Libraries versions

Hey!
I've been trying to run this script, although I am getting some troubles, which I suspect it may be due to a divergence on libraries versions.

DeepChem 1.x (Requires RDKit)
Pandas (Prediction is tested with Pandas 0.22)
Tensorflow 1.3
Keras
XGBoost
ScikitLearn

The libraries that I have installed are (pip):

base) benjamin@benjamin-T460:~/owen$ conda activate test-env
(test-env) benjamin@benjamin-T460:~/owen$ pip list
Package                 Version
----------------------- ---------------
apturl                  0.5.2
asttokens               2.0.8
backcall                0.2.0
bcrypt                  3.2.0
blinker                 1.4
Brlapi                  0.8.3
certifi                 2020.6.20
chardet                 4.0.0
cli-helpers             2.2.1
click                   8.0.3
cmake                   3.22.5
colorama                0.4.4
command-not-found       0.3
configobj               5.0.6
cryptography            3.4.8
cupshelpers             1.0
dbus-python             1.2.18
decorator               5.1.1
defer                   1.0.6
delegator.py            0.1.1
distlib                 0.3.5
distro                  1.7.0
distro-info             1.1build1
dlib                    19.24.0
duplicity               0.8.21
executing               1.1.0
face-recognition        1.3.0
face-recognition-models 0.3.0
fasteners               0.14.1
filelock                3.7.1
Flask                   2.1.2
Flask-Cors              3.0.10
frida                   15.1.22
frida-tools             10.6.1
future                  0.18.2
gpg                     1.16.0-unknown
httplib2                0.20.2
idna                    3.3
importlib-metadata      4.6.4
ipython                 8.5.0
itsdangerous            2.1.2
jedi                    0.18.1
jeepney                 0.7.1
Jinja2                  3.1.2
keyring                 23.5.0
language-selector       0.1
launchpadlib            1.10.16
lazr.restfulclient      0.14.4
lazr.uri                1.0.6
litecli                 1.8.0
lockfile                0.12.2
louis                   3.20.0
macaroonbakery          1.3.1
Mako                    1.1.3
MarkupSafe              2.0.1
matplotlib-inline       0.1.6
monotonic               1.6
more-itertools          8.10.0
mysql-connector-python  8.0.30
netifaces               0.11.0
numpy                   1.23.1
oauthlib                3.2.0
objection               1.11.0
olefile                 0.46
opencv-python           4.6.0.66
paramiko                2.9.3
parso                   0.8.3
pexpect                 4.8.0
pickleshare             0.7.5
Pillow                  9.0.1
pip                     22.2.2
pipenv                  2022.7.4
platformdirs            2.5.2
prompt-toolkit          3.0.29
protobuf                3.12.4
ptyprocess              0.7.0
pure-eval               0.2.2
pycairo                 1.20.1
pycups                  2.0.1
Pygments                2.12.0
PyGObject               3.42.1
PyJWT                   2.4.0
pymacaroons             0.13.0
PyNaCl                  1.5.0
pyparsing               2.4.7
PyPDF2                  2.8.1
pyRFC3339               1.1
python-apt              2.3.0+ubuntu2.1
python-dateutil         2.8.1
python-debian           0.1.43ubuntu1
python-dotenv           0.21.0
pytz                    2022.1
pyxdg                   0.27
PyYAML                  5.4.1
rdkit                   2022.3.5
reportlab               3.6.8
requests                2.25.1
SecretStorage           3.3.1
semver                  2.13.0
setuptools              59.6.0
six                     1.16.0
sqlparse                0.4.2
stack-data              0.5.1
systemd-python          234
tabulate                0.8.9
traitlets               5.4.0
ubuntu-advantage-tools  27.9
ubuntu-drivers-common   0.0.0
ufw                     0.36.1
unattended-upgrades     0.1
urllib3                 1.26.5
usb-creator             0.3.7
vboxapi                 1.0
virtualenv              20.15.1
virtualenv-clone        0.5.7
wadllib                 1.3.6
wcwidth                 0.2.5
websockets              10.3
Werkzeug                2.1.2
wheel                   0.37.1
xdg                     5
xkit                    0.0.0
zipp                    1.0.0
(test-env) benjamin@benjamin-T460:~/owen$ conda list
# packages in environment at /home/benjamin/anaconda3/envs/test-env:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
_openmp_mutex             5.1                       1_gnu  
absl-py                   0.15.0                   pypi_0    pypi
astor                     0.8.1                    pypi_0    pypi
backports                 1.0                        py_2    conda-forge
backports-functools-lru-cache 1.6.4                    pypi_0    pypi
backports-shutil-get-terminal-size 1.0.0                    pypi_0    pypi
backports.weakref         1.0rc1                   py27_1    conda-forge
blas                      1.0                         mkl  
bleach                    1.5.0                    py27_0    conda-forge
blosc                     1.21.0               h8c45485_0  
boost                     1.63.0           py27hd84f722_1    rdkit
bzip2                     1.0.8                h7b6447c_0  
ca-certificates           2022.07.19           h06a4308_0  
cairo                     1.16.0               hf32fb01_1  
certifi                   2020.6.20          pyhd3eb1b0_3  
cycler                    0.10.0                   pypi_0    pypi
decorator                 4.4.2                    pypi_0    pypi
deepchem                  1.3.2.dev290             pypi_0    pypi
eigen                     3.3.7                hd09550d_1  
enum34                    1.1.10                   pypi_0    pypi
fftw3f                    3.3.4                         2    omnia
fontconfig                2.13.1               h6c09931_0  
freetype                  2.11.0               h70c0345_0  
funcsigs                  1.0.2                      py_3    conda-forge
futures                   3.3.0                    pypi_0    pypi
