Molecular dataset formatting

EasternCaveMan commented 1 year ago

Something goes wrong using the data and code below. According to the docs the format should be right for a molecular dataset?

Code

mydata = DataSet(datasail.reader.utils.read_csv('output2500.tsv'))
run_ecfp(mydata)

Data

molecule_chembl_id  canonical_smiles    IC50
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 41.0
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 300.0
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 7820.0

Output

ValueError: ECFP with Tanimoto-scores can only be applied to molecular data.

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

I see the point of this not working. In line 26 of ecfp.py I check if the dataset has been labeled as a molecular dataset, which is not the case if you create it the way, you do.

I'd suggest to use

from datasail.reader.read_molecules import read_molecule_data
from datasail.cluster.ecfp import run_ecfp

mydata = read_molecule_data('output2500.tsv')
names, cluster_map, sim_matrix = run_ecfp(mydata)

Please let me know if it works for you or if you encountered any other issues.

Best, Roman

EasternCaveMan commented 1 year ago

I followed the structure you mentioned :

mydata = read_molecule_data('output2500.tsv')
names, cluster_map, sim_matrix = run_ecfp(mydata)

I got this error : output

TypeError: read_molecule_data() missing 8 required positional arguments: 'weights', 'sim', 'dist', 'max_sim', 'max_dist', 'id_map', 'inter', and 'index'

which doesn't make sense to me, because on the package website (https://datasail.readthedocs.io/en/latest/workflow/input.html) it is said that for SMILES: A TSV file with the molecule’s ID in the first column and a SMILES string in the second column. Further columns will be ignored.

I dont have the following arguments: 'weights', 'sim', 'dist'. do you mean by 'weights' the molecular weights? and about 'sim', and 'dist' do I have to by myself write functions to calculate them? if yes what should be the format of 'sim' and 'dis' files? I didn't understand this part too --> 'dist: Distance file or metric'. instead of Distance file i can use metric, what do you mean by metric? does it mean I can use the key function to calculate the Distance file? I really appreciate your help, I am a master's student at Saarland University, and your package was introduced to me by my supervisor, it has all functions I need to do my master's seminar.

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

First of all, I'm happy to help you with any questions you have. Second, the package is still actively developed, therefore, it might be a little buggy/confusing sometimes, and the documentation is far from complete. Third, I'm a Ph.D. student in Saarbruecken as well and know Michael ;-)

Answering your question: You can set every argument to None, which should solve the issues. The reason for this "bug" is that DataSAIL was first implemented as a command-line tool and it was not intended to be "hacked" the way you do. But that's fine. I'm currently working on making DataSAIL conda-installable and therefore will address these things.

In general, maybe this docu-page might be helpful for you. With

dist: Distance file or metric

I mean you either provide a CSV-file storing a similarity-matrix or distance-matrix of your data points of a metric's name to be used for this. For molecular data, only "ECFP" is available.

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand , I hope that you are doing well, Thank you so much for your kind words. I have followed the structure which you mentioned: Input : input2500.tsv

molecule_chembl_id  canonical_smiles    IC50
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 41.0
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 300.0
CHEMBL68920 Cc1cc(C)c(/C=C2\C(=O)Nc3ncnc(Nc4ccc(F)c(Cl)c4)c32)[nH]1 7820.0

code

from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from datasail.reader.read_molecules import read_molecule_data
from datasail.cluster.ecfp import run_ecfp
mydata = read_molecule_data('input2500.tsv', weights=None, sim=None, dist=None, max_sim=None, max_dist=None, id_map=None, inter=None,index=None)
names, cluster_map, sim_matrix = run_ecfp(mydata)

here is the following error:

AttributeError: 'tuple' object has no attribute 'type'

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

I'm sorry to hear that you still encounter issues. Could you please provide the full stack trace of the output to that I can see in which line the code broke? Furthermore, there has been a lot going on in the code of DataSAIL. Maybe, you can update your version to the latest state of the repo or install it from conda: https://datasail.readthedocs.io/en/latest/index.html

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand , Thanks for your quick response. Yes, actually, I have realized that there have been a lot of changes since last week.

here is the full stack trace of the output:

Traceback (most recent call last):
  File "/Users/vahidatabaigi/anaconda3/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3460, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-2-09fea9123334>", line 1, in <module>
    runfile('/Users/vahidatabaigi/PycharmProjects/test.py', wdir='/Users/vahidatabaigi/PycharmProjects')
  File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_bundle/pydev_umd.py", line 198, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/Users/vahidatabaigi/PycharmProjects/test.py", line 7, in <module>
    names, cluster_map, sim_matrix = run_ecfp(mydata)
  File "/Users/vahidatabaigi/anaconda3/lib/python3.10/site-packages/datasail/cluster/ecfp.py", line 26, in run_ecfp
    if dataset.type != "M":
AttributeError: 'tuple' object has no attribute 'type'

Old-Shatterhand commented 1 year ago

The issue is that read_molecule_data returns a tuple of the dataset and a list of interactions (which is not interesting in your case). If you change the code to

from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from datasail.reader.read_molecules import read_molecule_data
from datasail.cluster.ecfp import run_ecfp

mydata, _ = read_molecule_data('input2500.tsv', weights=None, sim=None, dist=None, max_sim=None, max_dist=None, id_map=None, inter=None,index=None)
names, cluster_map, sim_matrix = run_ecfp(mydata)

it should work.

EasternCaveMan commented 1 year ago

thank you so much ☺️ 😊 , it worked

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand, I hope you are doing well, I had a problem with the installation of the package. I tried to install it through CLI, but every time I got this error:

(base) vahidatabaigi@vahids-MacBook-Pro ~ % conda install -c kalininalab -c conda-forge -c mosek datasail
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/kalininalab/osx-arm64
  - https://conda.anaconda.org/kalininalab/noarch
  - https://conda.anaconda.org/conda-forge/osx-arm64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/mosek/osx-arm64
  - https://conda.anaconda.org/mosek/noarch
  - https://repo.anaconda.com/pkgs/main/osx-arm64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/osx-arm64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.

to solve this problem I downloaded the package and went to the directory in which the file setup.py exist, and ran the following code:

pip install .

so that I was able to install and import the package in Pycharm. if you remember last time I was able to run the following code:

from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from datasail.reader.read_molecules import read_molecule_data
from datasail.cluster.ecfp import run_ecfp

mydata, _ = read_molecule_data('input2500.tsv', weights=None, sim=None, dist=None, max_sim=None, max_dist=None, id_map=None, inter=None,index=None)
names, cluster_map, sim_matrix = run_ecfp(mydata)

In the next step, I want to split the data based on clusters into training and test sets, I have tried to import a function from the package to do this, but I was not able to find one. however, in the package's document, you mentioned how to split the data through CLI: I did use the following code to split data in CLI

python sail --e-type M --e-data input2500.tsv --e-sim ecfp --output PycharmProjects/CADDSeminar_2023/notebooks/T001_Scaffold-based-data-split --technique CCS --splits 0.8 0.2

but I got this error:

(base) vahidatabaigi@vahids-MacBook-Pro ~ % python sail --e-type M --e-data input2500.tsv --e-sim ecfp --output PycharmProjects/CADDSeminar_2023/notebooks/T001_Scaffold-based-data-split --technique CCS --splits 0.8 0.2
python: can't open file '/Users/vahidatabaigi/sail': [Errno 2] No such file or directory

I am sorry if the message is too long, but I thought it would be better to explain everything to give all information. have a good day

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

I appreciate your patience with DataSAIL. Thank you for giving me that much information about the problem. That helps a lot.

Regarding the installation: Please try conda install -c kalininalab -c conda-forge -c mosek -c bioconda datasail
Regarding the execution: When running the CLI interface of datasail, you don't need to prepend python. Please try datasail -h

Furthermore, I advise installing datasail into a separate conda environment.
Regarding importing stuff form datasail: I've tested the import statement in a conda environment with datasail installed and it worked fine. Here'r what I tried:
```
import datasail
form datasail import reader
from datasail.reader.read_molecules import read_molecular_data
```

I hope I could help to fix your problems. If not, please let me know.

