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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Analysis of Middle Low German and Nowegian Nynorsk\n",
    "\n",
    "In this analysis we will walk through the results of the baseline neural transducer models for two target low-resource languages, Middle Low German and Norwegian Nynorsk, as well as their source languages, English, German, and Icelandic.\n",
    "\n",
    "Ontop of a basic Analysis we will be taking an in-depth look at how a lemma is effected in the predictions and training, and by training models with custom training data, hope to gain an insight into what source languages pair with the target languages best, as well as how the models react to adding more data to train on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     family language          model   prediction        truth      lemma  \\\n0  germanic      gml  hall-mono-hmm      achtede      achtede     achten   \n1  germanic      gml  hall-mono-hmm        vrîen        vrîen       vrie   \n2  germanic      gml  hall-mono-hmm     spö̂dede     spö̂dede     spoden   \n3  germanic      gml  hall-mono-hmm  jachtereden  jachtereden  jachteren   \n4  germanic      gml  hall-mono-hmm   jachterest   jachterest  jachteren   \n\n                 tag      loss  distance  is_correct  is_lemma  \n0   V;IND;SG;3;PST\\n  0.006583         0           1         0  \n1  ADJ;GEN;LGSPEC1\\n  0.036813         0           1         0  \n2  V;SBJV;SG;1;PST\\n  0.001421         0           1         0  \n3     V;IND;PL;PST\\n  0.001089         0           1         0  \n4  V;SBJV;SG;2;PRS\\n  0.000505         0           1         0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>family</th>\n      <th>language</th>\n      <th>model</th>\n      <th>prediction</th>\n      <th>truth</th>\n      <th>lemma</th>\n      <th>tag</th>\n      <th>loss</th>\n      <th>distance</th>\n      <th>is_correct</th>\n      <th>is_lemma</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>germanic</td>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>achtede</td>\n      <td>achtede</td>\n      <td>achten</td>\n      <td>V;IND;SG;3;PST\\n</td>\n      <td>0.006583</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>germanic</td>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>vrîen</td>\n      <td>vrîen</td>\n      <td>vrie</td>\n      <td>ADJ;GEN;LGSPEC1\\n</td>\n      <td>0.036813</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>germanic</td>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>spö̂dede</td>\n      <td>spö̂dede</td>\n      <td>spoden</td>\n      <td>V;SBJV;SG;1;PST\\n</td>\n      <td>0.001421</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>germanic</td>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>jachtereden</td>\n      <td>jachtereden</td>\n      <td>jachteren</td>\n      <td>V;IND;PL;PST\\n</td>\n      <td>0.001089</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>germanic</td>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>jachterest</td>\n      <td>jachterest</td>\n      <td>jachteren</td>\n      <td>V;SBJV;SG;2;PRS\\n</td>\n      <td>0.000505</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# packages\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# lists\n",
    "languages = 'gml nno deu eng isl'.split(' ')\n",
    "baseraw = pd.read_csv('scraped_data/2020-07-10-tsv.predictions.csv')\n",
    "\n",
    "# column for if full lemma is in truth word\n",
    "baseraw['is_lemma'] = [1 if str(row[6]) in str(row[5]) else 0 for row in baseraw.itertuples()]\n",
    "\n",
    "baseraw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown, this is what the raw data from our base predictions looks like. This data consists of every prediction, ground truth, lemma, and tag for the development data for all the languages we want to take a look at. There are also additional columns that are derived from those 4 mentioned columns.\n",
    "\n",
    "# Simple analysis\n",
    "\n",
    "Lets take a look at some basic statistics of all our languages, trying to see if we can find any patterns in the test data, that can be utilized for different training methods.\n",
    "\n",
    "## How well did languages perform?\n",
    "\n",
    "Lets start with looking at the mean accuracy for each language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  language  nwords tested  mean accuracy  mean distance  mean loss\n0      deu          14201      97.829378       0.051387   0.387351\n1      eng          11553      96.516056       0.088181   0.398252\n2      gml            127      60.826772       1.137795   0.524684\n3      isl           7690      96.605982       0.073700   0.391719\n4      nno           1443      84.736660       0.241511   0.440174",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>language</th>\n      <th>nwords tested</th>\n      <th>mean accuracy</th>\n      <th>mean distance</th>\n      <th>mean loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>deu</td>\n      <td>14201</td>\n      <td>97.829378</td>\n      <td>0.051387</td>\n      <td>0.387351</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>eng</td>\n      <td>11553</td>\n      <td>96.516056</td>\n      <td>0.088181</td>\n      <td>0.398252</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gml</td>\n      <td>127</td>\n      <td>60.826772</td>\n      <td>1.137795</td>\n      <td>0.524684</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>isl</td>\n      <td>7690</td>\n      <td>96.605982</td>\n      <td>0.073700</td>\n      <td>0.391719</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>nno</td>\n      <td>1443</td>\n      <td>84.736660</td>\n      <td>0.241511</td>\n      <td>0.440174</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "# get mean accuracy, loss, distance\n",
    "avg_langs = baseraw.groupby(['language']).agg({'prediction':'count', 'is_correct':'mean', 'distance':'mean', 'loss':'mean'}).reset_index()\n",
    "avg_langs.columns = ['language', 'nwords tested', 'mean accuracy', 'mean distance', 'mean loss']\n",
    "# get actual nwords, as the first part was not grouped by model\n",
    "avg_langs['nwords tested'] = baseraw.groupby(['language', 'model']).agg({'prediction':'count'})['prediction'].reset_index().groupby('language').agg({'prediction':'max'}).reset_index()['prediction']\n",
    "avg_langs['mean accuracy'] = avg_langs['mean accuracy'] * 100\n",
    "\n",
    "avg_langs.sort_values(by=['language'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see how many words each development data test set had. Which shows that Middle Low German (gml) was only tested on 127 words. We can also see the mean accuracy for each language.\n",
    "\n",
    "We notice that the mean accuracy for the source languages are > 96%, while the target languages tend to be lower. We can also see that Middle Lowe German had the highest mean leveshtein distance with 1.1, it also has the highest mean loss at 0.52.\n",
    "\n",
    "## How well did individual model types perform?\n",
    "\n",
