cpp 1.39.1 h850795e_1 conda-forge grpcio 1.39.0 py39hff7568b_0 conda-forge gsl 2.6 he838d99_2 conda-forge gstreamer-orc 0.4.32 h7f98852_1 conda-forge gtk2 2.24.33 h539f30e_1 conda-forge gts 0.7.6 h64030ff_2 conda-forge gunicorn 20.1.0 pypi_0 pypi gxx_impl_linux-64 9.3.0 hd87eabc_19 conda-forge gxx_linux-64 9.3.0 h3fbe746_30 conda-forge h5py 3.1.0 nompi_py39h25020de_100 conda-forge harfbuzz 2.9.1 h83ec7ef_0 conda-forge hdf4 4.2.15 h10796ff_3 conda-forge hdf5 1.10.6 nompi_h6a2412b_1114 conda-forge icu 68.2 h9c3ff4c_0 conda-forge idna 3.3 pyhd8ed1ab_0 conda-forge igraph 0.9.7 hf5496dd_0 conda-forge imagemagick 7.1.0_7 pl5321hb118871_0 conda-forge importlib-metadata 4.4.0 py39hf3d152e_0 conda-forge importlib_metadata 4.4.0 hd8ed1ab_0 conda-forge intervaltree 3.1.0 pypi_0 pypi ipykernel 5.5.5 py39hef51801_0 conda-forge ipython 8.1.1 py39h06a4308_0 ipython_genutils 0.2.0 pyhd3eb1b0_1 itsdangerous 2.1.1 pypi_0 pypi jbig 2.1 hdba287a_0 jedi 0.18.0 py39h06a4308_1 jinja2 3.0.1 pyhd8ed1ab_0 conda-forge jmespath 1.0.1 pypi_0 pypi joblib 1.1.0 pypi_0 pypi jpeg 9d h516909a_0 conda-forge jq 1.6 h36c2ea0_1000 conda-forge json-c 0.15 h98cffda_0 conda-forge jsonschema 3.2.0 py_2 jupyter_client 6.1.12 pyhd3eb1b0_0 jupyter_core 4.7.1 py39h06a4308_0 jupyterlab_pygments 0.1.2 py_0 kealib 1.4.14 hcc255d8_2 conda-forge keras 2.6.0 pyhd8ed1ab_1 conda-forge keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge kernel-headers_linux-64 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conda-forge udunits2 2.2.27.27 h975c496_1 conda-forge umap-learn 0.5.3 pypi_0 pypi urllib3 1.26.9 pyhd8ed1ab_0 conda-forge wcwidth 0.2.5 py_0 webencodings 0.5.1 py39h06a4308_1 werkzeug 2.0.3 pyhd8ed1ab_1 conda-forge wheel 0.36.2 pyhd3eb1b0_0 wrapt 1.12.1 py39h3811e60_3 conda-forge xerces-c 3.2.3 h9d8b166_2 conda-forge xorg-kbproto 1.0.7 h14c3975_1002 conda-forge xorg-libice 1.0.10 h516909a_0 conda-forge xorg-libsm 1.2.3 hd9c2040_1000 conda-forge xorg-libx11 1.7.1 h7f98852_0 conda-forge xorg-libxext 1.3.4 h7f98852_1 conda-forge xorg-libxrender 0.9.10 h7f98852_1003 conda-forge xorg-libxt 1.2.1 h7f98852_2 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h27cfd23_1007 xz 5.2.5 h7b6447c_0 yarl 1.7.2 py39h3811e60_1 conda-forge zeromq 4.3.4 h2531618_0 zipp 3.4.1 pyhd3eb1b0_0 zlib 1.2.11 h36c2ea0_1013 conda-forge zstd 1.5.0 ha95c52a_0 conda-forge ```

[byeongchangsim]

Try checking your version of python scipy I reinstalled to 1.7 and that fixed the error.

[psgit2022]

@byeongchangsim - did you use the scipy version 1.7.0 ?

[byeongchangsim]

That's right.

2023년 3월 13일 (월) 오후 10:38, Paddy @.***>님이 작성:

@byeongchangsim https://github.com/byeongchangsim - did you use the scipy version 1.7.0 ?

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