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand, sorry for bothering you again, I have tried to install the dataSAIL as you said. In order to minimize the potential for conflicts with other packages, I uninstalled Anaconda and installed Miniconda instead. Additionally, I created a separate conda environment specifically for dataSAIL. Despite these efforts, I encountered the same error as before. I even tried installing dataSAIL on different computers with various operating systems, but unfortunately, the issue persisted. The error I received on Windows was identical to the one I encountered on macOS.

(base) C:\Users\Ali>conda install -c kalininalab -c conda-forge -c mosek -c bioconda datasail
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/kalininalab/win-64
  - https://conda.anaconda.org/kalininalab/noarch
  - https://conda.anaconda.org/conda-forge/win-64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/mosek/win-64
  - https://conda.anaconda.org/mosek/noarch
  - https://conda.anaconda.org/bioconda/win-64
  - https://conda.anaconda.org/bioconda/noarch
  - https://repo.anaconda.com/pkgs/main/win-64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/win-64
  - https://repo.anaconda.com/pkgs/r/noarch
  - https://repo.anaconda.com/pkgs/msys2/win-64
  - https://repo.anaconda.com/pkgs/msys2/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org/

and use the search bar at the top of the page.

I believe there may have been a misunderstanding in our previous conversation. As I mentioned before, I am able to install your package locally by executing the setup.py file using the following code:

(base) vahidatabaigi@vahids-MBP DataSAIL-main % ls
LICENSE     build.sh    datasail    environment.yml pytest.ini  tests
README.md   conftest.py docs        meta.yaml   setup.py
(base) vahidatabaigi@vahids-MBP DataSAIL-main % pip install .
Processing /Users/vahidatabaigi/Downloads/DataSAIL-main
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: DataSAIL
  Building wheel for DataSAIL (setup.py) ... done
  Created wheel for DataSAIL: filename=DataSAIL-0.0.10-py3-none-any.whl size=71719 sha256=db087a38e28034f85139e6b8bd1c51ad8d6e804403c526ec690c5ec62eab68e8
  Stored in directory: /Users/vahidatabaigi/Library/Caches/pip/wheels/4b/d8/f8/9f443bb8564b7e70617c7eb219ad154fe56845435678f3b890
Successfully built DataSAIL
Installing collected packages: DataSAIL
  Attempting uninstall: DataSAIL
    Found existing installation: DataSAIL 0.0.10
    Uninstalling DataSAIL-0.0.10:
      Successfully uninstalled DataSAIL-0.0.10
Successfully installed DataSAIL-0.0.10

However, I encountered an error when attempting to split my data. The specific error message I received is:

(base) vahidatabaigi@vahids-MBP DataSAIL-main % sail --e-type M --e-data input2500.tsv --e-sim ecfp --output /Users/vahidatabaigi/PycharmProjects/CADDSeminar_2023/notebooks --technique CCS --splits 0.8 0.2
zsh: command not found: sail

However, although I am able to import dataSAIL successfully in PyCharm, I am unsure which specific function or module I should import in order to correctly split the data after clustering. Here is the code I have been using for clustering my data. Additionally, I would like to have the ability to handle the threshold for clustering. but I don't know how to do that:

from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
from datasail.reader.read_molecules import read_molecule_data
from datasail.cluster.ecfp import run_ecfp

mydata, _ = read_molecule_data('input2500.tsv', weights=None, sim=None, dist=None, max_sim=None, max_dist=None, id_map=None, inter=None,index=None)
names, cluster_map, sim_matrix = run_ecfp(mydata)

kind regards, Vahid

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

no worries, I'm happy to fix any bug/problem you find. Let's go through this step by step:

Conda installation

Apparently, this does not work for me either. I guess the conda dependency-solver is not powerful enough to build the environment. Here, I'd suggest using mamba instead. Even though, they say, to not install it in conda's base environment, I had no issues with this so far. Then, you can simply run

mamba install -c kalininalab -c conda-forge -c mosek -c bioconda datasail
pip install grakel

Setup.py installation

This is not a supported way of installing DataSAIL so far. It technically runs without errors, but no dependencies are installed. (And as you've encountered, the command line tool is also not available). I'll maybe add this functionality later, but so far, the setup.py is just an artifact, left over from the very early stages of the project.

Clustering

There is no real clustering happening in the run_ecfp method. If out check it out (code and docu), it comprises three steps

Compute Murcko scaffolds
Compute 1024-bit ECFPs
Compute pairwise Tanimoto coefficients

The aggregation of data points occurs for molecules with the same Murcko scaffold. DataSAIL then just computes a similarity matrix for all scaffolds. There is no threshold to be controlled.

Splitting

In order to split the data, you have to run run_solver (code). But this will not work! As you might have 2500 clusters (judging from your input file's name), the constraint optimization problem becomes way too big (I tested that and it exceeds 1TB of CPU memory). The easiest way is to use DataSAIL in the way, it has been designed for. Just call

from datasail.sail import datasail

_, splits, _ = datasail(techniques=["CCSe"], e_type="M", e_data="input2500.tsv")

The reason why you cannot just run the splitting after the ECFP clustering is that ECFP returns 2500 clusters in your case (judging from the input file's name). Internally, DataSAIL runs affinity propagation (sklearn-clustering) on the ECFP clusters to actually introduce clusters. This method cannot be easily extracted from DataSAIL as you did with reading the molecules and clustering them by yourself.

I hope, I could help you with this. If not, or if you encounter any other issues, please let me know.

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand, I install the mamba and then run your script. Thank you for your patience with me. Here is the error I encountered. I think it is the same as the previous error

(py310_caddseminar2023) vahidatabaigi@vahids-MBP CADDSeminar_2023 % conda install mamba
Collecting package metadata (current_repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
  current version: 23.3.1
  latest version: 23.5.0

Please update conda by running

    $ conda update -n base -c defaults conda

Or to minimize the number of packages updated during conda update use

     conda install conda=23.5.0

## Package Plan ##

  environment location: /Users/vahidatabaigi/miniconda3/envs/py310_caddseminar2023

  added / updated specs:
    - mamba

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
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    certifi-2023.5.7           |  py310hca03da5_0         153 KB
    conda-22.11.1              |  py310hca03da5_5         957 KB
    conda-package-handling-2.1.0|  py310hca03da5_0         270 KB
    conda-package-streaming-0.8.0|  py310hca03da5_0          29 KB
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    yaml-cpp-0.7.0             |       hc377ac9_1         427 KB
    ------------------------------------------------------------
                                           Total:         8.7 MB

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  zstandard          pkgs/main/osx-arm64::zstandard-0.19.0-py310h80987f9_0

The following packages will be UPDATED:

  ca-certificates    conda-forge::ca-certificates-2023.5.7~ --> pkgs/main::ca-certificates-2023.05.30-hca03da5_0

The following packages will be SUPERSEDED by a higher-priority channel:

  certifi            conda-forge/noarch::certifi-2023.5.7-~ --> pkgs/main/osx-arm64::certifi-2023.5.7-py310hca03da5_0

Proceed ([y]/n)? y

Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(py310_caddseminar2023) vahidatabaigi@vahids-MBP CADDSeminar_2023 % which mamba
/Users/vahidatabaigi/miniconda3/envs/py310_caddseminar2023/bin/mamba
(py310_caddseminar2023) vahidatabaigi@vahids-MBP CADDSeminar_2023 % mamba install -c kalininalab -c conda-forge -c mosek -c bioconda datasail

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                 /  \  /  \  /  \  /  \
                /    \/    \/    \/    \
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              /  / \   / \   / \   / \  \____
             /  /   \_/   \_/   \_/   \    o \__,
            / _/                       \_____/  `
            |/
        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗
        ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗
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        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║
        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝

        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
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█████████████████████████████████████████████████████████████

Looking for: ['datasail']

mosek/osx-arm64                                      4.2kB @   8.7kB/s  0.5s
pkgs/r/osx-arm64                                   118.0 B @ 167.0 B/s  0.2s
kalininalab/noarch                                 135.0 B @ 173.0 B/s  0.8s
kalininalab/osx-arm64                              136.0 B @ 142.0 B/s  1.0s
mosek/noarch                                       135.0 B @ 116.0 B/s  0.2s
pkgs/main/noarch                                   837.8kB @ 148.5kB/s  4.5s
pkgs/r/noarch                                        1.3MB @ 189.3kB/s  6.1s
bioconda/osx-arm64                                 129.0 B @  17.0 B/s  0.6s
pkgs/main/osx-arm64                                  1.7MB @ 140.7kB/s  6.2s
conda-forge/osx-arm64                                6.5MB @ 429.4kB/s 15.3s
bioconda/noarch                                      4.3MB @ 279.4kB/s 14.7s
conda-forge/noarch                                  12.5MB @ 667.8kB/s 19.1s

Pinned packages:
  - python 3.10.*

Could not solve for environment specs
The following package could not be installed
└─ datasail   does not exist (perhaps a typo or a missing channel).
(py310_caddseminar2023) vahidatabaigi@vahids-MBP CADDSeminar_2023 % conda search -c conda-forge datasail
Loading channels: done
No match found for: datasail. Search: *datasail*

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/conda-forge/osx-arm64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://repo.anaconda.com/pkgs/main/osx-arm64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/osx-arm64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.

best regards Vahid

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

which OS are you using? Due to its dependencies, it's not available for Windows. I just tested

mamba install -c kalininalab -c conda-forge -c mosek -c bioconda datasail

on my Linux computer and it works fine. TBH, I haven't tested the installation on OSX but will include this in the GitHub CI/CD.

