Datenaufbereitung_ver2.ipynb

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      "Tiny-VErsions\n",
      "      LP  Score       Time    Memory  LM_Score   LM_Time  LM_Usage\n",
      "0  cs-en  0.235  15.855671  3.558393     0.442  9.398126  0.964480\n",
      "1  de-en  0.395  13.523358  2.286151     0.485  9.307220  0.877646\n",
      "2  fi-en  0.337  13.095006  2.195590     0.622  9.155854  0.491279\n",
      "3  lv-en  0.222  14.211346  3.180200     0.591  7.273632  0.303442\n",
      "4  ru-en  0.342  15.529096  2.705046     0.491  9.320954  0.264931\n",
      "5  tr-en  0.341  14.867953  3.258908     0.474  9.124849  0.227061\n",
      "6  zh-en  0.369  16.058394  2.547295     0.456  9.213624  0.818694\n",
      "       LP  Score       Time    Memory  LM_Score   LM_Time  LM_Usage\n",
      "14  cs-en  0.522  13.434774  3.119256     0.550  9.357930  0.781249\n",
      "15  de-en  0.589  13.019631  2.614598     0.586  9.367455  0.711064\n",
      "16  fi-en  0.697  11.060187  2.085313     0.716  9.247418  0.332877\n",
      "17  lv-en  0.610  11.423567  2.570462     0.689  7.224518  0.248850\n",
      "18  ru-en  0.640  13.486646  2.560999     0.650  9.348438  0.264931\n",
      "19  tr-en  0.637  11.945083  2.574326     0.626  9.324737  0.227117\n",
      "20  zh-en  0.540  12.567549  2.387927     0.558  9.309676  0.818638\n",
      "       LP  Score       Time    Memory  LM_Score   LM_Time  LM_Usage\n",
      "7   cs-en  0.058  11.825575  3.475181     0.391  9.347923  0.781649\n",
      "8   de-en  0.291  10.474583  2.264133     0.454  9.315544  0.711144\n",
      "9   fi-en  0.134  10.293857  2.184611     0.586  9.161947  0.332957\n",
      "10  lv-en -0.049  11.559869  3.173271     0.539  7.214934  0.248930\n",
      "11  ru-en  0.209  12.320973  2.701613     0.444  9.189983  0.264907\n",
      "12  tr-en  0.081  11.502283  3.258275     0.414  9.166659  0.227253\n",
      "13  zh-en  0.302  12.655150  2.544646     0.422  9.258699  0.818718\n",
      "       LP  Score       Time    Memory  LM_Score   LM_Time  LM_Usage\n",
      "21  cs-en  0.161   9.066475  1.931327     0.299  9.444648  0.781249\n",
      "22  de-en  0.178  10.487882  1.964911     0.310  9.298769  0.711008\n",
      "23  fi-en  0.338   9.785728  1.709673     0.548  9.276955  0.332821\n",
      "24  lv-en  0.210   7.456413  2.194751     0.434  7.251883  0.248954\n",
      "25  ru-en  0.210  32.350783  6.495305     0.366  9.202943  0.264875\n",
      "26  tr-en  0.250  11.101510  2.818414     0.379  9.197371  0.227061\n",
      "27  zh-en  0.330  12.846279  2.144281     0.437  9.270823  0.818638\n"
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   "source": [
    "# Daten Einlesen\n",
    "import pandas as pd\n",
    "filename1 = 'XMOVERScore_FinalBench.csv'\n",
    "\n",
    "\n",
    "data = pd.read_csv(filename1)\n",
    "\n",
    "\n",
    "\n",
    "bert = data.iloc[0:7]\n",
    "distilbert = data.iloc[7:14]\n",
    "xlmr = data.iloc[14:21]\n",
    "tiny = data.iloc[21:]\n",
    "\n",
    "\n",
    "models_df = [bert, xlmr,distilbert, tiny ]\n",
    "for mod in models_df:\n",
    "    print(mod)"
   ],
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      "[]\n"
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   "source": [
    "# Aufbereitung\n",
    "dist = distilbert.copy()\n",
    "xlm= xlmr.copy()\n",
    "tiny = tiny.copy()\n",
    "xlm_var = []\n",
    "dist_var = []\n",
    "tiny_var = []\n",
    "\n",
    "\n",
    "for col in range(1,xlm.shape[1]):\n",
    "    #xlm_var.append(xlm[col].mean(axis = 0))\n",
    "\n",