    "Lets dive one level deeper and take a look at how these langauges did in comparison to the model types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   language             model  nwords trained  nwords tested  lemma_in_word  \\\n0       deu     hall-mono-hmm           10000          14201      61.523836   \n1       deu  hall-transformer           10000          14201      61.523836   \n2       deu          mono-hmm           99405          14201      61.523836   \n3       deu       transformer           99405          14201      61.523836   \n4       eng     hall-mono-hmm           10000          11553      87.881935   \n5       eng  hall-transformer           10000          11553      87.881935   \n6       eng          mono-hmm           80864          11553      87.881935   \n7       eng       transformer           80864          11553      87.881935   \n8       gml     hall-mono-hmm           10000            127      11.811024   \n9       gml  hall-transformer           10000            127      11.811024   \n10      gml          mono-hmm             890            127      11.811024   \n11      gml       transformer             890            127      11.811024   \n12      isl     hall-mono-hmm           10000           7690      45.708713   \n13      isl  hall-transformer           10000           7690      45.708713   \n14      isl          mono-hmm           53841           7690      45.708713   \n15      isl       transformer           53841           7690      45.708713   \n16      nno     hall-mono-hmm           10000           1443      81.704782   \n17      nno  hall-transformer           10000           1443      81.704782   \n18      nno          mono-hmm           10101           1443      81.704782   \n19      nno       transformer           10101           1443      81.704782   \n\n    meanloss  meandist   accuracy  \n0   0.005871  0.075276  98.331103  \n1   0.769088  0.038448  96.894585  \n2   0.005425  0.061686  98.591648  \n3   0.769019  0.030139  97.500176  \n4   0.020817  0.149225  95.654808  \n5   0.776552  0.062754  96.918549  \n6   0.017318  0.081364  96.485761  \n7   0.778323  0.059379  97.005107  \n8   0.227857  1.220472  54.330709  \n9   0.814902  0.944882  65.354331  \n10  0.134304  1.236220  62.204724  \n11  0.921674  1.149606  61.417323  \n12  0.019361  0.083745  96.657997  \n13  0.765096  0.078674  96.150845  \n14  0.017599  0.062549  97.022107  \n15  0.764821  0.069831  96.592978  \n16  0.093843  0.281358  82.605683  \n17  0.789725  0.214830  86.347886  \n18  0.090520  0.293139  81.288981  \n19  0.786608  0.176715  88.704089  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>language</th>\n      <th>model</th>\n      <th>nwords trained</th>\n      <th>nwords tested</th>\n      <th>lemma_in_word</th>\n      <th>meanloss</th>\n      <th>meandist</th>\n      <th>accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>deu</td>\n      <td>hall-mono-hmm</td>\n      <td>10000</td>\n      <td>14201</td>\n      <td>61.523836</td>\n      <td>0.005871</td>\n      <td>0.075276</td>\n      <td>98.331103</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>deu</td>\n      <td>hall-transformer</td>\n      <td>10000</td>\n      <td>14201</td>\n      <td>61.523836</td>\n      <td>0.769088</td>\n      <td>0.038448</td>\n      <td>96.894585</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>deu</td>\n      <td>mono-hmm</td>\n      <td>99405</td>\n      <td>14201</td>\n      <td>61.523836</td>\n      <td>0.005425</td>\n      <td>0.061686</td>\n      <td>98.591648</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>deu</td>\n      <td>transformer</td>\n      <td>99405</td>\n      <td>14201</td>\n      <td>61.523836</td>\n      <td>0.769019</td>\n      <td>0.030139</td>\n      <td>97.500176</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>eng</td>\n      <td>hall-mono-hmm</td>\n      <td>10000</td>\n      <td>11553</td>\n      <td>87.881935</td>\n      <td>0.020817</td>\n      <td>0.149225</td>\n      <td>95.654808</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>eng</td>\n      <td>hall-transformer</td>\n      <td>10000</td>\n      <td>11553</td>\n      <td>87.881935</td>\n      <td>0.776552</td>\n      <td>0.062754</td>\n      <td>96.918549</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>eng</td>\n      <td>mono-hmm</td>\n      <td>80864</td>\n      <td>11553</td>\n      <td>87.881935</td>\n      <td>0.017318</td>\n      <td>0.081364</td>\n      <td>96.485761</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>eng</td>\n      <td>transformer</td>\n      <td>80864</td>\n      <td>11553</td>\n      <td>87.881935</td>\n      <td>0.778323</td>\n      <td>0.059379</td>\n      <td>97.005107</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>10000</td>\n      <td>127</td>\n      <td>11.811024</td>\n      <td>0.227857</td>\n      <td>1.220472</td>\n      <td>54.330709</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>gml</td>\n      <td>hall-transformer</td>\n      <td>10000</td>\n      <td>127</td>\n      <td>11.811024</td>\n      <td>0.814902</td>\n      <td>0.944882</td>\n      <td>65.354331</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>gml</td>\n      <td>mono-hmm</td>\n      <td>890</td>\n      <td>127</td>\n      <td>11.811024</td>\n      <td>0.134304</td>\n      <td>1.236220</td>\n      <td>62.204724</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>gml</td>\n      <td>transformer</td>\n      <td>890</td>\n      <td>127</td>\n      <td>11.811024</td>\n      <td>0.921674</td>\n      <td>1.149606</td>\n      <td>61.417323</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>isl</td>\n      <td>hall-mono-hmm</td>\n      <td>10000</td>\n      <td>7690</td>\n      <td>45.708713</td>\n      <td>0.019361</td>\n      <td>0.083745</td>\n      <td>96.657997</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>isl</td>\n      <td>hall-transformer</td>\n      <td>10000</td>\n      <td>7690</td>\n      <td>45.708713</td>\n      <td>0.765096</td>\n      <td>0.078674</td>\n      <td>96.150845</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>isl</td>\n      <td>mono-hmm</td>\n      <td>53841</td>\n      <td>7690</td>\n      <td>45.708713</td>\n      <td>0.017599</td>\n      <td>0.062549</td>\n      <td>97.022107</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>isl</td>\n      <td>transformer</td>\n      <td>53841</td>\n      <td>7690</td>\n      <td>45.708713</td>\n      <td>0.764821</td>\n      <td>0.069831</td>\n      <td>96.592978</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>nno</td>\n      <td>hall-mono-hmm</td>\n      <td>10000</td>\n      <td>1443</td>\n      <td>81.704782</td>\n      <td>0.093843</td>\n      <td>0.281358</td>\n      <td>82.605683</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>nno</td>\n      <td>hall-transformer</td>\n      <td>10000</td>\n      <td>1443</td>\n      <td>81.704782</td>\n      <td>0.789725</td>\n      <td>0.214830</td>\n      <td>86.347886</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>nno</td>\n      <td>mono-hmm</td>\n      <td>10101</td>\n      <td>1443</td>\n      <td>81.704782</td>\n      <td>0.090520</td>\n      <td>0.293139</td>\n      <td>81.288981</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>nno</td>\n      <td>transformer</td>\n      <td>10101</td>\n      <td>1443</td>\n      <td>81.704782</td>\n      <td>0.786608</td>\n      <td>0.176715</td>\n      <td>88.704089</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "# aggregating dataframe for number of words, mean loss, and mean distance\n",
    "aggdf = baseraw.groupby(['language','model']).agg({'prediction':'count', 'is_lemma':'mean', 'loss':'mean', 'distance':'mean', 'is_correct':'mean'}).reset_index()\n",
    "# rename\n",
    "aggdf.columns = ['language', 'model', 'nwords tested', 'lemma_in_word', 'meanloss', 'meandist', 'accuracy']\n",
    "aggdf['accuracy'] = aggdf['accuracy'] * 100\n",
    "aggdf['lemma_in_word'] = aggdf['lemma_in_word'] * 100\n",
    "aggdf['nwords trained'] = [10000, 10000, 99405, 99405, 10000, 10000, 80864, 80864, 10000, 10000, 890, 890, 10000, 10000, 53841, 53841, 10000, 10000, 10101, 10101]\n",
    "\n",