gast                      0.5.3                    pypi_0    pypi
glib                      2.69.1               h4ff587b_1  
grpcio                    1.41.1                   pypi_0    pypi
h5py                      2.10.0                   pypi_0    pypi
hdf5                      1.10.4               hb1b8bf9_0  
html5lib                  0.9999999                py27_0    conda-forge
icu                       58.2                 he6710b0_3  
intel-openmp              2022.0.1          h06a4308_3633  
ipython                   5.5.0                    pypi_0    pypi
ipython-genutils          0.2.0                    pypi_0    pypi
joblib                    0.13.0                   pypi_0    pypi
jpeg                      9e                   h7f8727e_0  
keras                     2.2.4                    pypi_0    pypi
keras-applications        1.0.8                    pypi_0    pypi
keras-preprocessing       1.1.2                    pypi_0    pypi
lerc                      3.0                  h295c915_0  
libboost                  1.65.1               habcd387_4  
libdeflate                1.8                  h7f8727e_5  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 11.2.0               h1234567_1  
libgfortran-ng            7.5.0               ha8ba4b0_17  
libgfortran4              7.5.0               ha8ba4b0_17  
libgomp                   11.2.0               h1234567_1  
libpng                    1.6.37               hbc83047_0  
libprotobuf               3.11.4               h8b12597_0    conda-forge
libstdcxx-ng              11.2.0               h1234567_1  
libtiff                   4.2.0                h85742a9_0  
libuuid                   1.0.3                h7f8727e_2  
libwebp-base              1.2.2                h7f8727e_0  
libxcb                    1.15                 h7f8727e_0  
libxml2                   2.9.12               h03d6c58_0  
lz4-c                     1.9.3                h295c915_1  
lzo                       2.10                 h7b6447c_2  
markdown                  3.1.1                      py_0    conda-forge
matplotlib                2.1.2                    pypi_0    pypi
mdtraj                    1.9.1                    py27_1    deepchem
mkl                       2020.2                      256  
mkl-service               2.3.0            py27he904b0f_0  
mkl_fft                   1.0.15           py27ha843d7b_0  
mkl_random                1.1.0            py27hd6b4f25_0  
mock                      3.0.5            py27h8c360ce_1    conda-forge
ncurses                   6.3                  h5eee18b_3  
numexpr                   2.7.0            py27h9e4a6bb_0  
numpy                     1.16.6                   pypi_0    pypi
numpy-base                1.16.6           py27hde5b4d6_0  
olefile                   0.46                     py27_0  
openmm                    7.3.1           py27_cuda92_rc_2    omnia
pandas                    0.22.0                   pypi_0    pypi
pathlib2                  2.3.7.post1              pypi_0    pypi
pcre                      8.45                 h295c915_0  
pdbfixer                  1.4                      py27_0    omnia
pexpect                   4.8.0                    pypi_0    pypi
pickleshare               0.7.5                    pypi_0    pypi
pillow                    6.2.1            py27h34e0f95_0  
pip                       19.3.1                   py27_0  
pixman                    0.40.0               h7f8727e_1  
prompt-toolkit            1.0.18                   pypi_0    pypi
protobuf                  3.11.4           py27he1b5a44_0    conda-forge
ptyprocess                0.7.0                    pypi_0    pypi
py-boost                  1.65.1           py27hf484d3e_4  
pygments                  2.5.2                    pypi_0    pypi
pyparsing                 2.4.7                    pypi_0    pypi
pytables                  3.5.2            py27h71ec239_1  
python                    2.7.18               ha1903f6_2  
python-dateutil           2.8.2              pyhd3eb1b0_0  
python_abi                2.7                    1_cp27mu    conda-forge
pytz                      2021.3             pyhd3eb1b0_0  
pyyaml                    5.4.1                    pypi_0    pypi
rdkit                     2018.09.1.0      py27h71b666b_1    rdkit
readline                  8.1.2                h7f8727e_1  
scandir                   1.10.0                   pypi_0    pypi
scikit-learn              0.19.1                   pypi_0    pypi
scipy                     1.2.3                    pypi_0    pypi
setuptools                44.0.0                   py27_0  
simdna                    0.4.2                    pypi_0    pypi
simplegeneric             0.8.1                    pypi_0    pypi
six                       1.16.0             pyhd3eb1b0_1  
sqlite                    3.38.2               hc218d9a_0  
subprocess32              3.5.4                    pypi_0    pypi
tensorboard               1.6.0                    pypi_0    pypi
tensorflow                1.6.0                    pypi_0    pypi
tensorflow-estimator      1.13.0                   pypi_0    pypi
tensorflow-tensorboard    1.5.1                    pypi_0    pypi
termcolor                 1.1.0                    pypi_0    pypi
tk                        8.6.11               h1ccaba5_0  
traitlets                 4.3.3                    pypi_0    pypi
typing                    3.10.0.0                 pypi_0    pypi
wcwidth                   0.2.5                    pypi_0    pypi
webencodings              0.5.1                      py_1    conda-forge
werkzeug                  1.0.1              pyh9f0ad1d_0    conda-forge
wheel                     0.37.1             pyhd3eb1b0_0  
xgboost                   0.80                     pypi_0    pypi
xz                        5.2.6                h5eee18b_0  
zlib                      1.2.11               h7f8727e_4  
zstd                      1.4.9                haebb681_0  

