Machine_Learning/code_examples/2020.01.22_Logistic_Regression.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODQEiw9TnjIY",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression in Python\n",
        "[Original article](https://realpython.com/logistic-regression-python/) by Mirko Stojiljkovic on Real Python website.\n",
        "\n",
        "Keep in mind that logistic regression is essentially a **linear classifier**.\n",
        "\n",
        "[Self-link](https://colab.research.google.com/drive/1TJEfTkfuXscEndBe0Yb95x-BEDbLejYq) on this CoLab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTpcHqS_uqpy",
        "colab_type": "text"
      },
      "source": [
        "## Example 1. Scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouw1irivneMp",
        "colab_type": "code",
        "outputId": "3d425c24-9727-48cf-fbc7-2188c231b5e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "x = np.arange(10).reshape(-1, 1)\n",
        "y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])\n",
        "\n",
        "print('x =', x)\n",
        "print('y =', y)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x = [[0]\n",
            " [1]\n",
            " [2]\n",
            " [3]\n",
            " [4]\n",
            " [5]\n",
            " [6]\n",
            " [7]\n",
            " [8]\n",
            " [9]]\n",
            "y = [0 0 0 0 1 1 1 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83VV7SWLvScB",
        "colab_type": "code",
        "outputId": "973c2ecb-e026-48f3-d9d9-f3091931c4d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "# Create model\n",
        "model = LogisticRegression(solver='liblinear', random_state=0)\n",
        "# Train model\n",
        "model.fit(x, y)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn5Zdh2EvdAq",
        "colab_type": "code",
        "outputId": "1c4e4b82-9299-4819-aef2-5d6b7664b688",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "# You can quickly get the attributes of your model\n",
        "print(model.classes_)\n",
        "# Value of the slope 𝑏₁ and the intercept 𝑏₀ of the linear function 𝑓\n",
        "print(model.intercept_)\n",
        "print(model.coef_)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1]\n",
            "[-1.04608067]\n",
            "[[0.51491375]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiUlzX5qwsaZ",
        "colab_type": "code",
        "outputId": "4ea61bf8-3456-43a2-9f20-413a62b59ad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "# Evaluate the Model\n",
        "# Return probability matrix that the predicted output is equal to 0 or 1\n",
        "print(model.predict_proba(x))\n",
        "# Get the actual predictions\n",
        "print(model.predict(x))\n",
        "# Get accuracy of the model\n",
        "print(model.score(x, y))\n",
        "# Get confusion matrix\n",
        "print(confusion_matrix(y, model.predict(x)))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.74002157 0.25997843]\n",
            " [0.62975524 0.37024476]\n",
            " [0.5040632  0.4959368 ]\n",
            " [0.37785549 0.62214451]\n",
            " [0.26628093 0.73371907]\n",
            " [0.17821501 0.82178499]\n",
            " [0.11472079 0.88527921]\n",
            " [0.07186982 0.92813018]\n",
            " [0.04422513 0.95577487]\n",
            " [0.02690569 0.97309431]]\n",
            "[0 0 0 1 1 1 1 1 1 1]\n",
            "0.9\n",
            "[[3 1]\n",
            " [0 6]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJB996m0xpvR",
        "colab_type": "code",
        "outputId": "392a5385-f2d2-4a6a-cc0c-859e3933de6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "# Visualize the confusion matrix\n",
        "cm = confusion_matrix(y, model.predict(x))\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "font_size = 32\n",
        "ax.imshow(cm)\n",
        "ax.grid(False)\n",
        "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
        "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
        "ax.set_ylim(1.5, -0.5)\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        ax.text(j, i, cm[i, j], ha='center', va='center',\n",
        "                color='red', fontsize=font_size)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dojOv+04H7srMZyNiaUQclZm/iYhTK+uOycw1ETE0M5dFxF8AF2XmHICInf5gngaOz8xN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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmlPG47p0SJ6",
        "colab_type": "code",
