statistical_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-26T10:28:08.193116500Z",
     "start_time": "2024-05-26T10:28:08.180493600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input lists are the same length\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import copy\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "#global projects \n",
    "projects= [\"lucida\", \"word\", \"anonymouth\",\"h2o-2\",\"phimpme-android\", \"GeometricWeather\", \"Arduino\" ,\"lbry-android\", \"litho\", \"PictureSelector\", \"SmarterStreaming\", \"lawnchair\", \"NekoX\", \"hudi\", \"PojavLauncher\", \"beam\" ]\n",
    "colours_for_projects = [\"red\",\"red\",\"red\",\"red\", \"red\", \"red\",\"red\", \"red\",\"green\",\"green\",\"green\", \"green\",\"green\",\"green\",\"green\", \"green\"]\n",
    "\n",
    "if len(projects) != len(colours_for_projects):\n",
    "    print(\"\\n Lists are NOT the same length!! Check you input\\n\\n\")\n",
    "    print(f\"{len(projects)} vs {len(colours_for_projects)}\")\n",
    "else:\n",
    "    print(\"input lists are the same length\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "\n",
    "def data_prep(metric_index:int, requested_projects=None):\n",
    "    \"\"\"\n",
    "    Prepare and fetch data for visualization.\n",
    "    \"\"\"\n",
    "\n",
    "    global projects\n",
    "    project_paths: list[str] = []\n",
    "    \n",
    "    if requested_projects is None:\n",
    "        requested_projects = copy.deepcopy(projects)\n",
    "    \n",
    "    \n",
    "    absolute_path = os.path.abspath(os.getcwd())\n",
    "    \n",
    "    data = {}\n",
    "\n",
    "    for project in requested_projects:\n",
    "        project_path = os.path.join(absolute_path, project)\n",
    "        project_paths.append(project_path)\n",
    "    \n",
    "        project_csv_path = os.path.join(project_path , project + \"class.csv\")\n",
    "        with open(project_csv_path, \"r\", encoding=\"UTF8\") as project_csv:\n",
    "    \n",
    "         \n",
    "            reader = csv.reader(project_csv)\n",
    "            data_list = []\n",
    "            next(reader)  # Skip the header\n",
    "            for idx, row in enumerate(reader, start=1):\n",
    "                #print(row[metric_index])\n",
    "                if len(row) > metric_index:\n",
    "                    data_list.append(int(row[metric_index]))\n",
    "                    \n",
    "        data[project] = data_list\n",
    "        \n",
    "        \n",
    "        #print(data_list)\n",
    "        \n",
    "    return data\n",
    "\n",
    "\n",
    "    \n",
    "def weighted_data_prep(metric_index:int, requested_projects=None):\n",
    "    \"\"\"\n",
    "    Prepare and fetch data for visualization.\n",
    "    \"\"\"\n",
    "\n",
    "    global projects\n",
    "    project_paths: list[str] = []\n",
    "    \n",
    "    if requested_projects is None:\n",
    "        requested_projects = projects\n",
    "    \n",
    "    \n",
    "    absolute_path = os.path.abspath(os.getcwd())\n",
    "    \n",
    "    data = {}\n",
    "    weight_index = 15 #at index 10 is totalMethodsQty\n",
    "\n",
    "    for project in requested_projects:\n",
    "        project_path = os.path.join(absolute_path, project)\n",
    "        project_paths.append(project_path)\n",
    "    \n",
    "        project_csv_path = os.path.join(project_path , project + \"class.csv\")\n",
    "        with open(project_csv_path, \"r\", encoding=\"UTF8\") as project_csv:\n",
    "    \n",
    "         \n",
    "            reader = csv.reader(project_csv)\n",
    "            data_list = []\n",
    "            next(reader)  # Skip the header\n",
    "            for idx, row in enumerate(reader, start=1):\n",
    "                #print(row[metric_index])\n",
    "                #print(f\"weight for {project} = {(row[weight_index])}\")\n",
    "                if len(row) > metric_index and int(row[weight_index]) != 0:\n",
    "                    data_list.append((int((row[metric_index])) / (int(row[weight_index]))))\n",