    "aggdf[['language', 'model', 'nwords trained', 'nwords tested', 'lemma_in_word', 'meanloss', 'meandist', 'accuracy']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial thoughts\n",
    "\n",
    "Middle Low German\n",
    "\n",
    "* Has a very low amount of development data\n",
    "* Has the lowest accuracy out of all languages\n",
    "    + hall-transformer seemed to perform the best out of the models at 65.354%\n",
    "\n",
    "Norwegian Nynorsk\n",
    "\n",
    "* A low amount of development data, although not the lowest\n",
    "* Good accuray results, with the transformer taking the lead at 88.704%\n",
    "\n",
    "English, German, Icelandic\n",
    "\n",
    "* Each having a good amount of development data\n",
    "* All accuracies are >= 95%\n",
    "    + For icelandic, mono-hmm did the best at 97%\n",
    "    + For german, mono-hmm did the bests at 98.592%\n",
    "    + For english, transformer did the best at 97%\n",
    "\n",
    "# Correlations and Pearsons r\n",
    "\n",
    "Next lets start by taking a look the matchups between the distance, loss, number of testing words, and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 900x900 with 30 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.pairplot(aggdf[['nwords tested', 'nwords trained', 'meanloss', 'meandist', 'accuracy']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                nwords tested  nwords trained  meanloss  meandist  accuracy\nnwords tested        1.000000        0.330275  0.013591  0.523978  0.668623\nnwords trained       0.330275        1.000000  0.004592  0.188719  0.227895\nmeanloss             0.013591        0.004592  1.000000  0.003196  0.006263\nmeandist             0.523978        0.188719  0.003196  1.000000  0.953489\naccuracy             0.668623        0.227895  0.006263  0.953489  1.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nwords tested</th>\n      <th>nwords trained</th>\n      <th>meanloss</th>\n      <th>meandist</th>\n      <th>accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>nwords tested</th>\n      <td>1.000000</td>\n      <td>0.330275</td>\n      <td>0.013591</td>\n      <td>0.523978</td>\n      <td>0.668623</td>\n    </tr>\n    <tr>\n      <th>nwords trained</th>\n      <td>0.330275</td>\n      <td>1.000000</td>\n      <td>0.004592</td>\n      <td>0.188719</td>\n      <td>0.227895</td>\n    </tr>\n    <tr>\n      <th>meanloss</th>\n      <td>0.013591</td>\n      <td>0.004592</td>\n      <td>1.000000</td>\n      <td>0.003196</td>\n      <td>0.006263</td>\n    </tr>\n    <tr>\n      <th>meandist</th>\n      <td>0.523978</td>\n      <td>0.188719</td>\n      <td>0.003196</td>\n      <td>1.000000</td>\n      <td>0.953489</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>0.668623</td>\n      <td>0.227895</td>\n      <td>0.006263</td>\n      <td>0.953489</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "## pearsons r squared\n",
    "aggdf[['nwords tested', 'nwords trained', 'meanloss', 'meandist', 'accuracy']].corr()**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation thoughs\n",
    "\n",
    "Pearsons r squared is being used here.\n",
    "\n",
    "* Mean levenshtein distance and accuracy have the highest R^2 value of ~0.95, this is an indicator that focusing on improving the metric might be the best way forward. A lower distance tends to show a higher accuracy.\n",
    "\n",
    "* The number of words is also heavily correlated to the accuracy and the mean distance, an attempt to boost the number of words might prove beneficial\n",
    "\n",
    "* The mean loss seems to have little to do with the models accuracy, although it is most likely a statistically significant r squared value.\n",
    "\n",
    "# Does the full lemma appear in the inflected word?\n",
    "\n",
    "Now that we have a general idea of how the baseline model performed, lets dive deeper into distinguishing a pattern in the two target languages: Middle Low German (gml) and Norwegian Nynorsk (nno).\n",
    "\n",
    "One factor to the language performance could be distinguishing whether the full lemma exists in the true word or not. It might make it easier for the Neural Network to correctly predict a word, if the lemma is unchanged and fully in the inflected word.\n",
    "\n",
    "Lets start digging into this idea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   language             model  nlemmas     %lemma  %false&lemma  %acc_diff\n8       gml     hall-mono-hmm       15  11.811024     12.068966  -0.997375\n9       gml  hall-transformer       15  11.811024     13.636364  -5.354331\n10      gml          mono-hmm       15  11.811024     12.500000  -2.204724\n11      gml       transformer       15  11.811024     14.285714  -8.083990\n16      nno     hall-mono-hmm     1179  81.704782     72.908367   1.872689\n17      nno  hall-transformer     1179  81.704782     77.157360   0.759832\n18      nno          mono-hmm     1179  81.704782     71.851852   2.256396\n19      nno       transformer     1179  81.704782     73.006135   1.202612",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>language</th>\n      <th>model</th>\n      <th>nlemmas</th>\n      <th>%lemma</th>\n      <th>%false&amp;lemma</th>\n      <th>%acc_diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>gml</td>\n      <td>hall-mono-hmm</td>\n      <td>15</td>\n      <td>11.811024</td>\n      <td>12.068966</td>\n      <td>-0.997375</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>gml</td>\n      <td>hall-transformer</td>\n      <td>15</td>\n      <td>11.811024</td>\n      <td>13.636364</td>\n      <td>-5.354331</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>gml</td>\n      <td>mono-hmm</td>\n      <td>15</td>\n      <td>11.811024</td>\n      <td>12.500000</td>\n      <td>-2.204724</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>gml</td>\n      <td>transformer</td>\n      <td>15</td>\n      <td>11.811024</td>\n      <td>14.285714</td>\n      <td>-8.083990</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>nno</td>\n      <td>hall-mono-hmm</td>\n      <td>1179</td>\n      <td>81.704782</td>\n      <td>72.908367</td>\n      <td>1.872689</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>nno</td>\n      <td>hall-transformer</td>\n      <td>1179</td>\n      <td>81.704782</td>\n      <td>77.157360</td>\n      <td>0.759832</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>nno</td>\n      <td>mono-hmm</td>\n      <td>1179</td>\n      <td>81.704782</td>\n      <td>71.851852</td>\n      <td>2.256396</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>nno</td>\n      <td>transformer</td>\n      <td>1179</td>\n      <td>81.704782</td>\n      <td>73.006135</td>\n      <td>1.202612</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# aggregate dataframe into each language -> model\n",
    "agglemma = baseraw[baseraw['is_lemma'] == 1].groupby(['language', 'model']).agg({'prediction':'count', 'loss':'mean', 'distance':'mean', 'is_correct':'mean'}).reset_index()\n",
    "agglemma.columns = ['language', 'model', 'nlemmas', 'meanloss', 'meandist', 'accuracy']\n",
    "\n",
    "# math to compare results\n",
    "agglemma['%acc_diff'] = (agglemma['accuracy']*100 - aggdf['accuracy'])\n",
    "# agglemma['dist_diff'] = agglemma['meandist'] - aggdf['meandist']\n",
    "# agglemma['loss_diff'] = agglemma['meanloss'] - aggdf['meanloss']\n",
    "agglemma['%lemma'] = agglemma['nlemmas'] / aggdf['numwords']*100\n",
    "\n",
    "# the proportion of words that are incorrect and have the full lemma, to the total incorrect words\n",
    "false = baseraw[baseraw.is_correct == 0]\n",
    "falseagg = false[false.is_lemma == 1].groupby(['language', 'model']).agg({'prediction':'count'}).reset_index()\n",
