motivation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>processing_delay (ms)</th>\n",
       "      <th>core</th>\n",
       "      <th>model_acc</th>\n",
       "      <th>ram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>2725.300789</td>\n",
       "      <td>half</td>\n",
       "      <td>64.1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>2869.115829</td>\n",
       "      <td>half</td>\n",
       "      <td>64.1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>2394.518852</td>\n",
       "      <td>half</td>\n",
       "      <td>64.1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>2873.452187</td>\n",
       "      <td>half</td>\n",
       "      <td>64.1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>2371.568680</td>\n",
       "      <td>half</td>\n",
       "      <td>64.1</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model  processing_delay (ms)  core  model_acc   ram\n",
       "0  yolov5m            2725.300789  half       64.1  1000\n",
       "1  yolov5m            2869.115829  half       64.1  1000\n",
       "2  yolov5m            2394.518852  half       64.1  1000\n",
       "3  yolov5m            2873.452187  half       64.1  1000\n",
       "4  yolov5m            2371.568680  half       64.1  1000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv')\n",
    "df.drop(columns=['Time', 'location', 'File Name', 'Propagation Delay (s)', 'E2E Delay (s)'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64.1, 45.7, 56.8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model_acc'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "latency_bound = 700  # in ms\n",
    "sla_violations = df[df['processing_delay (ms)'] > latency_bound]\n",
    "\n",
    "# Count SLA violations for each core\n",
    "sla_violation_counts = sla_violations.groupby(['model','core']).size().reset_index(name='violation_count')\n",
    "sla_violation_counts['per_core_violations'] = (sla_violation_counts['violation_count']/1000)*100\n",
    "# Generate all possible combinations of model and core\n",
    "models = df['model'].unique()\n",
    "cores = df['core'].unique()\n",
    "\n",
    "all_combinations = pd.DataFrame(list(itertools.product(models, cores)), columns=['model', 'core'])\n",
    "\n",
    "# Merge with sla_violation_counts to ensure all combinations are included\n",
    "complete_sla_violation_counts = all_combinations.merge(sla_violation_counts, on=['model', 'core'], how='left')\n",
    "# Fill missing values with zero\n",
    "complete_sla_violation_counts['violation_count'] = complete_sla_violation_counts['violation_count'].fillna(0)\n",
    "complete_sla_violation_counts['per_core_violations'] = complete_sla_violation_counts['per_core_violations'].fillna(0)\n",
    "sla_violate =  complete_sla_violation_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>core</th>\n",
       "      <th>violation_count</th>\n",
       "      <th>per_core_violations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>half</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>half</td>\n",
       "      <td>367.0</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>half</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>one</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>one</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>one</td>\n",
       "      <td>327.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>two</td>\n",
       "      <td>514.0</td>\n",
       "      <td>51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>two</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>two</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model  core  violation_count  per_core_violations\n",
       "0  yolov5m  half           1000.0                100.0\n",
       "3  yolov5n  half            367.0                 36.7\n",
       "6  yolov5s  half           1000.0                100.0\n",
       "1  yolov5m   one           1000.0                100.0\n",
       "4  yolov5n   one              1.0                  0.1\n",
       "7  yolov5s   one            327.0                 32.7\n",
       "2  yolov5m   two            514.0                 51.4\n",
       "5  yolov5n   two              0.0                  0.0\n",
       "8  yolov5s   two              0.0                  0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sla_violate.sort_values(by=['core'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>core</th>\n",
       "      <th>violation_count</th>\n",
       "      <th>per_core_violations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>half</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>half</td>\n",
       "      <td>367.0</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>half</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>one</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>one</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>one</td>\n",
       "      <td>327.0</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yolov5m</td>\n",
       "      <td>two</td>\n",
