c4v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# code made by team: leet\n",
    "#Participants:\n",
    "#charles-francis damedey\n",
    "#Benjamin Dancause\n",
    "#Dimitrios Nassif\n",
    "#-----------------------\n",
    "# importing libraries\n",
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.utils import shuffle\n",
    "##############################################################\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn import feature_extraction\n",
    "from sklearn import pipeline\n",
    "from sklearn import linear_model\n",
    "from IPython.display import display\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"datasets.csv\"\n",
    "\n",
    "# dataset manipulations\n",
    "names = ['ID', 'function_source', 'CWE-119', 'CWE-120',\t'CWE-476', 'CWE-468', 'CWE-other']\n",
    "df = pd.read_csv(path, names=names  )\n",
    "\n",
    "#print(df.shape);print(df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning set from 1 to 85805\n",
    "\n",
    "#learn_set = df.iloc[1:85805]\n",
    "\n",
    "#cut the data because its too big\n",
    "learn_set = df.iloc[0:10000]\n",
    "\n",
    "x = learn_set.iloc[:,1]\n",
    "\n",
    "#cwe\n",
    "y1 = learn_set.iloc[:,2]\n",
    "#cwe\n",
    "y2 = learn_set.iloc[:,3]\n",
    "#cwe\n",
    "y3 = learn_set.iloc[:,4]\n",
    "#cwe\n",
    "y4 = learn_set.iloc[:,5]\n",
    "#other\n",
    "y5 = learn_set.iloc[:,6]\n",
    "\n",
    "\n",
    "#print(y1);print(y5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test set from 85806 to 105231\n",
    "\n",
    "X = df.iloc[85805:105232]\n",
    "X = X.iloc[:,1]\n",
    "#cwe\n",
    "Y1 = df.iloc[85805:105232]\n",
    "Y1 = Y1.iloc[:,2]\n",
    "#cwe\n",
    "Y2 = df.iloc[85805:105232]\n",
    "Y2 = Y2.iloc[:,3]\n",
    "#cwe\n",
    "Y3 = df.iloc[85805:105232]\n",
    "Y3 = Y3.iloc[:,4]\n",
    "#cwe\n",
    "Y4 = df.iloc[85805:105232]\n",
    "Y4 = Y4.iloc[:,5]\n",
    "#other\n",
    "Y5 = df.iloc[85805:105232]\n",
    "Y5 = Y5.iloc[:,6]\n",
    "\n",
    "\n",
    "#print(Y1);print(Y5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collection the first row is x\n",
    "# # Target or the labels we want to predict (i.e., the 13 different complaints of products)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y1, test_size=0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9999,)\n"
     ]
    }
   ],
   "source": [
    "#print(train.head(5));print(test.head(5))\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = feature_extraction.text.TfidfVectorizer(ngram_range=(1,2),analyzer='char')\n",
    "pipe = pipeline.Pipeline([('vec', vec), ('clf', linear_model.LogisticRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vec&#x27;, TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 2))),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vec&#x27;, TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 2))),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(analyzer=&#x27;char&#x27;, ngram_range=(1, 2))</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vec', TfidfVectorizer(analyzer='char', ngram_range=(1, 2))),\n",
       "                ('clf', LogisticRegression())])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model learning, takes a long time\n",
    "# the model learns on the x_train and y_train data\n",
    "pipe.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0' '0' '0' ... '0' '0' '0']\n"
     ]
    }
   ],
   "source": [
    "# make the model predict on a set of data\n",
    "# the model makes predictions on the x_test and y_test data\n",
    "predicted_var = pipe.predict(x_test)\n",
    "print(predicted_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.25"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we see the accuracy of the predictions made by the model\n",
    "metrics.accuracy_score(y_test, predicted_var)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105231\n",
      "['0' '0' '0' ... '0' '0' '0']\n"
     ]
    }
   ],
   "source": [
    "# make the model predict on the rest of the dataset that we don't know the language\n",
    "predicted_var = pipe.predict(df.function_source)\n",
    "print(len(predicted_var))\n",
    "print(predicted_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         0\n",
      "1         0\n",
      "2         0\n",
      "3         0\n",
      "4         0\n",
      "         ..\n",
      "105226    0\n",
      "105227    0\n",
      "105228    0\n",
      "105229    0\n",
      "105230    0\n",
      "Name: CWE-119, Length: 105231, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# learn_set = df.iloc[1:85805]\n",
    "# test set from 85806 to 105231\n",
    "\n",
    "#CWE-119', 'CWE-120', 'CWE-476', 'CWE-468', 'CWE-other'\n",
    "df['CWE-119'] = predicted_var \n",
    "df.fillna(0)\n",
    "print(df['CWE-119'])\n",
    "#print(temp) \n",
    "\n",
    "df.to_csv(r'C:\\Users\\mstr\\Documents\\GitHub\\CodeML22 code\\challenge 4\\output.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (microsoft store)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "74240e1388f775f6a25796995e248cd3c384138bceb666c80cf8a8cc28a81df4"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}