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand, I am using macOS ventura 13.4, chip M1

Old-Shatterhand commented 1 year ago

Ok, I didn't expect that. For better tractability, I opened another issue as the discussion is now drifting away from the initial topic. As I assume the original question regarding dataset formatting is solved, I close this issue and will work on the OSX installability in Issue #3. If you think, your initial question(s) regarding the dataset format is not answered sufficiently, please reopen this issue and I will help you with this. Otherwise, see you in issue #3.

EasternCaveMan commented 1 year ago

I tried to split the data based one CCSe but it give the emtpy dict by name splits

from datasail.sail import datasail

_, splits, _ = datasail(techniques=["CCSe"], e_type="M", e_data="input2500.tsv")

error

import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['/Users/vahidatabaigi/PycharmProjects'])
PyDev console: starting.
Python 3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ] on darwin
runfile('/Users/vahidatabaigi/PycharmProjects/datasail_package.py', wdir='/Users/vahidatabaigi/PycharmProjects')
/Users/vahidatabaigi/miniconda3/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")
No duplicates found.
1201 / 1290Converged after 62 iterations.
Converged after 26 iterations.
===============================================================================
                                     CVXPY                                     
                                     v1.3.1                                    
===============================================================================
(CVXPY) Jun 09 02:45:13 PM: Your problem has 87 variables, 10 constraints, and 0 parameters.
(CVXPY) Jun 09 02:45:13 PM: It is compliant with the following grammars: DCP, DQCP
(CVXPY) Jun 09 02:45:13 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)
(CVXPY) Jun 09 02:45:13 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.
2023-06-09 14:45:13,010 Splitting failed for CCS, try to increase the timelimit or the epsilon value.
No duplicates found.
1201 / 1290

I added the parameter epsilon = 0.05 and increased it by 0.2, but I got the same error, I did not find parameter timelimit

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

that is like an issue with the data (they might be too similar). Is is possible to upload them here (if they are not proprietary)? Anyways, can you please paste the output you get, when running the same command with verbose="I" to get more logging messages?

BTW: The field for timeout is max_sec but that wouldn't help you here, as CvXPY is not even able to compile the program.

Best, Roman

EasternCaveMan commented 1 year ago

here is the my data

input2500.tsv.zip

with parameter verbose="I"

PyDev console: starting.
Python 3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ] on darwin
runfile('/Users/vahidatabaigi/PycharmProjects/datasail_package.py', wdir='/Users/vahidatabaigi/PycharmProjects')
/Users/vahidatabaigi/miniconda3/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")
2023-06-09 15:08:56,062 Validating arguments
2023-06-09 15:08:56,062 Read data
No duplicates found.
2023-06-09 15:08:56,314 Cluster first set of entities.
2023-06-09 15:08:56,314 Start ECFP clustering
2023-06-09 15:08:58,119 Reduced 1764 molecules to 1290
2023-06-09 15:08:58,119 Compute Tanimoto Coefficients
1201 / 12902023-06-09 15:08:58,273 Cluster 1290 items based on similarities
Converged after 58 iterations.
2023-06-09 15:08:59,958 Reduced number of clusters to 158.
2023-06-09 15:08:59,958 Cluster 158 items based on similarities
Converged after 26 iterations.
2023-06-09 15:08:59,980 Reduced number of clusters to 29.
2023-06-09 15:08:59,980 Split data
2023-06-09 15:08:59,980 Define optimization problem
2023-06-09 15:08:59,980 CCSe
2023-06-09 15:08:59,980 Clustering 29 clusters into 3 splits.
2023-06-09 15:08:59,982 Start solving with MOSEK
2023-06-09 15:08:59,982 The problem has 87 variables and 2558 constraints.
===============================================================================
                                     CVXPY                                     
                                     v1.3.1                                    
===============================================================================
(CVXPY) Jun 09 03:08:59 PM: Your problem has 87 variables, 10 constraints, and 0 parameters.
(CVXPY) Jun 09 03:08:59 PM: It is compliant with the following grammars: DCP, DQCP
(CVXPY) Jun 09 03:08:59 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)
(CVXPY) Jun 09 03:08:59 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.
2023-06-09 15:08:59,983 Splitting failed for CCS, try to increase the timelimit or the epsilon value.
2023-06-09 15:08:59,984 Store results
2023-06-09 15:08:59,984 BQP splitting finished and results stored.
2023-06-09 15:08:59,984 Total runtime: 3.92160s

but still is the empty dict

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

sorry for this problem. It's actually not data-related. It's because of the choice of the solving algorithm. There are two options to fix this:

Get a MOSEK license (free for academics) or

Use SCIP:

from datasail.sail import datasail

splits, _, _ = datasail(techniques=["CCSe"], e_type="M", e_data="input2500.tsv", solver="SCIP")

Please note, in this code, I changes the position of splits!

I hope this finally solves your problem. If not, please ask further questions.

Old-Shatterhand commented 1 year ago

Please note that the output only contains split assignment for one CHEMBL-ID per SMILES string. As your dataset contains duplicate SMILES, DataSAIL removes them first.

I thought they would appear in the output, but it seems like this is only true for the CLI version. I will implement this for package usage ASAP.

EasternCaveMan commented 1 year ago

Hi@Old-Shatterhand,

I apologize for the delayed response. I initially thought that the issue might be related to my operating system, so I opted to install Ubuntu Linux arm64. However, despite following your suggestion to split the data, I am still encountering the same error. Additionally, I attempted to resolve the issue by changing the solver to Mosek, but unfortunately, it did not make any difference.

As for my endeavor to install the package on Ubuntu, I am still facing the same error.

(base) vahidata@ubuntu:~$ conda env list
# conda environments:
#
base                  *  /home/vahidata/miniconda3
(base) vahidata@ubuntu:~$ conda create -n sail -c conda-forge -c kalininalab -c mosek -c bioconda datasail
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/conda-forge/linux-aarch64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/kalininalab/linux-aarch64
  - https://conda.anaconda.org/kalininalab/noarch
  - https://conda.anaconda.org/mosek/linux-aarch64
  - https://conda.anaconda.org/mosek/noarch
  - https://conda.anaconda.org/bioconda/linux-aarch64
  - https://conda.anaconda.org/bioconda/noarch
  - https://repo.anaconda.com/pkgs/main/linux-aarch64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/linux-aarch64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.

(base) vahidata@ubuntu:~$ conda install -c conda-forge -c kalininalab -c mosek -c bioconda datasail
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/conda-forge/linux-aarch64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/kalininalab/linux-aarch64
  - https://conda.anaconda.org/kalininalab/noarch
  - https://conda.anaconda.org/mosek/linux-aarch64
  - https://conda.anaconda.org/mosek/noarch
  - https://conda.anaconda.org/bioconda/linux-aarch64
  - https://conda.anaconda.org/bioconda/noarch
  - https://repo.anaconda.com/pkgs/main/linux-aarch64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/linux-aarch64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.
(base) vahidata@ubuntu:~$ conda install -c kalininalab -c conda-forge -c mosek datasail
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

  - datasail

Current channels:

  - https://conda.anaconda.org/kalininalab/linux-aarch64
  - https://conda.anaconda.org/kalininalab/noarch
  - https://conda.anaconda.org/conda-forge/linux-aarch64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/mosek/linux-aarch64
  - https://conda.anaconda.org/mosek/noarch
  - https://repo.anaconda.com/pkgs/main/linux-aarch64
  - https://repo.anaconda.com/pkgs/main/noarch
  - https://repo.anaconda.com/pkgs/r/linux-aarch64
  - https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you're
looking for, navigate to

    https://anaconda.org

and use the search bar at the top of the page.

