sergeyk/gist:6106248

## gistfile1.txt
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A single prediction run cross-validates mixed $L_1$, $L_2$ regularization.\n",
      "\n",
      "    Caching data\n",
      "    Caching data\n",
      "    Caching data\n",
      "    Running VW training for 18 param settings, 5 at a time\n",
      "       l1_weight l2_weight      loss  num_passes train_score val_score\n",
      "    0    0.0e+00   0.0e+00  logistic           2       0.628     0.576\n",
      "    1    0.0e+00   0.0e+00  logistic          20       0.736     0.624\n",
      "    2    0.0e+00   0.0e+00  logistic          40       0.763     0.632\n",
      "    3    0.0e+00   1.0e-06  logistic           2       0.626     0.575\n",
      "    4    0.0e+00   1.0e-06  logistic          20       0.730     0.623\n",
      "    5    0.0e+00   1.0e-06  logistic          40       0.756     0.631\n",
      "    6    1.0e-04   0.0e+00  logistic           2       0.665     0.603\n",
      "    7    1.0e-04   0.0e+00  logistic          20       0.632     0.578\n",
      "    8    1.0e-04   0.0e+00  logistic          40       0.617     0.570\n",
      "    9    1.0e-04   1.0e-06  logistic           2       0.665     0.602\n",
      "    10   1.0e-04   1.0e-06  logistic          20       0.631     0.578\n",
      "    11   1.0e-04   1.0e-06  logistic          40       0.616     0.569\n",
      "    12   1.0e-06   0.0e+00  logistic           2       0.626     0.575\n",
      "    13   1.0e-06   0.0e+00  logistic          20       0.732     0.624\n",
      "    14   1.0e-06   0.0e+00  logistic          40       0.759     0.633\n",
      "    15   1.0e-06   1.0e-06  logistic           2       0.624     0.575\n",
      "    16   1.0e-06   1.0e-06  logistic          20       0.726     0.622\n",
      "    17   1.0e-06   1.0e-06  logistic          40       0.752     0.633\n",
      "    Best setting: {'l2_weight': 0.0, 'l1_weight': 9.9999999999999995e-07, 'num_passes': 40, 'loss': 'logistic'}\n",
      "    Best score: 0.633\n",
      "    Running VW prediction\n",
      "    Final score: 0.626"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Results\n",
      "\n",
      "All prediction results are stored in one database.\n",
      "Here, we plot classification and regression results vs. num of training images for different $\\delta$ settings, for different feature channels and combinations of channels."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import aphrodite\n",
      "import pandas\n",
      "import numpy as np\n",
      "\n",
      "# Load all prediction runs into one DataFrame.\n",
      "collection = aphrodite.util.get_mongodb_client().ava.predict_vw\n",
      "df = pandas.DataFrame(list(collection.find()))\n",
      "# Make the features list hashable for filtering/joins.\n",
      "df['features'] = df['features'].apply(lambda x: frozenset(x))\n",
      "print df[['num_train', 'num_test', 'data', 'task', 'delta', 'features', 'score_test', 'score_val']].to_string()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    num_train  num_test    data  task  delta                    features  score_test  score_val\n",
