Resultados.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recovering models metadata from pickle dump...\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import pickle\n",
    "\n",
    "font = {'family' : 'sans-serif',\n",
    "        #'weight' : 'bold',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "#Results Beta\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv1.1.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv1.1.2_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv1.2.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv1.2.2_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.1.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.1.2_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.2.1_C_1.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.2.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.2.1_C_3.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.2.1_C_3.5.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv2.2.2_C_3.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv3.1.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv3.1.2_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv3.2.1_C_2.0.csv\"\n",
    "#file = \"./results_casted_beta/results.accuracy.results_tfv3.2.2_C_2.0.csv\"\n",
    "\n",
    "#Final results\n",
    "#file = \"./results_shifted_all_10it/results.accuracy.mean.csv\"\n",
    "#file = \"./results_shifted_expanded_10it/results.accuracy.mean.csv\"\n",
    "#file = \"./results_casted_all_10it/results.accuracy.mean.csv\"\n",
    "file = \"./results_casted_all_10it/results.accuracy.all.top1.mean.csv\"\n",
    "#file = \"./results_casted_alma_10it/results.accuracy.all.top1.mean.csv\"\n",
    "\n",
    "#Obtain models info\n",
    "model_names = list()\n",
    "model_accuracys = list()\n",
    "with open(file) as f:\n",
    "    for line in f:\n",
    "        tokens = line.split(',')\n",
    "        model_names.append(tokens[0])\n",
    "        model_accuracys.append(float(tokens[1].rstrip()))\n",
    "\n",
    "#Obtain models metadata\n",
    "models_metadata = dict()\n",
    "for model in model_names:\n",
    "\tmodels_metadata[model] = dict()\n",
    "\n",
    "pickle_models_filename = 'models_metadata.dmp'\n",
    "\n",
    "if not os.path.isfile(pickle_models_filename):\n",
    "\tprint(\"Reading models metadata...\")\n",
    "\t#Obtain number of features (vocabulary) per model\n",
    "\tfor i,model in enumerate(model_names):\n",
    "\t    with open('./llda_train_input/'+model+'_features.dat') as f:\n",
    "\t        line = f.readline()\n",
    "\t        models_metadata[model]['vocabulary_len'] = len(line.split())\n",
    "\t#Obtain number of tokens per model\n",
    "\tfor model in model_names:\n",
    "\t    #model_data = \"_\".join(model.split('_')[:-1]) if \"expanded\" not in model else \"_\".join(model.split('_')[:-3]) \n",
    "\t    with open('./llda_train_input/'+model+'.dat') as f:\n",
    "\t        tokens_count = 0\n",
    "\t        for line in f:\n",
    "\t                tokens_count += len(line.split())\n",
    "\t        models_metadata[model]['tokens_len'] = tokens_count\n",
    "\n",
    "\t#Obtain number of topics per model\n",
    "\tfor model in model_names:\n",
    "\t    if 'expanded' in model:\n",
    "\t        tokens = model.split('_expanded')\n",
    "\t        labelmap_path = './llda_train_input/'+tokens[0]+'_labelmap.sub'\n",
    "\t    else:\n",
