fremtpl.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fremtpl.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/RonRichman/fastai_on_freMTPL/blob/master/fremtpl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "BooIvQjZfGfE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install requirements and get the data into Pandas"
      ]
    },
    {
      "metadata": {
        "id": "1ngTQZmlSvOD",
        "colab_type": "code",
        "outputId": "72993289-4f00-4061-b219-994317a0a1c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 10900
        }
      },
      "cell_type": "code",
      "source": [
        "!apt-get update\n",
        "!apt-get install r-base\n",
        "!pip install rpy2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Hit:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  Release\n",
            "Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  Release\n",
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            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  autoconf automake autopoint autotools-dev bzip2-doc cdbs debhelper\n",
            "  dh-autoreconf dh-strip-nondeterminism dh-translations file gettext\n",
            "  gettext-base intltool intltool-debian jq libarchive-cpio-perl\n",
            "  libarchive-zip-perl libauthen-sasl-perl libbz2-dev libdata-dump-perl\n",
            "  libencode-locale-perl libfile-basedir-perl libfile-desktopentry-perl\n",
            "  libfile-listing-perl libfile-mimeinfo-perl libfile-stripnondeterminism-perl\n",
            "  libfile-which-perl libfont-afm-perl libhtml-form-perl libhtml-format-perl\n",
            "  libhtml-parser-perl libhtml-tagset-perl libhtml-tree-perl\n",
            "  libhttp-cookies-perl libhttp-daemon-perl libhttp-date-perl\n",
            "  libhttp-message-perl libhttp-negotiate-perl libio-html-perl\n",
            "  libio-socket-ssl-perl libipc-system-simple-perl libjq1\n",
            "  liblwp-mediatypes-perl liblwp-protocol-https-perl libmagic-mgc libmagic1\n",
            "  libmail-sendmail-perl libmailtools-perl libnet-dbus-perl libnet-http-perl\n",
            "  libnet-smtp-ssl-perl libnet-ssleay-perl libonig4 libpaper-utils libpaper1\n",
            "  libreadline-dev libsigsegv2 libsys-hostname-long-perl libtext-iconv-perl\n",
            "  libtie-ixhash-perl libtimedate-perl libtool libtry-tiny-perl liburi-perl\n",
            "  libwww-perl libwww-robotrules-perl libx11-protocol-perl libxml-parser-perl\n",
            "  libxml-twig-perl libxml-xpathengine-perl libxmuu1 libxxf86dga1 m4 netbase\n",
            "  perl-openssl-defaults po-debconf python3-pkg-resources python3-scour\n",
            "  python3-six r-base-core r-base-dev r-base-html r-cran-boot r-cran-class\n",
            "  r-cran-cluster r-cran-codetools r-cran-foreign r-cran-kernsmooth\n",
            "  r-cran-lattice r-cran-mass r-cran-matrix r-cran-mgcv r-cran-nlme r-cran-nnet\n",
            "  r-cran-rpart r-cran-spatial r-cran-survival r-doc-html r-recommended scour\n",
            "  x11-utils x11-xserver-utils xauth xdg-utils\n",
            "Suggested packages:\n",
            "  autoconf-archive gnu-standards autoconf-doc devscripts dh-make dwz\n",
            "  gettext-doc libasprintf-dev libgettextpo-dev libdigest-hmac-perl\n",
            "  libgssapi-perl libcrypt-ssleay-perl readline-doc libtool-doc gcj-jdk\n",
            "  libauthen-ntlm-perl libunicode-map8-perl libunicode-string-perl\n",
            "  xml-twig-tools m4-doc libmail-box-perl python3-setuptools ess r-doc-info\n",
            "  | r-doc-pdf r-mathlib texlive-base texlive-latex-base\n",
            "  texlive-generic-recommended texlive-fonts-recommended texlive-fonts-extra\n",
            "  texlive-extra-utils texlive-latex-recommended texlive-latex-extra texinfo\n",
            "  mozilla | www-browser gir1.2-rsvg-2.0 python3-gi-cairo mesa-utils nickle\n",
            "  cairo-5c xorg-docs-core\n",
            "The following NEW packages will be installed:\n",
            "  autoconf automake autopoint autotools-dev bzip2-doc cdbs debhelper\n",
            "  dh-autoreconf dh-strip-nondeterminism dh-translations file gettext\n",
            "  gettext-base intltool intltool-debian jq libarchive-cpio-perl\n",
            "  libarchive-zip-perl libauthen-sasl-perl libbz2-dev libdata-dump-perl\n",
            "  libencode-locale-perl libfile-basedir-perl libfile-desktopentry-perl\n",
            "  libfile-listing-perl libfile-mimeinfo-perl libfile-stripnondeterminism-perl\n",
            "  libfile-which-perl libfont-afm-perl libhtml-form-perl libhtml-format-perl\n",
            "  libhtml-parser-perl libhtml-tagset-perl libhtml-tree-perl\n",
            "  libhttp-cookies-perl libhttp-daemon-perl libhttp-date-perl\n",
            "  libhttp-message-perl libhttp-negotiate-perl libio-html-perl\n",
            "  libio-socket-ssl-perl libipc-system-simple-perl libjq1\n",
            "  liblwp-mediatypes-perl liblwp-protocol-https-perl libmagic-mgc libmagic1\n",
            "  libmail-sendmail-perl libmailtools-perl libnet-dbus-perl libnet-http-perl\n",
            "  libnet-smtp-ssl-perl libnet-ssleay-perl libonig4 libpaper-utils libpaper1\n",
            "  libreadline-dev libsigsegv2 libsys-hostname-long-perl libtext-iconv-perl\n",
            "  libtie-ixhash-perl libtimedate-perl libtool libtry-tiny-perl liburi-perl\n",
            "  libwww-perl libwww-robotrules-perl libx11-protocol-perl libxml-parser-perl\n",
            "  libxml-twig-perl libxml-xpathengine-perl libxmuu1 libxxf86dga1 m4 netbase\n",
            "  perl-openssl-defaults po-debconf python3-pkg-resources python3-scour\n",
            "  python3-six r-base r-base-core r-base-dev r-base-html r-cran-boot\n",
            "  r-cran-class r-cran-cluster r-cran-codetools r-cran-foreign\n",
            "  r-cran-kernsmooth r-cran-lattice r-cran-mass r-cran-matrix r-cran-mgcv\n",
            "  r-cran-nlme r-cran-nnet r-cran-rpart r-cran-spatial r-cran-survival\n",
            "  r-doc-html r-recommended scour x11-utils x11-xserver-utils xauth xdg-utils\n",
            "0 upgraded, 106 newly installed, 0 to remove and 12 not upgraded.\n",
            "Need to get 48.5 MB of archives.\n",
            "After this operation, 93.8 MB of additional disk space will be used.\n",
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            "(Reading database ... 110842 files and directories currently installed.)\n",
