f1.ipynb

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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "display_name": "Python 3.8.5 64-bit"
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\nThreshold = 0.5833, train accuracy = 0.9696\nThreshold = 0.7762, train accuracy = 0.9269\nThreshold = 0.4439, train accuracy = 0.9122\nThreshold = 0.0600, train accuracy = 0.9600\nThreshold = 0.7105, train accuracy = 0.9299\nThreshold = -8.7397, train accuracy = 0.9555\nThreshold = 0.9008, train accuracy = 0.9899\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| Algorithm   |   Accuracy |   Precision |   Recall |   F1 |   ROC |   TP |   TN |   FP |   FN |\n+=============+============+=============+==========+======+=======+======+======+======+======+\n| LCS         |       0.95 |        0.98 |     0.92 | 0.95 |  1.00 |  339 |  360 |    8 |   29 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| COS         |       0.89 |        0.99 |     0.79 | 0.88 |  0.99 |  289 |  366 |    2 |   79 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| LEV         |       0.94 |        0.94 |     0.93 | 0.94 |  0.98 |  344 |  347 |   21 |   24 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| LSH         |       0.94 |        0.97 |     0.92 | 0.94 |  0.98 |  338 |  357 |   11 |   30 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| D2VK        |       0.95 |        0.97 |     0.94 | 0.95 |  0.99 |  346 |  356 |   12 |   22 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| WMDK        |       0.93 |        0.98 |     0.88 | 0.93 |  1.00 |  323 |  363 |    5 |   45 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n| SiamX2      |       0.92 |        0.95 |     0.88 | 0.92 |  0.98 |  325 |  352 |   16 |   43 |\n+-------------+------------+-------------+----------+------+-------+------+------+------+------+\n"
    }
   ],
   "source": [
    "from helpers.similarity_generator import get_algorithm_by_name\n",
    "from metrics import metrics\n",
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "\n",
    "stats = {}\n",
    "table = []\n",
    "headers = ['Algorithm', 'Accuracy', 'Precision', 'Recall', 'F1', 'ROC', 'TP', 'TN', 'FP', 'FN']\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2VK', 'WMDK']:\n",
    "    alg = get_algorithm_by_name(name)\n",
    "    s = metrics.train_test(metrics.get_metrics)('data/train.csv', 'data/test.csv', alg, True, name)\n",
    "    stats[name] = s\n",
    "    table.append((name, s['accuracy'], s['precision'], s['recall'], s['f1'], s['roc'], s['tp'], s['tn'], s['fp'], s['fn']))\n",
    "\n",
    "name = 'SiamX2'\n",
    "siamx = get_algorithm_by_name(name)\n",
    "df_train = pd.read_csv('data/train.csv', index_col=0)\n",
    "df_test = pd.read_csv('data/test.csv', index_col=0)\n",
    "siamx.train(df_train, True, name)\n",
    "y_pred = siamx.run_similarity(df_test)\n",
    "y_test = df_test['label'].to_numpy()\n",
    "s = metrics.get_metrics(siamx, y_test, y_pred)\n",
    "stats[name] = s\n",
    "table.append((name, s['accuracy'], s['precision'], s['recall'], s['f1'], s['roc'], s['tp'], s['tn'], s['fp'], s['fn']))\n",
    "\n",
    "print(tabulate(table, headers, tablefmt='grid', floatfmt='.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "LCS 15.346621078000013\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       179409    179409  179409  179409  179409  179409\n1          972       972     972     972     972     972\nCOS 18.71035759899999\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       179817    179817  179817  179817  179817  179817\n1          564       564     564     564     564     564\nLEV 161.394464712\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       178359    178359  178359  178359  178359  178359\n1         2022      2022    2022    2022    2022    2022\nLSH 625.3815550520001\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       179077    179077  179077  179077  179077  179077\n1         1304      1304    1304    1304    