17-Recommender_Systems.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from scipy import sparse"
      ],
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      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Data/Dataset.csv')\n",
        "#df = df.set_index(['user','item'])\n",
        "df.head()"
      ],
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              "   user  item         x  observed  liked\n",
              "0     1     1  1.546881         1      1\n",
              "1     1     2  0.850043         1      1\n",
              "2     1     3  0.344194         1      1\n",
              "3     1     4  1.621203         1      1\n",
              "4     1     5  0.714086         1      1"
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        "df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2800 entries, 0 to 2799\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   user      2800 non-null   int64  \n",
            " 1   item      2800 non-null   int64  \n",
            " 2   x         2800 non-null   float64\n",
            " 3   observed  2800 non-null   int64  \n",
            " 4   liked     2800 non-null   int64  \n",
            "dtypes: float64(1), int64(4)\n",
            "memory usage: 109.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Find the signal put into data"
      ],
      "metadata": {
        "id": "FC2vsG277sa1"
      }
    },
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      "cell_type": "code",
      "source": [
        "dfs = df.reset_index().pivot(columns='item',index='user',values='liked')"
      ],
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        "id": "FILgvy-D7VqS"
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      "execution_count": null,
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      "source": [
        "fig, ax = plt.subplots(figsize=(10, 40))\n",
        "cbar_kws=dict(use_gridspec=False,location=\"top\",pad=0.02,shrink=1)\n",
        "sns.heatmap(dfs, cmap= 'seismic', cbar_kws = cbar_kws,vmin=0,vmax=1,linewidths=0.1)\n",
        "ax.xaxis.tick_top()\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.set_xlabel('items', fontsize=20)\n",
        "ax.set_ylabel('users', fontsize=20)\n",
        "ax.collections[0].colorbar.set_label(\"liked\")"
      ],
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x2880 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Diagonal signals are visible in data\n",
        "To clean the added signal diagonal cells were replaced by 0 instead of 1"
      ],
      "metadata": {
        "id": "Zvn3St2DLkzM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = dfs.copy()"
      ],
      "metadata": {
        "id": "MsffwY7wLZMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(2,100,4):\n",
        "  j = 0\n",
        "  #print('i is:', i)\n",
        "  while i >= 0 and j < 28:\n",
        "    df0.iloc[i, j] = 0\n",
        "    i -= 1\n",
        "    j += 1\n",
        "for i in range(103, 127,4):\n",
        "  j = i%100\n",
        "  i = 99\n",
        "  #print('i is:',99)\n",
        "  while i >= 0 and j < 28:\n",
        "    df0.iloc[i, j] = 0\n",
        "    i -= 1\n",
        "    j += 1\n"
      ],
      "metadata": {
        "id": "nsnt67I9AByG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 40))\n",
        "cbar_kws=dict(use_gridspec=False,location=\"top\",pad=0.02,shrink=1)\n",
        "sns.heatmap(df0, cmap= 'seismic', cbar_kws = cbar_kws,vmin=0,vmax=1,linewidths=0.1)\n",
        "ax.xaxis.tick_top()\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.set_xlabel('items', fontsize=20)\n",
        "ax.set_ylabel('users', fontsize=20)\n",
        "ax.collections[0].colorbar.set_label(\"liked\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UlNVK2EfMMPc",
        "outputId": "081aec2d-de3e-4cf9-8b96-fbd30bd4c957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x2880 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df0.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "y4yVXB6gqldh",
        "outputId": "db63e558-96b2-4d93-ac9b-ec3bb92b75c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "item  1   2   3   4   5   6   7   8   9   10  ...  19  20  21  22  23  24  25  \\\n",
              "user                                          ...                               \n",
              "1      1   1   0   1   1   0   0   0   0   0  ...   0   0   0   0   0   1   0   \n",
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              "5      0   1   0   0   0   1   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
              "\n",
              "item  26  27  28  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_i = df[df.user == 1]"
      ],
      "metadata": {
        "id": "pg3WphkfnBxi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_i.liked = df0.iloc[0,:].values"
      ],
      "metadata": {
        "id": "V5ch-OItn3O6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_base = user_i.copy()"
      ],
      "metadata": {
        "id": "dY8zu1Aco8K6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for u in range(2,101):\n",
        "  user_i = df[df.user == u]\n",
        "  user_i.liked = df0.iloc[u-1,:].values\n",
