{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self made simplified I-KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import helpers\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sparse\n",
    "from collections import defaultdict\n",
    "from itertools import chain\n",
    "import random\n",
    "\n",
    "train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None)\n",
    "test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
    "train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code = helpers.data_to_csr(train_read, test_read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IKNN():\n",
    "    \n",
    "    def fit(self, train_ui):\n",
    "        self.train_ui=train_ui\n",
    "        \n",
    "        train_iu=train_ui.transpose()\n",
    "        norms=np.linalg.norm(train_iu.A, axis=1) # here we compute lenth of each item ratings vector\n",
    "        norms=np.vectorize(lambda x: max(x,1))(norms[:,None]) # to avoid dividing by zero\n",
    "\n",
    "        normalized_train_iu=sparse.csr_matrix(train_iu/norms)\n",
    "\n",
    "        self.similarity_matrix_ii=normalized_train_iu*normalized_train_iu.transpose()\n",
    "        \n",
    "        self.estimations=np.array(train_ui*self.similarity_matrix_ii/((train_ui>0)*self.similarity_matrix_ii))\n",
    "        \n",
    "    def recommend(self, user_code_id, item_code_id, topK=10):\n",
    "        \n",
    "        top_k = defaultdict(list)\n",
    "        for nb_user, user in enumerate(self.estimations):\n",
    "            \n",
    "            user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n",
    "            for item, score in enumerate(user):\n",
    "                if item not in user_rated and not np.isnan(score):\n",
    "                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n",
    "        result=[]\n",
    "        # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n",
    "        for uid, item_scores in top_k.items():\n",
    "            item_scores.sort(key=lambda x: x[1], reverse=True)\n",
    "            result.append([uid]+list(chain(*item_scores[:topK])))\n",
    "        return result\n",
    "    \n",
    "    def estimate(self, user_code_id, item_code_id, test_ui):\n",
    "        result=[]\n",
    "        for user, item in zip(*test_ui.nonzero()):\n",
    "            result.append([user_code_id[user], item_code_id[item], \n",
    "                           self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "toy train ui:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 0, 0, 5, 0, 0, 4],\n",
       "       [0, 1, 2, 3, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.9701425 , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 1.        ],\n",
       "       [0.9701425 , 1.        , 0.24253563, 0.12478355, 0.9701425 ,\n",
       "        0.        , 0.        , 0.9701425 ],\n",
       "       [0.        , 0.24253563, 1.        , 0.51449576, 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.12478355, 0.51449576, 1.        , 0.        ,\n",
       "        0.85749293, 0.85749293, 0.        ],\n",
       "       [1.        , 0.9701425 , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 1.        ],\n",
       "       [0.        , 0.        , 0.        , 0.85749293, 0.        ,\n",
       "        1.        , 1.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.85749293, 0.        ,\n",
       "        1.        , 1.        , 0.        ],\n",
       "       [1.        , 0.9701425 , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 1.        ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimations matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[4.        , 4.        , 4.        , 4.        , 4.        ,\n",
       "               nan,        nan, 4.        ],\n",
       "       [1.        , 1.35990333, 2.15478388, 2.53390319, 1.        ,\n",
       "        3.        , 3.        , 1.        ],\n",
       "       [       nan, 5.        , 5.        , 4.05248907,        nan,\n",
       "        3.95012863, 3.95012863,        nan]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[0, 20, 4.0, 30, 4.0],\n",
       " [10, 50, 3.0, 60, 3.0, 0, 1.0, 40, 1.0, 70, 1.0],\n",
       " [20, 10, 5.0, 20, 5.0]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# toy example\n",
    "toy_train_read=pd.read_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
    "toy_test_read=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
