Killian / decodopr

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  {
   "cells": [
    {
     "cell_type": "code",
     "execution_count": 2,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "import shelve
  ",
      "import numpy
  ",
      "import pandas
  ",
      "import sklearn.manifold
  ",
      "import matplotlib.pyplot as plt"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 3,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "%matplotlib inline"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 4,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"./Sparse_mat.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 5,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "array_test=data[\"ASR\"][\"DEV\"].toarray()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 6,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "(175, 1060)"
        ]
       },
       "execution_count": 6,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "array_test.shape"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 7,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "max_list=numpy.max(array_test,axis=1)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 8,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "a=pandas.DataFrame([numpy.divide(x,numpy.float(max(x))) for x in array_test])"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 9,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
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        ],
        "text/plain": [
         "       0         1         2     3         4     5     6     7         8     \\
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         "0  1.000000  0.700000  0.100000   0.1  0.000000     0     0     0  0.100000   
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         "1  0.500000  0.000000  0.250000   0.0  0.000000     0     0     0  0.000000   
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  ",
         "[5 rows x 1060 columns]"
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       },
       "execution_count": 9,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "a.head()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 16,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "trans=sklearn.manifold.TSNE()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 17,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "tsne=trans.fit_transform(a.values)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 18,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
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        ]
       },
       "execution_count": 18,
       "metadata": {},
       "output_type": "execute_result"
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f0e613b0fd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "plt.scatter([x[0] for x in tsne],[y[1] for y in tsne])"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 19,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "plt.show()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 20,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'title' is not defined",
       "output_type": "error",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-20-7cea0497790c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m
  \u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m",
        "\u001b[1;31mNameError\u001b[0m: name 'title' is not defined"
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f0e612bef50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "plt.figure()
  ",
      "plt.axes()
  ",
      "plt.title(title)
  "
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "for i,pos in enumerate(zip([x[0] for x in tsne],[y[1] for y in tsne])):
  ",
      "    plt.text(pos[0],pos[1],i)
  ",
      "    if i > 30:
  ",
      "        break"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "enumerate(zip([x[0] for x in tsne],[y[1] for y in tsne])).next()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "plt.axes()"
     ]
    }
   ],
   "metadata": {
    "kernelspec": {
     "display_name": "Python 2",
     "language": "python",
     "name": "python2"
    },
    "language_info": {
     "codemirror_mode": {
      "name": "ipython",
      "version": 2
     },
     "file_extension": ".py",
     "mimetype": "text/x-python",
     "name": "python",
     "nbconvert_exporter": "python",
     "pygments_lexer": "ipython2",
     "version": "2.7.10"
    }
   },
   "nbformat": 4,
   "nbformat_minor": 0
  }