        "  df_base = df_base.append(user_i, ignore_index=True)"
      ],
      "metadata": {
        "id": "OnZjBkwLnvzX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Divide Data to Train and Test"
      ],
      "metadata": {
        "id": "iHM6ONzz8swh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train data which are observed"
      ],
      "metadata": {
        "id": "WIzb2qYoOaEW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test data which aren't observed"
      ],
      "metadata": {
        "id": "pN99_l6zPbF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_base = df_base.rename(columns={'user':'userID','item':'itemID','liked':'rating'})"
      ],
      "metadata": {
        "id": "hCneQEYy2O8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain = df_base[df_base.observed==1]\n",
        "dftest = df_base[df_base.observed==0]\n",
        "dftrain = dftrain.drop(['observed'], axis = 1)\n",
        "dftest = dftest.drop(['observed'], axis = 1)"
      ],
      "metadata": {
        "id": "_-JMKSlvC6l0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.reset_index(inplace=True,drop= True)\n",
        "dftest.reset_index(inplace=True,drop= True)"
      ],
      "metadata": {
        "id": "1fWZVzCKupfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualize train set"
      ],
      "metadata": {
        "id": "7AbC3z3uPtQS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = dftrain.reset_index().pivot(columns='itemID',index='userID',values='rating')"
      ],
      "metadata": {
        "id": "uqVVXO9Mw9UE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 40))\n",
        "cbar_kws=dict(use_gridspec=False,location=\"top\",pad=0.02,shrink=1)\n",
        "sns.heatmap(df1, cmap= 'seismic', cbar_kws = cbar_kws,vmin=0,vmax=1,linewidths=0.1)\n",
        "ax.xaxis.tick_top()\n",
        "ax.xaxis.set_label_position('top')\n",
        "ax.set_xlabel('items', fontsize=20)\n",
        "ax.set_ylabel('users', fontsize=20)\n",
        "ax.collections[0].colorbar.set_label(\"liked\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IcvRB9h6OF-q",
        "outputId": "4b1de8f5-dbec-4972-9954-72c34a338408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x2880 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Popular items"
      ],
      "metadata": {
        "id": "DZyvcJ1lJXoX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "popular_items = df1.sum(axis=0,numeric_only = True)\n",
        "most_popular = popular_items.sort_values(ascending=False)\n",
        "most_popular.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NrhFv9aIa2z",
        "outputId": "7459b36f-e80c-441b-e830-97ac740fa9a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "itemID\n",
              "5     36.0\n",
              "6     35.0\n",
              "4     30.0\n",
              "14    21.0\n",
              "16    20.0\n",
              "1     17.0\n",
              "18    17.0\n",
              "11    16.0\n",
              "19    15.0\n",
              "2     15.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "How many items each user liked"
      ],
      "metadata": {
        "id": "mHkr44lIL91i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1.sum(axis=1,numeric_only = True).count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mP9r4p4Edv1D",
        "outputId": "eeaaaf2b-512b-4c19-9373-c25d6e5afba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method Series.count of userID\n",
            "1      5.0\n",
            "2      3.0\n",
            "3      4.0\n",
            "4      4.0\n",
            "5      4.0\n",
            "      ... \n",
            "96     4.0\n",
            "97     4.0\n",
            "98     6.0\n",
            "99     6.0\n",
            "100    3.0\n",
            "Length: 100, dtype: float64>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "Wi7O8x29qL_W",
        "outputId": "c5a12559-f676-413a-af8f-65e9e5af96c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "itemID   1    2    3    4    5    6    7    8    9    10  ...   19   20   21  \\\n",
              "userID                                                    ...                  \n",
              "1       1.0  1.0  0.0  1.0  1.0  0.0  0.0  NaN  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "2       0.0  0.0  0.0  0.0  1.0  0.0  NaN  NaN  0.0  0.0  ...  1.0  0.0  NaN   \n",
              "3       0.0  0.0  0.0  0.0  NaN  1.0  1.0  1.0  NaN  0.0  ...  0.0  0.0  0.0   \n",
              "4       NaN  NaN  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...  1.0  0.0  0.0   \n",
              "5       0.0  1.0  0.0  0.0  NaN  1.0  NaN  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "\n",
              "itemID   22   23   24   25   26   27   28  \n",
              "userID                                     \n",
              "1       0.0  0.0  1.0  0.0  0.0  0.0  0.0  \n",
              "2       0.0  NaN  0.0  0.0  0.0  0.0  0.0  \n",
              "3       0.0  0.0  0.0  NaN  1.0  NaN  0.0  \n",
              "4       0.0  1.0  0.0  0.0  1.0  0.0  0.0  \n",
