From 6ae309603291fce021380f84bfd99eb84514e655 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 11:18:46 +0100 Subject: [PATCH 1/9] environment.yml + pip - prefix from conda/environment.yml --- environment.yml | 314 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 314 insertions(+) create mode 100644 environment.yml diff --git a/environment.yml b/environment.yml new file mode 100644 index 00000000..8fae40b5 --- /dev/null +++ b/environment.yml @@ -0,0 +1,314 @@ +name: atomsci +channels: + - pytorch + - omnia + - deepchem + - rdkit + - conda-forge + - defaults +dependencies: + - _ipyw_jlab_nb_ext_conf=0.1.0=py36he11e457_0 + - absl-py=0.4.1=py_0 + - alabaster=0.7.10=py36h306e16b_0 + - anaconda-client=1.6.14=py36_0 + - anaconda-navigator=1.8.7=py36_0 + - anaconda-project=0.8.2=py36h44fb852_0 + - asn1crypto=0.24.0=py36_0 + - astor=0.7.1=py_0 + - astroid=1.6.3=py36_0 + - astropy=3.0.2=py36h3010b51_1 + - attrs=18.1.0=py36_0 + - babel=2.5.3=py36_0 + - backcall=0.1.0=py36_0 + - backports=1.0=py36hfa02d7e_1 + - backports.shutil_get_terminal_size=1.0.0=py36hfea85ff_2 + - beautifulsoup4=4.6.0=py36h49b8c8c_1 + - bitarray=0.8.1=py36h14c3975_1 + - bkcharts=0.2=py36h735825a_0 + - blas=1.0=mkl + - blaze=0.11.3=py36h4e06776_0 + - bleach=1.5.0=py36_0 + - blosc=1.14.3=hdbcaa40_0 + - bokeh=0.12.16=py36_0 + - boost=1.63.0=py36h415b752_1 + - boto=2.48.0=py36h6e4cd66_1 + - bottleneck=1.2.1=py36haac1ea0_0 + - bzip2=1.0.6=h14c3975_5 + - c-ares=1.14.0=h470a237_0 + - ca-certificates=2019.1.23=0 + - cairo=1.14.12=h7636065_2 + - certifi=2019.3.9=py36_0 + - cffi=1.11.5=py36h9745a5d_0 + - chardet=3.0.4=py36h0f667ec_1 + - click=6.7=py36h5253387_0 + - cloudpickle=0.5.3=py36_0 + - clyent=1.2.2=py36h7e57e65_1 + - colorama=0.3.9=py36h489cec4_0 + - conda=4.6.12=py36_1 + - conda-build=3.10.5=py36_0 + - conda-env=2.6.0=h36134e3_1 + - conda-verify=2.0.0=py36h98955d8_0 + - contextlib2=0.5.5=py36h6c84a62_0 + - cryptography=2.2.2=py36h14c3975_0 + - cudatoolkit=9.0=h13b8566_0 + - curl=7.60.0=h84994c4_0 + - cycler=0.10.0=py36h93f1223_0 + - cython=0.28.2=py36h14c3975_0 + - cytoolz=0.9.0.1=py36h14c3975_0 + - dask=0.17.5=py36_0 + - dask-core=0.17.5=py36_0 + - datashape=0.5.4=py36h3ad6b5c_0 + - dbus=1.13.2=h714fa37_1 + - decorator=4.3.0=py36_0 + - deepchem=2.1.0=py36_0 + - distributed=1.21.8=py36_0 + - docutils=0.14=py36hb0f60f5_0 + - entrypoints=0.2.3=py36h1aec115_2 + - et_xmlfile=1.0.1=py36hd6bccc3_0 + - expat=2.2.5=he0dffb1_0 + - fastcache=1.0.2=py36h14c3975_2 + - fftw3f=3.3.4=2 + - filelock=3.0.4=py36_0 + - flask=1.0.2=py36_1 + - flask-cors=3.0.4=py36_0 + - fontconfig=2.12.6=h49f89f6_0 + - freetype=2.8=hab7d2ae_1 + - gast=0.2.0=py_0 + - get_terminal_size=1.0.0=haa9412d_0 + - gevent=1.3.0=py36h14c3975_0 + - glib=2.56.1=h000015b_0 + - glob2=0.6=py36he249c77_0 + - gmp=6.1.2=h6c8ec71_1 + - gmpy2=2.0.8=py36hc8893dd_2 + - graphite2=1.3.11=h16798f4_2 + - greenlet=0.4.13=py36h14c3975_0 + - grpcio=1.14.1=py36hd60e7a3_0 + - gst-plugins-base=1.14.0=hbbd80ab_1 + - gstreamer=1.14.0=hb453b48_1 + - h5py=2.7.1=py36ha1f6525_2 + - harfbuzz=1.7.6=h5f0a787_1 + - hdf5=1.10.2=hba1933b_1 + - heapdict=1.0.0=py36_2 + - html5lib=0.9999999=py36_0 + - icu=58.2=h9c2bf20_1 + - idna=2.6=py36h82fb2a8_1 + - imageio=2.3.0=py36_0 + - imagesize=1.0.0=py36_0 + - intel-openmp=2018.0.0=8 + - ipykernel=4.8.2=py36_0 + - ipython=6.4.0=py36_0 + - ipython_genutils=0.2.0=py36hb52b0d5_0 + - ipywidgets=7.2.1=py36_0 + - isort=4.3.4=py36_0 + - itsdangerous=0.24=py36h93cc618_1 + - jbig=2.1=hdba287a_0 + - jdcal=1.4=py36_0 + - jedi=0.12.0=py36_1 + - jinja2=2.10=py36ha16c418_0 + - joblib=0.11=py36_0 + - jpeg=9b=h024ee3a_2 + - jsonschema=2.6.0=py36h006f8b5_0 + - jupyter=1.0.0=py36_4 + - jupyter_client=5.2.3=py36_0 + - jupyter_console=5.2.0=py36he59e554_1 + - jupyter_core=4.4.0=py36h7c827e3_0 + - jupyterlab=0.32.1=py36_0 + - jupyterlab_launcher=0.10.5=py36_0 + - kiwisolver=1.0.1=py36h764f252_0 + - lazy-object-proxy=1.3.1=py36h10fcdad_0 + - libcurl=7.60.0=h1ad7b7a_0 + - libedit=3.1.20170329=h6b74fdf_2 + - libffi=3.2.1=hd88cf55_4 + - libgcc-ng=8.2.0=hdf63c60_1 + - libgfortran-ng=7.2.0=hdf63c60_3 + - libpng=1.6.34=hb9fc6fc_0 + - libprotobuf=3.6.0=hd28b015_0 + - libsodium=1.0.16=h1bed415_0 + - libssh2=1.8.0=h9cfc8f7_4 + - libstdcxx-ng=7.2.0=hdf63c60_3 + - libtiff=4.0.9=he85c1e1_1 + - libtool=2.4.6=h544aabb_3 + - libxcb=1.13=h1bed415_1 + - libxml2=2.9.8=h26e45fe_1 + - libxslt=1.1.32=h1312cb7_0 + - llvmlite=0.23.1=py36hdbcaa40_0 + - locket=0.2.0=py36h787c0ad_1 + - lxml=4.2.1=py36h23eabaa_0 + - lzo=2.10=h49e0be7_2 + - markdown=2.6.11=py_0 + - markupsafe=1.0=py36hd9260cd_1 + - matplotlib=2.2.2=py36h0e671d2_1 + - mccabe=0.6.1=py36h5ad9710_1 + - mdtraj=1.9.1=py36_1 + - mistune=0.8.3=py36h14c3975_1 + - mkl=2018.0.3=1 + - mkl-service=1.1.2=py36h17a0993_4 + - mkl_fft=1.0.4=py36h4414c95_1 + - mkl_random=1.0.1=py36h629b387_0 + - more-itertools=4.1.0=py36_0 + - mpc=1.0.3=hec55b23_5 + - mpfr=3.1.5=h11a74b3_2 + - mpmath=1.0.0=py36hfeacd6b_2 + - msgpack-python=0.5.6=py36h6bb024c_0 + - multipledispatch=0.5.0=py36_0 + - navigator-updater=0.2.1=py36_0 + - nbconvert=5.3.1=py36hb41ffb7_0 + - nbformat=4.4.0=py36h31c9010_0 + - ncurses=6.1=hf484d3e_0 + - networkx=2.1=py36_0 + - ninja=1.8.2=py36h6bb024c_1 + - nltk=3.3.0=py36_0 + - nose=1.3.7=py36hcdf7029_2 + - notebook=5.5.0=py36_0 + - numba=0.38.0=py36h637b7d7_0 + - numexpr=2.6.5=py36h7bf3b9c_0 + - numpydoc=0.8.0=py36_0 + - odo=0.5.1=py36h90ed295_0 + - olefile=0.45.1=py36_0 + - openmm=7.2.2=py36_1 + - openpyxl=2.5.3=py36_0 + - openssl=1.0.2r=h7b6447c_0 + - packaging=17.1=py36_0 + - pandas=0.22.0=py36_1 + - pandoc=1.19.2.1=hea2e7c5_1 + - pandocfilters=1.4.2=py36ha6701b7_1 + - pango=1.41.0=hd475d92_0 + - parso=0.2.0=py36_0 + - partd=0.3.8=py36h36fd896_0 + - patchelf=0.9=hf79760b_2 + - path.py=11.0.1=py36_0 + - pathlib2=2.3.2=py36_0 + - patsy=0.5.0=py36_0 + - pcre=8.42=h439df22_0 + - pdbfixer=1.4=py36_0 + - pep8=1.7.1=py36_0 + - pexpect=4.5.0=py36_0 + - pickleshare=0.7.4=py36h63277f8_0 + - pillow=5.0.0=py36_0 + - pixman=0.34.0=hceecf20_3 + - pkginfo=1.4.2=py36_1 + - pluggy=0.6.0=py36hb689045_0 + - ply=3.11=py36_0 + - prompt_toolkit=1.0.15=py36h17d85b1_0 + - protobuf=3.6.0=py36hfc679d8_0 + - psutil=5.4.5=py36h14c3975_0 + - ptyprocess=0.5.2=py36h69acd42_0 + - py=1.5.3=py36_0 + - pycodestyle=2.4.0=py36_0 + - pycosat=0.6.3=py36h0a5515d_0 + - pycparser=2.18=py36hf9f622e_1 + - pycrypto=2.6.1=py36h14c3975_8 + - pycurl=7.43.0.1=py36hb7f436b_0 + - pyflakes=1.6.0=py36h7bd6a15_0 + - pygments=2.2.0=py36h0d3125c_0 + - pylint=1.8.4=py36_0 + - pyodbc=4.0.23=py36hf484d3e_0 + - pyopenssl=18.0.0=py36_0 + - pyparsing=2.2.0=py36hee85983_1 + - pyqt=5.9.2=py36h751905a_0 + - pysocks=1.6.8=py36_0 + - pytables=3.4.3=py36h02b9ad4_2 + - pytest=3.5.1=py36_0 + - pytest-arraydiff=0.2=py36_0 + - pytest-astropy=0.3.0=py36_0 + - pytest-doctestplus=0.1.3=py36_0 + - pytest-openfiles=0.3.0=py36_0 + - pytest-remotedata=0.2.1=py36_0 + - python=3.6.6=hc3d631a_0 + - python-dateutil=2.7.3=py36_0 + - pytorch=1.0.1=py3.6_cuda9.0.176_cudnn7.4.2_2 + - pytz=2018.4=py36_0 + - pywavelets=0.5.2=py36he602eb0_0 + - pyyaml=3.12=py36hafb9ca4_1 + - pyzmq=17.0.0=py36h14c3975_0 + - qt=5.9.5=h7e424d6_0 + - qtawesome=0.4.4=py36h609ed8c_0 + - qtconsole=4.3.1=py36h8f73b5b_0 + - qtpy=1.4.1=py36_0 + - rdkit=2017.09.1=py36_1 + - readline=7.0=ha6073c6_4 + - requests=2.18.4=py36he2e5f8d_1 + - rope=0.10.7=py36h147e2ec_0 + - ruamel_yaml=0.15.35=py36h14c3975_1 + - scikit-image=0.13.1=py36h14c3975_1 + - scikit-learn=0.19.1=py36h7aa7ec6_0 + - scipy=1.1.0=py36hfc37229_0 + - seaborn=0.9.0=py36_0 + - send2trash=1.5.0=py36_0 + - setuptools=39.1.0=py36_0 + - simdna=0.4.2=py_0 + - simplegeneric=0.8.1=py36_2 + - singledispatch=3.4.0.3=py36h7a266c3_0 + - sip=4.19.8=py36hf484d3e_0 + - six=1.11.0=py36h372c433_1 + - snappy=1.1.7=hbae5bb6_3 + - snowballstemmer=1.2.1=py36h6febd40_0 + - sortedcollections=0.6.1=py36_0 + - sortedcontainers=1.5.10=py36_0 + - sphinx=1.7.4=py36_0 + - sphinxcontrib=1.0=py36h6d0f590_1 + - sphinxcontrib-websupport=1.0.1=py36hb5cb234_1 + - spyder=3.2.8=py36_0 + - sqlalchemy=1.2.7=py36h6b74fdf_0 + - sqlite=3.24.0=h84994c4_0 + - statsmodels=0.9.0=py36h3010b51_0 + - sympy=1.1.1=py36hc6d1c1c_0 + - tblib=1.3.2=py36h34cf8b6_0 + - tensorboard=1.6.0=py36_0 + - tensorflow=1.6.0=py36_0 + - termcolor=1.1.0=py_2 + - terminado=0.8.1=py36_1 + - testpath=0.3.1=py36h8cadb63_0 + - tk=8.6.7=hc745277_3 + - toolz=0.9.0=py36_0 + - torchvision=0.2.2=py_3 + - tornado=5.0.2=py36_0 + - traitlets=4.3.2=py36h674d592_0 + - typing=3.6.4=py36_0 + - unicodecsv=0.14.1=py36ha668878_0 + - unixodbc=2.3.6=h1bed415_0 + - urllib3=1.22=py36hbe7ace6_0 + - wcwidth=0.1.7=py36hdf4376a_0 + - webencodings=0.5.1=py36h800622e_1 + - werkzeug=0.14.1=py36_0 + - wheel=0.31.1=py36_0 + - widgetsnbextension=3.2.1=py36_0 + - wrapt=1.10.11=py36h28b7045_0 + - xgboost=0.6a2=py36_2 + - xlrd=1.1.0=py36h1db9f0c_1 + - xlsxwriter=1.0.4=py36_0 + - xlwt=1.3.0=py36h7b00a1f_0 + - xz=5.2.4=h14c3975_4 + - yaml=0.1.7=had09818_2 + - zeromq=4.2.5=h439df22_0 + - zict=0.1.3=py36h3a3bf81_0 + - zlib=1.2.11=ha838bed_2 + - pip + - pip: + - anyconfig==0.9.7 + - bravado==10.2.1 + - bravado-core==5.10.0 + - feather-format==0.4.0 + - jsonref==0.2 + - keras==2.2.4 + - keras-applications==1.0.7 + - keras-preprocessing==1.0.9 + - libroadrunner==1.5.3 + - molvs==0.1.1 + - monotonic==1.5 + - mordred==1.2.0 + - msgpack==0.6.1 + - numpy==1.16.2 + - pip==19.3.1 + - py4j==0.10.7 + - pyaml==18.11.0 + - pyarrow==0.12.0 + - pyspark==2.4.0 + - rfc3987==1.3.8 + - simplejson==3.16.0 + - strict-rfc3339==0.7 + - swagger-spec-validator==2.4.1 + - umap-learn==0.3.7 + - webcolors==1.8.1 +prefix: /home/mt424895/anaconda3/envs/atomsci + From 9f4926df9f33d2c48cbc58b1484078d86b1950d1 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 13:32:36 +0100 Subject: [PATCH 2/9] prefix modified for mybinder.org --- environment.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index 8fae40b5..ce5d1976 100644 --- a/environment.yml +++ b/environment.yml @@ -310,5 +310,4 @@ dependencies: - swagger-spec-validator==2.4.1 - umap-learn==0.3.7 - webcolors==1.8.1 -prefix: /home/mt424895/anaconda3/envs/atomsci - +prefix: /srv/conda/envs/notebook/envs/atomsci From 9dfa42927db378ed68a200d0ba934a5e29f193e7 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:20:46 +0100 Subject: [PATCH 3/9] fix TOPDIR --- build.sh | 2 +- install.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/build.sh b/build.sh index 97b990c0..d55d1e81 100755 --- a/build.sh +++ b/build.sh @@ -5,7 +5,7 @@ APP=ampl DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" cd $DIR -TOPDIR=`readlink -f .` +TOPDIR=`readlink -f .`/ BUILD_DIR=${TOPDIR}.build/$APP DIST_DIR=${TOPDIR}.dist diff --git a/install.sh b/install.sh index 22d13b29..8281551c 100755 --- a/install.sh +++ b/install.sh @@ -12,7 +12,7 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" echo "DIR: $DIR" cd $DIR -TOPDIR=`readlink -f .` +TOPDIR=`readlink -f .