recommendation_Engine.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "recommendation Engine.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shegun93/Artficial-Neural-Network/blob/main/recommendation_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4f4JG1gdKqj"
      },
      "source": [
        "#AutoEncoders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jbiqOK7dLGG"
      },
      "source": [
        "##Downloading the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XL5MEkLcfRD2"
      },
      "source": [
        "###ML-100K"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjOPzue7FCXJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91abf78f-afca-4c0d-9772-3a72b8a245e2"
      },
      "source": [
        "!wget \"http://files.grouplens.org/datasets/movielens/ml-100k.zip\"\n",
        "!unzip ml-100k.zip\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-11 07:52:52--  http://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4924029 (4.7M) [application/zip]\n",
            "Saving to: ‘ml-100k.zip’\n",
            "\n",
            "ml-100k.zip         100%[===================>]   4.70M  5.89MB/s    in 0.8s    \n",
            "\n",
            "2021-06-11 07:52:53 (5.89 MB/s) - ‘ml-100k.zip’ saved [4924029/4924029]\n",
            "\n",
            "Archive:  ml-100k.zip\n",
            "   creating: ml-100k/\n",
            "  inflating: ml-100k/allbut.pl       \n",
            "  inflating: ml-100k/mku.sh          \n",
            "  inflating: ml-100k/README          \n",
            "  inflating: ml-100k/u.data          \n",
            "  inflating: ml-100k/u.genre         \n",
            "  inflating: ml-100k/u.info          \n",
            "  inflating: ml-100k/u.item          \n",
            "  inflating: ml-100k/u.occupation    \n",
            "  inflating: ml-100k/u.user          \n",
            "  inflating: ml-100k/u1.base         \n",
            "  inflating: ml-100k/u1.test         \n",
            "  inflating: ml-100k/u2.base         \n",
            "  inflating: ml-100k/u2.test         \n",
            "  inflating: ml-100k/u3.base         \n",
            "  inflating: ml-100k/u3.test         \n",
            "  inflating: ml-100k/u4.base         \n",
            "  inflating: ml-100k/u4.test         \n",
            "  inflating: ml-100k/u5.base         \n",
            "  inflating: ml-100k/u5.test         \n",
            "  inflating: ml-100k/ua.base         \n",
            "  inflating: ml-100k/ua.test         \n",
            "  inflating: ml-100k/ub.base         \n",
            "  inflating: ml-100k/ub.test         \n",
            "ml-100k  ml-100k.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Xis6ldDfTs6"
      },
      "source": [
        "###ML-1M"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOly1yfAfTjd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c87230c-d9d9-403f-a96d-cacd2f8908d4"
      },
      "source": [
        "!wget \"http://files.grouplens.org/datasets/movielens/ml-1m.zip\"\n",
        "!unzip ml-1m.zip\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-11 07:53:18--  http://files.grouplens.org/datasets/movielens/ml-1m.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5917549 (5.6M) [application/zip]\n",
            "Saving to: ‘ml-1m.zip’\n",
            "\n",
            "ml-1m.zip           100%[===================>]   5.64M  7.08MB/s    in 0.8s    \n",
            "\n",
            "2021-06-11 07:53:19 (7.08 MB/s) - ‘ml-1m.zip’ saved [5917549/5917549]\n",
            "\n",
            "Archive:  ml-1m.zip\n",
            "   creating: ml-1m/\n",
            "  inflating: ml-1m/movies.dat        \n",
            "  inflating: ml-1m/ratings.dat       \n",
            "  inflating: ml-1m/README            \n",
            "  inflating: ml-1m/users.dat         \n",
            "ml-100k  ml-100k.zip  ml-1m  ml-1m.zip\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOBJ8UCXdY0g"
      },
      "source": [
        "##Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LvGeU1CeCtg"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.parallel\n",
        "import torch.optim as optim\n",
        "import torch.utils.data\n",
        "from torch.autograd import Variable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pM04FyMudkoK"
      },
      "source": [
        "## Importing the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJw2p3-Cewo4"
      },
      "source": [
        "# We won't be using this dataset.\n",
