lab10.ipynb

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      "name": "lab10.ipynb",
      "provenance": []
    },
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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Lab 10: Regression \n",
        "\n",
        "Kobie Williams \n",
        "\n",
        "3/8/2022 \n",
        "\n",
        "The purpose of this lab is to predict continuous values using regression "
      ],
      "metadata": {
        "id": "MO9RePrIqygC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "N4y-6xiwpoMy"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://drive.google.com/uc?export=download&id=1XHF2E7uXPqXSmGi2X1E9_o9VmIh89bfV') \n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "0zt2vPTbqxFA",
        "outputId": "0ab8fd50-8247-4699-bc3f-ad9a88ccc122"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>is_train</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000799</td>\n",
              "      <td>1.699337</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.010214</td>\n",
              "      <td>-0.436091</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.071439</td>\n",
              "      <td>-1.873174</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.076680</td>\n",
              "      <td>15.854541</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.115109</td>\n",
              "      <td>-0.831285</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "          x          y  is_train\n",
              "0  0.000799   1.699337       1.0\n",
              "1  0.010214  -0.436091       1.0\n",
              "2  0.071439  -1.873174       1.0\n",
              "3  0.076680  15.854541       1.0\n",
              "4  0.115109  -0.831285       1.0"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pandas Series \n",
        "\n",
        "#Creating Xtrain variable \n",
        "Xtrain = df['x'][df['is_train'] == 1] \n",
        "\n",
        "#Creating ytrain variable \n",
        "ytrain = df['y'][df['is_train'] == 1] \n",
        "\n",
        "#Creating Xtest variable \n",
        "Xtest = df['x'][df['is_train'] == 0] \n",
        "\n",
        "#Creating ytest variable\n",
        "ytest = df['y'][df['is_train'] == 0] "
      ],
      "metadata": {
        "id": "qW1T5Cn8rTmo"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NumPy arrays \n",
        "\n",
        "#Creating Xtrain variable \n",
        "Xtrain = df['x'][df['is_train'] == 1].values.reshape((-1,1))\n",
        "\n",
        "#Creating ytrain variable \n",
        "ytrain = df['y'][df['is_train'] == 1].values.reshape((-1,1))\n",
        "\n",
        "#Creating Xtest variable \n",
        "Xtest = df['x'][df['is_train'] == 0].values.reshape((-1,1))\n",
        "\n",
        "#Creating ytest variable\n",
        "ytest = df['y'][df['is_train'] == 0].values.reshape((-1,1))"
      ],
      "metadata": {
        "id": "aUPqzdpcxyxc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Scatterplot of the data \n",
        "\n",
        "plt.scatter(Xtrain, ytrain) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "9fKJBSWv0Dwj",
        "outputId": "09af546b-ae82-428c-a562-80df021ddc30"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa07f955c90>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures "
      ],
      "metadata": {
        "id": "zjzcyM8W05iJ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import make_pipeline \n",
        "\n",
        "poly_model = make_pipeline(PolynomialFeatures(4), LinearRegression()) "
      ],
      "metadata": {
        "id": "z9DNN41f3_nq"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_model.fit(Xtrain, ytrain) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5DynN_c4U3c",
        "outputId": "17d31adc-22bf-44b1-c6e6-870255ef4b27"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=4)),\n",
              "                ('linearregression', LinearRegression())])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict the test data \n",
        "yfit = poly_model.predict(Xtest) "
      ],
      "metadata": {
        "id": "_MBVEWbx46wa"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Show predictions on our data \n",
        "plt.scatter(Xtrain, ytrain)\n",
        "#Plot our line  \n",
        "plt.plot(Xtest, yfit, 'r') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "UvErsBCk5Ght",
