Result Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f14b8616-ae1c-4191-ba8b-3f3ac50447f7",
   "metadata": {},
   "source": [
    "# Results visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95904abf-f705-4e53-8bce-a6c3ecb6d93e",
   "metadata": {},
   "source": [
    "## Objective of the project \n",
    "\n",
    "This study seeks to conduct a thorough comparative analysis of these three models, focusing\n",
    "on their performance with regards to accuracy, computational complexity, scalability, and their\n",
    "effectiveness in handling data sparsity and dynamically changing environments. By evaluat-\n",
    "ing these aspects, the research aims to illuminate the operational strengths and weaknesses\n",
    "of each model, providing clear insights that could guide the development and deployment of\n",
    "future recommender systems. Through this comparative framework, we aspire to answer which\n",
    "model, under what conditions, provides the most reliable and robust recommendations, thereby\n",
    "significantly contributing to the optimization of digital services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec1c5afc-8ec3-4beb-9777-ef5cd6260e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "from collections import Counter, defaultdict\n",
    "from surprise import Dataset, Reader, KNNBasic, SVD, CoClustering, accuracy\n",
    "from sklearn.model_selection import train_test_split\n",
    "from surprise.accuracy import rmse, mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89d2f34e-8624-4280-9fb9-28ed84c78540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Links':    movieId  imdbId   tmdbId\n",
       " 0        1  114709    862.0\n",
       " 1        2  113497   8844.0\n",
       " 2        3  113228  15602.0\n",
       " 3        4  114885  31357.0\n",
       " 4        5  113041  11862.0,\n",
       " 'Movies':    movieId                               title  \\\n",
       " 0        1                    Toy Story (1995)   \n",
       " 1        2                      Jumanji (1995)   \n",
       " 2        3             Grumpier Old Men (1995)   \n",
       " 3        4            Waiting to Exhale (1995)   \n",
       " 4        5  Father of the Bride Part II (1995)   \n",
       " \n",
       "                                         genres  \n",
       " 0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       " 1                   Adventure|Children|Fantasy  \n",
       " 2                               Comedy|Romance  \n",
       " 3                         Comedy|Drama|Romance  \n",
       " 4                                       Comedy  ,\n",
       " 'Ratings':    userId  movieId  rating  timestamp\n",
       " 0       1        1     4.0  964982703\n",
       " 1       1        3     4.0  964981247\n",
       " 2       1        6     4.0  964982224\n",
       " 3       1       47     5.0  964983815\n",
       " 4       1       50     5.0  964982931,\n",
       " 'Tags':    userId  movieId              tag   timestamp\n",
       " 0       2    60756            funny  1445714994\n",
       " 1       2    60756  Highly quotable  1445714996\n",
       " 2       2    60756     will ferrell  1445714992\n",
       " 3       2    89774     Boxing story  1445715207\n",
       " 4       2    89774              MMA  1445715200}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_df = pd.read_csv('MovieLens_100k/links.csv')\n",
    "movies_df = pd.read_csv('MovieLens_100k/movies.csv')\n",
    "ratings_df = pd.read_csv('MovieLens_100k/ratings.csv')\n",
    "tags_df = pd.read_csv('MovieLens_100k/tags.csv')\n",
    "\n",
    "datasets = {\n",
    "    \"Links\": links_df,\n",
    "    \"Movies\": movies_df,\n",
    "    \"Ratings\": ratings_df,\n",
    "    \"Tags\": tags_df\n",
    "}\n",
    "\n",
    "datasets_info = {name: df.head() for name, df in datasets.items()}\n",
    "datasets_info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffed4e8-5b41-485b-8277-e2a6fba0e61b",
   "metadata": {},
   "source": [
    "## Dataset structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "862584cf-a7e4-415e-b6bc-ec7f894fb287",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in Links dataset:\n",
      "movieId    0\n",
      "imdbId     0\n",
