13_AMPL_HPO_Part2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "13_AMPL_HPO_Part2.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "SZ70ierx3pq2",
        "mTOwqhvR3xL3",
        "XA8ARleo3h53",
        "pKP_mFuB5giz",
        "dBQ-bRaW9oq5",
        "vK6rAXyO9rL8",
        "quStVkT996lA",
        "o1rIVfBKCPT5",
        "oHjYDaTNBYjQ"
      ],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ravichas/AMPL-Tutorial/blob/master/13_AMPL_HPO_Part2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ70ierx3pq2"
      },
      "source": [
        "### Install AMPL packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYPEij6rwDmP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7a92d80-2cd5-4b47-ceb5-2eb83aa12e59"
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNuv4Q5KFABw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99000104-d5be-46a6-b4fc-5bc0b26f683b"
      },
      "source": [
        "! wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
        "! chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
        "! bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
        "\n",
        "# ! wget -c https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh \n",
        "# ! chmod +x Miniconda3-py37_4.9.2-Linux-x86_64.sh\n",
        "# ! time bash Miniconda3-py37_4.9.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "\n",
        "! time conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-10 03:02:31--  https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 530308481 (506M) [application/x-sh]\n",
            "Saving to: ‘Anaconda3-2019.10-Linux-x86_64.sh’\n",
            "\n",
            "Anaconda3-2019.10-L 100%[===================>] 505.74M   183MB/s    in 2.8s    \n",
            "\n",
            "2021-04-10 03:02:34 (183 MB/s) - ‘Anaconda3-2019.10-Linux-x86_64.sh’ saved [530308481/530308481]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - _ipyw_jlab_nb_ext_conf==0.1.0=py37_0\n",
            "    - _libgcc_mutex==0.1=main\n",
            "    - alabaster==0.7.12=py37_0\n",
            "    - anaconda-client==1.7.2=py37_0\n",
            "    - anaconda-navigator==1.9.7=py37_0\n",
            "    - anaconda-project==0.8.3=py_0\n",
            "    - anaconda==2019.10=py37_0\n",
            "    - asn1crypto==1.0.1=py37_0\n",
            "    - astroid==2.3.1=py37_0\n",
            "    - astropy==3.2.2=py37h7b6447c_0\n",
            "    - atomicwrites==1.3.0=py37_1\n",
            "    - attrs==19.2.0=py_0\n",
            "    - babel==2.7.0=py_0\n",
            "    - backcall==0.1.0=py37_0\n",
            "    - backports.functools_lru_cache==1.5=py_2\n",
            "    - backports.os==0.1.1=py37_0\n",
            "    - backports.shutil_get_terminal_size==1.0.0=py37_2\n",
            "    - backports.tempfile==1.0=py_1\n",
            "    - backports.weakref==1.0.post1=py_1\n",
            "    - backports==1.0=py_2\n",
            "    - beautifulsoup4==4.8.0=py37_0\n",
            "    - bitarray==1.0.1=py37h7b6447c_0\n",
            "    - bkcharts==0.2=py37_0\n",
            "    - blas==1.0=mkl\n",
            "    - bleach==3.1.0=py37_0\n",
            "    - blosc==1.16.3=hd408876_0\n",
            "    - bokeh==1.3.4=py37_0\n",
            "    - boto==2.49.0=py37_0\n",
            "    - bottleneck==1.2.1=py37h035aef0_1\n",
            "    - bzip2==1.0.8=h7b6447c_0\n",
            "    - ca-certificates==2019.8.28=0\n",
            "    - cairo==1.14.12=h8948797_3\n",
            "    - certifi==2019.9.11=py37_0\n",
            "    - cffi==1.12.3=py37h2e261b9_0\n",
            "    - chardet==3.0.4=py37_1003\n",
            "    - click==7.0=py37_0\n",
            "    - cloudpickle==1.2.2=py_0\n",
            "    - clyent==1.2.2=py37_1\n",
            "    - colorama==0.4.1=py37_0\n",
            "    - conda-build==3.18.9=py37_3\n",
            "    - conda-env==2.6.0=1\n",
            "    - conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "    - conda-verify==3.4.2=py_1\n",
            "    - conda==4.7.12=py37_0\n",
            "    - contextlib2==0.6.0=py_0\n",
            "    - cryptography==2.7=py37h1ba5d50_0\n",
            "    - curl==7.65.3=hbc83047_0\n",
            "    - cycler==0.10.0=py37_0\n",
            "    - cython==0.29.13=py37he6710b0_0\n",
            "    - cytoolz==0.10.0=py37h7b6447c_0\n",
            "    - dask-core==2.5.2=py_0\n",
            "    - dask==2.5.2=py_0\n",
            "    - dbus==1.13.6=h746ee38_0\n",
            "    - decorator==4.4.0=py37_1\n",
            "    - defusedxml==0.6.0=py_0\n",
            "    - distributed==2.5.2=py_0\n",
            "    - docutils==0.15.2=py37_0\n",
            "    - entrypoints==0.3=py37_0\n",
            "    - et_xmlfile==1.0.1=py37_0\n",
            "    - expat==2.2.6=he6710b0_0\n",
            "    - fastcache==1.1.0=py37h7b6447c_0\n",
            "    - filelock==3.0.12=py_0\n",
            "    - flask==1.1.1=py_0\n",
            "    - fontconfig==2.13.0=h9420a91_0\n",
            "    - freetype==2.9.1=h8a8886c_1\n",
            "    - fribidi==1.0.5=h7b6447c_0\n",
            "    - fsspec==0.5.2=py_0\n",
            "    - future==0.17.1=py37_0\n",
            "    - get_terminal_size==1.0.0=haa9412d_0\n",
            "    - gevent==1.4.0=py37h7b6447c_0\n",
            "    - glib==2.56.2=hd408876_0\n",
            "    - glob2==0.7=py_0\n",
            "    - gmp==6.1.2=h6c8ec71_1\n",
            "    - gmpy2==2.0.8=py37h10f8cd9_2\n",
            "    - graphite2==1.3.13=h23475e2_0\n",
            "    - greenlet==0.4.15=py37h7b6447c_0\n",
            "    - gst-plugins-base==1.14.0=hbbd80ab_1\n",
            "    - gstreamer==1.14.0=hb453b48_1\n",
            "    - h5py==2.9.0=py37h7918eee_0\n",
            "    - harfbuzz==1.8.8=hffaf4a1_0\n",
            "    - hdf5==1.10.4=hb1b8bf9_0\n",
            "    - heapdict==1.0.1=py_0\n",
            "    - html5lib==1.0.1=py37_0\n",
            "    - icu==58.2=h9c2bf20_1\n",
            "    - idna==2.8=py37_0\n",
            "    - imageio==2.6.0=py37_0\n",
            "    - imagesize==1.1.0=py37_0\n",
            "    - importlib_metadata==0.23=py37_0\n",
            "    - intel-openmp==2019.4=243\n",
            "    - ipykernel==5.1.2=py37h39e3cac_0\n",
            "    - ipython==7.8.0=py37h39e3cac_0\n",
            "    - ipython_genutils==0.2.0=py37_0\n",
            "    - ipywidgets==7.5.1=py_0\n",
            "    - isort==4.3.21=py37_0\n",
            "    - itsdangerous==1.1.0=py37_0\n",
            "    - jbig==2.1=hdba287a_0\n",
            "    - jdcal==1.4.1=py_0\n",
            "    - jedi==0.15.1=py37_0\n",
            "    - jeepney==0.4.1=py_0\n",
            "    - jinja2==2.10.3=py_0\n",
            "    - joblib==0.13.2=py37_0\n",
            "    - jpeg==9b=h024ee3a_2\n",
            "    - json5==0.8.5=py_0\n",
            "    - jsonschema==3.0.2=py37_0\n",
            "    - jupyter==1.0.0=py37_7\n",
            "    - jupyter_client==5.3.3=py37_1\n",
            "    - jupyter_console==6.0.0=py37_0\n",
            "    - jupyter_core==4.5.0=py_0\n",
            "    - jupyterlab==1.1.4=pyhf63ae98_0\n",
            "    - jupyterlab_server==1.0.6=py_0\n",
            "    - keyring==18.0.0=py37_0\n",
            "    - kiwisolver==1.1.0=py37he6710b0_0\n",
            "    - krb5==1.16.1=h173b8e3_7\n",
            "    - lazy-object-proxy==1.4.2=py37h7b6447c_0\n",
            "    - libarchive==3.3.3=h5d8350f_5\n",
            "    - libcurl==7.65.3=h20c2e04_0\n",
            "    - libedit==3.1.20181209=hc058e9b_0\n",
            "    - libffi==3.2.1=hd88cf55_4\n",
            "    - libgcc-ng==9.1.0=hdf63c60_0\n",
            "    - libgfortran-ng==7.3.0=hdf63c60_0\n",
            "    - liblief==0.9.0=h7725739_2\n",
            "    - libpng==1.6.37=hbc83047_0\n",
            "    - libsodium==1.0.16=h1bed415_0\n",
            "    - libssh2==1.8.2=h1ba5d50_0\n",
            "    - libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "    - libtiff==4.0.10=h2733197_2\n",
            "    - libtool==2.4.6=h7b6447c_5\n",
            "    - libuuid==1.0.3=h1bed415_2\n",
            "    - libxcb==1.13=h1bed415_1\n",
            "    - libxml2==2.9.9=hea5a465_1\n",
            "    - libxslt==1.1.33=h7d1a2b0_0\n",
            "    - llvmlite==0.29.0=py37hd408876_0\n",
            "    - locket==0.2.0=py37_1\n",
            "    - lxml==4.4.1=py37hefd8a0e_0\n",
            "    - lz4-c==1.8.1.2=h14c3975_0\n",
            "    - lzo==2.10=h49e0be7_2\n",
            "    - markupsafe==1.1.1=py37h7b6447c_0\n",
            "    - matplotlib==3.1.1=py37h5429711_0\n",
            "    - mccabe==0.6.1=py37_1\n",
            "    - mistune==0.8.4=py37h7b6447c_0\n",
            "    - mkl-service==2.3.0=py37he904b0f_0\n",
            "    - mkl==2019.4=243\n",
            "    - mkl_fft==1.0.14=py37ha843d7b_0\n",
            "    - mkl_random==1.1.0=py37hd6b4f25_0\n",
            "    - mock==3.0.5=py37_0\n",
            "    - more-itertools==7.2.0=py37_0\n",
            "    - mpc==1.1.0=h10f8cd9_1\n",
            "    - mpfr==4.0.1=hdf1c602_3\n",
            "    - mpmath==1.1.0=py37_0\n",
            "    - msgpack-python==0.6.1=py37hfd86e86_1\n",
            "    - multipledispatch==0.6.0=py37_0\n",
            "    - navigator-updater==0.2.1=py37_0\n",
            "    - nbconvert==5.6.0=py37_1\n",
            "    - nbformat==4.4.0=py37_0\n",
            "    - ncurses==6.1=he6710b0_1\n",
            "    - networkx==2.3=py_0\n",
            "    - nltk==3.4.5=py37_0\n",
            "    - nose==1.3.7=py37_2\n",
            "    - notebook==6.0.1=py37_0\n",
            "    - numba==0.45.1=py37h962f231_0\n",
            "    - numexpr==2.7.0=py37h9e4a6bb_0\n",
            "    - numpy-base==1.17.2=py37hde5b4d6_0\n",
            "    - numpy==1.17.2=py37haad9e8e_0\n",
            "    - numpydoc==0.9.1=py_0\n",
            "    - olefile==0.46=py37_0\n",
            "    - openpyxl==3.0.0=py_0\n",
            "    - openssl==1.1.1d=h7b6447c_2\n",
            "    - packaging==19.2=py_0\n",
            "    - pandas==0.25.1=py37he6710b0_0\n",
            "    - pandoc==2.2.3.2=0\n",
            "    - pandocfilters==1.4.2=py37_1\n",
            "    - pango==1.42.4=h049681c_0\n",
            "    - parso==0.5.1=py_0\n",
            "    - partd==1.0.0=py_0\n",
            "    - patchelf==0.9=he6710b0_3\n",
            "    - path.py==12.0.1=py_0\n",
            "    - pathlib2==2.3.5=py37_0\n",
            "    - patsy==0.5.1=py37_0\n",
            "    - pcre==8.43=he6710b0_0\n",
            "    - pep8==1.7.1=py37_0\n",
            "    - pexpect==4.7.0=py37_0\n",
            "    - pickleshare==0.7.5=py37_0\n",
            "    - pillow==6.2.0=py37h34e0f95_0\n",
            "    - pip==19.2.3=py37_0\n",
            "    - pixman==0.38.0=h7b6447c_0\n",
            "    - pkginfo==1.5.0.1=py37_0\n",
            "    - pluggy==0.13.0=py37_0\n",
            "    - ply==3.11=py37_0\n",
            "    - prometheus_client==0.7.1=py_0\n",
            "    - prompt_toolkit==2.0.10=py_0\n",
            "    - psutil==5.6.3=py37h7b6447c_0\n",
            "    - ptyprocess==0.6.0=py37_0\n",
            "    - py-lief==0.9.0=py37h7725739_2\n",
            "    - py==1.8.0=py37_0\n",
            "    - pycodestyle==2.5.0=py37_0\n",
            "    - pycosat==0.6.3=py37h14c3975_0\n",
            "    - pycparser==2.19=py37_0\n",
            "    - pycrypto==2.6.1=py37h14c3975_9\n",
            "    - pycurl==7.43.0.3=py37h1ba5d50_0\n",
            "    - pyflakes==2.1.1=py37_0\n",
            "    - pygments==2.4.2=py_0\n",
            "    - pylint==2.4.2=py37_0\n",
            "    - pyodbc==4.0.27=py37he6710b0_0\n",
            "    - pyopenssl==19.0.0=py37_0\n",
            "    - pyparsing==2.4.2=py_0\n",
            "    - pyqt==5.9.2=py37h05f1152_2\n",
            "    - pyrsistent==0.15.4=py37h7b6447c_0\n",
            "    - pysocks==1.7.1=py37_0\n",
            "    - pytables==3.5.2=py37h71ec239_1\n",
            "    - pytest-arraydiff==0.3=py37h39e3cac_0\n",
            "    - pytest-astropy==0.5.0=py37_0\n",
            "    - pytest-doctestplus==0.4.0=py_0\n",
            "    - pytest-openfiles==0.4.0=py_0\n",
            "    - pytest-remotedata==0.3.2=py37_0\n",
            "    - pytest==5.2.1=py37_0\n",
            "    - python-dateutil==2.8.0=py37_0\n",
            "    - python-libarchive-c==2.8=py37_13\n",
            "    - python==3.7.4=h265db76_1\n",
            "    - pytz==2019.3=py_0\n",
            "    - pywavelets==1.0.3=py37hdd07704_1\n",
            "    - pyyaml==5.1.2=py37h7b6447c_0\n",
            "    - pyzmq==18.1.0=py37he6710b0_0\n",
            "    - qt==5.9.7=h5867ecd_1\n",
            "    - qtawesome==0.6.0=py_0\n",
            "    - qtconsole==4.5.5=py_0\n",
            "    - qtpy==1.9.0=py_0\n",
            "    - readline==7.0=h7b6447c_5\n",
            "    - requests==2.22.0=py37_0\n",
            "    - ripgrep==0.10.0=hc07d326_0\n",
            "    - rope==0.14.0=py_0\n",
            "    - ruamel_yaml==0.15.46=py37h14c3975_0\n",
            "    - scikit-image==0.15.0=py37he6710b0_0\n",
            "    - scikit-learn==0.21.3=py37hd81dba3_0\n",
            "    - scipy==1.3.1=py37h7c811a0_0\n",
            "    - seaborn==0.9.0=py37_0\n",
            "    - secretstorage==3.1.1=py37_0\n",
            "    - send2trash==1.5.0=py37_0\n",
            "    - setuptools==41.4.0=py37_0\n",
            "    - simplegeneric==0.8.1=py37_2\n",
            "    - singledispatch==3.4.0.3=py37_0\n",
            "    - sip==4.19.8=py37hf484d3e_0\n",
            "    - six==1.12.0=py37_0\n",
            "    - snappy==1.1.7=hbae5bb6_3\n",
            "    - snowballstemmer==2.0.0=py_0\n",
            "    - sortedcollections==1.1.2=py37_0\n",
            "    - sortedcontainers==2.1.0=py37_0\n",
            "    - soupsieve==1.9.3=py37_0\n",
            "    - sphinx==2.2.0=py_0\n",
            "    - sphinxcontrib-applehelp==1.0.1=py_0\n",
            "    - sphinxcontrib-devhelp==1.0.1=py_0\n",
            "    - sphinxcontrib-htmlhelp==1.0.2=py_0\n",
            "    - sphinxcontrib-jsmath==1.0.1=py_0\n",
            "    - sphinxcontrib-qthelp==1.0.2=py_0\n",
            "    - sphinxcontrib-serializinghtml==1.1.3=py_0\n",
            "    - sphinxcontrib-websupport==1.1.2=py_0\n",
            "    - sphinxcontrib==1.0=py37_1\n",
            "    - spyder-kernels==0.5.2=py37_0\n",
            "    - spyder==3.3.6=py37_0\n",
            "    - sqlalchemy==1.3.9=py37h7b6447c_0\n",
            "    - sqlite==3.30.0=h7b6447c_0\n",
            "    - statsmodels==0.10.1=py37hdd07704_0\n",
            "    - sympy==1.4=py37_0\n",
            "    - tbb==2019.4=hfd86e86_0\n",
            "    - tblib==1.4.0=py_0\n",
            "    - terminado==0.8.2=py37_0\n",
            "    - testpath==0.4.2=py37_0\n",
            "    - tk==8.6.8=hbc83047_0\n",
            "    - toolz==0.10.0=py_0\n",
            "    - tornado==6.0.3=py37h7b6447c_0\n",
            "    - tqdm==4.36.1=py_0\n",
            "    - traitlets==4.3.3=py37_0\n",
            "    - unicodecsv==0.14.1=py37_0\n",
            "    - unixodbc==2.3.7=h14c3975_0\n",
            "    - urllib3==1.24.2=py37_0\n",
            "    - wcwidth==0.1.7=py37_0\n",
            "    - webencodings==0.5.1=py37_1\n",
            "    - werkzeug==0.16.0=py_0\n",
            "    - wheel==0.33.6=py37_0\n",
            "    - widgetsnbextension==3.5.1=py37_0\n",
            "    - wrapt==1.11.2=py37h7b6447c_0\n",
            "    - wurlitzer==1.0.3=py37_0\n",
            "    - xlrd==1.2.0=py37_0\n",
            "    - xlsxwriter==1.2.1=py_0\n",
            "    - xlwt==1.3.0=py37_0\n",
            "    - xz==5.2.4=h14c3975_4\n",
            "    - yaml==0.1.7=had09818_2\n",
            "    - zeromq==4.3.1=he6710b0_3\n",
            "    - zict==1.0.0=py_0\n",
            "    - zipp==0.6.0=py_0\n",
            "    - zlib==1.2.11=h7b6447c_3\n",
            "    - zstd==1.3.7=h0b5b093_0\n",
            "\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _ipyw_jlab_nb_ext~ pkgs/main/linux-64::_ipyw_jlab_nb_ext_conf-0.1.0-py37_0\n",
            "  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
            "  alabaster          pkgs/main/linux-64::alabaster-0.7.12-py37_0\n",
            "  anaconda           pkgs/main/linux-64::anaconda-2019.10-py37_0\n",
            "  anaconda-client    pkgs/main/linux-64::anaconda-client-1.7.2-py37_0\n",
            "  anaconda-navigator pkgs/main/linux-64::anaconda-navigator-1.9.7-py37_0\n",
            "  anaconda-project   pkgs/main/noarch::anaconda-project-0.8.3-py_0\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-1.0.1-py37_0\n",
            "  astroid            pkgs/main/linux-64::astroid-2.3.1-py37_0\n",
            "  astropy            pkgs/main/linux-64::astropy-3.2.2-py37h7b6447c_0\n",
            "  atomicwrites       pkgs/main/linux-64::atomicwrites-1.3.0-py37_1\n",
            "  attrs              pkgs/main/noarch::attrs-19.2.0-py_0\n",
            "  babel              pkgs/main/noarch::babel-2.7.0-py_0\n",
            "  backcall           pkgs/main/linux-64::backcall-0.1.0-py37_0\n",
            "  backports          pkgs/main/noarch::backports-1.0-py_2\n",
            "  backports.functoo~ pkgs/main/noarch::backports.functools_lru_cache-1.5-py_2\n",
            "  backports.os       pkgs/main/linux-64::backports.os-0.1.1-py37_0\n",
            "  backports.shutil_~ pkgs/main/linux-64::backports.shutil_get_terminal_size-1.0.0-py37_2\n",
            "  backports.tempfile pkgs/main/noarch::backports.tempfile-1.0-py_1\n",
            "  backports.weakref  pkgs/main/noarch::backports.weakref-1.0.post1-py_1\n",
            "  beautifulsoup4     pkgs/main/linux-64::beautifulsoup4-4.8.0-py37_0\n",
            "  bitarray           pkgs/main/linux-64::bitarray-1.0.1-py37h7b6447c_0\n",
            "  bkcharts           pkgs/main/linux-64::bkcharts-0.2-py37_0\n",
            "  blas               pkgs/main/linux-64::blas-1.0-mkl\n",
            "  bleach             pkgs/main/linux-64::bleach-3.1.0-py37_0\n",
            "  blosc              pkgs/main/linux-64::blosc-1.16.3-hd408876_0\n",
            "  bokeh              pkgs/main/linux-64::bokeh-1.3.4-py37_0\n",
            "  boto               pkgs/main/linux-64::boto-2.49.0-py37_0\n",
            "  bottleneck         pkgs/main/linux-64::bottleneck-1.2.1-py37h035aef0_1\n",
            "  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n",
            "  ca-certificates    pkgs/main/linux-64::ca-certificates-2019.8.28-0\n",
            "  cairo              pkgs/main/linux-64::cairo-1.14.12-h8948797_3\n",
            "  certifi            pkgs/main/linux-64::certifi-2019.9.11-py37_0\n",
            "  cffi               pkgs/main/linux-64::cffi-1.12.3-py37h2e261b9_0\n",
            "  chardet            pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
            "  click              pkgs/main/linux-64::click-7.0-py37_0\n",
            "  cloudpickle        pkgs/main/noarch::cloudpickle-1.2.2-py_0\n",
            "  clyent             pkgs/main/linux-64::clyent-1.2.2-py37_1\n",
            "  colorama           pkgs/main/linux-64::colorama-0.4.1-py37_0\n",
            "  conda              pkgs/main/linux-64::conda-4.7.12-py37_0\n",
            "  conda-build        pkgs/main/linux-64::conda-build-3.18.9-py37_3\n",
            "  conda-env          pkgs/main/linux-64::conda-env-2.6.0-1\n",
            "  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
            "  conda-verify       pkgs/main/noarch::conda-verify-3.4.2-py_1\n",
            "  contextlib2        pkgs/main/noarch::contextlib2-0.6.0-py_0\n",
            "  cryptography       pkgs/main/linux-64::cryptography-2.7-py37h1ba5d50_0\n",
            "  curl               pkgs/main/linux-64::curl-7.65.3-hbc83047_0\n",
            "  cycler             pkgs/main/linux-64::cycler-0.10.0-py37_0\n",
            "  cython             pkgs/main/linux-64::cython-0.29.13-py37he6710b0_0\n",
            "  cytoolz            pkgs/main/linux-64::cytoolz-0.10.0-py37h7b6447c_0\n",
            "  dask               pkgs/main/noarch::dask-2.5.2-py_0\n",
            "  dask-core          pkgs/main/noarch::dask-core-2.5.2-py_0\n",
            "  dbus               pkgs/main/linux-64::dbus-1.13.6-h746ee38_0\n",
            "  decorator          pkgs/main/linux-64::decorator-4.4.0-py37_1\n",
            "  defusedxml         pkgs/main/noarch::defusedxml-0.6.0-py_0\n",
            "  distributed        pkgs/main/noarch::distributed-2.5.2-py_0\n",
            "  docutils           pkgs/main/linux-64::docutils-0.15.2-py37_0\n",
            "  entrypoints        pkgs/main/linux-64::entrypoints-0.3-py37_0\n",
            "  et_xmlfile         pkgs/main/linux-64::et_xmlfile-1.0.1-py37_0\n",
            "  expat              pkgs/main/linux-64::expat-2.2.6-he6710b0_0\n",
            "  fastcache          pkgs/main/linux-64::fastcache-1.1.0-py37h7b6447c_0\n",
            "  filelock           pkgs/main/noarch::filelock-3.0.12-py_0\n",
            "  flask              pkgs/main/noarch::flask-1.1.1-py_0\n",
            "  fontconfig         pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0\n",
            "  freetype           pkgs/main/linux-64::freetype-2.9.1-h8a8886c_1\n",
            "  fribidi            pkgs/main/linux-64::fribidi-1.0.5-h7b6447c_0\n",
            "  fsspec             pkgs/main/noarch::fsspec-0.5.2-py_0\n",
            "  future             pkgs/main/linux-64::future-0.17.1-py37_0\n",
            "  get_terminal_size  pkgs/main/linux-64::get_terminal_size-1.0.0-haa9412d_0\n",
            "  gevent             pkgs/main/linux-64::gevent-1.4.0-py37h7b6447c_0\n",
            "  glib               pkgs/main/linux-64::glib-2.56.2-hd408876_0\n",
            "  glob2              pkgs/main/noarch::glob2-0.7-py_0\n",
            "  gmp                pkgs/main/linux-64::gmp-6.1.2-h6c8ec71_1\n",
            "  gmpy2              pkgs/main/linux-64::gmpy2-2.0.8-py37h10f8cd9_2\n",
            "  graphite2          pkgs/main/linux-64::graphite2-1.3.13-h23475e2_0\n",
            "  greenlet           pkgs/main/linux-64::greenlet-0.4.15-py37h7b6447c_0\n",
            "  gst-plugins-base   pkgs/main/linux-64::gst-plugins-base-1.14.0-hbbd80ab_1\n",
            "  gstreamer          pkgs/main/linux-64::gstreamer-1.14.0-hb453b48_1\n",
            "  h5py               pkgs/main/linux-64::h5py-2.9.0-py37h7918eee_0\n",
            "  harfbuzz           pkgs/main/linux-64::harfbuzz-1.8.8-hffaf4a1_0\n",
            "  hdf5               pkgs/main/linux-64::hdf5-1.10.4-hb1b8bf9_0\n",
            "  heapdict           pkgs/main/noarch::heapdict-1.0.1-py_0\n",
            "  html5lib           pkgs/main/linux-64::html5lib-1.0.1-py37_0\n",
            "  icu                pkgs/main/linux-64::icu-58.2-h9c2bf20_1\n",
            "  idna               pkgs/main/linux-64::idna-2.8-py37_0\n",
            "  imageio            pkgs/main/linux-64::imageio-2.6.0-py37_0\n",
            "  imagesize          pkgs/main/linux-64::imagesize-1.1.0-py37_0\n",
            "  importlib_metadata pkgs/main/linux-64::importlib_metadata-0.23-py37_0\n",
            "  intel-openmp       pkgs/main/linux-64::intel-openmp-2019.4-243\n",
            "  ipykernel          pkgs/main/linux-64::ipykernel-5.1.2-py37h39e3cac_0\n",
            "  ipython            pkgs/main/linux-64::ipython-7.8.0-py37h39e3cac_0\n",
            "  ipython_genutils   pkgs/main/linux-64::ipython_genutils-0.2.0-py37_0\n",
            "  ipywidgets         pkgs/main/noarch::ipywidgets-7.5.1-py_0\n",
            "  isort              pkgs/main/linux-64::isort-4.3.21-py37_0\n",
            "  itsdangerous       pkgs/main/linux-64::itsdangerous-1.1.0-py37_0\n",
            "  jbig               pkgs/main/linux-64::jbig-2.1-hdba287a_0\n",
            "  jdcal              pkgs/main/noarch::jdcal-1.4.1-py_0\n",
            "  jedi               pkgs/main/linux-64::jedi-0.15.1-py37_0\n",
            "  jeepney            pkgs/main/noarch::jeepney-0.4.1-py_0\n",
            "  jinja2             pkgs/main/noarch::jinja2-2.10.3-py_0\n",
            "  joblib             pkgs/main/linux-64::joblib-0.13.2-py37_0\n",
            "  jpeg               pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n",
            "  json5              pkgs/main/noarch::json5-0.8.5-py_0\n",
            "  jsonschema         pkgs/main/linux-64::jsonschema-3.0.2-py37_0\n",
            "  jupyter            pkgs/main/linux-64::jupyter-1.0.0-py37_7\n",
            "  jupyter_client     pkgs/main/linux-64::jupyter_client-5.3.3-py37_1\n",
            "  jupyter_console    pkgs/main/linux-64::jupyter_console-6.0.0-py37_0\n",
            "  jupyter_core       pkgs/main/noarch::jupyter_core-4.5.0-py_0\n",
            "  jupyterlab         pkgs/main/noarch::jupyterlab-1.1.4-pyhf63ae98_0\n",
            "  jupyterlab_server  pkgs/main/noarch::jupyterlab_server-1.0.6-py_0\n",
            "  keyring            pkgs/main/linux-64::keyring-18.0.0-py37_0\n",
            "  kiwisolver         pkgs/main/linux-64::kiwisolver-1.1.0-py37he6710b0_0\n",
            "  krb5               pkgs/main/linux-64::krb5-1.16.1-h173b8e3_7\n",
            "  lazy-object-proxy  pkgs/main/linux-64::lazy-object-proxy-1.4.2-py37h7b6447c_0\n",
            "  libarchive         pkgs/main/linux-64::libarchive-3.3.3-h5d8350f_5\n",
            "  libcurl            pkgs/main/linux-64::libcurl-7.65.3-h20c2e04_0\n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
            "  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
            "  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
            "  libgfortran-ng     pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0\n",
            "  liblief            pkgs/main/linux-64::liblief-0.9.0-h7725739_2\n",
            "  libpng             pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n",
            "  libsodium          pkgs/main/linux-64::libsodium-1.0.16-h1bed415_0\n",
            "  libssh2            pkgs/main/linux-64::libssh2-1.8.2-h1ba5d50_0\n",
            "  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
            "  libtiff            pkgs/main/linux-64::libtiff-4.0.10-h2733197_2\n",
            "  libtool            pkgs/main/linux-64::libtool-2.4.6-h7b6447c_5\n",
            "  libuuid            pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2\n",
            "  libxcb             pkgs/main/linux-64::libxcb-1.13-h1bed415_1\n",
            "  libxml2            pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n",
            "  libxslt            pkgs/main/linux-64::libxslt-1.1.33-h7d1a2b0_0\n",
            "  llvmlite           pkgs/main/linux-64::llvmlite-0.29.0-py37hd408876_0\n",
            "  locket             pkgs/main/linux-64::locket-0.2.0-py37_1\n",
            "  lxml               pkgs/main/linux-64::lxml-4.4.1-py37hefd8a0e_0\n",