I am running the script in a conda venv,

Thank you very much for your time. I appreciate that even with this repository deactivate, you still helping the community :)

And, just an append, the error/output that I get is:

(test-env) benjamin@benjamin-T460:~/owen/GPCR_LigandClassify$ python GPCR_LigandClassify.py --input_file sample_input.csv --output_file output.csv --n_rows_to_read 1200
/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
Using TensorFlow backend.
PANDAS version  0.22.0
########################
 The following python librares are required to run the models:

* Python 2.7 (tested with Anaconda distribution, Linux OS (Mint 19.1 for a local PC, and 3.10.0-957.12.2.el7.x86_64 GNU/Linux for ComputeCanada clusters). Running the models on MAC may be cumbersome because of the recent XGBoost updates. We did not test the prediction on Windows.)
* DeepChem 1.x (Require RDKit)
* Pandas (Prediction is tested with Pandas 0.22)
* Tensorflow 1.3
* Keras
* XGBoost
* ScikitLearn

########################
In order for the script to run, and in addition to the input file (see below), the following files should exist in the running directory:

* dl_model_fp.json
* dl_model_fp.h5
* mlp_rdkit_classify_fp.sav
* xgb_rdkit_classify_fp.sav
* rfc_rdkit_classify_fp.sav
* svm_rdkit_classify_fp.sav
* coded_gpcr_list.csv

NB: The rfc_rdkit_classify_fp.sav & svm_rdkit_classify_fp.sav & mlp_rdkit_classify_fp.sav models are required only if the [--ignore_rf_svm argument] option in the script is set to False (True is the default behaviour)
. The models are not deposited in the github repository because of size limits, to get these two models a direct request should be sent to mmahmed@ualberta.ca & kbarakat@ualberta.ca