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            "Selecting previously unselected package libtext-iconv-perl.\n",
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            "Selecting previously unselected package libfile-stripnondeterminism-perl.\n",
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            "Selecting previously unselected package libtimedate-perl.\n",
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            "Selecting previously unselected package dh-strip-nondeterminism.\n",
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            "Selecting previously unselected package gettext.\n",
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            "Selecting previously unselected package intltool-debian.\n",
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            "Selecting previously unselected package liburi-perl.\n",
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            "Selecting previously unselected package libhttp-date-perl.\n",
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            "Selecting previously unselected package libhtml-tagset-perl.\n",
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            "Selecting previously unselected package libhtml-tree-perl.\n",
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            "Selecting previously unselected package libio-html-perl.\n",
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            "Selecting previously unselected package liblwp-mediatypes-perl.\n",
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            "Selecting previously unselected package libhttp-message-perl.\n",
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            "Selecting previously unselected package libhttp-cookies-perl.\n",
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            "Selecting previously unselected package libhttp-negotiate-perl.\n",
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            "Selecting previously unselected package perl-openssl-defaults:amd64.\n",
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            "Selecting previously unselected package libnet-ssleay-perl.\n",
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            "Selecting previously unselected package libio-socket-ssl-perl.\n",
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            "Selecting previously unselected package libnet-http-perl.\n",
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            "Selecting previously unselected package libtry-tiny-perl.\n",
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            "Selecting previously unselected package libwww-robotrules-perl.\n",
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            "Selecting previously unselected package libxml-parser-perl.\n",
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            "Selecting previously unselected package intltool.\n",
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            "Selecting previously unselected package libonig4:amd64.\n",
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            "Selecting previously unselected package libjq1:amd64.\n",
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            "Selecting previously unselected package libfile-which-perl.\n",
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            "Selecting previously unselected package dh-translations.\n",
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            "Selecting previously unselected package python3-six.\n",
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            "Preparing to unpack .../057-cdbs_0.4.156ubuntu4_all.deb ...\n",
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            "Selecting previously unselected package libarchive-cpio-perl.\n",
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            "Selecting previously unselected package libdata-dump-perl.\n",
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            "Selecting previously unselected package libipc-system-simple-perl.\n",
            "Preparing to unpack .../061-libipc-system-simple-perl_1.25-4_all.deb ...\n",
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            "Selecting previously unselected package libfile-basedir-perl.\n",
            "Preparing to unpack .../062-libfile-basedir-perl_0.07-1_all.deb ...\n",
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            "Selecting previously unselected package libfile-desktopentry-perl.\n",
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            "Selecting previously unselected package libfile-mimeinfo-perl.\n",
            "Preparing to unpack .../064-libfile-mimeinfo-perl_0.28-1_all.deb ...\n",
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            "Selecting previously unselected package libfont-afm-perl.\n",
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            "Selecting previously unselected package libhtml-form-perl.\n",
            "Preparing to unpack .../066-libhtml-form-perl_6.03-1_all.deb ...\n",
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            "Selecting previously unselected package libhtml-format-perl.\n",
            "Preparing to unpack .../067-libhtml-format-perl_2.12-1_all.deb ...\n",
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            "Selecting previously unselected package libhttp-daemon-perl.\n",
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            "Selecting previously unselected package libsys-hostname-long-perl.\n",
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            "Selecting previously unselected package libmail-sendmail-perl.\n",
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            "Selecting previously unselected package libnet-smtp-ssl-perl.\n",
            "Preparing to unpack .../071-libnet-smtp-ssl-perl_1.04-1_all.deb ...\n",
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            "Selecting previously unselected package libmailtools-perl.\n",
            "Preparing to unpack .../072-libmailtools-perl_2.18-1_all.deb ...\n",
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            "Selecting previously unselected package libxml-twig-perl.\n",
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            "Selecting previously unselected package libnet-dbus-perl.\n",
            "Preparing to unpack .../074-libnet-dbus-perl_1.1.0-4build2_amd64.deb ...\n",
