KNNBasicWithZScore.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KNNBasicWithZScore",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNrJV/Kgdy14Fwp0dG/xZ28"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-hAxBHOIbv5",
        "colab_type": "code",
        "outputId": "00b23014-c270-4bb6-a14a-d1057e623836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "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": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3rkcfKq4C_6u",
        "colab_type": "code",
        "outputId": "787bc430-d388-4f32-a08f-37aea4e6b3ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "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": "6456b99b-8bc9-44cb-e32f-03488923b887",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "gpu()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=364.7011>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFj33Jiqp7tA",
        "colab_type": "code",
        "outputId": "403bedf9-c363-4442-833c-1580e6064ec8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "!pip install surprise"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting surprise\n",
            "  Downloading https://files.pythonhosted.org/packages/61/de/e5cba8682201fcf9c3719a6fdda95693468ed061945493dea2dd37c5618b/surprise-0.1-py2.py3-none-any.whl\n",
            "Collecting scikit-surprise\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/da/b5700d96495fb4f092be497f02492768a3d96a3f4fa2ae7dea46d4081cfa/scikit-surprise-1.1.0.tar.gz (6.4MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /tensorflow-2.1.0/python3.6 (from scikit-surprise->surprise) (1.18.1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /tensorflow-2.1.0/python3.6 (from scikit-surprise->surprise) (1.4.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /tensorflow-2.1.0/python3.6 (from scikit-surprise->surprise) (1.14.0)\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-1.1.0-cp36-cp36m-linux_x86_64.whl size=1678189 sha256=dd3e2cf996d1bb23c81f1f83d6b2e6e76391a19dc858ae1130627ff1ce559c92\n",
            "  Stored in directory: /root/.cache/pip/wheels/cc/fa/8c/16c93fccce688ae1bde7d979ff102f7bee980d9cfeb8641bcf\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise, surprise\n",
            "Successfully installed scikit-surprise-1.1.0 surprise-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AZW-Xe9p_TN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "fc0a79b3-137d-4002-c938-83d5e438ea0a"
      },
      "source": [
        "from surprise import KNNWithZScore\n",
        "from surprise import Dataset, Reader\n",
        "from surprise.model_selection import cross_validate\n",
        "from surprise import accuracy\n",
        "from surprise.model_selection import KFold\n",
        "import time\n",
        "\n",
        "time1=time.time()\n",
        "\n",
        "# 数据读取\n",
        "reader = Reader(line_format='user item rating timestamp', sep=',', skip_lines=1)\n",
        "data = Dataset.load_from_file('ratings.csv', reader=reader)\n",
        "trainset = data.build_full_trainset()\n",
        "\n",
        "# 取最相似的用户计算时，只取最相似的k个\n",
        "algo = KNNWithZScore(k=50, sim_options={'user_based': False, 'verbose': 'True'})\n",
        "\n",
        "# 定义K折交叉验证迭代器，K=3\n",
        "kf = KFold(n_splits=3)\n",
        "for trainset, testset in kf.split(data):\n",
        "    # 训练并预测\n",
        "    algo.fit(trainset)\n",
        "    predictions = algo.test(testset)\n",
        "    # 计算RMSE\n",
        "    accuracy.rmse(predictions, verbose=True)\n",
        "    accuracy.mae(predictions,verbose=True)\n",
        "\n",
        "\n",
        "time2=time.time()\n",
        "print(time2-time1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "RMSE: 0.8574\n",
            "MAE:  0.6550\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "RMSE: 0.8588\n",
            "MAE:  0.6557\n",
            "Computing the msd similarity matrix...\n",
            "Done computing similarity matrix.\n",
            "RMSE: 0.8591\n",
            "MAE:  0.6562\n",
            "395.24113416671753\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}