Food_Review_SVD++.ipynb

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      "metadata": {
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "from surprise import Dataset, Reader\n",
        "from surprise import SVDpp\n",
        "from surprise.model_selection import GridSearchCV, cross_validate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "dhz3psfgSdh2",
        "outputId": "a1236fdc-9fb2-4ab0-d4fb-551cfc1fa42c"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-surprise\n",
            "  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m772.0/772.0 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.10.1)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp39-cp39-linux_x86_64.whl size=3195838 sha256=fa8d927a95256378cf0a3f80c07d06e76c2733b753a6bc31dc0295b423ff3a9f\n",
            "  Stored in directory: /root/.cache/pip/wheels/c6/3a/46/9b17b3512bdf283c6cb84f59929cdd5199d4e754d596d22784\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise\n",
            "Successfully installed scikit-surprise-1.1.3\n"
          ]
        }
      ],
      "source": [
        "pip install scikit-surprise"
      ]
    },
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          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\", force_remount=True)"
      ]
    },
    {
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id   ProductId          UserId ProfileName  HelpfulnessNumerator  \\\n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW  delmartian                     1   \n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK      dll pa                     0   \n",
              "\n",
              "   HelpfulnessDenominator  Score        Time                Summary  \\\n",
              "0                       1      5  1303862400  Good Quality Dog Food   \n",
              "1                       0      1  1346976000      Not as Advertised   \n",
              "\n",
              "                                                Text  \n",
              "0  I have bought several of the Vitality canned d...  \n",
              "1  Product arrived labeled as Jumbo Salted Peanut...  "
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              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "df = pd.read_csv('/content/gdrive/MyDrive/GTMSA/Text Mining/project/Reviews.csv')\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D_sdbEJ2JGkR"
      },
      "outputs": [],
      "source": [
        "ratings = df[['UserId', 'ProductId', 'Score']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTLr_XySpbUG",
        "outputId": "ec8ba9e0-d7cf-4b38-a9f0-273c87a990d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "568454"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "len(ratings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVggby7XDQx3"
      },
      "source": [
        "Matrix Factorization-Based Algorithms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ngdeUmDnfQJr"
      },
      "outputs": [],
      "source": [
        "# try surprise package\n",
        "from surprise import Reader, SVDpp, accuracy, Dataset\n",
        "from surprise.model_selection import cross_validate\n",
        "from surprise.model_selection import GridSearchCV\n",
        "from surprise.model_selection import GridSearchCV, train_test_split\n",
        "\n",
        "\n",
        "# when importing from a DF, only need to specify the scale of the ratings.\n",
        "reader = Reader(rating_scale=(1,5)) \n",
        "#Initialize a recommender instance and apply its function using surprise:\n",
        "dataset = Dataset.load_from_df(ratings, reader)\n",
        "trainset, testset = train_test_split(dataset, test_size=0.25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXdMnp56D1EM"
      },
      "source": [
        "Hyper-tuning for SVDpp parameters\n",
        "- n factors: 50, 60 （50 better than 55, (50 better than 40)， (60 better than 70), 50 better than 45）(49 better than 48) (50 better than 49)\n",
        "- n_epochs: 35, 40 (35 is better than 40)\n",
        "- lr_all: 0.085 (0.25 better than 0.3) (0.12 better than 0.25) (0.09 better than 0.12) (0.085 better than 0.09)\n",
        "- reg_all: 0.051 （0.1 better than 0.05）(0.08 better than 0.1) 0.07 better than 0.08 (0.06 better than 0.07)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grid Search Method"
      ],
      "metadata": {
        "id": "KKXS0CnxDih9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMSQtcVLPTV9",
        "outputId": "81eccc3d-a74e-48e9-9d55-bd225b6699c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best RMSE:  0.9995167619814358\n",
            "Best hyperparameters:  {'n_factors': 50, 'n_epochs': 35, 'lr_all': 0.085, 'reg_all': 0.06, 'random_state': 10086}\n"
          ]
        }
      ],
      "source": [
        "# Define the hyperparameter grid\n",
        "# SVDpp with sgd, use gridsearch to tune parameters\n",
        "\n",
        "param_grid = {\n",
        "    'n_factors': [50],\n",
        "    'n_epochs': [35],\n",
        "    'lr_all': [0.085],\n",
        "    'reg_all': [0.06],\n",
        "    'random_state': [10086]\n",
        "}\n",
        "# Choose the best model\n",
        "grid_search = GridSearchCV(SVDpp, param_grid, measures=['rmse'], cv=8, n_jobs=-1)\n",
        "\n",
        "grid_search.fit(dataset)\n",
        "\n",
        "# best RMSE score\n",
        "print(\"Best RMSE: \", grid_search.best_score['rmse'])\n",
        "\n",
        "# combination of parameters that gave the best RMSE score\n",