            "Unpacking libnet-dbus-perl (1.1.0-4build2) ...\n",
            "Selecting previously unselected package libpaper1:amd64.\n",
            "Preparing to unpack .../075-libpaper1_1.1.24+nmu5ubuntu1_amd64.deb ...\n",
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            "Selecting previously unselected package libpaper-utils.\n",
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            "Selecting previously unselected package libreadline-dev:amd64.\n",
            "Preparing to unpack .../077-libreadline-dev_7.0-3_amd64.deb ...\n",
            "Unpacking libreadline-dev:amd64 (7.0-3) ...\n",
            "Selecting previously unselected package libtie-ixhash-perl.\n",
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            "Unpacking libtie-ixhash-perl (1.23-2) ...\n",
            "Selecting previously unselected package libx11-protocol-perl.\n",
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            "Selecting previously unselected package libxml-xpathengine-perl.\n",
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            "Selecting previously unselected package xdg-utils.\n",
            "Preparing to unpack .../081-xdg-utils_1.1.2-1ubuntu2.3_all.deb ...\n",
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            "Selecting previously unselected package r-base-core.\n",
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            "Selecting previously unselected package r-cran-boot.\n",
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            "Selecting previously unselected package r-cran-cluster.\n",
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            "Unpacking r-cran-cluster (2.0.6-2build1) ...\n",
            "Selecting previously unselected package r-cran-foreign.\n",
            "Preparing to unpack .../085-r-cran-foreign_0.8.69-1build1_amd64.deb ...\n",
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            "Selecting previously unselected package r-cran-mass.\n",
            "Preparing to unpack .../086-r-cran-mass_7.3-49-1_amd64.deb ...\n",
            "Unpacking r-cran-mass (7.3-49-1) ...\n",
            "Selecting previously unselected package r-cran-kernsmooth.\n",
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            "Unpacking r-cran-kernsmooth (2.23-15-3build1) ...\n",
            "Selecting previously unselected package r-cran-lattice.\n",
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            "Unpacking r-cran-lattice (0.20-35-1build1) ...\n",
            "Selecting previously unselected package r-cran-nlme.\n",
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            "Unpacking r-cran-nlme (3.1.131-3build1) ...\n",
            "Selecting previously unselected package r-cran-matrix.\n",
            "Preparing to unpack .../090-r-cran-matrix_1.2-12-1_amd64.deb ...\n",
            "Unpacking r-cran-matrix (1.2-12-1) ...\n",
            "Selecting previously unselected package r-cran-mgcv.\n",
            "Preparing to unpack .../091-r-cran-mgcv_1.8-23-1_amd64.deb ...\n",
            "Unpacking r-cran-mgcv (1.8-23-1) ...\n",
            "Selecting previously unselected package r-cran-survival.\n",
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            "Unpacking r-cran-survival (2.41-3-2build1) ...\n",
            "Selecting previously unselected package r-cran-rpart.\n",
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            "Unpacking r-cran-rpart (4.1-13-1) ...\n",
            "Selecting previously unselected package r-cran-class.\n",
            "Preparing to unpack .../094-r-cran-class_7.3-14-2build1_amd64.deb ...\n",
            "Unpacking r-cran-class (7.3-14-2build1) ...\n",
            "Selecting previously unselected package r-cran-nnet.\n",
            "Preparing to unpack .../095-r-cran-nnet_7.3-12-2build1_amd64.deb ...\n",
            "Unpacking r-cran-nnet (7.3-12-2build1) ...\n",
            "Selecting previously unselected package r-cran-spatial.\n",
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            "Unpacking r-cran-spatial (7.3-11-2build1) ...\n",
            "Selecting previously unselected package r-cran-codetools.\n",
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            "Unpacking r-cran-codetools (0.2-15-1.1) ...\n",
            "Selecting previously unselected package r-recommended.\n",
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            "Selecting previously unselected package r-base.\n",
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            "Selecting previously unselected package r-doc-html.\n",
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            "Selecting previously unselected package x11-utils.\n",
            "Preparing to unpack .../102-x11-utils_7.7+3build1_amd64.deb ...\n",
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            "\n",
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            "\n",
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            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "Collecting rpy2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/d1/074ffbbe7b4bf74c60b75d74c8e67a1e4515b0d85f85cd6540e39610754a/rpy2-2.9.5.tar.gz (194kB)\n",
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            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from rpy2) (1.11.0)\n",
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            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->rpy2) (1.1.0)\n",
            "Building wheels for collected packages: rpy2\n",
            "  Running setup.py bdist_wheel for rpy2 ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/9b/28/7e/a7d20210b71aae01e3000db80e60755ba1ef9fa1d15b47ae1f\n",
            "Successfully built rpy2\n",
            "Installing collected packages: rpy2\n",
            "Successfully installed rpy2-2.9.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7iAtXIl9cx5g",
        "colab_type": "code",
        "outputId": "f5c6d048-065a-4939-b847-7ea82786b903",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "! pip install tzlocal"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tzlocal\n",
            "  Downloading https://files.pythonhosted.org/packages/cb/89/e3687d3ed99bc882793f82634e9824e62499fdfdc4b1ae39e211c5b05017/tzlocal-1.5.1.tar.gz\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from tzlocal) (2018.7)\n",
            "Building wheels for collected packages: tzlocal\n",