            "  lz4-c              pkgs/main/linux-64::lz4-c-1.8.1.2-h14c3975_0\n",
            "  lzo                pkgs/main/linux-64::lzo-2.10-h49e0be7_2\n",
            "  markupsafe         pkgs/main/linux-64::markupsafe-1.1.1-py37h7b6447c_0\n",
            "  matplotlib         pkgs/main/linux-64::matplotlib-3.1.1-py37h5429711_0\n",
            "  mccabe             pkgs/main/linux-64::mccabe-0.6.1-py37_1\n",
            "  mistune            pkgs/main/linux-64::mistune-0.8.4-py37h7b6447c_0\n",
            "  mkl                pkgs/main/linux-64::mkl-2019.4-243\n",
            "  mkl-service        pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n",
            "  mkl_fft            pkgs/main/linux-64::mkl_fft-1.0.14-py37ha843d7b_0\n",
            "  mkl_random         pkgs/main/linux-64::mkl_random-1.1.0-py37hd6b4f25_0\n",
            "  mock               pkgs/main/linux-64::mock-3.0.5-py37_0\n",
            "  more-itertools     pkgs/main/linux-64::more-itertools-7.2.0-py37_0\n",
            "  mpc                pkgs/main/linux-64::mpc-1.1.0-h10f8cd9_1\n",
            "  mpfr               pkgs/main/linux-64::mpfr-4.0.1-hdf1c602_3\n",
            "  mpmath             pkgs/main/linux-64::mpmath-1.1.0-py37_0\n",
            "  msgpack-python     pkgs/main/linux-64::msgpack-python-0.6.1-py37hfd86e86_1\n",
            "  multipledispatch   pkgs/main/linux-64::multipledispatch-0.6.0-py37_0\n",
            "  navigator-updater  pkgs/main/linux-64::navigator-updater-0.2.1-py37_0\n",
            "  nbconvert          pkgs/main/linux-64::nbconvert-5.6.0-py37_1\n",
            "  nbformat           pkgs/main/linux-64::nbformat-4.4.0-py37_0\n",
            "  ncurses            pkgs/main/linux-64::ncurses-6.1-he6710b0_1\n",
            "  networkx           pkgs/main/noarch::networkx-2.3-py_0\n",
            "  nltk               pkgs/main/linux-64::nltk-3.4.5-py37_0\n",
            "  nose               pkgs/main/linux-64::nose-1.3.7-py37_2\n",
            "  notebook           pkgs/main/linux-64::notebook-6.0.1-py37_0\n",
            "  numba              pkgs/main/linux-64::numba-0.45.1-py37h962f231_0\n",
            "  numexpr            pkgs/main/linux-64::numexpr-2.7.0-py37h9e4a6bb_0\n",
            "  numpy              pkgs/main/linux-64::numpy-1.17.2-py37haad9e8e_0\n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.17.2-py37hde5b4d6_0\n",
            "  numpydoc           pkgs/main/noarch::numpydoc-0.9.1-py_0\n",
            "  olefile            pkgs/main/linux-64::olefile-0.46-py37_0\n",
            "  openpyxl           pkgs/main/noarch::openpyxl-3.0.0-py_0\n",
            "  openssl            pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_2\n",
            "  packaging          pkgs/main/noarch::packaging-19.2-py_0\n",
            "  pandas             pkgs/main/linux-64::pandas-0.25.1-py37he6710b0_0\n",
            "  pandoc             pkgs/main/linux-64::pandoc-2.2.3.2-0\n",
            "  pandocfilters      pkgs/main/linux-64::pandocfilters-1.4.2-py37_1\n",
            "  pango              pkgs/main/linux-64::pango-1.42.4-h049681c_0\n",
            "  parso              pkgs/main/noarch::parso-0.5.1-py_0\n",
            "  partd              pkgs/main/noarch::partd-1.0.0-py_0\n",
            "  patchelf           pkgs/main/linux-64::patchelf-0.9-he6710b0_3\n",
            "  path.py            pkgs/main/noarch::path.py-12.0.1-py_0\n",
            "  pathlib2           pkgs/main/linux-64::pathlib2-2.3.5-py37_0\n",
            "  patsy              pkgs/main/linux-64::patsy-0.5.1-py37_0\n",
            "  pcre               pkgs/main/linux-64::pcre-8.43-he6710b0_0\n",
            "  pep8               pkgs/main/linux-64::pep8-1.7.1-py37_0\n",
            "  pexpect            pkgs/main/linux-64::pexpect-4.7.0-py37_0\n",
            "  pickleshare        pkgs/main/linux-64::pickleshare-0.7.5-py37_0\n",
            "  pillow             pkgs/main/linux-64::pillow-6.2.0-py37h34e0f95_0\n",
            "  pip                pkgs/main/linux-64::pip-19.2.3-py37_0\n",
            "  pixman             pkgs/main/linux-64::pixman-0.38.0-h7b6447c_0\n",
            "  pkginfo            pkgs/main/linux-64::pkginfo-1.5.0.1-py37_0\n",
            "  pluggy             pkgs/main/linux-64::pluggy-0.13.0-py37_0\n",
            "  ply                pkgs/main/linux-64::ply-3.11-py37_0\n",
            "  prometheus_client  pkgs/main/noarch::prometheus_client-0.7.1-py_0\n",
            "  prompt_toolkit     pkgs/main/noarch::prompt_toolkit-2.0.10-py_0\n",
            "  psutil             pkgs/main/linux-64::psutil-5.6.3-py37h7b6447c_0\n",
            "  ptyprocess         pkgs/main/linux-64::ptyprocess-0.6.0-py37_0\n",
            "  py                 pkgs/main/linux-64::py-1.8.0-py37_0\n",
            "  py-lief            pkgs/main/linux-64::py-lief-0.9.0-py37h7725739_2\n",
            "  pycodestyle        pkgs/main/linux-64::pycodestyle-2.5.0-py37_0\n",
            "  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py37h14c3975_0\n",
            "  pycparser          pkgs/main/linux-64::pycparser-2.19-py37_0\n",
            "  pycrypto           pkgs/main/linux-64::pycrypto-2.6.1-py37h14c3975_9\n",
            "  pycurl             pkgs/main/linux-64::pycurl-7.43.0.3-py37h1ba5d50_0\n",
            "  pyflakes           pkgs/main/linux-64::pyflakes-2.1.1-py37_0\n",
            "  pygments           pkgs/main/noarch::pygments-2.4.2-py_0\n",
            "  pylint             pkgs/main/linux-64::pylint-2.4.2-py37_0\n",
            "  pyodbc             pkgs/main/linux-64::pyodbc-4.0.27-py37he6710b0_0\n",
            "  pyopenssl          pkgs/main/linux-64::pyopenssl-19.0.0-py37_0\n",
            "  pyparsing          pkgs/main/noarch::pyparsing-2.4.2-py_0\n",
            "  pyqt               pkgs/main/linux-64::pyqt-5.9.2-py37h05f1152_2\n",
            "  pyrsistent         pkgs/main/linux-64::pyrsistent-0.15.4-py37h7b6447c_0\n",
            "  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
            "  pytables           pkgs/main/linux-64::pytables-3.5.2-py37h71ec239_1\n",
            "  pytest             pkgs/main/linux-64::pytest-5.2.1-py37_0\n",
            "  pytest-arraydiff   pkgs/main/linux-64::pytest-arraydiff-0.3-py37h39e3cac_0\n",
            "  pytest-astropy     pkgs/main/linux-64::pytest-astropy-0.5.0-py37_0\n",
            "  pytest-doctestplus pkgs/main/noarch::pytest-doctestplus-0.4.0-py_0\n",
            "  pytest-openfiles   pkgs/main/noarch::pytest-openfiles-0.4.0-py_0\n",
            "  pytest-remotedata  pkgs/main/linux-64::pytest-remotedata-0.3.2-py37_0\n",
            "  python             pkgs/main/linux-64::python-3.7.4-h265db76_1\n",
            "  python-dateutil    pkgs/main/linux-64::python-dateutil-2.8.0-py37_0\n",
            "  python-libarchive~ pkgs/main/linux-64::python-libarchive-c-2.8-py37_13\n",
            "  pytz               pkgs/main/noarch::pytz-2019.3-py_0\n",
            "  pywavelets         pkgs/main/linux-64::pywavelets-1.0.3-py37hdd07704_1\n",
            "  pyyaml             pkgs/main/linux-64::pyyaml-5.1.2-py37h7b6447c_0\n",
            "  pyzmq              pkgs/main/linux-64::pyzmq-18.1.0-py37he6710b0_0\n",
            "  qt                 pkgs/main/linux-64::qt-5.9.7-h5867ecd_1\n",
            "  qtawesome          pkgs/main/noarch::qtawesome-0.6.0-py_0\n",
            "  qtconsole          pkgs/main/noarch::qtconsole-4.5.5-py_0\n",
            "  qtpy               pkgs/main/noarch::qtpy-1.9.0-py_0\n",
            "  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
            "  requests           pkgs/main/linux-64::requests-2.22.0-py37_0\n",
            "  ripgrep            pkgs/main/linux-64::ripgrep-0.10.0-hc07d326_0\n",
            "  rope               pkgs/main/noarch::rope-0.14.0-py_0\n",
            "  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.46-py37h14c3975_0\n",
            "  scikit-image       pkgs/main/linux-64::scikit-image-0.15.0-py37he6710b0_0\n",
            "  scikit-learn       pkgs/main/linux-64::scikit-learn-0.21.3-py37hd81dba3_0\n",
            "  scipy              pkgs/main/linux-64::scipy-1.3.1-py37h7c811a0_0\n",
            "  seaborn            pkgs/main/linux-64::seaborn-0.9.0-py37_0\n",
            "  secretstorage      pkgs/main/linux-64::secretstorage-3.1.1-py37_0\n",
            "  send2trash         pkgs/main/linux-64::send2trash-1.5.0-py37_0\n",
            "  setuptools         pkgs/main/linux-64::setuptools-41.4.0-py37_0\n",
            "  simplegeneric      pkgs/main/linux-64::simplegeneric-0.8.1-py37_2\n",
            "  singledispatch     pkgs/main/linux-64::singledispatch-3.4.0.3-py37_0\n",
            "  sip                pkgs/main/linux-64::sip-4.19.8-py37hf484d3e_0\n",
            "  six                pkgs/main/linux-64::six-1.12.0-py37_0\n",
            "  snappy             pkgs/main/linux-64::snappy-1.1.7-hbae5bb6_3\n",
            "  snowballstemmer    pkgs/main/noarch::snowballstemmer-2.0.0-py_0\n",
            "  sortedcollections  pkgs/main/linux-64::sortedcollections-1.1.2-py37_0\n",
            "  sortedcontainers   pkgs/main/linux-64::sortedcontainers-2.1.0-py37_0\n",
            "  soupsieve          pkgs/main/linux-64::soupsieve-1.9.3-py37_0\n",
            "  sphinx             pkgs/main/noarch::sphinx-2.2.0-py_0\n",
            "  sphinxcontrib      pkgs/main/linux-64::sphinxcontrib-1.0-py37_1\n",
            "  sphinxcontrib-app~ pkgs/main/noarch::sphinxcontrib-applehelp-1.0.1-py_0\n",
            "  sphinxcontrib-dev~ pkgs/main/noarch::sphinxcontrib-devhelp-1.0.1-py_0\n",
            "  sphinxcontrib-htm~ pkgs/main/noarch::sphinxcontrib-htmlhelp-1.0.2-py_0\n",
            "  sphinxcontrib-jsm~ pkgs/main/noarch::sphinxcontrib-jsmath-1.0.1-py_0\n",
            "  sphinxcontrib-qth~ pkgs/main/noarch::sphinxcontrib-qthelp-1.0.2-py_0\n",
            "  sphinxcontrib-ser~ pkgs/main/noarch::sphinxcontrib-serializinghtml-1.1.3-py_0\n",
            "  sphinxcontrib-web~ pkgs/main/noarch::sphinxcontrib-websupport-1.1.2-py_0\n",
            "  spyder             pkgs/main/linux-64::spyder-3.3.6-py37_0\n",
            "  spyder-kernels     pkgs/main/linux-64::spyder-kernels-0.5.2-py37_0\n",
            "  sqlalchemy         pkgs/main/linux-64::sqlalchemy-1.3.9-py37h7b6447c_0\n",
            "  sqlite             pkgs/main/linux-64::sqlite-3.30.0-h7b6447c_0\n",
            "  statsmodels        pkgs/main/linux-64::statsmodels-0.10.1-py37hdd07704_0\n",
            "  sympy              pkgs/main/linux-64::sympy-1.4-py37_0\n",
            "  tbb                pkgs/main/linux-64::tbb-2019.4-hfd86e86_0\n",
            "  tblib              pkgs/main/noarch::tblib-1.4.0-py_0\n",
            "  terminado          pkgs/main/linux-64::terminado-0.8.2-py37_0\n",
            "  testpath           pkgs/main/linux-64::testpath-0.4.2-py37_0\n",
            "  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
            "  toolz              pkgs/main/noarch::toolz-0.10.0-py_0\n",
            "  tornado            pkgs/main/linux-64::tornado-6.0.3-py37h7b6447c_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.36.1-py_0\n",
            "  traitlets          pkgs/main/linux-64::traitlets-4.3.3-py37_0\n",
            "  unicodecsv         pkgs/main/linux-64::unicodecsv-0.14.1-py37_0\n",
            "  unixodbc           pkgs/main/linux-64::unixodbc-2.3.7-h14c3975_0\n",
            "  urllib3            pkgs/main/linux-64::urllib3-1.24.2-py37_0\n",
            "  wcwidth            pkgs/main/linux-64::wcwidth-0.1.7-py37_0\n",
            "  webencodings       pkgs/main/linux-64::webencodings-0.5.1-py37_1\n",
            "  werkzeug           pkgs/main/noarch::werkzeug-0.16.0-py_0\n",
            "  wheel              pkgs/main/linux-64::wheel-0.33.6-py37_0\n",
            "  widgetsnbextension pkgs/main/linux-64::widgetsnbextension-3.5.1-py37_0\n",
            "  wrapt              pkgs/main/linux-64::wrapt-1.11.2-py37h7b6447c_0\n",
            "  wurlitzer          pkgs/main/linux-64::wurlitzer-1.0.3-py37_0\n",
            "  xlrd               pkgs/main/linux-64::xlrd-1.2.0-py37_0\n",
            "  xlsxwriter         pkgs/main/noarch::xlsxwriter-1.2.1-py_0\n",
            "  xlwt               pkgs/main/linux-64::xlwt-1.3.0-py37_0\n",
            "  xz                 pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
            "  yaml               pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
            "  zeromq             pkgs/main/linux-64::zeromq-4.3.1-he6710b0_3\n",
            "  zict               pkgs/main/noarch::zict-1.0.0-py_0\n",
            "  zipp               pkgs/main/noarch::zipp-0.6.0-py_0\n",
            "  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
            "  zstd               pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n",
            "\n",
            "\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Anaconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Anaconda3: /usr/local\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 4.7.12\n",
            "  latest version: 4.10.0\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - deepchem-gpu=2.3.0\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _py-xgboost-mutex-2.0      |            cpu_0           8 KB  conda-forge\n",
            "    _tflow_select-2.1.0        |              gpu           2 KB\n",
            "    absl-py-0.12.0             |     pyhd8ed1ab_0          96 KB  conda-forge\n",
            "    astor-0.8.1                |     pyh9f0ad1d_0          25 KB  conda-forge\n",
            "    astunparse-1.6.3           |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    c-ares-1.17.1              |       h36c2ea0_0         111 KB  conda-forge\n",
            "    certifi-2019.9.11          |           py37_0         147 KB  conda-forge\n",
            "    conda-4.10.0               |   py37h89c1867_1         3.1 MB  conda-forge\n",
            "    cudatoolkit-10.1.243       |       h6bb024c_0       347.4 MB\n",
            "    cudnn-7.6.5.32             |       hc0a50b0_1       250.7 MB  conda-forge\n",
            "    cupti-10.1.168             |                0         1.4 MB\n",
            "    deepchem-gpu-2.3.0         |           py37_0         2.1 MB  deepchem\n",
            "    fftw3f-3.3.4               |                2         1.2 MB  omnia\n",
            "    gast-0.4.0                 |     pyh9f0ad1d_0          12 KB  conda-forge\n",
            "    google-pasta-0.2.0         |     pyh8c360ce_0          42 KB  conda-forge\n",
            "    grpcio-1.23.0              |   py37hb0870dc_1         1.1 MB  conda-forge\n",
            "    importlib-metadata-3.10.0  |   py37h89c1867_0          27 KB  conda-forge\n",
            "    keras-applications-1.0.8   |             py_1          30 KB  conda-forge\n",
            "    keras-preprocessing-1.1.2  |     pyhd8ed1ab_0          34 KB  conda-forge\n",
            "    libboost-1.67.0            |       h46d08c1_4        13.0 MB\n",
            "    libprotobuf-3.13.0.1       |       h8b12597_0         2.3 MB  conda-forge\n",
            "    libxgboost-1.2.0           |       he1b5a44_0         3.1 MB  conda-forge\n",
            "    markdown-3.3.4             |     pyhd8ed1ab_0          67 KB  conda-forge\n",
            "    mdtraj-1.9.5               |   py37h113d463_0         1.7 MB  conda-forge\n",
            "    openmm-7.4.2               |py37_cuda101_rc_1        11.9 MB  omnia\n",
            "    pdbfixer-1.6               |             py_1         167 KB  omnia\n",
            "    protobuf-3.13.0.1          |   py37h745909e_1         704 KB  conda-forge\n",
            "    py-boost-1.67.0            |   py37h04863e7_4         278 KB\n",
            "    py-xgboost-1.2.0           |   py37hc8dfbb8_0         1.7 MB  conda-forge\n",
            "    python_abi-3.7             |          1_cp37m           4 KB  conda-forge\n",
            "    rdkit-2020.03.3.0          |   py37hc20afe1_1        24.8 MB  rdkit\n",
            "    simdna-0.4.2               |             py_0         627 KB  deepchem\n",
            "    tensorboard-1.14.0         |           py37_0         3.2 MB  conda-forge\n",
            "    tensorflow-1.14.0          |gpu_py37h74c33d7_0           4 KB\n",
            "    tensorflow-base-1.14.0     |gpu_py37he45bfe2_0       146.3 MB\n",
            "    tensorflow-estimator-1.14.0|   py37h5ca1d4c_0         645 KB  conda-forge\n",
            "    tensorflow-gpu-1.14.0      |       h0d30ee6_0           3 KB\n",
            "    termcolor-1.1.0            |             py_2           6 KB  conda-forge\n",
            "    typing_extensions-3.7.4.3  |             py_0          25 KB  conda-forge\n",
            "    xgboost-1.2.0              |   py37h3340039_0          11 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       817.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _py-xgboost-mutex  conda-forge/linux-64::_py-xgboost-mutex-2.0-cpu_0\n",
            "  _tflow_select      pkgs/main/linux-64::_tflow_select-2.1.0-gpu\n",
            "  absl-py            conda-forge/noarch::absl-py-0.12.0-pyhd8ed1ab_0\n",
            "  astor              conda-forge/noarch::astor-0.8.1-pyh9f0ad1d_0\n",
            "  astunparse         conda-forge/noarch::astunparse-1.6.3-pyhd8ed1ab_0\n",
            "  c-ares             conda-forge/linux-64::c-ares-1.17.1-h36c2ea0_0\n",
            "  cudatoolkit        pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0\n",
            "  cudnn              conda-forge/linux-64::cudnn-7.6.5.32-hc0a50b0_1\n",
            "  cupti              pkgs/main/linux-64::cupti-10.1.168-0\n",
            "  deepchem-gpu       deepchem/linux-64::deepchem-gpu-2.3.0-py37_0\n",
            "  fftw3f             omnia/linux-64::fftw3f-3.3.4-2\n",
            "  gast               conda-forge/noarch::gast-0.4.0-pyh9f0ad1d_0\n",
            "  google-pasta       conda-forge/noarch::google-pasta-0.2.0-pyh8c360ce_0\n",
            "  grpcio             conda-forge/linux-64::grpcio-1.23.0-py37hb0870dc_1\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-3.10.0-py37h89c1867_0\n",
            "  keras-applications conda-forge/noarch::keras-applications-1.0.8-py_1\n",
            "  keras-preprocessi~ conda-forge/noarch::keras-preprocessing-1.1.2-pyhd8ed1ab_0\n",
            "  libboost           pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.13.0.1-h8b12597_0\n",
            "  libxgboost         conda-forge/linux-64::libxgboost-1.2.0-he1b5a44_0\n",
            "  markdown           conda-forge/noarch::markdown-3.3.4-pyhd8ed1ab_0\n",
            "  mdtraj             conda-forge/linux-64::mdtraj-1.9.5-py37h113d463_0\n",
            "  openmm             omnia/linux-64::openmm-7.4.2-py37_cuda101_rc_1\n",
            "  pdbfixer           omnia/noarch::pdbfixer-1.6-py_1\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.13.0.1-py37h745909e_1\n",
            "  py-boost           pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n",
            "  py-xgboost         conda-forge/linux-64::py-xgboost-1.2.0-py37hc8dfbb8_0\n",
            "  python_abi         conda-forge/linux-64::python_abi-3.7-1_cp37m\n",
            "  rdkit              rdkit/linux-64::rdkit-2020.03.3.0-py37hc20afe1_1\n",
            "  simdna             deepchem/noarch::simdna-0.4.2-py_0\n",
            "  tensorboard        conda-forge/linux-64::tensorboard-1.14.0-py37_0\n",
            "  tensorflow         pkgs/main/linux-64::tensorflow-1.14.0-gpu_py37h74c33d7_0\n",
            "  tensorflow-base    pkgs/main/linux-64::tensorflow-base-1.14.0-gpu_py37he45bfe2_0\n",
            "  tensorflow-estima~ conda-forge/linux-64::tensorflow-estimator-1.14.0-py37h5ca1d4c_0\n",
            "  tensorflow-gpu     pkgs/main/linux-64::tensorflow-gpu-1.14.0-h0d30ee6_0\n",
            "  termcolor          conda-forge/noarch::termcolor-1.1.0-py_2\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-3.7.4.3-py_0\n",
            "  xgboost            conda-forge/linux-64::xgboost-1.2.0-py37h3340039_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda                      pkgs/main::conda-4.7.12-py37_0 --> conda-forge::conda-4.10.0-py37h89c1867_1\n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  certifi                                         pkgs/main --> conda-forge\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "gast-0.4.0           | 12 KB     | : 100% 1.0/1 [00:00<00:00,  7.87it/s]\n",
            "typing_extensions-3. | 25 KB     | : 100% 1.0/1 [00:00<00:00, 20.06it/s]\n",
            "openmm-7.4.2         | 11.9 MB   | : 100% 1.0/1 [00:02<00:00,  2.91s/it]\n",
            "tensorflow-1.14.0    | 4 KB      | : 100% 1.0/1 [00:00<00:00,  5.42it/s]\n",
            "google-pasta-0.2.0   | 42 KB     | : 100% 1.0/1 [00:00<00:00, 18.02it/s]\n",
            "termcolor-1.1.0      | 6 KB      | : 100% 1.0/1 [00:00<00:00, 21.99it/s]\n",
            "tensorflow-estimator | 645 KB    | : 100% 1.0/1 [00:00<00:00,  4.34it/s]\n",
            "libprotobuf-3.13.0.1 | 2.3 MB    | : 100% 1.0/1 [00:00<00:00,  2.01it/s]\n",
            "protobuf-3.13.0.1    | 704 KB    | : 100% 1.0/1 [00:00<00:00,  4.25it/s]\n",
            "tensorboard-1.14.0   | 3.2 MB    | : 100% 1.0/1 [00:00<00:00, 16.73s/it]                \n",
            "pdbfixer-1.6         | 167 KB    | : 100% 1.0/1 [00:00<00:00,  5.02it/s]\n",
            "python_abi-3.7       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 25.94it/s]\n",
            "astor-0.8.1          | 25 KB     | : 100% 1.0/1 [00:00<00:00, 21.87it/s]\n",
            "deepchem-gpu-2.3.0   | 2.1 MB    | : 100% 1.0/1 [00:00<00:00,  1.55it/s]\n",
            "astunparse-1.6.3     | 15 KB     | : 100% 1.0/1 [00:00<00:00, 25.45it/s]\n",
            "importlib-metadata-3 | 27 KB     | : 100% 1.0/1 [00:00<00:00, 17.46it/s]\n",
            "py-boost-1.67.0      | 278 KB    | : 100% 1.0/1 [00:00<00:00,  5.41it/s]\n",
            "certifi-2019.9.11    | 147 KB    | : 100% 1.0/1 [00:00<00:00,  7.18it/s]\n",
            "rdkit-2020.03.3.0    | 24.8 MB   | : 100% 1.0/1 [00:04<00:00,  4.48s/it]\n",
            "markdown-3.3.4       | 67 KB     | : 100% 1.0/1 [00:00<00:00, 18.42it/s]\n",
            "c-ares-1.17.1        | 111 KB    | : 100% 1.0/1 [00:00<00:00, 15.42it/s]\n",
            "conda-4.10.0         | 3.1 MB    | : 100% 1.0/1 [00:00<00:00,  1.51it/s]\n",
            "mdtraj-1.9.5         | 1.7 MB    | : 100% 1.0/1 [00:00<00:00,  2.78it/s]\n",
            "libboost-1.67.0      | 13.0 MB   | : 100% 1.0/1 [00:01<00:00,  1.02it/s]               \n",
            "cupti-10.1.168       | 1.4 MB    | : 100% 1.0/1 [00:00<00:00,  6.38it/s]\n",
            "keras-applications-1 | 30 KB     | : 100% 1.0/1 [00:00<00:00, 18.97it/s]\n",
            "absl-py-0.12.0       | 96 KB     | : 100% 1.0/1 [00:00<00:00, 15.11it/s]\n",
            "grpcio-1.23.0        | 1.1 MB    | : 100% 1.0/1 [00:00<00:00,  4.07it/s]\n",
            "_tflow_select-2.1.0  | 2 KB      | : 100% 1.0/1 [00:00<00:00,  8.09it/s]\n",
            "_py-xgboost-mutex-2. | 8 KB      | : 100% 1.0/1 [00:00<00:00, 24.04it/s]\n",
            "tensorflow-base-1.14 | 146.3 MB  | : 100% 1.0/1 [00:08<00:00,  8.12s/it]               \n",
            "tensorflow-gpu-1.14. | 3 KB      | : 100% 1.0/1 [00:00<00:00,  6.27it/s]\n",
            "libxgboost-1.2.0     | 3.1 MB    | : 100% 1.0/1 [00:00<00:00,  1.66it/s]               \n",
            "xgboost-1.2.0        | 11 KB     | : 100% 1.0/1 [00:00<00:00, 23.29it/s]\n",
            "py-xgboost-1.2.0     | 1.7 MB    | : 100% 1.0/1 [00:00<00:00,  2.84it/s]\n",
            "cudatoolkit-10.1.243 | 347.4 MB  | : 100% 1.0/1 [00:18<00:00, 18.62s/it]              \n",
            "keras-preprocessing- | 34 KB     | : 100% 1.0/1 [00:00<00:00, 15.27it/s]\n",
            "simdna-0.4.2         | 627 KB    | : 100% 1.0/1 [00:05<00:00,  5.42s/it]\n",
            "cudnn-7.6.5.32       | 250.7 MB  | : 100% 1.0/1 [00:34<00:00, 34.24s/it]               \n",
            "fftw3f-3.3.4         | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.79it/s]\n",
            "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ b'By downloading and using the cuDNN conda packages, you accept the terms and conditions of the NVIDIA cuDNN EULA -\\n  https://docs.nvidia.com/deeplearning/cudnn/sla/index.html\\n'\n",
            "\b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "real\t5m40.597s\n",
            "user\t5m1.920s\n",
            "sys\t0m18.124s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4AB0U-9gyJNB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "465954b4-5dee-4e45-892f-3f3370d2dc43"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/ravichas/AMPL-Tutorial/master/config/install_AMPL_GPU_test.sh"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-10 03:09:41--  https://raw.githubusercontent.com/ravichas/AMPL-Tutorial/master/config/install_AMPL_GPU_test.sh\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1100 (1.1K) [text/plain]\n",
            "Saving to: ‘install_AMPL_GPU_test.sh’\n",
            "\n",
            "install_AMPL_GPU_te 100%[===================>]   1.07K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-04-10 03:09:41 (71.1 MB/s) - ‘install_AMPL_GPU_test.sh’ saved [1100/1100]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aadk1g7ZzPdp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1e242a6-3e91-48bb-ad6f-83b5a6c98f28"
      },
      "source": [
        "! chmod u+x install_AMPL_GPU_test.sh\n",
        "! time ./install_AMPL_GPU_test.sh"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'AMPL'...\n",
            "remote: Enumerating objects: 388, done.\u001b[K\n",
            "remote: Counting objects: 100% (388/388), done.\u001b[K\n",
            "remote: Compressing objects: 100% (217/217), done.\u001b[K\n",
            "remote: Total 1896 (delta 278), reused 242 (delta 171), pack-reused 1508\u001b[K\n",
            "Receiving objects: 100% (1896/1896), 9.71 MiB | 21.89 MiB/s, done.\n",
            "Resolving deltas: 100% (1090/1090), done.\n",
            "Branch 'pkg_upgrade' set up to track remote branch 'pkg_upgrade' from 'origin'.\n",
            "Switched to a new branch 'pkg_upgrade'\n",
            "patching file /content/github/AMPL/atomsci/ddm/pipeline/transformations.py\n",
            "patching file /content/github/AMPL/atomsci/ddm/__init__.py\n",
            "running build\n",
            "running build_py\n",
            "creating /content/github/AMPL.build/ampl/lib\n",
            "creating /content/github/AMPL.build/ampl/lib/atomsci\n",
            "copying atomsci/__init__.py -> /content/github/AMPL.build/ampl/lib/atomsci\n",
            "creating /content/github/AMPL.build/ampl/lib/atomsci/ddm\n",
            "copying atomsci/ddm/__init__.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm\n",
