{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utility function for visualizing confusion matrices via matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `from mlxtend.plotting import plot_confusion_matrix`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on confusion matrices, please see [`mlxtend.evaluate.confusion_matrix`](../evaluate/confusion_matrix.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Binary and Multi-Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SxqXrSNLZkmZLWijpZUk/LXqdvSTNTOc/TnLPrta+Vxen22iU9Jak8yXVNbPc8Wn9jen7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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f75550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "binary = np.array([[4, 1],\n",
    "                   [1, 2]])\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e19c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiclass = np.array([[2, 1, 0, 0],\n",
    "                       [1, 2, 0, 0],\n",
    "                       [0, 0, 1, 0],\n",
    "                       [0, 0, 0, 1]])\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=multiclass)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_confusion_matrix\n",
      "\n",
      "*plot_confusion_matrix(conf_mat, hide_spines=False, hide_ticks=False, figsize=(2.5, 2.5), cmap=None, alpha=0.3)*\n",
      "\n",
      "Plot a confusion matrix via matplotlib.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `conf_mat` : array-like, shape = [n_classes, n_classes]\n",
      "\n",
      "    Confusion matrix from evaluate.confusion matrix.\n",
      "\n",
      "- `hide_spines` : bool (default: False)\n",
      "\n",
      "    Hides axis spines if True.\n",
      "\n",
      "- `hide_ticks` : bool (default: False)\n",
      "\n",
      "    Hides axis ticks if True\n",
      "\n",
      "- `figsize` : tuple (default: (2.5, 2.5))\n",
      "\n",
      "    Height and width of the figure\n",
      "\n",
      "- `cmap` : matplotlib colormap (default: `None`)\n",
      "\n",
      "    Uses matplotlib.pyplot.cm.Blues if `None`\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `fig, ax` : matplotlib.pyplot subplot objects\n",
      "\n",
      "    Figure and axis elements of the subplot.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_confusion_matrix.md', 'r') as f:\n",
    "    s = f.read() \n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}