            "  Running setup.py bdist_wheel for tzlocal ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/15/ae/df/a67bf1ed84e9bf230187d36d8dcfd30072bea0236cb059ed91\n",
            "Successfully built tzlocal\n",
            "Installing collected packages: tzlocal\n",
            "Successfully installed tzlocal-1.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vgoZauGXSxAN",
        "colab_type": "code",
        "outputId": "707869bd-c1b1-47a4-b614-40c6e0197f7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1533
        }
      },
      "cell_type": "code",
      "source": [
        "  !curl https://course-v3.fast.ai/setup/colab | bash"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "100   665  100   665    0     0    840      0 --:--:-- --:--:-- --:--:--   839\n",
            "Collecting pillow==4.1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/36/e5/88b3d60924a3f8476fa74ec086f5fbaba56dd6cee0d82845f883b6b6dd18/Pillow-4.1.1-cp36-cp36m-manylinux1_x86_64.whl (5.7MB)\n",
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            "Installing collected packages: pillow\n",
            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
            "      Successfully uninstalled Pillow-4.0.0\n",
            "Successfully installed pillow-4.1.1\n",
            "Looking in links: https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html\n",
            "Collecting torch_nightly\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/nightly/cu92/torch_nightly-1.0.0.dev20181206-cp36-cp36m-linux_x86_64.whl (576.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 576.2MB 25kB/s \n",
            "tcmalloc: large alloc 1073750016 bytes == 0x632b6000 @  0x7f052eb8e2a4 0x591a07 0x5b5d56 0x502e9a 0x506859 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x504c28 0x502540 0x502f3d 0x507641\n",
            "\u001b[?25hInstalling collected packages: torch-nightly\n",
            "Successfully installed torch-nightly-1.0.0.dev20181206\n",
            "Cloning into 'course-v3'...\n",
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            "Receiving objects: 100% (2760/2760), 105.07 MiB | 10.57 MiB/s, done.\n",
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            "Collecting fastai\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/17/b5ab3f9a44b45661d9eb6e29001fe39c39ebb287ce9a9f1670d07bda0d4d/fastai-1.0.39-py3-none-any.whl (150kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from fastai) (2.1.2)\n",
            "Collecting torch (from fastai)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/60/66415660aa46b23b5e1b72bc762e816736ce8d7260213e22365af51e8f9c/torch-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (591.8MB)\n",
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            "\u001b[?25hCollecting bottleneck (from fastai)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/05/ae/cedf5323f398ab4e4ff92d6c431a3e1c6a186f9b41ab3e8258dff786a290/Bottleneck-1.2.1.tar.gz (105kB)\n",
            "\u001b[K    100% |████████████████████████████████| 112kB 30.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: spacy>=2.0.18 in /usr/local/lib/python3.6/dist-packages (from fastai) (2.0.18)\n",
            "Collecting dataclasses; python_version < \"3.7\" (from fastai)\n",
            "  Downloading https://files.pythonhosted.org/packages/26/2f/1095cdc2868052dd1e64520f7c0d5c8c550ad297e944e641dbf1ffbb9a5d/dataclasses-0.6-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.12 in /usr/local/lib/python3.6/dist-packages (from fastai) (1.14.6)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from fastai) (0.22.0)\n",
            "Requirement already satisfied, skipping upgrade: Pillow in /usr/local/lib/python3.6/dist-packages (from fastai) (4.1.1)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.6/dist-packages (from fastai) (3.13)\n",
            "Collecting nvidia-ml-py3 (from fastai)\n",
            "  Downloading https://files.pythonhosted.org/packages/6d/64/cce82bddb80c0b0f5c703bbdafa94bfb69a1c5ad7a79cff00b482468f0d3/nvidia-ml-py3-7.352.0.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from fastai) (18.0)\n",
            "Collecting torchvision (from fastai)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/0d/f00b2885711e08bd71242ebe7b96561e6f6d01fdb4b9dcf4d37e2e13c5e1/torchvision-0.2.1-py2.py3-none-any.whl (54kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 19.8MB/s \n",
            "\u001b[?25hCollecting typing (from fastai)\n",
            "  Downloading https://files.pythonhosted.org/packages/4a/bd/eee1157fc2d8514970b345d69cb9975dcd1e42cd7e61146ed841f6e68309/typing-3.6.6-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from fastai) (1.1.0)\n",
            "Collecting fastprogress>=0.1.18 (from fastai)\n",
            "  Downloading https://files.pythonhosted.org/packages/78/57/24a5e20f4a357f7f1c90dd5250071951c832b2480fd4fefd7be48edf4180/fastprogress-0.1.18-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: numexpr in /usr/local/lib/python3.6/dist-packages (from fastai) (2.6.8)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from fastai) (2.18.4)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (2.5.3)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (1.11.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (2.3.0)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.6/dist-packages (from matplotlib->fastai) (2018.7)\n",
            "Requirement already satisfied, skipping upgrade: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (2.0.2)\n",
            "Requirement already satisfied, skipping upgrade: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: thinc<6.13.0,>=6.12.1 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (6.12.1)\n",
            "Requirement already satisfied, skipping upgrade: plac<1.0.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (0.9.6)\n",
            "Requirement already satisfied, skipping upgrade: dill<0.3,>=0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (0.2.8.2)\n",
            "Requirement already satisfied, skipping upgrade: regex==2018.01.10 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (2018.1.10)\n",
            "Requirement already satisfied, skipping upgrade: preshed<2.1.0,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (2.0.1)\n",
            "Requirement already satisfied, skipping upgrade: ujson>=1.35 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0.18->fastai) (1.35)\n",