        "0      200000     50000  rating   clf    2.0            (gist_256, size)    0.543680   0.549990\n",
        "1      200000     50000  rating   clf    2.0                  (gist_256)    0.544303   0.549829\n",
        "2      200000     50000  rating   clf    0.0                      (size)    0.540347   0.544357\n",
        "3      200000     50000  rating   clf    0.0                  (gist_256)    0.524004   0.528827\n",
        "4      200000     50000  rating   clf    0.0            (gist_256, size)    0.523702   0.529230\n",
        "5       25000     50000  rating   clf    0.0                      (size)    0.545086   0.550895\n",
        "6       25000     50000  rating   clf    0.5                      (size)    0.544372   0.550050\n",
        "7       25000     50000  rating   clf    1.0                      (size)    0.544704   0.548904\n",
        "8       50000     50000  rating   clf    0.0                      (size)    0.539926   0.543191\n",
        "9       50000     50000  rating   clf    0.5                      (size)    0.544171   0.549547\n",
        "10      50000     50000  rating   clf    1.0                      (size)    0.541030   0.544257\n",
        "11      75000     50000  rating   clf    1.0                      (size)    0.544002   0.549909\n",
        "12      75000     50000  rating   clf    0.0                      (size)    0.539424   0.542647\n",
        "13     100000     50000  rating   clf    1.0                      (size)    0.544002   0.549909\n",
        "14     100000     50000  rating   clf    0.0                      (size)    0.539223   0.542185\n",
        "15     125000     50000  rating   clf    0.0                      (size)    0.539584   0.543673\n",
        "16     175000     50000  rating   clf    1.0                      (size)    0.544002   0.549909\n",
        "17     150000     50000  rating   clf    1.0                      (size)    0.544002   0.549909\n",
        "18     175000     50000  rating   clf    0.0                      (size)    0.540347   0.544357\n",
        "19     125000     50000  rating   clf    1.0                      (size)    0.544002   0.549909\n",
        "20     150000     50000  rating   clf    0.0                      (size)    0.540689   0.544558\n",
        "21       5000     50000  rating   clf    0.0            (gist_256, size)    0.520952   0.525790\n",
        "22      15000     50000  rating   clf    0.0            (gist_256, size)    0.521514   0.521444\n",
        "23       5000     50000  rating   clf    0.0                  (gist_256)    0.519084   0.525226\n",
        "24       5000     50000  rating   clf    2.0                  (gist_256)    0.541231   0.546449\n",
        "25      25000     50000  rating   clf    0.0            (gist_256, size)    0.517679   0.519352\n",
        "26     100000     50000  rating   clf    2.0                  (gist_256)    0.544303   0.549829\n",
        "27      25000     50000  rating   clf    2.0                  (gist_256)    0.544222   0.548200\n",
