{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. _quickstart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "nbsphinx": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rich/datasets\n"
     ]
    }
   ],
   "source": [
    "cd ~/datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['savefig.dpi'] = 100\n",
    "\n",
    "pd.options.display.max_columns = 10\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be working on a mutagenicity dataset, released by [Kazius et al.](http://pubs.acs.org/doi/abs/10.1021/jm040835a).  4337 compounds, provided as the file `mols.sdf`, were subjected to the [AMES test](https://en.wikipedia.org/wiki/Ames_test).  The results are given in `labels.csv`.  We will clean the molecules, perform a brief chemical space  analysis before finally assessing potential predictive models built on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "\n",
    "**scikit-chem** imports all subpackages with the main package, so all we need to do is import the main package, `skchem`.  We will also need `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import skchem\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `skchem.read_sdf` to import the sdf file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batch\n",
       "1728-95-6     <Mol: COc1ccc(-c2nc(-c3ccccc3)c(-c3ccccc3)[nH]...\n",
       "91-08-7                            <Mol: Cc1c(N=C=O)cccc1N=C=O>\n",
       "89786-04-9      <Mol: CC1(Cn2ccnn2)C(C(=O)O)N2C(=O)CC2S1(=O)=O>\n",
       "2439-35-2                               <Mol: C=CC(=O)OCCN(C)C>\n",
       "95-94-3                             <Mol: Clc1cc(Cl)c(Cl)cc1Cl>\n",
       "                                    ...                        \n",
       "89930-60-9    <Mol: CCCn1cc2c3c(cccc31)C1C=C(C)CN(C)C1C2.O=C...\n",
       "9002-92-0             <Mol: CCCCCCCCCCCCOCCOCCOCCOCCOCCOCCOCCO>\n",
       "90597-22-1               <Mol: Nc1ccn(C2C=C(CO)C(O)C2O)c(=O)n1>\n",
       "924-43-6                                      <Mol: CCC(C)ON=O>\n",
       "97534-21-9              <Mol: O=C1NC(=S)NC(=O)C1C(=O)Nc1ccccc1>\n",
       "Name: structure, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms_raw = skchem.read_sdf('mols.sdf'); ms_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And pandas to import the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "1728-95-6        mutagen\n",
       "91-08-7          mutagen\n",
       "89786-04-9    nonmutagen\n",
       "2439-35-2     nonmutagen\n",
       "95-94-3       nonmutagen\n",
       "                 ...    \n",
       "89930-60-9       mutagen\n",
       "9002-92-0     nonmutagen\n",
       "90597-22-1    nonmutagen\n",
       "924-43-6         mutagen\n",
       "97534-21-9    nonmutagen\n",
       "Name: Ames test categorisation, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_raw = pd.read_csv('labels.csv').set_index('name').squeeze(); y_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quickly check the class balance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121204278>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg0AAAGoCAYAAADFIdZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1210f4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_raw.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And binarize them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "1728-95-6     1\n",
       "91-08-7       1\n",
       "89786-04-9    0\n",
       "2439-35-2     0\n",
       "95-94-3       0\n",
       "             ..\n",
       "89930-60-9    1\n",
       "9002-92-0     0\n",
       "90597-22-1    0\n",
       "924-43-6      1\n",
       "97534-21-9    0\n",
       "Name: Ames test categorisation, dtype: uint8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_bin = (y_raw == 'mutagen').astype(np.uint8); y_bin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classes are (mercifully) quite balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "\n",
    "The data is unlikely to be canonicalized, and potentially contain broken or difficult molecules, so we will now clean it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardization\n",
    "\n",
    "The first step is to apply a `Transformer` to canonicalize the representations.  Specifically, we will use the ChemAxon Standardizer wrapper.  Some compounds are likely to fail this procedure, however they are likely to still be valid structures, so we will use the `keep_failed` configuration option on the object to keep these, rather than returning a None, or raising an error."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. tip::\n",
    "\n",
    "    ``Transformer`` s implement the ``transform`` method, which converts ``Mol`` s into *something else*.  This can either be another ``Mol``, such as in this case, or into a vector or even a number.  The result will be packaged as a ``pandas`` data structure of appropriate dimensionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "std = skchem.standardizers.ChemAxonStandardizer(keep_failed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ChemAxonStandardizer: 100% (4337 of 4337) |####################################| Elapsed Time: 0:00:32 Time: 0:00:32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "batch\n",
       "1728-95-6     <Mol: COc1ccc(-c2nc(-c3ccccc3)c(-c3ccccc3)[nH]...\n",
       "91-08-7                            <Mol: Cc1c(N=C=O)cccc1N=C=O>\n",
       "89786-04-9      <Mol: CC1(Cn2ccnn2)C(C(=O)O)N2C(=O)CC2S1(=O)=O>\n",
       "2439-35-2                               <Mol: C=CC(=O)OCCN(C)C>\n",
       "95-94-3                             <Mol: Clc1cc(Cl)c(Cl)cc1Cl>\n",
       "                                    ...                        \n",
       "89930-60-9          <Mol: CCCn1cc2c3c(cccc31)C1C=C(C)CN(C)C1C2>\n",
       "9002-92-0             <Mol: CCCCCCCCCCCCOCCOCCOCCOCCOCCOCCOCCO>\n",
       "90597-22-1               <Mol: Nc1ccn(C2C=C(CO)C(O)C2O)c(=O)n1>\n",
       "924-43-6                                      <Mol: CCC(C)ON=O>\n",
       "97534-21-9             <Mol: O=C(Nc1ccccc1)c1c(O)nc(=S)[nH]c1O>\n",
       "Name: structure, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms = std.transform(ms_raw); ms"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. tip::\n",
    "\n",
    "    This pattern is the typical way to handle all operations while using **scikit-chem**.  The available configuration options for all classes may be found in the class's docstring, available in the :ref:`documentation <api>` or using the builtin ``help`` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter undesirable molecules\n",
    "\n",
    "Next, we will remove molecules that are likely to not work well with the circular descriptors that we will use.  These are usually *large* or *inorganic* molecules.\n",
    "\n",
    "To do this, we will use some `Filters`, which implement the `filter` method."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. tip::\n",
    "\n",