      "tmdbId     8\n",
      "dtype: int64\n",
      "\n",
      "Missing values in Movies dataset:\n",
      "movieId    0\n",
      "title      0\n",
      "genres     0\n",
      "dtype: int64\n",
      "\n",
      "Missing values in Ratings dataset:\n",
      "userId       0\n",
      "movieId      0\n",
      "rating       0\n",
      "timestamp    0\n",
      "dtype: int64\n",
      "\n",
      "Missing values in Tags dataset:\n",
      "userId       0\n",
      "movieId      0\n",
      "tag          0\n",
      "timestamp    0\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values in each dataset\n",
    "missing_values = {name: df.isnull().sum() for name, df in datasets.items()}\n",
    "\n",
    "# Print the information about missing values\n",
    "for name, missing in missing_values.items():\n",
    "    print(f\"Missing values in {name} dataset:\\n{missing}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "713549fa-4fca-4d77-9bb7-7d1f73ef073d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the Links DataFrame is: (9742, 3)\n",
      "The shape of the Movies DataFrame is: (9742, 3)\n",
      "The shape of the Ratings DataFrame is: (100836, 4)\n",
      "The shape of the Tags DataFrame is: (3683, 4)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of each DataFrame\n",
    "for name, df in datasets.items():\n",
    "    print(f\"The shape of the {name} DataFrame is: {df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "083bc746-5ced-4d3e-a934-90d141950572",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>13136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>26818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>8551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>13211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating  count\n",
       "0     0.5   1370\n",
       "1     1.0   2811\n",
       "2     1.5   1791\n",
       "3     2.0   7551\n",
       "4     2.5   5550\n",
       "5     3.0  20047\n",
       "6     3.5  13136\n",
       "7     4.0  26818\n",
       "8     4.5   8551\n",
       "9     5.0  13211"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution_of_ratings = ratings_df.groupby('rating').size().reset_index(name='count')\n",
    "distribution_of_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf7c0d1-1cc0-442a-aae0-f0561808966c",
   "metadata": {},
   "source": [
    "### Combined result for hypergraphs\n",
    "\n",
    "This data is obtained from the notebooks for HyperGCN and Node2vec. Please check it for more information on how it was obtained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c751ce47-85f8-451b-a83d-3f86041eb1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_hypergcn = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"HyperGCN\", \"HyperGCN\", \"HyperGCN\"],\n",
    "    \"MSE\": [5.608074, 13.239963, 5.691181],\n",
    "    \"RMSE\": [2.368137, 3.638676, 2.385620],\n",
    "    \"MAE\": [2.050150, 3.480585, 2.063831],\n",
    "    \"Precision@10\": [0.116924, 0.000000, 0.105558],\n",
    "    \"Recall@10\": [0.009341, 0.000000, 0.008382],\n",
    "    \"Running Time (s)\": [86.924503, 11.528216, 88.792880]\n",
    "}\n",
    "\n",
    "results_HyperGCN_combined = pd.DataFrame(data_hypergcn)\n",
    "\n",
    "data_node2vec = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"Node2Vec\", \"Node2Vec\", \"Node2Vec\"],\n",
    "    \"MSE\": [47.267650, 48.482455, 47.357681],\n",
    "    \"RMSE\": [6.875147, 6.962934, 6.881692],\n",
    "    \"MAE\": [5.017519, 5.095941, 4.988233],\n",
    "    \"Precision@10\": [0.473556, 0.407286, 0.442582],\n",
    "    \"Recall@10\": [0.104442, 0.099925, 0.097093],\n",
    "    \"Running Time (s)\": [57.877078, 38.807848, 59.448123]\n",
    "}\n",
    "\n",
    "results_Node2Vec_combined = pd.DataFrame(data_node2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a364d839-65ef-4b20-abb1-8a2b782ecbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scenario</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Running Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>HyperGCN</td>\n",
       "      <td>5.608074</td>\n",
       "      <td>2.368137</td>\n",
       "      <td>2.050150</td>\n",
       "      <td>0.116924</td>\n",
