Solpto3.ipynb

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      "authorship_tag": "ABX9TyMJf+L4DJovtqGtBGmzuALy",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/malzate10/mental-Ataraxia/blob/main/Solpto3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Osk17VZg1OF1",
        "outputId": "9fe2824c-5ee5-4874-ce16-74f91756aae5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-surprise\n",
            "  Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m357.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.32.3)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.4.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise) (1.13.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
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            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp310-cp310-linux_x86_64.whl size=2357290 sha256=6af0f0d49946b64d5917d962594b11b8db479a99f674d6d99f5a0961d1f70d3b\n",
            "  Stored in directory: /root/.cache/pip/wheels/4b/3f/df/6acbf0a40397d9bf3ff97f582cc22fb9ce66adde75bc71fd54\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise\n",
            "Successfully installed scikit-surprise-1.1.4\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-surprise pandas numpy matplotlib kaggle requests\n",
        "import pandas as pd\n",
        "from surprise import Dataset, Reader, KNNBasic, SVD\n",
        "from surprise.model_selection import train_test_split, GridSearchCV\n",
        "from surprise import accuracy\n",
        "import subprocess"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/Ratings.csv.zip')\n",
        "data.head()\n"
      ],
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          "height": 206
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      },
      "execution_count": 13,
      "outputs": [
        {
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              "   User-ID        ISBN  Book-Rating\n",
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              "2   276727  0446520802            0\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1149780 entries, 0 to 1149779\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count    Dtype \n",
            "---  ------       --------------    ----- \n",
            " 0   User-ID      1149780 non-null  int64 \n",
            " 1   ISBN         1149780 non-null  object\n",
            " 2   Book-Rating  1149780 non-null  int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 26.3+ MB\n"
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      "metadata": {
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          "height": 178
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        "id": "rLj-Mwvl8tvi",
        "outputId": "fd28549a-0e6e-4523-ad67-dd66666c0008"
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        "outputId": "79c182dd-3c3a-4f7d-e0e7-c69cbb7f8129"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         User-ID         ISBN  Book-Rating\n",
              "1149770   276704   0679752714            0\n",
              "1149771   276704   0743211383            7\n",
              "1149772   276704   080410526X            0\n",
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              "1149774   276704   0876044011            0\n",
              "1149775   276704   1563526298            9\n",
              "1149776   276706   0679447156            0\n",
              "1149777   276709   0515107662           10\n",
              "1149778   276721   0590442449           10\n",
              "1149779   276723  05162443314            8"
            ],
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"User-ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 276704,\n        \"max\": 276723,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          276706,\n          276723,\n          276709\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"0590442449\",\n          \"0743211383\",\n          \"1563526298\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          7,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-Mfzpt8_Nfd",
        "outputId": "2bfbe06f-9d86-42c7-f568-657646625336"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1149780, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluacion de modelo SVD"
      ],
      "metadata": {
        "id": "O8qXvsNML6fu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svd=SVD()\n",
        "reader = Reader(rating_scale=(1, 5))\n",
        "data = Dataset.load_from_df(data[['User-ID', 'ISBN', 'Book-Rating']], reader)\n",
        "cross_validate(svd, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbUsuVKo_4yH",
        "outputId": "d9e465e0-79cd-47f6-cb99-b008aa512a34"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    3.5026  3.5057  3.5068  3.5031  3.5033  3.5043  0.0017  \n",
            "MAE (testset)     2.9805  2.9804  2.9823  2.9815  2.9797  2.9809  0.0009  \n",
            "Fit time          24.87   25.24   25.55   26.48   25.90   25.61   0.55    \n",
            "Test time         2.57    2.58    2.93    2.39    3.26    2.75    0.31    \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_rmse': array([3.50255245, 3.50566595, 3.50681272, 3.50307276, 3.50334189]),\n",
              " 'test_mae': array([2.98049011, 2.98035677, 2.98232212, 2.98150776, 2.97968817]),\n",
              " 'fit_time': (24.87066960334778,\n",
              "  25.237791776657104,\n",
              "  25.55373239517212,\n",
              "  26.47884225845337,\n",
              "  25.896082401275635),\n",
              " 'test_time': (2.5745849609375,\n",
              "  2.577214241027832,\n",
              "  2.9314963817596436,\n",
              "  2.3943138122558594,\n",
              "  3.2597885131835938)}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluacion de modelo SVD"
      ],
      "metadata": {
        "id": "43lFPY01LoxM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainset, testset = train_test_split(data,test_size=0.30)\n",
        "#Run\n",
        "svd=SVD()\n",
        "#Test\n",
        "svd.fit(trainset)\n",
        "predictions = svd.test(testset)\n",
        "#compute error\n",
        "accuracy.rmse(predictions)\n",
        "accuracy.mae(predictions)\n",
        "print(\"Rmse model SVD:\", accuracy.rmse(predictions))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4jzN9ZCDWJx",
        "outputId": "3a718e19-e0a6-4989-da6c-93ceb7e2948d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 3.5061\n",
            "MAE:  2.9849\n",
            "RMSE: 3.5061\n",
            "Rmse model SVD: 3.5061386903727487\n"
          ]
        }
      ]
    }
  ]
}