    "\n",
    "toy_train_ui, toy_test_ui, toy_user_code_id, toy_user_id_code, \\\n",
    "toy_item_code_id, toy_item_id_code = helpers.data_to_csr(toy_train_read, toy_test_read)\n",
    "\n",
    "\n",
    "model=IKNN()\n",
    "model.fit(toy_train_ui)\n",
    "\n",
    "print('toy train ui:')\n",
    "display(toy_train_ui.A)\n",
    "\n",
    "print('similarity matrix:')\n",
    "display(model.similarity_matrix_ii.A)\n",
    "\n",
    "print('estimations matrix:')\n",
    "display(model.estimations)\n",
    "\n",
    "model.recommend(toy_user_code_id, toy_item_code_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=IKNN()\n",
    "model.fit(train_ui)\n",
    "\n",
    "top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))\n",
    "\n",
    "top_n.to_csv('Recommendations generated/ml-100k/Self_IKNN_reco.csv', index=False, header=False)\n",
    "\n",
    "estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))\n",
    "estimations.to_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "943it [00:00, 12783.59it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_05</th>\n",
       "      <th>precision_super</th>\n",
       "      <th>recall_super</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>mAP</th>\n",
       "      <th>MRR</th>\n",
       "      <th>LAUC</th>\n",
       "      <th>HR</th>\n",
       "      <th>Reco in test</th>\n",
       "      <th>Test coverage</th>\n",
       "      <th>Shannon</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.018363</td>\n",
       "      <td>0.808793</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.496391</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.392153</td>\n",
       "      <td>0.11544</td>\n",
       "      <td>4.174741</td>\n",
       "      <td>0.965327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RMSE       MAE  precision    recall      F_1      F_05  \\\n",
       "0  1.018363  0.808793   0.000318  0.000108  0.00014  0.000189   \n",
       "\n",
       "   precision_super  recall_super      NDCG       mAP       MRR      LAUC  \\\n",
       "0              0.0           0.0  0.000214  0.000037  0.000368  0.496391   \n",
       "\n",
       "         HR  Reco in test  Test coverage   Shannon      Gini  \n",
       "0  0.003181      0.392153        0.11544  4.174741  0.965327  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import evaluation_measures as ev\n",
    "estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', header=None)\n",
    "reco=np.loadtxt('Recommendations generated/ml-100k/Self_IKNN_reco.csv', delimiter=',')\n",
    "\n",
    "ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None),\n",
    "            estimations_df=estimations_df, \n",
    "            reco=reco,\n",
    "            super_reactions=[4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "943it [00:00, 11968.82it/s]\n",
      "943it [00:00, 12116.70it/s]\n",
      "943it [00:00, 12439.74it/s]\n",
      "943it [00:00, 11821.76it/s]\n",
      "943it [00:00, 12771.20it/s]\n",
      "943it [00:00, 12128.51it/s]\n",
      "943it [00:00, 10501.52it/s]\n",
      "943it [00:00, 11386.54it/s]\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\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>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_05</th>\n",
       "      <th>precision_super</th>\n",
       "      <th>recall_super</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>mAP</th>\n",
       "      <th>MRR</th>\n",
       "      <th>LAUC</th>\n",
       "      <th>HR</th>\n",
       "      <th>Reco in test</th>\n",
       "      <th>Test coverage</th>\n",
       "      <th>Shannon</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_TopPop</td>\n",
       "      <td>2.508258</td>\n",
       "      <td>2.217909</td>\n",
       "      <td>0.188865</td>\n",
       "      <td>0.116919</td>\n",
       "      <td>0.118732</td>\n",
       "      <td>0.141584</td>\n",
       "      <td>0.130472</td>\n",
       "      <td>0.137473</td>\n",
       "      <td>0.214651</td>\n",
       "      <td>0.111707</td>\n",
       "      <td>0.400939</td>\n",
       "      <td>0.555546</td>\n",
       "      <td>0.765642</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038961</td>\n",
       "      <td>3.159079</td>\n",
       "      <td>0.987317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_Baseline</td>\n",
       "      <td>0.949459</td>\n",
       "      <td>0.752487</td>\n",