            "creating /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/datastore_functions.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/curate_data.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/genTestset.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/hyperparam_search_wrapper.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/rdkit_easy.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/pubchem_utils.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/open-docs.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/__init__.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/struct_utils.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/data_curation_functions.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/process_slurm.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/llnl_utils.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/utils\n",
            "creating /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/parameter_parser.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/transformations.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/ave_splitter.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_pipeline.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/perf_data.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/compare_models.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_wrapper.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/predict_from_model.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/chem_diversity.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/hyper_perf_plots.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_datasets.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/temporal_splitter.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/__init__.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/diversity_plots.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/splitting.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/featurization.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/dist_metrics.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/perf_plots.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_tracker.py -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/pipeline\n",
            "running egg_info\n",
            "creating /content/github/AMPL.build/ampl/atomsci_ampl.egg-info\n",
            "writing /content/github/AMPL.build/ampl/atomsci_ampl.egg-info/PKG-INFO\n",
            "writing dependency_links to /content/github/AMPL.build/ampl/atomsci_ampl.egg-info/dependency_links.txt\n",
            "writing namespace_packages to /content/github/AMPL.build/ampl/atomsci_ampl.egg-info/namespace_packages.txt\n",
            "writing top-level names to /content/github/AMPL.build/ampl/atomsci_ampl.egg-info/top_level.txt\n",
            "writing manifest file '/content/github/AMPL.build/ampl/atomsci_ampl.egg-info/SOURCES.txt'\n",
            "reading manifest file '/content/github/AMPL.build/ampl/atomsci_ampl.egg-info/SOURCES.txt'\n",
            "reading manifest template 'MANIFEST.in'\n",
            "warning: no files found matching 'atomsci/*/*.yaml'\n",
            "writing manifest file '/content/github/AMPL.build/ampl/atomsci_ampl.egg-info/SOURCES.txt'\n",
            "creating /content/github/AMPL.build/ampl/lib/atomsci/ddm/data\n",
            "copying atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv -> /content/github/AMPL.build/ampl/lib/atomsci/ddm/data\n",
            "running bdist_wheel\n",
            "installing to /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel\n",
            "running install\n",
            "running install_lib\n",
            "Skipping installation of /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/__init__.py (namespace package)\n",
            "copying atomsci/ddm/__init__.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm\n",
            "copying atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/data\n",
            "copying atomsci/ddm/utils/datastore_functions.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/curate_data.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/genTestset.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/hyperparam_search_wrapper.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/rdkit_easy.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/pubchem_utils.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/open-docs.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/__init__.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/struct_utils.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/data_curation_functions.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/process_slurm.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/utils/llnl_utils.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/utils\n",
            "copying atomsci/ddm/pipeline/parameter_parser.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/transformations.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/ave_splitter.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_pipeline.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/perf_data.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/compare_models.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_wrapper.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/predict_from_model.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/chem_diversity.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/hyper_perf_plots.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_datasets.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/temporal_splitter.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/__init__.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/diversity_plots.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/splitting.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/featurization.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/dist_metrics.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/perf_plots.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "copying atomsci/ddm/pipeline/model_tracker.py -> /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci/ddm/pipeline\n",
            "running install_egg_info\n",
            "Copying /content/github/AMPL.build/ampl/atomsci_ampl.egg-info to /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci_ampl-1.0.0.dev1-py3.7.egg-info\n",
            "Installing /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci_ampl-1.0.0.dev1-py3.7-nspkg.pth\n",
            "running install_scripts\n",
            "adding license file \"LICENSE\" (matched pattern \"LICEN[CS]E*\")\n",
            "adding license file \"NOTICE\" (matched pattern \"NOTICE*\")\n",
            "creating /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel/atomsci_ampl-1.0.0.dev1.dist-info/WHEEL\n",
            "creating '/content/github/AMPL.dist/atomsci_ampl-1.0.0.dev1-py3-none-any.whl' and adding '/content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel' to it\n",
            "adding 'atomsci_ampl-1.0.0.dev1-py3.7-nspkg.pth'\n",
            "adding 'atomsci/ddm/__init__.py'\n",
            "adding 'atomsci/ddm/data/descriptor_sets_sources_by_descr_type.csv'\n",
            "adding 'atomsci/ddm/pipeline/__init__.py'\n",
            "adding 'atomsci/ddm/pipeline/ave_splitter.py'\n",
            "adding 'atomsci/ddm/pipeline/chem_diversity.py'\n",
            "adding 'atomsci/ddm/pipeline/compare_models.py'\n",
            "adding 'atomsci/ddm/pipeline/dist_metrics.py'\n",
            "adding 'atomsci/ddm/pipeline/diversity_plots.py'\n",
            "adding 'atomsci/ddm/pipeline/featurization.py'\n",
            "adding 'atomsci/ddm/pipeline/hyper_perf_plots.py'\n",
            "adding 'atomsci/ddm/pipeline/model_datasets.py'\n",
            "adding 'atomsci/ddm/pipeline/model_pipeline.py'\n",
            "adding 'atomsci/ddm/pipeline/model_tracker.py'\n",
            "adding 'atomsci/ddm/pipeline/model_wrapper.py'\n",
            "adding 'atomsci/ddm/pipeline/parameter_parser.py'\n",
            "adding 'atomsci/ddm/pipeline/perf_data.py'\n",
            "adding 'atomsci/ddm/pipeline/perf_plots.py'\n",
            "adding 'atomsci/ddm/pipeline/predict_from_model.py'\n",
            "adding 'atomsci/ddm/pipeline/splitting.py'\n",
            "adding 'atomsci/ddm/pipeline/temporal_splitter.py'\n",
            "adding 'atomsci/ddm/pipeline/transformations.py'\n",
            "adding 'atomsci/ddm/utils/__init__.py'\n",
            "adding 'atomsci/ddm/utils/curate_data.py'\n",
            "adding 'atomsci/ddm/utils/data_curation_functions.py'\n",
            "adding 'atomsci/ddm/utils/datastore_functions.py'\n",
            "adding 'atomsci/ddm/utils/genTestset.py'\n",
            "adding 'atomsci/ddm/utils/hyperparam_search_wrapper.py'\n",
            "adding 'atomsci/ddm/utils/llnl_utils.py'\n",
            "adding 'atomsci/ddm/utils/open-docs.py'\n",
            "adding 'atomsci/ddm/utils/process_slurm.py'\n",
            "adding 'atomsci/ddm/utils/pubchem_utils.py'\n",
            "adding 'atomsci/ddm/utils/rdkit_easy.py'\n",
            "adding 'atomsci/ddm/utils/struct_utils.py'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/LICENSE'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/METADATA'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/NOTICE'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/WHEEL'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/namespace_packages.txt'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/top_level.txt'\n",
            "adding 'atomsci_ampl-1.0.0.dev1.dist-info/RECORD'\n",
            "removing /content/github/AMPL.build/ampl/bdist.linux-x86_64/wheel\n",
            "DIR: /content/github/AMPL\n",
            "Ignoring indexes: https://pypi.org/simple\n",
            "Created temporary directory: /tmp/pip-ephem-wheel-cache-5g2cbin0\n",
            "Created temporary directory: /tmp/pip-req-tracker-elosdf_t\n",
            "Created requirements tracker '/tmp/pip-req-tracker-elosdf_t'\n",
            "Created temporary directory: /tmp/pip-install-974ng5ci\n",
            "Looking in links: /content/github/AMPL.dist\n",
            "Collecting atomsci_ampl\n",
            "  0 location(s) to search for versions of atomsci-ampl:\n",
            "  Skipping link: unsupported archive format: .dist: /content/github/AMPL.dist (from -f)\n",
            "  Found link file:///content/github/AMPL.dist/atomsci_ampl-1.0.0.dev1-py3-none-any.whl, version: 1.0.0.dev1\n",
            "  Local files found: /content/github/AMPL.dist/atomsci_ampl-1.0.0.dev1-py3-none-any.whl\n",
            "  Given no hashes to check 1 links for project 'atomsci-ampl': discarding no candidates\n",
            "  Using version 1.0.0.dev1 (newest of versions: 1.0.0.dev1)\n",
            "  Added atomsci_ampl from file:///content/github/AMPL.dist/atomsci_ampl-1.0.0.dev1-py3-none-any.whl to build tracker '/tmp/pip-req-tracker-elosdf_t'\n",
            "  Removed atomsci_ampl from file:///content/github/AMPL.dist/atomsci_ampl-1.0.0.dev1-py3-none-any.whl from build tracker '/tmp/pip-req-tracker-elosdf_t'\n",
            "Installing collected packages: atomsci-ampl\n",
            "\n",
            "Successfully installed atomsci-ampl-1.0.0.dev1\n",
            "Cleaning up...\n",
            "Removed build tracker '/tmp/pip-req-tracker-elosdf_t'\n",
            "\n",
            "real\t0m3.315s\n",
            "user\t0m2.148s\n",
            "sys\t0m0.568s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gvmh_JjmFYbJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7e2e198-f6f6-48e2-af8d-367e154c9b5c"
      },
      "source": [
        "! time pip install mordred bravado molvs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mordred\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/3d/26c908ece761adafcea06320bf8fe73f4de69979273fb164226dc6038c39/mordred-1.2.0.tar.gz (128kB)\n",
            "\r\u001b[K     |██▌                             | 10kB 11.5MB/s eta 0:00:01\r\u001b[K     |█████                           | 20kB 10.8MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 30kB 8.5MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 40kB 7.4MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 51kB 4.7MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 61kB 5.2MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 71kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████▍           | 81kB 5.5MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 92kB 5.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▌      | 102kB 4.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 112kB 4.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 122kB 4.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 133kB 4.5MB/s \n",
            "\u001b[?25hCollecting bravado\n",
            "  Downloading https://files.pythonhosted.org/packages/21/ed/03b0c36b5bcafbe2938ed222f9a164a6c0367ce99a9d2d502e462853571d/bravado-11.0.3-py2.py3-none-any.whl\n",
            "Collecting molvs\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/dc/d948e83b97f2c420cb6c7e2143ae349560d3b5b061945f1b2a4eefb0231c/MolVS-0.1.1.tar.gz (61kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: six==1.* in /usr/local/lib/python3.7/site-packages (from mordred) (1.12.0)\n",
            "Requirement already satisfied: numpy==1.* in /usr/local/lib/python3.7/site-packages (from mordred) (1.17.2)\n",
            "Requirement already satisfied: networkx==2.* in /usr/local/lib/python3.7/site-packages (from mordred) (2.3)\n",
            "Collecting simplejson (from bravado)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/04/377418ac1e530ce2a196b54c6552c018fdf1fe776718053efb1f216bffcd/simplejson-3.17.2-cp37-cp37m-manylinux2010_x86_64.whl (128kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 7.1MB/s \n",
            "\u001b[?25hCollecting monotonic (from bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/9a/67/7e8406a29b6c45be7af7740456f7f37025f0506ae2e05fb9009a53946860/monotonic-1.6-py2.py3-none-any.whl\n",
            "Collecting bravado-core>=5.16.1 (from bravado)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/11/18e9d28a156c33f2d5f15a5e155dc7130250acb0a569255a2b6b307b596d/bravado_core-5.17.0-py2.py3-none-any.whl (67kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/site-packages (from bravado) (3.7.4.3)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.7/site-packages (from bravado) (0.6.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/site-packages (from bravado) (2.8.0)\n",
            "Requirement already satisfied: requests>=2.17 in /usr/local/lib/python3.7/site-packages (from bravado) (2.22.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/site-packages (from bravado) (5.1.2)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/site-packages (from networkx==2.*->mordred) (4.4.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/site-packages (from bravado-core>=5.16.1->bravado) (2019.3)\n",
            "Collecting jsonref (from bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/07/92/f8e4ac824b14af77e613984e480fa818397c72d4141fc466decb26752749/jsonref-0.2-py3-none-any.whl\n",
            "Requirement already satisfied: jsonschema[format]>=2.5.1 in /usr/local/lib/python3.7/site-packages (from bravado-core>=5.16.1->bravado) (3.0.2)\n",
            "Collecting swagger-spec-validator>=2.0.1 (from bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/09/de/e78cefbf5838b434b63a789264b79821cb2267f1498fbed23ef8590133e4/swagger_spec_validator-2.7.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests>=2.17->bravado) (1.24.2)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests>=2.17->bravado) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests>=2.17->bravado) (2019.9.11)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests>=2.17->bravado) (3.0.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado) (41.4.0)\n",
            "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.7/site-packages (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado) (0.15.4)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/site-packages (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado) (19.2.0)\n",
            "Collecting jsonpointer>1.13; extra == \"format\" (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/23/52/05f67532aa922e494c351344e0d9624a01f74f5dd8402fe0d1b563a6e6fc/jsonpointer-2.1-py2.py3-none-any.whl\n",
            "Collecting rfc3987; extra == \"format\" (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/65/d4/f7407c3d15d5ac779c3dd34fbbc6ea2090f77bd7dd12f207ccf881551208/rfc3987-1.3.8-py2.py3-none-any.whl\n",
            "Collecting webcolors; extra == \"format\" (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/12/05/3350559de9714b202e443a9e6312937341bd5f79f4e4f625744295e7dd17/webcolors-1.11.1-py3-none-any.whl\n",
            "Collecting strict-rfc3339; extra == \"format\" (from jsonschema[format]>=2.5.1->bravado-core>=5.16.1->bravado)\n",
            "  Downloading https://files.pythonhosted.org/packages/56/e4/879ef1dbd6ddea1c77c0078cd59b503368b0456bcca7d063a870ca2119d3/strict-rfc3339-0.7.tar.gz\n",
            "Building wheels for collected packages: mordred, molvs, strict-rfc3339\n",
            "  Building wheel for mordred (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mordred: filename=mordred-1.2.0-cp37-none-any.whl size=176721 sha256=160f5fffef0136ee24558f2290bf938151974b69d3209438cc619b578bbae58c\n",
            "  Stored in directory: /root/.cache/pip/wheels/ac/74/3f/2fd81b1187013f2eadb15620434813f1824c4c03b7bd1f94f6\n",
            "  Building wheel for molvs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for molvs: filename=MolVS-0.1.1-cp37-none-any.whl size=32376 sha256=40f26f97470cfbb583ce61fde737df3ce20b2bced03339a38d0f07625517d2ee\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/37/a8/8ac8147605c9de6b45ffd66d1cc19761d41467db12b34a0de8\n",
            "  Building wheel for strict-rfc3339 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for strict-rfc3339: filename=strict_rfc3339-0.7-cp37-none-any.whl size=18120 sha256=3ce98ff81a04b000dfb93c7f25968c2d8a70180f778dec2a4762543af6fdcba1\n",
            "  Stored in directory: /root/.cache/pip/wheels/bb/af/c9/b6e9fb5f9b2470e4ed2a7241c9ab3a8cdd3bc8555ae02ca2e6\n",
            "Successfully built mordred molvs strict-rfc3339\n",
            "Installing collected packages: mordred, simplejson, monotonic, jsonref, swagger-spec-validator, bravado-core, bravado, molvs, jsonpointer, rfc3987, webcolors, strict-rfc3339\n",
            "Successfully installed bravado-11.0.3 bravado-core-5.17.0 jsonpointer-2.1 jsonref-0.2 molvs-0.1.1 monotonic-1.6 mordred-1.2.0 rfc3987-1.3.8 simplejson-3.17.2 strict-rfc3339-0.7 swagger-spec-validator-2.7.3 webcolors-1.11.1\n",
            "\n",
            "real\t0m7.431s\n",
            "user\t0m5.993s\n",
            "sys\t0m0.985s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InUQE4II1noi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3af126fc-1252-4828-9324-5ab67e45e271"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/ravichas/AMPL-Tutorial/master/datasets/H1_std.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-10 03:09:52--  https://raw.githubusercontent.com/ravichas/AMPL-Tutorial/master/datasets/H1_std.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 136759 (134K) [text/plain]\n",
            "Saving to: ‘H1_std.csv’\n",
            "\n",
            "H1_std.csv          100%[===================>] 133.55K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2021-04-10 03:09:52 (5.20 MB/s) - ‘H1_std.csv’ saved [136759/136759]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PO0-2HKvVpn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1567e3aa-f69a-4b48-b171-24a3e4269af9"
      },
      "source": [
        "!pip install hyperopt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting hyperopt\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a6/07/bd524635d218adae139be320eeac87fb4fbbd45c63b0bd58930c9e91f1fc/hyperopt-0.2.5-py2.py3-none-any.whl (965kB)\n",
            "\u001b[K     |████████████████████████████████| 972kB 4.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.7/site-packages (from hyperopt) (2.3)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/site-packages (from hyperopt) (1.2.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/site-packages (from hyperopt) (0.17.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from hyperopt) (1.17.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/site-packages (from hyperopt) (4.36.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from hyperopt) (1.12.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from hyperopt) (1.3.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/site-packages (from networkx>=2.2->hyperopt) (4.4.0)\n",
            "Installing collected packages: hyperopt\n",
            "Successfully installed hyperopt-0.2.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTOwqhvR3xL3"
      },
      "source": [
        "### Load packages and dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeN9kEMO2R9Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2befbb67-c789-4374-8114-4b47545b7955"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "sns.set_context(\"poster\")\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.set_palette(\"Set2\")\n",
        "\n",
        "import pandas as pd\n",
        "import os, json, sys, glob, pickle\n",
        "\n",
        "from atomsci.ddm.pipeline import model_pipeline as mp\n",
        "from atomsci.ddm.pipeline import parameter_parser as parse\n",
        "from atomsci.ddm.pipeline import perf_data\n",
        "from atomsci.ddm.pipeline import compare_models as cmp\n",
        "\n",
        "from hyperopt import fmin, tpe, hp, Trials, STATUS_OK"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0zYnjPsU1TZ",
        "outputId": "83826469-2bdb-4d8a-ca7a-5c9712dfa4e8"
      },
      "source": [
        "# clean up unnecessary files\n",
        "! conda clean -a -y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cache location: /usr/local/pkgs\n",
            "Will remove the following tarballs:\n",
            "\n",
            "/usr/local/pkgs\n",
            "---------------\n",
            "secretstorage-3.1.1-py37_0.conda              24 KB\n",
            "html5lib-1.0.1-py37_0.conda                  185 KB\n",
            "ncurses-6.1-he6710b0_1.conda                 777 KB\n",
            "pcre-8.43-he6710b0_0.conda                   209 KB\n",
            "wrapt-1.11.2-py37h7b6447c_0.conda             49 KB\n",
            "importlib_metadata-0.23-py37_0.tar.bz2        43 KB\n",
            "qtconsole-4.5.5-py_0.tar.bz2                  94 KB\n",
            "pandoc-2.2.3.2-0.conda                      14.0 MB\n",
            "mdtraj-1.9.5-py37h113d463_0.tar.bz2          1.7 MB\n",
            "libsodium-1.0.16-h1bed415_0.conda            214 KB\n",
            "cupti-10.1.168-0.conda                       1.4 MB\n",
            "sphinxcontrib-qthelp-1.0.2-py_0.tar.bz2       26 KB\n",
            "conda-4.10.0-py37h89c1867_1.tar.bz2          3.1 MB\n",
            "jsonschema-3.0.2-py37_0.conda                 92 KB\n",
            "certifi-2019.9.11-py37_0.tar.bz2             147 KB\n",
            "backports.tempfile-1.0-py_1.tar.bz2           12 KB\n",
            "gst-plugins-base-1.14.0-hbbd80ab_1.conda     4.8 MB\n",
            "scipy-1.3.1-py37h7c811a0_0.conda            14.0 MB\n",
            "msgpack-python-0.6.1-py37hfd86e86_1.conda      87 KB\n",
            "olefile-0.46-py37_0.conda                     50 KB\n",
            "conda-env-2.6.0-1.conda                        3 KB\n",
            "xlrd-1.2.0-py37_0.conda                      175 KB\n",
            "backports.shutil_get_terminal_size-1.0.0-py37_2.conda       8 KB\n",
            "dask-2.5.2-py_0.tar.bz2                       12 KB\n",
            "termcolor-1.1.0-py_2.tar.bz2                   6 KB\n",
            "pyparsing-2.4.2-py_0.tar.bz2                  61 KB\n",
            "json5-0.8.5-py_0.tar.bz2                      25 KB\n",
            "numpy-1.17.2-py37haad9e8e_0.conda              4 KB\n",
            "pycparser-2.19-py37_0.conda                  171 KB\n",
            "tensorflow-gpu-1.14.0-h0d30ee6_0.conda         3 KB\n",
            "python-3.7.4-h265db76_1.conda               32.1 MB\n",
            "glob2-0.7-py_0.tar.bz2                        14 KB\n",
            "mccabe-0.6.1-py37_1.conda                     14 KB\n",
            "libuuid-1.0.3-h1bed415_2.conda                15 KB\n",
            "send2trash-1.5.0-py37_0.conda                 16 KB\n",
            "astropy-3.2.2-py37h7b6447c_0.tar.bz2         7.1 MB\n",
            "graphite2-1.3.13-h23475e2_0.conda             98 KB\n",
            "sqlalchemy-1.3.9-py37h7b6447c_0.tar.bz2      1.8 MB\n",
            "babel-2.7.0-py_0.tar.bz2                     5.8 MB\n",
            "libffi-3.2.1-hd88cf55_4.conda                 40 KB\n",
            "soupsieve-1.9.3-py37_0.tar.bz2                60 KB\n",
            "sphinxcontrib-jsmath-1.0.1-py_0.tar.bz2        8 KB\n",
            "patchelf-0.9-he6710b0_3.conda                 68 KB\n",
            "pyyaml-5.1.2-py37h7b6447c_0.conda            179 KB\n",
            "keyring-18.0.0-py37_0.conda                   59 KB\n",
            "grpcio-1.23.0-py37hb0870dc_1.tar.bz2         1.1 MB\n",
            "bottleneck-1.2.1-py37h035aef0_1.conda        120 KB\n",
            "ripgrep-0.10.0-hc07d326_0.conda              1.2 MB\n",
            "cairo-1.14.12-h8948797_3.conda               906 KB\n",
            "asn1crypto-1.0.1-py37_0.tar.bz2              161 KB\n",
            "click-7.0-py37_0.conda                       120 KB\n",
            "sphinxcontrib-htmlhelp-1.0.2-py_0.tar.bz2      28 KB\n",
            "prometheus_client-0.7.1-py_0.tar.bz2          42 KB\n",
            "fftw3f-3.3.4-2.tar.bz2                       1.2 MB\n",
            "attrs-19.2.0-py_0.tar.bz2                     39 KB\n",
            "libgfortran-ng-7.3.0-hdf63c60_0.conda       1006 KB\n",
            "libxml2-2.9.9-hea5a465_1.conda               1.6 MB\n",
            "astunparse-1.6.3-pyhd8ed1ab_0.tar.bz2         15 KB\n",
            "wheel-0.33.6-py37_0.tar.bz2                   40 KB\n",
            "pip-19.2.3-py37_0.tar.bz2                    1.9 MB\n",
            "nltk-3.4.5-py37_0.conda                      1.7 MB\n",
            "distributed-2.5.2-py_0.tar.bz2               396 KB\n",
            "imagesize-1.1.0-py37_0.conda                   9 KB\n",
            "pytest-openfiles-0.4.0-py_0.tar.bz2           10 KB\n",
            "gevent-1.4.0-py37h7b6447c_0.conda            2.0 MB\n",
            "mistune-0.8.4-py37h7b6447c_0.conda            55 KB\n",
            "llvmlite-0.29.0-py37hd408876_0.conda        14.9 MB\n",
            "ptyprocess-0.6.0-py37_0.conda                 23 KB\n",
            "libcurl-7.65.3-h20c2e04_0.conda              431 KB\n",
            "libtiff-4.0.10-h2733197_2.conda              435 KB\n",
            "defusedxml-0.6.0-py_0.tar.bz2                 23 KB\n",
            "pytz-2019.3-py_0.tar.bz2                     231 KB\n",