        "outputId": "1d079481-5389-42b5-b716-df870bc8a3a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        }
      },
      "source": [
        "# Get a more comprehensive report on the classification\n",
        "print(classification_report(y, model.predict(x)))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.75      0.86         4\n",
            "           1       0.86      1.00      0.92         6\n",
            "\n",
            "    accuracy                           0.90        10\n",
            "   macro avg       0.93      0.88      0.89        10\n",
            "weighted avg       0.91      0.90      0.90        10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouvBsXGN1GTq",
        "colab_type": "code",
        "outputId": "a7bb2c17-ad9d-45ab-f0be-a141cda09ce4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "# Improve the Model\n",
        "model = LogisticRegression(solver='liblinear', C=10.0, random_state=0)\n",
        "model.fit(x, y)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bf9jV0L909BI",
        "colab_type": "code",
        "outputId": "da889cfe-cbe4-4535-f5a7-e03133d4f9c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        }
      },
      "source": [
        "# Check it\n",
        "print(model.intercept_)\n",
        "print(model.coef_)\n",
        "print(model.predict_proba(x))\n",
        "print(model.predict(x))\n",
        "print()\n",
        "print(model.score(x, y))\n",
        "print(confusion_matrix(y, model.predict(x)))\n",
        "print(classification_report(y, model.predict(x)))\n",
        "\n",
        "# Visualize the confusion matrix\n",
        "cm = confusion_matrix(y, model.predict(x))\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\n",
        "font_size = 32\n",
        "ax.imshow(cm)\n",
        "ax.grid(False)\n",
        "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
        "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
        "ax.set_ylim(1.5, -0.5)\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        ax.text(j, i, cm[i, j], ha='center', va='center',\n",
        "                color='red', fontsize=font_size)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-3.51335372]\n",
            "[[1.12066084]]\n",
            "[[0.97106534 0.02893466]\n",
            " [0.9162684  0.0837316 ]\n",
            " [0.7810904  0.2189096 ]\n",
            " [0.53777071 0.46222929]\n",
            " [0.27502212 0.72497788]\n",
            " [0.11007743 0.88992257]\n",
            " [0.03876835 0.96123165]\n",
            " [0.01298011 0.98701989]\n",
            " [0.0042697  0.9957303 ]\n",
            " [0.00139621 0.99860379]]\n",
            "[0 0 0 0 1 1 1 1 1 1]\n",
            "\n",
            "1.0\n",
            "[[4 0]\n",
            " [0 6]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         4\n",
            "           1       1.00      1.00      1.00         6\n",
            "\n",
            "    accuracy                           1.00        10\n",
            "   macro avg       1.00      1.00      1.00        10\n",
            "weighted avg       1.00      1.00      1.00        10\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XlqdHxNyIeCwi7o+Iw3uaR2auy8z7gG0uv4iIpoi4ISIWVeby33bhzz/gbaKputxMRw8j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\n4P8DyOcTryhJLuEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpLyMyF72sgc",
        "colab_type": "text"
      },
      "source": [
        "## Example 2. Scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFv1ET7G2BaU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Step 1: Import packages, functions, and classes\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Step 2: Get data\n",
        "x = np.arange(10).reshape(-1, 1)\n",
        "y = np.array([0, 1, 0, 0, 1, 1, 1, 1, 1, 1])\n",
        "\n",
        "# Step 3: Create a model and train it\n",
        "model = LogisticRegression(solver='liblinear', C=10.0, random_state=0)\n",
        "model.fit(x, y)\n",
        "\n",
        "# Step 4: Evaluate the model\n",
        "p_pred = model.predict_proba(x)\n",
        "y_pred = model.predict(x)\n",
        "score_ = model.score(x, y)\n",
        "conf_m = confusion_matrix(y, y_pred)\n",
        "report = classification_report(y, y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSevCBve2_O7",
        "colab_type": "code",
        "outputId": "d6e3009e-6edf-43f2-afff-d2679559465d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        }
      },
      "source": [