              "5       0.0  NaN  0.0  0.0  NaN  0.0  0.0  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cfa06f68-b327-4728-8f44-9becd04e6700\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>itemID</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>...</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfa06f68-b327-4728-8f44-9becd04e6700')\"\n",
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              "        \n",
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              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cfa06f68-b327-4728-8f44-9becd04e6700');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Item-Item Similarity\n",
        "Calculating cosin similarity matrix"
      ],
      "metadata": {
        "id": "GyVtYOvEthYU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Cosine(data):\n",
        "  data_sparse = sparse.csr_matrix(data)\n",
        "  similarity = cosine_similarity(data_sparse.transpose())\n",
        "  Cos = pd.DataFrame(data=similarity, index= data.columns, columns= data.columns)\n",
        "  return Cos"
      ],
      "metadata": {
        "id": "XwO2i9sJQeUb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df1.fillna(0)"
      ],
      "metadata": {
        "id": "Jw2UVctdtB-K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cos_item_matrix = Cosine(df1)\n",
        "print('10 first similar items to the item #1')\n",
        "print(cos_item_matrix.iloc[0].nlargest(10))"
      ],
      "metadata": {
        "id": "FPXx1UYpttjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ede89bb-8217-4a3d-e241-6bf58ba52216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 first similar items to the item #1\n",
            "itemID\n",
            "1     1.000000\n",
            "9     0.365636\n",
            "6     0.327968\n",
            "4     0.309965\n",
            "5     0.282958\n",
            "25    0.257248\n",
            "2     0.250490\n",
            "19    0.250490\n",
            "18    0.235294\n",
            "23    0.219382\n",
            "Name: 1, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## User-User Similarity\n",
        "Calculate cosin similarity matrix"
      ],
      "metadata": {
        "id": "Lu8J6WO5Jlpv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the similarity matrix\n",
        "cos_user_matrix = Cosine(df1.transpose())"
      ],
      "metadata": {
        "id": "MB9LyPLjfHcG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('10 first similar users to the user # 1')\n",
        "print(cos_user_matrix.iloc[0].nlargest(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_2EXMkCt0YJ",
        "outputId": "3f56aa4c-7d98-48da-f348-635eeeaa6c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 first similar users to the user # 1\n",
            "userID\n",
            "1     1.000000\n",
            "54    0.670820\n",
            "14    0.600000\n",
            "29    0.600000\n",
            "77    0.600000\n",
            "37    0.547723\n",
            "45    0.547723\n",
            "98    0.547723\n",
            "22    0.516398\n",
            "30    0.516398\n",
            "Name: 1, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Cosine Similarity find the most similar user behaviours"
      ],
      "metadata": {
        "id": "kJ3pirlv1Fpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_neighbours = pd.DataFrame(index=cos_user_matrix.columns, columns=range(1,7))\n",
        "for i in range(0, len(cos_user_matrix.columns)):\n",
        "    data_neighbours.iloc[i,:6] = cos_user_matrix.iloc[0:,i].sort_values(ascending=False)[:6].index"
      ],
      "metadata": {
        "id": "NAjMvH1I1I-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_neighbours.drop(columns=[1],axis= 0).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "KVw71ppV1Vvg",
        "outputId": "619518cb-37b1-4af2-bc3f-dc7d12ae524b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          2   3    4   5   6\n",
              "userID                      \n",
              "1        54  77   29  14  45\n",
              "2        52  86  100  96   4\n",
              "3        71  48   91  51   8\n",
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              "5        57  77   23  39  26\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-edce3081-3b57-4001-8c7f-605008b805ca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The users with similar bahaviour with the user # 1"
      ],
      "metadata": {
        "id": "KIGXgAh12FVV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in data_neighbours.iloc[0].values:\n",
        "  known_user_likes = df1.iloc[i-1]\n",
        "  known_user_likes = known_user_likes[known_user_likes >0].index.values\n",
        "  print('Items liked by user #',i,'are:',known_user_likes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNmGAsK92B_V",
        "outputId": "1963bc95-b8a6-4684-f9fc-83055f6c37cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Items liked by user # 1 are: [ 1  2  4  5 24]\n",
            "Items liked by user # 54 are: [ 1  5  8 24]\n",
            "Items liked by user # 77 are: [ 1  2  4  6 16]\n",
            "Items liked by user # 29 are: [ 4  5 14 24 26]\n",
            "Items liked by user # 14 are: [ 1  4  5 17 20]\n",