            "parso-0.5.1-py_0.tar.bz2                      68 KB\n",
            "tk-8.6.8-hbc83047_0.conda                    2.8 MB\n",
            "zlib-1.2.11-h7b6447c_3.conda                 103 KB\n",
            "liblief-0.9.0-h7725739_2.conda               2.9 MB\n",
            "atomicwrites-1.3.0-py37_1.conda               13 KB\n",
            "libprotobuf-3.13.0.1-h8b12597_0.tar.bz2      2.3 MB\n",
            "boto-2.49.0-py37_0.conda                     1.2 MB\n",
            "mpc-1.1.0-h10f8cd9_1.conda                    90 KB\n",
            "patsy-0.5.1-py37_0.conda                     274 KB\n",
            "backports.os-0.1.1-py37_0.conda               15 KB\n",
            "cytoolz-0.10.0-py37h7b6447c_0.conda          374 KB\n",
            "sqlite-3.30.0-h7b6447c_0.tar.bz2             1.9 MB\n",
            "_tflow_select-2.1.0-gpu.conda                  2 KB\n",
            "mkl_random-1.1.0-py37hd6b4f25_0.conda        321 KB\n",
            "jupyter_core-4.5.0-py_0.tar.bz2               48 KB\n",
            "scikit-learn-0.21.3-py37hd81dba3_0.conda     4.9 MB\n",
            "c-ares-1.17.1-h36c2ea0_0.tar.bz2             111 KB\n",
            "h5py-2.9.0-py37h7918eee_0.conda              991 KB\n",
            "conda-4.7.12-py37_0.tar.bz2                  3.0 MB\n",
            "krb5-1.16.1-h173b8e3_7.conda                 1.2 MB\n",
            "readline-7.0-h7b6447c_5.conda                324 KB\n",
            "pycosat-0.6.3-py37h14c3975_0.conda            79 KB\n",
            "pickleshare-0.7.5-py37_0.conda                13 KB\n",
            "anaconda-project-0.8.3-py_0.tar.bz2          212 KB\n",
            "libxcb-1.13-h1bed415_1.conda                 421 KB\n",
            "toolz-0.10.0-py_0.tar.bz2                     50 KB\n",
            "pep8-1.7.1-py37_0.conda                       53 KB\n",
            "cloudpickle-1.2.2-py_0.tar.bz2                29 KB\n",
            "openssl-1.1.1d-h7b6447c_2.conda              2.5 MB\n",
            "jupyter-1.0.0-py37_7.conda                     6 KB\n",
            "heapdict-1.0.1-py_0.conda                      9 KB\n",
            "pytest-arraydiff-0.3-py37h39e3cac_0.conda      15 KB\n",
            "libboost-1.67.0-h46d08c1_4.conda            13.0 MB\n",
            "cryptography-2.7-py37h1ba5d50_0.conda        544 KB\n",
            "zeromq-4.3.1-he6710b0_3.conda                496 KB\n",
            "pycodestyle-2.5.0-py37_0.conda                61 KB\n",
            "python-libarchive-c-2.8-py37_13.conda         24 KB\n",
            "jeepney-0.4.1-py_0.tar.bz2                    21 KB\n",
            "nbformat-4.4.0-py37_0.conda                  128 KB\n",
            "blas-1.0-mkl.conda                             6 KB\n",
            "testpath-0.4.2-py37_0.conda                   86 KB\n",
            "requests-2.22.0-py37_0.conda                  90 KB\n",
            "pyodbc-4.0.27-py37he6710b0_0.conda            68 KB\n",
            "pysocks-1.7.1-py37_0.tar.bz2                  30 KB\n",
            "notebook-6.0.1-py37_0.tar.bz2                6.0 MB\n",
            "anaconda-client-1.7.2-py37_0.conda           147 KB\n",
            "pandocfilters-1.4.2-py37_1.conda              13 KB\n",
            "kiwisolver-1.1.0-py37he6710b0_0.conda         82 KB\n",
            "ipykernel-5.1.2-py37h39e3cac_0.conda         170 KB\n",
            "bokeh-1.3.4-py37_0.conda                     3.6 MB\n",
            "cython-0.29.13-py37he6710b0_0.conda          2.0 MB\n",
            "et_xmlfile-1.0.1-py37_0.conda                 21 KB\n",
            "mpfr-4.0.1-hdf1c602_3.conda                  429 KB\n",
            "sphinx-2.2.0-py_0.tar.bz2                    1.5 MB\n",
            "tblib-1.4.0-py_0.tar.bz2                      14 KB\n",
            "matplotlib-3.1.1-py37h5429711_0.conda        5.0 MB\n",
            "nose-1.3.7-py37_2.conda                      214 KB\n",
            "backports.weakref-1.0.post1-py_1.tar.bz2       7 KB\n",
            "jinja2-2.10.3-py_0.tar.bz2                    95 KB\n",
            "numpy-base-1.17.2-py37hde5b4d6_0.conda       4.2 MB\n",
            "backports-1.0-py_2.tar.bz2                   139 KB\n",
            "sortedcontainers-2.1.0-py37_0.conda           43 KB\n",
            "unicodecsv-0.14.1-py37_0.conda                25 KB\n",
            "bleach-3.1.0-py37_0.conda                    220 KB\n",
            "locket-0.2.0-py37_1.conda                      9 KB\n",
            "deepchem-gpu-2.3.0-py37_0.tar.bz2            2.1 MB\n",
            "pygments-2.4.2-py_0.tar.bz2                  664 KB\n",
            "libxgboost-1.2.0-he1b5a44_0.tar.bz2          3.1 MB\n",
            "entrypoints-0.3-py37_0.conda                  12 KB\n",
            "markupsafe-1.1.1-py37h7b6447c_0.conda         29 KB\n",
            "pytables-3.5.2-py37h71ec239_1.conda          1.2 MB\n",
            "multipledispatch-0.6.0-py37_0.conda           22 KB\n",
            "hdf5-1.10.4-hb1b8bf9_0.conda                 3.9 MB\n",
            "tensorflow-1.14.0-gpu_py37h74c33d7_0.conda       4 KB\n",
            "conda-verify-3.4.2-py_1.tar.bz2               25 KB\n",
            "setuptools-41.4.0-py37_0.tar.bz2             651 KB\n",
            "clyent-1.2.2-py37_1.conda                     19 KB\n",
            "jedi-0.15.1-py37_0.conda                     704 KB\n",
            "pylint-2.4.2-py37_0.tar.bz2                  429 KB\n",
            "tornado-6.0.3-py37h7b6447c_0.conda           584 KB\n",
            "xlwt-1.3.0-py37_0.conda                      158 KB\n",
            "pluggy-0.13.0-py37_0.tar.bz2                  31 KB\n",
            "mkl_fft-1.0.14-py37ha843d7b_0.conda          155 KB\n",
            "pyzmq-18.1.0-py37he6710b0_0.conda            455 KB\n",
            "statsmodels-0.10.1-py37hdd07704_0.conda      7.4 MB\n",
            "simplegeneric-0.8.1-py37_2.conda              10 KB\n",
            "pytest-astropy-0.5.0-py37_0.conda              7 KB\n",
            "fribidi-1.0.5-h7b6447c_0.conda               101 KB\n",
            "sip-4.19.8-py37hf484d3e_0.conda              274 KB\n",
            "yaml-0.1.7-had09818_2.conda                   73 KB\n",
            "typing_extensions-3.7.4.3-py_0.tar.bz2        25 KB\n",
            "flask-1.1.1-py_0.tar.bz2                      73 KB\n",
            "numexpr-2.7.0-py37h9e4a6bb_0.conda           183 KB\n",
            "fontconfig-2.13.0-h9420a91_0.conda           227 KB\n",
            "bzip2-1.0.8-h7b6447c_0.conda                  78 KB\n",
            "wurlitzer-1.0.3-py37_0.conda                  14 KB\n",
            "decorator-4.4.0-py37_1.conda                  19 KB\n",
            "libarchive-3.3.3-h5d8350f_5.conda            725 KB\n",
            "greenlet-0.4.15-py37h7b6447c_0.conda          20 KB\n",
            "traitlets-4.3.3-py37_0.tar.bz2               138 KB\n",
            "harfbuzz-1.8.8-hffaf4a1_0.conda              507 KB\n",
            "pixman-0.38.0-h7b6447c_0.conda               364 KB\n",
            "snappy-1.1.7-hbae5bb6_3.conda                 35 KB\n",
            "dask-core-2.5.2-py_0.tar.bz2                 579 KB\n",
            "pdbfixer-1.6-py_1.tar.bz2                    167 KB\n",
            "pandas-0.25.1-py37he6710b0_0.conda           8.8 MB\n",
            "widgetsnbextension-3.5.1-py37_0.conda        862 KB\n",
            "pytest-doctestplus-0.4.0-py_0.tar.bz2         18 KB\n",
            "bkcharts-0.2-py37_0.conda                    133 KB\n",
            "gmp-6.1.2-h6c8ec71_1.conda                   514 KB\n",
            "sphinxcontrib-serializinghtml-1.1.3-py_0.tar.bz2      24 KB\n",
            "_py-xgboost-mutex-2.0-cpu_0.tar.bz2            8 KB\n",
            "gmpy2-2.0.8-py37h10f8cd9_2.conda             150 KB\n",
            "simdna-0.4.2-py_0.tar.bz2                    627 KB\n",
            "lazy-object-proxy-1.4.2-py37h7b6447c_0.conda      29 KB\n",
            "pathlib2-2.3.5-py37_0.tar.bz2                 37 KB\n",
            "sortedcollections-1.1.2-py37_0.conda          18 KB\n",
            "numba-0.45.1-py37h962f231_0.conda            2.8 MB\n",
            "openpyxl-3.0.0-py_0.tar.bz2                  157 KB\n",
            "expat-2.2.6-he6710b0_0.conda                 146 KB\n",
            "python_abi-3.7-1_cp37m.tar.bz2                 4 KB\n",
            "psutil-5.6.3-py37h7b6447c_0.conda            313 KB\n",
            "imageio-2.6.0-py37_0.tar.bz2                 3.4 MB\n",
            "jupyterlab_server-1.0.6-py_0.tar.bz2          26 KB\n",
            "cudnn-7.6.5.32-hc0a50b0_1.tar.bz2          250.7 MB\n",
            "itsdangerous-1.1.0-py37_0.conda               28 KB\n",
            "lzo-2.10-h49e0be7_2.conda                    182 KB\n",
            "mkl-2019.4-243.conda                       131.2 MB\n",
            "py-lief-0.9.0-py37h7725739_2.conda           1.3 MB\n",
            "terminado-0.8.2-py37_0.conda                  23 KB\n",
            "future-0.17.1-py37_0.conda                   616 KB\n",
            "sphinxcontrib-1.0-py37_1.conda                 4 KB\n",
            "pytest-5.2.1-py37_0.tar.bz2                  364 KB\n",
            "packaging-19.2-py_0.tar.bz2                   30 KB\n",
            "rope-0.14.0-py_0.tar.bz2                     113 KB\n",
            "icu-58.2-h9c2bf20_1.conda                   10.3 MB\n",
            "pexpect-4.7.0-py37_0.conda                    80 KB\n",
            "pycrypto-2.6.1-py37h14c3975_9.conda          362 KB\n",
            "ipython_genutils-0.2.0-py37_0.conda           39 KB\n",
            "wcwidth-0.1.7-py37_0.conda                    22 KB\n",
            "mkl-service-2.3.0-py37he904b0f_0.conda       218 KB\n",
            "pyflakes-2.1.1-py37_0.conda                  106 KB\n",
            "singledispatch-3.4.0.3-py37_0.conda           16 KB\n",
            "_ipyw_jlab_nb_ext_conf-0.1.0-py37_0.conda       4 KB\n",
            "blosc-1.16.3-hd408876_0.conda                 71 KB\n",
            "pywavelets-1.0.3-py37hdd07704_1.conda        3.5 MB\n",
            "ipython-7.8.0-py37h39e3cac_0.conda           985 KB\n",
            "spyder-kernels-0.5.2-py37_0.tar.bz2           69 KB\n",
            "markdown-3.3.4-pyhd8ed1ab_0.tar.bz2           67 KB\n",
            "fastcache-1.1.0-py37h7b6447c_0.conda          30 KB\n",
            "joblib-0.13.2-py37_0.conda                   343 KB\n",
            "bitarray-1.0.1-py37h7b6447c_0.conda           60 KB\n",
            "freetype-2.9.1-h8a8886c_1.conda              550 KB\n",
            "backports.functools_lru_cache-1.5-py_2.tar.bz2       9 KB\n",
            "cycler-0.10.0-py37_0.conda                    13 KB\n",
            "anaconda-navigator-1.9.7-py37_0.tar.bz2      4.8 MB\n",
            "intel-openmp-2019.4-243.conda                729 KB\n",
            "tensorflow-estimator-1.14.0-py37h5ca1d4c_0.tar.bz2     645 KB\n",
            "tensorboard-1.14.0-py37_0.tar.bz2            3.2 MB\n",
            "spyder-3.3.6-py37_0.conda                    2.2 MB\n",
            "ruamel_yaml-0.15.46-py37h14c3975_0.conda     237 KB\n",
            "seaborn-0.9.0-py37_0.conda                   355 KB\n",
            "mock-3.0.5-py37_0.conda                       49 KB\n",
            "unixodbc-2.3.7-h14c3975_0.conda              285 KB\n",
            "sympy-1.4-py37_0.conda                       7.9 MB\n",
            "rdkit-2020.03.3.0-py37hc20afe1_1.tar.bz2    24.8 MB\n",
            "six-1.12.0-py37_0.conda                       23 KB\n",
            "gstreamer-1.14.0-hb453b48_1.conda            3.1 MB\n",
            "xz-5.2.4-h14c3975_4.conda                    283 KB\n",
            "qtpy-1.9.0-py_0.tar.bz2                       39 KB\n",
            "libtool-2.4.6-h7b6447c_5.conda               389 KB\n",
            "tbb-2019.4-hfd86e86_0.conda                  1.1 MB\n",
            "zict-1.0.0-py_0.tar.bz2                       12 KB\n",
            "mpmath-1.1.0-py37_0.conda                    766 KB\n",
            "pango-1.42.4-h049681c_0.conda                464 KB\n",
            "jupyterlab-1.1.4-pyhf63ae98_0.conda          4.4 MB\n",
            "tqdm-4.36.1-py_0.conda                        50 KB\n",
            "py-boost-1.67.0-py37h04863e7_4.conda         278 KB\n",
            "jpeg-9b-h024ee3a_2.conda                     214 KB\n",
            "python-dateutil-2.8.0-py37_0.conda           266 KB\n",
            "contextlib2-0.6.0-py_0.tar.bz2                16 KB\n",
            "pyqt-5.9.2-py37h05f1152_2.conda              4.5 MB\n",
            "qtawesome-0.6.0-py_0.tar.bz2                 671 KB\n",
            "libxslt-1.1.33-h7d1a2b0_0.conda              426 KB\n",
            "backcall-0.1.0-py37_0.conda                   20 KB\n",
            "jupyter_console-6.0.0-py37_0.conda            37 KB\n",
            "dbus-1.13.6-h746ee38_0.conda                 499 KB\n",
            "more-itertools-7.2.0-py37_0.conda            100 KB\n",
            "glib-2.56.2-hd408876_0.conda                 3.9 MB\n",
            "conda-package-handling-1.6.0-py37h7b6447c_0.conda     797 KB\n",
            "xgboost-1.2.0-py37h3340039_0.tar.bz2          11 KB\n",
            "jbig-2.1-hdba287a_0.conda                     40 KB\n",
            "pytest-remotedata-0.3.2-py37_0.conda          15 KB\n",
            "libedit-3.1.20181209-hc058e9b_0.conda        163 KB\n",
            "lz4-c-1.8.1.2-h14c3975_0.conda               130 KB\n",
            "py-1.8.0-py37_0.conda                        148 KB\n",
            "anaconda-2019.10-py37_0.tar.bz2               11 KB\n",
            "conda-build-3.18.9-py37_3.conda              506 KB\n",
            "werkzeug-0.16.0-py_0.tar.bz2                 255 KB\n",
            "partd-1.0.0-py_0.tar.bz2                      19 KB\n",
            "idna-2.8-py37_0.conda                         85 KB\n",
            "ipywidgets-7.5.1-py_0.tar.bz2                107 KB\n",
            "protobuf-3.13.0.1-py37h745909e_1.tar.bz2     704 KB\n",
            "google-pasta-0.2.0-pyh8c360ce_0.tar.bz2       42 KB\n",
            "zipp-0.6.0-py_0.tar.bz2                        9 KB\n",
            "ply-3.11-py37_0.conda                         82 KB\n",
            "keras-preprocessing-1.1.2-pyhd8ed1ab_0.tar.bz2      34 KB\n",
            "libstdcxx-ng-9.1.0-hdf63c60_0.conda          3.1 MB\n",
            "ca-certificates-2019.8.28-0.tar.bz2          132 KB\n",
            "importlib-metadata-3.10.0-py37h89c1867_0.tar.bz2      27 KB\n",
            "pillow-6.2.0-py37h34e0f95_0.tar.bz2          643 KB\n",
            "isort-4.3.21-py37_0.conda                     69 KB\n",
            "chardet-3.0.4-py37_1003.conda                174 KB\n",
            "jdcal-1.4.1-py_0.tar.bz2                      11 KB\n",
            "docutils-0.15.2-py37_0.conda                 660 KB\n",
            "nbconvert-5.6.0-py37_1.tar.bz2               491 KB\n",
            "navigator-updater-0.2.1-py37_0.conda         691 KB\n",
            "openmm-7.4.2-py37_cuda101_rc_1.tar.bz2      11.9 MB\n",
            "pyopenssl-19.0.0-py37_0.conda                 84 KB\n",
            "cffi-1.12.3-py37h2e261b9_0.conda             222 KB\n",
            "numpydoc-0.9.1-py_0.tar.bz2                   31 KB\n",
            "get_terminal_size-1.0.0-haa9412d_0.conda       3 KB\n",
            "fsspec-0.5.2-py_0.tar.bz2                     46 KB\n",
            "webencodings-0.5.1-py37_1.conda               19 KB\n",
            "zstd-1.3.7-h0b5b093_0.conda                  401 KB\n",
            "beautifulsoup4-4.8.0-py37_0.conda            152 KB\n",
            "filelock-3.0.12-py_0.tar.bz2                  12 KB\n",
            "qt-5.9.7-h5867ecd_1.conda                   68.5 MB\n",
            "pkginfo-1.5.0.1-py37_0.conda                  44 KB\n",
            "libssh2-1.8.2-h1ba5d50_0.conda               226 KB\n",
            "jupyter_client-5.3.3-py37_1.tar.bz2          137 KB\n",
            "astor-0.8.1-pyh9f0ad1d_0.tar.bz2              25 KB\n",
            "_libgcc_mutex-0.1-main.conda                   3 KB\n",
            "sphinxcontrib-applehelp-1.0.1-py_0.tar.bz2      29 KB\n",
            "curl-7.65.3-hbc83047_0.conda                 130 KB\n",
            "cudatoolkit-10.1.243-h6bb024c_0.conda      347.4 MB\n",
            "keras-applications-1.0.8-py_1.tar.bz2         30 KB\n",
            "py-xgboost-1.2.0-py37hc8dfbb8_0.tar.bz2      1.7 MB\n",
            "snowballstemmer-2.0.0-py_0.tar.bz2            58 KB\n",
            "path.py-12.0.1-py_0.tar.bz2                   23 KB\n",
            "absl-py-0.12.0-pyhd8ed1ab_0.tar.bz2           96 KB\n",
            "sphinxcontrib-devhelp-1.0.1-py_0.tar.bz2      23 KB\n",
            "lxml-4.4.1-py37hefd8a0e_0.conda              1.4 MB\n",
            "urllib3-1.24.2-py37_0.conda                  159 KB\n",
            "networkx-2.3-py_0.tar.bz2                    1.1 MB\n",
            "libpng-1.6.37-hbc83047_0.conda               278 KB\n",
            "libgcc-ng-9.1.0-hdf63c60_0.conda             5.1 MB\n",
            "tensorflow-base-1.14.0-gpu_py37he45bfe2_0.conda   146.3 MB\n",
            "colorama-0.4.1-py37_0.conda                   25 KB\n",
            "pyrsistent-0.15.4-py37h7b6447c_0.tar.bz2      92 KB\n",
            "xlsxwriter-1.2.1-py_0.tar.bz2                106 KB\n",
            "astroid-2.3.1-py37_0.tar.bz2                 288 KB\n",
            "alabaster-0.7.12-py37_0.conda                 18 KB\n",
            "pycurl-7.43.0.3-py37h1ba5d50_0.conda          67 KB\n",
            "prompt_toolkit-2.0.10-py_0.tar.bz2           227 KB\n",
            "scikit-image-0.15.0-py37he6710b0_0.conda    24.8 MB\n",
            "sphinxcontrib-websupport-1.1.2-py_0.tar.bz2      35 KB\n",
            "gast-0.4.0-pyh9f0ad1d_0.tar.bz2               12 KB\n",
            "\n",
            "---------------------------------------------------\n",
            "Total:                                      1.28 GB\n",
            "\n",
            "Removed secretstorage-3.1.1-py37_0.conda\n",
            "Removed html5lib-1.0.1-py37_0.conda\n",
            "Removed ncurses-6.1-he6710b0_1.conda\n",
            "Removed pcre-8.43-he6710b0_0.conda\n",
            "Removed wrapt-1.11.2-py37h7b6447c_0.conda\n",
            "Removed importlib_metadata-0.23-py37_0.tar.bz2\n",
            "Removed qtconsole-4.5.5-py_0.tar.bz2\n",
            "Removed pandoc-2.2.3.2-0.conda\n",
            "Removed mdtraj-1.9.5-py37h113d463_0.tar.bz2\n",
            "Removed libsodium-1.0.16-h1bed415_0.conda\n",
            "Removed cupti-10.1.168-0.conda\n",
            "Removed sphinxcontrib-qthelp-1.0.2-py_0.tar.bz2\n",
            "Removed conda-4.10.0-py37h89c1867_1.tar.bz2\n",
            "Removed jsonschema-3.0.2-py37_0.conda\n",
            "Removed certifi-2019.9.11-py37_0.tar.bz2\n",
            "Removed backports.tempfile-1.0-py_1.tar.bz2\n",
            "Removed gst-plugins-base-1.14.0-hbbd80ab_1.conda\n",
            "Removed scipy-1.3.1-py37h7c811a0_0.conda\n",
            "Removed msgpack-python-0.6.1-py37hfd86e86_1.conda\n",
            "Removed olefile-0.46-py37_0.conda\n",
            "Removed conda-env-2.6.0-1.conda\n",
            "Removed xlrd-1.2.0-py37_0.conda\n",
            "Removed backports.shutil_get_terminal_size-1.0.0-py37_2.conda\n",
            "Removed dask-2.5.2-py_0.tar.bz2\n",
            "Removed termcolor-1.1.0-py_2.tar.bz2\n",
            "Removed pyparsing-2.4.2-py_0.tar.bz2\n",
            "Removed json5-0.8.5-py_0.tar.bz2\n",
            "Removed numpy-1.17.2-py37haad9e8e_0.conda\n",
            "Removed pycparser-2.19-py37_0.conda\n",
            "Removed tensorflow-gpu-1.14.0-h0d30ee6_0.conda\n",
            "Removed python-3.7.4-h265db76_1.conda\n",
            "Removed glob2-0.7-py_0.tar.bz2\n",
            "Removed mccabe-0.6.1-py37_1.conda\n",
            "Removed libuuid-1.0.3-h1bed415_2.conda\n",
            "Removed send2trash-1.5.0-py37_0.conda\n",
            "Removed astropy-3.2.2-py37h7b6447c_0.tar.bz2\n",
            "Removed graphite2-1.3.13-h23475e2_0.conda\n",
            "Removed sqlalchemy-1.3.9-py37h7b6447c_0.tar.bz2\n",
            "Removed babel-2.7.0-py_0.tar.bz2\n",
            "Removed libffi-3.2.1-hd88cf55_4.conda\n",
            "Removed soupsieve-1.9.3-py37_0.tar.bz2\n",
            "Removed sphinxcontrib-jsmath-1.0.1-py_0.tar.bz2\n",
            "Removed patchelf-0.9-he6710b0_3.conda\n",
            "Removed pyyaml-5.1.2-py37h7b6447c_0.conda\n",
            "Removed keyring-18.0.0-py37_0.conda\n",
            "Removed grpcio-1.23.0-py37hb0870dc_1.tar.bz2\n",
            "Removed bottleneck-1.2.1-py37h035aef0_1.conda\n",
            "Removed ripgrep-0.10.0-hc07d326_0.conda\n",
            "Removed cairo-1.14.12-h8948797_3.conda\n",
            "Removed asn1crypto-1.0.1-py37_0.tar.bz2\n",
            "Removed click-7.0-py37_0.conda\n",
            "Removed sphinxcontrib-htmlhelp-1.0.2-py_0.tar.bz2\n",
            "Removed prometheus_client-0.7.1-py_0.tar.bz2\n",
            "Removed fftw3f-3.3.4-2.tar.bz2\n",
            "Removed attrs-19.2.0-py_0.tar.bz2\n",
            "Removed libgfortran-ng-7.3.0-hdf63c60_0.conda\n",
            "Removed libxml2-2.9.9-hea5a465_1.conda\n",
            "Removed astunparse-1.6.3-pyhd8ed1ab_0.tar.bz2\n",
            "Removed wheel-0.33.6-py37_0.tar.bz2\n",
            "Removed pip-19.2.3-py37_0.tar.bz2\n",
            "Removed nltk-3.4.5-py37_0.conda\n",
            "Removed distributed-2.5.2-py_0.tar.bz2\n",
            "Removed imagesize-1.1.0-py37_0.conda\n",
            "Removed pytest-openfiles-0.4.0-py_0.tar.bz2\n",
            "Removed gevent-1.4.0-py37h7b6447c_0.conda\n",
            "Removed mistune-0.8.4-py37h7b6447c_0.conda\n",
            "Removed llvmlite-0.29.0-py37hd408876_0.conda\n",
            "Removed ptyprocess-0.6.0-py37_0.conda\n",
            "Removed libcurl-7.65.3-h20c2e04_0.conda\n",
            "Removed libtiff-4.0.10-h2733197_2.conda\n",
            "Removed defusedxml-0.6.0-py_0.tar.bz2\n",
            "Removed pytz-2019.3-py_0.tar.bz2\n",
            "Removed parso-0.5.1-py_0.tar.bz2\n",
            "Removed tk-8.6.8-hbc83047_0.conda\n",
            "Removed zlib-1.2.11-h7b6447c_3.conda\n",
            "Removed liblief-0.9.0-h7725739_2.conda\n",
            "Removed atomicwrites-1.3.0-py37_1.conda\n",
            "Removed libprotobuf-3.13.0.1-h8b12597_0.tar.bz2\n",
            "Removed boto-2.49.0-py37_0.conda\n",
            "Removed mpc-1.1.0-h10f8cd9_1.conda\n",
            "Removed patsy-0.5.1-py37_0.conda\n",
            "Removed backports.os-0.1.1-py37_0.conda\n",
            "Removed cytoolz-0.10.0-py37h7b6447c_0.conda\n",
            "Removed sqlite-3.30.0-h7b6447c_0.tar.bz2\n",
            "Removed _tflow_select-2.1.0-gpu.conda\n",
            "Removed mkl_random-1.1.0-py37hd6b4f25_0.conda\n",
            "Removed jupyter_core-4.5.0-py_0.tar.bz2\n",
            "Removed scikit-learn-0.21.3-py37hd81dba3_0.conda\n",
            "Removed c-ares-1.17.1-h36c2ea0_0.tar.bz2\n",
            "Removed h5py-2.9.0-py37h7918eee_0.conda\n",
            "Removed conda-4.7.12-py37_0.tar.bz2\n",
            "Removed krb5-1.16.1-h173b8e3_7.conda\n",
            "Removed readline-7.0-h7b6447c_5.conda\n",
            "Removed pycosat-0.6.3-py37h14c3975_0.conda\n",
            "Removed pickleshare-0.7.5-py37_0.conda\n",
            "Removed anaconda-project-0.8.3-py_0.tar.bz2\n",
            "Removed libxcb-1.13-h1bed415_1.conda\n",
            "Removed toolz-0.10.0-py_0.tar.bz2\n",
            "Removed pep8-1.7.1-py37_0.conda\n",
            "Removed cloudpickle-1.2.2-py_0.tar.bz2\n",
            "Removed openssl-1.1.1d-h7b6447c_2.conda\n",
            "Removed jupyter-1.0.0-py37_7.conda\n",
            "Removed heapdict-1.0.1-py_0.conda\n",
            "Removed pytest-arraydiff-0.3-py37h39e3cac_0.conda\n",
            "Removed libboost-1.67.0-h46d08c1_4.conda\n",
            "Removed cryptography-2.7-py37h1ba5d50_0.conda\n",
            "Removed zeromq-4.3.1-he6710b0_3.conda\n",
            "Removed pycodestyle-2.5.0-py37_0.conda\n",
            "Removed python-libarchive-c-2.8-py37_13.conda\n",
            "Removed jeepney-0.4.1-py_0.tar.bz2\n",
            "Removed nbformat-4.4.0-py37_0.conda\n",
            "Removed blas-1.0-mkl.conda\n",
            "Removed testpath-0.4.2-py37_0.conda\n",
            "Removed requests-2.22.0-py37_0.conda\n",
            "Removed pyodbc-4.0.27-py37he6710b0_0.conda\n",
            "Removed pysocks-1.7.1-py37_0.tar.bz2\n",
            "Removed notebook-6.0.1-py37_0.tar.bz2\n",
            "Removed anaconda-client-1.7.2-py37_0.conda\n",
            "Removed pandocfilters-1.4.2-py37_1.conda\n",
            "Removed kiwisolver-1.1.0-py37he6710b0_0.conda\n",
            "Removed ipykernel-5.1.2-py37h39e3cac_0.conda\n",
            "Removed bokeh-1.3.4-py37_0.conda\n",
            "Removed cython-0.29.13-py37he6710b0_0.conda\n",
            "Removed et_xmlfile-1.0.1-py37_0.conda\n",
            "Removed mpfr-4.0.1-hdf1c602_3.conda\n",
            "Removed sphinx-2.2.0-py_0.tar.bz2\n",
            "Removed tblib-1.4.0-py_0.tar.bz2\n",
            "Removed matplotlib-3.1.1-py37h5429711_0.conda\n",
            "Removed nose-1.3.7-py37_2.conda\n",
            "Removed backports.weakref-1.0.post1-py_1.tar.bz2\n",
            "Removed jinja2-2.10.3-py_0.tar.bz2\n",
            "Removed numpy-base-1.17.2-py37hde5b4d6_0.conda\n",
            "Removed backports-1.0-py_2.tar.bz2\n",
            "Removed sortedcontainers-2.1.0-py37_0.conda\n",
            "Removed unicodecsv-0.14.1-py37_0.conda\n",
            "Removed bleach-3.1.0-py37_0.conda\n",
            "Removed locket-0.2.0-py37_1.conda\n",
            "Removed deepchem-gpu-2.3.0-py37_0.tar.bz2\n",
            "Removed pygments-2.4.2-py_0.tar.bz2\n",
            "Removed libxgboost-1.2.0-he1b5a44_0.tar.bz2\n",
            "Removed entrypoints-0.3-py37_0.conda\n",
            "Removed markupsafe-1.1.1-py37h7b6447c_0.conda\n",
            "Removed pytables-3.5.2-py37h71ec239_1.conda\n",
            "Removed multipledispatch-0.6.0-py37_0.conda\n",
            "Removed hdf5-1.10.4-hb1b8bf9_0.conda\n",
            "Removed tensorflow-1.14.0-gpu_py37h74c33d7_0.conda\n",
            "Removed conda-verify-3.4.2-py_1.tar.bz2\n",
            "Removed setuptools-41.4.0-py37_0.tar.bz2\n",
            "Removed clyent-1.2.2-py37_1.conda\n",
            "Removed jedi-0.15.1-py37_0.conda\n",
            "Removed pylint-2.4.2-py37_0.tar.bz2\n",
            "Removed tornado-6.0.3-py37h7b6447c_0.conda\n",
            "Removed xlwt-1.3.0-py37_0.conda\n",
            "Removed pluggy-0.13.0-py37_0.tar.bz2\n",
            "Removed mkl_fft-1.0.14-py37ha843d7b_0.conda\n",
            "Removed pyzmq-18.1.0-py37he6710b0_0.conda\n",
            "Removed statsmodels-0.10.1-py37hdd07704_0.conda\n",
            "Removed simplegeneric-0.8.1-py37_2.conda\n",
            "Removed pytest-astropy-0.5.0-py37_0.conda\n",
            "Removed fribidi-1.0.5-h7b6447c_0.conda\n",
            "Removed sip-4.19.8-py37hf484d3e_0.conda\n",