        "28     150000     50000  rating   clf    2.0                  (gist_256)    0.544303   0.549829\n",
        "29      50000     50000  rating   clf    2.0                  (gist_256)    0.544303   0.549829\n",
        "30      50000     50000  rating   clf    0.0                  (gist_256)    0.516012   0.517622\n",
        "31       5000     50000  rating   clf    2.0            (gist_256, size)    0.540147   0.546550\n",
        "32     150000     50000  rating   clf    0.0                  (gist_256)    0.524204   0.528928\n",
        "33      25000     50000  rating   clf    2.0            (gist_256, size)    0.543158   0.548461\n",
        "34      25000     50000  rating   clf    0.0                  (gist_256)    0.516816   0.518950\n",
        "35      50000     50000  rating   clf    0.0            (gist_256, size)    0.517097   0.519453\n",
        "36     100000     50000  rating   clf    2.0            (gist_256, size)    0.543680   0.549990\n",
        "37     100000     50000  rating   clf    0.0            (gist_256, size)    0.520289   0.523557\n",
        "38      50000     50000  rating   clf    2.0            (gist_256, size)    0.543680   0.549990\n",
        "39     150000     50000  rating   clf    2.0            (gist_256, size)    0.543680   0.549990\n",
        "40     150000     50000  rating   clf    0.0            (gist_256, size)    0.523361   0.528646\n",
        "41     100000     50000  rating   clf    0.0                  (gist_256)    0.519366   0.521766\n",
        "42       5000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.536214   0.537188\n",
        "43       5000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.543102   0.543726\n",
        "44      25000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.545009   0.545617\n",
        "45      50000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.543563   0.543988\n",
        "46      25000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.542419   0.542358\n",
        "47     100000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.544286   0.544169\n",
        "48     100000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.545873   0.545396\n",
        "49     200000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.545873   0.545396\n",
        "50     150000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.544989   0.544893\n",
        "51     200000     50000  rating   clf    0.0  (color_K150_s1.00_3x3_1x1)    0.544326   0.544652\n",
        "52      50000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.543985   0.546020\n",
        "53     150000     50000  rating   clf    1.0  (color_K150_s1.00_3x3_1x1)    0.545873   0.545396\n",
        "54       5000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)    0.011500   0.009956\n",
        "55       5000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.003588  -0.004786\n",
        "56      25000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.011013  -0.015206\n",
        "57      25000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)   -0.003275  -0.006252\n",