    "    for row in range(xlm.shape[0]):\n",
    "        xlm.iloc[row, col] =  abs(xlm.iloc[row, col].copy() / bert.iloc[row, col].copy())\n",
    "        dist.iloc[row, col] = abs(dist.iloc[row, col].copy() / bert.iloc[row, col].copy())\n",
    "        tiny.iloc[row, col] =  abs(tiny.iloc[row, col].copy() / bert.iloc[row, col].copy())\n",
    "models=[dist,xlm,tiny]\n",
    "\n",
    "\n",
    "# Calculate the variance of scores\n"
   ],
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   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio"
   ],
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     "metadata": {},
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    }
   ],
   "source": [
    "modelle = models_df\n",
    "lang = ['cs', 'de', 'fi' , 'lv', 'ru', 'tr', 'zh']\n",
    "columm ='Score'\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[0][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='mBERT'\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r=  modelle[1][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='XLM-RoBERTa'\n",
    "))\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[2][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='DistilBERT'\n",
    "))\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[3][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='TinyBERT'\n",
    "))\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "  polar=dict(\n",
    "    radialaxis=dict(\n",
    "      visible=True,\n",
    "      range=[0, 1]\n",
    "    )),\n",
    "    #colorway = ['deepskyblue','orange', 'mediumaquamarine', 'tomato'],\n",
    "  showlegend=True,\n",
    "        #title = \"Model Score\"\n",
    ")\n",
    "pio.write_image(fig,'images/ModelScore.png' , format='png')\n",
    "fig.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
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                       {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('f2451784-fd1d-46e4-8576-00691c070f57');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "lang = ['cs', 'de', 'fi' , 'lv', 'ru', 'tr', 'zh']\n",
    "columm ='LM_Score'\n",
    "modelle = models_df\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[0][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='mBERT'\n",
    "))\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r=  modelle[1][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='XLM-RoBERTa'\n",
    "))\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[2][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='DistilBERT'\n",
    "))\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "      r= modelle[3][columm].to_numpy(),\n",
    "      theta=lang,\n",
    "      fill='toself',\n",
    "      name='TinyBERT'\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "  polar=dict(\n",
    "    radialaxis=dict(\n",
    "      visible=True,\n",
    "      range=[0, 1]\n",
    "    )),\n",
    "  showlegend=True,\n",
    "    #title = \"Model + LM(GP2) for remapping score\"\n",
    ")\n",
    "pio.write_image(fig,'images/ModelLMScore.png' , format='png')\n",
    "fig.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " # ###    Show increasement for LM\n",
    "modelle_diff=[]\n",
    "langs = ['cs', 'de', 'fi' , 'lv', 'ru', 'tr', 'zh']\n",
    "performance_models = ['BERT','XLM-RoBERTa','DistilBERT', 'TinyBERT']\n",
    "\n",
    "\n",
    "for i in range(4):\n",
    "    scor = modelle[i]['Score'].to_numpy()\n",
    "    scor_lm = modelle[i]['LM_Score'].to_numpy()\n",
    "\n",
    "    diff = scor_lm - scor\n",
    "    modelle_diff.append(diff)\n",