    "                    \n",
    "        data[project] = data_list\n",
    "        \n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-26T10:28:11.478229600Z",
     "start_time": "2024-05-26T10:28:11.421425300Z"
    }
   },
   "id": "3a65e25883eb3aa3"
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "import csv \n",
    "\n",
    "\n",
    "indexes = [3,8,7,9,10,11]\n",
    "aggregated_data = {\n",
    "    \"lucida\": [\"dead\"],\n",
    "    \"word\": [\"dead\"],\n",
    "    \"anonymouth\": [\"dead\"],\n",
    "    \"h2o-2\": [\"dead\"],\n",
    "    \"phimpme-android\": [\"dead\"],\n",
    "    \"GeometricWeather\": [\"dead\"],\n",
    "    \"Arduino\": [\"dead\"],\n",
    "    \"lbry-android\": [\"dead\"],\n",
    "    \"litho\": [\"alive\"],\n",
    "    \"PictureSelector\": [\"alive\"],\n",
    "    \"SmarterStreaming\": [\"alive\"],\n",
    "    \"lawnchair\": [\"alive\"],\n",
    "    \"NekoX\": [\"alive\"],\n",
    "    \"hudi\": [\"alive\"],\n",
    "    \"PojavLauncher\": [\"alive\"],\n",
    "    \"beam\": [\"alive\"]\n",
    "}\n",
    "\n",
    "for metric in indexes:\n",
    "    data = data_prep(metric)\n",
    "    averaged_data = {p:sum(v)/len(v) for (p,v) in data.items()}\n",
    "    \n",
    "    #print(averaged_data)\n",
    "    for project,project_data in averaged_data.items(): \n",
    "        aggregated_data[project].append(project_data)\n",
    " \n",
    "       \n",
    "with open (r\"average_values.csv\", \"w\") as file:\n",
    "    \n",
    "    file.write(\"project,status,CBO,DIT,WMC,NCO,RFC,LCOM\")   \n",
    "    \n",
    "    for project in aggregated_data:\n",
    "        new_line = f\"\\n{project}\"\n",
    "        for metric in aggregated_data[project]:\n",
    "            new_line += f\",{metric}\"\n",
    "        \n",
    "        file.write(new_line)\n",
    "            \n",
    "            \n",
    "    \n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-26T11:18:03.122352300Z",
     "start_time": "2024-05-26T11:18:01.820513700Z"
    }
   },
   "id": "45bd00104aa6416e"
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      1.00      0.86         3\n",
      "           1       1.00      0.50      0.67         2\n",
      "\n",
      "    accuracy                           0.80         5\n",
      "   macro avg       0.88      0.75      0.76         5\n",
      "weighted avg       0.85      0.80      0.78         5\n",
      "\n",
      "Feature Importance:\n",
      "       importance\n",
      "CBO     0.344169\n",
      "RFC     0.168406\n",
      "DIT     0.160135\n",
      "LCOM    0.141761\n",
      "NCO     0.115755\n",
      "WMC     0.069773\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv('average_values.csv')\n",
    "\n",
    "# Prepare the features and target variable\n",
    "X = data[['CBO', 'DIT', 'WMC', 'NCO', 'RFC', 'LCOM']]\n",
    "y = data['status'].apply(lambda x: 1 if x == 'alive' else 0)\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Initialize and train the Random Forest Classifier\n",
    "clf = RandomForestClassifier(random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# Determine feature importance\n",
    "feature_importances = pd.DataFrame(clf.feature_importances_, index=X.columns, columns=['importance']).sort_values('importance', ascending=False)\n",
    "print(\"Feature Importance:\\n\", feature_importances)\n",
    "\n",
    "# Plot feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=feature_importances.importance, y=feature_importances.index)\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-26T11:39:40.677918200Z",
     "start_time": "2024-05-26T11:39:40.383007800Z"
    }
   },
   "id": "6c9b47b41dff0881"
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "import csv\n",
    "# Step 1: Data Preparation\n",
    "# Assuming you have extracted the CBO values for red and green projects from the CSV file\n",
    "\n",
    "dead_projects = [\"lucida\", \"word\", \"anonymouth\",\"h2o-2\",\"phimpme-android\", \"GeometricWeather\", \"Arduino\" ,\"lbry-android\"]\n",
