P1 - User To User.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be3d3d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb594d80",
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       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>movieID</th>\n",
       "      <th>rating</th>\n",
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      "text/plain": [
       "   userID  movieID  rating\n",
       "0    4490     2109       4\n",
       "1    5839     3471       4\n",
       "2    5382      150       3\n",
       "3    1262     1237       5\n",
       "4    6005     2273       4"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(\"train.csv\")\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "306fab1b",
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       "      <th>movieID</th>\n",
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      "text/plain": [
       "movieID  1     2     3     4     5     6     7     8     9     10    ...   \n",
       "userID                                                               ...   \n",
       "1         3.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...  \\\n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
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       "5         NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "movieID  3943  3944  3945  3946  3947  3948  3949  3950  3951  3952  \n",
       "userID                                                               \n",
       "1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "5         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 3705 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pivot_uu = raw_data.pivot(index=\"userID\", columns= \"movieID\", values=\"rating\")\n",
    "pivot_uu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b0dad81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_mean = pivot_uu.mean().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "886b1ad1",
   "metadata": {},
   "outputs": [
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "pivot_uu.shape"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5efb30b",
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      "text/plain": [
       "movieID      1     2     3     4     5         6     7     8     9     10     \n",
       "userID                                                                        \n",
       "1       -0.571429   0.0   0.0   0.0   0.0  0.000000   0.0   0.0   0.0   0.0  \\\n",
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       "5        0.000000   0.0   0.0   0.0   0.0 -0.299312   0.0   0.0   0.0   0.0   \n",
       "\n",
       "movieID  ...  3943  3944  3945  3946  3947  3948  3949  3950  3951  3952  \n",
       "userID   ...                                                              \n",
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       "\n",
       "[5 rows x 3705 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df = pivot_uu - pivot_uu.mean(axis=1).values.reshape(-1, 1)\n",
    "scaled_df.fillna(0, inplace=True)\n",
    "scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6732e3a3",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDs</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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      "text/plain": [
       "         IDs\n",
       "0   629_2683\n",
       "1    601_904\n",
       "2  5673_3717\n",
       "3  5616_1721\n",
       "4  1099_3697"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_no_labels = pd.read_csv(\"test_without_labels.csv\")\n",
    "test_no_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ce246c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>IDs</th>\n",
       "      <th>userID</th>\n",
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      ],
      "text/plain": [
       "         IDs  userID  movieID\n",
       "0   629_2683     629     2683\n",
       "1    601_904     601      904\n",
       "2  5673_3717    5673     3717\n",
       "3  5616_1721    5616     1721\n",
       "4  1099_3697    1099     3697"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_no_labels[\"userID\"] = test_no_labels.IDs.str.split('_').str[0].astype(int)\n",
    "test_no_labels[\"movieID\"] = test_no_labels.IDs.str.split('_').str[1].astype(int)\n",
    "test_no_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca1a9629",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_uu = test_no_labels.drop(\"IDs\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84308a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ratings_uu = []\n",
    "\n",
    "for userId,movie in test_uu.itertuples(index=False):\n",
    "    k = 50\n",
    "    rated_indices = pivot_uu[np.isnan(pivot_uu[movie]) == False].index\n",
    "    filtered_df = scaled_df.loc[rated_indices, :]\n",
    "    if len(filtered_df) == 0:\n",
    "        pred = all_mean\n",
    "    else:\n",
    "        if len(filtered_df) < k:\n",
    "            k = len(filtered_df)\n",
    "        \n",
    "        nn = NearestNeighbors(n_neighbors=k, algorithm=\"kd_tree\")\n",
    "        nn.fit(filtered_df)\n",
    "        test_user = scaled_df.loc[[userId], :]\n",
    "        close_indices = nn.kneighbors(test_user ,return_distance=False)[0]\n",
    "        close_friends = filtered_df.iloc[close_indices, :]\n",
    "        pred = pivot_uu.loc[userId, :].mean() + close_friends[movie].mean()\n",
    "        pred_ratings_uu.append(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51a3fdfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred_ratings_uu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aa8444d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(pred_ratings_uu)): \n",
    "    if pred_ratings_uu[i] > 5:\n",
    "        pred_ratings_uu[i] = 5\n",
    "    elif pred_ratings_uu[i] < 1:\n",
    "        pred_ratings_uu[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a021cc7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_no_labels[\"rating\"] = pred_ratings_uu\n",
    "test_no_labels.drop(columns=[\"userID\",\"movieID\"],axis=1,inplace=True)\n",
    "test_no_labels.to_csv(\"result_uu_2804.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8b11e7a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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