    "    ``Filter`` s drop compounds that fail a predicicate. The results of the predicate can be found by using ``transform`` - that's right, each ``Filter`` is also a ``Transformer`` !  Labels with similar index can be passed in as a second argument, and will also be filtered and returned as a second return value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OrganicFilter: 100% (4337 of 4337) |###########################################| Elapsed Time: 0:00:05 Time: 0:00:05\n"
     ]
    }
   ],
   "source": [
    "of = skchem.filters.OrganicFilter()\n",
    "\n",
    "ms, y = of.filter(ms, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "MassFilter:   0% (  38 of 4337) |                                              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   1% (  75 of 4337) |                                              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   2% ( 113 of 4337) |#                                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   3% ( 150 of 4337) |#                                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   4% ( 187 of 4337) |#                                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   5% ( 225 of 4337) |##                                            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   6% ( 262 of 4337) |##                                            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   6% ( 300 of 4337) |###                                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   7% ( 337 of 4337) |###                                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   8% ( 374 of 4337) |###                                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:   9% ( 412 of 4337) |####                                          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  10% ( 449 of 4337) |####                                          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  11% ( 487 of 4337) |#####                                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  12% ( 524 of 4337) |#####                                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  12% ( 561 of 4337) |#####                                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  13% ( 599 of 4337) |######                                        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  14% ( 636 of 4337) |######                                        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  15% ( 673 of 4337) |#######                                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  16% ( 711 of 4337) |#######                                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  17% ( 748 of 4337) |#######                                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  18% ( 786 of 4337) |########                                      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  18% ( 823 of 4337) |########                                      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  19% ( 860 of 4337) |#########                                     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  20% ( 898 of 4337) |#########                                     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  21% ( 935 of 4337) |#########                                     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  22% ( 973 of 4337) |##########                                    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  23% (1010 of 4337) |##########                                    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  24% (1047 of 4337) |###########                                   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  25% (1085 of 4337) |###########                                   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  25% (1122 of 4337) |###########                                   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  26% (1160 of 4337) |############                                  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  27% (1197 of 4337) |############                                  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  28% (1234 of 4337) |#############                                 | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  29% (1272 of 4337) |#############                                 | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  30% (1309 of 4337) |#############                                 | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  31% (1346 of 4337) |##############                                | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  31% (1384 of 4337) |##############                                | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  32% (1421 of 4337) |###############                               | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  33% (1459 of 4337) |###############                               | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  34% (1496 of 4337) |###############                               | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  35% (1533 of 4337) |################                              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  36% (1571 of 4337) |################                              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  37% (1608 of 4337) |#################                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  37% (1646 of 4337) |#################                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  38% (1683 of 4337) |#################                             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  39% (1720 of 4337) |##################                            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  40% (1758 of 4337) |##################                            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  41% (1795 of 4337) |###################                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  42% (1833 of 4337) |###################                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  43% (1870 of 4337) |###################                           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  43% (1907 of 4337) |####################                          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  44% (1945 of 4337) |####################                          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  45% (1982 of 4337) |#####################                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  46% (2019 of 4337) |#####################                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  47% (2057 of 4337) |#####################                         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  48% (2094 of 