        "outputId": "12d9a00e-2ccd-4dd1-dc6a-81c1e54c4e21"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa07f851d10>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Simple to complex models \n",
        "degrees = [1,4,15] \n",
        "\n",
        "plt.figure(figsize=(14,5)) \n",
        "\n",
        "#Loop over each model \n",
        "for i in range(len(degrees)): \n",
        "  ax = plt.subplot(1,len(degrees), i + 1) \n",
        "  plt.setp(ax, xticks=(), yticks=()) \n",
        "\n",
        "  poly_model = make_pipeline(PolynomialFeatures(degrees[i]), LinearRegression()) \n",
        "\n",
        "  poly_model.fit(Xtrain, ytrain) \n",
        "\n",
        "  plt.plot(Xtest, poly_model.predict(Xtest), label=\"Model\")\n",
        "  plt.plot(Xtest, ytest, label=\"True function\")\n",
        "  plt.scatter(Xtrain, ytrain, edgecolor='b', s=20, label=\"Samples\")\n",
        "  plt.xlabel(\"x\")\n",
        "  plt.ylabel(\"y\")\n",
        "  plt.xlim((0, 1))\n",
        "  plt.ylim((-2, 2))\n",
        "  plt.legend(loc=\"best\")\n",
        "  plt.title(\"Degree {}\".format(degrees[i]))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "K92yMRBp5RaT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "b712dfb6-048a-4a79-93bc-1b8be558698d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "poly_model = make_pipeline(PolynomialFeatures(1),\n",
        "                           LinearRegression())\n",
        "\n",
        "poly_model.fit(Xtrain, ytrain)\n",
        "ypred = poly_model.predict(Xtest)\n",
        "\n",
        "mae = mean_absolute_error(ytest, ypred)\n",
        "mse = mean_squared_error(ytest, ypred)\n",
        "\n",
        "print('MAE: {:.4f}, MSE: {:.4f}'.format(mae, mse)) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkROLS4M13Nk",
        "outputId": "762ef02b-02b9-4466-e1f6-75d8af8b1b02"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 6.2029, MSE: 63.5673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly_model = make_pipeline(PolynomialFeatures(4),\n",
        "                           LinearRegression())\n",
        "\n",
        "poly_model.fit(Xtrain, ytrain)\n",
        "ypred = poly_model.predict(Xtest)\n",
        "\n",
        "mae = mean_absolute_error(ytest, ypred)\n",
        "mse = mean_squared_error(ytest, ypred)\n",
        "\n",
        "print('MAE: {:.4f}, MSE: {:.4f}'.format(mae, mse))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xw7dt813ABM",
        "outputId": "926cd94e-b5b1-4c4e-c365-a0b3387d828a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 5.5844, MSE: 50.4123\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Best MAE experiments \n",
        "#Simple to complex models \n",
        "degrees = [15,16,17] \n",
        "\n",
        "plt.figure(figsize=(14,5)) \n",
        "\n",
        "#Loop over each model \n",
        "for i in range(len(degrees)): \n",
        "  ax = plt.subplot(1,len(degrees), i + 1) \n",
        "  plt.setp(ax, xticks=(), yticks=()) \n",
        "\n",
        "  poly_model = make_pipeline(PolynomialFeatures(degrees[i]), LinearRegression()) \n",
        "\n",
        "  poly_model.fit(Xtrain, ytrain) \n",
        "  ypred = poly_model.predict(Xtest)\n",
        "\n",
        "  mae = mean_absolute_error(ytest, ypred)\n",
        "  mse = mean_squared_error(ytest, ypred)\n",
        "\n",
        "  print('MAE: {:.4f}, MSE: {:.4f}'.format(mae, mse) + 'degree: ' + str(degrees[i]))\n",
        "\n",
        "  plt.plot(Xtest, poly_model.predict(Xtest), label=\"Model\")\n",
        "  plt.plot(Xtest, ytest, label=\"True function\", color='red')\n",
        "  plt.scatter(Xtrain, ytrain, edgecolor='b', s=20, label=\"Samples\")\n",
        "  plt.xlabel(\"x\")\n",
        "  plt.ylabel(\"y\")\n",
        "  plt.xlim((0, 1))\n",
        "  plt.ylim((-2, 2))\n",
        "  plt.legend(loc=\"best\")\n",
        "  plt.title(\"Degree {}\".format(degrees[i]))\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "EjjwpJFF3WNg",
        "outputId": "746cec6f-16f5-4509-c367-9e88e9b7b0b3"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1.6709, MSE: 4.6215degree: 15\n",
            "MAE: 1.2164, MSE: 3.2042degree: 16\n",
            "MAE: 1.3376, MSE: 2.7855degree: 17\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The best MAE we can get is with 16 degrees "
      ],
      "metadata": {
        "id": "aq9ONlJp8cLG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}