EasternCaveMan commented 1 year ago

I attempted to resolve the issue by using the following command with Mamba( as you recommended), but unfortunately, I encountered the same error as I encountered on Mac and Windows systems.

Console output

``` (base) vahidata@ubuntu:~$ wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh --2023-06-10 19:21:28-- https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh Resolving github.com (github.com)... 140.82.121.3 Connecting to github.com (github.com)|140.82.121.3|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://github.com/conda-forge/miniforge/releases/download/23.1.0-1/Miniforge3-Linux-aarch64.sh [following] --2023-06-10 19:21:29-- https://github.com/conda-forge/miniforge/releases/download/23.1.0-1/Miniforge3-Linux-aarch64.sh Reusing existing connection to github.com:443. HTTP request sent, awaiting response... 302 Found Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/221584272/decb9bbb-59b8-4a7a-9442-7c16427de068?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230610%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230610T192129Z&X-Amz-Expires=300&X-Amz-Signature=341c8b7257b4829c89950d7c522ab0c8db892c0dbdc2373035ffb68ff3de317d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=221584272&response-content-disposition=attachment%3B%20filename%3DMiniforge3-Linux-aarch64.sh&response-content-type=application%2Foctet-stream [following] --2023-06-10 19:21:29-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/221584272/decb9bbb-59b8-4a7a-9442-7c16427de068?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230610%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230610T192129Z&X-Amz-Expires=300&X-Amz-Signature=341c8b7257b4829c89950d7c522ab0c8db892c0dbdc2373035ffb68ff3de317d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=221584272&response-content-disposition=attachment%3B%20filename%3DMiniforge3-Linux-aarch64.sh&response-content-type=application%2Foctet-stream Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ... Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.109.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 54512822 (52M) [application/octet-stream] Saving to: ‘Miniforge3-Linux-aarch64.sh’ Miniforge3-Linux-aarch64.sh 100%[================================================================================================>] 51.99M 1.50MB/s in 35s 2023-06-10 19:22:04 (1.50 MB/s) - ‘Miniforge3-Linux-aarch64.sh’ saved [54512822/54512822] (base) vahidata@ubuntu:~$ chmod +x Miniforge3-Linux-aarch64.sh (base) vahidata@ubuntu:~$ ./Miniforge3-Linux-aarch64.sh Welcome to Miniforge3 23.1.0-1 In order to continue the installation process, please review the license agreement. Please, press ENTER to continue >>> Miniforge installer code uses BSD-3-Clause license as stated below. Binary packages that come with it have their own licensing terms and by installing miniforge you agree to the licensing terms of individual packages as well. They include different OSI-approved licenses including the GNU General Public License and can be found in pkgs//info/licenses folders. Miniforge installer comes with a boostrapping executable that is used when installing miniforge and is deleted after miniforge is installed. The bootstrapping executable uses micromamba, cli11, cpp-filesystem, curl, c-ares, krb5, libarchive, libev, lz4, nghttp2, openssl, libsolv, nlohmann-json, reproc and zstd which are licensed under BSD-3-Clause, MIT and OpenSSL licenses. Licenses and copyright notices of these projects can be found at the following URL. https://github.com/conda-forge/micromamba-feedstock/tree/master/recipe. ============================================================================= Copyright (c) 2019-2022, conda-forge All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Do you accept the license terms? [yes|no] [no] >>> zes Please answer 'yes' or 'no':' >>> yes Miniforge3 will now be installed into this location: /home/vahidata/miniforge3 - Press ENTER to confirm the location - Press CTRL-C to abort the installation - Or specify a different location below [/home/vahidata/miniforge3] >>> PREFIX=/home/vahidata/miniforge3 Unpacking payload ... Extracting ca-certificates-2022.12.7-h4fd8a4c_0.conda Extracting ld_impl_linux-aarch64-2.40-h2d8c526_0.conda Extracting libgomp-12.2.0-h607ecd0_19.tar.bz2 Extracting libstdcxx-ng-12.2.0-hc13a102_19.tar.bz2 Extracting python_abi-3.10-3_cp310.conda Extracting tzdata-2023c-h71feb2d_0.conda Extracting _openmp_mutex-4.5-2_gnu.tar.bz2 Extracting libgcc-ng-12.2.0-h607ecd0_19.tar.bz2 Extracting bzip2-1.0.8-hf897c2e_4.tar.bz2 Extracting libffi-3.4.2-h3557bc0_5.tar.bz2 Extracting libnsl-2.0.0-hf897c2e_0.tar.bz2 Extracting libuuid-2.38.1-hb4cce97_0.conda Extracting libzlib-1.2.13-h4e544f5_4.tar.bz2 Extracting ncurses-6.3-headf329_1.tar.bz2 Extracting openssl-3.1.0-hb4cce97_0.conda Extracting xz-5.2.6-h9cdd2b7_0.tar.bz2 Extracting libsqlite-3.40.0-hf9034f9_0.tar.bz2 Extracting readline-8.2-h8fc344f_1.conda Extracting tk-8.6.12-hd8af866_0.tar.bz2 Extracting zstd-1.5.2-h44f6412_6.conda Extracting python-3.10.10-ha43d526_0_cpython.conda Extracting certifi-2022.12.7-pyhd8ed1ab_0.conda Extracting charset-normalizer-3.1.0-pyhd8ed1ab_0.conda Extracting colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 Extracting idna-3.4-pyhd8ed1ab_0.tar.bz2 Extracting pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 Extracting pycosat-0.6.4-py310h761cc84_1.tar.bz2 Extracting pycparser-2.21-pyhd8ed1ab_0.tar.bz2 Extracting pysocks-1.7.1-pyha2e5f31_6.tar.bz2 Extracting ruamel.yaml.clib-0.2.7-py310hb89b984_1.conda Extracting setuptools-65.6.3-pyhd8ed1ab_0.conda Extracting toolz-0.12.0-pyhd8ed1ab_0.tar.bz2 Extracting wheel-0.40.0-pyhd8ed1ab_0.conda Extracting cffi-1.15.1-py310hf0c4615_3.conda Extracting pip-23.0.1-pyhd8ed1ab_0.conda Extracting ruamel.yaml-0.17.21-py310hb89b984_3.conda Extracting tqdm-4.65.0-pyhd8ed1ab_1.conda Extracting brotlipy-0.7.0-py310h761cc84_1005.tar.bz2 Extracting cryptography-40.0.1-py310he4ba0b1_0.conda Extracting zstandard-0.19.0-py310hde4b81c_1.conda Extracting conda-package-streaming-0.7.0-pyhd8ed1ab_1.conda Extracting pyopenssl-23.1.1-pyhd8ed1ab_0.conda Extracting conda-package-handling-2.0.2-pyh38be061_0.conda Extracting urllib3-1.26.15-pyhd8ed1ab_0.conda Extracting requests-2.28.2-pyhd8ed1ab_1.conda Extracting conda-23.1.0-py310h4c7bcd0_0.conda Installing base environment... __ __ ______ ___ ____ _____ ___ / /_ ____ _ / / / / __ `__ \/ __ `/ __ `__ \/ __ \/ __ `/ / /_/ / / / / / / /_/ / / / / / / /_/ / /_/ / / .