238 lines (237 with data), 91.0 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "def plot_tls_from_dir(text):\n",
    "    filedir = 'save_'+text+'/train_losses/'\n",
    "    tls = sorted(os.listdir(filedir))\n",
    "    \n",
    "    # A bunch of dictionaries for parameters to a multidimensional matrix of training losses\n",
    "    d = np.array([i.split('_') for i in tls])\n",
    "    maps = []\n",
    "    for indi,i in enumerate(range(len(d[0]))):\n",
    "        maps.append({})\n",
    "        for indj,j in enumerate(np.unique(d[:,i])):\n",
    "            maps[indi][j] = indj\n",
    "\n",
    "    # Save mean of last 50 training losses in 4D tensor for each combo of parameters\n",
    "    X = np.zeros([len(i) for i in maps])\n",
    "    \n",
    "    min_tl = ('sup',float('inf'))\n",
    "    plt.figure(figsize=(10,4))\n",
    "    for tl in tls:\n",
    "        x = np.exp(np.loadtxt(filedir+tl))\n",
    "        if len(x) > 0:\n",
    "            plt.plot(x,label=tl)\n",
    "            if np.mean(x[-50:]) < min_tl[1]:\n",
    "                min_tl = (tl,np.mean(x[-50:]))\n",
    "        X[tuple([maps[i][key] for i,key in enumerate(tl.split('_'))])] = np.mean(x[-50:])\n",
    "\n",
    "    # Parameters of each training loss are: CELLTYPE_LENGTHSEQ_NUMLAYERS_LEARNINGRATE\n",
    "#     plt.legend(bbox_to_anchor=(1.22, 1))\n",
    "    plt.ylabel('perplexity')\n",
    "    plt.xlabel('batch')\n",
    "\n",
    "    print min_tl, min_tl[1]\n",
    "    \n",
    "    return maps,X\n",
    "\n",
    "def choose_best_parameters(maps,X):\n",
    "    # Count the number of times each parameter \"wins\"\n",
    "    for i,m in enumerate(maps):\n",
    "        h = np.bincount(np.argmin(X,axis=i).flatten())\n",
    "        xax = range(len(h))\n",
    "        xax_labels = [i[1] for i in sorted([(m[key],key) for key in m])]\n",
    "        fig,ax = plt.subplots()\n",
    "        ax.bar(xax, h)\n",
    "        ax.set_xticks([float(i)+0.4 for i in xax])\n",
    "        ax.set_xticklabels(tuple(xax_labels))\n",
    "        ax.set_title(' '.join(xax_labels))\n",
    "        ax.set_xlim([np.min(xax)-0.2,np.max(xax)+1])\n",
    "        ax.set_ylim([0,np.max(h)*1.1])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('gru_50_3_0.0008', 3.6795794075634718) 3.67957940756\n"
     ]
    },
    {
     "data": {
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KIXnsGPzuKLydWpOUaNmwZgmsqKHNDWeT/+aXxA3vTf7z72MnTMD91v9o2q8t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\nfvVn7T281+TZ8s57v6rqKmOrsP3qz61pR94OwyR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23c18bc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23c18bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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pwHeBa4GzqmrHUoqUJM1vwzQnVdX9Sc4B/hk4APiQYS1Jy2uqGbYkaeX5ScdFSnJxkl1J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\nbElqxP8BOrZRs7JFuokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ca21210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff196fb8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V+jxsvBrcXTagt6eTfX79HPAdYC/wBOC1SZ4wZQ0aw8CeD18Gzh5aP5vBlcy4Po/v+qzU\n/uXu9XL3ay1J9gJfmWHNasfJPr9+BfhoVX23qr4KfAr42Rkcx7ZnYM+Hm4EnJdmf5DTgl4HrRvpc\nB/wqnHjS9Gj36+i4fa8DLu5eXwx8aGTMaa6o1L6TfX59EXhuN9YZwDMBn9OYhapymYMFeBGDp0fv\nBt7QtV0CXDLU593d9luAnx63b9e+C/gYcBdwPbBzaNth4GvAMeC/gCdv9X8Dl8U4vxjcaHw/cDtw\nCPjtrT7+RVl8cEaSGuGUiCQ1wsCWpEYY2JLUCANbkhphYEtSIwxsSWqEgS1JjTCwJakR/w8oje6m\n+xVN4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ca89190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maps,X = plot_tls_from_dir('ecoligenome')\n",
    "choose_best_parameters(maps,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('rnn_100_3_0.0001', 2.9385979100742547) 2.93859791007\n"
     ]
    },
    {
     "data": {
      "image/png": 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VFAAAIKesrExlZWV5P0/YzvtJ1liJiJslLbJ9V27ZJSU/0cJxD6tVp1YFrh0AAMCqRYRs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\nJBAhDQAAIIEIaQAAAAn0/0b16SaqQ0/lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff196f42110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff196f42fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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wSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaNTXAk3w2yf4kd03oc1WS+5PsSXL2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\nkhplgEtSowxwSWqUAS5JjTLAJalR/x/BJszBDBzreAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23c770510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff19c18e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEKCAYAAAA2Mm/+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1969eef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maps,X = plot_tls_from_dir('malariagenome')\n",
    "choose_best_parameters(maps,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}