            "Requirement already satisfied, skipping upgrade: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow->fastai) (0.46)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->fastai) (2.6)\n",
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            "Requirement already satisfied, skipping upgrade: msgpack<0.6.0,>=0.5.6 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (0.5.6)\n",
            "Requirement already satisfied, skipping upgrade: cytoolz<0.10,>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (0.9.0.1)\n",
            "Requirement already satisfied, skipping upgrade: wrapt<1.11.0,>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (1.10.11)\n",
            "Requirement already satisfied, skipping upgrade: msgpack-numpy<0.4.4 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (0.4.3.2)\n",
            "Requirement already satisfied, skipping upgrade: tqdm<5.0.0,>=4.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (4.28.1)\n",
            "Requirement already satisfied, skipping upgrade: toolz>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from cytoolz<0.10,>=0.9.0->thinc<6.13.0,>=6.12.1->spacy>=2.0.18->fastai) (0.9.0)\n",
            "Building wheels for collected packages: bottleneck, nvidia-ml-py3\n",
            "  Running setup.py bdist_wheel for bottleneck ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/f2/bf/ec/e0f39aa27001525ad455139ee57ec7d0776fe074dfd78c97e4\n",
            "  Running setup.py bdist_wheel for nvidia-ml-py3 ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/e4/1d/06/640c93f5270d67d0247f30be91f232700d19023f9e66d735c7\n",
            "Successfully built bottleneck nvidia-ml-py3\n",
            "Installing collected packages: torch, bottleneck, dataclasses, nvidia-ml-py3, torchvision, typing, fastprogress, fastai\n",
            "Successfully installed bottleneck-1.2.1 dataclasses-0.6 fastai-1.0.39 fastprogress-0.1.18 nvidia-ml-py3-7.352.0 torch-1.0.0 torchvision-0.2.1 typing-3.6.6\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IWSAHA-TWcsU",
        "colab_type": "code",
        "outputId": "d187b80e-52e3-4d06-db0c-9945abe0a4b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "! wget http://cas.uqam.ca/pub/R/bin/windows/contrib/3.2/CASdatasets_1.0-6.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-01-01 04:09:22--  http://cas.uqam.ca/pub/R/bin/windows/contrib/3.2/CASdatasets_1.0-6.zip\n",
            "Resolving cas.uqam.ca (cas.uqam.ca)... 132.208.138.146\n",
            "Connecting to cas.uqam.ca (cas.uqam.ca)|132.208.138.146|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 99795419 (95M) [application/zip]\n",
            "Saving to: ‘CASdatasets_1.0-6.zip’\n",
            "\n",
            "CASdatasets_1.0-6.z 100%[===================>]  95.17M  5.44MB/s    in 20s     \n",
            "\n",
            "2019-01-01 04:09:43 (4.81 MB/s) - ‘CASdatasets_1.0-6.zip’ saved [99795419/99795419]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Wi8aMaX_bQmj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile('CASdatasets_1.0-6.zip', 'r') as zip_ref:\n",
        "    zip_ref.extractall()\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fSCHrdmSbaaM",
        "colab_type": "code",
        "outputId": "cdb966e3-8b22-429c-ad83-6c73b61365ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "! ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CASdatasets  CASdatasets_1.0-6.zip  course-v3  data  models\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZPXYmlPBba9q",
        "colab_type": "code",
        "outputId": "077123cb-6361-4834-a3a2-8ae88646690b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import rpy2.robjects as robjects\n",
        "from rpy2.robjects import pandas2ri\n",
        "from rpy2.robjects.packages import importr\n",
        "pandas2ri.activate()\n",
        "\n",
        "! ls CASdatasets//data//freMTPL2freq.rda\t"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CASdatasets//data//freMTPL2freq.rda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "N0xtlFSubtUS",
        "colab_type": "code",
        "outputId": "68845034-3535-4c2a-cbc7-657b969ce203",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "base = importr('base')\n",
        "base.load('.//CASdatasets//data//freMTPL2freq.rda')    \n",
        "rdf_List = base.mget(base.ls())\n",
        "\n",
        "# ITERATE THROUGH LIST OF R DFs \n",
        "pydf_dict = {}\n",
        "\n",
        "for i,f in enumerate(base.names(rdf_List)):\n",
        "    pydf_dict[f] = pandas2ri.ri2py_dataframe(rdf_List[i])\n",
        "\n",
        "for k,v in pydf_dict.items():\n",
        "    print(v.head())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   IDpol  ClaimNb  Exposure Area  VehPower  VehAge  DrivAge  BonusMalus  \\\n",
            "0    1.0      1.0      0.10    D         5       0       55          50   \n",
            "1    3.0      1.0      0.77    D         5       0       55          50   \n",
            "2    5.0      1.0      0.75    B         6       2       52          50   \n",
            "3   10.0      1.0      0.09    B         7       0       46          50   \n",
            "4   11.0      1.0      0.84    B         7       0       46          50   \n",
            "\n",
            "  VehBrand   VehGas  Density Region  \n",
            "0      B12  Regular     1217    R82  \n",
            "1      B12  Regular     1217    R82  \n",
            "2      B12   Diesel       54    R22  \n",
            "3      B12   Diesel       76    R72  \n",
            "4      B12   Diesel       76    R72  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g-5_sxbYdh5m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "freMTPL2freq = pydf_dict['freMTPL2freq'].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gbTtFGgtfTaW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Run the Fast.AI Tabular Learner"
      ]
    },
    {
      "metadata": {
        "id": "n5knpME-cn_k",
        "colab_type": "code",
        "outputId": "685073e9-b766-4939-c224-d048bbb8f2e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "freMTPL2freq.head(5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>IDpol</th>\n",
              "      <th>ClaimNb</th>\n",
              "      <th>Exposure</th>\n",
              "      <th>Area</th>\n",
              "      <th>VehPower</th>\n",
              "      <th>VehAge</th>\n",
              "      <th>DrivAge</th>\n",
              "      <th>BonusMalus</th>\n",