___/_/ /_/ /_/\__,_/_/ /_/ /_/_.___/\__,_/ /_/ Transaction Prefix: /home/vahidata/miniforge3 Updating specs: - conda-forge/linux-aarch64::ca-certificates==2022.12.7=h4fd8a4c_0[md5=2450fbcaf65634e0d071e47e2b8487b4] - conda-forge/linux-aarch64::ld_impl_linux-aarch64==2.40=h2d8c526_0[md5=16246d69e945d0b1969a6099e7c5d457] - conda-forge/linux-aarch64::libgomp==12.2.0=h607ecd0_19[md5=65b9cb876525dcb2e74a90cf02c6762a] - conda-forge/linux-aarch64::libstdcxx-ng==12.2.0=hc13a102_19[md5=981741cd4321edd5c504b48f74fe91f2] - conda-forge/linux-aarch64::python_abi==3.10=3_cp310[md5=7f4f00b03d3a7c4d4b8b987e5da461a9] - conda-forge/noarch::tzdata==2023c=h71feb2d_0[md5=939e3e74d8be4dac89ce83b20de2492a] - conda-forge/linux-aarch64::_openmp_mutex==4.5=2_gnu[md5=6168d71addc746e8f2b8d57dfd2edcea] - conda-forge/linux-aarch64::libgcc-ng==12.2.0=h607ecd0_19[md5=8456a29b6d9fc3123ccb9a966b6b2c49] - conda-forge/linux-aarch64::bzip2==1.0.8=hf897c2e_4[md5=2d787570a729e273a4e75775ddf3348a] - conda-forge/linux-aarch64::libffi==3.4.2=h3557bc0_5[md5=dddd85f4d52121fab0a8b099c5e06501] - conda-forge/linux-aarch64::libnsl==2.0.0=hf897c2e_0[md5=36fdbc05c9d9145ece86f5a63c3f352e] - conda-forge/linux-aarch64::libuuid==2.38.1=hb4cce97_0[md5=000e30b09db0b7c775b21695dff30969] - conda-forge/linux-aarch64::libzlib==1.2.13=h4e544f5_4[md5=88596b6277fe6d39f046983aae6044db] - conda-forge/linux-aarch64::ncurses==6.3=headf329_1[md5=486b68148e121bc8bbadc3cefae4c04f] - conda-forge/linux-aarch64::openssl==3.1.0=hb4cce97_0[md5=bee9334d1f911b1e05aee5bfcc82f04b] - conda-forge/linux-aarch64::xz==5.2.6=h9cdd2b7_0[md5=83baad393a31d59c20b63ba4da6592df] - conda-forge/linux-aarch64::libsqlite==3.40.0=hf9034f9_0[md5=9afb0d5dbaa403858a660cd0b4a31d29] - conda-forge/linux-aarch64::readline==8.2=h8fc344f_1[md5=105eb1e16bf83bfb2eb380a48032b655] - conda-forge/linux-aarch64::tk==8.6.12=hd8af866_0[md5=7894e82ff743bd96c76585ddebe28e2a] - conda-forge/linux-aarch64::zstd==1.5.2=h44f6412_6[md5=6d0d1cd6d184129eabb96bb220afb5b2] - conda-forge/linux-aarch64::python==3.10.10=ha43d526_0_cpython[md5=1e9b20478f1c2edffbcf2a40442dc31c] - conda-forge/noarch::certifi==2022.12.7=pyhd8ed1ab_0[md5=fb9addc3db06e56abe03e0e9f21a63e6] - conda-forge/noarch::charset-normalizer==3.1.0=pyhd8ed1ab_0[md5=7fcff9f6f123696e940bda77bd4d6551] - conda-forge/noarch::colorama==0.4.6=pyhd8ed1ab_0[md5=3faab06a954c2a04039983f2c4a50d99] - conda-forge/noarch::idna==3.4=pyhd8ed1ab_0[md5=34272b248891bddccc64479f9a7fffed] - conda-forge/noarch::pluggy==1.0.0=pyhd8ed1ab_5[md5=7d301a0d25f424d96175f810935f0da9] - conda-forge/linux-aarch64::pycosat==0.6.4=py310h761cc84_1[md5=c701cff6d6e7907c93ab603e58082a7c] - conda-forge/noarch::pycparser==2.21=pyhd8ed1ab_0[md5=076becd9e05608f8dc72757d5f3a91ff] - conda-forge/noarch::pysocks==1.7.1=pyha2e5f31_6[md5=2a7de29fb590ca14b5243c4c812c8025] - conda-forge/linux-aarch64::ruamel.yaml.clib==0.2.7=py310hb89b984_1[md5=89972c78c36ed3261c22bde7c012be03] - conda-forge/noarch::setuptools==65.6.3=pyhd8ed1ab_0[md5=9600fc9524d3f821e6a6d58c52f5bf5a] - conda-forge/noarch::toolz==0.12.0=pyhd8ed1ab_0[md5=92facfec94bc02d6ccf42e7173831a36] - conda-forge/noarch::wheel==0.40.0=pyhd8ed1ab_0[md5=49bb0d9e60ce1db25e151780331bb5f3] - conda-forge/linux-aarch64::cffi==1.15.1=py310hf0c4615_3[md5=a2bedcb1d205485ea32fe5d2bd6fd970] - conda-forge/noarch::pip==23.0.1=pyhd8ed1ab_0[md5=8025ca83b8ba5430b640b83917c2a6f7] - conda-forge/linux-aarch64::ruamel.yaml==0.17.21=py310hb89b984_3[md5=3c88178b698c68cdacf6f4703f57516d] - conda-forge/noarch::tqdm==4.65.0=pyhd8ed1ab_1[md5=ed792aff3acb977d09c7013358097f83] - conda-forge/linux-aarch64::brotlipy==0.7.0=py310h761cc84_1005[md5=66934993368d01f896652925d3ac7e66] - conda-forge/linux-aarch64::cryptography==40.0.1=py310he4ba0b1_0[md5=ba0e59c3d8973392e4df0c6622d79f25] - conda-forge/linux-aarch64::zstandard==0.19.0=py310hde4b81c_1[md5=d4b3cc980179c38949c83fe23057d97c] - conda-forge/noarch::conda-package-streaming==0.7.0=pyhd8ed1ab_1[md5=1a2fa9e53cfbc2e4d9ab21990805a436] - conda-forge/noarch::pyopenssl==23.1.1=pyhd8ed1ab_0[md5=0b34aa3ab7e7ccb1765a03dd9ed29938] - conda-forge/noarch::conda-package-handling==2.0.2=pyh38be061_0[md5=44800e9bd13143292097c65e57323038] - conda-forge/noarch::urllib3==1.26.15=pyhd8ed1ab_0[md5=27db656619a55d727eaf5a6ece3d2fd6] - conda-forge/noarch::requests==2.28.2=pyhd8ed1ab_1[md5=3bfbd6ead1d7299ed46dab3a7bf0bc8c] - conda-forge/linux-aarch64::conda==23.1.0=py310h4c7bcd0_0[md5=2706278dbe45c60c9dc2a2232f77f323] Package Version Build Channel Size ──────────────────────────────────────────────────────────────────────────────────── Install: ──────────────────────────────────────────────────────────────────────────────────── + _openmp_mutex 4.5 2_gnu conda-forge Cached + brotlipy 0.7.0 py310h761cc84_1005 conda-forge Cached + bzip2 1.0.8 hf897c2e_4 conda-forge Cached + ca-certificates 2022.12.7 h4fd8a4c_0 conda-forge Cached + certifi 2022.12.7 pyhd8ed1ab_0 conda-forge Cached + cffi 1.15.1 py310hf0c4615_3 conda-forge Cached + charset-normalizer 3.1.0 pyhd8ed1ab_0 conda-forge Cached + colorama 0.4.6 pyhd8ed1ab_0 conda-forge Cached + conda 23.1.0 py310h4c7bcd0_0 conda-forge Cached + conda-package-handling 2.0.2 pyh38be061_0 conda-forge Cached + conda-package-streaming 0.7.0 pyhd8ed1ab_1 conda-forge Cached + cryptography 40.0.1 py310he4ba0b1_0 conda-forge Cached + idna 3.4 pyhd8ed1ab_0 conda-forge Cached + ld_impl_linux-aarch64 2.40 h2d8c526_0 conda-forge Cached + libffi 3.4.2 h3557bc0_5 conda-forge Cached + libgcc-ng 12.2.0 h607ecd0_19 conda-forge Cached + libgomp 12.2.0 h607ecd0_19 conda-forge Cached + libnsl 2.0.0 hf897c2e_0 conda-forge Cached + libsqlite 3.40.0 hf9034f9_0 conda-forge Cached + libstdcxx-ng 12.2.0 hc13a102_19 conda-forge Cached + libuuid 2.38.1 hb4cce97_0 conda-forge Cached + libzlib 1.2.13 h4e544f5_4 conda-forge Cached + ncurses 6.3 headf329_1 conda-forge Cached + openssl 3.1.0 hb4cce97_0 conda-forge Cached + pip 23.0.1 pyhd8ed1ab_0 conda-forge Cached + pluggy 1.0.0 pyhd8ed1ab_5 conda-forge Cached + pycosat 0.6.4 py310h761cc84_1 conda-forge Cached + pycparser 2.21 pyhd8ed1ab_0 conda-forge Cached + pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge Cached + pysocks 1.7.1 pyha2e5f31_6 conda-forge Cached + python 3.10.10 ha43d526_0_cpython conda-forge Cached + python_abi 3.10 3_cp310 conda-forge Cached + readline 8.2 h8fc344f_1 conda-forge Cached + requests 2.28.2 pyhd8ed1ab_1 conda-forge Cached + ruamel.yaml 0.17.21 py310hb89b984_3 conda-forge Cached + ruamel.yaml.clib 0.2.7 py310hb89b984_1 conda-forge Cached + setuptools 65.6.3 pyhd8ed1ab_0 conda-forge Cached + tk 8.6.12 hd8af866_0 conda-forge Cached + toolz 0.12.0 pyhd8ed1ab_0 conda-forge Cached + tqdm 4.65.0 pyhd8ed1ab_1 conda-forge Cached + tzdata 2023c h71feb2d_0 conda-forge Cached + urllib3 1.26.15 pyhd8ed1ab_0 conda-forge Cached + wheel 0.40.0 pyhd8ed1ab_0 conda-forge Cached + xz 5.2.6 h9cdd2b7_0 conda-forge Cached + zstandard 0.19.0 py310hde4b81c_1 conda-forge Cached + zstd 1.5.2 h44f6412_6 conda-forge Cached Summary: Install: 46 packages Total download: 0 B ──────────────────────────────────────────────────────────────────────────────────── Transaction starting Linking ca-certificates-2022.12.7-h4fd8a4c_0 Linking ld_impl_linux-aarch64-2.40-h2d8c526_0 Linking libgomp-12.2.0-h607ecd0_19 Linking libstdcxx-ng-12.2.0-hc13a102_19 Linking python_abi-3.10-3_cp310 Linking tzdata-2023c-h71feb2d_0 Linking _openmp_mutex-4.5-2_gnu Linking libgcc-ng-12.2.0-h607ecd0_19 Linking bzip2-1.0.8-hf897c2e_4 Linking libffi-3.4.2-h3557bc0_5 Linking libnsl-2.0.0-hf897c2e_0 Linking libuuid-2.38.1-hb4cce97_0 Linking libzlib-1.2.13-h4e544f5_4 Linking ncurses-6.3-headf329_1 Linking openssl-3.1.0-hb4cce97_0 Linking xz-5.2.6-h9cdd2b7_0 Linking libsqlite-3.40.0-hf9034f9_0 Linking readline-8.2-h8fc344f_1 Linking tk-8.6.12-hd8af866_0 Linking zstd-1.5.2-h44f6412_6 Linking python-3.10.10-ha43d526_0_cpython Linking certifi-2022.12.7-pyhd8ed1ab_0 Linking charset-normalizer-3.1.0-pyhd8ed1ab_0 Linking colorama-0.4.6-pyhd8ed1ab_0 Linking idna-3.4-pyhd8ed1ab_0 Linking pluggy-1.0.0-pyhd8ed1ab_5 Linking pycosat-0.6.4-py310h761cc84_1 Linking pycparser-2.21-pyhd8ed1ab_0 Linking pysocks-1.7.1-pyha2e5f31_6 Linking ruamel.yaml.clib-0.2.7-py310hb89b984_1 Linking setuptools-65.6.3-pyhd8ed1ab_0 Linking toolz-0.12.0-pyhd8ed1ab_0 Linking wheel-0.40.0-pyhd8ed1ab_0 Linking cffi-1.15.1-py310hf0c4615_3 Linking pip-23.0.1-pyhd8ed1ab_0 Linking ruamel.yaml-0.17.21-py310hb89b984_3 Linking tqdm-4.65.0-pyhd8ed1ab_1 Linking brotlipy-0.7.0-py310h761cc84_1005 Linking cryptography-40.0.1-py310he4ba0b1_0 Linking zstandard-0.19.0-py310hde4b81c_1 Linking conda-package-streaming-0.7.0-pyhd8ed1ab_1 Linking pyopenssl-23.1.1-pyhd8ed1ab_0 Linking conda-package-handling-2.0.2-pyh38be061_0 Linking urllib3-1.26.15-pyhd8ed1ab_0 Linking requests-2.28.2-pyhd8ed1ab_1 Linking conda-23.1.0-py310h4c7bcd0_0 Transaction finished installation finished. Do you wish the installer to initialize Miniforge3 by running conda init? [yes|no] [no] >>> yes no change /home/vahidata/miniforge3/condabin/conda no change /home/vahidata/miniforge3/bin/conda no change /home/vahidata/miniforge3/bin/conda-env no change /home/vahidata/miniforge3/bin/activate no change /home/vahidata/miniforge3/bin/deactivate modified /home/vahidata/miniforge3/etc/profile.d/conda.sh modified /home/vahidata/miniforge3/etc/fish/conf.d/conda.fish no change /home/vahidata/miniforge3/shell/condabin/Conda.psm1 no change /home/vahidata/miniforge3/shell/condabin/conda-hook.ps1 no change /home/vahidata/miniforge3/lib/python3.10/site-packages/xontrib/conda.xsh modified /home/vahidata/miniforge3/etc/profile.d/conda.csh modified /home/vahidata/.bashrc ==> For changes to take effect, close and re-open your current shell. <== If you'd prefer that conda's base environment not be activated on startup, set the auto_activate_base parameter to false: conda config --set auto_activate_base false Thank you for installing Miniforge3! (base) vahidata@ubuntu:~$ conda create -n mamba Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 23.3.1 latest version: 23.5.0 Please update conda by running $ conda update -n base -c defaults conda Or to minimize the number of packages updated during conda update use conda install conda=23.5.0 ## Package Plan ## environment location: /home/vahidata/miniconda3/envs/mamba Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate mamba # # To deactivate an active environment, use # # $ conda deactivate (base) vahidata@ubuntu:~$ conda activate mamba (mamba) vahidata@ubuntu:~$ conda install mamba -c conda-forge Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 23.3.1 latest version: 23.5.0 Please update conda by running $ conda update -n base -c defaults conda Or to minimize the number of packages updated during conda update use conda install conda=23.5.0 ## Package Plan ## environment location: /home/vahidata/miniconda3/envs/mamba added / updated specs: - mamba The following packages will be downloaded: package | build ---------------------------|----------------- _openmp_mutex-4.5 | 2_gnu 23 KB conda-forge boltons-23.0.0 | pyhd8ed1ab_0 296 KB conda-forge brotli-1.0.9 | h4e544f5_8 19 KB conda-forge brotli-bin-1.0.9 | h4e544f5_8 20 KB conda-forge bzip2-1.0.8 | hf897c2e_4 396 KB conda-forge c-ares-1.19.1 | h31becfc_0 115 KB conda-forge ca-certificates-2023.5.7 | hcefe29a_0 145 KB conda-forge certifi-2023.5.7 | pyhd8ed1ab_0 149 KB conda-forge cffi-1.15.1 | py311h2e7898d_3 305 KB conda-forge charset-normalizer-3.1.0 | pyhd8ed1ab_0 44 KB conda-forge colorama-0.4.6 | pyhd8ed1ab_0 25 KB conda-forge conda-23.3.1 | py311hec3470c_0 1.2 MB conda-forge