4337) |######################                        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  49% (2132 of 4337) |######################                        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  50% (2169 of 4337) |#######################                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  50% (2206 of 4337) |#######################                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  51% (2244 of 4337) |#######################                       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  52% (2281 of 4337) |########################                      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  53% (2319 of 4337) |########################                      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  54% (2356 of 4337) |########################                      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  55% (2393 of 4337) |#########################                     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  56% (2431 of 4337) |#########################                     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  56% (2468 of 4337) |##########################                    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  57% (2505 of 4337) |##########################                    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  58% (2543 of 4337) |##########################                    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  59% (2580 of 4337) |###########################                   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  60% (2618 of 4337) |###########################                   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  61% (2655 of 4337) |############################                  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  62% (2692 of 4337) |############################                  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  62% (2730 of 4337) |############################                  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  63% (2767 of 4337) |#############################                 | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  64% (2805 of 4337) |#############################                 | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  65% (2842 of 4337) |##############################                | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  66% (2879 of 4337) |##############################                | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  67% (2917 of 4337) |##############################                | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  68% (2954 of 4337) |###############################               | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  68% (2992 of 4337) |###############################               | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  69% (3029 of 4337) |################################              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  70% (3066 of 4337) |################################              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  71% (3104 of 4337) |################################              | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  72% (3141 of 4337) |#################################             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  73% (3178 of 4337) |#################################             | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  74% (3216 of 4337) |##################################            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  75% (3253 of 4337) |##################################            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  75% (3291 of 4337) |##################################            | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  76% (3328 of 4337) |###################################           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  77% (3365 of 4337) |###################################           | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  78% (3403 of 4337) |####################################          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  79% (3440 of 4337) |####################################          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  80% (3478 of 4337) |####################################          | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  81% (3515 of 4337) |#####################################         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  81% (3552 of 4337) |#####################################         | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  82% (3590 of 4337) |######################################        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  83% (3627 of 4337) |######################################        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  84% (3665 of 4337) |######################################        | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  85% (3702 of 4337) |#######################################       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  86% (3739 of 4337) |#######################################       | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  87% (3777 of 4337) |########################################      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  87% (3814 of 4337) |########################################      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  88% (3851 of 4337) |########################################      | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  89% (3889 of 4337) |#########################################     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  90% (3926 of 4337) |#########################################     | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  91% (3964 of 4337) |##########################################    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  92% (4001 of 4337) |##########################################    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  93% (4038 of 4337) |##########################################    | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  93% (4076 of 4337) |###########################################   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  94% (4113 of 4337) |###########################################   | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  95% (4151 of 4337) |############################################  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  96% (4188 of 4337) |############################################  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  97% (4225 of 4337) |############################################  | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  98% (4263 of 4337) |############################################# | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter:  99% (4300 of 4337) |############################################# | Elapsed Time: 0:00:00 ETA:  0:00:00\r",