            "Items liked by user # 45 are: [ 4  5 16 18 24 28]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in data_neighbours.iloc[8].values:\n",
        "  known_user_likes = df1.iloc[i-1]\n",
        "  known_user_likes = known_user_likes[known_user_likes >0].index.values\n",
        "  print('Items liked by user #',i,'are:',known_user_likes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e59l0qvaBJoi",
        "outputId": "be30d858-986f-41f4-d091-389925d3bf0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Items liked by user # 9 are: [16 17 20]\n",
            "Items liked by user # 69 are: [ 2  8 17 20]\n",
            "Items liked by user # 14 are: [ 1  4  5 17 20]\n",
            "Items liked by user # 63 are: [10 15 16 20 26]\n",
            "Items liked by user # 93 are: [ 6 12 14 17 20]\n",
            "Items liked by user # 43 are: [ 8 14 16 20 26 27]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Cosine_item_Neighbours "
      ],
      "metadata": {
        "id": "JbbZUah_NiIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data_neighbours = pd.DataFrame(index=cos_item_matrix.columns, columns=range(1,28))\n",
        "for i in range(0, len(cos_item_matrix.columns)):\n",
        "    all_data_neighbours.iloc[i,:27] = cos_item_matrix.iloc[0:,i].sort_values(ascending=False)[:27].index"
      ],
      "metadata": {
        "id": "Ny4_P3pws7uA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = {}\n",
        "for user_index in range(len(all_data_neighbours)):\n",
        "  known_user_likes = df1.iloc[user_index]\n",
        "  known_user_likes = known_user_likes[known_user_likes >0].index.values\n",
        "  most_similar_to_likes = all_data_neighbours.iloc[known_user_likes]\n",
        "  similar_list = most_similar_to_likes.values.tolist()\n",
        "  similar_list = list(set([item for sublist in similar_list for item in sublist]))\n",
        "  neighbourhood = cos_item_matrix[similar_list].loc[similar_list]\n",
        "  user_vector = df1.iloc[user_index].loc[similar_list]\n",
        "  score = neighbourhood.dot(user_vector).div(neighbourhood.sum(axis=1))\n",
        "  score = score.drop(index=known_user_likes,errors='ignore')\n",
        "  score.sort_values(ascending= False)\n",
        "  top3 = list(score.nlargest(3).index.values)\n",
        "  res[user_index] = top3\n",
        "  print('item',user_index+1, 'is similar to items', top3)\n",
        "#print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsTDp55Uow-9",
        "outputId": "e8e0c8e5-1c18-465a-d08e-01ec5b35035f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "item 1 is similar to items [9, 13, 16]\n",
            "item 2 is similar to items [3, 25, 21]\n",
            "item 3 is similar to items [23, 24, 20]\n",
            "item 4 is similar to items [25, 11, 7]\n",
            "item 5 is similar to items [14, 1, 12]\n",
            "item 6 is similar to items [11, 14, 6]\n",
            "item 7 is similar to items [5, 16, 18]\n",
            "item 8 is similar to items [24, 25, 11]\n",
            "item 9 is similar to items [8, 4, 15]\n",
            "item 10 is similar to items [9, 20, 3]\n",
            "item 11 is similar to items [24, 22, 13]\n",
            "item 12 is similar to items [28, 19, 2]\n",
            "item 13 is similar to items [12, 16, 17]\n",
            "item 14 is similar to items [9, 16, 24]\n",
            "item 15 is similar to items [13, 26, 25]\n",
            "item 16 is similar to items [19, 9, 25]\n",
            "item 17 is similar to items [24, 28, 11]\n",
            "item 18 is similar to items [24, 12, 13]\n",
            "item 19 is similar to items []\n",
            "item 20 is similar to items [22, 6, 11]\n",
            "item 21 is similar to items [25, 1, 21]\n",
            "item 22 is similar to items [20, 24, 9]\n",
            "item 23 is similar to items [13, 24, 12]\n",
            "item 24 is similar to items [9, 19, 3]\n",
            "item 25 is similar to items [4, 28, 2]\n",
            "item 26 is similar to items [4, 24, 12]\n",
            "item 27 is similar to items [23, 24, 5]\n",
            "item 28 is similar to items [18, 20, 15]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Machine Learning"
      ],
      "metadata": {
        "id": "NMclI7QZ2hUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K2ym3cp9ufHP",
        "outputId": "2338e717-85f2-4a1f-810a-f5df49626ae4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userID  itemID         x  rating\n",
              "0       1       1  1.546881       1\n",
              "1       1       2  0.850043       1\n",
              "2       1       3  0.344194       0\n",
              "3       1       4  1.621203       1\n",
              "4       1       5  0.714086       1"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "execution_count": 34
        }
      ]
    },
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      "cell_type": "code",
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        "x_train, y_train = dftrain.drop([\"rating\"], axis = 1), dftrain[\"rating\"]"
      ],
      "metadata": {
        "id": "A7Eu5KCb72oH"
      },
      "execution_count": null,
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        "dftest.head()"