        "# Evaluate results\n",
        "print('x:', x, sep='\\n')\n",
        "print('y:', y, sep='\\n', end='\\n\\n')\n",
        "print('intercept:', model.intercept_)\n",
        "print('coef:', model.coef_, end='\\n\\n')\n",
        "print('p_pred:', p_pred, sep='\\n', end='\\n\\n')\n",
        "print('y_pred:', y_pred, end='\\n\\n')\n",
        "print('score_:', score_, end='\\n\\n')\n",
        "print('conf_m:', conf_m, sep='\\n', end='\\n\\n')\n",
        "print('report:', report, sep='\\n')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x:\n",
            "[[0]\n",
            " [1]\n",
            " [2]\n",
            " [3]\n",
            " [4]\n",
            " [5]\n",
            " [6]\n",
            " [7]\n",
            " [8]\n",
            " [9]]\n",
            "y:\n",
            "[0 1 0 0 1 1 1 1 1 1]\n",
            "\n",
            "intercept: [-1.51632619]\n",
            "coef: [[0.703457]]\n",
            "\n",
            "p_pred:\n",
            "[[0.81999686 0.18000314]\n",
            " [0.69272057 0.30727943]\n",
            " [0.52732579 0.47267421]\n",
            " [0.35570732 0.64429268]\n",
            " [0.21458576 0.78541424]\n",
            " [0.11910229 0.88089771]\n",
            " [0.06271329 0.93728671]\n",
            " [0.03205032 0.96794968]\n",
            " [0.0161218  0.9838782 ]\n",
            " [0.00804372 0.99195628]]\n",
            "\n",
            "y_pred: [0 0 0 1 1 1 1 1 1 1]\n",
            "\n",
            "score_: 0.8\n",
            "\n",
            "conf_m:\n",
            "[[2 1]\n",
            " [1 6]]\n",
            "\n",
            "report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.67      0.67         3\n",
            "           1       0.86      0.86      0.86         7\n",
            "\n",
            "    accuracy                           0.80        10\n",
            "   macro avg       0.76      0.76      0.76        10\n",
            "weighted avg       0.80      0.80      0.80        10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MS9ejSZm35Hb",
        "colab_type": "text"
      },
      "source": [
        "## Example 3. StatsModels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXi4Vn523GTz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import statsmodels.api as sm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QVFZeIm4kCT",
        "colab_type": "code",
        "outputId": "05c23fd6-3418-4b6c-be1f-0fad3194f356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "x = np.arange(10).reshape(-1, 1)\n",
        "y = np.array([0, 1, 0, 0, 1, 1, 1, 1, 1, 1])\n",
        "x = sm.add_constant(x)\n",
        "print(x)\n",
        "print(y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0.]\n",
            " [1. 1.]\n",
            " [1. 2.]\n",
            " [1. 3.]\n",
            " [1. 4.]\n",
            " [1. 5.]\n",
            " [1. 6.]\n",
            " [1. 7.]\n",
            " [1. 8.]\n",
            " [1. 9.]]\n",
            "[0 1 0 0 1 1 1 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KY4m9oCJ42RV",
        "colab_type": "code",
        "outputId": "c4b995e7-879b-4026-bdf2-442e41f3622e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "# Create a model\n",
        "# Your logistic regression model is going to be an instance\n",
        "# of the class `statsmodels.discrete.discrete_model.Logit`\n",
        "model = sm.Logit(y, x)\n",
        "# Train a model\n",
        "# If you want to apply L1 regularization, with model.fit_regularized()\n",
        "result = model.fit(method='newton')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.350471\n",
            "         Iterations 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxRtAPBQ6D5r",
        "colab_type": "code",
        "outputId": "021efc4f-f1ad-4d8d-d0b4-ea4a5a1523be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Obtain the values of 𝑏₀ and 𝑏₁ with .params\n",
        "result.params"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.972805  ,  0.82240094])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVpKBs-I6PKc",
        "colab_type": "code",
        "outputId": "8111183b-7025-4b0a-d1bd-6eaf35bce182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "source": [
        "print(result.predict(x))\n",
        "lst = (result.predict(x) >= 0.5).astype(int)\n",
        "print(lst, end='\\n\\n')\n",
        "print(result.pred_table())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.12208792 0.24041529 0.41872657 0.62114189 0.78864861 0.89465521\n",