    "alive_projects = [\"litho\", \"PictureSelector\", \"SmarterStreaming\", \"lawnchair\", \"NekoX\", \"hudi\", \"PojavLauncher\", \"beam\"]\n",
    "\n",
    "# Step 2: Data Preprocessing\n",
    "#deadProj_weigh_data = weighted_data_prep(3, requested_projects=dead_projects)\n",
    "#aliveProj_weigh_data = weighted_data_prep(3, requested_projects=alive_projects)\n",
    "\n",
    "# Step 2: Data Preprocessing\n",
    "deadProj_weigh_data = data_prep(11, requested_projects=dead_projects)\n",
    "aliveProj_weigh_data = data_prep(11, requested_projects=alive_projects)\n",
    "\n",
    "deadProj_data = []\n",
    "aliveProj_data = []\n",
    "\n",
    "for proj in dead_projects:\n",
    "    deadProj_data += deadProj_weigh_data[proj]\n",
    "\n",
    "for proj in alive_projects:\n",
    "    aliveProj_data += aliveProj_weigh_data[proj]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-24T12:01:34.870117300Z",
     "start_time": "2024-05-24T12:01:34.633030300Z"
    }
   },
   "id": "d9034fe44d168e25"
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dead Projects:\n",
      "  Median: 0.0\n",
      "  Lower Quartile (Q1): 0.0\n",
      "  Upper Quartile (Q3): 1.0\n",
      "  Lower Whisker: 0.0\n",
      "  Upper Whisker: 2.0\n",
      "  Outliers: [ 8  3  6  4  6  7  3 10  7  5  3  5  5  4  3  6  5  9  6  6  3  5  5  5\n",
      "  3  3  5  3  7  3  6  7  7 10  6  3  6 10  6  7  4  9  8  6  3 10 10  6\n",
      "  3 10  8  6  3 10  9  3  6  3 10  3 10  6  3  4  6  3  6  6  3  3 10  3\n",
      "  6  6  6  6  6  3  6  3 10  4  7 10 10  5  6 10  6  6  6  6  6  8  3  8\n",
      "  6  8  7  8  3  3  3  6  6  8  3  9  9  6 10  6  4  3  4  7  6  3  4  7\n",
      "  6  4  8  9  6  6  9  3  3  3  4  6  3  3  3 10  4  6 10  3  6 10  3  8\n",
      "  6 10 10  7  3  6  5  5  6  3 10  4  6  4  8  3  4  9  6  9  3  3 10  6\n",
      "  5  6  3  6  9  3  6 10  6 10  6  3  3  4  9  6  6  3  3  6  3  6 10 10\n",
      "  3  3  3 10  3  5 10  5  6 10  6  4  6  4  8  8  3  6  8 10  3 10  6  8\n",
      "  6  3  3  3  6  3 10 10  6  3  3  3  6  3  7  3 10  9  6  6  3  3  6  3\n",
      "  6  4  3  6  6  3  3  3 10  3  6  4  3  6  4  3 10  6  4  6  6  7  6  6\n",
      " 10  6  3  3  3  4 10  6  8  6  7 10  4  6  6  6  3  3  9  6  6  6  7  3\n",
      "  3  6  6  6  6  6  4  3  8  3  6  9  3 10  6  6  3  3  3  3  3  3  6  6\n",
      "  3  3  6  3  3  3  6  6  3  6  4  7  6 10  4  8  3  6 10  6  3  4  6  3\n",
      " 10  3  4  6  8  3  3  3  3  4  6  6  3  6  6 10 10 10  3  4  7  3  4  6\n",
      "  3  6  3  5  3  9  3  4  8  6  6  6  3  4  3  6  6 10 10  4  3 10  8  6\n",
      "  3  3  6  6  5  6 10  6  3  4 10  3  3 10  6  3 10  6  6  6 10  6  6  3\n",
      "  6  3  6  3  6  6  6  6  9  3  6  6  6  8  3  6  3 10  4  3  3  6 10  6\n",
      " 10  4  9  6  3  3  6  6  3  3 10  3  9  3  3 10 10  3  6  4  3  3 10  3\n",
      "  3 10 10  3  6  6  3  6  3  8  3  6  9  6  6  8  4  3 10 10  3  6  3 10\n",
      "  3  7  9  5  3  7  3 10  4 10  6  6  6  3  3  3  3  6 10  6  4  3  3 10\n",
      "  3  3 10  6  3  7 10  6  9 10  6  3  6  3  3  6  4 10  7  6  6  3  6  4\n",
      "  5  3  6  3  3  3 10  3  3  4  6  3  3  6  4  3  3  3  6  3  3  9  3  3\n",
      "  3  3  6  8  6  9  3  3 10  3  3  4  3  6  3  3  3  4  3 10  3  3  3  3\n",
      "  3  3  3  8  3  3  3  3 10  3  3  3  3  3  3  3  6  3  5  3  3  3  6  6\n",
      " 10  3  4  5 10  6  3  3  4 10 10  7  9  5  3  3  6  3  4  6  3  8  3  6\n",
      "  3  3  6  6  6  4  6  3  3 10  3  3  6  3  8  5  6  3  3  6  3  3  3  4\n",
      "  6  3  6  3  9  6  3 10  5 10  3  9  6  6 10  6  9  4  4  6  6  6  3  6\n",
      "  4  4 10  6  3  6  4  3  9  6  3  3  3  3  4  3  4  3  3  6  9  6  6  6\n",
      "  3  4  9  6  3  6  3  6  6  3  4  6  5  3  3  6  4 10  3  9  6  3  6  3\n",
      "  6 10  4  9  3  3  6  3  6  4  6  6  9  6  3 10  3 10  4  9  4 10  4  4\n",
      "  6  7  9  3  6  3  3  3  3  6  4  6  9  9  3 10  9  3  3 10  9  9  6  3\n",
      "  8 10  7  4 10 10  6  6  8  3  4  9  4 10  3  3  6  6  6 10 10  4  9  3\n",
      "  4  7  6  3  3  4  9  3  6  3  3  3  8  9  3  4  6  3  6  7  4  6  3  3\n",
