MovieRating_using_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MovieRating using Linear Regression.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pksX01/TensorFlow-Examples/blob/master/MovieRating_using_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2ig6Ukh6qqW",
        "colab_type": "code",
        "outputId": "f632b765-5379-441c-8c3e-d0b3b7f1fb74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Wo8Uj5j7gdE",
        "colab_type": "code",
        "outputId": "9981789b-b116-441d-87d3-0f3c5cc4aa9c",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "file=files.upload()\n",
        "movie_init_df = pd.read_csv(\"movie_metadata.csv\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a0825674-abcc-4438-bb83-afbd75a6d877\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-a0825674-abcc-4438-bb83-afbd75a6d877\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving movie_metadata.csv to movie_metadata.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYSMiLgvUJub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_regression(tensor_data,movie_init_df):\n",
        "    \n",
        "    def feature_normalize(dataset):\n",
        "        mu = np.mean(dataset,axis=0)\n",
        "        sigma = np.std(dataset,axis=0)\n",
        "        return (dataset - mu)/sigma\n",
        "    \n",
        "    def append_bias_reshape(features,labels):\n",
        "        n_training_samples = features.shape[0]\n",
        "        n_dim = features.shape[1]\n",
        "        f = np.reshape(np.c_[np.ones(n_training_samples),features],[n_training_samples,n_dim + 1])\n",
        "        l = np.reshape(labels,[n_training_samples,1])\n",
        "        return f, l\n",
        "    \n",
        "    features = np.array(tensor_data)\n",
        "    labels = np.array(movie_init_df['imdb_score'])\n",
        "    normalized_features = feature_normalize(features)\n",
        "    f, l = append_bias_reshape(normalized_features,labels)\n",
        "    n_dim = f.shape[1]\n",
        "    \n",
        "    rnd_indices = np.random.rand(len(f)) < 0.80\n",
        "    \n",
        "    train_x = f[rnd_indices]\n",
        "    train_y = l[rnd_indices]\n",
        "    test_x = f[~rnd_indices]\n",
        "    test_y = l[~rnd_indices]\n",
        "    \n",
        "    learning_rate = 0.01\n",
        "    training_epochs = 1000\n",
        "    cost_history = np.empty(shape=[1],dtype=float)\n",
        "    \n",
        "    X = tf.placeholder(tf.float32,[None,n_dim])\n",
        "    Y = tf.placeholder(tf.float32,[None,1])\n",
        "    W = tf.Variable(tf.ones([n_dim,1]))\n",
        "    \n",
        "    init = tf.initialize_all_variables()\n",
        "    \n",
        "    \n",
        "    y_ = tf.matmul(X, W)\n",
        "    cost = tf.reduce_mean(tf.square(y_ - Y))\n",
        "    training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
        "    \n",
        "    sess = tf.Session()\n",
        "    sess.run(init)\n",
        "    \n",
        "    for epoch in range(training_epochs):\n",
        "        sess.run(training_step,feed_dict={X:train_x,Y:train_y})\n",
        "        cost_history = np.append(cost_history,sess.run(cost,feed_dict={X: train_x,Y: train_y}))\n",
        "        \n",
        "    plt.plot(range(len(cost_history)),cost_history)\n",
        "    plt.axis([0,training_epochs,0,np.max(cost_history)])\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Error')\n",
        "    plt.show()\n",
        "    \n",
        "    pred_y = sess.run(y_, feed_dict={X: test_x})\n",
        "    mse = tf.reduce_mean(tf.square(pred_y - test_y))\n",
        "    print(\"MSE: %.4f\" % sess.run(mse)) \n",
        "    \n",
        "    fig, ax = plt.subplots()\n",
        "    ax.scatter(test_y, pred_y)\n",
        "    ax.plot([test_y.min(), test_y.max()], [test_y.min(), test_y.max()], 'k--', lw=3)\n",
        "    ax.set_xlabel('Measured')\n",
        "    ax.set_ylabel('Predicted')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPiACBxBULFg",
        "colab_type": "code",
        "outputId": "6986b508-dc35-462d-f48a-d7b620776140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        }
      },
      "source": [
        "\n",
        "movie_init_df = movie_init_df.dropna()\n",
        "tensor_data = movie_init_df[['num_critic_for_reviews','duration','director_facebook_likes',\n",
        "                             'actor_3_facebook_likes','actor_1_facebook_likes',\n",
        "                             'num_voted_users','cast_total_facebook_likes',\n",
        "                             'num_user_for_reviews','title_year',\n",
        "                             'actor_2_facebook_likes','movie_facebook_likes']]\n",
        "run_regression(tensor_data,movie_init_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py:193: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
            "Instructions for updating:\n",
            "Use `tf.global_variables_initializer` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HAKB3PQa8YRg9fbvPBQNeKngAACywtBd9u86Bn4nwzwl4WWQHAIAFPa6if/fdd3XVVVd1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\ngg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE: 0.8081\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8qitLUFF0GbHJaV69jsPJuNX8rqjyb6JV4+F6lVKKapvnEIRUWnsjIoTNyzrk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\nJ6HSFhERcRIqbRERESfx3wPD6/m4Z6oaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9JS08hFUVag",
        "colab_type": "code",
        "outputId": "4b66fc0a-1d24-4fb1-f25e-031783a91d1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        }
      },
      "source": [
        "tensor_data_1 = movie_init_df[['num_critic_for_reviews','duration','director_facebook_likes',\n",
        "                             'actor_3_facebook_likes','actor_1_facebook_likes',\n",
        "                             'num_voted_users','cast_total_facebook_likes',\n",
        "                             'num_user_for_reviews','title_year',\n",
        "                             'actor_2_facebook_likes','movie_facebook_likes','facenumber_in_poster']]\n",
        "run_regression(tensor_data_1,movie_init_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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doRFn9k1h6+zhMQyF+vZSqG+vLt/XHI3raEOz6hujamqOqbEppsbm1v86vI7G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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSE: 0.6599\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z0hN1SEmIgt3hwGerD7usWNeplTDbGpcAJTZKDVOl60I1ueOGxCiXCl1yD0K5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\nXY152Kr9s3X34CelKZeVDKW1POSe26B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DiYiowRi0I4QgCPj88yVYsGAO/vrrnOjclVde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\niIiIRfwJ9HPOm0nAV4QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}