    "falseagg.columns = ['language', 'model', 'nwords']\n",
    "\n",
    "# finding proportion of false words that contain the full lemma\n",
    "false_lemma = [n for n in falseagg['nwords']]\n",
    "false = [ n for n in false.groupby(['language', 'model']).agg({'prediction':'count'}).reset_index()['prediction']]\n",
    "agglemma['%false&lemma'] = [false_lemma[i] / false[i] * 100 for i in range(len(false))]\n",
    "\n",
    "\n",
    "agglemma = agglemma[['language', 'model', 'nlemmas', '%lemma', '%false&lemma', '%acc_diff']]\n",
    "\n",
    "agglemma[[True if lang in ('gml', 'nno') else False for lang in agglemma.language]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this chart we can see the difference in the testing data after subsetting for words where the lemma appears unchanged in the inflected word. One thing that we notice is that only 11% of the Middle Low German development data is in this subset, while 81% is in Norwegian Nynorsk.\n",
    "\n",
    "We can also see that the accuracy dropped for all model types in Middle Low German, suggesting that this is a weak spot in the training. However, for Norwegian Nynorsk, the accuracy increased, suggesting that this might not be the best approach to helping this language train.\n",
    "\n",
    "Nonetheless, lets run some more models and see what results we get!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Augmentation\n",
    "\n",
    "One base method for training augmentation was done here. We had taken the target languages, Middle Low German and Norwegian Nynorsk and have added an additional buffer of data to train from the source languages, English, German, Icelandic. The amount of data added is a direct percentage of the existing target language, for example we added an additional 25%, 50%, .., 200% training data, when allowable. This data was randomly chosen from a subset of the source languages. The subset is the words where the lemma is in the inflected word, unchanged.\n",
    "\n",
    "This was done to test the hypothesis that if we augment the training data with a subset of source data, we can achieve a higher accuracy in training. Ultimately supporting the idea that targeted language feature modifications can help improve low-resource languages.\n",
    "\n",
    "## Basic Overview\n",
    "\n",
    "Lets start by taking a look at how each language performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  target language  mean augmented accuracy  mean baseline accuracy\n0             gml                65.485564               60.826772\n1             nno                86.050211               84.736660",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target language</th>\n      <th>mean augmented accuracy</th>\n      <th>mean baseline accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gml</td>\n      <td>65.485564</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>nno</td>\n      <td>86.050211</td>\n      <td>84.736660</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# reading in and adding columns for the lemma prediction raw data\n",
    "augraw = pd.read_csv('scraped_data/8-13-20.lemma.dev.predictions.csv')\n",
    "trglang = [lang.split('+')[0] for lang in augraw.language]\n",
    "srclang = [lang.split('+')[-1] for lang in augraw.language]\n",
    "percent_added = [int(lang.split('+')[1].split('p')[0]) for lang in augraw.language] \n",
    "\n",
    "augraw['target'], augraw['add_lang'], augraw['%added'] = trglang, srclang, percent_added\n",
    "# column for if full lemma is in truth word\n",
    "augraw['is_lemma'] = [1 if str(row[6]) in str(row[5]) else 0\n",
    "        for row in augraw.itertuples()]\n",
    "\n",
    "\n",
    "# reading in basic language model data\n",
    "basic_models = pd.read_csv('scraped_data/2020-07-10-tsv.predictions.csv')\n",
    "aggog = basic_models.groupby(['language','model']).agg({'loss':'mean', 'distance':'mean', 'is_correct':'mean'}).reset_index()\n",
    "aggog.columns = ['language', 'model', 'meanloss', 'meandist', 'accuracy']\n",
    "\n",
    "# get gml and nno baseline accuracies\n",
    "gml_acc = float(aggog[aggog.language == 'gml'].groupby(['language']).agg({'accuracy':'mean'}).reset_index()['accuracy'])*100\n",
    "nno_acc = float(aggog[aggog.language == 'nno'].groupby(['language']).agg({'accuracy':'mean'}).reset_index()['accuracy'])*100\n",
    "base_acc = [gml_acc, nno_acc]\n",
    "\n",
    "# aggregating dataframe for number of words, mean loss, and mean distance\n",
    "agglemma = augraw.groupby(['language','model']).agg({'target':'max', 'add_lang':'max', '%added':'max', 'loss':'mean', 'distance':'mean', 'is_correct':'mean'}).reset_index()\n",
    "# rename\n",
    "agglemma.columns = ['language', 'model', 'target', 'add_lang', '%added', 'meanloss', 'meandist', 'accuracy']\n",
    "\n",
    "agglemma['accuracy'] = agglemma['accuracy'] * 100\n",
    "\n",
    "comparison = agglemma.groupby(['target']).agg({'accuracy':'mean'}).reset_index()\n",
    "comparison.columns = ['target language', 'mean augmented accuracy']\n",
    "comparison['mean baseline accuracy'] = base_acc\n",
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, with the addition of another language for training the models perform, on average, better than the baseline.\n",
    "\n",
    "## Best language to add for training\n",
    "\n",
    "Lets take a look into which additional language performed the best for each target language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  target language addition language  mean augmented accuracy\n0             gml               deu                65.157480\n1             gml               eng                66.437008\n2             gml               isl                64.714567\n3             gml               nno                65.682415\n4             nno               deu                85.635086\n5             nno               eng                86.204336\n6             nno               isl                86.235274\n7             nno               nno                86.321899",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target language</th>\n      <th>addition language</th>\n      <th>mean augmented accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gml</td>\n      <td>deu</td>\n      <td>65.157480</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gml</td>\n      <td>eng</td>\n      <td>66.437008</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gml</td>\n      <td>isl</td>\n      <td>64.714567</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gml</td>\n      <td>nno</td>\n      <td>65.682415</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>nno</td>\n      <td>deu</td>\n      <td>85.635086</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>nno</td>\n      <td>eng</td>\n      <td>86.204336</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>nno</td>\n      <td>isl</td>\n      <td>86.235274</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>nno</td>\n      <td>nno</td>\n      <td>86.321899</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "lang_comparison = agglemma.groupby(['target','add_lang']).agg({'accuracy':'mean'}).reset_index()\n",
    "lang_comparison.columns = ['target language', 'addition language', 'mean augmented accuracy']\n",
    "lang_comparison['mean baseline accuracy'] = [acc for item in [[accuracy for i in range(4)] for accuracy in base_acc] for acc in item]\n",
    "\n",
    "lang_comparison[['target language', 'addition language', 'mean augmented accuracy']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that for both target languages, the custom training models out performed the baseline.\n",