              "      <th>VehBrand</th>\n",
              "      <th>VehGas</th>\n",
              "      <th>Density</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>D</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Regular</td>\n",
              "      <td>1217</td>\n",
              "      <td>R82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.77</td>\n",
              "      <td>D</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Regular</td>\n",
              "      <td>1217</td>\n",
              "      <td>R82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>B</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>52</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>54</td>\n",
              "      <td>R22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>B</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>76</td>\n",
              "      <td>R72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.84</td>\n",
              "      <td>B</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>76</td>\n",
              "      <td>R72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   IDpol  ClaimNb  Exposure Area  VehPower  VehAge  DrivAge  BonusMalus  \\\n",
              "0    1.0      1.0      0.10    D         5       0       55          50   \n",
              "1    3.0      1.0      0.77    D         5       0       55          50   \n",
              "2    5.0      1.0      0.75    B         6       2       52          50   \n",
              "3   10.0      1.0      0.09    B         7       0       46          50   \n",
              "4   11.0      1.0      0.84    B         7       0       46          50   \n",
              "\n",
              "  VehBrand   VehGas  Density Region  \n",
              "0      B12  Regular     1217    R82  \n",
              "1      B12  Regular     1217    R82  \n",
              "2      B12   Diesel       54    R22  \n",
              "3      B12   Diesel       76    R72  \n",
              "4      B12   Diesel       76    R72  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "WuAbi245fSPL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "freMTPL2freq['id'] = freMTPL2freq.index + 1\n",
        "freMTPL2freq['Density'] = np.log(freMTPL2freq.Density)\n",
        "freMTPL2freq.loc[ freMTPL2freq['ClaimNb'] > 4.0, 'ClaimNb' ] = 4.0\n",
        "freMTPL2freq.loc[ freMTPL2freq['Exposure'] > 1.0, 'Exposure' ] = 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rHD1DO3e2cJp",
        "colab_type": "code",
        "outputId": "21c39ed9-9f96-491d-fa4b-efeb435f39fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(freMTPL2freq,test_size = .05)\n",
        "train_idx, validation_idx = train_test_split(train['id'],test_size = .05)\n",
        "\n",
        "# Show validation set\n",
        "freMTPL2freq[freMTPL2freq.id.isin(validation_idx)].head()"
      ],
      "execution_count": 15,
      "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>IDpol</th>\n",
              "      <th>ClaimNb</th>\n",
              "      <th>Exposure</th>\n",
              "      <th>Area</th>\n",
              "      <th>VehPower</th>\n",
              "      <th>VehAge</th>\n",
              "      <th>DrivAge</th>\n",
              "      <th>BonusMalus</th>\n",
              "      <th>VehBrand</th>\n",
              "      <th>VehGas</th>\n",
              "      <th>Density</th>\n",
              "      <th>Region</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.71</td>\n",
              "      <td>C</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>68</td>\n",
              "      <td>B12</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>4.919981</td>\n",
              "      <td>R91</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>72.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.39</td>\n",
              "      <td>D</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>85</td>\n",
              "      <td>B12</td>\n",
              "      <td>Regular</td>\n",
              "      <td>6.278521</td>\n",
              "      <td>R93</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>121.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>C</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>55</td>\n",
              "      <td>B12</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>5.713733</td>\n",
              "      <td>R11</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>161.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.81</td>\n",
              "      <td>E</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8.140316</td>\n",
              "      <td>R93</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>465.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.79</td>\n",
              "      <td>E</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>50</td>\n",
              "      <td>B12</td>\n",
              "      <td>Regular</td>\n",
              "      <td>8.635509</td>\n",
              "      <td>R11</td>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     IDpol  ClaimNb  Exposure Area  VehPower  VehAge  DrivAge  BonusMalus  \\\n",
              "8     18.0      1.0      0.71    C         7       0       33          68   \n",
              "34    72.0      1.0      0.39    D         4       0       23          85   \n",
              "59   121.0      1.0      0.05    C         6       0       37          55   \n",
              "78   161.0      1.0      0.81    E         5       0       53          50   \n",
              "224  465.0      1.0      0.79    E        10       3       40          50   \n",
              "\n",
              "    VehBrand   VehGas   Density Region   id  \n",
              "8        B12   Diesel  4.919981    R91    9  \n",
              "34       B12  Regular  6.278521    R93   35  \n",
              "59       B12   Diesel  5.713733    R11   60  \n",
              "78       B12  Regular  8.140316    R93   79  \n",
              "224      B12  Regular  8.635509    R11  225  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "Brv_UpzVfZpL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cats = ['Area', 'VehPower', 'VehBrand', 'VehGas', 'Region']\n",
        "cont = ['VehAge', 'DrivAge', 'BonusMalus', 'Density', 'Exposure']\n",
        "dep_var = 'ClaimNb'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A2rKdMBAfkoK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai.tabular import *\n",