        "movies = pd.read_csv('ml-1m/movies.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
        "users = pd.read_csv('ml-1m/users.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
        "ratings = pd.read_csv('ml-1m/ratings.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTIbE2tkdkwP"
      },
      "source": [
        "## Preparing the training set and the test set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2usLKJBEgPE2"
      },
      "source": [
        "training_set = pd.read_csv('ml-100k/u1.base', delimiter = '\\t')\n",
        "training_set = np.array(training_set, dtype = 'int')\n",
        "test_set = pd.read_csv('ml-100k/u1.test', delimiter = '\\t')\n",
        "test_set = np.array(test_set, dtype = 'int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCf8HjSydk4s"
      },
      "source": [
        "## Getting the number of users and movies\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPaGZqdniC5m"
      },
      "source": [
        "nb_users = int(max(max(training_set[:, 0], ), max(test_set[:, 0])))\n",
        "nb_movies = int(max(max(training_set[:, 1], ), max(test_set[:, 1])))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-w4-hVidlAm"
      },
      "source": [
        "## Converting the data into an array with users in lines and movies in columns\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wASs2YFiDaa"
      },
      "source": [
        "def convert(data):\n",
        "  new_data = []\n",
        "  for id_users in range(1, nb_users + 1):\n",
        "    id_movies = data[:, 1] [data[:, 0] == id_users]\n",
        "    id_ratings = data[:, 2] [data[:, 0] == id_users]\n",
        "    ratings = np.zeros(nb_movies)\n",
        "    ratings[id_movies - 1] = id_ratings\n",
        "    new_data.append(list(ratings))\n",
        "  return new_data\n",
        "training_set = convert(training_set)\n",
        "test_set = convert(test_set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWq5OcH3EaB2"
      },
      "source": [
        "# Converting the data into Torch tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwD-KD8yiEEw"
      },
      "source": [
        "training_set = torch.FloatTensor(training_set)\n",
        "test_set = torch.FloatTensor(test_set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kkL8NkkdlZj"
      },
      "source": [
        "## Creating the architecture of the Neural Network\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oU2nyh76iE6M"
      },
      "source": [
        "class SAE(nn.Module):\n",
        "    def __init__(self, ):\n",
        "        super(SAE, self).__init__()\n",
        "        self.fc1 = nn.Linear(nb_movies, 20)\n",
        "        self.fc2 = nn.Linear(20, 10)\n",
        "        self.fc3 = nn.Linear(10, 20)\n",
        "        self.fc4 = nn.Linear(20, nb_movies)\n",
        "        self.activation = nn.Sigmoid()\n",
        "    def forward(self, x):\n",
        "        x = self.activation(self.fc1(x))\n",
        "        x = self.activation(self.fc2(x))\n",
        "        x = self.activation(self.fc3(x))\n",
        "        x = self.fc4(x)\n",
        "        return x\n",
        "sae = SAE()\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.RMSprop(sae.parameters(), lr = 0.01, weight_decay = 0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gy59alAdloL"
      },
      "source": [
        "## Training the SAE\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEz9hRaciFTs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e503d03a-125d-479d-85a5-c68d132d54f5"
      },
      "source": [
        "nb_epoch = 200\n",
        "for epoch in range(1, nb_epoch + 1):\n",
        "  train_loss = 0\n",
        "  s = 0.\n",
        "  for id_user in range(nb_users):\n",
        "    input = Variable(training_set[id_user]).unsqueeze(0)\n",
        "    target = input.clone()\n",
        "    if torch.sum(target.data > 0) > 0:\n",
        "      output = sae(input)\n",
        "      target.require_grad = False\n",
        "      output[target == 0] = 0\n",
        "      loss = criterion(output, target)\n",
        "      mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)\n",
        "      loss.backward()\n",
        "      train_loss += np.sqrt(loss.data*mean_corrector)\n",
        "      s += 1.\n",
        "      optimizer.step()\n",
        "  print('epoch: '+str(epoch)+'loss: '+ str(train_loss/s))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 1loss: tensor(1.7713)\n",