        "58      50000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)   -0.016124  -0.018660\n",
        "59     150000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.096495  -0.099558\n",
        "60     200000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.096495  -0.099558\n",
        "61     100000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.096495  -0.099558\n",
        "62     150000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)   -0.108299  -0.110322\n",
        "63     200000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)   -0.110477  -0.112824\n",
        "64      50000     50000  rating  regr    1.0  (color_K150_s1.00_3x3_1x1)   -0.059257  -0.062712\n",
        "65     100000     50000  rating  regr    0.0  (color_K150_s1.00_3x3_1x1)   -0.076324  -0.078060\n",
        "66       5000     50000  rating   clf    1.0            (dsift_llc_1000)    0.610746   0.610324\n",
        "67       5000     50000  rating   clf    0.0            (dsift_llc_1000)    0.592233   0.598797\n",
        "68      25000     50000  rating   clf    1.0            (dsift_llc_1000)    0.627372   0.634626\n",
        "69      25000     50000  rating   clf    0.0            (dsift_llc_1000)    0.604120   0.611089\n",
        "70      50000     50000  rating   clf    0.0            (dsift_llc_1000)    0.617131   0.623079\n",
        "71      50000     50000  rating   clf    1.0            (dsift_llc_1000)    0.625846   0.633158\n",
        "72     200000     50000  rating   clf    1.0            (dsift_llc_1000)    0.625665   0.630603\n",
        "73     150000     50000  rating   clf    1.0            (dsift_llc_1000)    0.625665   0.630603\n",
        "74     100000     50000  rating   clf    1.0            (dsift_llc_1000)    0.625665   0.630603\n",
        "75     100000     50000  rating   clf    0.0            (dsift_llc_1000)    0.603136   0.607045\n",
        "76     150000     50000  rating   clf    0.0            (dsift_llc_1000)    0.602494   0.606200\n",
        "77     200000     50000  rating   clf    0.0            (dsift_llc_1000)    0.602855   0.606240\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_results_for_exeriment(df, features, task='clf'):\n",
      "    name = {'clf': 'Classification', 'regr': 'Regression'}\n",
      "    \n",
      "    # Filter on experimental settings and take only the necessary columns.\n",
      "    ix = (df['features'] == set(features)) & (df['task'] == task)\n",
      "    df2 = df[ix][['actual_num_train', 'delta', 'score_test']]\n",
      "    df2['score_test'] = df2['score_test'].abs()\n",
      "\n",
      "    # Pivot table.\n",
      "    df2 = df2.drop_duplicates()\n",
      "    df_pivoted = df2.pivot(index='actual_num_train', columns='delta', values='score_test')\n",
      "    #df_pivoted = df_pivoted.fillna(method='pad')\n",
      "    \n",
      "    # Plot.\n",
      "    fig = plt.figure(figsize=(10, 8), dpi=300)\n",
      "    ax = df_pivoted.plot(fig=fig, marker='s', style='--', linewidth=2)\n",
      "    plt.gcf().autofmt_xdate()\n",
      "    if task == 'clf':\n",