        "\n",
        "procs=[Categorify, Normalize]\n",
        "\n",
        "path = Path('')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iwguHcKTNdo5",
        "colab_type": "code",
        "outputId": "b2e73006-1f6e-4b2d-d834-6dae7a55c9d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "cell_type": "code",
      "source": [
        "data = (TabularList.from_df(freMTPL2freq, path=path, cat_names=cats, cont_names=cont, procs=procs)\n",
        "                   .split_by_idx(validation_idx)\n",
        "                   .label_from_df(cols=dep_var, label_cls=FloatList, log=False)\n",
        "                   .databunch(bs=256))\n",
        "\n",
        "data"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TabularDataBunch;\n",
              "\n",
              "Train: LabelList\n",
              "y: FloatList (645807 items)\n",
              "[FloatItem 1.0, FloatItem 1.0, FloatItem 1.0, FloatItem 1.0, FloatItem 1.0]...\n",
              "Path: .\n",
              "x: TabularList (645807 items)\n",
              "[TabularLine Area D; VehPower 5; VehBrand B12; VehGas Regular; Region R82; VehAge -1.2432; DrivAge 0.6726; BonusMalus -0.6244; Density 0.5998; Exposure -1.1768; , TabularLine Area D; VehPower 5; VehBrand B12; VehGas Regular; Region R82; VehAge -1.2432; DrivAge 0.6726; BonusMalus -0.6244; Density 0.5998; Exposure 0.6634; , TabularLine Area B; VehPower 6; VehBrand B12; VehGas Diesel; Region R22; VehAge -0.8903; DrivAge 0.4603; BonusMalus -0.6244; Density -1.0659; Exposure 0.6085; , TabularLine Area B; VehPower 7; VehBrand B12; VehGas Diesel; Region R72; VehAge -1.2432; DrivAge 0.0358; BonusMalus -0.6244; Density -0.8832; Exposure -1.2043; , TabularLine Area B; VehPower 7; VehBrand B12; VehGas Diesel; Region R72; VehAge -1.2432; DrivAge 0.0358; BonusMalus -0.6244; Density -0.8832; Exposure 0.8557; ]...\n",
              "Path: .;\n",
              "\n",
              "Valid: LabelList\n",
              "y: FloatList (32206 items)\n",
              "[FloatItem 1.0, FloatItem 0.0, FloatItem 0.0, FloatItem 0.0, FloatItem 0.0]...\n",
              "Path: .\n",
              "x: TabularList (32206 items)\n",
              "[TabularLine Area C; VehPower 5; VehBrand B2; VehGas Diesel; Region R26; VehAge -0.0080; DrivAge -1.0963; BonusMalus 1.9339; Density -0.3296; Exposure 0.1965; , TabularLine Area C; VehPower 4; VehBrand B1; VehGas Regular; Region R82; VehAge 0.1684; DrivAge 0.6018; BonusMalus -0.6244; Density -0.1690; Exposure 1.2952; , TabularLine Area C; VehPower 9; VehBrand B3; VehGas Regular; Region R53; VehAge 1.4036; DrivAge 0.3896; BonusMalus -0.6244; Density -0.4654; Exposure -1.2043; , TabularLine Area D; VehPower 7; VehBrand B5; VehGas Diesel; Region R25; VehAge 0.3449; DrivAge 1.2386; BonusMalus -0.6244; Density 0.6284; Exposure 1.2952; , TabularLine Area C; VehPower 9; VehBrand B13; VehGas Regular; Region R53; VehAge -0.1845; DrivAge -1.0963; BonusMalus -0.3685; Density -0.2934; Exposure 1.2952; ]...\n",
              "Path: .;\n",
              "\n",
              "Test: None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "-SX9NMeTRnLQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def poisson_dev(pred:Tensor, targ:Tensor)->Rank0Tensor:\n",
        "    \"Poisson deviance between `pred` and `targ`.\"\n",
        "    eps = 10e-4\n",
        "    return 2*torch.mean(pred - targ - targ*(torch.log(torch.clamp(pred,min = eps))-torch.log(torch.clamp(targ,min = eps))))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-J3uskdFZ8fZ",
        "colab_type": "code",
        "outputId": "69312210-00d4-44a3-8dab-2094fff7590a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max_y = np.max(train['ClaimNb'])*1\n",
        "print(max_y)\n",
        "y_range = torch.tensor([0, max_y], device=defaults.device)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "22vgNqLES6fm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = tabular_learner(data,emb_szs={'Area':2, 'VehPower':2, 'VehBrand':5, 'VehGas':1, 'Region':5},\n",
        "                        layers=[32,32], ps=[0.05,0.05], emb_drop=0,\n",
        "                      metrics=poisson_dev,y_range=y_range, wd=0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qVxQkNcbvdn6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "f518704e-97ff-451a-b087-92f1ef3c0bb9"
      },
      "cell_type": "code",
      "source": [
        "learn.layer_groups[0]\n"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Embedding(7, 2)\n",
              "  (1): Embedding(13, 2)\n",
              "  (2): Embedding(12, 5)\n",
              "  (3): Embedding(3, 1)\n",
              "  (4): Embedding(23, 5)\n",
              "  (5): Dropout(p=0)\n",
              "  (6): BatchNorm1d(5, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (7): Linear(in_features=20, out_features=32, bias=True)\n",
              "  (8): ReLU(inplace)\n",
              "  (9): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (10): Dropout(p=0.05)\n",
              "  (11): Linear(in_features=32, out_features=32, bias=True)\n",
              "  (12): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (13): Dropout(p=0.05)\n",
              "  (14): Linear(in_features=32, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "d_aJLowsTIet",
        "colab_type": "code",
        "outputId": "6627430a-28c7-4986-9a13-d8d15a86eb30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "learn.model"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TabularModel(\n",
              "  (embeds): ModuleList(\n",
              "    (0): Embedding(7, 2)\n",
              "    (1): Embedding(13, 2)\n",
              "    (2): Embedding(12, 5)\n",
              "    (3): Embedding(3, 1)\n",
              "    (4): Embedding(23, 5)\n",
              "  )\n",
              "  (emb_drop): Dropout(p=0)\n",
              "  (bn_cont): BatchNorm1d(5, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (layers): Sequential(\n",
              "    (0): Linear(in_features=20, out_features=32, bias=True)\n",
              "    (1): ReLU(inplace)\n",
              "    (2): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Dropout(p=0.05)\n",
              "    (4): Linear(in_features=32, out_features=32, bias=True)\n",
              "    (5): ReLU(inplace)\n",
              "    (6): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (7): Dropout(p=0.05)\n",
              "    (8): Linear(in_features=32, out_features=1, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "metadata": {