       "      <td>0.091410</td>\n",
       "      <td>0.037652</td>\n",
       "      <td>0.046030</td>\n",
       "      <td>0.061286</td>\n",
       "      <td>0.079614</td>\n",
       "      <td>0.056463</td>\n",
       "      <td>0.095957</td>\n",
       "      <td>0.043178</td>\n",
       "      <td>0.198193</td>\n",
       "      <td>0.515501</td>\n",
       "      <td>0.437964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033911</td>\n",
       "      <td>2.836513</td>\n",
       "      <td>0.991139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_GlobalAvg</td>\n",
       "      <td>1.125760</td>\n",
       "      <td>0.943534</td>\n",
       "      <td>0.061188</td>\n",
       "      <td>0.025968</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0.041343</td>\n",
       "      <td>0.040558</td>\n",
       "      <td>0.032107</td>\n",
       "      <td>0.067695</td>\n",
       "      <td>0.027470</td>\n",
       "      <td>0.171187</td>\n",
       "      <td>0.509546</td>\n",
       "      <td>0.384942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>2.711772</td>\n",
       "      <td>0.992003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_TopRated</td>\n",
       "      <td>1.244026</td>\n",
       "      <td>1.000400</td>\n",
       "      <td>0.061188</td>\n",
       "      <td>0.025968</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0.041343</td>\n",
       "      <td>0.040558</td>\n",
       "      <td>0.032107</td>\n",
       "      <td>0.067695</td>\n",
       "      <td>0.027470</td>\n",
       "      <td>0.171187</td>\n",
       "      <td>0.509546</td>\n",
       "      <td>0.384942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>2.711772</td>\n",
       "      <td>0.992003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_Random</td>\n",
       "      <td>1.518962</td>\n",
       "      <td>1.216659</td>\n",
       "      <td>0.046978</td>\n",
       "      <td>0.019905</td>\n",
       "      <td>0.023281</td>\n",
       "      <td>0.030951</td>\n",
       "      <td>0.029399</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.052066</td>\n",
       "      <td>0.021101</td>\n",
       "      <td>0.129283</td>\n",
       "      <td>0.506463</td>\n",
       "      <td>0.314952</td>\n",
       "      <td>0.987911</td>\n",
       "      <td>0.184704</td>\n",
       "      <td>5.110269</td>\n",
       "      <td>0.905724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_BaselineIU</td>\n",
       "      <td>0.958136</td>\n",
       "      <td>0.754051</td>\n",
       "      <td>0.000954</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.496433</td>\n",
       "      <td>0.009544</td>\n",
       "      <td>0.699046</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.995669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_BaselineUI</td>\n",
       "      <td>0.967585</td>\n",
       "      <td>0.762740</td>\n",
       "      <td>0.000954</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.496424</td>\n",
       "      <td>0.009544</td>\n",
       "      <td>0.600530</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>1.803126</td>\n",
       "      <td>0.996380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_IKNN</td>\n",
       "      <td>1.018363</td>\n",
       "      <td>0.808793</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.496391</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.392153</td>\n",
       "      <td>0.115440</td>\n",
       "      <td>4.174741</td>\n",
       "      <td>0.965327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Model      RMSE       MAE  precision    recall       F_1  \\\n",
       "0      Self_TopPop  2.508258  2.217909   0.188865  0.116919  0.118732   \n",
       "0   Ready_Baseline  0.949459  0.752487   0.091410  0.037652  0.046030   \n",
       "0   Self_GlobalAvg  1.125760  0.943534   0.061188  0.025968  0.031383   \n",
       "0    Self_TopRated  1.244026  1.000400   0.061188  0.025968  0.031383   \n",
       "0     Ready_Random  1.518962  1.216659   0.046978  0.019905  0.023281   \n",
       "0  Self_BaselineIU  0.958136  0.754051   0.000954  0.000188  0.000298   \n",
       "0  Self_BaselineUI  0.967585  0.762740   0.000954  0.000170  0.000278   \n",
       "0        Self_IKNN  1.018363  0.808793   0.000318  0.000108  0.000140   \n",
       "\n",
       "       F_05  precision_super  recall_super      NDCG       mAP       MRR  \\\n",
