Analyze len of train.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# About\n",
      "\n",
      "Compare different train size 0.08, 0.1, 0.13, 0.16, 0.2, 0.3, 0.4, 0.5\n",
      "\n",
      "svr signal / svr bck\n",
      "1) 235446 / 201421 (0.08 size from all tuples)\n",
      "\n",
      "\n",
      "2) 297085 / 201421 (0.1)\n",
      "\n",
      "\n",
      "3) 386588 / 201421 (0.13)\n",
      "\n",
      "\n",
      "4) 547786 / 201421 (0.16)\n",
      "\n",
      "\n",
      "5) 667524 / 201421 (0.2)\n",
      "\n",
      "\n",
      "6) 1445821 / 201421 (0.3)\n",
      "\n",
      "\n",
      "7) 1834195 / 201421 (0.4)\n",
      "\n",
      "\n",
      "8) 2231811 / 201421 (0.5)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "channels_all = [\"mod_11114001.csv\", \"mod_11124001.csv\", \n",
      "                \"mod_11296013.csv\", \"mod_11874042.csv\", \n",
      "                \"mod_12103035.csv\", \"mod_12165106.csv\", \n",
      "                \"mod_12265042.csv\", \"mod_30000000.csv\"]\n",
      "folder = \"Topo/csv_data/\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import io\n",
      "from IPython.nbformat import current\n",
      "\n",
      "def execute_notebook(nbfile):\n",
      "    \n",
      "    with io.open(nbfile) as f:\n",
      "        nb = current.read(f, 'json')\n",
      "    \n",
      "    ip = get_ipython()\n",
      "    \n",
      "    for cell in nb.worksheets[0].cells:\n",
      "        if cell.cell_type != 'code':\n",
      "            continue\n",
      "        ip.run_cell(cell.input)\n",
      "\n",
      "        \n",
      "import sys\n",
      "sys.path.insert(0, '/local/userdata/shelf/notebook/antares/work/REP')\n",
      "import cern_utils\n",
      "from cern_utils import converter_csv\n",
      "\n",
      "execute_notebook('metric_generate.ipynb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# head = True\n",
      "# for channel in channels_all[:-1]:\n",
      "#     t = pandas.read_csv(folder + channel, sep='\\t')\n",
      "#     t.to_csv(folder + 'signal.csv', header=head, index=False, sep='\\t', mode='a')\n",
      "#     head = False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables = [\"sumpt\",\n",
      "             \"m\",\n",
      "             \"mcor\",\n",
      "             \"ipchi2\",\n",
      "             \"vchi2\",\n",
      "             \"sumipchi2\",\n",
      "             \"fdr\",\n",
      "             \"nlt16\",\n",
      "             \"minpt\",\n",
      "             \"ptau\",\n",
      "             \"q\",\n",
      "             \"eta\",\n",
      "             \"nhlt1\",\n",
      "             \"nmu\",\n",
      "             \"n\",\n",
      "             \"fdchi2\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig_data = converter_csv.load_from_csv(folder + 'signal.csv', sep='\\t')\n",
      "bck_data = converter_csv.load_from_csv(folder + channels_all[-1], sep='\\t')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_channel_labels(ds):\n",
      "    channel_label = []\n",
      "\n",
      "    for element in ds['# event_id']:\n",
      "        channel_label.append(int(element.split('_')[0]))\n",
      "    return channel_label"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import LabelEncoder\n",
      "from sklearn import preprocessing\n",
      "le = preprocessing.LabelEncoder()\n",
      "le.fit(map(lambda x: int(x[4:-4]), channels_all))\n",
      "sig_data = sig_data.add_columns({'channel': le.transform(get_channel_labels(sig_data)), \n",
      "                                 'channel_label': get_channel_labels(sig_data)})\n",
      "bck_data = bck_data.add_columns({'channel': le.transform(get_channel_labels(bck_data)),\n",
      "                                 'channel_label': get_channel_labels(bck_data)})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bck_data_train, bck_data_test = bck_data.get_train_test(train_size=0.5, \n",
      "                                                        test_size=0.5, group_columns='# event_id')\n",
      "bck_data_train = bck_data_train.select('presel==1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(sig_data), len(bck_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(11011932, 1562996)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_, sig_data_test = sig_data.get_train_test(test_size=0.5, group_columns='# event_id')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Training of Matrixet "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn\n",
      "from cern_utils import sklearn_classifier, ef_classifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mns = []\n",
      "for num, size in enumerate([0.08, 0.1, 0.13, 0.16, 0.2, 0.3, 0.4, 0.5]):\n",
      "    sig_data_train, _ = sig_data.get_train_test(train_size=size, \n",
      "                                                            test_size=0.5, group_columns='# event_id')\n",
      "    sig_data_train = sig_data_train.select('presel==1')\n",
      "    \n",
      "    print len(sig_data_train), len(bck_data_train)\n",
      "    \n",
      "    mns.append(ef_classifier.ClassifierEF(directory='train_anal/mn_new_%d' % num, formula_name='topo_mn_train_new', \n",
      "                                         dataset_name='topo_mn_train_new_%d' %num, url_base='test02cern.vs.os.yandex.net'))\n",
      "    mns[-1].set_params(features=variables, iterations=5000, sync=False)\n",
      "    mns[-1].fit(sig_data_train, bck_data_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "235446 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "201421\n",
        "297085"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "386588"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "547786"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "667524"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "1445821"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "1834195"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n",
        "2231811"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 201421\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Training of Ada"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import AdaBoostClassifier\n",