      "        ax.set_ylim([.5, .64])\n",
      "    ax.set_title('{} with {}'.format(name[task], features))\n",
      "    ax.set_xlabel('Number of training images (test always 50K)')\n",
      "    ax.set_ylabel('Score')\n",
      "    \n",
      "    return fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plot_results_for_exeriment(df, ['color_K150_s1.00_3x3_1x1'], 'clf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1111fa750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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beHt7Y+/evXBycuLKFBQUoF+/fjh+/DjEYjHy8vJgZWXVqHkB4ZxNRAjRDJp6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\n7i2Nwi4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1111a89d0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plot_results_for_exeriment(df, ['color_K150_s1.00_3x3_1x1'], 'regr')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x111732210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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168O5c2BmIj9nlbAvQV5NpJfnzOjrWTVFHbjj4uLo27evdrhZs2b8+eef3L9/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\n2hzbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109439710>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plot_results_for_exeriment(df, ['dsift_llc_1000'], 'clf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x11172f210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9fvy42ssSiefIoFynTkT+/kT79hEVFf3z+oQJ88nfX/FnwoT52orKXnO6cjRa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\nIkE6A4lEIpEgnYFEIpFIgP8ApEvL+Fw+4IgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111718890>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plot_results_for_exeriment(df, ['gist_256'], 'clf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1111f2650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BeHh4ICgoCACQnp6O0NDQWst7e3sjKysL2dnZqKioQGxsrFz5Bw8ecK1Tamoq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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111726910>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plot_results_for_exeriment(df, ['gist_256', 'size'], 'clf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1117b4450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MCCHkvVXrnIFEIsHUqVMRGRmJ5ORk7Nu3Dzdv3pQr5+Pjg6SkJCQlJXEdQWpq\nKrZu3YrExET8/fffkEgk2L9/P98RCSGEvIP3ziAhIQH29vawtbWFrq4uAgMDcfjwYblyinonY2Nj\n6OrqIj8/HyUlJcjPz4eVlRXfEauNUMYRKSe/KCd/hJAREE5OZXT4rjA9PR02NjbcsrW1NS5evChT\nRiQSIS4uDq6urrCyssKKFSvg6OgIc3NzfPPNN2jcuDFq164NPz8/dO/eXeFxgoKCYGtrCwAwNTWF\nm5sbfH19Afz7i1H3chlNyVPe8pUrVzQqD72f1bNcRlPyCHn5ypUrGpWnbFksFiM8PBwAuPayPLzP\nGRw6dAiRkZHYunUrAGD37t24ePEi1q9fz5V5/fo1tLW1YWBggJMnT2L69Om4ffs27t69i759++Lc\nuXMwMTHBkCFDMHjwYIwYMUI2NM0ZEELIe/vgOYP8/Hz8888/lTqYlZUV0tLSuOW0tDRYW1vLlDEy\nMoKBgQEAwN/fH8XFxXj58iUuXbqE9u3bw8LCAjo6Ohg4cCDi4uIqdVxCCCFVV2FncOTIEbi7u8PP\nzw8AkJSUhICAgHLLe3p6IiUlBampqSgqKkJERIRc+WfPnnG9U0JCAhhjsLCwQIsWLRAfH4+CggIw\nxhAVFQVHR8cPeX1q9e7puKainPyinPwRQkZAODmVqXDOIDQ0FBcvXkSXLl0AAO7u7rh37175Fero\nICwsDH5+fpBIJBg3bhwcHBywefNmAMDEiRNx8OBBbNy4ETo6OjAwMOCuGHJzc8Po0aPh6ekJLS0t\neHh44IsvvuDjdRJCCFGiwjkDLy8vXLx4Ee7u7khKSgIAuLi44Nq1a9USUBGaMyCEkPf3QXMGrVq1\nwp49e1BSUoKUlBRMmzYN7du35z0kIYQQ9amwMwgLC8ONGzegp6eH4cOHw9jYGGvWrKmObIInlHFE\nyskvyskfIWQEhJNTGaVzBiUlJejduzfOnDmDxYsXV1cmQggh1azCOYNu3brh0KFDMDU1ra5MFaI5\nA0IIeX/K2s4KryaqU6cOnJ2d0aNHD9SpU4ercN26dfymJIQQojYVzhkMHDgQP/74I3x8fODp6YnW\nrVujdevW1ZFN8IQyjkg5+UU5+SOEjIBwcipT4ZlBUFAQ3rx5g9u3bwMAWrZsCV1dXZUHI4QQUn0q\nnDMQi8UYM2YMPvnkEwDAw4cPsWPHDvj4+FRLQEVozoAQQt6fsrazws7Aw8MD+/btQ4sWLQAAt2/f\nRmBgIBKjZXIKAAAgAElEQVQTE/lPWknUGRBCyPv7oJvOSkpKuI4AAJo3b46SkhL+0n3EhDKOSDn5\nRTn5I4SMgHByKlPhnEHr1q0xfvx4jBw5Eowx7NmzB56entWRjRBCSDWpcJiosLAQP//8M86fPw8A\n6NSpEyZPniz3XOLqRMNEhBDy/j5oziAvLw/6+vrQ1tYGIH3G8Zs3b7jnEagDdQaEEPL+PmjOoGvX\nrigoKOCW8/Pzy30UJZEllHFEyskvyskfIWQEhJNTmQo7gzdv3sDQ0JBbNjIyQn5+vkpDEUIIqV4V\nDhN16NAB69at4+46vnTpEqZNm4YLFy5US0BFaJiIEELe3wd9N9GaNWswdOhQNGzYEADw9OlT7slk\nhBBCPg7lDhMlJCTgyZMnaNOmDW7evInAwEDUqlULfn5+aNKkSXVmFCyhjCNSTn5RTv4IISMgnJzK\nlNsZTJw4kbt8ND4+HosWLcKUKVNgZmZGzyUmhJCPTLlzBq6urrh69SoAYMqUKahXrx5CQ0PltikS\nGRmJ4OBgSCQSjB8/HiEhITLbxWIx+vXrx51hDBo0CHPnzgUAZGVlYfz48bhx4wZEIhG2b98Ob29v\n2dA0Z0AIIe+tSnMGEokExcXF0NXVRVRUFLZs2cJtU/Z1FBKJBFOnTkVUVBSsrKzQpk0bBAQEwMHB\nQaacj48Pjhw5Irf/9OnT0atXLxw8eBAlJSXIy8ur8AUSQgj5MOUOEw0fPhw+Pj4ICAiAgYEBOnXq\nBABISUlR+tSzhIQE2Nvbw9bWFrq6uggMDMThw4flyinqnbKzs3Hu3Dl8/vnnAAAdHR2YmJi894vS\nFEIZR6Sc/KKc/BFCRkA4OZUptzOYM2cOVq5cibFjxyI2NhZaWtKijDGsX7++3ArT09NhY2PDLVtb\nWyM9PV2mjEgkQlxcHFxdXdGrVy8kJycDAO7fv4969eph7Nix8PDwwIQJE+ieBkIIqQZKLy1t166d\n3LrmzZsrrVAkElV4UA8PD6SlpcHAwAAnT55E//79cfv2bZSUlCAxMRFhYWFo06YNgoODsXTpUixY\nsECujqCgINja2gIATE1N4ebmBl9fXwD/9tK0XLnlsnWakkfoy2XrNCWPkJd9fX01Ko+y5TKakqfs\nvQsPDwcArr0sT4U3nb2v+Ph4hIaGIjIyEgCwZMkSaGlpyU0iv83Ozg6XL19GUVER2rVrh/v37wMA\nYmNjsXTpUhw7dkw2NE0gE0LIe/ug7yZ6X56enkhJSUFqaiqKiooQERGBgIAAmTLPnj3jAiUkJIAx\nBnNzc1haWsLGxoZ7xGZUVBRatWrFd8Rq8+4nBk1FOflFOfkjhIyAcHIqU+EdyO9doY4OwsLC4Ofn\nB4lEgnHjxsHBwQGbN28GIL1/4eDBg9i4cSN0dHRgYGAgc0fz+vXrMWLECBQVFaFp06b49ddf+Y5I\nCCHkHbwPE1UHGiYihJD3V63DRIQQQoSHOgMVEso4IuXkF+XkjxAyAsLJqQx1BoQQQmjOgBBCagqa\nMyCEEKIUdQYqJJRxRMrJL8rJHyFkBISTUxnqDAghhNCcASGE1BQ0Z0AIIUQp6gxUSCjjiJSTX5ST\nP0LICAgnpzLUGRBCCKE5A0IIqSlozoAQQohS1BmokFDGESknvygnf4SQERBOTmWoMyCEEEJzBoQQ\nUlMoazt5f9IZeT9BwUFIzUqVW29raovwNeHVnocQUjPRMJEKVTSOGBQchD/i/0CMXYzcj6IOQlWE\nMt5JOfklhJxCyAgIJ6cy1BmoUWpWKrJrZ6s7BiGE0JyBOvkG+SLmQQzgK7/N574PxOHi6o5ECPmI\nVft9BpGRkWjZsiWaNWuGZcuWyW0Xi8UwMTGBu7s73N3dsXDhQpntEokE7u7u6Nu3ryriEUIIeQfv\nnYFEIsHUqVMRGRmJ5ORk7Nu3Dzdv3pQr5+Pjg6SkJCQlJWHu3Lky29auXQtHR0eIRKIqZQgKDoJv\nkK/cT1BwUJXqqyqhjCNSTn5RTv4IISMgnJzK8H41UUJCAuzt7WFrawsACAwMxOHDh+Hg4CBTrrxT\nlUePHuHEiROYM2cOVq1aVaUMqVmpiLGLkd9wv0rVqZ5YdtGkwAS23rbqSEIIqaF47wzS09NhY2PD\nLVtbW+PixYsyZUQiEeLi4uDq6gorKyusWLECjo6OAIAZM2Zg+fLlyMnJ4TtatfP19VW63dbUttz1\n1XlZaUU5NQXl5JcQcgohIyCcnMrw3hlUZmjHw8MDaWlpMDAwwMmTJ9G/f3/cvn0bx44dQ/369eHu\n7l7xZZlBQdzZh6mpKdzc3LhfSNbTLEAEwPZ/hVNl9y2ru6y8upbLGnxF28Visdrz0TIt07Kwl8Vi\nMcLDwwGAay/LxXh24cIF5ufnxy0vXryYLV26VOk+tra2LCMjg82ePZtZW1szW1tbZmlpyQwMDNio\nUaPkylcU22eMD0Mo5H58xvhU6TVV1ZkzZ6r1eFVFOflFOfkjhIyMCSensraT9wlkT09PpKSkIDU1\nFUVFRYiIiEBAQIBMmWfPnnFzBgkJCWCMwcLCAosXL0ZaWhru37+P/fv3o2vXrti5cyffEQkhhLyD\n92EiHR0dhIWFwc/PDxKJBOPGjYODgwM2b94MAJg4cSIOHjyIjRs3QkdHBwYGBti/f7/Cuqp6NZGt\nqa3CyeLyxuhVpey0TdNRTn5RTv4IISMgnJzK0E1nhBBSQ9DDbdSkbCJH01FOflFO/gghIyCcnMpQ\nZ0AIIYSGiQghpKagYSJCCCFKUWegQkIZR6Sc/KKc/BFCRkA4OZWhzoAQQgjNGRBCSE1BcwaEEEKU\nos5AhYQyjkg5+UU5+SOEjIBwcipDnQEhhBCaMyCEkJqC5gwIIYQoRZ2BCgllHJFy8oty8kcIGQHh\n5FSGOgNCCCE0Z0AIITUFzRkQQghRivcnnZF/id96qL0mo5z8opz8EUJGgL+cQUGhSE2VX29rC4SH\nh35weWWoMyCEEA2RmgrExIQq2PLvusxM4PJlIC8PSEgAbt5UXr6yqDNQISF8ogEoJ98oJ3+EkBGQ\nzZmVBSQlSRvrt3/q1weGDZPf9/JlIDhYWubWrYqPlZgIfPopf9nLUGdQSXyejhFC1CcnB7h6Vbah\nzs8HzM2BoUPly1+7BkyfLt+4t24NREYqLt+1q/z6Dh0UdwYFBUBsbOXzW1oC3boBdeoA8fHA8+eV\n31cZlXUGkZGRCA4OhkQiwfjx4xESEiKzXSwWo1+/fmjSpAkAYNCgQZg7dy7S0tIwevRoPH/+HCKR\nCF988QW++uqr9zr2hzbcxcXSP478fOkvSkurcqdv76pp452qRjk/3Nv/N7KyUmFqagtAsz7U5OZK\nG1TpMIgYdna+yMsDTEwUN9Y3bwJffSXbsOflAc7OwKlT8uVv3AA6d5Zf37at4voLCgBFtxFkZPz7\n77d/5/XqAT4+gIGBtMEu+2neXPHrdXYGYmKkZb78Evi//1NcroyTExAVJf23r6+GdwYSiQRTp05F\nVFQUrKys0KZNGwQEBMDBwUGmnI+PD44cOSKzTldXF6tXr4abmxtyc3PRunVr9OjRQ25fZcpruG/d\nCsXgwUDTpsCyZfL7xcYCXboAJSWy6zt1knYIirx4Abx8CVhYVDoeIWoj+39DDMD3f/8OlS9cjvx8\n4Pr1fxvdsh9DQ2DIEPnyKSnAtGmyDXVenrRxjI6WL//PP9JP0e9yd1fcWBcW/ts4vq1ePcX569aV\n1v92Q12nDmBvr7i8g4O0/nfLGxqWX/597kEzMfm3czIwqPx+fFNJZ5CQkAB7e3vY2toCAAIDA3H4\n8GG5Bl3R9a6WlpawtLQEABgaGsLBwQGPHz9+r86gPM+eAYcOAW3aKN6upyftCLS0pL9sAwPpj6Vl\n+b1vcrL0j87NDejeXfrTsaN0P039dPguyskvdecsLJQ2qO8Oa+jrv1vSl/vX9evSoY23y9vZAadP\ny9efkgJ4ecmvb9VKcWfw5o3iT+jlNaZmZtL6pY2uL/d/8X+DCHLs7aX1V7axbtbs/YZljI2lwzLK\n8PU7lzaZoeWs//DyyqikM0hPT4eNjQ23bG1tjYsXL8qUEYlEiIuLg6urK6ysrLBixQo4OjrKlElN\nTUVSUhK8FP3lVUHLlsCCBUCDBoq3e3hI/3B1dQGRSHZbeb9rU1Ppp52kJOnP8uXAhAnAli28RCYf\noeJiID1dOhxS1vDm5gI6OkCvXvLlHz8GvvlGtnxeHtCoEfDnn/Ll79+Xfjh5V/PmQMOGijO9fAmc\nOSO7TltbcVkzM8DTU34YpHFjxeVtbYHjx/8tV7afkZHi8k2aSMfCK8vISDUTqurwvkN1fA7tqaQz\nEL3bkirg4eGBtLQ0GBgY4OTJk+jfvz9u377Nbc/NzcXgwYOxdu1aGCro4oOCgrgzD1NTU7i5uXG9\nc1ZWKmRPgcUApJ3AkCHS8T2x+N/evOx7RXx9faGtLbtctl1aJ2TqA3zh6grMni3G9evAixe+iI4G\nGjYUc6eJvr6+XH3Gxr7Q1weePRNDJALCw8VITQVXd9n4rb5+KmbNClKYTxXLa9askXn/VH28qi6X\nrVP18U6fFiM3F3B390VuLnD2rBiFhYCrqy98feXL//GHGNu2SX+/eXnAzZtroKPjBjs7Xxw7Jl/+\nt9/EGDkSePfv087OF/fuyZePiRFj/3758q9fK85/44YYdnZA/fq+MDQECgrE0NcHPD19If1M9u/7\nKa1TDDu7VPzyi7ShTk6Wlu/RQ3H99+6JsXx55d/PS5fEMDAAfHwqV/7t5Xd/9++7f3UtX7lyBcHB\nwRqTp2xZLBYjPDwcALj2sjwq+TqK+Ph4hIaGIvJ/U+1LliyBlpaW3CTy2+zs7HD58mWYm5ujuLgY\nffr0gb+/P/cGy4Su4OsofH1DFc4Z+PiEQiyWX18ZVZmUfncisWdP6elsw4bS087/+79Q/PMPvzmr\ngq8JT1VfcfV2zuJi2U/JubmARKJ4CDArC1i4UL68kRFw8KB8+QcPFJ9mW1kBjx7Jr09PB6ytZZIC\n8IWlJfDkiXz5Z8/eHgb598fKCti8Wb58fj5w+LB8eWNj4JNP5MsrI/t/Q5oTqP6/ucri629T1YSS\nU1nbqZIzA09PT6SkpCA1NRWNGjVCREQE9u3bJ1Pm2bNnqF+/PkQiERISEsAYg7m5ORhjGDduHBwd\nHRV2BJXB5zhamao0Zu/+cTRuLD07efIE2L276ln4xtcfcXkT90VFobhzBygqAt4ZCQQgbZhXrZIf\n465VS/Z9Ksv55Il0iORd9etLG1r54wMrV8qvL2/S39BQOvxXNu5c1vj+bypLYT0bN75d1pdrrBVp\n0AAKO83yGBgAw4dXvrwy8v83xG+t1zxCaGAB4eRURiWdgY6ODsLCwuDn5weJRIJx48bBwcEBm//3\nsWfixIk4ePAgNm7cCB0dHRgYGGC/9DwY58+fx+7du+Hi4gJ3d3cA0jOLnj17Vvr4mnKJ3Lu2bJF+\n8rtxQ3oVxY8/SsdqP3YXLkgn7czMpHdPvqu4GJg3T359eY1pnTrSSf63G2pDw/KvHjExkV499m75\n8uq3sABevarcawOkE7OTJlW+vDpp6v8NogGYAAkl9pkzZ5Ru9/GZxwAm9+PjM69a8pWpKGdllfd6\ndHXnMTs7