conda-package-handling-2.0.2| pyh38be061_0 247 KB conda-forge conda-package-streaming-0.8.0| pyhd8ed1ab_0 18 KB conda-forge cryptography-41.0.1 | py311hd42d77a_0 1.9 MB conda-forge fmt-9.1.0 | hdd96247_0 186 KB conda-forge icu-72.1 | hcf00150_0 11.5 MB conda-forge idna-3.4 | pyhd8ed1ab_0 55 KB conda-forge jsonpatch-1.32 | pyhd8ed1ab_0 14 KB conda-forge jsonpointer-2.0 | py_0 9 KB conda-forge keyutils-1.6.1 | h4e544f5_0 110 KB conda-forge krb5-1.20.1 | h113d92e_0 1.4 MB conda-forge ld_impl_linux-aarch64-2.40 | h2d8c526_0 721 KB conda-forge libarchive-3.6.2 | h566b526_1 931 KB conda-forge libbrotlicommon-1.0.9 | h4e544f5_8 66 KB conda-forge libbrotlidec-1.0.9 | h4e544f5_8 34 KB conda-forge libbrotlienc-1.0.9 | h4e544f5_8 293 KB conda-forge libcurl-8.1.2 | hc34909b_0 373 KB conda-forge libedit-3.1.20191231 | he28a2e2_2 131 KB conda-forge libev-4.33 | h516909a_1 103 KB conda-forge libexpat-2.5.0 | hd600fc2_1 75 KB conda-forge libffi-3.4.2 | h3557bc0_5 58 KB conda-forge libgcc-ng-13.1.0 | h2b4548d_0 446 KB conda-forge libgomp-13.1.0 | h2b4548d_0 413 KB conda-forge libiconv-1.17 | h9cdd2b7_0 1.4 MB conda-forge libmamba-1.4.2 | h4a4fd89_0 1.5 MB conda-forge libmambapy-1.4.2 | py311hf0bf98e_0 257 KB conda-forge libnghttp2-1.52.0 | h250e5c5_0 657 KB conda-forge libnsl-2.0.0 | hf897c2e_0 31 KB conda-forge libsolv-0.7.24 | h3cb9bc8_0 457 KB conda-forge libsqlite-3.42.0 | h194ca79_0 992 KB conda-forge libssh2-1.11.0 | h492db2e_0 278 KB conda-forge libstdcxx-ng-13.1.0 | h452befe_0 3.6 MB conda-forge libuuid-2.38.1 | hb4cce97_0 35 KB conda-forge libxml2-2.11.4 | h164fba4_0 732 KB conda-forge libzlib-1.2.13 | h4e544f5_4 72 KB conda-forge lz4-c-1.9.4 | hd600fc2_0 160 KB conda-forge lzo-2.10 | h516909a_1000 276 KB conda-forge mamba-1.4.2 | py311hb6c5aa6_0 64 KB conda-forge ncurses-6.4 | h2e1726e_0 900 KB conda-forge openssl-3.1.1 | h31becfc_1 2.5 MB conda-forge packaging-23.1 | pyhd8ed1ab_0 45 KB conda-forge pip-23.1.2 | pyhd8ed1ab_0 1.3 MB conda-forge pluggy-1.0.0 | pyhd8ed1ab_5 16 KB conda-forge pybind11-abi-4 | hd8ed1ab_3 10 KB conda-forge pycosat-0.6.4 | py311hdfa8b44_1 113 KB conda-forge pycparser-2.21 | pyhd8ed1ab_0 100 KB conda-forge pyopenssl-23.2.0 | pyhd8ed1ab_1 126 KB conda-forge pysocks-1.7.1 | pyha2e5f31_6 19 KB conda-forge python-3.11.4 |h43d1f9e_0_cpython 14.6 MB conda-forge python_abi-3.11 | 3_cp311 6 KB conda-forge readline-8.2 | h8fc344f_1 287 KB conda-forge reproc-14.2.4 | hb4cce97_0 31 KB conda-forge reproc-cpp-14.2.4 | hd600fc2_0 21 KB conda-forge requests-2.31.0 | pyhd8ed1ab_0 55 KB conda-forge ruamel.yaml-0.17.31 | py311hcd402e7_0 274 KB conda-forge ruamel.yaml.clib-0.2.7 | py311h1d6c08a_1 125 KB conda-forge setuptools-67.7.2 | pyhd8ed1ab_0 569 KB conda-forge tk-8.6.12 | hd8af866_0 3.4 MB conda-forge toolz-0.12.0 | pyhd8ed1ab_0 48 KB conda-forge tqdm-4.65.0 | pyhd8ed1ab_1 86 KB conda-forge tzdata-2023c | h71feb2d_0 115 KB conda-forge urllib3-2.0.3 | pyhd8ed1ab_0 96 KB conda-forge wheel-0.40.0 | pyhd8ed1ab_0 54 KB conda-forge xz-5.2.6 | h9cdd2b7_0 430 KB conda-forge yaml-cpp-0.7.0 | h4de3ea5_2 206 KB conda-forge zstandard-0.19.0 | py311hd2ac760_1 370 KB conda-forge zstd-1.5.2 | h44f6412_6 390 KB conda-forge ------------------------------------------------------------ Total: 58.3 MB The following NEW packages will be INSTALLED: _openmp_mutex conda-forge/linux-aarch64::_openmp_mutex-4.5-2_gnu boltons conda-forge/noarch::boltons-23.0.0-pyhd8ed1ab_0 brotli conda-forge/linux-aarch64::brotli-1.0.9-h4e544f5_8 brotli-bin conda-forge/linux-aarch64::brotli-bin-1.0.9-h4e544f5_8 bzip2 conda-forge/linux-aarch64::bzip2-1.0.8-hf897c2e_4 c-ares conda-forge/linux-aarch64::c-ares-1.19.1-h31becfc_0 ca-certificates conda-forge/linux-aarch64::ca-certificates-2023.5.7-hcefe29a_0 certifi conda-forge/noarch::certifi-2023.5.7-pyhd8ed1ab_0 cffi conda-forge/linux-aarch64::cffi-1.15.1-py311h2e7898d_3 charset-normalizer conda-forge/noarch::charset-normalizer-3.1.0-pyhd8ed1ab_0 colorama conda-forge/noarch::colorama-0.4.6-pyhd8ed1ab_0 conda conda-forge/linux-aarch64::conda-23.3.1-py311hec3470c_0 conda-package-han~ conda-forge/noarch::conda-package-handling-2.0.2-pyh38be061_0 conda-package-str~ conda-forge/noarch::conda-package-streaming-0.8.0-pyhd8ed1ab_0 cryptography conda-forge/linux-aarch64::cryptography-41.0.1-py311hd42d77a_0 fmt conda-forge/linux-aarch64::fmt-9.1.0-hdd96247_0 icu conda-forge/linux-aarch64::icu-72.1-hcf00150_0 idna conda-forge/noarch::idna-3.4-pyhd8ed1ab_0 jsonpatch conda-forge/noarch::jsonpatch-1.32-pyhd8ed1ab_0 jsonpointer conda-forge/noarch::jsonpointer-2.0-py_0 keyutils conda-forge/linux-aarch64::keyutils-1.6.1-h4e544f5_0 krb5 conda-forge/linux-aarch64::krb5-1.20.1-h113d92e_0 ld_impl_linux-aar~ conda-forge/linux-aarch64::ld_impl_linux-aarch64-2.40-h2d8c526_0 libarchive conda-forge/linux-aarch64::libarchive-3.6.2-h566b526_1 libbrotlicommon conda-forge/linux-aarch64::libbrotlicommon-1.0.9-h4e544f5_8 libbrotlidec conda-forge/linux-aarch64::libbrotlidec-1.0.9-h4e544f5_8 libbrotlienc conda-forge/linux-aarch64::libbrotlienc-1.0.9-h4e544f5_8 libcurl conda-forge/linux-aarch64::libcurl-8.1.2-hc34909b_0 libedit conda-forge/linux-aarch64::libedit-3.1.20191231-he28a2e2_2 libev conda-forge/linux-aarch64::libev-4.33-h516909a_1 libexpat conda-forge/linux-aarch64::libexpat-2.5.0-hd600fc2_1 libffi conda-forge/linux-aarch64::libffi-3.4.2-h3557bc0_5 libgcc-ng conda-forge/linux-aarch64::libgcc-ng-13.1.0-h2b4548d_0 libgomp conda-forge/linux-aarch64::libgomp-13.1.0-h2b4548d_0 libiconv conda-forge/linux-aarch64::libiconv-1.17-h9cdd2b7_0 libmamba conda-forge/linux-aarch64::libmamba-1.4.2-h4a4fd89_0 libmambapy conda-forge/linux-aarch64::libmambapy-1.4.2-py311hf0bf98e_0 libnghttp2 conda-forge/linux-aarch64::libnghttp2-1.52.0-h250e5c5_0 libnsl conda-forge/linux-aarch64::libnsl-2.0.0-hf897c2e_0 libsolv conda-forge/linux-aarch64::libsolv-0.7.24-h3cb9bc8_0 libsqlite conda-forge/linux-aarch64::libsqlite-3.42.0-h194ca79_0 libssh2 conda-forge/linux-aarch64::libssh2-1.11.0-h492db2e_0 libstdcxx-ng conda-forge/linux-aarch64::libstdcxx-ng-13.1.0-h452befe_0 libuuid