1304    1304\nD2VK 3156.966807996\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       178936    178936  178936  178936  178936  178936\n1         1445      1445    1445    1445    1445    1445\nWMDK 1949.152138073\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       179700    179700  179700  179700  179700  179700\n1          681       681     681     681     681     681\nSiamX2 82.11296640699948\n      comment1  comment2   meta1   meta2   label   score\npred                                                    \n0       178918    178918  178918  178918  178918  178918\n1         1463      1463    1463    1463    1463    1463\n"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import time\n",
    "\n",
    "df = pd.read_csv('data/pairs/junit4.csv', index_col=0, na_filter=False)\n",
    "X = list(zip(df.comment1, df.comment2))\n",
    "times = {}\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2VK', 'WMDK', 'SiamX2']:\n",
    "    alg = get_algorithm_by_name(name, load=True)\n",
    "\n",
    "    start_time = time.process_time()\n",
    "    if name == 'SiamX2':\n",
    "        scores = np.array(alg.run_similarity(df))\n",
    "    else:\n",
    "        scores = np.array(alg.run_similarity(X))\n",
    "    times[name] = time.process_time() - start_time\n",
    "\n",
    "    predictions = alg.predict(scores)\n",
    "    df_pred = df.copy()\n",
    "    df_pred.drop(columns=['name1', 'name2'], inplace=True)\n",
    "    df_pred.insert(len(df_pred.columns), 'score', scores)\n",
    "    df_pred.insert(len(df_pred.columns), 'pred', predictions)\n",
    "    df_pred.sort_values(by='score', ascending=False, inplace=True)\n",
    "    df_pred.to_csv('data/pred/' + name + '.csv')\n",
    "\n",
    "    print(name, times[name])\n",
    "    print(df_pred.groupby('pred').count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd \n",
    "\n",
    "with open('data/up/accepted/all.pkl', 'rb') as f:\n",
    "    all = pickle.load(f)\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2VK', 'WMDK', 'SiamX2']:\n",
    "    df = pd.read_csv('data/pred/' + name + '.csv', index_col=0)\n",
    "    df.loc[all, 'label'] = 1\n",
    "    df.to_csv('data/pred/part/' + name + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\n"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from helpers.similarity_generator import get_algorithm_by_name\n",
    "\n",
    "df = pd.read_csv('data/test.csv', index_col=0, na_filter=False)\n",
    "X = list(zip(df.comment1, df.comment2))\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2VK', 'WMDK', 'SiamX2']:\n",
    "    alg = get_algorithm_by_name(name, load=True)\n",
    "    if name == 'SiamX2':\n",
    "        scores = np.array(alg.run_similarity(df))\n",
    "    else:\n",
    "        scores = np.array(alg.run_similarity(X))\n",
    "    df_pred = df.copy()\n",
    "    df_pred.insert(len(df_pred.columns), 'score', scores)\n",
    "    df_pred.to_csv('data/pred/test/' + name + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "for name, title in zip(['LCS', 'COS', 'LEV', 'LSH', 'D2VK', 'WMDK', 'SiamX2'],\n",
    "    ['LCS', 'COS', 'LEV', 'LSH', 'D2V', 'WMD', 'SNN']):\n",
    "    df = pd.read_csv('data/pred/test/' + name + '.csv', index_col=0)\n",
    "    precision, recall, thresholds = precision_recall_curve(df.label, df.score)\n",
    "    plt.plot(recall, precision, label=title)\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend()\n",
    "plt.savefig('stats/pr1.png', bbox_inches='tight', pad_inches=0.1, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n-1    156494\n 0     23285\n 1       602\nName: label, dtype: int64\n-1    156491\n 0     23285\n 1       605\nName: label, dtype: int64\n"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import pickle\n",
    "\n",
    "all = set()\n",
    "for name in ['LCS', 'LSH', 'D2V']:\n",
    "    with open('data/up/accepted/plain/'+name+'.pkl', 'rb') as f:\n",
    "        x = pickle.load(f)\n",