    "\n",
    "For Middle Low German the best performing additional language was Norwegian Nynorsk coming, with an average accuracy of 65.06% compared to the baseline of 60.83%.\n",
    "\n",
    "For Norwegian Nynorsk the best performing addition language was FIXING NNO+NNO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What percentage of the target language does best?\n",
    "\n",
    "Lets take a look at the how each target language dealt with additional training data, on average. We can hope to gain insights on how much data to buffer with these low-resource languages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   target language  percent of target  mean augmented accuracy  \\\n0              gml                 25                64.468504   \n1              gml                 50                64.173228   \n2              gml                 75                64.566929   \n3              gml                100                65.452756   \n4              gml                125                67.322835   \n5              gml                150                65.452756   \n6              gml                175                66.404199   \n7              gml                200                66.535433   \n8              nno                 25                85.732848   \n9              nno                 50                86.053361   \n10             nno                 75                86.763687   \n11             nno                100                86.200624   \n12             nno                125                85.793486   \n13             nno                150                85.221760   \n14             nno                175                86.036036   \n\n    mean baseline accuracy  \n0                60.826772  \n1                60.826772  \n2                60.826772  \n3                60.826772  \n4                60.826772  \n5                60.826772  \n6                60.826772  \n7                60.826772  \n8                84.736660  \n9                84.736660  \n10               84.736660  \n11               84.736660  \n12               84.736660  \n13               84.736660  \n14               84.736660  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target language</th>\n      <th>percent of target</th>\n      <th>mean augmented accuracy</th>\n      <th>mean baseline accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>gml</td>\n      <td>25</td>\n      <td>64.468504</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gml</td>\n      <td>50</td>\n      <td>64.173228</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gml</td>\n      <td>75</td>\n      <td>64.566929</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gml</td>\n      <td>100</td>\n      <td>65.452756</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>gml</td>\n      <td>125</td>\n      <td>67.322835</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>gml</td>\n      <td>150</td>\n      <td>65.452756</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>gml</td>\n      <td>175</td>\n      <td>66.404199</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>gml</td>\n      <td>200</td>\n      <td>66.535433</td>\n      <td>60.826772</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>nno</td>\n      <td>25</td>\n      <td>85.732848</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>nno</td>\n      <td>50</td>\n      <td>86.053361</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>nno</td>\n      <td>75</td>\n      <td>86.763687</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>nno</td>\n      <td>100</td>\n      <td>86.200624</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>nno</td>\n      <td>125</td>\n      <td>85.793486</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>nno</td>\n      <td>150</td>\n      <td>85.221760</td>\n      <td>84.736660</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>nno</td>\n      <td>175</td>\n      <td>86.036036</td>\n      <td>84.736660</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# change agglemma to bins of 25%\n",
    "agglemma['%added'] = [p if p%25==0 else (p+(25-(p%25))) for p in agglemma['%added']]\n",
    "\n",
    "percent_comparison = agglemma.groupby(['target', '%added']).agg({'accuracy':'mean'}).reset_index()\n",
    "percent_comparison.columns = ['target language', 'percent of target', 'mean augmented accuracy']\n",
    "# percent_comparison['mean baseline accuracy'] = [acc for item in [[accuracy for i in range(9)] for accuracy in base_acc] for acc in item]\n",
    "\n",
    "\n",
    "percent_comparison['mean baseline accuracy'] = [acc for item in [[accuracy for i in range(8)] for accuracy in base_acc] for acc in item][:-1]\n",
    "percent_comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here if we ignore which language was added, we can see that adding more language for training Middle Low German produces a more accurate model. While, for Norwegian Nynorsk it seems that around an additional 75% training data improves performance.\n",
    "\n",
    "## Does this change depending on the language?\n",
    "\n",
    "Lets break this down even further into seeing if these changes in accuracy depends on the language being added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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f34bZ0OYfkHqw/q++41pVxVIuSb2z4d6SNJjUs1Yr33+SdpK0bvbdOgLYkrrrETezelbRxftmZoUi0jCie0jXWP2DNLV8oyLpAtJkNZdExEf1HY81GuuShu11Ig01PTYi3qjfkGpFsZSrHWnIZTfSrQ7+CDxUS/velDQstC1p1tsDsuuUzawR8PBLMzMzMzOzIubhl2ZmZmZmZkXMjToza1Ak9ZIUqvjecNXZ1zBJMypJv0XShYXMo8y6IyW9kPN6oaR8p2KvTkyPZdfkrOp+SsVrpdXmuVqNPEudb5L+K2lYXeVvZmaF4WvqzKyoSZoC/CwinqrvWOraKtzsuar9/rC622STvnwENC8z9b2txiKiT33HYGZmq849dWZmZpm67DWzwvH7aGaNjRt1Zla0JN0O9AQeyYYi/jIn+TBJ0yTNlXR2zjZNJJ0pabKkeZLulVTuDblztjkr288USYdVsE5HSY9KmpPdxPhRST1y0teSdLOkj7P0cqdbl3SSpHdyt60krpC0Ufb8FknXSPqHpC8lvSJpw5x1e0t6UtJnkt6TdFAl+x0r6WfZ840kPStpQXYM7qlgs+eyv59n78W2Ofu7LCvzR9l9CUuWt5d0o6RZkmZKulBS0wpiGizpJUmfZ+tfLWmNLO1aSZeVWf8hSf8ve95N0gPZe/ORpJNy1jtf0v2S7lC6ifbIyvLKttk9O4YLJP0lOz4/y0k/WtKkrMz/klTVPRiPzs6LWZJOy7PMknSFpE+zOP4jqW+W1iI75tMkzc6OT6sKjusUSbvmHIt7Jd2WnUP/lTQwZ90Kj2M5+/2xpDckfSFpuqTzy6TvIOnFrGzTJY3MlreS9EdJU7NyvZAtW2mYcjmxV/d97KPvPhOzlT7n60paJKlTznoDsjI3r+J9NDOrN27UmVnRiogjgGnAXhHRNiJ+n5O8A2mK712AcyVtli0/CdgX2Ik0rfh84JpKslkXWBvoDowArpe0aTnrNQFuBtYnNTQXA1fnpN8OtAb6AF2AK8ruQNI5wEhgp4jI6zq7Mg4h3S6iI/A/4KJsv22AJ4G7srwPAf4iKZ+hdxcAT2T77AH8uYL1hmZ/O2TvxUvZ622A90jH8PfAjZKUpd0KLAM2ArYGdgd+RvmWk24BsTawLel9PS5LuwsYXrJfSR2zfY2R1AR4BHiT9B7uApwiaY+cfe8D3A90AO6sLC9Ja2fr/oo0ff57wHYlO5K0L3AW6f6DnYHnSVPYV+b7wMZZzGeWNFSqKPPupGO+SRb3cGBelva7bHk/0rHtTv63DdkbGJPt82GyczjP45jrK+DIbD8/Bo7Njg2SegKPkc6lzlmcE7PtLgMGkI7pWsAvgW/zjL0672M74CnSfdy6kY7T0xHxCTAWyP2nx+HAmIhYmmccZmZ1LyL88MMPP0o9gCnArvUdR01iBXoBAfTIWfYqcHD2fBKwS05aV2Ap0KycfQ8jNTra5Cy7Fzgne34LcGEFcfUD5ufk8S3QsYI8ZgKXAy8A7Ssp60jghZzXAWyUE8vfctJ+BLybPR8OPF9mX9cB51WQz1jSdYoAtwHX5x7PCrYpOe7NysT7v5zXrbN11gXWAb4GWuWkHwL8O8/3/RTgwey5SI37odnrUcAz2fNtgGlltv0VcHP2/HzguWrkdSTwUk6agOk5x+sx4Kc56U2ARcD6lRyz3jnLfg/cmEccOwPvA0OAJmXi+QrYMGfZtsBHOefbjPI+P9mxeConbXNgcT7HMY/360rgipztHixnnSakf4ZsVcHnZEaZZWVjr877eAjwRgXrDQfGZc+bAp8Ag/Mppx9++OFHfT3cU2dWjtxhPY0x/wbik5zni0g35IXUk/ZgNiTrc1IjbzmpkVGe+RHxVc7rqaT/7JciqbWk67JhY1+QhiN2UBpOuB7wWUTMryCPDsAxpBuRL8izfOWprMzblJQ5K/dhpMZVVX5Jaii8mg3HO7qmMUXEouxp2yym5sCsnJiuI/UkrkTSJkpDWj/Jju/FpB4YIiJIvUuHZKsfSuqpIcunW5myn0Xp93t6vnmR3vsV62d55/aqrg9clZPXZ6Tj172SY5Sb/4rzq4oyP0PqRbsGmC3peklrknq+WgMTcmJ4PFuej7LnUEul69PyOY4rSNpG0r+zYYsLgJ/z3TFcj3Rz7LLWBlpWkJaP6ryPFcUA6WbgmyvNLLsbsCAiXq1hTA1SfddR9Z2/2erIjTqzAlAF1wVZQUQ1158O/DAiOuQ8WkbEzArW75gNXyzRE/i4nPVOIw333CYi1uS74YglPTlrSepQQR7zgT