      "MassFilter: 100% (4337 of 4337) |##############################################| Elapsed Time: 0:00:00 Time: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "mf = skchem.filters.MassFilter(above=100, below=900)\n",
    "\n",
    "ms, y = mf.filter(ms, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AtomNumberFilter: 100% (4068 of 4068) |########################################| Elapsed Time: 0:00:00 Time: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "nf = skchem.filters.AtomNumberFilter(above=5, below=100, include_hydrogens=True)\n",
    "\n",
    "ms, y = nf.filter(ms, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize Geometry\n",
    "\n",
    "We would like to calculate some features that require three dimensional coordinates, so we will next calculate three dimensional conformers using the Universal Force Field.  Additionally, some compounds may be unfeasible - these should be dropped from the dataset. In order to do this, we will use the `transform_filter` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 109883-99-0\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 135768-83-1\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 13366-73-9\n",
      "  warnings.warn(msg)\n",
      "UFF: 100% (4046 of 4046) |#####################################################| Elapsed Time: 0:01:47 Time: 0:01:47\n"
     ]
    }
   ],
   "source": [
    "uff = skchem.forcefields.UFF()\n",
    "\n",
    "ms, y = uff.transform_filter(ms, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4043"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we get a warning that 3 molecules failed to embed, have been dropped.  If we didn't care about the warnings, we could have set the `warn_on_fail` property to `False` (or set it using a keyword argument at initialization).  Conversely, if we *really* cared about failures, we could have set `error_on_fail` to True, which would raise an Error if any `Mol`s failed to embed."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. tip::\n",
    "\n",
    "    ``TransformFilter`` s implement the ``transform_filter`` method.  This is a combination of ``transform`` and ``filter`` , which converts ``Mol`` s into *something else* **and** drops instances that fail the predicate.  The ``ChemAxonStandardizer`` object is also a ``TransformFilter``, as it can drop ``Mol`` s that fail to standardize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Chemical Space\n",
    "\n",
    "**scikit-chem** adds a custom `mol` accessor to `pandas.Series`, which provides a shorthand for calling methods on all `Mol`s in the collection.  This is analogous to the `str` accessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutagen</th>\n",
       "      <th>nonmutagen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728-95-6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91-08-7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89786-04-9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439-35-2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-94-3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89930-60-9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9002-92-0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90597-22-1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924-43-6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97534-21-9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4043 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mutagen  nonmutagen\n",
       "name                           \n",
       "1728-95-6         1           0\n",
       "91-08-7           1           0\n",
       "89786-04-9        0           1\n",
       "2439-35-2         0           1\n",
       "95-94-3           0           1\n",
       "...             ...         ...\n",
       "89930-60-9        1           0\n",
       "9002-92-0         0           1\n",
       "90597-22-1        0           1\n",
       "924-43-6          1           0\n",
       "97534-21-9        0           1\n",
       "\n",
       "[4043 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_raw.str.get_dummies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this function to binarize the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = y.str.get_dummies()['mutagen']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amongst other options, it is provides access to chemical space plotting functionality.  This will featurize the molecules using a passed featurizer (or a string shortcut), and a dimensionality reduction technique to reduce the feature space to two dimensions, which are then plotted.  In this example, we use circular Morgan fingerprints, reduced by [t-SNE](https://lvdmaaten.github.io/tsne) to visualize structural diversity in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcsAAAFYCAYAAAA88NeqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuWCyJCIicvF/U4gw02pJHvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126d0c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms.mol.visualize(fper='morgan',\n",
    "                 dim_red='tsne', dim_red_kw={'method': 'exact'}, \n",
    "                 c=y, \n",
    "                 cmap='copper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data appears to be reasonably separable in structural space, so we may suspect that Morgan fingerprints will be a good representation for modelling the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Featurizing the data\n",
    "\n",
    "As previously noted, Morgan fingerprints would be a good fit for this data.  To calculate them, we will use the `MorganFeaturizer` class, which is a `Transformer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MorganFeaturizer: 100% (4043 of 4043) |########################################| Elapsed Time: 0:00:01 Time: 0:00:01\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>morgan_fp_idx</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>...</th>\n",
       "      <th>2043</th>\n",
       "      <th>2044</th>\n",
       "      <th>2045</th>\n",
       "      <th>2046</th>\n",
       "      <th>2047</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728-95-6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91-08-7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89786-04-9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439-35-2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-94-3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89930-60-9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9002-92-0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90597-22-1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924-43-6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97534-21-9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4043 rows × 2048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "morgan_fp_idx  0     1     2     3     4     ...   2043  2044  2045  2046  \\\n",