       "      <td>514.0</td>\n",
       "      <td>51.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>yolov5n</td>\n",
       "      <td>two</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>yolov5s</td>\n",
       "      <td>two</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model  core  violation_count  per_core_violations\n",
       "0  yolov5m  half           1000.0                100.0\n",
       "3  yolov5n  half            367.0                 36.7\n",
       "6  yolov5s  half           1000.0                100.0\n",
       "1  yolov5m   one           1000.0                100.0\n",
       "4  yolov5n   one              1.0                  0.1\n",
       "7  yolov5s   one            327.0                 32.7\n",
       "2  yolov5m   two            514.0                 51.4\n",
       "5  yolov5n   two              0.0                  0.0\n",
       "8  yolov5s   two              0.0                  0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sla_violate.sort_values(by=['core', 'model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data\n",
    "models = ['Nano', 'Small', 'Medium']\n",
    "sla_half = [36.7, 100.0, 100.0]\n",
    "sla_one = [0.1, 32.7, 100.0]\n",
    "sla_two = [0.0, 0.0, 51.4]\n",
    "accuracy = [45.7, 56.8, 64.1]\n",
    "\n",
    "# Create figure and axis objects\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(7,3.3))\n",
    "\n",
    "# Plot throughput on the first y-axis\n",
    "ax1.set_xlabel('Yolov5 Model Variants')\n",
    "ax1.set_ylabel('SLA Violation (%)')\n",
    "bar_width = 0.2\n",
    "x = np.arange(len(models))\n",
    "ax1.bar(x - bar_width, sla_half, bar_width, label='Core = 0.5', color='red')\n",
    "ax1.bar(x, sla_one, bar_width, label='Core = 1', color='lightblue')\n",
    "ax1.bar(x + bar_width, sla_two, bar_width, label='Core = 2', color='orange')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Create the second y-axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Accuracy (%)')\n",
    "line, = ax2.plot(x, accuracy, marker='d', color='black', label='Accuracy')\n",
    "\n",
    "# Combine legends\n",
    "handles1, labels1 = ax1.get_legend_handles_labels()\n",
    "handles2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles1 + [line], labels1 + labels2, loc='upper left')\n",
    "\n",
    "fig.tight_layout()\n",
    "# Set x-axis labels\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(models)\n",
    "plt.savefig('motivation_sla_800.pdf', format='pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Switching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfms = pd.read_csv('model_switching.csv')\n",
    "model_accuracies = {\n",
    "    'yolov5n': 0.457,\n",
    "    'yolov5s': 0.568,\n",
    "    'yolov5m': 0.641\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfms.drop(columns=['time', 'location', 'file_name', 'propagation_delay (s)', 'e2e_delay (s)'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 500, 1000, 2000])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfms['core'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yolov5s', 'yolov5m'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfms1 = dfms[dfms['cid']==1]\n",
    "dfms1['model_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "core\n",
       "500     57\n",
       "1000    51\n",
       "2000     7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sla_violations = dfms[dfms['processing_delay (ms)'] > 700].groupby('core').size()\n",
    "sla_violations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "core\n",
       "500     56.435644\n",
       "1000    50.495050\n",
       "2000     6.930693\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_traffic = dfms.groupby('core').size()\n",
    "sla_violation_rate = (sla_violations / total_traffic) * 100\n",
    "sla_violation_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>cid</th>\n",
       "      <th>model_name</th>\n",
       "      <th>processing_delay (ms)</th>\n",
       "      <th>core</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>yolov5n</td>\n",
       "      <td>846.629620</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>yolov5s</td>\n",
       "      <td>662.415028</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>yolov5m</td>\n",
       "      <td>552.519083</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>yolov5n</td>\n",
       "      <td>1033.409357</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>yolov5s</td>\n",
       "      <td>727.887869</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1</td>\n",
       "      <td>yolov5m</td>\n",
       "      <td>1285.557747</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>2</td>\n",
       "      <td>yolov5m</td>\n",
       "      <td>693.729639</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0</td>\n",
       "      <td>yolov5n</td>\n",
       "      <td>542.279243</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1</td>\n",
       "      <td>yolov5m</td>\n",
       "      <td>1071.745872</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2</td>\n",