    "\t        labelmap_path = './llda_train_input/'+model+'_labelmap.sub'\n",
    "\t    \n",
    "\t    with open(labelmap_path) as f:\n",
    "\t        topics_count = 0\n",
    "\t        for line in f:\n",
    "\t        \ttopics_count += 1\n",
    "\t        models_metadata[model]['topics_count'] = topics_count\n",
    "\n",
    "\tpickleout = open(pickle_models_filename,'wb')\n",
    "\tpickle.dump(models_metadata,pickleout)\n",
    "\tpickleout.close()\n",
    "else:\n",
    "\tprint(\"Recovering models metadata from pickle dump...\")\n",
    "\tpicklein = open(pickle_models_filename,'rb')\n",
    "\tmodels_metadata = pickle.load(picklein)\n",
    "\tpicklein.close()\n",
    "    \n",
    "model_vocabulary_len = []\n",
    "model_tokens_len = []\n",
    "model_topics_len = []\n",
    "for model in model_names:\n",
    "    if \"expanded_00010\" in model or \"expanded_00002\" in model:\n",
    "        continue\n",
    "    model_vocabulary_len.append(models_metadata[model]['vocabulary_len'])\n",
    "    model_tokens_len.append(models_metadata[model]['tokens_len'])\n",
    "    model_topics_len.append(models_metadata[model]['topics_count'])\n",
    "    \n",
    "\n",
    "results = zip(model_names, model_accuracys, model_vocabulary_len, model_topics_len)\n",
    "\n",
    "results_tr = list()\n",
    "results_2 = list()\n",
    "results_full = list()\n",
    "\n",
    "results_full_ex3_00050 = list()\n",
    "results_full_ex3_00100 = list()\n",
    "results_full_ex3_01000 = list()\n",
    "\n",
    "for model, accuracy, vlen, topics in results:\n",
    "    if model.endswith('tr'):\n",
    "        results_tr.append((model,accuracy,vlen,topics))\n",
    "    elif model.endswith('2'):\n",
    "        results_2.append((model,accuracy,vlen,topics))\n",
    "    elif model.endswith('full'):\n",
    "        results_full.append((model,accuracy,vlen,topics))\n",
    "    elif model.endswith('full_expanded_00050_x3'):\n",
    "        results_full_ex3_00050.append((model,accuracy,vlen,topics))\n",
    "    elif model.endswith('full_expanded_00100_x3'):\n",
    "        results_full_ex3_00100.append((model,accuracy,vlen,topics))\n",
    "    elif model.endswith('full_expanded_01000_x3'):\n",
    "        results_full_ex3_01000.append((model,accuracy,vlen,topics))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('planetary_tr', 0.1, 6195, 20), ('diffuse_clouds_tr', 0.0444444444444, 7684, 24), ('dark_clouds_tr', 0.0363636363636, 8830, 58), ('extragalactic_tr', 0.0454545454546, 9032, 36), ('comets_tr', 0.0777777777777, 9349, 30), ('agb_ppn_pn_tr', 0.0, 9597, 71), ('hot_cores_tr', 0.0, 9701, 85)]\n",
      "[('planetary_2', 0.1875, 21973, 20), ('extragalactic_2', 0.381818181818, 51664, 36), ('comets_2', 0.633333333334, 70080, 30), ('diffuse_clouds_2', 0.644444444445, 73104, 24), ('agb_ppn_pn_2', 0.3, 84566, 71), ('dark_clouds_2', 0.263636363636, 117834, 58), ('hot_cores_2', 0.318181818182, 186902, 85)]\n",
      "[('planetary_full', 0.1875, 24006, 20), ('extragalactic_full', 0.363636363636, 65293, 36), ('comets_full', 0.566666666667, 98875, 30), ('diffuse_clouds_full', 0.488888888889, 120444, 24), ('agb_ppn_pn_full', 0.377777777777, 181268, 71), ('dark_clouds_full', 0.418181818181, 227544, 58), ('hot_cores_full', 0.481818181818, 596096, 85)]\n",