`/ DIST_DIR=${TOPDIR}.dist From 22c6db55c80fd914cf30a4289c801ec57038a9d2 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:21:20 +0100 Subject: [PATCH 4/9] copy the example to the top level and change the path of the data set --- Delaney_Example.ipynb | 335 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 335 insertions(+) create mode 100644 Delaney_Example.ipynb diff --git a/Delaney_Example.ipynb b/Delaney_Example.ipynb new file mode 100644 index 00000000..6bbfea87 --- /dev/null +++ b/Delaney_Example.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predicting solubility using the ATOM Modeling Pipeline (AMPL) on the public Delaney solubility dataset\n", + "\n", + "In this notebook, we describe the AMPL used to curate a public dataset, fit a simple model to predict solubility from chemical structure, and predict solubility for withheld compounds.\n", + "\n", + "## Set up\n", + "We first import the AMPL modules for use in this notebook.\n", + "\n", + "The relevant AMPL modules for this example are listed below:\n", + "\n", + "|module|Description|\n", + "|-|-|\n", + "|`atomsci.ddm.pipeline.model_pipeline`|The model pipeline module is used to fit models and load models for prediction.|\n", + "|`atomsci.ddm.pipeline.parameter_parser`|The parameter parser reads through pipeline options for the model pipeline.|\n", + "|`atomsci.ddm.utils.curate_data`|The curate data module is used for data loading and pre-processing.|\n", + "|`atomsci.ddm.utils.struct_utils`|The structure utilities module is used to process loaded structures.|\n", + "|`atomsci.ddm.pipeline.perf_plots`|Perf plots contains a variety of plotting functions.|" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# We temporarily disable warnings for demonstration.\n", + "# FutureWarnings and DeprecationWarnings are present from some of the AMPL dependency modules.\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import requests\n", + "import sys\n", + "\n", + "import atomsci.ddm.pipeline.model_pipeline as mp\n", + "import atomsci.ddm.pipeline.parameter_parser as parse\n", + "import atomsci.ddm.utils.curate_data as curate_data\n", + "import atomsci.ddm.utils.struct_utils as struct_utils\n", + "from atomsci.ddm.pipeline import perf_plots as pp\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data curation\n", + "\n", + "We then download and do very simple curation to the related dataset.\n", + "\n", + "We need to set the directory we want to save files to. Next we download the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "working_dir = '/home/jovyan/atomsci/ddm/data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Download the Delaney dataset\n", + "dataset_file = os.path.join(working_dir, 'delaney-processed.csv')\n", + "if (not os.path.isfile(dataset_file)):\n", + " r = requests.get('http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/delaney-processed.csv', verify=True)\n", + " with open(dataset_file, 'wb') as f:\n", + " f.write(r.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the downloaded dataset, and process the compound structures:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Delaney dataset\n", + "raw_df = pd.read_csv(dataset_file)\n", + "\n", + "# Generate SMILES, InChI keys for dataset with curation and structure modules.