       "      <td>yolov5m</td>\n",
       "      <td>498.083830</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cid model_name  processing_delay (ms)  core\n",
       "0      0    yolov5n             846.629620   500\n",
       "1      1    yolov5s             662.415028  1000\n",
       "2      2    yolov5m             552.519083  2000\n",
       "3      0    yolov5n            1033.409357   500\n",
       "4      1    yolov5s             727.887869  1000\n",
       "..   ...        ...                    ...   ...\n",
       "298    1    yolov5m            1285.557747  1000\n",
       "299    2    yolov5m             693.729639  2000\n",
       "300    0    yolov5n             542.279243   500\n",
       "301    1    yolov5m            1071.745872  1000\n",
       "302    2    yolov5m             498.083830  2000\n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the average accuracy for each core\n",
    "dfms['model_accuracy'] = dfms['model_name'].map(model_accuracies)\n",
    "traffic_by_core_model = dfms.groupby(['core', 'model_name']).size().unstack(fill_value=0)\n",
    "average_accuracies = {}\n",
    "for core in traffic_by_core_model.index:\n",
    "    total_traffic_core = total_traffic[core]\n",
    "    weighted_sum = 0\n",
    "    for model in traffic_by_core_model.columns:\n",
    "        traffic_by_model = traffic_by_core_model.loc[core, model]\n",
    "        if traffic_by_model > 0:\n",
    "            model_accuracy = model_accuracies[model]\n",
    "            weighted_sum += (traffic_by_model / total_traffic_core) * model_accuracy\n",
    "    average_accuracies[core] = weighted_sum * 100  # Convert to percentage\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>model_name</th>\n",
       "      <th>yolov5m</th>\n",
       "      <th>yolov5n</th>\n",
       "      <th>yolov5s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>core</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_name  yolov5m  yolov5n  yolov5s\n",
       "core                                 \n",
       "500               0      101        0\n",
       "1000             49        0       52\n",
       "2000            101        0        0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_by_core_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data for plotting\n",
    "cores = list(sla_violation_rate.index)\n",
    "sla_rates = list(sla_violation_rate.values)\n",
    "accuracies = [average_accuracies[core] for core in cores]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45.7, 60.34158415841584, 64.1]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data\n",
    "cores = [500, 1000, 2000]\n",
    "sla_rates = [56.435, 50.495, 6.930]\n",
    "accuracies = [45.7, 60.34158415841584, 64.1]\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(7,3.3))\n",
    "\n",
    "bar_width = 0.35\n",
    "x1 = np.arange(len(cores))\n",
    "x2 = [x + bar_width for x in x1]\n",
    "\n",
    "ax1.bar(x1, sla_rates, width=bar_width, label='SLA Violations', color='lightblue')\n",
    "\n",
    "ax1.set_xlabel('Yolov5 Model Variants')\n",
    "ax1.set_ylabel('SLA Violation (%)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Create the second y-axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Accuracy (%)')\n",
    "line, = ax2.plot(x, accuracies, marker='d', color='black', label='Accuracy')\n",
    "\n",
    "handles1, labels1 = ax1.get_legend_handles_labels()\n",
    "handles2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles1 + [line], labels1 + labels2, loc='upper left')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax1.set_xticks(x)\n",
    "#plt.savefig('motivation_sla_800.pdf', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x280 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data for figure 1\n",
    "cores = [500, 1000, 2000]\n",
    "sla_rates = [56.435, 50.495, 6.930]\n",
    "accuracies = [45.7, 60.34158415841584, 64.1]\n",
    "\n",
    "# Data for figure 2\n",
    "models = ['Nano', 'Small', 'Medium']\n",
    "sla_half = [36.7, 100.0, 100.0]\n",
    "sla_one = [0.1, 32.7, 100.0]\n",
    "sla_two = [0.0, 0.0, 51.4]\n",
    "accuracy = [45.7, 56.8, 64.1]\n",
    "\n",
    "# Create figure and axis objects for subplots\n",
    "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(8,2.8))\n",
    "\n",
    "# Plot figure 1\n",
    "bar_width = 0.5\n",
    "x3 = np.arange(len(cores))\n",
    "\n",
    "ax3.bar(x3, sla_rates, width=bar_width, label='SLA Violations', color='lightblue')\n",
    "ax3.set_xlabel('Container Cores', labelpad=5)\n",
    "ax3.set_ylabel('SLA Violation (%)', labelpad=5)\n",
    "ax3.set_ylim([0,100])\n",
    "ax3.legend(loc='upper left')\n",
    "\n",
    "# Create the second y-axis for figure 1\n",
    "ax4 = ax3.twinx()\n",
    "ax4.set_ylabel('Accuracy (%)', labelpad=5)\n",
    "line1, = ax4.plot(x3, accuracies, marker='d', color='black', label='Accuracy')\n",
    "\n",
    "handles1, labels1 = ax3.get_legend_handles_labels()\n",
    "handles2, labels2 = ax4.get_legend_handles_labels()\n",