2BmyVtX83y5GM68GyZMreNiGOr2jAiPomIURHRDRhNGra5UXmr1iCmr4G1c2JaMyqejfGvwLvAxtnxPYt0bEvcDRygdP3aNsADOfl8VKbs7SLiR5XEXlles0jDUIF0bVvu6yy/0WXyaxURL1ZyLNbLeZ57flVa5oj4U0QMIA3p3QT4BTCX1NvVJyf/9rHqM6Xmcxxz3UUavrleRLQHrs2JfTqwYTnbzAWWVJD2FamxCqz4ji3bUK3O+1hRDETEElKP/GHAEaSh01aLXEea1T436szKUAWTb0i6L/uP7wJJz+Vej6Q0ScVfJf1T0lfA9yX1V5oo4Mts23uUc08zSXtKmpj91/tFSVtWln8ecf9SaTKAjyX9TCtPovEXpfuPLZQ0TmlCgCuVJnN4V9LWtXYQ69ZsoDr3arsWuCj78Y+kzpL2qWKb30haQ9KOpMbXfeWs0470Y/pzpYlXzitJiIhZpGF5f1GaUKW5pKG5G0fEWNKPyAclbVON8uTjUWATSUdkeTeXNEjfXWdYIUkH6rtJW+aTfjgvL2fVOaQhpnm9F9kxeQL4o6Q1lSaw2VDSThVs0g74AlgoqTdQqkEaEW9kMfwN+FdEfJ4lvQp8IekMpQk3mkrqK2lQJeFVltc/gC0k7Zv1YB1P6R7Pa4FflXw/KE0Gc2AVh+McpZ7ePsBRQMlkNBXGkb1/2yhN3vEVqTG0PCK+BW4ArpDUJVu3uyq+9i1f1T2O7Ui900skDSb1npa4E9hV0kGSmknqJKlfFvtNwOVKk7I0lbStpBakoaYtlSZgaQ78GmhRRcyVvY+PAutKOkVpYpl2ZT53t5GGD+8N3JHH8Wk0irGOVDbRjqTTlCYXmiXpqDLxVTbR1HaSXsvK9pqk7crPyaz+uFFnVkZUPPnGY6TJDLoAr/Pd8K4Sh5ImpmhH+gH0IOk6p7VIvQj7lawoqT/px8to0mQL1wEPS2pRUf5Ks9vl/jAiZ38/AP4fsCvpgv/yfhgfRPohtDaph+SlrBwlEz9cnsfhWR1dAvw6q/hPz2P9q0g9CE9I+hJ4mdSzU5FPSI2Zj0nv+c8j4t1y1rsSaEXqbXiZNOQt1xGka/feBT4lXd9TSkQ8SfpR/7CkAXmUJS8R8SVpYo2DSeX4hDSZRlU/igEGAa9IWkg6bidHxEfl5LGIdP6Py96LIXns+0hgDeAd0jG+n3T9YXlOJ33GviQ1WsqbhfNu0mfgrpy4lgN7ka5x/Ij0/vwNaF9JXBXmFRFzgQNJ177NI113Np70mSIiHiQd2zFKQ/7eBqq679+zpIltngYui4gn8ijzmtmy+aQhm/NIk4wAnJHt7+UshqdIvcg1VoPjeBzw2+wzdi6p56tkX9NI13yeRhqeOhHYKks+HXgLeC1L+x3pmsEF2T7/RroG9StKD3stT2Xv45ekoZV7kT4PH5AmrClJH0f6J8XrETGlinwalWKsIzPrks7X7sBPgWuUJlUqUdFEU2uR/pnzpyyWy4F/KGeGVLPVgdLlAGaWS1Xc0FppGN180kx/CyTdQvrhcWSWPpRUSfXIrrlB0gvA2Ij4taS/AnMj4pycfb4HHBMRz1aVfznx3ATMjohfZa83Iv1I2Tgi/pfFtzQiRmXpJwLHRcRm2estSBNpdMin/GaWKM0KOQM4LCL+Xd/xWO2R9AxwV0T8rb5jWd0UYR05jNTobBcRy7JlnwJ7R8TLWXzLIqLkVi4/Ai6PiN6SjgBOjIjBOft7CbguIm7JJ3+zuuCeOrM8ZMOALlW6t9kXpFnX4LuL7qH0RfrdgJkllVU56esDp6n0pAPrUc4EHHkqNXlDmeclZuc8X1zO61W95sasUZC0h6QO2bDAkuu0Xq7nsKwWZcNK+1N+j7CVUQR1JMC8kgZdJncyKah4oqlupN7wXFOpfPIjszrnRp1Z+cp2YR9KugfSrqThG72y5apgm1lAd0m56bmTIUwHLioz6UDriCi5n1V1u9BLTd5QJi8zq13bkmZOnEsavrdvRCyu35Cstki6lTRk9ZRsmKatrNjqyFXxMamRmasnaRiw2WrDjTqz8pWdfKMd6ZqZeaQZ2C6uYvuXSJNJnKA0EcA+wOCc9BuAnytNdCBJbZQmAGhXQf5VuRc4StJmklqT/42GzayaIuL8iOiUzf64TUS8Ut8xWe2JiBHZjKG31Hcsq7FiqyNXxT9JE00dmsU6nHQt7aN1lL9ZXtyoMytf2ck3biMNt5hJmtSh0qFWEfEN8BPSxdifA4eTKoCSyRTGk26QfDXpuoP/kWZaqyh/lO4RdlgF+T1Guoj739m+XsqSvq5Wqc3MzKpWVHXkqoiIeaRZj08jNVp/CeyZTZpkttrwRClmdUTSK8C1EXFzHeS1GWnWvRZlriEwMzNb7dRlHWnWELmnzqxAJO2kdC+4ZpJGAFuy8jT3tZnffkr3UutImgb8ETfozMxsdVTXdaRZQ1fQRp2kkyW9nXWJn5ItW0vSk5I+yP52rGo/ZkVqU+BNYAFp2MYB2Q2XC2U06ebLk0nXKhxb+epmZmb1pq7rSLMGrWDDLyX1BcaQLnz9hvTfl2NJY6Q/i4hLJZ0JdIyIMwoShJmZmZmZWQNXyJ66zYCXI2JRNgTsWWA/0pS3t2br3ArsW8AYzMzMzMzMGrRCNureBoZK6pRNsf4j0j1I1inpXs/+dilgDGZmZmZmZg1as0LtOCImSfod8CSwkDRuOu9JGyQdAxwD0KZNmwG9e/cuSJxmZrb6mDBhwtyI6FzfcazOXD+amTVOldWRdXZLA0kXAzOAk4FhETFLUldgbERsWtm2AwcOjPHjx9dFmGZmVo8kTYiIgfUdR7Fw/Whm1nhUVkcWevbLLtnfnqSbTN4NPAyMyFYZATxUyBjMzMzMzMwasoINv8w8IKkTsBQ4PiLmS7oUuFfST4FpwIEFjsHMzMzMzKzBKmijLiJ2LGfZPGCXQuZrZmZmZmbWWBS6p65gli5dyowZM1iyZEl9h1KrWrZsSY8ePWjevHl9h2JmZmZmZkWgaBt1M2bMoF27dvTq1QtJ9R1OrYgI5s2bx4wZM9hggw3qOxwzMzMzMysCBZ0opZCWLFlCp06dGkyDDkASnTp1anC9j2ZmZmZmVjhF26gDGlSDrkRDLJOZmZmZmRVOUTfqzMzMzMzMGrtG16i75ZZbOOGEE8pNa9u2bYXbTZkyhb59+xYqLDMzMzMzsxppdI06MzMzMzOzhqTBNer23XdfBgwYQJ8+fbj++usBuPnmm9lkk03YaaedGDdu3Ip1P/roI7bddlsGDRrEOeeck3ceU6ZMYccdd6R///7079+fF198EYCxY8cybNgwDjjgAHr37s1hhx1GRADwz3/+k969e7PDDjtw0kknseeee9Ziqc3MzMzMrLFqcI26m266iQkTJjB+/Hj+9Kc/MXPmTM477zzGjRvHk08+yTvvvLNi3ZNPPpljjz2W1157jXXXXTfvPLp06cKTTz7J66+/zj333MNJJ520Iu2NN97gyiuv5J133uHDDz9k3LhxLFmyhNGjR/PYY4/xwgsvMGfOnFots5mZmZmZNV4NrlH3pz/9ia222oohQ4Ywffp0br/9doYNG0bnzp1ZY401GD58+Ip1x40bxyGHHALAEUcckXceS5cuZdSoUWyxxRYceOCBpRqKgwcPpkePHjRp0oR+/foxZcoU3n33Xb73ve+tuPdcSZ5mZmZmZmarqmhvPl6esWPH8tRTT/HSSy/RunVrhg0bRu/evZk0aVKF29TkFgJXXHEF66yzDm+++SbffvstLVu2XJHWokWLFc+bNm3KsmXLVgzBNDMzMzMzq20NqqduwTup1C0AACAASURBVIIFdOzYkdatW/Puu+/y8ssvs3jxYsaOHcu8efNYunQp991334r1t99+e8aMGQPAnXfeWa18unbtSpMmTbj99ttZvnx5pev37t2bDz/8kClTpgBwzz33VL9wZmZmZmZm5WhQjbof/OAHLFu2jC233JJzzjmHIUOG0LVrV84//3y23XZbdt11V/r3779i/auuuoprrrmGQYMGsWDBgrzzOe6447j11lsZMmQI77//Pm3atKl0/VatWvGXv/yFH/zgB+ywww6ss846tG/fvsblNDMzMzMzK6FiGBo4cODAGD9+fKllkyZNYrPNNquniKpv4cKFtG3blojg+OOPZ+ONN+bUU08td91iK5uZWW2RNCEiBtZ3HMWivPrRzMwapsrqyAZ1Td3q7IYbbuDWW2/lm2++Yeutt2b06NH1HZKZmZkVoWm/3SKv9Xqe+1aBIzGz1YUbdWW89dZbK82E2aJFC1555ZVV2u+pp55aYc+cmZlZfcm3gQBuJDRkzw7dKe91d3ru2QJGYmY14UZdGVtssQUTJ06s7zDMzMysjAG/uC3vdSf84cgCRmJmtnpxo87MzMzMGqx8eyHdA2nFrEHNfmlmZmZmZtbYuKfOzMzMrJ5VZ2jpg+3yW2/7P2+f9z4vLrKfhA25bGY14Z66WnL++edz2WWX1XcYZmZmZmbWyDSYf11U5z9c+fAF1mZmZo1DdXp9xp04roCRNG7VmYmVjmsWLhBrkBr6REsNplFXHy666CJuu+021ltvPTp37syAAQOYPHkyxx9/PHPmzKF169bccMMN9O7dm5EjR7LnnntywAEHANC2bVsWLlxYzyUwMzMzs0LybUOsLrhRV0MTJkxgzJgxvPHGGyxbtoz+/fszYMAAjjnmGK699lo23nhjXnnlFY477jieeeaZ+g7XzMysTnnGQTMrVsXYEHejroaef/559ttvP1q3bg3A3nvvzZIlS3jxxRc58MADV6z39ddf11eIZmZmtcqTU5iZrZ4K+o0r6VTgZ0AAbwFHAWcCo4A52WpnRcQ/CxlHoUgq9frbb7+lQ4cO5d68vFmzZnz77bcARATffPNNncRoZmZmtSffHkhwL2RDVojZSotRvj1aq0tvVkNWsEadpO7AScDmEbFY0r3AwVnyFRFR1FNFDh06lJEjR3LmmWeybNkyHnnkEUaPHs0GG2zAfffdx4EHHkhE8J///IetttqKXr16MWHCBA466CAeeughli5dWt9FMDMza7A86cbqwY0fs7pR6FsaNANaSWoGtAY+LnB+daZ///4MHz6cfv36sf/++7PjjjsCcOedd3LjjTey1VZb0adPHx566CEARo0axbPPPsvgwYN55ZVXaNOmTX2Gb2ZmZmZmDUTBeuoiYqaky4BpwGLgiYh4QtJ2wAmSjgTGA6dFxPxVza8+ph49++yzOfvss1da/vjjj6+0bJ111uHll19e8fqSSy4paGxmZmZmZtY4FKynTlJHYB9gA6Ab0EbS4cBfgQ2BfsAs4I8VbH+MpPGSxs+ZM6e8VczMzBod149mZlZWISdK2RX4KCLmAEj6O7BdRNxRsoKkG4BHy9s4Iq4HrgcYOHBgFDBOMzOzopFbP7Zr1y6GDRtWKv2ggw7iuOOOY9GiRfzoRz9aafuRI0cycuRI5s6dywEHHMDXUz8slX74oE7s1bc9Hy/4hlP/PqNU2jvNmtHt+91Ya4u1WDx7MZPvmbzS/nvs0YMOm3bgg4ULufrDldNH9epF3zXb8/YXC7hhyhQ6lIn/yiuvpF+/fjz11FNceOGFpdLenzybnruPpOVaXfl88ht8+trKI2N6/egY1lizE4+8vYA7Xpu3UvpfD+rJWm2acd8b87l/4vwV5Sqx2c83o+kaTZn1/CzmvbHy9nTtB8CYGdN56bPPSiW1aNKE3/dN1/LdNm0q55UpW6dOnXjggQcA+NWvfsVLL71UqmzN23Vkgx//HIDpz9zJ4k+nld7/Wuuw/u5HA3DmwzP5aF7pGbY3X7cV5/2wKwAnPzCdtxeV/vnUrlc71t97fQDevfFdln21bEXayXPEgA4dOLJnSv/l22/xdTbBW4lt11qLg3usB0DZ8w7KP/fenzz7u/L33YFOfXdk2aIv+fDhq1fa/pFtv6rw3AMYtd3a7Lrpmkye+zVv3/72Sukl595XM77io79/VKpssPK5V9YtEydWeO4BXHfddWy66aY88sgjvD9m5RFXJefeZ+++wtyJ393Oaniz9D6Wd+6Vyv+wXgD85S9/4d57710pfezYsQBcdtllPPpo6Z/PrVq14rHHHgPgggsu4Omnny6VXtm5B9CjRw/uuCP9RO8yYI9Kz72pT9zE15+l97VvVray594nX5SeO2LY4l+tGKW2//77M29e6c/WLrvswjnnnAPAD3/4QxYvXlwqfc899+T0009P+8rz3Ms1cuRIoEmF597a/XZmrd7b8M0X85jyz+tXvGclcs+9sx6ZWfrYPDOMX//61+y6665MnDiRU045ZaX9X3zxxWy33Xa8+OKLnHXWWSulV/a9B9+de5Up5DV104AhklorTRO5CzBJUtecdfYDVv5UmpmZmZmZWV4UUbhOMEm/AYYDy4A3SLc3+Btp6GUAU4DRETGrsv0MHDgwxo8fX2rZpEmT2GyzzQoQdf1ryGUzM6uMpAkRMbC+4ygW5dWP1VWdWSIPqcYskRffl99goOpM+1+9mRT/kPe6hSgX1H/ZGmq5oOGei6vD1P+FKFtDLRfUbdkqqyMLep+6iDgPOK/M4iMKmaeZmZmZmVljUuhbGpiZmZmZmVkBFbSnzszMzOqeb/hsZta4NJhGXXWuCcjH6jD218zMzMzMrCoefrkK7rjjDgYPHky/fv0YPXo0y5cvp23btpx99tlstdVWDBkyhNmz05SvkydPZsiQIQwaNIhzzz2Xtm3b1nP0ZmZmZmbWELhRV0OTJk3innvuYdy4cUycOJGmTZty55138tVXXzFkyBDefPNNhg4dyg033ADAySefzMknn8xrr71Gt27d6jl6MzMzMzNrKNyoq6Gnn36aCRMmMGjQIPr168fTTz/Nhx9+yBprrMGee+4JwIABA5iS3dzypZde4sADDwTg0EMPra+wzczMzMysgWkw19TVtYhgxIgRXHLJJaWWX3bZZaR7rUPTpk1ZtmxZfYRnZmZmZmaNhHvqamiXXXbh/vvv59NPPwXgs88+Y+rUqRWuP2TIEB544AEAxowZUycxmpmZmZlZw+dGXQ1tvvnmXHjhhey+++5sueWW7LbbbsyaNavC9a+88kouv/xyBg8ezKxZs2jfvn0dRmtmZmZmZg1Vgxl+WR+3IBg+fDjDhw8vtWzhwoUrnh9wwAEccMABAHTv3p2XX34ZSYwZM4aBAwfWaaxmZmZmZtYwNZhG3epuwoQJnHDCCUQEHTp04KabbqrvkMzMzMzMrAFwo66O7Ljjjrz55pv1HYaZmZmZmTUwvqbOzMzMzMysiLlRZ2ZmZmZmVsTcqDMzMzMzMytibtSZmZmZmZkVMTfqVsF2221XaXqvXr2YO3duHUVjZmZmZmaNUYOZ/XL7P29fq/sbd+K4Ktd58cUXazVPMzMzMzOz6nJP3Spo27YtALNmzWLo0KH069ePvn378vzzz9dzZGZmZmZm1lg0mJ66+nTXXXexxx57cPbZZ7N8+XIWLVpU3yGZmZmZmVkj4UZdLRg0aBBHH300S5cuZd9996Vfv371HZKZmZmZmTUSHn5ZC4YOHcpzzz1H9+7dOeKII7jtttvqOyQzMzMzM2sk3KirBVOnTqVLly6MGjWKn/70p7z++uv1HZKZmZmZmTUSHn5ZC8aOHcsf/vAHmjdvTtu2bd1TZ2ZmZmZmdabBNOryuQVBbVu4cCEAI0aMYMSIESulT5kypY4jMjMzMzOzxqagwy8lnSrpv5LelnS3pJaS1pL0pKQPsr8dCxmDmZmZmZlZQ1awRp2k7sBJwMCI6As0BQ4GzgSejoiNgaez12ZmZmZmZlYDhZ4opRnQSlIzoDXwMbAPcGuWfiuwb4FjMDMzMzMza7AK1qiLiJnAZcA0YBawICKeANaJiFnZOrOALoWKwczMzMzMrKEr5PDLjqReuQ2AbkAbSYdXY/tjJI2XNH7OnDmFCtPMzKyouH40M7OyCjn8clfgo4iYExFLgb8D2wGzJXUFyP5+Wt7GEXF9RAyMiIGdO3cuYJhmZmbFw/WjmZmVVchG3TRgiKTWkgTsAkwCHgZK5v8fATxUwBjMzMzMzMwatILdpy4iXpF0P/A6sAx4A7geaAvcK+mnpIbfgbWR37NDd6qN3ayw03PP1ur+zMzMzMzMCqGgs19GxHkR0Tsi+kbEERHxdUTMi4hdImLj7O9nhYyhkKZMmcJmm23GqFGj6NOnD7vvvjuLFy9m2LBhnHHGGQwePJhNNtmE559/HoAlS5Zw1FFHscUWW7D11lvz73//u55LYGZmZmZmxa7QtzRo8D744AOOP/54/vvf/9KhQwceeOABAJYtW8arr77KlVdeyW9+8xsArrnmGgDeeust7r77bkaMGMGSJUvqLXYzMzMzMyt+btStog022IB+/foBMGDAAKZMmQLAT37yk5WWvfDCCxxxxBEA9O7dm/XXX5/333+/zmM2MzMzM7OGw426VdSiRYsVz5s2bcqyZctKLc9dFhF1H6CZmZmZmTVobtTVoaFDh3LnnXcC8P777zNt2jQ23XTTeo7KzMzMzMyKmRt1dei4445j+fLlbLHFFgwfPpxbbrmlVE+fmZmZmZlZdRXslgZ1rT5uQdCrVy/efvvtFa9PP/30ldZZe+21V1xT17JlS2655ZY6is7MzMzMzBoD99SZmZmZmZkVMTfqzMzMzMzMipgbdWZmZmZmZkWsqBt1DfEWAQ2xTGZmZmZmVjhF26hr2bIl8+bNa1CNoIhg3rx5tGzZsr5DMTMzMzOzIlG0s1/26NGDGTNmMGfOnPoOpVa1bNmSHj161HcYZmZmZmZWJIq2Ude8eXM22GCD+g7DzMzMzMysXhXt8EszMzMzMzNzo87MzMzMzKyouVFnZmZmZmZWxNyoMzMzMzMzK2Ju1JmZmZmZmRUxN+rMzMzMzMyKmBt1ZmZmZmZmRcyNOjMzMzMzsyLmRp2ZmZmZmVkRc6POzMzMzMysiLlRZ2ZmZmZmVsTcqDMzMzMzMytizQq1Y0mbAvfkLPoecC7QARgFzMmWnxUR/yxUHGZmZmZmZg1ZwRp1EfEe0A9AUlNgJvAgcBRwRURcVqi8zczMzMzMGou6Gn65CzA5IqbWUX5mZmZmZmaNQl016g4G7s55fYKk/0i6SVLHOorBzMzMzMyswSl4o07SGsDewH3Zor8CG5KGZs4C/ljBdsdIGi9p/Jw5c8pbxczMrNFx/WhmZmXVRU/dD4HXI2I2QETMjojlEfEtcAMwuLyNIuL6iBgYEQM7d+5cB2GamZmt/lw/mplZWXXRqDuEnKGXkrrmpO0HvF0HMZiZmZmZmTVIVc5+KWkgsCPQDVhMaoQ9FRGf5bFta2A3YHTO4t9L6gcEMKVMmpmZWaO0KvWtmZk1bhX21EkaKel14FdAK+A94FNgB+BJSbdK6lnZziNiUUR0iogFOcuOiIgtImLLiNg7ImbVTlHMzMyKT23Ut2Zm1rhV1lPXBtg+IhaXl5j1tm0MTCtEYGZmZo2E61szM1slFTbqIuKayjaMiIm1H46ZmVnj4vrWzMxWVd4TpUjaS9IrkiZKOq6QQZmZmTVWrm/NzKy6Krumbqsyi44AhgD9gWMLGZSZmVlj4frWzMxWVWXX1B0nScC5EfEJMB24CPgW+LgugjMzM2sEXN+amdkqqeyautHZfw+vkzQeOAfYDmgNXFBH8ZmZmTVorm/NzGxVVXpNXUS8GRH7ABOBh4GuEfFwRHxdJ9GZmZk1Aq5vzcxsVVR2Td3PJb2R3TunDfADoKOkf0nasc4iNDMza8Bc35qZ2aqqrKfuuIjYmnSx9i8iYllE/Ak4GNivTqIzMzNr+FzfmpnZKqlsopSZki4AWgHvliyMiPnA/yt0YGZmZo2E61szM1sllTXq9gH2AJYCT9ZNOGZmZo2O61szM1sllTXqukXEIxUlZtMvd4+IGbUflpmZWaPh+tbMzFZJZY26P0hqAjwETADmAC2BjYDvA7sA5wGuZMzMzGrO9a2Zma2Syu5Td6CkzYHDgKOBrsAiYBLwT+CiiFhSJ1GamZk1UK5vzcxsVVXWU0dEvAOcXUexmJmZNUqub83MbFVUevNxMzMzMzMzW725UWdmZmZmZlbE3KgzMzMzMzMrYlU26iQ9IOnH2cxcZmZmVgCub83MrKbyqTj+ChwKfCDpUkm9CxyTmZlZY+T61szMaqTKRl1EPBURhwH9gSnAk5JelHSUpOaFDtDMzKwxcH1rZmY1ldcQD0mdgJHAz4A3gKtIlc6TBYvMzMyskXF9a2ZmNVHpfeoAJP0d6A3cDuwVEbOypHskjS9kcGZmZo2F61szM6upKht1wNUR8Ux5CRExsJbjMTMza6xc35qZWY3kM/xyM0kdSl5I6ijpuALGZGZm1hi5vjUzsxrJp1E3KiI+L3kREfOBUVVtJGlTSRNzHl9IOkXSWpKelPRB9rfjqhTAzMysgahRfWtmZpZPo66JJJW8kNQUWKOqjSLivYjoFxH9gAHAIuBB4Ezg6YjYGHg6e21mZtbY1ai+NTMzy6dR9y/gXkm7SNoZuBt4vJr57AJMjoipwD7ArdnyW4F9q7kvMzOzhqg26lszM2uE8pko5QxgNHAsIOAJ4G/VzOdgUuUEsE7JjF4RMUtSl2ruy8wqMe23W+S1Xs9z3ypwJGZWTbVR35qZWSNUZaMuIr4F/po9qk3SGsDewK+qud0xwDEAPXv2rEnWZmZmRSPf+tb1o5mZlVXl8EtJG0u6X9I7kj4seVQjjx8Cr0fE7Oz1bElds313BT4tb6OIuD4iBkbEwM6dO1cjOzMzs+KTb33r+tHMzMrK55q6m0n/NVwGfB+4jXRj1HwdwndDLwEeBkZkz0cAD1VjX2ZmZg3Vqta3ZmbWSOXTqGsVEU8DioipEXE+sHM+O5fUGtgN+HvO4kuB3SR9kKVdWr2QzczMGqQa17dmZta45TNRyhJJTYAPJJ0AzATymtwkIhYBncosm0eaDdPMzMy+U+P61szMGrd8eupOAVoDJ5HuN3c43w2fNDMzs9rh+tbMzGqk0p667ManB0XEL4CFwFF1EpWZmVkj4vrWzMxWRaU9dRGxHBggSXUUj5mZWaPj+tbMzFZFPtfUvQE8JOk+4KuShRHx94o3MTMzs2pyfWtmZjWST6NuLWAepWfgCkrPaGlmZmarxvWtmZnVSJWNuojwuH4zM7MCc31rZmY1VWWjTtLNpP8UlhIRRxckIjMzs0bI9a2ZmdVUPsMvH8153hLYD/i4MOGYmZk1Wq5vzcysRvIZfvlA7mtJdwNPFSwiM7PV0IBf3JbXehP+cGSBIymcab/dIq/1ep77VoEjaZxc35qZWU3lc/PxsjYGetZ2IGZmZlaK61szM8tLPtfUfUnpMf6fAGcULCIzsyKWT2+Xe7qsPK5vzcyspvIZftmuLgIxMzNrzFzfmplZTVU5/FLSfpLa57zuIGnfwoZlZmbWuLi+NTOzmsrnmrrzImJByYuI+Bw4r3AhmZmZNUqub83MrEbyadSVt04+t0IwMzOz/Lm+NTOzGsmnUTde0uWSNpT0PUlXABMKHZiZmVkj4/rWzMxqJJ//AJ4InAPck71+Avh1wSIys9XGs0N3ymu9nZ57tsCR1Eyxx2+NjutbMzOrkXxmv/wKOLMOYjEzM2u0XN+amVlN5XOfuieBA7MLtpHUERgTEXsUOjgzK5zt/7x9letcXA+X8+Rznzfwvd6qa8AvbqtynQc9oX69cn1rZmY1lc81dWuXVDAAETEf6FK4kMzMzBol17dmZlYj+TTqvpXUs+SFpPWBKFxIZmZmjZLrWzMzq5F8xladDbwgqWQmgaHAMYULyaz+5TME0MP/zKyWub41M7MayWeilMcl9QeGAAJOjYi5BY/MzMysEXF9a2ZmNZXvLAjLgU+BlsDmkoiI5woXlpmZWaPk+tbMzKotn9kvfwacDPQAJpL+g/gSsHNhQzNbveUzeyTAuBPH1VqensHQrOFyfWtmZjWVz0QpJwODgKkR8X1ga2BOPjuX1EHS/ZLelTRJ0raSzpc0U9LE7PGjVYjfzMysoahxfWtmZo1bPsMvl0TEEklIahER70raNM/9XwU8HhEHSFoDaA3sAVwREZfVNOj68OzQnfJab6fnnq16JauWYj/2+cS/usZuZnVqVepbMzNrxPJp1M2Q1AH4P+BJSfOBj6vaSNKapJm7RgJExDfAN5JqHq2ZmVnDVaP61szMLJ/ZL/fLnp4v6d9Ae+DxPPb9PdKwkZslbQVMIA0tAThB0pHAeOC07AarZmZmjdYq1LdmZtbI5Tv7JQARUZ0xYs2A/sCJEfGKpKuAM4GrgQtIN1S9APgjcHTZjSUdQ3Z/np49e5ZNrlX5THhxcfUOVa3wvdKsIclnkhfwRC/FIJ/vzNqcIKgxqqy+rcv60czMikM+E6XU1AxgRkS8kr2+H+gfEbMjYnlEfAvcAAwub+OIuD4iBkbEwM6dOxcwTDMzs+Lh+tHMzMoqWKMuIj4Bpudc5L0L8I6krjmr7Qe8XagYzMzMzMzMGrpCjyk8Ebgzm/nyQ+Ao4E+S+pGGX04BRhc4Bqtl+Qyjm/CHI/PaV10PffUQQDMzMzNraAraqIuIicDAMouPKGSeZmZmZmZmjUndz/5Rh/KZaASAjmsWNpACyqenC+Di+6p+q32vNDMzMzOz4lPIiVLMzMzMzMyswNyoMzMzMzMzK2INevil1Z/GMPTVzMzMzGx14J46MzMzMzOzIuZGnZmZmZmZWREr2uGX+dxvbHW915jvlWa26ur6Hoe1Kd9Za8edOK7AkZiZmVlD4J46MzMzMzOzIuZGnZmZmZmZWRFzo87MzMzMzKyIuVFnZmZmZmZWxNyoMzMzMzMzK2Ju1JmZmZmZmRWx1XO+bzMzMzMrGkuXLmXGjBksWbKk1PI/7LdZ3vtYoCvzXvfCJvn3S6zZN7/1Jk2atNKyli1b0qNHD5o3b553fmb1wY06MzMzM1slM2bMoF27dvTq1QtJK5bH9Ll572PDprPzXrd506Z5r9t9fn7rtevdu9TriGDevHnMmDGDDTbYIO/8zOqDh1+amZmZ2SpZsmQJnTp1KtWgK3aS6NSp00q9j2arIzfqzMzMzGyVNaQGXYmGWCZrmNyoMzMzMzMzK2Ju1JmZmZlZvXnwvru58Jwzyk3rtPGgCrebOW0me+20V6HCMisqbtSZmZmZmZkVMc9+aWZmZmYFc+LPjuSTj2fy9ddfc/jRx3DQYUfy4L13ccM1V9G5yzqsv8GGrNFiDQA+mjaDkcefwbLly9ht2A555zFz2kzOOPEMFi1aBMA5F5/D1oO25tVxr3LDpVfTqWNH3vngA/r16cPffv97JPGvZ5/lrN/9jk4dO7LV5pszY/58Hn300YIcA7NCc6POzMzMzArmgsuuokOHjixZspjhe+7OTrvsxtWX/577/vEUbdutyVHD92WzvlsAcPq5lzLqyIM4/MB9uPaWu/POY6211+LGe26kRcsWTPlwCqf//HTuf+J+AP4zaRKvPPIIXbt0YbdDD+Xl119n6759OeX883ns9tvp1aMHR512GnhSFCtiRdGoe++99xg2bFipZXOarU/nrXfh26Vf878HLl9pm059d4Bt4bOvlnHsvdNWSj98UCf26tuejxd8w9u3v71Serfvd2OtLdZi8ezFTL5nMifPKf1BP2K9ngzs2JEPFi7k6g8nr1jeIYvz4osvZrvttuPFF1/krLPOKrXt+5Nn02PnQ2ndZX2+mPpfPnnp4ZXy77n7SGgHT733BTe8uPI9Xq74SQ+6tV+Dua/P5ZMXPlkpfdOjN6V52+Z8+sqnfPrKpyvF/7s+fWnZtCn/9/HH/HvunFKxA4wdOxaAyy67bKX/Wv1v6mdsdMDpAMx66SG+nPpOqfRmrdrC4Vk+T37C6zMWlUpfd83mXLX/egB89MBHfDXzq1Lprbq0YsODNwRg8pjJnPzf0lMJb9SmLSdumNIvfPdd5nzzdan4t912Wy655BIA9t9/f+bNm7di2/cnz6bd+pvTddt9Ulnuv4xvly0ttf/2G24FO6fnw2/+kLJ+3Kc9Rw7uxPJvljPp2pVvVNplmy502aYLSxcu5b2b3lvp2O/TtSs7d+7Cp18v4aL33isVO8Bpp53GXnvtxXvvvcfo0aNLbfv+5Nmsu+3erLl+HxZ9OpUZz9y1Uv7j9/iGgT1bM37aIv7w9Mrnxrk/6Eqfrq34/L3PmfGvGSulbzh8Q1qt04rP3vqMk/8za6X0szfdlC4tWvLMnE95aNasleK///77WXvttbnlllu45ZZbSsUOsNH+/48mzVsw542nmf/eqyvtn1Hpa+m6cXN55v0vSiW1aNaE247oBcD0x6ez4P0FpdKbtWlG75+m+wxNfXgqJ/9nYan0zmu04NfZfYj+PHky//tqYan4N9lkE66//noAjjnmGN5///1S8bfq0pP1dj4MgI/+cS1Lvyx986M23TaEH6fno8dM4/PFy0qlb7dBW04e1gWAd/76Dt8u/bZUesc+Hem+S3f+f3t3Hh9Vdfdx/PMjrGHHACLIakUBWSNVcQHBBSoQiqKoLahI0boXq9TWlcfaR1vt01IVWgWtylIkgKCiKLiigCyCUlT2iGylAiJbOM8fcxOHZO5kskxm7uT7fr3ymjvnnnvv75zc3F/O3GUAVv3fKnpO73nM/CFDhnDjjTeyf/9++vXrR0HDhw9n+PDh7Ny5k0svvfSYeWu/2kZG5/NpcMqPbgx13AAAGlNJREFUObRnFxvmji+0/Jvn7qdP2zp8tfMgv5mdU2j+zec24uw2tfhuy3esf3l9ofnNL2lOndZ12LNuT6FjNsATTzxB586defPNNxk7dmyh+U8//XShMhEJvheemcCbr88F4JutOcyaPpXuZ/SgwXEZAPTtn8WG9aH/pT5cvIzJEx4H4MrB/bnnfwr/jxfJkSNHeOg3D7Fm1RrS0tLYsG5D/rxup51G0+OPB6DjKaewMSeHmunptGzWjJbNmgFw2U9+wvNz55ZJe0USIRCDOhEREREJno8/fJ8P31vIi9lzqVEjneFDBtL6pB+x7su1vsuU5GsEJj09iYyMDLLfyubo0aN0btE5f17VqlXzpyulpZGbm4tzrtjbEElmgRjUtW3bNv/MUZ5udz4HQKUq1Tj5ijE+Sy6iQc3KTLmmte+6T6hblQ63dPCdX6NxDTrc0oGHp0Xuqh/VqsWfO3bKf39egTjPOuss39gB6rRoT50W7X2336dtHfq0reM7P6NrBhldM3zn55018os/64QTyDrhhIixA4wePZrRo0f7xt/kzIH5Z72O9SgAd11wvG9sAK0Gt4o6v80VbXxjB/