            "Removed yaml-0.1.7-had09818_2.conda\n",
            "Removed typing_extensions-3.7.4.3-py_0.tar.bz2\n",
            "Removed flask-1.1.1-py_0.tar.bz2\n",
            "Removed numexpr-2.7.0-py37h9e4a6bb_0.conda\n",
            "Removed fontconfig-2.13.0-h9420a91_0.conda\n",
            "Removed bzip2-1.0.8-h7b6447c_0.conda\n",
            "Removed wurlitzer-1.0.3-py37_0.conda\n",
            "Removed decorator-4.4.0-py37_1.conda\n",
            "Removed libarchive-3.3.3-h5d8350f_5.conda\n",
            "Removed greenlet-0.4.15-py37h7b6447c_0.conda\n",
            "Removed traitlets-4.3.3-py37_0.tar.bz2\n",
            "Removed harfbuzz-1.8.8-hffaf4a1_0.conda\n",
            "Removed pixman-0.38.0-h7b6447c_0.conda\n",
            "Removed snappy-1.1.7-hbae5bb6_3.conda\n",
            "Removed dask-core-2.5.2-py_0.tar.bz2\n",
            "Removed pdbfixer-1.6-py_1.tar.bz2\n",
            "Removed pandas-0.25.1-py37he6710b0_0.conda\n",
            "Removed widgetsnbextension-3.5.1-py37_0.conda\n",
            "Removed pytest-doctestplus-0.4.0-py_0.tar.bz2\n",
            "Removed bkcharts-0.2-py37_0.conda\n",
            "Removed gmp-6.1.2-h6c8ec71_1.conda\n",
            "Removed sphinxcontrib-serializinghtml-1.1.3-py_0.tar.bz2\n",
            "Removed _py-xgboost-mutex-2.0-cpu_0.tar.bz2\n",
            "Removed gmpy2-2.0.8-py37h10f8cd9_2.conda\n",
            "Removed simdna-0.4.2-py_0.tar.bz2\n",
            "Removed lazy-object-proxy-1.4.2-py37h7b6447c_0.conda\n",
            "Removed pathlib2-2.3.5-py37_0.tar.bz2\n",
            "Removed sortedcollections-1.1.2-py37_0.conda\n",
            "Removed numba-0.45.1-py37h962f231_0.conda\n",
            "Removed openpyxl-3.0.0-py_0.tar.bz2\n",
            "Removed expat-2.2.6-he6710b0_0.conda\n",
            "Removed python_abi-3.7-1_cp37m.tar.bz2\n",
            "Removed psutil-5.6.3-py37h7b6447c_0.conda\n",
            "Removed imageio-2.6.0-py37_0.tar.bz2\n",
            "Removed jupyterlab_server-1.0.6-py_0.tar.bz2\n",
            "Removed cudnn-7.6.5.32-hc0a50b0_1.tar.bz2\n",
            "Removed itsdangerous-1.1.0-py37_0.conda\n",
            "Removed lzo-2.10-h49e0be7_2.conda\n",
            "Removed mkl-2019.4-243.conda\n",
            "Removed py-lief-0.9.0-py37h7725739_2.conda\n",
            "Removed terminado-0.8.2-py37_0.conda\n",
            "Removed future-0.17.1-py37_0.conda\n",
            "Removed sphinxcontrib-1.0-py37_1.conda\n",
            "Removed pytest-5.2.1-py37_0.tar.bz2\n",
            "Removed packaging-19.2-py_0.tar.bz2\n",
            "Removed rope-0.14.0-py_0.tar.bz2\n",
            "Removed icu-58.2-h9c2bf20_1.conda\n",
            "Removed pexpect-4.7.0-py37_0.conda\n",
            "Removed pycrypto-2.6.1-py37h14c3975_9.conda\n",
            "Removed ipython_genutils-0.2.0-py37_0.conda\n",
            "Removed wcwidth-0.1.7-py37_0.conda\n",
            "Removed mkl-service-2.3.0-py37he904b0f_0.conda\n",
            "Removed pyflakes-2.1.1-py37_0.conda\n",
            "Removed singledispatch-3.4.0.3-py37_0.conda\n",
            "Removed _ipyw_jlab_nb_ext_conf-0.1.0-py37_0.conda\n",
            "Removed blosc-1.16.3-hd408876_0.conda\n",
            "Removed pywavelets-1.0.3-py37hdd07704_1.conda\n",
            "Removed ipython-7.8.0-py37h39e3cac_0.conda\n",
            "Removed spyder-kernels-0.5.2-py37_0.tar.bz2\n",
            "Removed markdown-3.3.4-pyhd8ed1ab_0.tar.bz2\n",
            "Removed fastcache-1.1.0-py37h7b6447c_0.conda\n",
            "Removed joblib-0.13.2-py37_0.conda\n",
            "Removed bitarray-1.0.1-py37h7b6447c_0.conda\n",
            "Removed freetype-2.9.1-h8a8886c_1.conda\n",
            "Removed backports.functools_lru_cache-1.5-py_2.tar.bz2\n",
            "Removed cycler-0.10.0-py37_0.conda\n",
            "Removed anaconda-navigator-1.9.7-py37_0.tar.bz2\n",
            "Removed intel-openmp-2019.4-243.conda\n",
            "Removed tensorflow-estimator-1.14.0-py37h5ca1d4c_0.tar.bz2\n",
            "Removed tensorboard-1.14.0-py37_0.tar.bz2\n",
            "Removed spyder-3.3.6-py37_0.conda\n",
            "Removed ruamel_yaml-0.15.46-py37h14c3975_0.conda\n",
            "Removed seaborn-0.9.0-py37_0.conda\n",
            "Removed mock-3.0.5-py37_0.conda\n",
            "Removed unixodbc-2.3.7-h14c3975_0.conda\n",
            "Removed sympy-1.4-py37_0.conda\n",
            "Removed rdkit-2020.03.3.0-py37hc20afe1_1.tar.bz2\n",
            "Removed six-1.12.0-py37_0.conda\n",
            "Removed gstreamer-1.14.0-hb453b48_1.conda\n",
            "Removed xz-5.2.4-h14c3975_4.conda\n",
            "Removed qtpy-1.9.0-py_0.tar.bz2\n",
            "Removed libtool-2.4.6-h7b6447c_5.conda\n",
            "Removed tbb-2019.4-hfd86e86_0.conda\n",
            "Removed zict-1.0.0-py_0.tar.bz2\n",
            "Removed mpmath-1.1.0-py37_0.conda\n",
            "Removed pango-1.42.4-h049681c_0.conda\n",
            "Removed jupyterlab-1.1.4-pyhf63ae98_0.conda\n",
            "Removed tqdm-4.36.1-py_0.conda\n",
            "Removed py-boost-1.67.0-py37h04863e7_4.conda\n",
            "Removed jpeg-9b-h024ee3a_2.conda\n",
            "Removed python-dateutil-2.8.0-py37_0.conda\n",
            "Removed contextlib2-0.6.0-py_0.tar.bz2\n",
            "Removed pyqt-5.9.2-py37h05f1152_2.conda\n",
            "Removed qtawesome-0.6.0-py_0.tar.bz2\n",
            "Removed libxslt-1.1.33-h7d1a2b0_0.conda\n",
            "Removed backcall-0.1.0-py37_0.conda\n",
            "Removed jupyter_console-6.0.0-py37_0.conda\n",
            "Removed dbus-1.13.6-h746ee38_0.conda\n",
            "Removed more-itertools-7.2.0-py37_0.conda\n",
            "Removed glib-2.56.2-hd408876_0.conda\n",
            "Removed conda-package-handling-1.6.0-py37h7b6447c_0.conda\n",
            "Removed xgboost-1.2.0-py37h3340039_0.tar.bz2\n",
            "Removed jbig-2.1-hdba287a_0.conda\n",
            "Removed pytest-remotedata-0.3.2-py37_0.conda\n",
            "Removed libedit-3.1.20181209-hc058e9b_0.conda\n",
            "Removed lz4-c-1.8.1.2-h14c3975_0.conda\n",
            "Removed py-1.8.0-py37_0.conda\n",
            "Removed anaconda-2019.10-py37_0.tar.bz2\n",
            "Removed conda-build-3.18.9-py37_3.conda\n",
            "Removed werkzeug-0.16.0-py_0.tar.bz2\n",
            "Removed partd-1.0.0-py_0.tar.bz2\n",
            "Removed idna-2.8-py37_0.conda\n",
            "Removed ipywidgets-7.5.1-py_0.tar.bz2\n",
            "Removed protobuf-3.13.0.1-py37h745909e_1.tar.bz2\n",
            "Removed google-pasta-0.2.0-pyh8c360ce_0.tar.bz2\n",
            "Removed zipp-0.6.0-py_0.tar.bz2\n",
            "Removed ply-3.11-py37_0.conda\n",
            "Removed keras-preprocessing-1.1.2-pyhd8ed1ab_0.tar.bz2\n",
            "Removed libstdcxx-ng-9.1.0-hdf63c60_0.conda\n",
            "Removed ca-certificates-2019.8.28-0.tar.bz2\n",
            "Removed importlib-metadata-3.10.0-py37h89c1867_0.tar.bz2\n",
            "Removed pillow-6.2.0-py37h34e0f95_0.tar.bz2\n",
            "Removed isort-4.3.21-py37_0.conda\n",
            "Removed chardet-3.0.4-py37_1003.conda\n",
            "Removed jdcal-1.4.1-py_0.tar.bz2\n",
            "Removed docutils-0.15.2-py37_0.conda\n",
            "Removed nbconvert-5.6.0-py37_1.tar.bz2\n",
            "Removed navigator-updater-0.2.1-py37_0.conda\n",
            "Removed openmm-7.4.2-py37_cuda101_rc_1.tar.bz2\n",
            "Removed pyopenssl-19.0.0-py37_0.conda\n",
            "Removed cffi-1.12.3-py37h2e261b9_0.conda\n",
            "Removed numpydoc-0.9.1-py_0.tar.bz2\n",
            "Removed get_terminal_size-1.0.0-haa9412d_0.conda\n",
            "Removed fsspec-0.5.2-py_0.tar.bz2\n",
            "Removed webencodings-0.5.1-py37_1.conda\n",
            "Removed zstd-1.3.7-h0b5b093_0.conda\n",
            "Removed beautifulsoup4-4.8.0-py37_0.conda\n",
            "Removed filelock-3.0.12-py_0.tar.bz2\n",
            "Removed qt-5.9.7-h5867ecd_1.conda\n",
            "Removed pkginfo-1.5.0.1-py37_0.conda\n",
            "Removed libssh2-1.8.2-h1ba5d50_0.conda\n",
            "Removed jupyter_client-5.3.3-py37_1.tar.bz2\n",
            "Removed astor-0.8.1-pyh9f0ad1d_0.tar.bz2\n",
            "Removed _libgcc_mutex-0.1-main.conda\n",
            "Removed sphinxcontrib-applehelp-1.0.1-py_0.tar.bz2\n",
            "Removed curl-7.65.3-hbc83047_0.conda\n",
            "Removed cudatoolkit-10.1.243-h6bb024c_0.conda\n",
            "Removed keras-applications-1.0.8-py_1.tar.bz2\n",
            "Removed py-xgboost-1.2.0-py37hc8dfbb8_0.tar.bz2\n",
            "Removed snowballstemmer-2.0.0-py_0.tar.bz2\n",
            "Removed path.py-12.0.1-py_0.tar.bz2\n",
            "Removed absl-py-0.12.0-pyhd8ed1ab_0.tar.bz2\n",
            "Removed sphinxcontrib-devhelp-1.0.1-py_0.tar.bz2\n",
            "Removed lxml-4.4.1-py37hefd8a0e_0.conda\n",
            "Removed urllib3-1.24.2-py37_0.conda\n",
            "Removed networkx-2.3-py_0.tar.bz2\n",
            "Removed libpng-1.6.37-hbc83047_0.conda\n",
            "Removed libgcc-ng-9.1.0-hdf63c60_0.conda\n",
            "Removed tensorflow-base-1.14.0-gpu_py37he45bfe2_0.conda\n",
            "Removed colorama-0.4.1-py37_0.conda\n",
            "Removed pyrsistent-0.15.4-py37h7b6447c_0.tar.bz2\n",
            "Removed xlsxwriter-1.2.1-py_0.tar.bz2\n",
            "Removed astroid-2.3.1-py37_0.tar.bz2\n",
            "Removed alabaster-0.7.12-py37_0.conda\n",
            "Removed pycurl-7.43.0.3-py37h1ba5d50_0.conda\n",
            "Removed prompt_toolkit-2.0.10-py_0.tar.bz2\n",
            "Removed scikit-image-0.15.0-py37he6710b0_0.conda\n",
            "Removed sphinxcontrib-websupport-1.1.2-py_0.tar.bz2\n",
            "Removed gast-0.4.0-pyh9f0ad1d_0.tar.bz2\n",
            "WARNING: /root/.conda/pkgs does not exist\n",
            "Cache location: /usr/local/pkgs\n",
            "Will remove the following packages:\n",
            "/usr/local/pkgs\n",
            "---------------\n",
            "\n",
            "get_terminal_size-1.0.0-haa9412d_0             7 KB\n",
            "conda-4.7.12-py37_0                         11.4 MB\n",
            "tensorflow-gpu-1.14.0-h0d30ee6_0              18 KB\n",
            "numpy-1.17.2-py37haad9e8e_0                   11 KB\n",
            "tensorflow-1.14.0-gpu_py37h74c33d7_0          12 KB\n",
            "python_abi-3.7-1_cp37m                        10 KB\n",
            "xgboost-1.2.0-py37h3340039_0                  32 KB\n",
            "_py-xgboost-mutex-2.0-cpu_0                   22 KB\n",
            "dask-2.5.2-py_0                               40 KB\n",
            "conda-env-2.6.0-1                              6 KB\n",
            "_tflow_select-2.1.0-gpu                        5 KB\n",
            "_libgcc_mutex-0.1-main                         7 KB\n",
            "blas-1.0-mkl                                  15 KB\n",
            "anaconda-2019.10-py37_0                       60 KB\n",
            "\n",
            "---------------------------------------------------\n",
            "Total:                                      11.6 MB\n",
            "\n",
            "removing get_terminal_size-1.0.0-haa9412d_0\n",
            "removing conda-4.7.12-py37_0\n",
            "removing tensorflow-gpu-1.14.0-h0d30ee6_0\n",
            "removing numpy-1.17.2-py37haad9e8e_0\n",
            "removing tensorflow-1.14.0-gpu_py37h74c33d7_0\n",
            "removing python_abi-3.7-1_cp37m\n",
            "removing xgboost-1.2.0-py37h3340039_0\n",
            "removing _py-xgboost-mutex-2.0-cpu_0\n",
            "removing dask-2.5.2-py_0\n",
            "removing conda-env-2.6.0-1\n",
            "removing _tflow_select-2.1.0-gpu\n",
            "removing _libgcc_mutex-0.1-main\n",
            "removing blas-1.0-mkl\n",
            "removing anaconda-2019.10-py37_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coNNIQafgoSC",
        "outputId": "c205e7a7-7957-44ff-b7c7-71ca39c89d78"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5J-cJNz2V5w"
      },
      "source": [
        "h1 = pd.read_csv(\"/content/drive/MyDrive/Columbia_E4511/HTR3A_curated.csv\", index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_fQuS473N1M"
      },
      "source": [
        "h1=h1[~h1.VALUE_NUM_mean.isna()]\n",
        "h1=h1[h1.VALUE_NUM_mean>2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XA8ARleo3h53"
      },
      "source": [
        "### Split (and featurize) the dataset\n",
        "- if you want to calculate molecular descriptors, do it here\n",
        "- change `featurizer` to 'computed_descriptors' and `descriptor_type` to 'rdkit_raw' or 'mordred_filtered'\n",
        "- each time you featurize, a new split table will be generated. moving forward, only pick one to use"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1eQC31_fA2wD"
      },
      "source": [
        "train_file = \"/content/drive/MyDrive/Columbia_E4511/HTR3A_curated.csv\"\n",
        "response_col = \"VALUE_NUM_mean\"\n",
        "compound_id = \"compound_id\"\n",
        "smiles_col = \"base_rdkit_smiles\"\n",
        "result_dir = \"/content/drive/MyDrive/Columbia_E4511/HTR3A_models\"\n",
        "params = {\n",
        "        \"system\": \"LC\",\n",
        "        \"lc_account\": 'None',\n",
        "        \"datastore\": \"False\",\n",
        "        \"save_results\": \"False\",\n",
        "        \"data_owner\": \"username\",\n",
        "        \"prediction_type\": \"regression\",\n",
        "        \"dataset_key\": train_file,\n",
        "        \"id_col\": compound_id,\n",
        "        \"smiles_col\": smiles_col,\n",
        "        \"response_cols\": response_col,\n",
        "        \"previously_split\": \"False\",\n",
        "        \"split_only\": \"True\",\n",
        "        \"featurizer\": \"computed_descriptors\",\n",
        "        \"model_type\": \"RF\",\n",
        "        \"verbose\": \"True\",\n",
        "        \"transformers\": \"True\",\n",
        "        'max_epochs': '70',\n",
        "        \"rerun\": \"False\",\n",
        "        \"result_dir\": result_dir,\n",
        "        \"descriptor_type\": \"rdkit_raw\",\n",
        "        \"split_valid_frac\": \"0.10\",\n",
        "        \"split_test_frac\": \"0.10\"\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcrEY3Bc471H"
      },
      "source": [
        "# # # dataset csv file needs to not have an unnamed first column in order to \n",
        "# # # compute moe descriptors for some reason /shrug\n",
        "# # # naming it index works, naming it idx messes up the id columns\n",
        "# # name it manually before running this\n",
        "# pparams = parse.wrapper(params)\n",
        "# MP = mp.ModelPipeline(pparams)\n",
        "\n",
        "# # # comment out this line after splitting once so you don't re-split\n",
        "# split_uuid = MP.split_dataset()\n",
        "# # pparams"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSrHN7pU5Cvs"
      },
      "source": [
        "# split_uuid\n",
        "# copy/paste this into the next cell to use the same split_uuid each time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PivCHbJZgtX9"
      },
      "source": [
        "split_uuid = 'c80c74b3-5c7e-4945-9979-94ccd26cdbf6' # 15/15/70 split\n",
        "split_uuid = 'ead9410d-7abc-41cc-8713-f78dac0106a8' # 10/10/80 split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKP_mFuB5giz"
      },
      "source": [
        "### Grid search\n",
        "#### Tunable parameters for Random Forest models\n",
        "- rf_estimators: number of estimators to use in random forest models\n",
        "- rf_max_depth: the maximum depth of a decision tree in the random forest\n",
        "- rf_max_features: max number of features to split random forest nodes (can't be greater than total number of features)\n",
        "\n",
        "#### NN models\n",
        "- learning_rate: >0.05 is probably too high\n",
        "- layer_sizes: ordered list of layer sizes. due to architecture of NNs you usually want this to be a power of 2 and reduce in size for each layer\n",
        "- dropouts: list of dropouts associated with each layer.\n",
        "- since dropouts and layer sizes must be the same length, either only do a grid search with 2 layers or 3 layers, or create a tuple of layers and dropouts\n",
        "\n",
        "#### XGBoost models\n",
        "- xgb_gamma\n",
        "- xgb_learning_rate: higher values can do better than NN learning rates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBQ-bRaW9oq5"
      },
      "source": [
        "##### RF Models\n",
        "add 'active' column to featurized data or the features will be recalculated for each model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0YQ1dfZZn8K"
      },
      "source": [
        "mf = pd.read_csv(\"/content/drive/MyDrive/Columbia_E4511/scaled_descriptors/HTR3A_curated_with_mordred_filtered_descriptors.csv\")\n",
        "#mf=mf.merge(h1[['base_rdkit_smiles','active']])\n",
        "#mf.to_csv(\"/content/drive/MyDrive/Columbia_E4511/scaled_descriptors/HTR3A_curated_with_mordred_filtered_descriptors.csv\", index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25hhGGh3IvqR"
      },
      "source": [
        "params={'data_owner': 'username',\n",
        " 'dataset_key': '/content/drive/MyDrive/Columbia_E4511/HTR3A_curated.csv',\n",
        " 'datastore': 'False',\n",
        " 'featurizer': 'ecfp',\n",
        "#  'descriptor_type':'mordred_filtered',\n",
        " 'id_col': 'compound_id',\n",
        " 'lc_account': 'None',\n",
        " 'max_epochs': '70',\n",
        " 'model_type': 'RF',\n",
        " 'prediction_type': 'classification',\n",
        " 'previously_split': 'True',\n",
        " 'rerun': 'False',\n",
        " 'response_cols': 'active',\n",
        " 'result_dir': '/content/drive/MyDrive/Columbia_E4511/HTR3A_models',\n",
        " 'save_results': 'False',\n",
        " 'smiles_col': 'base_rdkit_smiles',\n",
        " 'split_uuid': 'ead9410d-7abc-41cc-8713-f78dac0106a8',\n",
        " 'system': 'LC',\n",
        " 'transformers': 'True',\n",
        " 'uncertainty': 'True',\n",
        " 'verbose': 'True'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6FBeUGh5mGJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2faeb4f4-5653-4604-d132-dda9cc30feaa"
      },
      "source": [
        "# 210 models per feature set\n",
        "estimator_choice = [16,24,32,64,128,256,512]\n",
        "depth_choice = [16,24,32,64,128,256]\n",
        "features_choice = [256,512] #16,24,32,64,128, # can only be < total number of features\n",
        "\n",
        "for rf_estimator in estimator_choice:\n",
        "    for rf_depth in depth_choice:\n",
        "        for rf_feature in features_choice:\n",
        "            params[\"rf_estimators\"] = rf_estimator\n",
        "            params[\"rf_max_depth\"] = rf_depth\n",
        "            params[\"rf_max_features\"] = rf_feature\n",
        "            tp = parse.wrapper(params)\n",
        "            pl = mp.ModelPipeline(tp)\n",
        "            pl.train_model()\n",
        "            pred_data = pl.model_wrapper.get_perf_data(subset=\"valid\", epoch_label=\"best\")\n",
        "            pred_results = pred_data.get_prediction_results()\n",
        "            print(f\"rf_estimators: {rf_estimator}, rf_max_depth: {rf_depth}, rf_max_features: {rf_feature}, valid_roc_auc: {pred_results['roc_auc_score']}\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:28:56,432 Previous dataset split restored\n",
            "2021-04-10 03:28:57,360 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/9c230321-9b3c-42f0-b975-5a475343f0c3/model_metadata.json\n",
            "2021-04-10 03:28:57,407 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/9c230321-9b3c-42f0-b975-5a475343f0c3/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_9c230321-9b3c-42f0-b975-5a475343f0c3.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8212071103761884\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:03,796 Previous dataset split restored\n",
            "2021-04-10 03:29:04,840 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bd0b7507-57ef-4875-b826-1c5fe6207811/model_metadata.json\n",
            "2021-04-10 03:29:04,860 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bd0b7507-57ef-4875-b826-1c5fe6207811/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_bd0b7507-57ef-4875-b826-1c5fe6207811.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8480777180653163\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:11,230 Previous dataset split restored\n",
            "2021-04-10 03:29:12,138 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7cc6406a-0ba5-45d5-b6b1-07897dc7548f/model_metadata.json\n",
            "2021-04-10 03:29:12,157 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7cc6406a-0ba5-45d5-b6b1-07897dc7548f/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_7cc6406a-0ba5-45d5-b6b1-07897dc7548f.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8600661430343118\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:18,475 Previous dataset split restored\n",
            "2021-04-10 03:29:19,521 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1c6f28f6-6369-450c-b8a7-0c3526d7956e/model_metadata.json\n",
            "2021-04-10 03:29:19,540 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1c6f28f6-6369-450c-b8a7-0c3526d7956e/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_1c6f28f6-6369-450c-b8a7-0c3526d7956e.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.8489045059942126\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:25,805 Previous dataset split restored\n",
            "2021-04-10 03:29:26,704 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/deb20d1a-bca1-4beb-b24a-84949047e6fc/model_metadata.json\n",
            "2021-04-10 03:29:26,751 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/deb20d1a-bca1-4beb-b24a-84949047e6fc/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_deb20d1a-bca1-4beb-b24a-84949047e6fc.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8460107482430755\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:33,094 Previous dataset split restored\n",
            "2021-04-10 03:29:34,115 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d6b15514-9876-4fd6-ad92-bbd200dcd5cc/model_metadata.json\n",
            "2021-04-10 03:29:34,136 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d6b15514-9876-4fd6-ad92-bbd200dcd5cc/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_d6b15514-9876-4fd6-ad92-bbd200dcd5cc.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8458040512608517\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:40,424 Previous dataset split restored\n",
            "2021-04-10 03:29:41,335 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/52e5e852-0563-42a1-85bd-befcc22b4a8d/model_metadata.json\n",
            "2021-04-10 03:29:41,356 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/52e5e852-0563-42a1-85bd-befcc22b4a8d/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_52e5e852-0563-42a1-85bd-befcc22b4a8d.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.871641174038859\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:47,709 Previous dataset split restored\n",
            "2021-04-10 03:29:48,724 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/10587e3a-1c7f-4bf9-8289-62b053fdb4f3/model_metadata.json\n",
            "2021-04-10 03:29:48,769 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/10587e3a-1c7f-4bf9-8289-62b053fdb4f3/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_10587e3a-1c7f-4bf9-8289-62b053fdb4f3.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8210004133939643\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:29:55,098 Previous dataset split restored\n",
            "2021-04-10 03:29:56,008 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d651df4a-e9d2-4608-a9f1-b6f6d60346f1/model_metadata.json\n",
            "2021-04-10 03:29:56,028 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d651df4a-e9d2-4608-a9f1-b6f6d60346f1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_d651df4a-e9d2-4608-a9f1-b6f6d60346f1.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8650268706076891\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:02,330 Previous dataset split restored\n",
            "2021-04-10 03:30:03,233 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fffaf81b-72e5-48f8-bb53-4acf993ee201/model_metadata.json\n",
            "2021-04-10 03:30:03,251 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fffaf81b-72e5-48f8-bb53-4acf993ee201/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_fffaf81b-72e5-48f8-bb53-4acf993ee201.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8468375361719719\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:09,541 Previous dataset split restored\n",
            "2021-04-10 03:30:10,476 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/739edb1f-79b3-47d8-bf9e-1b34b7e1ac72/model_metadata.json\n",
            "2021-04-10 03:30:10,516 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/739edb1f-79b3-47d8-bf9e-1b34b7e1ac72/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_739edb1f-79b3-47d8-bf9e-1b34b7e1ac72.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8646134766432412\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:16,727 Previous dataset split restored\n",
            "2021-04-10 03:30:17,746 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/737521a0-6450-4e98-83c1-c612776d3b19/model_metadata.json\n",
            "2021-04-10 03:30:17,765 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/737521a0-6450-4e98-83c1-c612776d3b19/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_737521a0-6450-4e98-83c1-c612776d3b19.tar.gz\n",
            "rf_estimators: 16, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.8501446878875568\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:24,026 Previous dataset split restored\n",
            "2021-04-10 03:30:25,054 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/29106a61-d97b-44ef-acef-75e89afe4edd/model_metadata.json\n",
            "2021-04-10 03:30:25,076 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/29106a61-d97b-44ef-acef-75e89afe4edd/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_29106a61-d97b-44ef-acef-75e89afe4edd.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8732947498966515\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:31,404 Previous dataset split restored\n",
            "2021-04-10 03:30:32,424 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0971850c-82e2-44aa-a7b0-d76c7a3f1687/model_metadata.json\n",
            "2021-04-10 03:30:32,444 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0971850c-82e2-44aa-a7b0-d76c7a3f1687/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_0971850c-82e2-44aa-a7b0-d76c7a3f1687.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8474576271186439\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:38,694 Previous dataset split restored\n",
            "2021-04-10 03:30:39,721 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bb33eff0-c0b8-40f1-9ca8-5a9ccca54aa1/model_metadata.json\n",