conda-forge/linux-aarch64::libuuid-2.38.1-hb4cce97_0 libxml2 conda-forge/linux-aarch64::libxml2-2.11.4-h164fba4_0 libzlib conda-forge/linux-aarch64::libzlib-1.2.13-h4e544f5_4 lz4-c conda-forge/linux-aarch64::lz4-c-1.9.4-hd600fc2_0 lzo conda-forge/linux-aarch64::lzo-2.10-h516909a_1000 mamba conda-forge/linux-aarch64::mamba-1.4.2-py311hb6c5aa6_0 ncurses conda-forge/linux-aarch64::ncurses-6.4-h2e1726e_0 openssl conda-forge/linux-aarch64::openssl-3.1.1-h31becfc_1 packaging conda-forge/noarch::packaging-23.1-pyhd8ed1ab_0 pip conda-forge/noarch::pip-23.1.2-pyhd8ed1ab_0 pluggy conda-forge/noarch::pluggy-1.0.0-pyhd8ed1ab_5 pybind11-abi conda-forge/noarch::pybind11-abi-4-hd8ed1ab_3 pycosat conda-forge/linux-aarch64::pycosat-0.6.4-py311hdfa8b44_1 pycparser conda-forge/noarch::pycparser-2.21-pyhd8ed1ab_0 pyopenssl conda-forge/noarch::pyopenssl-23.2.0-pyhd8ed1ab_1 pysocks conda-forge/noarch::pysocks-1.7.1-pyha2e5f31_6 python conda-forge/linux-aarch64::python-3.11.4-h43d1f9e_0_cpython python_abi conda-forge/linux-aarch64::python_abi-3.11-3_cp311 readline conda-forge/linux-aarch64::readline-8.2-h8fc344f_1 reproc conda-forge/linux-aarch64::reproc-14.2.4-hb4cce97_0 reproc-cpp conda-forge/linux-aarch64::reproc-cpp-14.2.4-hd600fc2_0 requests conda-forge/noarch::requests-2.31.0-pyhd8ed1ab_0 ruamel.yaml conda-forge/linux-aarch64::ruamel.yaml-0.17.31-py311hcd402e7_0 ruamel.yaml.clib conda-forge/linux-aarch64::ruamel.yaml.clib-0.2.7-py311h1d6c08a_1 setuptools conda-forge/noarch::setuptools-67.7.2-pyhd8ed1ab_0 tk conda-forge/linux-aarch64::tk-8.6.12-hd8af866_0 toolz conda-forge/noarch::toolz-0.12.0-pyhd8ed1ab_0 tqdm conda-forge/noarch::tqdm-4.65.0-pyhd8ed1ab_1 tzdata conda-forge/noarch::tzdata-2023c-h71feb2d_0 urllib3 conda-forge/noarch::urllib3-2.0.3-pyhd8ed1ab_0 wheel conda-forge/noarch::wheel-0.40.0-pyhd8ed1ab_0 xz conda-forge/linux-aarch64::xz-5.2.6-h9cdd2b7_0 yaml-cpp conda-forge/linux-aarch64::yaml-cpp-0.7.0-h4de3ea5_2 zstandard conda-forge/linux-aarch64::zstandard-0.19.0-py311hd2ac760_1 zstd conda-forge/linux-aarch64::zstd-1.5.2-h44f6412_6 Proceed ([y]/n)? y Downloading and Extracting Packages Preparing transaction: done Verifying transaction: done Executing transaction: done (mamba) vahidata@ubuntu:~$ mamba install -c kalininalab -c conda-forge -c mosek -c bioconda datasail __ __ __ __ / \ / \ / \ / \ / \/ \/ \/ \ ███████████████/ /██/ /██/ /██/ /████████████████████████ / / \ / \ / \ / \ \____ / / \_/ \_/ \_/ \ o \__, / _/ \_____/ ` |/ ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗ ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗ ██╔████╔██║███████║██╔████╔██║██████╔╝███████║ ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║ ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝ mamba (1.4.2) supported by @QuantStack GitHub: https://github.com/mamba-org/mamba Twitter: https://twitter.com/QuantStack █████████████████████████████████████████████████████████████ Looking for: ['datasail'] kalininalab/linux-aarch64 144.0 B @ 246.0 B/s 0.6s mosek/linux-aarch64 8.6kB @ 12.3kB/s 0.7s bioconda/linux-aarch64 128.0 B @ 134.0 B/s 0.3s kalininalab/noarch 135.0 B @ 98.0 B/s 1.4s pkgs/r/linux-aarch64 122.0 B @ 87.0 B/s 0.8s pkgs/main/noarch 837.8kB @ 215.7kB/s 2.5s pkgs/r/noarch 1.3MB @ 280.6kB/s 3.7s mosek/noarch 135.0 B @ 25.0 B/s 0.7s pkgs/main/linux-aarch64 2.0MB @ 320.7kB/s 4.8s conda-forge/linux-aarch64 8.9MB @ 784.7kB/s 11.5s bioconda/noarch 4.3MB @ 325.6kB/s 9.3s conda-forge/noarch 12.5MB @ 638.2kB/s 19.9s Pinned packages: - python 3.11.* Could not solve for environment specs The following package could not be installed └─ datasail does not exist (perhaps a typo or a missing channel). (mamba) vahidata@ubuntu:~$ ```

Old-Shatterhand commented 1 year ago

Hi @atabaigi,

I'm sorry you still have issues installing DataSAIL from the kalininalab conda-channel. To be honest, I don't quite understand why you do the steps above.

As sorted out here, installing DataSAIL using conda directly does not work for me either. I suggested using mamba.

As sorted out throughout this issue and issue #3, DataSAIL cannot be installed and executed on Windows OS, but perfectly on MacOS and Linux. You just have to use mamba for this. I'll copy the instruction I gave in #3 here and put a bit more explanation to it:

You start in your base environment of a Linux OS or MacOS machine!
You install mamba to the base environment! Therefore, run conda install -c conda-forge mamba in your base environment. Further information can be found in the mamba documentation.

Then, you can start installing DataSAIL:

conda create -n <env_name> python=3.10
conda activate <env_name>
mamba install -c mosek -c conda-forge -c bioconda -y numpy pandas networkx matplotlib pytest setuptools pyscipopt foldseek mmseqs2 cd-hit mash tmalign cvxpy pytest-cov rdkit pytest-cases scikit-learn
pip install grakel

It is important to use python=3.10 as DataSAIL has no Python 3.11 builds yet. This is actually the issue with your last try to install DataSAIL using mamba

I'm pretty sure if you follow these steps rigorously, you will have installed DataSAIL v0.0.10 successfully and can use it as a Python package and as a CLI tool.

If you're still struggling with the installation, Michael or I can help you in person (he can give you some contact details of me).

Best, Roman

EasternCaveMan commented 1 year ago

Hi @Old-Shatterhand , Thanks for your message, I followed the steps, but it didn't work on Linux and macOS on ARM architecture. However, it works with Linux and macOS on ADM architecture. For completeness, I added one more step to successfully install dataSAIL

conda create -n <env_name> python=3.10
conda activate <env_name>
conda install -c conda-forge mamba
mamba install -c mosek -c conda-forge -c bioconda -y numpy pandas networkx matplotlib pytest setuptools pyscipopt foldseek mmseqs2 cd-hit mash tmalign cvxpy pytest-cov rdkit pytest-cases scikit-learn
pip install grakel
mamba install -c kalininalab -c conda-forge -c mosek -c bioconda datasail

Best regards Vahid

kalininalab / DataSAIL