    "ax3.legend(handles1 + [line1], labels1 + labels2, loc='upper left')\n",
    "\n",
    "ax3.set_xticks(x3)\n",
    "ax3.set_xticklabels(['0.5', '1', '2'])\n",
    "\n",
    "# Plot figure 2\n",
    "bar_width = 0.25\n",
    "x1 = np.arange(len(models))\n",
    "ax1.bar(x1 - bar_width, sla_half, bar_width, label='Core = 0.5', color='red')\n",
    "ax1.bar(x1, sla_one, bar_width, label='Core = 1', color='lightblue')\n",
    "ax1.bar(x1 + bar_width, sla_two, bar_width, label='Core = 2', color='orange')\n",
    "ax1.set_xlabel('Yolo5 Model Variants', labelpad=5)\n",
    "ax1.set_ylabel('SLA Violation (%)', labelpad=5)\n",
    "#ax1.set_ylim([0,100])\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Create the second y-axis for figure 2\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Accuracy (%)', labelpad=5)\n",
    "line2, = ax2.plot(x1, accuracy, marker='d', color='black', label='Accuracy')\n",
    "\n",
    "handles3, labels3 = ax1.get_legend_handles_labels()\n",
    "handles4, labels4 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles3 + [line2], labels3 + labels4, loc='upper left')\n",
    "\n",
    "ax1.set_xticks(x1)\n",
    "ax1.set_xticklabels(models)\n",
    "\n",
    "# Add subplot labels\n",
    "fig.text(0.25, 0.04, '(a)', ha='center', va='center', fontsize=10)\n",
    "fig.text(0.75, 0.04, '(b)', ha='center', va='center', fontsize=10)\n",
    "\n",
    "fig.subplots_adjust(wspace=0.2, bottom=0.25)  # Increased bottom margin significantly\n",
    "fig.tight_layout(rect=[0, 0.03, 1, 1])\n",
    "plt.savefig('motivation_sla_combined.pdf', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15360/967422714.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(cores)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data\n",
    "cores = [500, 1000, 2000]\n",
    "sla_rates = [56.435, 50.495, 6.930]\n",
    "core_model0 = np.array([101, 0, 0])\n",
    "core_model1 = np.array([0, 52, 49])\n",
    "core_model2 = np.array([0, 0, 101])\n",
    "\n",
    "# Create figure and axis objects\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(7,3.3))\n",
    "\n",
    "bar_width = 0.35\n",
    "x1 = np.arange(len(cores))\n",
    "x2 = [x + bar_width for x in x1]\n",
    "\n",
    "ax1.bar(x1, sla_rates, width=bar_width, label='SLA Violations', color='lightblue')\n",
    "\n",
    "# Plotting the stacked bar graph\n",
    "#ax1.bar(x2, core_model0, color='r', width=bar_width, label='Nano')\n",
    "#ax1.bar(x2, core_model1, color='lightblue', bottom=core_model0, width=bar_width, label='Small')\n",
    "#ax1.bar(x2, core_model2, color='orange', bottom=core_model0+core_model1, width=bar_width, label='Medium')\n",
    "\n",
    "#ax1.set_xticks([r + bar_width/2 for r in range(len(cores))])\n",
    "#ax1.set_xticklabels(cores)\n",
    "ax1.set_xlabel('Yolov5 Model Variants')\n",
    "ax1.set_ylabel('SLA Violation (%)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Create the second y-axis\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Accuracy (%)')\n",
    "line, = ax2.plot(x, accuracies, marker='d', color='black', label='Accuracy')\n",
    "\n",
    "# Combine legends\n",
    "handles1, labels1 = ax1.get_legend_handles_labels()\n",
    "handles2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(handles1 + [line], labels1 + labels2, loc='upper left')\n",
    "\n",
    "fig.tight_layout()\n",
    "# Set x-axis labels\n",
    "#ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(cores)\n",
    "#plt.savefig('motivation_sla_800.pdf', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cores = [500, 1000, 2000]\n",
    "sla_rates = [56.43564356435643, 50.495049504950494, 6.93]\n",
    "core_model0 = np.array([10, 15, 20])\n",
    "core_model1 = np.array([5, 10, 15])\n",
    "core_model2 = np.array([2, 4, 6])\n",
    "\n",
    "# Parameters for bar width and positions\n",
    "bar_width = 0.35\n",
    "x1 = np.arange(len(cores))\n",
    "x2 = x1 + bar_width\n",
    "\n",
    "# Create subplots\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plotting the straight bar graph\n",
    "ax1.bar(x1, sla_rates, width=bar_width, label='SLA Violations', color='red')\n",
    "\n",
    "# Plotting the stacked bar graph\n",
    "ax1.bar(x2, core_model0, color='blue', width=bar_width, label='Stacked Bar 1')\n",
    "ax1.bar(x2, core_model1, bottom=core_model0, color='green', width=bar_width, label='Stacked Bar 2')\n",
    "ax1.bar(x2, core_model2, bottom=core_model0 + core_model1, color='orange', width=bar_width, label='Stacked Bar 3')\n",
    "\n",
    "# Setting x-ticks, labels, and legend\n",
    "ax1.set_xticks(x1 + bar_width / 2)\n",
    "ax1.set_xticklabels(cores)\n",
    "ax1.set_xlabel('Cores')\n",
    "ax1.set_ylabel('SLA Violation (%)')\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# Display the plot\n",
    "plt.title('SLA Violation and Core Model Stacked Bar Graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[500, 1000, 2000]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>model_name</th>\n",
       "      <th>yolov5m</th>\n",