########################
Welcome to GPCR_LigandClassify, this is how you can use the program using the models to make novel predictions, we hope you find these predictions useful for your task: 
       python GPCR_LigandClassify.py --input_file input.csv --output_file output.csv [--n_rows_to_read <INTEGER>] [--mwt_lower_bound <FLOAT>] [--mwt_upper_bound <FLOAT>] [--logp_lower_bound <FLOAT>] [--logp_upper_bound <FLOAT>] [--ignore_rf_svm <True/False>]
########################
The input & output file names arguments are mandatory arguments, --n_rows_to_read argument determines how many rows you want to read from the input CSV files (default 9999999999 rows)
, the rest are optional with default same as input dataset used for models training.
########################
The --ignore_rf_svm argument will ignore the RF and the SVM models which are pretty large, suitable in case of limited computational resourcses, particularly memory. Default is True (Ignore Randomforests and SVM models.)
########################
Please note that a today date string will be attached to the output file name.
########################
Please note that the script will only save ligands where all models predictions agree.
########################
For the input file, please keep the same format as the attached sample input file. In case of data coming from different source, you can populate the rest of columns with fake data.
With the exception of the SMILES column, other columns may be left blank (not recommended).
########################
For the models and auxiliary files, please visit the following github repository:
      https://github.com/mmagithub/GPCR_LigandClassify
########################
inputfile: /home/benjamin/owen/GPCR_LigandClassify/sample_input.csv
outputfile: /home/benjamin/owen/GPCR_LigandClassify/output_2022-10-07.csv
GPCR_LigandClassify.py:207: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  drug_bank_df_selected_cols.dropna(subset = ['smiles'],inplace=True)
Featurizing sample 0
[21:04:14] Explicit valence for atom # 2 O, 3, is greater than permitted
RDKit ERROR: [21:04:14] Explicit valence for atom # 2 O, 3, is greater than permitted
[21:04:17] Explicit valence for atom # 0 N, 4, is greater than permitted
RDKit ERROR: [21:04:17] Explicit valence for atom # 0 N, 4, is greater than permitted
Featurizing sample 1000
[21:04:30] Explicit valence for atom # 2 O, 3, is greater than permitted
RDKit ERROR: [21:04:30] Explicit valence for atom # 2 O, 3, is greater than permitted
[21:04:31] Explicit valence for atom # 0 N, 4, is greater than permitted
RDKit ERROR: [21:04:31] Explicit valence for atom # 0 N, 4, is greater than permitted
/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/rdkit/Chem/PandasTools.py:302: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  lambda smiles: _MolPlusFingerprint(Chem.MolFromSmiles(smiles)))
GPCR_LigandClassify.py:221: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  drug_bank_df_selected_cols_featurized_filtered.dropna(inplace=True)
2022-10-07 21:04:34.752815: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Loaded model from disk
dl_model_fp prediction made
/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:
xgb_fp prediction made
Index([u'index', u'drugbank_id', u'name', u'smiles', u'pubchem_substance_id', u'drug_groups', u'prediction_class_dl_model_fp_prediction', u'prediction_class_dl_model_fp_prediction_proba', u'prediction_class_xgb_fp_prediction', u'prediction_class_xgb_fp_prediction_proba', u'Unnamed: 0', u'gpcr_name', u'first_seg', u'second_seg', u'gpcr_binding_encoded'], dtype='object')
Traceback (most recent call last):
  File "GPCR_LigandClassify.py", line 308, in <module>
    merged_predictions_selcols = merged_predictions_fullcols[['drugbank_id', 'name', u'pubchem_substance_id', 'drug_groups', 'prediction_class_dl_model_fp_prediction', 'prediction_class_dl_model_fp_prediction_proba', 'prediction_class_mlp_fp_prediction', 'prediction_class_mlp_fp_prediction_proba', 'prediction_class_xgb_fp_prediction', 'prediction_class_xgb_fp_prediction_proba', 'prediction_class_rfc_fp_prediction', 'prediction_class_rfc_fp_prediction_proba', 'prediction_class_svm_fp_prediction', 'prediction_class_svm_fp_prediction_proba', 'first_seg','gpcr_binding_encoded']]
  File "/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/pandas/core/frame.py", line 2133, in __getitem__
    return self._getitem_array(key)
  File "/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/pandas/core/frame.py", line 2177, in _getitem_array
    indexer = self.loc._convert_to_indexer(key, axis=1)
  File "/home/benjamin/anaconda3/envs/test-env/lib/python2.7/site-packages/pandas/core/indexing.py", line 1269, in _convert_to_indexer
    .format(mask=objarr[mask]))
KeyError: "[u'prediction_class_mlp_fp_prediction'\n u'prediction_class_mlp_fp_prediction_proba'\n u'prediction_class_rfc_fp_prediction'\n u'prediction_class_rfc_fp_prediction_proba'\n u'prediction_class_svm_fp_prediction'\n u'prediction_class_svm_fp_prediction_proba'] not in index"