            "2021-04-10 03:30:39,743 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bb33eff0-c0b8-40f1-9ca8-5a9ccca54aa1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_bb33eff0-c0b8-40f1-9ca8-5a9ccca54aa1.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8728813559322034\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:46,066 Previous dataset split restored\n",
            "2021-04-10 03:30:47,079 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/13fd9764-c4da-4efa-a9cd-b625b8375f2b/model_metadata.json\n",
            "2021-04-10 03:30:47,101 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/13fd9764-c4da-4efa-a9cd-b625b8375f2b/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_13fd9764-c4da-4efa-a9cd-b625b8375f2b.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.833195535345184\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:30:53,410 Previous dataset split restored\n",
            "2021-04-10 03:30:54,446 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f877a801-1b7f-49ce-859d-afcbd220f016/model_metadata.json\n",
            "2021-04-10 03:30:54,466 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f877a801-1b7f-49ce-859d-afcbd220f016/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_f877a801-1b7f-49ce-859d-afcbd220f016.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8584125671765191\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:00,700 Previous dataset split restored\n",
            "2021-04-10 03:31:01,709 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2f5e2190-35aa-4df8-bd01-55cae25088d3/model_metadata.json\n",
            "2021-04-10 03:31:01,731 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2f5e2190-35aa-4df8-bd01-55cae25088d3/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_2f5e2190-35aa-4df8-bd01-55cae25088d3.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8712277800744109\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:08,001 Previous dataset split restored\n",
            "2021-04-10 03:31:09,020 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1f9e6a24-ac9b-4f20-838c-1aaf18627b0c/model_metadata.json\n",
            "2021-04-10 03:31:09,042 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1f9e6a24-ac9b-4f20-838c-1aaf18627b0c/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_1f9e6a24-ac9b-4f20-838c-1aaf18627b0c.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8629599007854487\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:15,349 Previous dataset split restored\n",
            "2021-04-10 03:31:16,395 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/38083f29-44ff-48b0-94df-5699b5151582/model_metadata.json\n",
            "2021-04-10 03:31:16,413 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/38083f29-44ff-48b0-94df-5699b5151582/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_38083f29-44ff-48b0-94df-5699b5151582.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8608929309632078\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:22,614 Previous dataset split restored\n",
            "2021-04-10 03:31:23,643 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/69504de5-b470-46bf-b386-a610b53fe80b/model_metadata.json\n",
            "2021-04-10 03:31:23,664 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/69504de5-b470-46bf-b386-a610b53fe80b/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_69504de5-b470-46bf-b386-a610b53fe80b.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8602728400165357\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:29,964 Previous dataset split restored\n",
            "2021-04-10 03:31:30,969 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/4adb54ac-cf45-48d0-948d-b6cde8930427/model_metadata.json\n",
            "2021-04-10 03:31:30,991 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/4adb54ac-cf45-48d0-948d-b6cde8930427/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_4adb54ac-cf45-48d0-948d-b6cde8930427.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8637866887143447\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:37,231 Previous dataset split restored\n",
            "2021-04-10 03:31:38,241 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a472cf5d-a9e1-4d02-8d76-5d187e529a2a/model_metadata.json\n",
            "2021-04-10 03:31:38,262 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a472cf5d-a9e1-4d02-8d76-5d187e529a2a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_a472cf5d-a9e1-4d02-8d76-5d187e529a2a.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8445638693675072\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:44,526 Previous dataset split restored\n",
            "2021-04-10 03:31:45,570 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/196b568f-cee0-41db-ab74-759d4be6f380/model_metadata.json\n",
            "2021-04-10 03:31:45,591 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/196b568f-cee0-41db-ab74-759d4be6f380/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_196b568f-cee0-41db-ab74-759d4be6f380.tar.gz\n",
            "rf_estimators: 24, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.843323687474163\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:51,912 Previous dataset split restored\n",
            "2021-04-10 03:31:52,935 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/203cd8c9-ba75-4f7d-8c2c-6d2c766d6e0a/model_metadata.json\n",
            "2021-04-10 03:31:52,957 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/203cd8c9-ba75-4f7d-8c2c-6d2c766d6e0a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_203cd8c9-ba75-4f7d-8c2c-6d2c766d6e0a.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8743282348077719\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:31:59,303 Previous dataset split restored\n",
            "2021-04-10 03:32:00,438 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bcf5e3ae-3722-4d67-bb2b-c6c219bd33ef/model_metadata.json\n",
            "2021-04-10 03:32:00,460 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bcf5e3ae-3722-4d67-bb2b-c6c219bd33ef/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_bcf5e3ae-3722-4d67-bb2b-c6c219bd33ef.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8553121124431583\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:06,797 Previous dataset split restored\n",
            "2021-04-10 03:32:07,845 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/ed60297e-f61c-4c24-a6e6-41fe408bed71/model_metadata.json\n",
            "2021-04-10 03:32:07,894 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/ed60297e-f61c-4c24-a6e6-41fe408bed71/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_ed60297e-f61c-4c24-a6e6-41fe408bed71.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8484911120297645\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:14,292 Previous dataset split restored\n",
            "2021-04-10 03:32:15,434 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/261cd904-3e40-4718-8b02-bfe91612199a/model_metadata.json\n",
            "2021-04-10 03:32:15,454 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/261cd904-3e40-4718-8b02-bfe91612199a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_261cd904-3e40-4718-8b02-bfe91612199a.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.8501446878875569\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:21,739 Previous dataset split restored\n",
            "2021-04-10 03:32:22,768 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e4298ef8-7697-42cc-93c1-cbf376110a86/model_metadata.json\n",
            "2021-04-10 03:32:22,789 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e4298ef8-7697-42cc-93c1-cbf376110a86/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_e4298ef8-7697-42cc-93c1-cbf376110a86.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8621331128565523\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:29,072 Previous dataset split restored\n",
            "2021-04-10 03:32:30,211 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/af7b0f98-cc01-46cf-8aad-33e81505fac3/model_metadata.json\n",
            "2021-04-10 03:32:30,252 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/af7b0f98-cc01-46cf-8aad-33e81505fac3/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_af7b0f98-cc01-46cf-8aad-33e81505fac3.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8573790822653989\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:36,527 Previous dataset split restored\n",
            "2021-04-10 03:32:37,555 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/068bb21f-37f3-4018-b645-b57349e8c1f8/model_metadata.json\n",
            "2021-04-10 03:32:37,578 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/068bb21f-37f3-4018-b645-b57349e8c1f8/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_068bb21f-37f3-4018-b645-b57349e8c1f8.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8691608102521703\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:43,902 Previous dataset split restored\n",
            "2021-04-10 03:32:44,943 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1dcb47dc-9b8a-4607-a78d-9d3eaeaae011/model_metadata.json\n",
            "2021-04-10 03:32:44,963 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1dcb47dc-9b8a-4607-a78d-9d3eaeaae011/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_1dcb47dc-9b8a-4607-a78d-9d3eaeaae011.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8418768085985945\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:51,261 Previous dataset split restored\n",
            "2021-04-10 03:32:52,294 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/66e94a80-e099-45a5-9eed-2264f5180d41/model_metadata.json\n",
            "2021-04-10 03:32:52,317 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/66e94a80-e099-45a5-9eed-2264f5180d41/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_66e94a80-e099-45a5-9eed-2264f5180d41.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8334022323274081\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:32:58,622 Previous dataset split restored\n",
            "2021-04-10 03:32:59,651 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/dd321e61-55a8-402b-8b94-34981ebb0813/model_metadata.json\n",
            "2021-04-10 03:32:59,672 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/dd321e61-55a8-402b-8b94-34981ebb0813/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_dd321e61-55a8-402b-8b94-34981ebb0813.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8350558081852005\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:06,007 Previous dataset split restored\n",
            "2021-04-10 03:33:07,035 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2216bc8d-bdb0-4c03-b0cf-288d515a764a/model_metadata.json\n",
            "2021-04-10 03:33:07,059 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2216bc8d-bdb0-4c03-b0cf-288d515a764a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_2216bc8d-bdb0-4c03-b0cf-288d515a764a.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8503513848697809\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:13,328 Previous dataset split restored\n",
            "2021-04-10 03:33:14,356 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/517eef95-37e4-4723-9709-ead4dff50183/model_metadata.json\n",
            "2021-04-10 03:33:14,377 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/517eef95-37e4-4723-9709-ead4dff50183/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_517eef95-37e4-4723-9709-ead4dff50183.tar.gz\n",
            "rf_estimators: 32, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.8458040512608516\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:20,662 Previous dataset split restored\n",
            "2021-04-10 03:33:21,846 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a0bf743c-cfb1-4f71-9706-bedce77e6499/model_metadata.json\n",
            "2021-04-10 03:33:21,871 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a0bf743c-cfb1-4f71-9706-bedce77e6499/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_a0bf743c-cfb1-4f71-9706-bedce77e6499.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8772219925589086\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:28,168 Previous dataset split restored\n",
            "2021-04-10 03:33:29,427 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e3e6205d-f820-4688-99b1-8dcc170c9bcd/model_metadata.json\n",
            "2021-04-10 03:33:29,452 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e3e6205d-f820-4688-99b1-8dcc170c9bcd/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_e3e6205d-f820-4688-99b1-8dcc170c9bcd.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.867713931376602\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:35,693 Previous dataset split restored\n",
            "2021-04-10 03:33:36,866 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/6ba9c6f9-5478-49e8-bc82-92ae20dc48e8/model_metadata.json\n",
            "2021-04-10 03:33:36,921 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/6ba9c6f9-5478-49e8-bc82-92ae20dc48e8/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_6ba9c6f9-5478-49e8-bc82-92ae20dc48e8.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8720545680033072\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:43,133 Previous dataset split restored\n",
            "2021-04-10 03:33:44,393 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/692a570f-6f14-4b6c-b628-cb63b9d91fee/model_metadata.json\n",
            "2021-04-10 03:33:44,473 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/692a570f-6f14-4b6c-b628-cb63b9d91fee/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_692a570f-6f14-4b6c-b628-cb63b9d91fee.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.8480777180653162\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:50,804 Previous dataset split restored\n",
            "2021-04-10 03:33:51,987 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f610684a-ef75-4d71-8a46-12d2371d5758/model_metadata.json\n",
            "2021-04-10 03:33:52,014 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f610684a-ef75-4d71-8a46-12d2371d5758/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_f610684a-ef75-4d71-8a46-12d2371d5758.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8660603555188093\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:33:58,369 Previous dataset split restored\n",
            "2021-04-10 03:33:59,663 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/9ea13be3-76b6-4c02-a01c-7d85a9829219/model_metadata.json\n",
            "2021-04-10 03:33:59,687 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/9ea13be3-76b6-4c02-a01c-7d85a9829219/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_9ea13be3-76b6-4c02-a01c-7d85a9829219.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8706076891277387\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:05,983 Previous dataset split restored\n",
            "2021-04-10 03:34:07,165 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2477df47-edd5-4a2d-8cdf-42a59e16726f/model_metadata.json\n",
            "2021-04-10 03:34:07,210 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2477df47-edd5-4a2d-8cdf-42a59e16726f/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_2477df47-edd5-4a2d-8cdf-42a59e16726f.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8811492352211657\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:13,445 Previous dataset split restored\n",
            "2021-04-10 03:34:14,715 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0b5ebf9f-aa74-43cc-91f4-c00826aba20d/model_metadata.json\n",
            "2021-04-10 03:34:14,741 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0b5ebf9f-aa74-43cc-91f4-c00826aba20d/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_0b5ebf9f-aa74-43cc-91f4-c00826aba20d.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8530384456386937\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:20,988 Previous dataset split restored\n",
            "2021-04-10 03:34:22,147 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/afe3d578-6019-420f-8652-603d9b573d0f/model_metadata.json\n",
            "2021-04-10 03:34:22,173 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/afe3d578-6019-420f-8652-603d9b573d0f/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_afe3d578-6019-420f-8652-603d9b573d0f.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8648201736254651\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:28,418 Previous dataset split restored\n",
            "2021-04-10 03:34:29,692 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/468b95a7-a7c7-4e44-ac72-645ab40c20e4/model_metadata.json\n",
            "2021-04-10 03:34:29,740 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/468b95a7-a7c7-4e44-ac72-645ab40c20e4/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_468b95a7-a7c7-4e44-ac72-645ab40c20e4.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8621331128565524\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:36,041 Previous dataset split restored\n",
            "2021-04-10 03:34:37,220 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/df691b2f-e757-4aa2-b58a-f4a741d377b1/model_metadata.json\n",
            "2021-04-10 03:34:37,246 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/df691b2f-e757-4aa2-b58a-f4a741d377b1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_df691b2f-e757-4aa2-b58a-f4a741d377b1.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8635799917321207\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:43,495 Previous dataset split restored\n",
            "2021-04-10 03:34:44,768 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/13aa769e-05e3-455b-bfde-d001eb3b1765/model_metadata.json\n",
            "2021-04-10 03:34:44,791 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/13aa769e-05e3-455b-bfde-d001eb3b1765/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_13aa769e-05e3-455b-bfde-d001eb3b1765.tar.gz\n",
            "rf_estimators: 64, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.8491112029764365\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:51,102 Previous dataset split restored\n",
            "2021-04-10 03:34:52,663 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/18f2d306-e54c-472f-a754-a522aa216c26/model_metadata.json\n",
            "2021-04-10 03:34:52,697 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/18f2d306-e54c-472f-a754-a522aa216c26/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_18f2d306-e54c-472f-a754-a522aa216c26.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8673005374121538\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:34:59,016 Previous dataset split restored\n",
            "2021-04-10 03:35:00,777 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f5049f3d-161d-4722-a717-bbec8d916dc4/model_metadata.json\n",
            "2021-04-10 03:35:00,810 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f5049f3d-161d-4722-a717-bbec8d916dc4/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_f5049f3d-161d-4722-a717-bbec8d916dc4.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8590326581231914\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:07,185 Previous dataset split restored\n",
            "2021-04-10 03:35:08,758 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f29cae92-eb7d-42c1-ab4c-3e256c878f32/model_metadata.json\n",
            "2021-04-10 03:35:08,791 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/f29cae92-eb7d-42c1-ab4c-3e256c878f32/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_f29cae92-eb7d-42c1-ab4c-3e256c878f32.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8699875981810665\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:15,014 Previous dataset split restored\n",
            "2021-04-10 03:35:16,785 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/5a3020e6-0dda-44ac-a0b5-e01e6116379a/model_metadata.json\n",
            "2021-04-10 03:35:16,818 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/5a3020e6-0dda-44ac-a0b5-e01e6116379a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_5a3020e6-0dda-44ac-a0b5-e01e6116379a.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.8664737494832575\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:23,099 Previous dataset split restored\n",
            "2021-04-10 03:35:24,688 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/caf98f09-c5f1-4035-a25b-f7c1c3eb64fc/model_metadata.json\n",
            "2021-04-10 03:35:24,724 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/caf98f09-c5f1-4035-a25b-f7c1c3eb64fc/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_caf98f09-c5f1-4035-a25b-f7c1c3eb64fc.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8565522943365027\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:30,988 Previous dataset split restored\n",
            "2021-04-10 03:35:32,748 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bd492377-58b0-4aaf-966d-38f14a7274e0/model_metadata.json\n",
            "2021-04-10 03:35:32,780 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/bd492377-58b0-4aaf-966d-38f14a7274e0/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_bd492377-58b0-4aaf-966d-38f14a7274e0.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8476643241008681\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:39,017 Previous dataset split restored\n",
            "2021-04-10 03:35:40,575 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/17b5234c-0fae-430b-b355-dd43aa6c5057/model_metadata.json\n",
            "2021-04-10 03:35:40,610 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/17b5234c-0fae-430b-b355-dd43aa6c5057/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_17b5234c-0fae-430b-b355-dd43aa6c5057.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8685407193054981\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:46,871 Previous dataset split restored\n",
            "2021-04-10 03:35:48,633 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e92a9072-d982-489c-84ab-201cf62bdec4/model_metadata.json\n",
            "2021-04-10 03:35:48,681 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/e92a9072-d982-489c-84ab-201cf62bdec4/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_e92a9072-d982-489c-84ab-201cf62bdec4.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8567589913187268\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:35:54,969 Previous dataset split restored\n",
            "2021-04-10 03:35:56,535 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/97116038-ca27-47f8-ba14-d727e5af92ed/model_metadata.json\n",
            "2021-04-10 03:35:56,569 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/97116038-ca27-47f8-ba14-d727e5af92ed/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_97116038-ca27-47f8-ba14-d727e5af92ed.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8697809011988425\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:02,818 Previous dataset split restored\n",
            "2021-04-10 03:36:04,576 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1a38b043-9903-47ff-98b3-441af2bac7da/model_metadata.json\n",
            "2021-04-10 03:36:04,610 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/1a38b043-9903-47ff-98b3-441af2bac7da/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_1a38b043-9903-47ff-98b3-441af2bac7da.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8617197188921042\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:10,861 Previous dataset split restored\n",
            "2021-04-10 03:36:12,441 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a5d7d5cc-376a-49d3-bbf1-9334ae241e47/model_metadata.json\n",
            "2021-04-10 03:36:12,495 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/a5d7d5cc-376a-49d3-bbf1-9334ae241e47/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_a5d7d5cc-376a-49d3-bbf1-9334ae241e47.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8743282348077718\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:18,727 Previous dataset split restored\n",
            "2021-04-10 03:36:20,484 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/68dd7a87-1e24-469e-8bc7-0cd43ac3bd08/model_metadata.json\n",
            "2021-04-10 03:36:20,516 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/68dd7a87-1e24-469e-8bc7-0cd43ac3bd08/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_68dd7a87-1e24-469e-8bc7-0cd43ac3bd08.tar.gz\n",
            "rf_estimators: 128, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.8557255064076065\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:26,787 Previous dataset split restored\n",
            "2021-04-10 03:36:29,146 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7ee430d9-edb8-4735-b331-a5110101c7c1/model_metadata.json\n",
            "2021-04-10 03:36:29,197 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7ee430d9-edb8-4735-b331-a5110101c7c1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_7ee430d9-edb8-4735-b331-a5110101c7c1.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8730880529144275\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:35,444 Previous dataset split restored\n",
            "2021-04-10 03:36:38,152 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/35534254-1531-4aee-b278-3f9710f5d8aa/model_metadata.json\n",
            "2021-04-10 03:36:38,200 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/35534254-1531-4aee-b278-3f9710f5d8aa/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_35534254-1531-4aee-b278-3f9710f5d8aa.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8441504754030591\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:44,508 Previous dataset split restored\n",
            "2021-04-10 03:36:46,919 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/de0431e5-3d89-4024-b1b9-136860be4335/model_metadata.json\n",
            "2021-04-10 03:36:46,968 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/de0431e5-3d89-4024-b1b9-136860be4335/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_de0431e5-3d89-4024-b1b9-136860be4335.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8710210830921868\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:36:53,206 Previous dataset split restored\n",
            "2021-04-10 03:36:56,016 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/84583c7e-4788-4b64-b81c-b100cbe10097/model_metadata.json\n",
            "2021-04-10 03:36:56,062 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/84583c7e-4788-4b64-b81c-b100cbe10097/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_84583c7e-4788-4b64-b81c-b100cbe10097.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.854071930549814\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:02,360 Previous dataset split restored\n",
            "2021-04-10 03:37:04,669 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d2e16a0f-9139-4f49-a470-2f57e9cdd0b8/model_metadata.json\n",