LPukDk+KdPn37M+/DYgfwzjoWtBoi676RVTYu671SpVSXqvtOoWvX8fSdS7NH2e4D0Ri0i7vuZtUN9n9k8PWr89drWo17ber7zG5zWgIfXNPKdf37DRpzfsJFv/HlnjSLFDtCwS28adukdYc2h+H/RI4Nf9PDft0+8+EROvPhE3/ktBrTg4YP++07e2V6IHH/eGbs8BeNv9ZNRPmsOxf/0Fc19tw3Q7oZ2Ued3uKUDC24uHBdAenp6oX0jXEZGRtR9p2qd4yLuO328fadNRrWo+07NZjWj7vt1WtdhweP+8fXp04c+ffr4zheR1LFv7x7q1K1HjRrprPvyC1YsW8plBw6weNEH/Hf3f6hZqzavz5lF23ah/4XOPL0LU2e+ypWD+zP55djvb9u7dy/HNzmeSpUqkT0lm9zc3Kj1T27dmg1btrAxJ4cWTZsy/dVXS9VOkUSL69Mvzayemf3LzNaY2edmdqaZNTCzN8zsC++1fjxjEBEREZHEOPu888nNPcKgC8/jL4/9nk5dutGwUWN+efudXJnVlxFXDqZdh4759R978G6enjiZHv0u59u9+6Ks+VhDhw8le2o2l/e7nA3rNpCenh61fo3q1fnTvffy0+uv58KrrqLRccdRt27dErdTJNHifabuz8BrzrlLzawqkA78BpjvnHvEzO4G7gYifzmJiIiIiARW1WrVePq5KYVnnNmDQUOuLFC4jVbNm7Fw9gv5JXfeNMJ33U2bN2X2wtkAtGzdkplvz8yfd8c9dwDQvUd3BrXrnl/+x9/9Ln/6nO7dWTp3Ls457njwQTK7/1BPJGjiNqgzszrAucBwAOfcIeCQmQ0EenrVJgEL0KBORERERMrRxGnTeGnmTA4dPkzHU08t9HAykSCJ55m61sAO4Fkz6wQsBW4FGjvntgI457aamf9NOyIiIiJSoa36fC3X3lLgPuBq1ZjyaoQzgMVw0/Dh3BR273dRl2yKJLN4DuoqA12Bm51zH5nZnwldahkTMxsJjARo3jz6AwdEREQqCuVHqWg6nHoyH79x7IPP1qelJSgakeQUzwelbAG2OOc+8t7/i9Agb5uZNQHwXrdHWtg5N945l+mcy2zYsGEcwxQREQkO5UcRESkoboM659w3wGYza+sV9QY+A2YBw7yyYcDMCIuLiIiIiIhIDOL99MubgRe8J1+uA64hNJCcambXAZuAy+Icg4iIiIiISMqK66DOObccyIwwK9I3DouIiIiIlIm/PvpX0mum87uh1yY6FJG4i/eZOhERERGpYLrd+VyZru+D27uV6fpEUk08H5QiIiIiIlJunnriKfr26Ms1l13D+q/WA7Bu0yYGXX895w4ezEVXX83adesAGDVmDNmvv56/bK1atRISs0hZ0Jk6EREREQm81StWMzd7Li+/8TK5ubkMvmAw7Tu259b77uPx++7jpJYtWbxiBXc8+CCvTJyY6HBFypQGdSIiIiISeEs+WkKfvn2okV4DgF4X9eLgwYN8tGwZw26/Pb/ewUOHEhWiSNxoUCciIiIiKcHMjnl/9OhR6tauzfszZhSqWzktjaNHjwLgnOOQBnsSYLqnTkREREQCL/OMTN589U0OfH+A7/Z9x9vz3qZGjRq0aNaMGa+9BoQGb5+uWQNA86ZNWb56NQBz5s/n8OHDCYtdpLQ0qBMRERGRwGvfsT19B/ZlUJ9B3HLdLWT+OPStWn9/9FGemz6ds7Ky6N6/P3Pmzwdg+GWX8d7ixfQcMoQlK1dSs2bNRIYvUiq6/FJEREREytTSR38OwGebd8a8TJu0baXe7qjbRjHqtlHHlDXdDTMmTChUt1FGBm9NmZL//o/jx5d6+yKJojN1IiIiIiIiAaZBnYiIiIiISIBpUCciIiIiIhJgGtSJiIiIiIgEmAZ1IiIiIiIiAaZBnYiIiIiISIBpUCciIiIiIhJg+p46ERERESlTmx48DYBaxVgm2rfUNR4xuVTxiKQ6DepEREREJCXM+tcs/vn3f3L48GE6du3IvY/cS5Nu3bnhZz/jtQULqF6tGpPHjaNRRgbrNm1ixJ13knv0KBeccw7jnnuOffv2JboJIiWiyy9FREREJPC+WvsVr858lRdmv8CM+TOoVKkSs6fP5rv9+zm9Uyc+yM6mR2YmE6dNA+Cuhx/mhp//nIXTptGkUaMERy9SOhrUiYiIiEjgLXp3EatXrmbIxUMY1HsQi95bxJaNW6hapQoX9+wJQOf27dmUkwPAx8uXM+iiiwC47JJLEhW2SJnQ5ZciIiIiEnjOObKGZHHHPXccUz7xyWcxMwDS0tI4kpubiPBE4kpn6kREREQk8M445wxef+V1du3YBcB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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "avg_lang = agglemma.groupby(['target', '%added', 'add_lang']).agg({'accuracy':'mean', 'meanloss':'mean', 'meandist':'mean'}).reset_index()\n",
    "\n",
    "avg_lang.columns = ['target', '%added', 'add_lang', 'avg_acc', 'avg_loss', 'avg_dist']\n",
    "\n",
    "avg_lang\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5), sharex= False, sharey=True)\n",
    "\n",
    "for i,tlang in enumerate(['gml', 'nno']):\n",
    "    targetdf = avg_lang[tlang == avg_lang.target]\n",
    "    # group into bins of 25, graph is unreadble otherwise..\n",
    "    targetdf['%added'] = [p if p%25==0 else (p+(25-(p%25))) for p in targetdf['%added']]\n",
    "    p1 = sns.barplot(data=targetdf.sort_values(by=['%added', 'add_lang']), x='%added', y='avg_acc', hue='add_lang', ax= ax[i]).set_title(f\"target: {tlang}\")\n",
    "    ax[i].set_xlabel(\"Percent of language added\")\n",
    "    ax[i].set_ylabel(\"accuracy (%)\")\n",
    "    ax[i].axhline(base_acc[i], ls='--', color='black') # sets line in graph for baseline comparison\n",
    "\n",
    "# ax[1].get_legend().set_visible(False)  \n",
    "# plt.xlim([0, 100])\n",
    "plt.ylim([50, 90])\n",
    "plt.suptitle('Average accuracy accross all models for a given target language\\nthe black line is the average baseline accuracy')\n",
    "\n",
    "# to save\n",
    "plt.savefig('target.accuracy.percent.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that most of the additional languages performed, on average, better than the baseline. Howevewr, some languages are improving with more training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing by model type and target languages\n",
    "\n",
    "Taking this even further, lets break it down by model type and language added. Can we see any trends for the accuracy depending on the model, additional language, or the percent of the target language added?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def calculate(row, baseline):\n",
    "    '''takes new row and subracts baseline results'''\n",
    "    baseline = baseline[baseline.language == row['target']]\n",
    "    baseline = baseline[baseline.model == row['model']]\n",
    "    acc_diff = float(row['accuracy'] - baseline['accuracy']*100)\n",
    "    loss_diff = float(row['meanloss'] - baseline['meanloss'])\n",
    "    dist_diff = float(row['meandist'] - baseline['meandist'])\n",
    "\n",
    "    return {\n",
    "        \"target\":row['target'],\n",
    "        \"add_lang\":row['add_lang'],\n",
    "        \"%added\":row['%added'],\n",
    "        \"model\":row['model'],\n",
    "        \"loss_difference\":loss_diff,\n",
    "        \"dist_difference\":dist_diff,\n",
    "        \"acc_difference\":acc_diff\n",
    "    }\n",
    "\n",
    "\n",
    "differences = pd.DataFrame([calculate(row, aggog) for index, row in agglemma.iterrows()])\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(15, 8), sharex= True, sharey=True)\n",
    "\n",
    "for i,tlang in enumerate(['gml', 'nno']):\n",
    "    targetdf = differences[tlang == differences.target]\n",
    "    for n, srclang in enumerate(['deu', 'eng', 'isl', 'nno']):\n",
    "        subset = targetdf[targetdf.add_lang == srclang]\n",
    "\n",
    "        # print(t_s_df.head(20))\n",
    "        for k, model in enumerate(['mono-hmm', 'transformer']):\n",
    "            data = subset[subset.model == model]\n",
    "            \n",
    "            p1 = sns.lineplot(data=data, x='%added', y='acc_difference', hue='model', dashes=True, ax= ax[i, n%4]).set_title(f\"target: {tlang}, addition: {srclang}\")\n",
    "            # if tlang != 'nno' or srclang != 'nno':\n",
    "            #     p1 = sns.lineplot(data=data, x='%added', y='acc_difference', hue='model', dashes=True, ax= ax[i, n%4]).set_title(f\"target: {tlang}, addition: {srclang}\")\n",
    "            if k == 0:\n",
    "                ax[i,n%4].lines[0].set_linestyle(\"--\")\n",
    "        ax[i, n%4].set_xlabel(\"Percent of language added\")\n",
    "        ax[i, n%4].get_legend().set_visible(False)\n",
    "    ax[i, 0].set_ylabel(\"Percent difference from baseline\")\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "line1, line2 = Line2D([0], [0], color='b', ls='-', lw=4), Line2D([0], [0], ls='--', lw=4)\n",
    "        \n",
    "plt.xlim([0, 200])\n",
    "plt.legend([line2, line1], labels=['mono-hmm', '', 'transformer'], loc=1, bbox_to_anchor=[0.8, 2.6])\n",
    "plt.suptitle(\"Each target and additional languages difference in accuracy from the baseline\")\n",
    "\n",
    "# to save\n",
    "plt.savefig('difference.modeltype.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a distinct pattern from this chart. The mono-hmm type models seem to be a better choice for lower resource languages. Which is especially true for Norwegian Nynorsk, where the mono-hmm outperformed the transformer model every time.\n",
    "\n",
    "We can also really see the differences, and the changes that an additional language produces. However, the best improvement was a 5.5% increase in accuracy from adding an additional 75% training data of Norwegian Nynorsk to the Middle Low German training set.\n",
    "\n",
    "ADDING MORE DATA TO THESE"
   ]
  }
 ]
}