Movielens_WDL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "movielens_WDL.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNCrKNsfaX65xirbskVhVdd"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-hAxBHOIbv5",
        "colab_type": "code",
        "outputId": "89307132-9ec5-4e3e-cfc4-a35aa1e76766",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks/\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rkcfKq4C_6u",
        "colab_type": "code",
        "outputId": "656ce492-f937-48df-cd92-fb42082ebde4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWJ44cVPIViy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gpu():\n",
        "  with tf.device('/device:GPU:0'):\n",
        "    random_image_gpu = tf.random.normal((100, 100, 100, 3))\n",
        "    net_gpu = tf.keras.layers.Conv2D(32, 7)(random_image_gpu)\n",
        "    return tf.math.reduce_sum(net_gpu)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZsX5hVnRxwh",
        "colab_type": "code",
        "outputId": "c668f764-7914-4039-d389-73cde75abe43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "gpu()\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=-4736.4688>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nyNWrHIkcpA",
        "colab_type": "code",
        "outputId": "c6b3c35a-2d6d-4bba-eef5-0619e0d2377a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "!pip install deepctr"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: deepctr in /usr/local/lib/python3.6/dist-packages (0.7.2)\n",
            "Requirement already satisfied: h5py in /tensorflow-2.1.0/python3.6 (from deepctr) (2.10.0)\n",
            "Requirement already satisfied: requests in /tensorflow-2.1.0/python3.6 (from deepctr) (2.22.0)\n",
            "Requirement already satisfied: numpy>=1.7 in /tensorflow-2.1.0/python3.6 (from h5py->deepctr) (1.18.1)\n",
            "Requirement already satisfied: six in /tensorflow-2.1.0/python3.6 (from h5py->deepctr) (1.14.0)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /tensorflow-2.1.0/python3.6 (from requests->deepctr) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /tensorflow-2.1.0/python3.6 (from requests->deepctr) (1.25.8)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /tensorflow-2.1.0/python3.6 (from requests->deepctr) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /tensorflow-2.1.0/python3.6 (from requests->deepctr) (2019.11.28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tk9YZLKkkQrZ",
        "colab_type": "code",
        "outputId": "78491bdc-5b3c-467e-ce32-49c327842fd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from deepctr.models import WDL\n",
        "from deepctr.inputs import SparseFeat,get_feature_names\n",
        "\n",
        "data = pd.read_csv(\"ratings.csv\")\n",
        "sparse_features = [\"movieId\", \"userId\"]\n",
        "target = ['rating']\n",
        "\n",
        "# 对特征标签进行编码\n",
        "for feature in sparse_features:\n",
        "    lbe = LabelEncoder()\n",
        "    data[feature] = lbe.fit_transform(data[feature])\n",
        "# 计算每个特征中的 不同特征值的个数\n",
        "fixlen_feature_columns = [SparseFeat(feature, data[feature].nunique()) for feature in sparse_features]\n",
        "linear_feature_columns = fixlen_feature_columns\n",
        "dnn_feature_columns = fixlen_feature_columns\n",
        "feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)\n",
        "\n",
        "# 将数据集切分成训练集和测试集\n",
        "train, test = train_test_split(data, test_size=0.2)\n",
        "train_model_input = {name:train[name].values for name in feature_names}\n",
        "test_model_input = {name:test[name].values for name in feature_names}\n",
        "\n",
        "# 使用WDL进行训练\n",
        "model = WDL(linear_feature_columns, dnn_feature_columns, task='regression')\n",
        "model.compile(\"adam\", \"mse\", metrics=['mse'], )\n",
        "history = model.fit(train_model_input, train[target].values, batch_size=256, epochs=1, verbose=True, validation_split=0.2, )\n",
        "# 使用WDL进行预测\n",
        "pred_ans = model.predict(test_model_input, batch_size=256)\n",
        "# 输出RMSE或MSE\n",
        "mse = round(mean_squared_error(test[target].values, pred_ans), 4)\n",
        "rmse = mse ** 0.5\n",
        "print(\"test RMSE\", rmse)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 671088 samples, validate on 167772 samples\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n",
            "/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "671088/671088 [==============================] - 24s 36us/sample - loss: 0.9478 - mse: 0.9469 - val_loss: 0.7411 - val_mse: 0.7395\n",
            "test RMSE 0.8595347578777719\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}