        "id": "W-BsDHWMfuuX",
        "colab_type": "code",
        "outputId": "66e7ae1e-b9f4-408b-9f33-b33c8353d020",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "learn.loss_func"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fastai.layers.FlattenedLoss at 0x7f616442ff98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "Tz00iMohgKWf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since the default loss function is a modified MSE, we need to set this to the Poisson loss"
      ]
    },
    {
      "metadata": {
        "id": "ucb4vg9Yfy5e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.loss_func = poisson_dev"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "52o7d52lTQOt",
        "colab_type": "code",
        "outputId": "5ab68ae3-2c58-44db-bb32-88a3c8c32364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find(stop_div=False)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table style='width:300px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>poisson_dev</th>\n",
              "  </tr>\n",
              "</table>\n",
              "\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='progress-bar-interrupted' max='2522', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      Interrupted\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rigbsQnrTVOV",
        "colab_type": "code",
        "outputId": "6d0630e4-8867-4797-9a59-6cb5e556124f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rDVqNTz6TkZFBmzZtAGjbti0zZ870aTAREak7KQkR9OoQz+afHWz/pYBubeLM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\nmMhzzz3H1q1bMQyDJ598km7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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60d8da70b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "o6yUwsOwTYiF",
        "colab_type": "code",
        "outputId": "2bcbf2dc-f1bd-49c5-8933-641f25a38fa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        }
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(2, (1e-2))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
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              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
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              "      50.00% [1/2 01:02<01:02]\n",
              "    </div>\n",
              "    \n",
              "<table style='width:300px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>poisson_dev</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>1</th>\n",
              "    <th>0.327328</th>\n",
              "    <th>0.314082</th>\n",
              "    <th>0.314082</th>\n",
              "  </tr>\n",
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              "\n",
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              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
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              "                border: none;\n",
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              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-b93097b8d495>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, wd, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor,\n\u001b[1;32m     21\u001b[0m                                         pct_start=pct_start, **kwargs))\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_it\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_div\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m         fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,\n\u001b[0;32m--> 172\u001b[0;31m             callbacks=self.callbacks+callbacks)\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, model, loss_func, opt, data, callbacks, metrics)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m                 \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mto_detach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-19-6f7006695aa6>\u001b[0m in \u001b[0;36mpoisson_dev\u001b[0;34m(pred, targ)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"Poisson deviance between `pred` and `targ`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0meps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10e-4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtarg\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtarg\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "Y3xG3vnLinK6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "ce1115d2-cbe7-4b64-b0d3-4725ad4bc9e5"
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/1 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table style='width:300px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>poisson_dev</th>\n",
              "  </tr>\n",
              "</table>\n",
              "\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
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              "      Interrupted\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Lj-o_I66mb8x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "89bf5147-a93c-459d-dae5-bb5ea98728cc"
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z3dwW6lFRURgMBtftmpoadDodAA0NDRQVFTF//nz8/PxYsmQJx48fx9fXl4iI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\nIiIOQk1dRETEQaipi4iIOAg1dREREQehpi4iIuI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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60d02473c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u2SegejkuAwQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "outputId": "f81490bf-7352-42a6-9d95-d32893b59a45"
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(2, slice(1e-4))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Total time: 02:01 <p><table style='width:300px; margin-bottom:10px'>\n",
              "  <tr>\n",
              "    <th>epoch</th>\n",
              "    <th>train_loss</th>\n",
              "    <th>valid_loss</th>\n",
              "    <th>poisson_dev</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>1</th>\n",
              "    <th>0.317366</th>\n",
              "    <th>0.313336</th>\n",
              "    <th>0.313336</th>\n",
              "  </tr>\n",
              "  <tr>\n",
              "    <th>2</th>\n",
              "    <th>0.325065</th>\n",
              "    <th>0.313574</th>\n",
              "    <th>0.313574</th>\n",
              "  </tr>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bmMlVZg0uGxB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}