       "0  0.141584         0.130472      0.137473  0.214651  0.111707  0.400939   \n",
       "0  0.061286         0.079614      0.056463  0.095957  0.043178  0.198193   \n",
       "0  0.041343         0.040558      0.032107  0.067695  0.027470  0.171187   \n",
       "0  0.041343         0.040558      0.032107  0.067695  0.027470  0.171187   \n",
       "0  0.030951         0.029399      0.019829  0.052066  0.021101  0.129283   \n",
       "0  0.000481         0.000644      0.000223  0.001043  0.000335  0.003348   \n",
       "0  0.000463         0.000644      0.000189  0.000752  0.000168  0.001677   \n",
       "0  0.000189         0.000000      0.000000  0.000214  0.000037  0.000368   \n",
       "\n",
       "       LAUC        HR  Reco in test  Test coverage   Shannon      Gini  \n",
       "0  0.555546  0.765642      1.000000       0.038961  3.159079  0.987317  \n",
       "0  0.515501  0.437964      1.000000       0.033911  2.836513  0.991139  \n",
       "0  0.509546  0.384942      1.000000       0.025974  2.711772  0.992003  \n",
       "0  0.509546  0.384942      1.000000       0.025974  2.711772  0.992003  \n",
       "0  0.506463  0.314952      0.987911       0.184704  5.110269  0.905724  \n",
       "0  0.496433  0.009544      0.699046       0.005051  1.945910  0.995669  \n",
       "0  0.496424  0.009544      0.600530       0.005051  1.803126  0.996380  \n",
       "0  0.496391  0.003181      0.392153       0.115440  4.174741  0.965327  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import imp\n",
    "imp.reload(ev)\n",
    "\n",
    "import evaluation_measures as ev\n",
    "dir_path=\"Recommendations generated/ml-100k/\"\n",
    "super_reactions=[4,5]\n",
    "test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
    "\n",
    "ev.evaluate_all(test, dir_path, super_reactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ready-made KNNs - Surprise implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I-KNN - basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Generating predictions...\n",
      "Generating top N recommendations...\n",
      "Generating predictions...\n"
     ]
    }
   ],
   "source": [
    "import helpers\n",
    "import surprise as sp\n",
    "import imp\n",
    "imp.reload(helpers)\n",
    "\n",
    "sim_options = {'name': 'cosine',\n",
    "              'user_based': False}  # compute similarities between items\n",
    "algo = sp.KNNBasic(sim_options=sim_options)\n",
    "\n",
    "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNN_reco.csv',\n",
    "          estimations_path='Recommendations generated/ml-100k/Ready_I-KNN_estimations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### U-KNN - basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Generating predictions...\n",
      "Generating top N recommendations...\n",
      "Generating predictions...\n"
     ]
    }
   ],
   "source": [
    "import helpers\n",
    "import surprise as sp\n",
    "import imp\n",
    "imp.reload(helpers)\n",
    "\n",
    "sim_options = {'name': 'cosine',\n",
    "              'user_based': True}  # compute similarities between users\n",
    "algo = sp.KNNBasic(sim_options=sim_options)\n",
    "\n",
    "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_U-KNN_reco.csv',\n",
    "          estimations_path='Recommendations generated/ml-100k/Ready_U-KNN_estimations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I-KNN - on top baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Generating predictions...\n",
      "Generating top N recommendations...\n",
      "Generating predictions...\n"
     ]
    }
   ],
   "source": [
    "import helpers\n",
    "import surprise as sp\n",
    "import imp\n",
    "imp.reload(helpers)\n",
    "\n",
    "sim_options = {'name': 'cosine',\n",
    "              'user_based': False}  # compute similarities between items\n",
    "algo = sp.KNNBaseline()\n",
    "\n",
    "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_reco.csv',\n",
    "          estimations_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_estimations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# project task 4:  use a version of your choice of Surprise KNNalgorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the docs and try to find best parameter configuration (let say in terms of RMSE)\n",
    "# https://surprise.readthedocs.io/en/stable/knn_inspired.html##surprise.prediction_algorithms.knns.KNNBaseline\n",