\n", + "# RDkit modules are used to process the SMILES strings\n", + "raw_df['rdkit_smiles'] = raw_df['smiles'].apply(curate_data.base_smiles_from_smiles)\n", + "raw_df['inchi_key'] = raw_df['smiles'].apply(struct_utils.smiles_to_inchi_key)\n", + "\n", + "data = raw_df\n", + "data['compound_id'] = data['inchi_key']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to address the case where we have multiple measurements for a single structure (by RDkit canonical SMILEs string). We have a function in the `curate_data()` module to address process compounds. The function parameters are listed below along with an explanation of each parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad duplicates removed from dataset\n", + "Dataframe size (1128, 17)\n", + "List of 'bad' duplicates removed\n", + "Empty DataFrame\n", + "Columns: [compound_id, measured log solubility in mols per litre, VALUE_NUM_mean, Perc_Var, VALUE_NUM_std]\n", + "Index: []\n", + "\n", + "Dataset de-duplicated\n", + "Dataframe size (1117, 17)\n", + "New column created with averaged values: VALUE_NUM_mean\n" + ] + } + ], + "source": [ + "# column: Response values column\n", + "column = 'measured log solubility in mols per litre'\n", + "\n", + "# tolerance: Percentage of individual respsonse values allowed to different from the average to be included in averaging\n", + "tolerance = 10\n", + "\n", + "# list_bad_duplicates: Print structures with bad duplicates\n", + "list_bad_duplicates = 'Yes'\n", + "\n", + "# max_std: Maximum allowed standard deviation for computed average response value\n", + "# NOTE: In this example, we set this value very high to disable this feature\n", + "max_std = 100000\n", + "\n", + "# compound_id: Compound ID column\n", + "compound_id = 'compound_id'\n", + "\n", + "# smiles_col: SMILES column\n", + "smiles_col = 'rdkit_smiles'\n", + "\n", + "curated_df = curate_data.average_and_remove_duplicates(column, tolerance, list_bad_duplicates, data, max_std,\n", + " compound_id=compound_id, smiles_col=smiles_col)\n", + "curated_file = os.path.join(working_dir, 'delaney_curated.csv')\n", + "curated_df.to_csv(curated_file, index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a curated dataset, we decide what type of featurizer and model we would like. See documentation for all available options. We also set the name of the new averaged response value column." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "featurizer = 'ecfp'\n", + "model_type = 'RF'\n", + "response_cols = ['VALUE_NUM_mean']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we set up the parameters for our model. We set datastore and save_results to False to indicate that we are reading the input file and saving the results directly to the file system. There are a wide range of settable parameters; see the documentation for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "params={\"datastore\": \"False\",\n", + " \"save_results\": \"False\",\n", + " \"id_col\": compound_id,\n", + " \"smiles_col\": smiles_col,\n", + " \"response_cols\": response_cols,\n", + " \"featurizer\": featurizer,\n", + " \"model_type\": model_type,\n", + " \"result_dir\": working_dir,\n", + " \"dataset_key\": curated_file}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use parse.wrapper to process our input configuration. We then build the model pipeline, train the model, and plot the predicted versus true values for our train, valid, test sets." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2019-12-05 23:35:26,946 Splitting data by scaffold\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of features: 1024\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2019-12-05 23:35:29,435 Dataset split table saved to /usr/local/data/delaney_curated_train_valid_test_scaffold_ad460782-3a43-462b-9c98-a7be72ed6a9d.csv\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_cnt [1117.]\n", + "y_means [-3.05005819]\n", + "y_stds [2.09451877]\n", + "TIMING: dataset construction took 0.302 s\n", + "Loading dataset from disk.\n", + "TIMING: dataset construction took 0.017 s\n", + "Loading dataset from disk.\n", + "TIMING: dataset construction took 0.017 s\n", + "Loading dataset from disk.