      "[('planetary_full_expanded_00050_x3', 0.8, 167604, 20), ('extragalactic_full_expanded_00050_x3', 0.772727272728, 452951, 36), ('comets_full_expanded_00050_x3', 0.688888888889, 686179, 30), ('diffuse_clouds_full_expanded_00050_x3', 0.877777777778, 837034, 24), ('agb_ppn_pn_full_expanded_00050_x3', 0.911111111111, 1212329, 71), ('dark_clouds_full_expanded_00050_x3', 0.709090909091, 1561982, 58), ('hot_cores_full_expanded_00050_x3', 0.563636363637, 4064069, 85)]\n",
      "[('planetary_full_expanded_00100_x3', 0.8125, 167626, 20), ('extragalactic_full_expanded_00100_x3', 0.8, 454462, 36), ('comets_full_expanded_00100_x3', 0.811111111111, 687863, 30), ('diffuse_clouds_full_expanded_00100_x3', 0.766666666667, 838155, 24), ('agb_ppn_pn_full_expanded_00100_x3', 0.888888888889, 1228478, 71), ('dark_clouds_full_expanded_00100_x3', 0.690909090909, 1574683, 58), ('hot_cores_full_expanded_00100_x3', 0.618181818182, 4091372, 85)]\n",
      "[('planetary_full_expanded_01000_x3', 0.825, 167776, 20), ('extragalactic_full_expanded_01000_x3', 0.945454545455, 455982, 36), ('comets_full_expanded_01000_x3', 0.9, 689658, 30), ('diffuse_clouds_full_expanded_01000_x3', 0.977777777778, 839856, 24), ('agb_ppn_pn_full_expanded_01000_x3', 0.922222222222, 1243658, 71), ('dark_clouds_full_expanded_01000_x3', 0.727272727273, 1584754, 58), ('hot_cores_full_expanded_01000_x3', 0.7, 4118798, 85)]\n"
     ]
    },
    {
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tfwG/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\n5cCmwLbAvsC/trncjkXEo8DMHOOM2vGSFgb2Ac6Oi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+FvhMRJxXPW2iRTmWrclxWW8sFpdGk/lyLebLNZkv98acuSfmzDWZM9dnvtwbt2wY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\nYHUOEgAAAAAAAACA1X0BgeRdlE0vjeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63b4545b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotScoreBars(channeling, results,rows,cols,subplot, title=\"\"):\n",
    "    axes = plt.gca()\n",
    "    \n",
    "    res_sortedby_vocabulary_len = sorted(results,key=lambda x: x[2])\n",
    "    res_sortedby_topics_len = sorted(results,key=lambda x: x[3])\n",
    "    \n",
    "    print(res_sortedby_vocabulary_len)\n",
    "    \n",
    "    x = range(len(results))\n",
    "    \n",
    "    plt.subplot(rows,cols,subplot)\n",
    "    plt.bar(x, [i[1] for i in res_sortedby_vocabulary_len], align='center')\n",
    "    plt.ylabel('Accuracy Score')\n",
    "    plt.title(title+'(sorted by n° of tokens)')\n",
    "    plt.xticks(x, [i[0] for i in res_sortedby_vocabulary_len],rotation='vertical')\n",
    "    plt.ylim(0, 1)\n",
    "    \n",
    "    #plt.subplot(rows,cols,subplot+3)\n",
    "    #plt.bar(x, [i[1] for i in res_sortedby_topics_len], align='center')\n",
    "    #plt.ylabel('Accuracy Score')\n",
    "    #plt.title(title+'(sorted by n° of topics)')\n",
    "    #plt.xticks(x, [i[0] for i in res_sortedby_topics_len],rotation='vertical')\n",
    "    #plt.ylim(0, 1)\n",
    "\n",
    "plt.close('all')\n",
    "fig = plt.figure(figsize=(30,20))\n",
    "\n",
    "plotScoreBars(0,results_tr,2,3,1,\"Truncated models\")\n",
    "plotScoreBars(2,results_2,2,3,2,\"Standard models\")\n",
    "plotScoreBars(5,results_full,2,3,3,\"Full models\")\n",
    "\n",
    "plotScoreBars(5,results_full_ex3_00050,2,3,4,\"Expanded models with \"+r\"$\\tau$\"+\" = 0.0005 [GHz]\")\n",
    "plotScoreBars(5,results_full_ex3_00100,2,3,5,\"Expanded models with \"+r\"$\\tau$\"+\" = 0.001 [GHz]\")\n",
    "plotScoreBars(5,results_full_ex3_01000,2,3,6,\"Expanded models with \"+r\"$\\tau$\"+\" = 0.01 [GHz]\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}