    "# the solution here can be similar to examples above\n",
    "# please save the output in 'Recommendations generated/ml-100k/Self_KNNSurprisetask_reco.csv' and\n",
    "# 'Recommendations generated/ml-100k/Self_KNNSurprisetask_estimations.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Generating predictions...\n",
      "Generating top N recommendations...\n",
      "Generating predictions...\n"
     ]
    }
   ],
   "source": [
    "import helpers\n",
    "import surprise as sp\n",
    "import imp\n",
    "imp.reload(helpers)\n",
    "\n",
    "sim_options = {'name': 'cosine',\n",
    "              'user_based': False}  # compute similarities between items\n",
    "#i=40\n",
    "#while i<=60:\n",
    "#    i += 2\n",
    "#    algo = sp.KNNWithMeans(k=i)\n",
    "#\n",
    "#    helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Self_KNNSurprisetaskMaxk' + str(i) + '_reco.csv',\n",
    "#              estimations_path='Recommendations generated/ml-100k/Self_KNNSurprisetaskMaxk' + str(i) + '_estimations.csv')\n",
    "\n",
    "#    #best max k = 48 with RMSE: 0.955432\n",
    "    \n",
    "#i=1\n",
    "#while i<=20:\n",
    "#    i += 1\n",
    "#    algo = sp.KNNWithMeans(k=48, min_k=i)\n",
    "#\n",
    "#    helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Self_KNNSurprisetaskMaxk48Mink' + str(i) + '_reco.csv',\n",
    "#              estimations_path='Recommendations generated/ml-100k/Self_KNNSurprisetaskMaxk48Mink' + str(i) + '_estimations.csv')\n",
    "\n",
    "#    #best max k = 48 and min_k = 3 with RMSE: 0.954453\n",
    "\n",
    "algo = sp.KNNWithMeans(k=48, min_k=3)\n",
    "\n",
    "helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Self_KNNSurprisetask_reco.csv',\n",
    "        estimations_path='Recommendations generated/ml-100k/Self_KNNSurprisetask_estimations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "943it [00:00, 10869.42it/s]\n",
      "943it [00:00, 11819.04it/s]\n",
      "943it [00:00, 12273.94it/s]\n",
      "943it [00:00, 12777.35it/s]\n",
      "943it [00:00, 12124.72it/s]\n",
      "943it [00:00, 12600.44it/s]\n",
      "943it [00:00, 11819.00it/s]\n",
      "943it [00:00, 10867.15it/s]\n",
      "943it [00:00, 12602.53it/s]\n",
      "943it [00:00, 12945.89it/s]\n",
      "943it [00:00, 11386.97it/s]\n",
      "943it [00:00, 12128.36it/s]\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>F_1</th>\n",
       "      <th>F_05</th>\n",
       "      <th>precision_super</th>\n",
       "      <th>recall_super</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>mAP</th>\n",
       "      <th>MRR</th>\n",
       "      <th>LAUC</th>\n",
       "      <th>HR</th>\n",
       "      <th>Reco in test</th>\n",
       "      <th>Test coverage</th>\n",
       "      <th>Shannon</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_TopPop</td>\n",
       "      <td>2.508258</td>\n",
       "      <td>2.217909</td>\n",
       "      <td>0.188865</td>\n",
       "      <td>0.116919</td>\n",
       "      <td>0.118732</td>\n",
       "      <td>0.141584</td>\n",
       "      <td>0.130472</td>\n",
       "      <td>0.137473</td>\n",
       "      <td>0.214651</td>\n",
       "      <td>0.111707</td>\n",
       "      <td>0.400939</td>\n",
       "      <td>0.555546</td>\n",
       "      <td>0.765642</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038961</td>\n",
       "      <td>3.159079</td>\n",
       "      <td>0.987317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_Baseline</td>\n",
       "      <td>0.949459</td>\n",
       "      <td>0.752487</td>\n",
       "      <td>0.091410</td>\n",
       "      <td>0.037652</td>\n",
       "      <td>0.046030</td>\n",
       "      <td>0.061286</td>\n",
       "      <td>0.079614</td>\n",
       "      <td>0.056463</td>\n",
       "      <td>0.095957</td>\n",
       "      <td>0.043178</td>\n",
       "      <td>0.198193</td>\n",
       "      <td>0.515501</td>\n",
       "      <td>0.437964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033911</td>\n",
       "      <td>2.836513</td>\n",
       "      <td>0.991139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_GlobalAvg</td>\n",
       "      <td>1.125760</td>\n",
       "      <td>0.943534</td>\n",
       "      <td>0.061188</td>\n",
       "      <td>0.025968</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0.041343</td>\n",
       "      <td>0.040558</td>\n",
       "      <td>0.032107</td>\n",