      "from sklearn.tree import DecisionTreeClassifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adas = []\n",
      "signal_size = {}\n",
      "for num, size in enumerate([0.08, 0.1, 0.13, 0.16, 0.2, 0.3, 0.4]):\n",
      "    sig_data_train, _ = sig_data.get_train_test(train_size=size, \n",
      "                                                            test_size=0.5, group_columns='# event_id')\n",
      "    sig_data_train = sig_data_train.select('presel==1')\n",
      "\n",
      "    print len(sig_data_train), len(bck_data_train)    \n",
      "    signal_size[size] = len(sig_data_train)\n",
      "    \n",
      "    ada_base = AdaBoostClassifier(n_estimators=400, learning_rate=0.01, random_state=13,\n",
      "                                  base_estimator=DecisionTreeClassifier(max_depth=6, min_samples_leaf=30, max_features=6,\n",
      "                                                                        random_state=13))\n",
      "    adas.append(sklearn_classifier.ClassifierSklearn(base_classifier=ada_base, directory='train_anal/ada_new_%d' % num))\n",
      "    adas[-1].set_params(features=variables)\n",
      "    adas[-1].fit(sig_data_train, bck_data_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "235446 201421\n",
        "297085"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "signal_labels = [1] * len(sig_data_test) + [0] * len(bck_data_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_all = sig_data_test.union([sig_data_test, bck_data_test])\n",
      "eff_to_plot = metric_generate(test_all.get_data(), metric_plot=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "182\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mns_prob = []\n",
      "for num, mn in enumerate(mns):\n",
      "    mns_prob.append(mn.predict(test_all))\n",
      "    print num"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "7"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adas_prob = []\n",
      "for num, ada in enumerate(adas):\n",
      "    adas_prob.append(ada.predict(test_all))\n",
      "    print num"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_plot_res = {}\n",
      "color = ['b', 'g', 'cyan', 'pink', 'orange', 'black', 'yellow', 'brown']\n",
      "for num, prob_mn in enumerate(mns_prob):\n",
      "    to_plot_res['mn_%d' % num] = {'data': eff_to_plot(signal_labels, prob_mn), \n",
      "                                  'color': color[num], 'move': 3 + num, 'alpha': 1,\n",
      "                                  'p_label': 'p_empty', 'total': 'total_empty'}\n",
      "    \n",
      "to_plot_res['BDT'] = {'data': eff_to_plot(signal_labels, numpy.zeros(len(signal_labels))), 'color': 'r', 'move': 3 + num + 1,\n",
      "                      'p_label': 'bdt_passed', 'total': 'total_empty'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "182\n",
        "0.941347133004\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.951637003699\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.963061963978\n",
        "182\n",
        "182"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.971630815463\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.977039314275\n",
        "182\n",
        "182"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.988971601971\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.991376084575\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.992837444618\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.0\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_plot_res['BDT']['move'] = 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_plot_res_ada = {}\n",
      "color = ['b', 'g', 'cyan', 'pink', 'orange', 'black', 'yellow', 'brown']\n",
      "for num, prob_ada in enumerate(adas_prob):\n",
      "    to_plot_res_ada['ada_%d' % num] = {'data': eff_to_plot(signal_labels, prob_ada),\n",
      "                                      'color': color[num], 'move': 3 + num, 'alpha': 1,\n",
      "                                      'p_label': 'p_empty', 'total': 'total_empty'}\n",
      "    \n",
      "to_plot_res_ada['BDT'] = to_plot_res['BDT']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "182\n",
        "0.647658413188\n",
        "182\n",
        "182"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "0.660595026126\n",
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      {
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       "stream": "stdout",
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        "0.674815047967\n",
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        "0.70296480302\n",
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "0.738452141806\n",
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "0.750954282914\n",
        "182\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplots(figsize=(15,7))\n",
      "plot_eff(to_plot_res)\n",
      "plt.xlabel('Modes')\n",
      "plt.ylabel('Inefficiency')\n",
      "plt.title('Classifier Inefficiency')\n",
      "plt.legend(loc='best')\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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6dN5rNzc38+IXvxiAlpYWbr/9du6///6pKl2SJEmSpLJhcDFNopyTIpPJcO7c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\nJGnmOQd05Wj7YZ42YOBjJ/MGG+MGH/E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       "text": [
        "<matplotlib.figure.Figure at 0x2c237cd0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplots(figsize=(15,7))\n",
      "plot_eff(to_plot_res_ada)\n",
      "plt.xlabel('Modes')\n",
      "plt.ylabel('Inefficiency')\n",
      "plt.title('Classifier Inefficiency')\n",
      "plt.legend(loc='best')\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FixYxffp0hgwZEsr1SJIkSZKUawU3nGSwiMfjvPDCC8RiMTZt2sSdd97Jrbfe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\n+ez+VddrPpPkcJKb+tb1vUn+V1Wmj6bcwt0xbD/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       "text": [
        "<matplotlib.figure.Figure at 0x2c237850>"
       ]
      }
     ],
     "prompt_number": 45
    }
   ],
   "metadata": {}
  }
 ]
}