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2019-12-05 23:35:34,791 Wrote model metadata to file /usr/local/data/delaney_curated/RF_ecfp_scaffold_regression/45423d3c-717c-4972-90fe-ae6d407f8ee3/model_metadata.json\n", + "2019-12-05 23:35:34,798 Wrote model metrics to file /usr/local/data/delaney_curated/RF_ecfp_scaffold_regression/45423d3c-717c-4972-90fe-ae6d407f8ee3/training_model_metrics.json\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pparams = parse.wrapper(params)\n", + "MP = mp.ModelPipeline(pparams)\n", + "MP.train_model()\n", + "pp.plot_pred_vs_actual(MP)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f20b2122dafd330ec877ec0a5be3942b37714dde Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:22:35 +0100 Subject: [PATCH 5/9] tentative mybinder --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index dea8d1ec..d1414315 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,9 @@ +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master) + + AMPL is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery. From 69e8b34bff462bd9b6b93c7226d52f3fed62b5ad Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:26:07 +0100 Subject: [PATCH 6/9] postinstallation script --- postBuild | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 postBuild diff --git a/postBuild b/postBuild new file mode 100644 index 00000000..6ec0a740 --- /dev/null +++ b/postBuild @@ -0,0 +1,2 @@ +./build.sh && ./install.sh + From 6b756789daa54c7230fce94da9af17b63d5d15e8 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:40:05 +0100 Subject: [PATCH 7/9] move the notebook back into the origina place --- Delaney_Example.ipynb | 335 ------------------------------ atomsci/ddm/Delaney_Example.ipynb | 2 +- 2 files changed, 1 insertion(+), 336 deletions(-) delete mode 100644 Delaney_Example.ipynb diff --git a/Delaney_Example.ipynb b/Delaney_Example.ipynb deleted file mode 100644 index 6bbfea87..00000000 --- a/Delaney_Example.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Predicting solubility using the ATOM Modeling Pipeline (AMPL) on the public Delaney solubility dataset\n", - "\n", - "In this notebook, we describe the AMPL used to curate a public dataset, fit a simple model to predict solubility from chemical structure, and predict solubility for withheld compounds.\n", - "\n", - "## Set up\n", - "We first import the AMPL modules for use in this notebook.\n", - "\n", - "The relevant AMPL modules for this example are listed below:\n", - "\n", - "|module|Description|\n", - "|-|-|\n", - "|`atomsci.ddm.pipeline.model_pipeline`|The model pipeline module is used to fit models and load models for prediction.|\n", - "|`atomsci.ddm.pipeline.parameter_parser`|The parameter parser reads through pipeline options for the model pipeline.|\n", - "|`atomsci.ddm.utils.curate_data`|The curate data module is used for data loading and pre-processing.|\n", - "|`atomsci.ddm.utils.struct_utils`|The structure utilities module is used to process loaded structures.|\n", - "|`atomsci.ddm.pipeline.perf_plots`|Perf plots contains a variety of plotting functions.|" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# We temporarily disable warnings for demonstration.\n", - "# FutureWarnings and DeprecationWarnings are present from some of the AMPL dependency modules.\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "import requests\n", - "import sys\n", - "\n", - "import atomsci.ddm.pipeline.model_pipeline as mp\n", - "import atomsci.ddm.pipeline.parameter_parser as parse\n", - "import atomsci.ddm.utils.curate_data as curate_data\n", - "import atomsci.ddm.utils.struct_utils as struct_utils\n", - "from atomsci.ddm.pipeline import perf_plots as pp\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data curation\n", - "\n", - "We then download and do very simple curation to the related dataset.