       "      <td>0.067695</td>\n",
       "      <td>0.027470</td>\n",
       "      <td>0.171187</td>\n",
       "      <td>0.509546</td>\n",
       "      <td>0.384942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>2.711772</td>\n",
       "      <td>0.992003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_TopRated</td>\n",
       "      <td>1.244026</td>\n",
       "      <td>1.000400</td>\n",
       "      <td>0.061188</td>\n",
       "      <td>0.025968</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0.041343</td>\n",
       "      <td>0.040558</td>\n",
       "      <td>0.032107</td>\n",
       "      <td>0.067695</td>\n",
       "      <td>0.027470</td>\n",
       "      <td>0.171187</td>\n",
       "      <td>0.509546</td>\n",
       "      <td>0.384942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>2.711772</td>\n",
       "      <td>0.992003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_Random</td>\n",
       "      <td>1.518962</td>\n",
       "      <td>1.216659</td>\n",
       "      <td>0.046978</td>\n",
       "      <td>0.019905</td>\n",
       "      <td>0.023281</td>\n",
       "      <td>0.030951</td>\n",
       "      <td>0.029399</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.052066</td>\n",
       "      <td>0.021101</td>\n",
       "      <td>0.129283</td>\n",
       "      <td>0.506463</td>\n",
       "      <td>0.314952</td>\n",
       "      <td>0.987911</td>\n",
       "      <td>0.184704</td>\n",
       "      <td>5.110269</td>\n",
       "      <td>0.905724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_KNNSurprisetask</td>\n",
       "      <td>0.954453</td>\n",
       "      <td>0.753598</td>\n",
       "      <td>0.039449</td>\n",
       "      <td>0.017706</td>\n",
       "      <td>0.021027</td>\n",
       "      <td>0.027213</td>\n",
       "      <td>0.035730</td>\n",
       "      <td>0.029334</td>\n",
       "      <td>0.034946</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.069045</td>\n",
       "      <td>0.505326</td>\n",
       "      <td>0.288441</td>\n",
       "      <td>0.653977</td>\n",
       "      <td>0.084416</td>\n",
       "      <td>3.351793</td>\n",
       "      <td>0.984540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_I-KNN</td>\n",
       "      <td>1.030386</td>\n",
       "      <td>0.813067</td>\n",
       "      <td>0.026087</td>\n",
       "      <td>0.006908</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>0.016046</td>\n",
       "      <td>0.021137</td>\n",
       "      <td>0.009522</td>\n",
       "      <td>0.024214</td>\n",
       "      <td>0.008958</td>\n",
       "      <td>0.048068</td>\n",
       "      <td>0.499885</td>\n",
       "      <td>0.154825</td>\n",
       "      <td>0.402333</td>\n",
       "      <td>0.434343</td>\n",
       "      <td>5.133650</td>\n",
       "      <td>0.877999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_I-KNNBaseline</td>\n",
       "      <td>0.935327</td>\n",
       "      <td>0.737424</td>\n",
       "      <td>0.002545</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>0.001602</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>0.000930</td>\n",
       "      <td>0.003444</td>\n",
       "      <td>0.001362</td>\n",
       "      <td>0.011760</td>\n",
       "      <td>0.496724</td>\n",
       "      <td>0.021209</td>\n",
       "      <td>0.482821</td>\n",
       "      <td>0.059885</td>\n",
       "      <td>2.232578</td>\n",
       "      <td>0.994487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready_U-KNN</td>\n",
       "      <td>1.023495</td>\n",
       "      <td>0.807913</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.496441</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.602121</td>\n",
       "      <td>0.010823</td>\n",
       "      <td>2.089186</td>\n",
       "      <td>0.995706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_BaselineIU</td>\n",
       "      <td>0.958136</td>\n",
       "      <td>0.754051</td>\n",
       "      <td>0.000954</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.496433</td>\n",
       "      <td>0.009544</td>\n",
       "      <td>0.699046</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.995669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_BaselineUI</td>\n",
       "      <td>0.967585</td>\n",
       "      <td>0.762740</td>\n",
       "      <td>0.000954</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.496424</td>\n",