       "      <th>yolov5n</th>\n",
       "      <th>yolov5s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_name  yolov5m  yolov5n  yolov5s\n",
       "cid                                  \n",
       "0                 0      101        0\n",
       "1                49        0       52\n",
       "2               101        0        0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_counts = dfms.groupby(['cid', 'model_name']).size().unstack(fill_value=0)\n",
    "model_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 500, 1000, 2000])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfms['core'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cid\n",
       "0    53.465347\n",
       "1    48.514851\n",
       "2     1.980198\n",
       "dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of occurrences for each core\n",
    "core_counts = dfms.groupby('cid').size()\n",
    "\n",
    "# Calculate the number of SLA violations for each cid\n",
    "sla_violations = dfms[dfms['processing_delay (ms)'] > 800].groupby('cid').size()\n",
    "(sla_violations/ core_counts)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "core\n",
       "500     53.465347\n",
       "1000    48.514851\n",
       "2000     1.980198\n",
       "dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_counts = dfms.groupby('core').size()\n",
    "\n",
    "# Calculate the number of SLA violations for each cid\n",
    "sla_violations = dfms[dfms['processing_delay (ms)'] > 800].groupby('core').size()\n",
    "(sla_violations/ core_counts)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Calculate the traffic handled by each model for each cid\n",
    "traffic_handled = dfms.groupby(['core', 'model_name']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate the total traffic for each cid\n",
    "total_traffic = dfms.groupby('cid').size()\n",
    "\n",
    "# Calculate the weighted average accuracy for each cid\n",
    "average_accuracies = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>model_name</th>\n",
       "      <th>yolov5m</th>\n",
       "      <th>yolov5n</th>\n",
       "      <th>yolov5s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>core</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_name  yolov5m  yolov5n  yolov5s\n",
       "core                                 \n",
       "500               0      101        0\n",
       "1000             49        0       52\n",
       "2000            101        0        0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_handled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([500, 1000, 2000], dtype='int64', name='core')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_handled.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "500",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 500",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[149], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cid \u001b[38;5;129;01min\u001b[39;00m traffic_handled\u001b[38;5;241m.\u001b[39mindex:\n\u001b[0;32m----> 2\u001b[0m     total_traffic_cid \u001b[38;5;241m=\u001b[39m \u001b[43mtotal_traffic\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcid\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      3\u001b[0m     weighted_sum \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m traffic_handled\u001b[38;5;241m.\u001b[39mcolumns:\n",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/core/series.py:1007\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1004\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m   1006\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1007\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1009\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[1;32m   1010\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[1;32m   1011\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1012\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/core/series.py:1116\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1115\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1116\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m   1119\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
      "File \u001b[0;32m~/miniconda3/envs/bitbrain/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3660\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 500"
     ]
    }
   ],
   "source": [
    "for cid in traffic_handled.index:\n",
    "    total_traffic_cid = total_traffic[cid]\n",
    "    weighted_sum = 0\n",
    "    for model in traffic_handled.columns:\n",
    "        traffic_by_model = traffic_handled.loc[cid, model]\n",
    "        if traffic_by_model > 0:\n",
    "            model_accuracy = model_accuracies[model]\n",
    "            weighted_sum += (traffic_by_model / total_traffic_cid) * model_accuracy\n",
    "    average_accuracies[cid] = weighted_sum\n",
    "\n",
    "print(\"Average accuracies for each cid:\")\n",
    "print(average_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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