            "2021-04-10 03:37:04,718 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/d2e16a0f-9139-4f49-a470-2f57e9cdd0b8/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_d2e16a0f-9139-4f49-a470-2f57e9cdd0b8.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8805291442744936\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:11,020 Previous dataset split restored\n",
            "2021-04-10 03:37:13,839 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7b986877-8461-4951-bdd8-c717e150e98e/model_metadata.json\n",
            "2021-04-10 03:37:13,884 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7b986877-8461-4951-bdd8-c717e150e98e/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_7b986877-8461-4951-bdd8-c717e150e98e.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8565522943365027\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:20,151 Previous dataset split restored\n",
            "2021-04-10 03:37:22,451 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/c5cff458-e489-42a5-82b1-664b64dc3d09/model_metadata.json\n",
            "2021-04-10 03:37:22,501 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/c5cff458-e489-42a5-82b1-664b64dc3d09/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_c5cff458-e489-42a5-82b1-664b64dc3d09.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8737081438610996\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:28,730 Previous dataset split restored\n",
            "2021-04-10 03:37:31,520 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/38433a5f-f4fe-4a46-bda2-19545559b364/model_metadata.json\n",
            "2021-04-10 03:37:31,569 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/38433a5f-f4fe-4a46-bda2-19545559b364/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_38433a5f-f4fe-4a46-bda2-19545559b364.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8590326581231914\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:37,854 Previous dataset split restored\n",
            "2021-04-10 03:37:40,292 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/15b7aa4f-2fde-48b0-bb04-e9c80e665da5/model_metadata.json\n",
            "2021-04-10 03:37:40,342 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/15b7aa4f-2fde-48b0-bb04-e9c80e665da5/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_15b7aa4f-2fde-48b0-bb04-e9c80e665da5.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8706076891277388\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:46,680 Previous dataset split restored\n",
            "2021-04-10 03:37:49,486 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/cda196bb-0f94-41fe-a362-2a3ce8bb0852/model_metadata.json\n",
            "2021-04-10 03:37:49,535 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/cda196bb-0f94-41fe-a362-2a3ce8bb0852/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_cda196bb-0f94-41fe-a362-2a3ce8bb0852.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.86151302190988\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:37:55,792 Previous dataset split restored\n",
            "2021-04-10 03:37:58,115 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/01fe975a-e02e-46a2-97a6-586b085bbbf1/model_metadata.json\n",
            "2021-04-10 03:37:58,168 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/01fe975a-e02e-46a2-97a6-586b085bbbf1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_01fe975a-e02e-46a2-97a6-586b085bbbf1.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8759818106655644\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:04,409 Previous dataset split restored\n",
            "2021-04-10 03:38:07,097 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/783de771-eb83-4b2d-a1b1-89b63339a66a/model_metadata.json\n",
            "2021-04-10 03:38:07,171 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/783de771-eb83-4b2d-a1b1-89b63339a66a/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_783de771-eb83-4b2d-a1b1-89b63339a66a.tar.gz\n",
            "rf_estimators: 256, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.8619264158743283\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:13,435 Previous dataset split restored\n",
            "2021-04-10 03:38:17,461 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/efbd0855-5160-4023-94cf-2917b8603a73/model_metadata.json\n",
            "2021-04-10 03:38:17,539 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/efbd0855-5160-4023-94cf-2917b8603a73/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_efbd0855-5160-4023-94cf-2917b8603a73.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 16, rf_max_features: 256, valid_roc_auc: 0.8697809011988425\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:23,754 Previous dataset split restored\n",
            "2021-04-10 03:38:28,578 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7318b713-8c68-4a2f-a351-909b10b8cb34/model_metadata.json\n",
            "2021-04-10 03:38:28,648 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/7318b713-8c68-4a2f-a351-909b10b8cb34/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_7318b713-8c68-4a2f-a351-909b10b8cb34.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 16, rf_max_features: 512, valid_roc_auc: 0.8639933856965689\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:34,966 Previous dataset split restored\n",
            "2021-04-10 03:38:38,893 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fd8df69b-e3eb-4cb3-801f-2143437f1e44/model_metadata.json\n",
            "2021-04-10 03:38:38,974 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fd8df69b-e3eb-4cb3-801f-2143437f1e44/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_fd8df69b-e3eb-4cb3-801f-2143437f1e44.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 24, rf_max_features: 256, valid_roc_auc: 0.8710210830921868\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:45,253 Previous dataset split restored\n",
            "2021-04-10 03:38:50,300 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0e2b51e6-971f-4f0a-9ab1-d6f351ec7ad8/model_metadata.json\n",
            "2021-04-10 03:38:50,375 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0e2b51e6-971f-4f0a-9ab1-d6f351ec7ad8/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_0e2b51e6-971f-4f0a-9ab1-d6f351ec7ad8.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 24, rf_max_features: 512, valid_roc_auc: 0.8652335675899132\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:38:56,675 Previous dataset split restored\n",
            "2021-04-10 03:39:00,706 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/6e382c81-54de-419a-b8f2-5ef510421549/model_metadata.json\n",
            "2021-04-10 03:39:00,788 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/6e382c81-54de-419a-b8f2-5ef510421549/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_6e382c81-54de-419a-b8f2-5ef510421549.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 32, rf_max_features: 256, valid_roc_auc: 0.8714344770566349\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:39:07,270 Previous dataset split restored\n",
            "2021-04-10 03:39:12,203 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0f187111-edaf-4863-8b47-e75749aa7d45/model_metadata.json\n",
            "2021-04-10 03:39:12,277 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0f187111-edaf-4863-8b47-e75749aa7d45/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_0f187111-edaf-4863-8b47-e75749aa7d45.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 32, rf_max_features: 512, valid_roc_auc: 0.8598594460520876\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:39:18,560 Previous dataset split restored\n",
            "2021-04-10 03:39:22,611 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/3f3b199f-f28c-44f8-8bcb-a4fe4f210dd1/model_metadata.json\n",
            "2021-04-10 03:39:22,688 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/3f3b199f-f28c-44f8-8bcb-a4fe4f210dd1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_3f3b199f-f28c-44f8-8bcb-a4fe4f210dd1.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 64, rf_max_features: 256, valid_roc_auc: 0.8706076891277387\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:39:28,980 Previous dataset split restored\n",
            "2021-04-10 03:39:33,941 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2f4e3cd1-7cab-48f3-bc62-f36ad81e0525/model_metadata.json\n",
            "2021-04-10 03:39:34,018 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/2f4e3cd1-7cab-48f3-bc62-f36ad81e0525/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_2f4e3cd1-7cab-48f3-bc62-f36ad81e0525.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 64, rf_max_features: 512, valid_roc_auc: 0.8644067796610169\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:39:40,293 Previous dataset split restored\n",
            "2021-04-10 03:39:44,303 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fe1e3288-755f-41e9-99c0-f01736b5c7fd/model_metadata.json\n",
            "2021-04-10 03:39:44,385 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/fe1e3288-755f-41e9-99c0-f01736b5c7fd/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_fe1e3288-755f-41e9-99c0-f01736b5c7fd.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 128, rf_max_features: 256, valid_roc_auc: 0.8701942951632906\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:39:50,592 Previous dataset split restored\n",
            "2021-04-10 03:39:55,569 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/3478f8c1-cd9d-4d57-9624-66470e8608d0/model_metadata.json\n",
            "2021-04-10 03:39:55,646 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/3478f8c1-cd9d-4d57-9624-66470e8608d0/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_3478f8c1-cd9d-4d57-9624-66470e8608d0.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 128, rf_max_features: 512, valid_roc_auc: 0.8639933856965689\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:40:01,933 Previous dataset split restored\n",
            "2021-04-10 03:40:05,987 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0495adda-ed01-44e7-9129-0c8766a77dea/model_metadata.json\n",
            "2021-04-10 03:40:06,069 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/0495adda-ed01-44e7-9129-0c8766a77dea/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_0495adda-ed01-44e7-9129-0c8766a77dea.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 256, rf_max_features: 256, valid_roc_auc: 0.8739148408433237\n",
            "number of features: 1024\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-10 03:40:12,350 Previous dataset split restored\n",
            "2021-04-10 03:40:17,302 Wrote model metadata to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/4f80bd89-d548-4718-8bda-7dbb230994f1/model_metadata.json\n",
            "2021-04-10 03:40:17,383 Wrote model metrics to file /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated/RF_ecfp_scaffold_classification/4f80bd89-d548-4718-8bda-7dbb230994f1/model_metrics.json\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Wrote model tarball to /content/drive/MyDrive/Columbia_E4511/HTR3A_models/HTR3A_curated_model_4f80bd89-d548-4718-8bda-7dbb230994f1.tar.gz\n",
            "rf_estimators: 512, rf_max_depth: 256, rf_max_features: 512, valid_roc_auc: 0.860686233980984\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bp86dseMwT8D",
        "outputId": "9a00b5ce-bfc4-4724-eaf6-20893a222ea5"
      },
      "source": [
        "pred_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy_score': 0.7708333333333334,\n",
              " 'confusion_matrix': [[[40, 15], [7, 34]]],\n",
              " 'cross_entropy': 0.4884305593366198,\n",
              " 'kappa': 0.5430549545651233,\n",
              " 'matthews_cc': 0.5507106607745376,\n",
              " 'model_choice_score': 0.8660753880266076,\n",
              " 'npv': 0.851063829787234,\n",
              " 'num_compounds': 96,\n",
              " 'prc_auc_score': 0.8497465088834931,\n",
              " 'precision': 0.6938775510204082,\n",
              " 'recall_score': 0.8292682926829268,\n",
              " 'roc_auc_score': 0.8660753880266076}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vK6rAXyO9rL8"
      },
      "source": [
        "##### NN Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePfWsakJd4Ya"
      },
      "source": [
        "params={'data_owner': 'username',\n",
        " 'dataset_key': '/content/drive/MyDrive/Columbia_E4511/HTR3A_curated.csv',\n",
        " 'datastore': 'False',\n",
        " 'featurizer': 'computed_descriptors',\n",
        " 'descriptor_type':'mordred_filtered',\n",
        " 'id_col': 'compound_id',\n",
        " 'lc_account': 'None',\n",
        " 'max_epochs': '70',\n",
        " 'model_type': 'NN',\n",
        " 'prediction_type': 'regression',\n",
        " 'previously_split': 'True',\n",
        " 'rerun': 'False',\n",
        " 'response_cols': 'VALUE_NUM_mean',\n",
        " 'result_dir': '/content/drive/MyDrive/Columbia_E4511/HTR3A_models',\n",
        " 'save_results': 'False',\n",
        " 'smiles_col': 'base_rdkit_smiles',\n",
        " 'split_uuid': 'ead9410d-7abc-41cc-8713-f78dac0106a8',\n",
        " 'system': 'LC',\n",
        " 'transformers': 'True',\n",
        " 'uncertainty': 'False',\n",
        " 'verbose': 'False'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMKz4mkma5ng"
      },
      "source": [
        "# # 200 models per feature set (SLOW - more than 12 hours)\n",
        "# layer_dropout = [\n",
        "#                 #  ('64,16','0.1,0.1'),\n",
        "#                 #  ('64,16','0.2,0.2'),\n",
        "#                  ('64,16','0.3,0.3'),\n",
        "#                  ('64,16','0.4,0.4'),\n",
        "#                  ('128,32','0.1,0.1'),\n",
        "#                  ('128,32','0.2,0.2'),\n",
        "#                  ('128,32','0.3,0.3'),\n",
        "#                  ('128,32','0.4,0.4'),\n",
        "#                  ('256,64','0.1,0.1'),\n",
        "#                  ('256,64','0.2,0.2'),\n",
        "#                  ('256,64','0.3,0.3'),\n",
        "#                  ('256,64','0.4,0.4'),\n",
        "#                  ('512,128','0.1,0.1'),\n",
        "#                  ('512,128','0.2,0.2'),\n",
        "#                  ('512,128','0.3,0.3'),\n",
        "#                  ('512,128','0.4,0.4'),\n",
        "#                  ('256,64,16','0.1,0.1,0.1'),\n",
        "#                  ('256,64,16','0.2,0.2,0.2'),\n",
        "#                  ('256,64,16','0.3,0.3,0.3'),\n",
        "#                  ('256,64,16','0.4,0.4,0.4'),\n",
        "# ]\n",
        "# lr_choice = [.00001,.00005,.0001,.0005,.001,.005,.01,.025,.05,.075]\n",
        "\n",
        "# for layers,dropouts in layer_dropout:\n",
        "#   for learning_rate in lr_choice:\n",
        "#     params[\"layer_sizes\"] = layers\n",
        "#     params[\"dropouts\"] = dropouts\n",
        "#     params[\"learning_rate\"] = learning_rate\n",
        "#     tp = parse.wrapper(params)\n",
        "#     pl = mp.ModelPipeline(tp)\n",
        "#     pl.train_model()\n",
        "#     pred_data = pl.model_wrapper.get_perf_data(subset=\"valid\", epoch_label=\"best\")\n",
        "#     pred_results = pred_data.get_prediction_results()\n",
        "#     print(f\"layers: {layers}, dropouts: {dropouts}, learning rate: {learning_rate}, valid_r2: {pred_results['r2_score']}\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quStVkT996lA"
      },
      "source": [
        "##### XGBoost models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7aRRhsAALxX"
      },
      "source": [
        "params={'data_owner': 'username',\n",
        " 'dataset_key': '/content/drive/MyDrive/Columbia_E4511/HTR3A_curated.csv',\n",
        " 'datastore': 'False',\n",
        " 'featurizer': 'computed_descriptors',\n",
        " 'descriptor_type':'mordred_filtered',\n",
        " 'id_col': 'compound_id',\n",
        " 'lc_account': 'None',\n",
        " 'max_epochs': '70',\n",
        " 'model_type': 'xgboost',\n",
        " 'prediction_type': 'regression',\n",
        " 'previously_split': 'True',\n",
        " 'rerun': 'False',\n",
        " 'response_cols': 'VALUE_NUM_mean',\n",
        " 'result_dir': '/content/drive/MyDrive/Columbia_E4511/HTR3A_models',\n",
        " 'save_results': 'False',\n",
        " 'smiles_col': 'base_rdkit_smiles',\n",
        " 'split_uuid': 'ead9410d-7abc-41cc-8713-f78dac0106a8',\n",
        " 'system': 'LC',\n",
        " 'transformers': 'True',\n",
        " 'uncertainty': 'True',\n",
        " 'verbose': 'False'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRJpm-FqALmh"
      },
      "source": [
        "# # 96 models per feature set, ~40 seconds per model; ~1:04 hours\n",
        "# gamma_choice = [0.0,0.05,0.1,0.2,0.3,0.4,0.5,1,2,4,8,16]\n",
        "# lr_choice = [0.25,0.2,0.15,0.10,0.05,0.01,0.001,0.0001]\n",
        "\n",
        "# for gamma in gamma_choice:\n",
        "#     for learning_rate in lr_choice:\n",
        "#         params[\"xgb_gamma\"] = gamma\n",
        "#         params[\"xgb_learning_rate\"] = learning_rate\n",
        "#         tp = parse.wrapper(params)\n",
        "#         pl = mp.ModelPipeline(tp)\n",
        "#         pl.train_model()\n",
        "#         pred_data = pl.model_wrapper.get_perf_data(subset=\"valid\", epoch_label=\"best\")\n",
        "#         pred_results = pred_data.get_prediction_results()\n",
        "#         print(f\"xgb gamma: {gamma}, xgb learning rate: {learning_rate}, valid_r2: {pred_results['r2_score']}\\n\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1rIVfBKCPT5"
      },
      "source": [
        "### Edit some AMPL functions to include xgboost parameters\n",
        "Just run this code once"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAiqmPOhCO9m"
      },
      "source": [
        "nan = np.float32('nan')\n",
        "def get_summary_perf_tables(collection_names=None, filter_dict={}, result_dir=None, prediction_type='regression', verbose=False):\n",
        "    \"\"\"\n",
        "    Load model parameters and performance metrics from model tracker for all models saved in the model tracker DB under\n",
        "    the given collection names (or result directory if Model tracker is not available) with the given prediction type. Tabulate the parameters and metrics including:\n",
        "        dataset (assay name, target, parameter, key, bucket)\n",
        "        dataset size (train/valid/test/total)\n",
        "        number of training folds\n",
        "        model type (NN or RF)\n",
        "        featurizer\n",
        "        transformation type\n",
        "        metrics: r2_score, mae_score and rms_score for regression, or ROC AUC for classification\n",
        "\n",
        "    result_dir: use result_dir when the model tracker is not available. Use a list format if you have multiple result direcotries.\n",
        "    \"\"\"\n",
        "    collection_list = []\n",
        "    model_uuid_list = []\n",
        "    time_built_list = []\n",
        "    model_type_list = []\n",
        "    dataset_key_list = []\n",
        "    bucket_list = []\n",
        "    param_list = []\n",
        "    featurizer_list = []\n",
        "    desc_type_list = []\n",
        "    transform_list = []\n",
        "    dset_size_list = []\n",
        "    splitter_list = []\n",
        "    split_strategy_list = []\n",
        "    split_uuid_list = []\n",
        "    rf_estimators_list = []\n",
        "    rf_max_features_list = []\n",
        "    rf_max_depth_list = []\n",
        "    best_epoch_list = []\n",
        "    max_epochs_list = []\n",
        "    learning_rate_list = []\n",
        "    layer_sizes_list = []\n",
        "    dropouts_list = []\n",
        "    xgb_gamma_list = []\n",
        "    xgb_learning_rate_list = []\n",
        "    umap_dim_list = []\n",
        "    umap_targ_wt_list = []\n",
        "    umap_neighbors_list = []\n",
        "    umap_min_dist_list = []\n",
        "    split_uuid_list=[]\n",
        "\n",
        "\n",
        "    if prediction_type == 'regression':\n",
        "        score_types = ['r2_score', 'mae_score', 'rms_score']\n",
        "    else:\n",
        "        # TODO: add more classification metrics later\n",
        "        score_types = ['roc_auc_score', 'prc_auc_score', 'accuracy_score', 'precision', 'recall_score', 'npv', 'matthews_cc']\n",
        "\n",
        "    subsets = ['train', 'valid', 'test']\n",
        "    score_dict = {}\n",
        "    ncmpd_dict = {}\n",
        "    for subset in subsets:\n",
        "        score_dict[subset] = {}\n",
        "        for score_type in score_types:\n",
        "            score_dict[subset][score_type] = []\n",
        "        ncmpd_dict[subset] = []\n",
        "\n",
        "    metadata_list_dict = {}\n",
        "    if result_dir:\n",
        "        if isinstance(result_dir, str):\n",
        "            result_dir = [result_dir]\n",
        "        for rd in result_dir:\n",
        "            if rd not in metadata_list_dict:\n",
        "                metadata_list_dict[rd] = []\n",
        "            for dirpath, dirnames, filenames in os.walk(rd):\n",
        "                if \"model_metadata.json\" in filenames:\n",
        "                    with open(os.path.join(dirpath, 'model_metadata.json')) as f:\n",
        "                        metadata_dict = json.load(f)\n",
        "                    metadata_list_dict[rd].append(metadata_dict)\n",
        "\n",
        "    for ss in metadata_list_dict:\n",
        "        for i, metadata_dict in enumerate(metadata_list_dict[ss]):\n",
        "            if (i % 10 == 0) and verbose:\n",
        "                print('Processing collection %s model %d' % (ss, i))\n",
        "            # Check that model has metrics before we go on\n",
        "            if not 'training_metrics' in metadata_dict:\n",
        "                continue\n",
        "            collection_list.append(ss)\n",
        "            model_uuid = metadata_dict['model_uuid']\n",
        "            model_uuid_list.append(model_uuid)\n",
        "            time_built = metadata_dict['time_built']\n",
        "            time_built_list.append(time_built)\n",
        "\n",
        "            model_params = metadata_dict['model_parameters']\n",
        "            model_type = model_params['model_type']\n",
        "            model_type_list.append(model_type)\n",
        "            featurizer = model_params['featurizer']\n",
        "            featurizer_list.append(featurizer)\n",
        "            if 'descriptor_specific' in metadata_dict:\n",
        "                desc_type = metadata_dict['descriptor_specific']['descriptor_type']\n",
        "            elif featurizer in ['graphconv', 'ecfp']:\n",
        "                desc_type = featurizer\n",
        "            else:\n",
        "                desc_type = ''\n",
        "            desc_type_list.append(desc_type)\n",
        "            dataset_key = metadata_dict['training_dataset']['dataset_key']\n",
        "            bucket = metadata_dict['training_dataset']['bucket']\n",
        "            dataset_key_list.append(dataset_key)\n",
        "            bucket_list.append(bucket)\n",
        "            dset_metadata = metadata_dict['training_dataset']['dataset_metadata']\n",
        "            param = metadata_dict['training_dataset']['response_cols'][0]\n",
        "            param_list.append(param)\n",
        "            transform_type = metadata_dict['training_dataset']['feature_transform_type']\n",
        "            transform_list.append(transform_type)\n",
        "            split_params = metadata_dict['splitting_parameters']\n",
        "            splitter_list.append(split_params['splitter'])\n",
        "            split_uuid_list.append(split_params.get('split_uuid', ''))\n",
        "            split_strategy = split_params['split_strategy']\n",
        "            split_strategy_list.append(split_strategy)\n",
        "\n",
        "            if 'umap_specific' in metadata_dict:\n",
        "                umap_params = metadata_dict['umap_specific']\n",
        "                umap_dim_list.append(umap_params['umap_dim'])\n",
        "                umap_targ_wt_list.append(umap_params['umap_targ_wt'])\n",
        "                umap_neighbors_list.append(umap_params['umap_neighbors'])\n",
        "                umap_min_dist_list.append(umap_params['umap_min_dist'])\n",
        "            else:\n",
        "                umap_dim_list.append(nan)\n",
        "                umap_targ_wt_list.append(nan)\n",
        "                umap_neighbors_list.append(nan)\n",
        "                umap_min_dist_list.append(nan)\n",
        "\n",
        "            if model_type == 'NN':\n",
        "                nn_params = metadata_dict['nn_specific']\n",
        "                max_epochs_list.append(nn_params['max_epochs'])\n",
        "                best_epoch_list.append(nn_params['best_epoch'])\n",
        "                learning_rate_list.append(nn_params['learning_rate'])\n",
        "                layer_sizes_list.append(','.join(['%d' % s for s in nn_params['layer_sizes']]))\n",
        "                dropouts_list.append(','.join(['%.2f' % d for d in nn_params['dropouts']]))\n",
        "                rf_estimators_list.append(nan)\n",
        "                rf_max_features_list.append(nan)\n",
        "                rf_max_depth_list.append(nan)\n",
        "                xgb_gamma_list.append(nan)\n",
        "                xgb_learning_rate_list.append(nan)\n",
        "            elif model_type == 'RF':\n",
        "                rf_params = metadata_dict['rf_specific']\n",
        "                rf_estimators_list.append(rf_params['rf_estimators'])\n",
        "                rf_max_features_list.append(rf_params['rf_max_features'])\n",
        "                rf_max_depth_list.append(rf_params['rf_max_depth'])\n",
        "                max_epochs_list.append(nan)\n",
        "                best_epoch_list.append(nan)\n",
        "                learning_rate_list.append(nan)\n",
        "                layer_sizes_list.append(nan)\n",
        "                dropouts_list.append(nan)\n",
        "                xgb_gamma_list.append(nan)\n",
        "                xgb_learning_rate_list.append(nan)\n",
        "            elif model_type == 'xgboost':\n",
        "                # TODO: Add xgboost parameters\n",
        "                xg_params = metadata_dict['xgb_specific']\n",
        "                xgb_gamma_list.append(xg_params['xgb_gamma'])\n",
        "                xgb_learning_rate_list.append(xg_params['xgb_learning_rate'])\n",
        "                max_epochs_list.append(nan)\n",
        "                best_epoch_list.append(nan)\n",
        "                learning_rate_list.append(nan)\n",
        "                layer_sizes_list.append(nan)\n",
        "                dropouts_list.append(nan)\n",