       "batch                                        ...                            \n",
       "1728-95-6         0     0     0     0     0  ...      0     0     0     0   \n",
       "91-08-7           0     0     0     0     0  ...      0     0     0     0   \n",
       "89786-04-9        0     0     0     0     0  ...      0     0     0     0   \n",
       "2439-35-2         0     0     0     0     0  ...      0     0     0     0   \n",
       "95-94-3           0     0     0     0     0  ...      0     0     0     0   \n",
       "...             ...   ...   ...   ...   ...  ...    ...   ...   ...   ...   \n",
       "89930-60-9        0     0     0     0     0  ...      0     0     0     0   \n",
       "9002-92-0         0     0     0     0     0  ...      1     0     1     0   \n",
       "90597-22-1        0     0     0     0     0  ...      0     0     0     0   \n",
       "924-43-6          0     0     0     0     0  ...      0     0     0     0   \n",
       "97534-21-9        0     0     0     0     0  ...      0     0     0     0   \n",
       "\n",
       "morgan_fp_idx  2047  \n",
       "batch                \n",
       "1728-95-6         0  \n",
       "91-08-7           0  \n",
       "89786-04-9        0  \n",
       "2439-35-2         0  \n",
       "95-94-3           0  \n",
       "...             ...  \n",
       "89930-60-9        0  \n",
       "9002-92-0         0  \n",
       "90597-22-1        0  \n",
       "924-43-6          0  \n",
       "97534-21-9        0  \n",
       "\n",
       "[4043 rows x 2048 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mf = skchem.descriptors.MorganFeaturizer()\n",
    "\n",
    "X, y = mf.transform(ms, y); X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipelining\n",
    "\n",
    "If this process appeared unnecessarily laborious (as it should!), **scikit-chem** provides a `Pipeline` class that will sequentially apply objects passed to it. For this example, we could have simply performed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ChemAxonStandardizer: 100% (4337 of 4337) |####################################| Elapsed Time: 0:00:22 Time: 0:00:22\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 37364-66-2\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 109883-99-0\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 135768-83-1\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 57817-89-7\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 58071-32-2\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 13366-73-9\n",
      "  warnings.warn(msg)\n",
      "/Users/rich/projects/scikit-chem/skchem/forcefields/base.py:54: UserWarning: Failed to Embed Molecule 89213-87-6\n",
      "  warnings.warn(msg)\n",
      "UFF: 100% (4337 of 4337) |#####################################################| Elapsed Time: 0:01:49 Time: 0:01:49\n",
      "OrganicFilter: 100% (4330 of 4330) |###########################################| Elapsed Time: 0:00:05 Time: 0:00:05\n",
      "MassFilter: 100% (4330 of 4330) |##############################################| Elapsed Time: 0:00:00 Time: 0:00:00\n",
      "AtomNumberFilter: 100% (4070 of 4070) |########################################| Elapsed Time: 0:00:00 Time: 0:00:00\n",
      "MorganFeaturizer: 100% (4047 of 4047) |########################################| Elapsed Time: 0:00:00 Time: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "pipeline = skchem.pipeline.Pipeline([\n",
    "        skchem.standardizers.ChemAxonStandardizer(keep_failed=True),\n",
    "        skchem.forcefields.UFF(),\n",
    "        skchem.filters.OrganicFilter(),\n",
    "        skchem.filters.MassFilter(above=100, below=1000),\n",
    "        skchem.filters.AtomNumberFilter(above=5, below=100),\n",
    "        skchem.descriptors.MorganFeaturizer()\n",
    "])\n",
    "\n",
    "X, y = pipeline.transform_filter(ms_raw, y_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will try building some basic [scikit-learn](http://scikit-learn.org) models on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partitioning the data\n",
    "\n",
    "To decide on the best model to use, we should perform some model selection.  This will require comparing the relative performance of a selection of candidate molecules each trained on the same **train** set, and evaluated on a **validation** set.\n",
    "\n",
    "In *cheminformatics*, partitioning datasets usually requires some thought, as chemical datasets usually vastly overrepresent certain *scaffolds*, and underrepresent others.  In order to get as unbiased an estimate of performance as possible, one can either downsample compounds in a region of high density, or artifically favor splits that pool in the same split molecules that are too close in chemical space.\n",
    "\n",
    "**scikit-chem** provides this functionality in the `SimThresholdSplit` class, which applies single link heirachical clustering to produce a large number of clusters consisting of highly similar compounds.  These clusters are then randomly assigned to the desired splits, such that no split contains compounds that are more similar to compounds in any other split than the clustering threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cv = skchem.cross_validation.SimThresholdSplit(fper=None, n_jobs=4).fit(X)\n",
    "train, valid, test = cv.split((60, 20, 20))\n",
    "X_train, X_valid, X_test = X[train], X[valid], X[test]\n",
    "y_train, y_valid, y_test = y[train], y[valid], y[test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. todo:: \n",
    "    \n",
    "    Improve the modelling section:\n",
    "    \n",
    "    - More features\n",
    "    - More models\n",
    "    - Grid Searches over skchem cross validation indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.ensemble\n",
    "import sklearn.linear_model\n",
    "import sklearn.naive_bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rf = sklearn.ensemble.RandomForestClassifier(n_estimators=100)\n",
    "nb = sklearn.naive_bayes.BernoulliNB()\n",
    "lr = sklearn.linear_model.LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2428, 2048), (2428,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.843016069221 0.812113720643 0.796044499382\n"
     ]
    }
   ],
   "source": [
    "rf_score = rf.fit(X_train, y_train).score(X_valid, y_valid)\n",
    "nb_score = nb.fit(X_train, y_train).score(X_valid, y_valid)\n",
    "lr_score = lr.fit(X_train, y_train).score(X_valid, y_valid)\n",
    "\n",
    "print(rf_score, nb_score, lr_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Random Forests appear to work best (although we should have chosen hyperparameters using Random or Grid search)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing the Final performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83580246913580247"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train.append(X_valid), y_train.append(y_valid)).score(X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}