       "      <td>0.009544</td>\n",
       "      <td>0.600530</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>1.803126</td>\n",
       "      <td>0.996380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Self_IKNN</td>\n",
       "      <td>1.018363</td>\n",
       "      <td>0.808793</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.496391</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>0.392153</td>\n",
       "      <td>0.115440</td>\n",
       "      <td>4.174741</td>\n",
       "      <td>0.965327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model      RMSE       MAE  precision    recall       F_1  \\\n",
       "0           Self_TopPop  2.508258  2.217909   0.188865  0.116919  0.118732   \n",
       "0        Ready_Baseline  0.949459  0.752487   0.091410  0.037652  0.046030   \n",
       "0        Self_GlobalAvg  1.125760  0.943534   0.061188  0.025968  0.031383   \n",
       "0         Self_TopRated  1.244026  1.000400   0.061188  0.025968  0.031383   \n",
       "0          Ready_Random  1.518962  1.216659   0.046978  0.019905  0.023281   \n",
       "0  Self_KNNSurprisetask  0.954453  0.753598   0.039449  0.017706  0.021027   \n",
       "0           Ready_I-KNN  1.030386  0.813067   0.026087  0.006908  0.010593   \n",
       "0   Ready_I-KNNBaseline  0.935327  0.737424   0.002545  0.000755  0.001105   \n",
       "0           Ready_U-KNN  1.023495  0.807913   0.000742  0.000205  0.000305   \n",
       "0       Self_BaselineIU  0.958136  0.754051   0.000954  0.000188  0.000298   \n",
       "0       Self_BaselineUI  0.967585  0.762740   0.000954  0.000170  0.000278   \n",
       "0             Self_IKNN  1.018363  0.808793   0.000318  0.000108  0.000140   \n",
       "\n",
       "       F_05  precision_super  recall_super      NDCG       mAP       MRR  \\\n",
       "0  0.141584         0.130472      0.137473  0.214651  0.111707  0.400939   \n",
       "0  0.061286         0.079614      0.056463  0.095957  0.043178  0.198193   \n",
       "0  0.041343         0.040558      0.032107  0.067695  0.027470  0.171187   \n",
       "0  0.041343         0.040558      0.032107  0.067695  0.027470  0.171187   \n",
       "0  0.030951         0.029399      0.019829  0.052066  0.021101  0.129283   \n",
       "0  0.027213         0.035730      0.029334  0.034946  0.010791  0.069045   \n",
       "0  0.016046         0.021137      0.009522  0.024214  0.008958  0.048068   \n",
       "0  0.001602         0.002253      0.000930  0.003444  0.001362  0.011760   \n",
       "0  0.000449         0.000536      0.000198  0.000845  0.000274  0.002744   \n",
       "0  0.000481         0.000644      0.000223  0.001043  0.000335  0.003348   \n",
       "0  0.000463         0.000644      0.000189  0.000752  0.000168  0.001677   \n",
       "0  0.000189         0.000000      0.000000  0.000214  0.000037  0.000368   \n",
       "\n",
       "       LAUC        HR  Reco in test  Test coverage   Shannon      Gini  \n",
       "0  0.555546  0.765642      1.000000       0.038961  3.159079  0.987317  \n",
       "0  0.515501  0.437964      1.000000       0.033911  2.836513  0.991139  \n",
       "0  0.509546  0.384942      1.000000       0.025974  2.711772  0.992003  \n",
       "0  0.509546  0.384942      1.000000       0.025974  2.711772  0.992003  \n",
       "0  0.506463  0.314952      0.987911       0.184704  5.110269  0.905724  \n",
       "0  0.505326  0.288441      0.653977       0.084416  3.351793  0.984540  \n",
       "0  0.499885  0.154825      0.402333       0.434343  5.133650  0.877999  \n",
       "0  0.496724  0.021209      0.482821       0.059885  2.232578  0.994487  \n",
       "0  0.496441  0.007423      0.602121       0.010823  2.089186  0.995706  \n",
       "0  0.496433  0.009544      0.699046       0.005051  1.945910  0.995669  \n",
       "0  0.496424  0.009544      0.600530       0.005051  1.803126  0.996380  \n",
       "0  0.496391  0.003181      0.392153       0.115440  4.174741  0.965327  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import imp\n",
    "imp.reload(ev)\n",
    "\n",
    "import evaluation_measures as ev\n",
    "dir_path=\"Recommendations generated/ml-100k/\"\n",
    "super_reactions=[4,5]\n",
    "test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
    "\n",
    "ev.evaluate_all(test, dir_path, super_reactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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