       "      <td>0.009341</td>\n",
       "      <td>86.924503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sparse</td>\n",
       "      <td>HyperGCN</td>\n",
       "      <td>13.239963</td>\n",
       "      <td>3.638676</td>\n",
       "      <td>3.480585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.528216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New User</td>\n",
       "      <td>HyperGCN</td>\n",
       "      <td>5.691181</td>\n",
       "      <td>2.385620</td>\n",
       "      <td>2.063831</td>\n",
       "      <td>0.105558</td>\n",
       "      <td>0.008382</td>\n",
       "      <td>88.792880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Normal</td>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>47.267650</td>\n",
       "      <td>6.875147</td>\n",
       "      <td>5.017519</td>\n",
       "      <td>0.473556</td>\n",
       "      <td>0.104442</td>\n",
       "      <td>57.877078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sparse</td>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>48.482455</td>\n",
       "      <td>6.962934</td>\n",
       "      <td>5.095941</td>\n",
       "      <td>0.407286</td>\n",
       "      <td>0.099925</td>\n",
       "      <td>38.807848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New User</td>\n",
       "      <td>Node2Vec</td>\n",
       "      <td>47.357681</td>\n",
       "      <td>6.881692</td>\n",
       "      <td>4.988233</td>\n",
       "      <td>0.442582</td>\n",
       "      <td>0.097093</td>\n",
       "      <td>59.448123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Scenario Algorithm        MSE      RMSE       MAE  Precision@10  Recall@10  \\\n",
       "0    Normal  HyperGCN   5.608074  2.368137  2.050150      0.116924   0.009341   \n",
       "1    Sparse  HyperGCN  13.239963  3.638676  3.480585      0.000000   0.000000   \n",
       "2  New User  HyperGCN   5.691181  2.385620  2.063831      0.105558   0.008382   \n",
       "3    Normal  Node2Vec  47.267650  6.875147  5.017519      0.473556   0.104442   \n",
       "4    Sparse  Node2Vec  48.482455  6.962934  5.095941      0.407286   0.099925   \n",
       "5  New User  Node2Vec  47.357681  6.881692  4.988233      0.442582   0.097093   \n",
       "\n",
       "   Running Time (s)  \n",
       "0         86.924503  \n",
       "1         11.528216  \n",
       "2         88.792880  \n",
       "3         57.877078  \n",
       "4         38.807848  \n",
       "5         59.448123  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_results_final = pd.concat([results_HyperGCN_combined, results_Node2Vec_combined], ignore_index=True)\n",
    "combined_results_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066a5818-afc8-4ad1-92d4-8ebe9d50323b",
   "metadata": {},
   "source": [
    "## RMSE visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9d6a25d9-1dd6-42d8-83fb-e2c6d223191d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the individual DataFrames with the provided data\n",
    "data_node2vec = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"Node2Vec\", \"Node2Vec\", \"Node2Vec\"],\n",
    "    \"MSE\": [47.267650, 48.482455, 47.357681],\n",
    "    \"RMSE\": [6.875147, 6.962934, 6.881692],\n",
    "    \"MAE\": [5.017519, 5.095941, 4.988233],\n",
    "    \"Precision@10\": [0.473556, 0.407286, 0.442582],\n",
    "    \"Recall@10\": [0.104442, 0.099925, 0.097093],\n",
    "    \"Running Time (s)\": [57.877078, 38.807848, 59.448123]\n",
    "}\n",
    "\n",
    "data_hypergcn = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"HyperGCN\", \"HyperGCN\", \"HyperGCN\"],\n",
    "    \"MSE\": [5.608074, 13.239963, 5.691181],\n",
    "    \"RMSE\": [2.368137, 3.638676, 2.385620],\n",
    "    \"MAE\": [2.050150, 3.480585, 2.063831],\n",
    "    \"Precision@10\": [0.116924, 0.000000, 0.105558],\n",
    "    \"Recall@10\": [0.009341, 0.000000, 0.008382],\n",
    "    \"Running Time (s)\": [86.924503, 11.528216, 88.792880]\n",
    "}\n",
    "\n",
    "data_lightgcn = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"LightGCN\", \"LightGCN\", \"LightGCN\"],\n",
    "    \"MSE\": [1.300472, 1.353399, 1.352309],\n",
    "    \"RMSE\": [1.140382, 1.163357, 1.162888],\n",