    "        all.update(x)\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2V', 'WMD', 'SNN']:\n",
    "    df = pd.read_csv('data/pred/full/' + name + '.csv', index_col=0, na_filter=False)\n",
    "    print(df.label.value_counts())\n",
    "    df.loc[all, 'label'] = 1\n",
    "    print(df.label.value_counts())\n",
    "    df.to_csv('data/pred/full/' + name + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "+-------------+------+------+-------------+----------+------+\n| Algorithm   |   TP |   FP |   Precision |   Recall |   F1 |\n+=============+======+======+=============+==========+======+\n| LCS         |  441 |  528 |        0.46 |     0.73 | 0.56 |\n+-------------+------+------+-------------+----------+------+\n| COS         |  351 |  208 |        0.63 |     0.58 | 0.60 |\n+-------------+------+------+-------------+----------+------+\n| LEV         |  480 | 1198 |        0.29 |     0.79 | 0.42 |\n+-------------+------+------+-------------+----------+------+\n| LSH         |  449 |  835 |        0.35 |     0.74 | 0.48 |\n+-------------+------+------+-------------+----------+------+\n| D2V         |  468 |  850 |        0.36 |     0.77 | 0.49 |\n+-------------+------+------+-------------+----------+------+\n| WMD         |  394 |  285 |        0.58 |     0.65 | 0.61 |\n+-------------+------+------+-------------+----------+------+\n| SNN         |  455 |  960 |        0.32 |     0.75 | 0.45 |\n+-------------+------+------+-------------+----------+------+\n"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from tabulate import tabulate\n",
    "\n",
    "table = []\n",
    "headers = ['Algorithm', 'TP', 'FP', 'Precision', 'Recall', 'F1']\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2V', 'WMD', 'SNN']:\n",
    "    df = pd.read_csv('data/pred/full/' + name + '.csv', index_col=0, na_filter=False)\n",
    "    tp = int(((df.label == 1) & (df.pred == 1)).value_counts()[True])\n",
    "    fp = int(((df.label == 0) & (df.pred == 1)).value_counts()[True])\n",
    "    fn = int(((df.label == 1) & (df.pred == 0)).value_counts()[True])\n",
    "    precision = tp/(tp+fp)\n",
    "    recall = tp/(tp+fn)\n",
    "    f1 = 2*precision*recall/(precision+recall)\n",
    "    table.append((name, tp, fp, precision, recall, f1))\n",
    "\n",
    "print(tabulate(table, headers, tablefmt='grid', floatfmt='.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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235.402095 \nL 406.166825 235.295604 \nL 406.166825 235.135399 \nL 405.496051 235.260611 \nL 405.496051 235.153578 \nL 405.496051 234.124063 \nL 404.825277 234.250312 \nL 404.825277 234.140672 \nL 404.825277 234.03077 \nL 404.154503 234.15734 \nL 404.154503 234.047261 \nL 404.154503 232.591903 \nL 402.812955 232.847911 \nL 402.812955 232.791104 \nL 402.812955 232.563184 \nL 402.142182 232.691639 \nL 402.142182 232.634528 \nL 402.142182 232.00168 \nL 400.800634 232.202244 \nL 400.800634 231.970055 \nL 398.788312 232.301469 \nL 398.788312 232.068375 \nL 398.117539 232.198755 \nL 398.117539 232.140345 \nL 398.117539 229.681437 \nL 396.775991 229.946474 \nL 396.775991 229.884984 \nL 396.775991 229.514394 \nL 396.105217 229.647485 \nL 396.105217 229.585488 \nL 396.105217 229.523411 \nL 395.434443 229.656765 \nL 395.434443 229.594654 \nL 395.434443 229.220307 \nL 394.763669 229.354154 \nL 394.763669 229.291527 \nL 394.763669 228.787579 \nL 394.092895 228.922015 \nL 394.092895 228.858699 \nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "for name in ['LCS', 'COS', 'LEV', 'LSH', 'D2V', 'WMD', 'SNN']:\n",
    "    df = pd.read_csv('data/pred/full/' + name + '.csv', index_col=0)\n",
    "    df = df[(df.label == 0) | (df.label == 1)]\n",
    "    precision, recall, thresholds = precision_recall_curve(df.label, df.score)\n",
    "    plt.plot(recall, precision, label=name)\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend()\n",
    "plt.savefig('stats/pr2.png', bbox_inches='tight', pad_inches=0.1, dpi=300)\n",
    "plt.show()"
   ]
  }
 ]
}