      "  3  8  7  7 10  3  6  3  6  7  4 10  7  3  3  3  3 10 10  3  3  3 10  6\n",
      "  3  7  9  3 10  4  8 10  3  6  6  3 10 10  3  8  3  4  3  6  6  6  4  3\n",
      "  3  8  3  3  3  3  6  3  3  3  3  3  3  3  3  3  3  8  3  3  3  3  3  3\n",
      "  3  6  4  3  3  3  6  3  3  3  6  3  3  3  4  4  4  3  3  8  3  3  3  3\n",
      "  7  8  3  3  3  4  6  3  3  3  7  3  8  8  3  3 10  8  3 10  3  3  4  3\n",
      "  3  3]\n",
      "\n",
      "Alive Projects:\n",
      "  Median: 0.0\n",
      "  Lower Quartile (Q1): 0.0\n",
      "  Upper Quartile (Q3): 1.0\n",
      "  Lower Whisker: 0.0\n",
      "  Upper Whisker: 2.0\n",
      "  Outliers: [ 4  3 10 ...  6 10  3]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define a function to remove outliers using IQR method\n",
    "def remove_outliers(data):\n",
    "    q1 = np.percentile(data, 25)\n",
    "    q3 = np.percentile(data, 75)\n",
    "    iqr = q3 - q1\n",
    "    lower_bound = q1 - 1.5 * iqr\n",
    "    upper_bound = q3 + 1.5 * iqr\n",
    "    return [x for x in data if x >= lower_bound and x <= upper_bound]\n",
    "\n",
    "# Remove outliers from the data\n",
    "deadProj_data_no_outliers = remove_outliers(deadProj_data)\n",
    "aliveProj_data_no_outliers = remove_outliers(aliveProj_data)\n",
    "\n",
    "\n",
    "# Combine the data for boxplot\n",
    "data_no_outliers = [deadProj_data_no_outliers, aliveProj_data_no_outliers]\n",
    "\n",
    "# Create a boxplot without outliers\n",
    "boxplot_dict = plt.boxplot(data_no_outliers, labels=['Dead Projects', 'Alive Projects'])\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Project Status')\n",
    "plt.ylabel('CBO Values')\n",
    "plt.title('Boxplot of CBO Values for Dead and Alive Projects (without outliers)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Extracting and printing box plot details\n",
    "def print_boxplot_details(boxplot_dict):\n",
    "    for i, label in enumerate(['Dead Projects', 'Alive Projects']):\n",
    "        print(f\"\\n{label}:\")\n",
    "        print(f\"  Median: {boxplot_dict['medians'][i].get_ydata()[0]}\")\n",
    "        print(f\"  Lower Quartile (Q1): {boxplot_dict['boxes'][i].get_ydata()[1]}\")\n",
    "        print(f\"  Upper Quartile (Q3): {boxplot_dict['boxes'][i].get_ydata()[2]}\")\n",
    "        print(f\"  Lower Whisker: {boxplot_dict['whiskers'][2*i].get_ydata()[1]}\")\n",
    "        print(f\"  Upper Whisker: {boxplot_dict['whiskers'][2*i+1].get_ydata()[1]}\")\n",
    "        print(f\"  Outliers: {boxplot_dict['fliers'][i].get_ydata()}\")\n",
    "\n",
    "# Print the details\n",
    "print_boxplot_details(boxplot_dict)\n"
   ],
   "metadata": {
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     "end_time": "2024-05-24T12:01:35.834202600Z",
     "start_time": "2024-05-24T12:01:35.670544100Z"
    }
   },
   "id": "54c2ab7523cdb5a6"
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney U test statistic: 67534509.0\n",
      "P-value: 0.16771933374681147\n",
      "Fail to reject the null hypothesis: There is no statistically significant difference.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Statistical Test (Mann-Whitney U test)\n",
    "statistic, p_value = mannwhitneyu(deadProj_data_no_outliers, aliveProj_data_no_outliers)\n",
    "# Interpretation\n",
    "alpha = 0.05  # significance level\n",
    "print(f\"Mann-Whitney U test statistic: {statistic}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "if p_value < alpha:\n",
    "    print(\"Reject the null hypothesis: There is a statistically significant difference.\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis: There is no statistically significant difference.\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-24T12:01:37.372234600Z",
     "start_time": "2024-05-24T12:01:37.309714700Z"
    }
   },
   "id": "6c196ea9f5ab4be7"
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   "execution_count": null,
   "outputs": [],
   "source": [],
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   "id": "d90497e7bef871bd"
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