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userID  itemID         x  rating\n",
              "0       1       8 -1.341051       0\n",
              "1       2       7 -1.922255       0\n",
              "2       2       8 -0.045707       0\n",
              "3       2      21 -0.425549       0\n",
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              "\n",
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              "          const element = document.querySelector('#df-e346873e-c907-4cfd-a925-01fdbc3987ef');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test, y_test = dftest.drop([\"rating\"], axis = 1), dftest[\"rating\"]"
      ],
      "metadata": {
        "id": "0U11A2Om8H-g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "def error_metrics(y_true, y_pred):\n",
        "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
        "    return rmse"
      ],
      "metadata": {
        "id": "HYO5lNS93LVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##XGB"
      ],
      "metadata": {
        "id": "g64Lsl0MCSqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "clf = xgb.XGBRegressor(objective=\"binary:logistic\", random_state=42)\n",
        "clf.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kZzYUQozoKO",
        "outputId": "ce634cab-bc85-42d8-82a6-284843fd4459"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(objective='binary:logistic', random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test = clf.predict(x_test)"
      ],
      "metadata": {
        "id": "SoMLf1jz3-h-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_test = error_metrics(y_test, y_pred_test)\n",
        "print(\"RMSE = {}\".format(rmse_test))\n",
        "print(\"score on test: \" + str(clf.score(x_test, y_test)))\n",
        "print(\"score on train: \"+ str(clf.score(x_train, y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMvzguR54Bh3",
        "outputId": "32f78904-5415-409f-d07c-e24fc3926d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE = 0.2584993890691428\n",
            "score on test: 0.4681940425962886\n",
            "score on train: 0.5622412666084355\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##KNN"
      ],
      "metadata": {
        "id": "KbV22jhCCMX4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(x_train, y_train)\n",
        "\n",
        "print(\"score on test: \" + str(knn.score(x_test, y_test)))\n",
        "print(\"score on train: \"+ str(knn.score(x_train, y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1iHWoNnCK_I",
        "outputId": "6e2bdd1d-196b-4d6b-e244-7ad6be284fed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score on test: 0.856140350877193\n",
            "score on train: 0.8815109343936381\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftest['knn_prediction'] = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "AQJnGAvsENAF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Logistic Regression"
      ],
      "metadata": {
        "id": "JZW3my23DL1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr=LogisticRegression(max_iter=1000)\n",
        "lr.fit(x_train, y_train)\n",
        "print(\"score on test: \" + str(lr.score(x_test, y_test)))\n",
        "print(\"score on train: \"+ str(lr.score(x_train, y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKUp4exjC9V0",
        "outputId": "f2896eaa-3857-4b25-ac2a-7506ce4f668a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score on test: 0.8807017543859649\n",
            "score on train: 0.8767395626242545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftest['lr_prediction'] = lr.predict(x_test)"
      ],
      "metadata": {
        "id": "v9ndGFSyEm0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SVM"
      ],
      "metadata": {
        "id": "cVwnRM4nE2KO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "svm=LinearSVC(C=0.0001)\n",
        "svm.fit(x_train, y_train)\n",
        "print(\"score on test: \" + str(svm.score(x_test, y_test)))\n",
        "print(\"score on train: \"+ str(svm.score(x_train, y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFXNY5-7E2Va",
        "outputId": "dd292f5f-b06b-4023-a92a-10bd2e9e3f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score on test: 0.8596491228070176\n",
            "score on train: 0.8357852882703777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftest['svm_prediction'] = svm.predict(x_test)"
      ],
      "metadata": {
        "id": "o_z2nR07E_ch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_importance(model, clf):\n",
        "    fig = plt.figure(figsize = (8, 6))\n",
        "    ax = fig.add_axes([0,0,1,1])\n",
        "    model.plot_importance(clf, ax = ax, height = 0.3)\n",
        "    plt.xlabel(\"F Score\", fontsize = 20)\n",
        "    plt.ylabel(\"Features\", fontsize = 20)\n",
        "    plt.title(\"Feature Importance\", fontsize = 20)\n",
        "    plt.tick_params(labelsize = 15)\n",
        "    \n",
        "    plt.show()\n",
        "plot_importance(xgb, clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "Cu020-Uh4F74",
        "outputId": "07f6c538-63f5-47bc-a399-886699f44d53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ]
}