xjw8FO9XVMTYnDmMLV7M2Lp1jG3bxtj+/YwdP644Z2mp9EdT8fV+qpoQcgohI2PCyams\n7aQ7kInKtW+v/LprXV3pmH5lVeL6BELIe6LOQIUqGkdUxdxGVQhlvJNy8ksIOYWQERBOTmWoM1Cj\nj238VlM6N0LI+6MnnamQUC43o5z8opz8EUJGQDg56UlnhBBClKIzA0IIqSHozIAQQohS1BmokFjZ\n9ZQahHLyi3LyRwgZAeHkVIY6A0IIITRnQAghNQXNGRBCCFGKOgMVEso4IuXkF+XkjxAyAsLJqQx1\nBoQQQmjOgBBCagqaMyCEEKIUdQYqJJRxRMrJL8rJHyFkBISTUxnqDAghhNCcASGE1BQ0Z0AIIUQp\n6gxUSCjjiJSTX5STP0LICAgnpzIq6QwiIyPRsmVLNGvWDMuWLZPbLhaLYWJiAnd3d7i7u2PhWw/A\nrWhfIbly5Yq6I1QK5eQX5eSPEDICwsmpDO+PvZRIJJg6dSqioqJgZWWFNm3aICAgAA4ODjLlfHx8\ncOTIkSrtKxRZWVnqjlAplJNflJM/QsgICCenMryfGSQkJMDe3h62trbQ1dVFYGAgDh8+LFdO0SRG\nZfclhBDCL947g/T0dNjY2HDL1tbWSE9PlykjEokQFxcHV1dX9OrVC8nJyZXeV0hSU1PVHaFSKCe/\nKCd/hJAREE5OZXgfJhKJRBWW8fDwQFpaGgwMDHDy5En0798ft2/f5v04mmDHjh3qjlAplJNflJM/\nQsgICCdneXjvDKysrJCWlsYtp6WlwdraWqaMkZER929/f39MnjwZmZmZsLa2rnBfQPEQEyGEkKrj\nfZjI09MTKSkpSE1NRVFRESIiIhAQECBT5tmzZ1yDnpCQAMYYzM3NK7UvIYQQ/vF+ZqCjo4OwsDD4\n+flBIpFg3LhxcHBwwObNmwEAEydOxMGDB7Fx40bo6OjAwMAA+/fvL3dfe3t7viMSQgh5h8Z+HYVE\nIkFISAiKiooQEBCA7t27qztSuZ4+fQpLS0t1x1BKCBnLCCGrEDICQGlpKbS0NP/eUsrJr6rk1A4N\nDQ1VTZyqKy0txdSpU/Hy5Ut06dIFP//8M3JycuDm5gZtbW11x+O8ePECY8eOxYYNG/D48WNYWFig\nQYMGYIxpzAS3EDKWEUJWIWQEgOLiYoSEhCAxMRG1a9dGo0aN1B1JIcrJrw/JqZFd3OvXr3HlyhVs\n2rQJI0aMwDfffIOUlBRERESoO5qMNWvWwNzcHH/++Sdq1aqFUaNGAdCsK52EkLGMELIKIWNeXh7G\njBmDjIwM1KlTB5MnT8bJkychkUjUHU0G5eTXh+bUyDMDfX19REdHIyMjA15eXrC0tERmZiYuXrwI\nDw8PmauRqltRURG0tbXBGENsbCycnZ3h7e2NTp06ISIiAi9fvkT79u3V+klRCBmFlFUIGd+WkZGB\ntWvXIjIyEl5eXpBIJLh06RL09fVha2ur7ngcysmvD82pkWcGADBw4EBcuXIFT548gaGhIVxcXKCn\np4enT5+qJc+FCxcwZMgQzJw5E8nJyRCJRCgoKEBGRgZXZunSpdiwYQPy8vLU0igIIaOQsgohIwA8\nevQICxcuxPXr15GXl4dGjRqhWbNm+O9//wsAGDRoEIyNjREfH4+cnBy1ZKScmp9TYzuDjh07om7d\nuggPDwcAtG7dGgkJCcjPz6/2LM+fP8fUqVPRq1cvWFhYYOXKlTh06BDGjh2LnTt3ch2Ul5cXXFxc\nsHLlSsoo8KxCyAgAx48fR/fu3XHv3j2sXr0awcHBAKQ3dl6/fh1ZWVmoV68e3N3dkZaWBolEopb7\ndCin5ufUyGEiQHpjWp06dbBx40YYGhrCwMAAJ06cQI8ePRTeiKZK58+fR1paGhYuXIg2bdrA0NAQ\nGzduxJgxY/DgwQPExcWhdevWMDAwQF5eHvT09NCmTRvKKOCsQsgIADExMbC2tsbatWvx6aefIjg4\nGC4uLmjatCkuX76MgoICODs7o0WLFpg+fTr69++PunXrUk7KKUdjOwMAsLGxgaWlJQ4fPoylS5di\nzJgxGDhwoMqPu3fvXhw4cAA5OTlo2bIljI2N8eOPP8Lf3x+WlpYwMzPDgwcPkJCQgLlz52L//v2I\nj49Heno6fvrpJwwYMEDl37QqhIxCyiqEjABw/fp13LhxA3Z2dgCA06dPo06dOvDw8IC+vj7Mzc2x\nbt06fPvtt8jIyMCBAwfQsGFDlJaW4uLFi+jXrx9MTU0pJ+WUxwTgzZs3rKSkROXHKS0tZRs2bGBu\nbm5s27ZtrFmzZmzr1q2soKCAzZ8/n02bNo0xxphEImFnz55l48ePZ7m5uezhw4ds9+7dbNiwYezY\nsWM1PqOQsgohY9nxJ02axFq0aMG6d+/OZs+eze7evcuioqJY165dWX5+PlfW09OT/fLLL4wxxnbs\n2MH69+/P7O3t2caNGykn5SyXIDqD6jR69Gi2b98+xhhjf/31F/vss8/YsWPH2OXLl5m/vz87deoU\nY4yxGzdusN69e7O8vDzKKPCsQsiYkZHBRo4cyUpLS9mzZ8/YypUr2fDhw1lpaSnr3bs3W7t2LZNI\nJIwxxiIiItiIESNYaWkpY4yxzMxMbhvlpJzl0dgJ5Oqyc+dOxMTEIDMzEwDg4OCA9PR0lJSUoHv3\n7nBycsKFCxdgYWGB4cOH4+uvv8adO3dw+vRpANLLDimjsLIKISMA3L17F3l5eQCAzMxMxMXFIT8/\nH/Xr18fgwYNRp04dbN++HStXrsR///tf/P777wCA27dvw8PDg7u6yczMTKV3zVLOjyOnRs8ZqApj\nDE+ePEHfvn1x9epVpKen448//kD37t3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       "text": [
        "<matplotlib.figure.Figure at 0x11151f990>"
       ]
      }
     ],
     "prompt_number": 59
    }
   ],
   "metadata": {}
  }
 ]
}