        "print(\"Best hyperparameters: \", grid_search.best_params['rmse'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the SVDpp model and use the optimial parameters on the train set"
      ],
      "metadata": {
        "id": "8_5CaTJw74JG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZpf-RpxZUPc",
        "outputId": "bdcc54dd-8d2e-4989-f95a-b3a7b7890915"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 1.0168\n",
            "RMSE on testset: 1.0168002535825797\n"
          ]
        }
      ],
      "source": [
        "# Train the SVDpp algorithm using the best parameters on the trainset\n",
        "algo = SVDpp(**grid_search.best_params['rmse'])\n",
        "algo.fit(trainset)\n",
        "\n",
        "# Test the trained model on the testset\n",
        "predictions = algo.test(testset)\n",
        "\n",
        "# Calculate RMSE for the testset predictions\n",
        "rmse = accuracy.rmse(predictions)\n",
        "print(\"RMSE on testset:\", rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 1000
        },
        "outputId": "c25a599f-f2ce-4e54-db60-b827f266101d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User ID: A3SGXH7AUHU8GW,\n",
            " Item ID: B001E4KFG0,\n",
            "  Predicted Score: 4.98,\n",
            "  Actual Rating: 5\n",
            "User ID: A1D87F6ZCVE5NK,\n",
            " Item ID: B00813GRG4,\n",
            "  Predicted Score: 1.04,\n",
            "  Actual Rating: 1\n",
            "User ID: ABXLMWJIXXAIN,\n",
            " Item ID: B000LQOCH0,\n",
            "  Predicted Score: 4.01,\n",
            "  Actual Rating: 4\n",
            "User ID: A395BORC6FGVXV,\n",
            " Item ID: B000UA0QIQ,\n",
            "  Predicted Score: 2.04,\n",
            "  Actual Rating: 2\n",
            "User ID: A1UQRSCLF8GW1T,\n",
            " Item ID: B006K2ZZ7K,\n",
            "  Predicted Score: 5.00,\n",
            "  Actual Rating: 5\n",
            "User ID: ADT0SRK1MGOEU,\n",
            " Item ID: B006K2ZZ7K,\n",
            "  Predicted Score: 4.54,\n",
            "  Actual Rating: 4\n",
            "User ID: A1SP2KVKFXXRU1,\n",
            " Item ID: B006K2ZZ7K,\n",
            "  Predicted Score: 4.93,\n",
            "  Actual Rating: 5\n",
            "User ID: A3JRGQVEQN31IQ,\n",
            " Item ID: B006K2ZZ7K,\n",
            "  Predicted Score: 4.99,\n",
            "  Actual Rating: 5\n",
            "User ID: A1MZYO9TZK0BBI,\n",
            " Item ID: B000E7L2R4,\n",
            "  Predicted Score: 4.18,\n",
            "  Actual Rating: 5\n",
            "User ID: A21BT40VZCCYT4,\n",
            " Item ID: B00171APVA,\n",
            "  Predicted Score: 4.98,\n",
            "  Actual Rating: 5\n",
            "User ID: A3HDKO7OW0QNK4,\n",
            " Item ID: B0001PB9FE,\n",
            "  Predicted Score: 4.97,\n",
            "  Actual Rating: 5\n",
            "User ID: A2725IB4YY9JEB,\n",
            " Item ID: B0009XLVG0,\n",
            "  Predicted Score: 4.92,\n",
            "  Actual Rating: 5\n",
            "User ID: A327PCT23YH90,\n",
            " Item ID: B0009XLVG0,\n",
            "  Predicted Score: 1.06,\n",
            "  Actual Rating: 1\n",
            "User ID: A18ECVX2RJ7HUE,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 4.05,\n",
            "  Actual Rating: 4\n",
            "User ID: A2MUGFV2TDQ47K,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 3.66,\n",
            "  Actual Rating: 5\n",
            "User ID: A1CZX3CP8IKQIJ,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 4.97,\n",
            "  Actual Rating: 5\n",
            "User ID: A3KLWF6WQ5BNYO,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 2.11,\n",
            "  Actual Rating: 2\n",
            "User ID: AFKW14U97Z6QO,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 4.98,\n",
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            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 4.90,\n",
            "  Actual Rating: 5\n",
            "User ID: A3IV7CL2C13K2U,\n",
            " Item ID: B001GVISJM,\n",
            "  Predicted Score: 4.95,\n",
            "  Actual Rating: 5\n"
          ]
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              "<Figure size 640x480 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get the first 20 records\n",
        "first_20_records = ratings.iloc[:20]\n",
        "\n",
        "predictions = []\n",
        "for _, row in first_20_records.iterrows():\n",
        "    user_id = row['UserId']\n",
        "    item_id = row['ProductId']\n",
        "    true_rating = row['Score']\n",
        "    pred_rating = algo.predict(user_id, item_id).est\n",
        "    predictions.append(pred_rating)\n",
        "    print(f\"User ID: {user_id},\\n Item ID: {item_id},\\n  Predicted Score: {pred_rating:.2f},\\n  Actual Rating: {true_rating}\")\n",
        "\n",
        "\n",
        "actual_ratings = first_20_records['Score'].tolist()\n",
        "\n",
        "# Plot the comparison between actual and predicted ratings\n",
        "plt.plot(actual_ratings, label='Actual Ratings', marker='o', linestyle='-')\n",
        "plt.plot(predictions, label='Predicted Ratings', marker='x', linestyle='--')\n",
        "plt.xlabel('Record Index')\n",
        "plt.ylabel('Rating Score')\n",
        "plt.legend()\n",
        "plt.title('Comparison of Actual and Predicted Ratings for the First 20 Records')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hIOoZvESPT8F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Fjevis7FJT3"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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