            "epoch: 2loss: tensor(1.0966)\n",
            "epoch: 3loss: tensor(1.0534)\n",
            "epoch: 4loss: tensor(1.0381)\n",
            "epoch: 5loss: tensor(1.0309)\n",
            "epoch: 6loss: tensor(1.0266)\n",
            "epoch: 7loss: tensor(1.0238)\n",
            "epoch: 8loss: tensor(1.0220)\n",
            "epoch: 9loss: tensor(1.0208)\n",
            "epoch: 10loss: tensor(1.0194)\n",
            "epoch: 11loss: tensor(1.0189)\n",
            "epoch: 12loss: tensor(1.0185)\n",
            "epoch: 13loss: tensor(1.0180)\n",
            "epoch: 14loss: tensor(1.0173)\n",
            "epoch: 15loss: tensor(1.0173)\n",
            "epoch: 16loss: tensor(1.0171)\n",
            "epoch: 17loss: tensor(1.0169)\n",
            "epoch: 18loss: tensor(1.0164)\n",
            "epoch: 19loss: tensor(1.0164)\n",
            "epoch: 20loss: tensor(1.0161)\n",
            "epoch: 21loss: tensor(1.0157)\n",
            "epoch: 22loss: tensor(1.0159)\n",
            "epoch: 23loss: tensor(1.0160)\n",
            "epoch: 24loss: tensor(1.0158)\n",
            "epoch: 25loss: tensor(1.0158)\n",
            "epoch: 26loss: tensor(1.0156)\n",
            "epoch: 27loss: tensor(1.0154)\n",
            "epoch: 28loss: tensor(1.0151)\n",
            "epoch: 29loss: tensor(1.0130)\n",
            "epoch: 30loss: tensor(1.0111)\n",
            "epoch: 31loss: tensor(1.0106)\n",
            "epoch: 32loss: tensor(1.0082)\n",
            "epoch: 33loss: tensor(1.0080)\n",
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            "epoch: 35loss: tensor(1.0025)\n",
            "epoch: 36loss: tensor(0.9990)\n",
            "epoch: 37loss: tensor(0.9981)\n",
            "epoch: 38loss: tensor(0.9960)\n",
            "epoch: 39loss: tensor(0.9950)\n",
            "epoch: 40loss: tensor(0.9921)\n",
            "epoch: 41loss: tensor(0.9929)\n",
            "epoch: 42loss: tensor(0.9879)\n",
            "epoch: 43loss: tensor(0.9879)\n",
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            "epoch: 48loss: tensor(0.9780)\n",
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            "epoch: 173loss: tensor(0.9229)\n",
            "epoch: 174loss: tensor(0.9222)\n",
            "epoch: 175loss: tensor(0.9221)\n",
            "epoch: 176loss: tensor(0.9216)\n",
            "epoch: 177loss: tensor(0.9215)\n",
            "epoch: 178loss: tensor(0.9215)\n",
            "epoch: 179loss: tensor(0.9214)\n",
            "epoch: 180loss: tensor(0.9215)\n",
            "epoch: 181loss: tensor(0.9211)\n",
            "epoch: 182loss: tensor(0.9213)\n",
            "epoch: 183loss: tensor(0.9208)\n",
            "epoch: 184loss: tensor(0.9199)\n",
            "epoch: 185loss: tensor(0.9202)\n",
            "epoch: 186loss: tensor(0.9201)\n",
            "epoch: 187loss: tensor(0.9200)\n",
            "epoch: 188loss: tensor(0.9204)\n",
            "epoch: 189loss: tensor(0.9195)\n",
            "epoch: 190loss: tensor(0.9198)\n",
            "epoch: 191loss: tensor(0.9198)\n",
            "epoch: 192loss: tensor(0.9196)\n",
            "epoch: 193loss: tensor(0.9188)\n",
            "epoch: 194loss: tensor(0.9191)\n",
            "epoch: 195loss: tensor(0.9192)\n",
            "epoch: 196loss: tensor(0.9186)\n",
            "epoch: 197loss: tensor(0.9181)\n",
            "epoch: 198loss: tensor(0.9184)\n",
            "epoch: 199loss: tensor(0.9180)\n",
            "epoch: 200loss: tensor(0.9178)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bak5uc8gd-gX"
      },
      "source": [
        "## Testing the SAE\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHmZrhTzId5T",
        "outputId": "be6f2d2f-1876-411d-f3c3-a70d35401a3e"
      },
      "source": [
        "test_loss = 0\n",
        "s = 0.\n",
        "for id_user in range(nb_users):\n",
        "  input = Variable(training_set[id_user]).unsqueeze(0)\n",
        "  target = Variable(test_set[id_user]).unsqueeze(0)\n",
        "  if torch.sum(target.data > 0) > 0:\n",
        "    output = sae(input)\n",
        "    target.require_grad = False\n",
        "    output[target == 0] = 0\n",
        "    loss = criterion(output, target)\n",
        "    mean_corrector = nb_movies/float(torch.sum(target.data > 0) + 1e-10)\n",
        "    test_loss += np.sqrt(loss.data*mean_corrector)\n",
        "    s += 1.\n",
        "print('test loss: '+str(test_loss/s))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test loss: tensor(0.9539)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}