    "\n",
    "\n",
    "bar_width = 0.1\n",
    "x = langs\n",
    "#negative_data = [-1,-4,-3,-2,-6,-2,-8]\n",
    "positive_data = modelle_diff[0]\n",
    "\n",
    "'''\n",
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(x, positive_data, width=1, color='b')\n",
    "'''\n",
    "hatches = [ \"*\",\"x\", \"o\", \"O\" ]\n",
    "\n",
    "x = np.arange(7)\n",
    "\n",
    "x1 = [a + bar_width for a in x]\n",
    "x2 = [a + bar_width for a in x1]\n",
    "x3 = [a + bar_width for a in x2]\n",
    "xs = [x, x1, x2, x3]\n",
    "\n",
    "for i in range(4):\n",
    "    colo = 'slateblue'\n",
    "    if i==1: colo = 'orangered'\n",
    "    if i==2: colo = 'mediumspringgreen'\n",
    "    if i==3: colo = 'darkorchid'\n",
    "    plt.bar(xs[i], modelle_diff[i], width= bar_width, label= performance_models[i], color = colo )\n",
    "\n",
    "plt.xticks([r + bar_width for r in range(len(langs))], langs)\n",
    "plt.ylabel('Score improvement')\n",
    "plt.xlabel('Languages for MT in combination with english')\n",
    "plt.legend()\n",
    "plt.savefig('images/XMover_MT-improvement.png')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.4669914510232293, 0.7821441077002221, 0.9939002438110375, 0.9111381264957114, 0.9976529804911394, 0.8742278865131842], [1.9616010994515627, 0.8447166123857348, 0.921774798405902, 1.2339607993674575, 1.0058174950486758, 0.8740081077665566], [0.7608763514411818, 0.89274310324058, 0.9969791580481014, 0.7763360363305353, 1.002253364756637, 0.8739662411350235]]\n"
     ]
    }
   ],
   "source": [
    "# Aufbereitung für größerer Daten Übersicht\n",
    "# Nehme gemittlelte Daten und Mittel sie über die Sprachen nochmal\n",
    "XLMR_avg = []\n",
    "DiBert_avg = []\n",
    "Tiny_avg = []\n",
    "avg_avg_mod = [ DiBert_avg, XLMR_avg, Tiny_avg]\n",
    "\n",
    "modelle_avg = models.copy()\n",
    "\n",
    "for j in range(len(modelle_avg)):\n",
    "    for col in range(1,modelle_avg[j].shape[1]):\n",
    "        temp=0\n",
    "        for row in range(modelle_avg[j].shape[0]):\n",
    "            temp += modelle_avg[j].iloc[row,col]\n",
    "        temp = temp/7\n",
    "        avg_avg_mod[j].append(temp)\n",
    "print(avg_avg_mod)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotte Parallele Daten\n",
    "\n",
    "modelle_diff=[]\n",
    "avg_avg_mod_descr = [\"Score\",\"Time\",\"Memory\",\"LM_Score\",\"LM_Time\",\"LM_Usage\"]\n",
    "performance_models = ['XLM-RoBERTa','DistilBERT', 'TinyBERT']\n",
    "\n",
    "bar_width = 0.1\n",
    "x = avg_avg_mod_descr\n",
    "\n",
    "\n",
    "\n",
    "var = [ 0.5 , 0.5, 0.5, 0.5 , 0.5, 0.5  ]\n",
    "\n",
    "x = np.arange(len(x))\n",
    "\n",
    "x1 = [a + bar_width for a in x]\n",
    "x2 = [a + bar_width for a in x1]\n",
    "x3 = [a + bar_width for a in x2]\n",
    "xs = [x, x1, x2]\n",
    "\n",
    "for i in range(3):\n",
    "\n",
    "    if i==0: colo = 'orangered'\n",
    "    if i==1: colo = 'mediumspringgreen'\n",
    "    if i==2: colo = 'darkorchid'\n",
    "    plt.bar(xs[i], avg_avg_mod[i], width= bar_width, label= performance_models[i] , color=colo  , capsize=3)\n",
    "\n",
    "plt.axhline(1, c='r', lw=0.3)\n",
    "\n",
    "plt.xticks([r + bar_width for r in range(len(avg_avg_mod_descr))], avg_avg_mod_descr)\n",
    "plt.ylabel('In relation to mBERT')\n",
    "plt.xlabel('Categories of interest for XMoverScore ')\n",
    "plt.legend()\n",
    "plt.savefig('images/XMover_Alldata.png')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ],
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