\n", - "\n", - "We need to set the directory we want to save files to. Next we download the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "working_dir = '/home/jovyan/atomsci/ddm/data'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the Delaney dataset\n", - "dataset_file = os.path.join(working_dir, 'delaney-processed.csv')\n", - "if (not os.path.isfile(dataset_file)):\n", - " r = requests.get('http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/delaney-processed.csv', verify=True)\n", - " with open(dataset_file, 'wb') as f:\n", - " f.write(r.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we load the downloaded dataset, and process the compound structures:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the Delaney dataset\n", - "raw_df = pd.read_csv(dataset_file)\n", - "\n", - "# Generate SMILES, InChI keys for dataset with curation and structure modules.\n", - "# RDkit modules are used to process the SMILES strings\n", - "raw_df['rdkit_smiles'] = raw_df['smiles'].apply(curate_data.base_smiles_from_smiles)\n", - "raw_df['inchi_key'] = raw_df['smiles'].apply(struct_utils.smiles_to_inchi_key)\n", - "\n", - "data = raw_df\n", - "data['compound_id'] = data['inchi_key']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next step is to address the case where we have multiple measurements for a single structure (by RDkit canonical SMILEs string). We have a function in the `curate_data()` module to address process compounds. The function parameters are listed below along with an explanation of each parameter:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad duplicates removed from dataset\n", - "Dataframe size (1128, 17)\n", - "List of 'bad' duplicates removed\n", - "Empty DataFrame\n", - "Columns: [compound_id, measured log solubility in mols per litre, VALUE_NUM_mean, Perc_Var, VALUE_NUM_std]\n", - "Index: []\n", - "\n", - "Dataset de-duplicated\n", - "Dataframe size (1117, 17)\n", - "New column created with averaged values: VALUE_NUM_mean\n" - ] - } - ], - "source": [ - "# column: Response values column\n", - "column = 'measured log solubility in mols per litre'\n", - "\n", - "# tolerance: Percentage of individual respsonse values allowed to different from the average to be included in averaging\n", - "tolerance = 10\n", - "\n", - "# list_bad_duplicates: Print structures with bad duplicates\n", - "list_bad_duplicates = 'Yes'\n", - "\n", - "# max_std: Maximum allowed standard deviation for computed average response value\n", - "# NOTE: In this example, we set this value very high to disable this feature\n", - "max_std = 100000\n", - "\n", - "# compound_id: Compound ID column\n", - "compound_id = 'compound_id'\n", - "\n", - "# smiles_col: SMILES column\n", - "smiles_col = 'rdkit_smiles'\n", - "\n", - "curated_df = curate_data.average_and_remove_duplicates(column, tolerance, list_bad_duplicates, data, max_std,\n", - " compound_id=compound_id, smiles_col=smiles_col)\n", - "curated_file = os.path.join(working_dir, 'delaney_curated.csv')\n", - "curated_df.to_csv(curated_file, index=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a curated dataset, we decide what type of featurizer and model we would like. See documentation for all available options. We also set the name of the new averaged response value column." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "featurizer = 'ecfp'\n", - "model_type = 'RF'\n", - "response_cols = ['VALUE_NUM_mean']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we set up the parameters for our model. We set datastore and save_results to False to indicate that we are reading the input file and saving the results directly to the file system. There are a wide range of settable parameters; see the documentation for more details." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "params={\"datastore\": \"False\",\n", - " \"save_results\": \"False\",\n", - " \"id_col\": compound_id,\n", - " \"smiles_col\": smiles_col,\n", - " \"response_cols\": response_cols,\n", - " \"featurizer\": featurizer,\n", - " \"model_type\": model_type,\n", - " \"result_dir\": working_dir,\n", - " \"dataset_key\": curated_file}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use parse.wrapper to process our input configuration. We then build the model pipeline, train the model, and plot the predicted versus true values for our train, valid, test sets." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2019-12-05 23:35:26,946 Splitting data by scaffold\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of features: 1024\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2019-12-05 23:35:29,435 Dataset split table saved to /usr/local/data/delaney_curated_train_valid_test_scaffold_ad460782-3a43-462b-9c98-a7be72ed6a9d.csv\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_cnt [1117.]\n", - "y_means [-3.05005819]\n", - "y_stds [2.09451877]\n", - "TIMING: dataset construction took 0.302 s\n", - "Loading dataset from disk.\n", - "TIMING: dataset construction took 0.017 s\n", - "Loading dataset from disk.\n", - "TIMING: dataset construction took 0.017 s\n", - "Loading dataset from disk.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2019-12-05 23:35:34,791 Wrote model metadata to file /usr/local/data/delaney_curated/RF_ecfp_scaffold_regression/45423d3c-717c-4972-90fe-ae6d407f8ee3/model_metadata.json\n", - "2019-12-05 23:35:34,798 Wrote model metrics to file /usr/local/data/delaney_curated/RF_ecfp_scaffold_regression/45423d3c-717c-4972-90fe-ae6d407f8ee3/training_model_metrics.json\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pparams = parse.wrapper(params)\n", - "MP = mp.ModelPipeline(pparams)\n", - "MP.train_model()\n", - "pp.plot_pred_vs_actual(MP)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/atomsci/ddm/Delaney_Example.ipynb b/atomsci/ddm/Delaney_Example.ipynb index 8e8cce32..6bbfea87 100644 --- a/atomsci/ddm/Delaney_Example.ipynb +++ b/atomsci/ddm/Delaney_Example.ipynb @@ -64,7 +64,7 @@ "metadata": {}, "outputs": [], "source": [ - "working_dir = '/usr/local/data'" + "working_dir = '/home/jovyan/atomsci/ddm/data'" ] }, { From 54678f1641b4599ef3cc27353d2217cba349d1b1 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:45:04 +0100 Subject: [PATCH 8/9] adding a second button for the notebook version --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d1414315..752d7ed8 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master?filepath=atomsci%2Fddm%2FDelaney_Example.ipynb) AMPL is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery. From 0295d56166632647b194e7221eab315bac74dfe0 Mon Sep 17 00:00:00 2001 From: "Tru Huynh (pasteur.fr)" Date: Fri, 6 Dec 2019 14:59:29 +0100 Subject: [PATCH 9/9] tentative label for the 2nd button --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 752d7ed8..d1e1d2eb 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master?filepath=atomsci%2Fddm%2FDelaney_Example.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg) with notebook](https://mybinder.org/v2/gh/truatpasteurdotfr/AMPL/master?filepath=atomsci%2Fddm%2FDelaney_Example.ipynb) AMPL is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.