        "                rf_estimators_list.append(nan)\n",
        "                rf_max_features_list.append(nan)\n",
        "                rf_max_depth_list.append(nan)\n",
        "            else:\n",
        "                raise Exception('Unexpected model type %s' % model_type)\n",
        "\n",
        "            # Get model metrics for this model\n",
        "            metrics_dicts = metadata_dict['training_metrics']\n",
        "            #print(\"Got %d metrics dicts for model %s\" % (len(metrics_dicts), model_uuid))\n",
        "            subset_metrics = {}\n",
        "            for metrics_dict in metrics_dicts:\n",
        "                if metrics_dict['label'] == 'best':\n",
        "                    subset = metrics_dict['subset']\n",
        "                    subset_metrics[subset] = metrics_dict['prediction_results']\n",
        "            if split_strategy == 'k_fold_cv':\n",
        "                dset_size = subset_metrics['train']['num_compounds'] + subset_metrics['test']['num_compounds']\n",
        "            else:\n",
        "                dset_size = subset_metrics['train']['num_compounds'] + subset_metrics['valid']['num_compounds'] + subset_metrics['test']['num_compounds']\n",
        "            for subset in subsets:\n",
        "                subset_size = subset_metrics[subset]['num_compounds']\n",
        "                for score_type in score_types:\n",
        "                    try:\n",
        "                        score = subset_metrics[subset][score_type]\n",
        "                    except KeyError:\n",
        "                        score = float('nan')\n",
        "                    score_dict[subset][score_type].append(score)\n",
        "                ncmpd_dict[subset].append(subset_size)\n",
        "            dset_size_list.append(dset_size)\n",
        "\n",
        "    col_dict = dict(\n",
        "                    collection=collection_list,\n",
        "                    model_uuid=model_uuid_list,\n",
        "                    time_built=time_built_list,\n",
        "                    model_type=model_type_list,\n",
        "                    featurizer=featurizer_list,\n",
        "                    features=desc_type_list,\n",
        "                    transformer=transform_list,\n",
        "                    splitter=splitter_list,\n",
        "                    split_strategy=split_strategy_list,\n",
        "                    split_uuid=split_uuid_list,\n",
        "                    umap_dim=umap_dim_list,\n",
        "                    umap_targ_wt=umap_targ_wt_list,\n",
        "                    umap_neighbors=umap_neighbors_list,\n",
        "                    umap_min_dist=umap_min_dist_list,\n",
        "                    layer_sizes=layer_sizes_list,\n",
        "                    dropouts=dropouts_list,\n",
        "                    learning_rate=learning_rate_list,\n",
        "                    best_epoch=best_epoch_list,\n",
        "                    max_epochs=max_epochs_list,\n",
        "                    rf_estimators=rf_estimators_list,\n",
        "                    rf_max_features=rf_max_features_list,\n",
        "                    rf_max_depth=rf_max_depth_list,\n",
        "                    xgb_gamma = xgb_gamma_list,\n",
        "                    xgb_learning_rate = xgb_learning_rate_list,\n",
        "                    dataset_bucket=bucket_list,\n",
        "                    dataset_key=dataset_key_list,\n",
        "                    dataset_size=dset_size_list,\n",
        "                    parameter=param_list\n",
        "                    )\n",
        "\n",
        "    perf_df = pd.DataFrame(col_dict)\n",
        "    for subset in subsets:\n",
        "        ncmpds_col = '%s_size' % subset\n",
        "        perf_df[ncmpds_col] = ncmpd_dict[subset]\n",
        "        for score_type in score_types:\n",
        "            metric_col = '%s_%s' % (subset, score_type)\n",
        "            perf_df[metric_col] = score_dict[subset][score_type]\n",
        "\n",
        "    return perf_df\n",
        "\n",
        "from atomsci.ddm.pipeline import predict_from_model as pfm\n",
        "\n",
        "def predict_from_model_file(model_path, input_df, id_col='compound_id', smiles_col='rdkit_smiles',\n",
        "                     response_col=None, is_featurized=False, dont_standardize=False):\n",
        "    \"\"\"\n",
        "    Loads a pretrained model from a model tarball file and runs predictions on compounds in an input\n",
        "    data frame.\n",
        "\n",
        "    Args:\n",
        "        model_path (str): File path of the model tarball file.\n",
        "\n",
        "        input_df (DataFrame): Input data to run predictions on; must at minimum contain SMILES strings.\n",
        "\n",
        "        id_col (str): Name of the column containing compound IDs. If none is provided, sequential IDs will be\n",
        "        generated.\n",
        "\n",
        "        smiles_col (str): Name of the column containing SMILES strings; required.\n",
        "\n",
        "        response_col (str): Name of an optional column containing actual response values; if it is provided, \n",
        "        the actual values will be included in the returned data frame to make it easier for you to assess performance.\n",
        "\n",
        "        dont_standardize (bool): By default, SMILES strings are salt-stripped and standardized using RDKit; \n",
        "        if you have already done this, or don't want them to be standardized, set dont_standardize to True.\n",
        "\n",
        "    Return: \n",
        "        A data frame with compound IDs, SMILES strings and predicted response values. Actual response values\n",
        "        will be included if response_col is provided. Standard prediction error estimates will be included\n",
        "        if the model was trained with uncertainty=True. Note that the predicted and actual response\n",
        "        columns will be labeled according to the response_col setting in the original training data,\n",
        "        not the response_col passed to this function; e.g. if the original model response_col was 'pIC50',\n",
        "        the returned data frame will contain columns 'pIC50_actual', 'pIC50_pred' and 'pIC50_std'.\n",
        "    \"\"\"\n",
        "\n",
        "    input_df, pred_params = pfm._prepare_input_data(input_df, id_col, smiles_col, response_col, dont_standardize)\n",
        "\n",
        "    has_responses = ('response_cols' in pred_params)\n",
        "    pred_params = parse.wrapper(pred_params)\n",
        "\n",
        "    pipe = mp.create_prediction_pipeline_from_file(pred_params, reload_dir=None, model_path=model_path)\n",
        "    if pipe.params.model_type == 'xgboost':\n",
        "        pipe.params.uncertainty = False\n",
        "    pred_df = pipe.predict_full_dataset(input_df, contains_responses=has_responses, is_featurized=is_featurized,\n",
        "                                        dset_params=pred_params)\n",
        "    pred_df = pred_df.sort_values(by=id_col)\n",
        "    return pred_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHjYDaTNBYjQ"
      },
      "source": [
        "### Monitor HP search with `groupby`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSDa2HTH6wbD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "b8f0dff1-fa69-44cc-8031-ff1ae14453d0"
      },
      "source": [
        "perf_df = get_summary_perf_tables(collection_names=None, filter_dict={}, result_dir=result_dir, prediction_type='regression', verbose=False)\n",
        "perf_df = perf_df[perf_df.rf_estimators!=500]\n",
        "perf_df.sort_values(by=\"valid_r2_score\", ascending=False).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>collection</th>\n",
              "      <th>model_uuid</th>\n",
              "      <th>time_built</th>\n",
              "      <th>model_type</th>\n",
              "      <th>featurizer</th>\n",
              "      <th>features</th>\n",
              "      <th>transformer</th>\n",
              "      <th>splitter</th>\n",
              "      <th>split_strategy</th>\n",
              "      <th>split_uuid</th>\n",
              "      <th>umap_dim</th>\n",
              "      <th>umap_targ_wt</th>\n",
              "      <th>umap_neighbors</th>\n",
              "      <th>umap_min_dist</th>\n",
              "      <th>layer_sizes</th>\n",
              "      <th>dropouts</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>best_epoch</th>\n",
              "      <th>max_epochs</th>\n",
              "      <th>rf_estimators</th>\n",
              "      <th>rf_max_features</th>\n",
              "      <th>rf_max_depth</th>\n",
              "      <th>xgb_gamma</th>\n",
              "      <th>xgb_learning_rate</th>\n",
              "      <th>dataset_bucket</th>\n",
              "      <th>dataset_key</th>\n",
              "      <th>dataset_size</th>\n",
              "      <th>parameter</th>\n",
              "      <th>train_size</th>\n",
              "      <th>train_r2_score</th>\n",
              "      <th>train_mae_score</th>\n",
              "      <th>train_rms_score</th>\n",
              "      <th>valid_size</th>\n",
              "      <th>valid_r2_score</th>\n",
              "      <th>valid_mae_score</th>\n",
              "      <th>valid_rms_score</th>\n",
              "      <th>test_size</th>\n",
              "      <th>test_r2_score</th>\n",
              "      <th>test_mae_score</th>\n",
              "      <th>test_rms_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1240</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>44080ec3-f88a-469e-ae3d-7a0c31d4a768</td>\n",
              "      <td>1.617830e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.20,0.20</td>\n",
              "      <td>0.010</td>\n",
              "      <td>38.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.917906</td>\n",
              "      <td>0.346156</td>\n",
              "      <td>0.457862</td>\n",
              "      <td>100</td>\n",
              "      <td>0.612270</td>\n",
              "      <td>0.783147</td>\n",
              "      <td>1.090699</td>\n",
              "      <td>101</td>\n",
              "      <td>0.636958</td>\n",
              "      <td>0.731418</td>\n",
              "      <td>1.041450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1281</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>ff0e342b-4e96-427c-84d4-23bdb9366929</td>\n",
              "      <td>1.617843e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128,32</td>\n",
              "      <td>0.20,0.20</td>\n",
              "      <td>0.025</td>\n",
              "      <td>67.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.861973</td>\n",
              "      <td>0.449256</td>\n",
              "      <td>0.593694</td>\n",
              "      <td>100</td>\n",
              "      <td>0.608775</td>\n",
              "      <td>0.803392</td>\n",
              "      <td>1.095604</td>\n",
              "      <td>101</td>\n",
              "      <td>0.650939</td>\n",
              "      <td>0.796721</td>\n",
              "      <td>1.021200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1271</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>241d00d3-69a0-416c-b48c-7193353cd81f</td>\n",
              "      <td>1.617839e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128,32</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.025</td>\n",
              "      <td>16.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.743212</td>\n",
              "      <td>0.605432</td>\n",
              "      <td>0.809780</td>\n",
              "      <td>100</td>\n",
              "      <td>0.598761</td>\n",
              "      <td>0.833737</td>\n",
              "      <td>1.109537</td>\n",
              "      <td>101</td>\n",
              "      <td>0.640857</td>\n",
              "      <td>0.747580</td>\n",
              "      <td>1.035844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1268</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>46f74299-e2a2-4276-8d0b-4b15be5398d1</td>\n",
              "      <td>1.617837e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128,32</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.001</td>\n",
              "      <td>9.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.839356</td>\n",
              "      <td>0.474190</td>\n",
              "      <td>0.640490</td>\n",
              "      <td>100</td>\n",
              "      <td>0.593710</td>\n",
              "      <td>0.807557</td>\n",
              "      <td>1.116499</td>\n",
              "      <td>101</td>\n",
              "      <td>0.716549</td>\n",
              "      <td>0.675669</td>\n",
              "      <td>0.920237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1250</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>8dc4b191-876c-46f3-bea9-352ddda840a2</td>\n",
              "      <td>1.617832e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>ecfp</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.30,0.30</td>\n",
              "      <td>0.010</td>\n",
              "      <td>25.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.877411</td>\n",
              "      <td>0.442408</td>\n",
              "      <td>0.559508</td>\n",
              "      <td>100</td>\n",
              "      <td>0.588992</td>\n",
              "      <td>0.830293</td>\n",
              "      <td>1.122963</td>\n",
              "      <td>101</td>\n",
              "      <td>0.598003</td>\n",
              "      <td>0.790241</td>\n",
              "      <td>1.095902</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             collection  ... test_rms_score\n",
              "1240  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.041450\n",
              "1281  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.021200\n",
              "1271  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.035844\n",
              "1268  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       0.920237\n",
              "1250  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.095902\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "IE8VVq4h2sXn",
        "outputId": "d29fc0c8-b017-42a4-d95b-09774283f32e"
      },
      "source": [
        "perf_df.groupby(by=['model_type', 'features']).count()[['model_uuid']].T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>model_type</th>\n",
              "      <th colspan=\"3\" halign=\"left\">NN</th>\n",
              "      <th colspan=\"3\" halign=\"left\">RF</th>\n",
              "      <th colspan=\"3\" halign=\"left\">xgboost</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>features</th>\n",
              "      <th>ecfp</th>\n",
              "      <th>mordred_filtered</th>\n",
              "      <th>rdkit_raw</th>\n",
              "      <th>ecfp</th>\n",
              "      <th>mordred_filtered</th>\n",
              "      <th>rdkit_raw</th>\n",
              "      <th>ecfp</th>\n",
              "      <th>mordred_filtered</th>\n",
              "      <th>rdkit_raw</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model_uuid</th>\n",
              "      <td>62</td>\n",
              "      <td>45</td>\n",
              "      <td>76</td>\n",
              "      <td>300</td>\n",
              "      <td>294</td>\n",
              "      <td>218</td>\n",
              "      <td>96</td>\n",
              "      <td>97</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "model_type   NN                             ... xgboost                           \n",
              "features   ecfp mordred_filtered rdkit_raw  ...    ecfp mordred_filtered rdkit_raw\n",
              "model_uuid   62               45        76  ...      96               97        96\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "PUs6NHZIPD06",
        "outputId": "d1e23005-aa6f-4fa0-8fad-8ca085fcb05a"
      },
      "source": [
        "nnmods = perf_df[perf_df.model_type==\"NN\"]\n",
        "nnmods.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>collection</th>\n",
              "      <th>model_uuid</th>\n",
              "      <th>time_built</th>\n",
              "      <th>model_type</th>\n",
              "      <th>featurizer</th>\n",
              "      <th>features</th>\n",
              "      <th>transformer</th>\n",
              "      <th>splitter</th>\n",
              "      <th>split_strategy</th>\n",
              "      <th>split_uuid</th>\n",
              "      <th>umap_dim</th>\n",
              "      <th>umap_targ_wt</th>\n",
              "      <th>umap_neighbors</th>\n",
              "      <th>umap_min_dist</th>\n",
              "      <th>layer_sizes</th>\n",
              "      <th>dropouts</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>best_epoch</th>\n",
              "      <th>max_epochs</th>\n",
              "      <th>rf_estimators</th>\n",
              "      <th>rf_max_features</th>\n",
              "      <th>rf_max_depth</th>\n",
              "      <th>xgb_gamma</th>\n",
              "      <th>xgb_learning_rate</th>\n",
              "      <th>dataset_bucket</th>\n",
              "      <th>dataset_key</th>\n",
              "      <th>dataset_size</th>\n",
              "      <th>parameter</th>\n",
              "      <th>train_size</th>\n",
              "      <th>train_r2_score</th>\n",
              "      <th>train_mae_score</th>\n",
              "      <th>train_rms_score</th>\n",
              "      <th>valid_size</th>\n",
              "      <th>valid_r2_score</th>\n",
              "      <th>valid_mae_score</th>\n",
              "      <th>valid_rms_score</th>\n",
              "      <th>test_size</th>\n",
              "      <th>test_r2_score</th>\n",
              "      <th>test_mae_score</th>\n",
              "      <th>test_rms_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>68dd1ae9-fbd5-41bf-8db2-e404e32a11ca</td>\n",
              "      <td>1.617859e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>computed_descriptors</td>\n",
              "      <td>mordred_filtered</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>69.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>-0.021531</td>\n",
              "      <td>1.249976</td>\n",
              "      <td>1.615965</td>\n",
              "      <td>100</td>\n",
              "      <td>-0.459866</td>\n",
              "      <td>1.689337</td>\n",
              "      <td>2.116394</td>\n",
              "      <td>101</td>\n",
              "      <td>-0.601774</td>\n",
              "      <td>1.810268</td>\n",
              "      <td>2.187565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>dd0aaba6-9ec1-4586-940e-f7caf4d866ae</td>\n",
              "      <td>1.617859e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>computed_descriptors</td>\n",
              "      <td>mordred_filtered</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.00005</td>\n",
              "      <td>69.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.618795</td>\n",
              "      <td>0.784715</td>\n",
              "      <td>0.987155</td>\n",
              "      <td>100</td>\n",
              "      <td>0.242201</td>\n",
              "      <td>1.244901</td>\n",
              "      <td>1.524815</td>\n",
              "      <td>101</td>\n",
              "      <td>0.381100</td>\n",
              "      <td>1.140487</td>\n",
              "      <td>1.359785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>e38f7e35-2997-4121-ba9f-2dc9a21da2bf</td>\n",
              "      <td>1.617859e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>computed_descriptors</td>\n",
              "      <td>mordred_filtered</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.00010</td>\n",
              "      <td>67.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.735145</td>\n",
              "      <td>0.619741</td>\n",
              "      <td>0.822830</td>\n",
              "      <td>100</td>\n",
              "      <td>0.338384</td>\n",
              "      <td>1.111783</td>\n",
              "      <td>1.424764</td>\n",
              "      <td>101</td>\n",
              "      <td>0.522538</td>\n",
              "      <td>0.949344</td>\n",
              "      <td>1.194345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>a61d8ce6-e75a-4f41-b410-8f95076c213e</td>\n",
              "      <td>1.617860e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>computed_descriptors</td>\n",
              "      <td>mordred_filtered</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.00050</td>\n",
              "      <td>69.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.885942</td>\n",
              "      <td>0.383724</td>\n",
              "      <td>0.539969</td>\n",
              "      <td>100</td>\n",
              "      <td>0.504730</td>\n",
              "      <td>0.871555</td>\n",
              "      <td>1.232710</td>\n",
              "      <td>101</td>\n",
              "      <td>0.629851</td>\n",
              "      <td>0.791005</td>\n",
              "      <td>1.051594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_mo...</td>\n",
              "      <td>9ec1acff-e3e7-4d69-99c7-d4eaae412a88</td>\n",
              "      <td>1.617860e+09</td>\n",
              "      <td>NN</td>\n",
              "      <td>computed_descriptors</td>\n",
              "      <td>mordred_filtered</td>\n",
              "      <td>normalization</td>\n",
              "      <td>scaffold</td>\n",
              "      <td>train_valid_test</td>\n",
              "      <td>ead9410d-7abc-41cc-8713-f78dac0106a8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,16</td>\n",
              "      <td>0.10,0.10</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>39.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>public</td>\n",
              "      <td>/content/drive/MyDrive/Columbia_E4511/HTR3A_cu...</td>\n",
              "      <td>1002</td>\n",
              "      <td>VALUE_NUM_mean</td>\n",
              "      <td>801</td>\n",
              "      <td>0.880167</td>\n",
              "      <td>0.397018</td>\n",
              "      <td>0.553469</td>\n",
              "      <td>100</td>\n",
              "      <td>0.527753</td>\n",
              "      <td>0.863128</td>\n",
              "      <td>1.203717</td>\n",
              "      <td>101</td>\n",
              "      <td>0.643010</td>\n",
              "      <td>0.781406</td>\n",
              "      <td>1.032733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            collection  ... test_rms_score\n",
              "194  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       2.187565\n",
              "195  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.359785\n",
              "196  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.194345\n",
              "197  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.051594\n",
              "198  /content/drive/MyDrive/Columbia_E4511/HTR3A_mo...  ...       1.032733\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "nlmghnGjPQ49",
        "outputId": "e29c27c5-b6a1-47c9-a9f1-d28b96a7d735"
      },
      "source": [
        "# should be 10 each, 20 groups per feature, if you got all the models created\n",
        "nnmods.groupby(by=['features','layer_sizes','dropouts']).count()[['model_uuid']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>model_uuid</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>features</th>\n",
              "      <th>layer_sizes</th>\n",
              "      <th>dropouts</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">ecfp</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">128,32</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20,0.20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30,0.30</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">64,16</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20,0.20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30,0.30</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.40,0.40</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">mordred_filtered</th>\n",
              "      <th>128,32</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">64,16</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20,0.20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30,0.30</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.40,0.40</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">rdkit_raw</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">128,32</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20,0.20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30,0.30</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.40,0.40</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">64,16</th>\n",
              "      <th>0.10,0.10</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20,0.20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30,0.30</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.40,0.40</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        model_uuid\n",
              "features         layer_sizes dropouts             \n",
              "ecfp             128,32      0.10,0.10          10\n",
              "                             0.20,0.20          10\n",
              "                             0.30,0.30           2\n",
              "                 64,16       0.10,0.10          10\n",
              "                             0.20,0.20          10\n",
              "                             0.30,0.30          10\n",
              "                             0.40,0.40          10\n",
              "mordred_filtered 128,32      0.10,0.10           4\n",
              "                 64,16       0.10,0.10          10\n",
              "                             0.20,0.20          10\n",
              "                             0.30,0.30          11\n",
              "                             0.40,0.40          10\n",
              "rdkit_raw        128,32      0.10,0.10          10\n",
              "                             0.20,0.20          10\n",
              "                             0.30,0.30          10\n",
              "                             0.40,0.40           2\n",
              "                 64,16       0.10,0.10          14\n",
              "                             0.20,0.20          10\n",
              "                             0.30,0.30          10\n",
              "                             0.40,0.40          10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_j2-s5COPvxx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}