    "    \"MAE\": [0.814420, 0.837602, 0.837188],\n",
    "    \"Precision@10\": [0.749278, 0.750687, 0.751206],\n",
    "    \"Recall@10\": [0.503184, 0.504528, 0.504964],\n",
    "    \"Running Time (s)\": [16.588426, 2.137099, 0.539947]\n",
    "}\n",
    "\n",
    "data_gat = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"GAT\", \"GAT\", \"GAT\"],\n",
    "    \"MSE\": [5.279527, 9.903407, 8.366129],\n",
    "    \"RMSE\": [2.297722, 3.146968, 2.892426],\n",
    "    \"MAE\": [1.813445, 2.508598, 2.294709],\n",
    "    \"Precision@10\": [0.640868, 0.665226, 0.656693],\n",
    "    \"Recall@10\": [0.339237, 0.375002, 0.358051],\n",
    "    \"Running Time (s)\": [76.762282, 9.373171, 2.174327]\n",
    "}\n",
    "\n",
    "data_graphsage = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"GraphSAGE\", \"GraphSAGE\", \"GraphSAGE\"],\n",
    "    \"MSE\": [0.995274, 1.191938, 2.352740],\n",
    "    \"RMSE\": [0.997634, 1.091759, 1.533864],\n",
    "    \"MAE\": [0.765923, 0.853637, 1.183540],\n",
    "    \"Precision@10\": [0.737043, 0.729891, 0.732416],\n",
    "    \"Recall@10\": [0.525224, 0.473050, 0.497194],\n",
    "    \"Running Time (s)\": [71.149196, 8.101512, 1.240817]\n",
    "}\n",
    "\n",
    "# Creating the DataFrame with the provided data for SVD\n",
    "data_svd = {\n",
    "    \"Scenario\": [\"Normal\", \"Sparse\", \"New User\"],\n",
    "    \"Algorithm\": [\"SVD\", \"SVD\", \"SVD\"],\n",
    "    \"RMSE\": [0.882703, 0.946178, 0.882193],\n",
    "    \"MAE\": [0.676092, 0.735762, 0.674224],\n",
    "    \"Precision@10\": [0.746351, 0.718301, 0.745780],\n",
    "    \"Recall@10\": [0.518142, 0.509393, 0.525009],\n",
    "    \"Running Time (s)\": [1.232592, 0.324698, 1.197195]\n",
    "}\n",
    "\n",
    "\n",
    "# Creating DataFrames\n",
    "df_svd = pd.DataFrame(data_svd)\n",
    "df_lightgcn = pd.DataFrame(data_lightgcn)\n",
    "df_gat = pd.DataFrame(data_gat)\n",
    "df_graphsage = pd.DataFrame(data_graphsage)\n",
    "df_hypergcn = pd.DataFrame(data_hypergcn)\n",
    "df_node2vec = pd.DataFrame(data_node2vec)\n",
    "\n",
    "\n",
    "# Combine all the DataFrames into one\n",
    "final_combined_results = pd.concat([df_node2vec, df_hypergcn, df_lightgcn, df_gat, df_graphsage], ignore_index=True)\n",
    "final_combined_results_svd = pd.concat([df_node2vec, df_hypergcn, df_lightgcn, df_gat, df_graphsage,df_svd], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e6305c48-ec5f-47f4-90af-cbbe0d718b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the RMSE values for all scenarios and algorithms in the combined DataFrame\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "for algorithm in final_combined_results['Algorithm'].unique():\n",
    "    subset = final_combined_results[final_combined_results['Algorithm'] == algorithm]\n",
    "    plt.plot(subset['Scenario'], subset['RMSE'], marker='o', label=algorithm)\n",
    "\n",
    "# Plot the best algorithm (SVD User-Based)\n",
    "plt.plot(df_svd['Scenario'], df_svd['RMSE'], marker='o', linestyle='--', color='red', label='SVD (User-Based)')\n",
    "\n",
    "plt.xlabel('Scenario')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('RMSE Values for Different Scenarios and Algorithms')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d8d35768-024a-4b98-a51c-6329ba72ad4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:1609: UserWarning: \n",
      "The markers list has fewer values (5) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
      "  func(x=x, y=y, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1130.5x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Selecting relevant columns for the pairplot\n",
    "pairplot_df = final_combined_results_svd[[\"Algorithm\", \"Scenario\", \"RMSE\", \"MAE\", \"Precision@10\", \"Recall@10\"]]\n",
    "\n",
    "# Generating the pairplot\n",
    "sns.pairplot(pairplot_df, hue=\"Algorithm\", markers=[\"o\", \"s\", \"D\", \"^\", \"v\"], plot_kws={'alpha':0.8})\n",
    "\n",
    "plt.suptitle('Pairwise Comparison of Metrics Across Algorithms and Scenarios', y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6eebaa4-62c6-4a1a-b6d0-fe28c9a64ffa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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