            " 0.95080891 0.97777369 0.99011108 0.99563083]\n",
            "[0 0 0 1 1 1 1 1 1 1]\n",
            "\n",
            "[[2. 1.]\n",
            " [1. 6.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NKih_gj6dSl",
        "colab_type": "code",
        "outputId": "d74f7bef-1c3d-4833-d997-97e8aad48124",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        }
      },
      "source": [
        "print(result.summary())\n",
        "print('\\n--- --- --- --- ---\\n')\n",
        "print(result.summary2())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                   10\n",
            "Model:                          Logit   Df Residuals:                        8\n",
            "Method:                           MLE   Df Model:                            1\n",
            "Date:                Wed, 22 Jan 2020   Pseudo R-squ.:                  0.4263\n",
            "Time:                        11:58:34   Log-Likelihood:                -3.5047\n",
            "converged:                       True   LL-Null:                       -6.1086\n",
            "Covariance Type:            nonrobust   LLR p-value:                   0.02248\n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.9728      1.737     -1.136      0.256      -5.377       1.431\n",
            "x1             0.8224      0.528      1.557      0.119      -0.213       1.858\n",
            "==============================================================================\n",
            "\n",
            "--- --- --- --- ---\n",
            "\n",
            "                        Results: Logit\n",
            "===============================================================\n",
            "Model:              Logit            Pseudo R-squared: 0.426   \n",
            "Dependent Variable: y                AIC:              11.0094 \n",
            "Date:               2020-01-22 11:58 BIC:              11.6146 \n",
            "No. Observations:   10               Log-Likelihood:   -3.5047 \n",
            "Df Model:           1                LL-Null:          -6.1086 \n",
            "Df Residuals:       8                LLR p-value:      0.022485\n",
            "Converged:          1.0000           Scale:            1.0000  \n",
            "No. Iterations:     7.0000                                     \n",
            "-----------------------------------------------------------------\n",
            "          Coef.    Std.Err.      z      P>|z|     [0.025   0.975]\n",
            "-----------------------------------------------------------------\n",
            "const    -1.9728     1.7366   -1.1360   0.2560   -5.3765   1.4309\n",
            "x1        0.8224     0.5281    1.5572   0.1194   -0.2127   1.8575\n",
            "===============================================================\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YKIFry676c1",
        "colab_type": "text"
      },
      "source": [
        "## Handwriting Recognition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPKZ2sRO7ZWs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_digits\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqJHVW-38mI1",
        "colab_type": "code",
        "outputId": "b5e20972-a579-4d77-f075-50c755ea94ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "x, y = load_digits(return_X_y=True)\n",
        "print('x:\\n', x)\n",
        "print('y:\\n', y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x:\n",
            " [[ 0.  0.  5. ...  0.  0.  0.]\n",
            " [ 0.  0.  0. ... 10.  0.  0.]\n",
            " [ 0.  0.  0. ... 16.  9.  0.]\n",
            " ...\n",
            " [ 0.  0.  1. ...  6.  0.  0.]\n",
            " [ 0.  0.  2. ... 12.  0.  0.]\n",
            " [ 0.  0. 10. ... 12.  1.  0.]]\n",
            "y:\n",
            " [0 1 2 ... 8 9 8]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPo0vyBU8rnV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split Data\n",
        "x_train, x_test, y_train, y_test = \\\n",
        "    train_test_split(x, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGQcjY-e9mFE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Standardize the inputs\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3CpaGqB_MUe",
        "colab_type": "code",
        "outputId": "f8c115d6-05cd-41a7-ad63-b0fe8d300ee0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# Create the model and train it\n",
        "model = LogisticRegression(\n",
        "    solver='liblinear', C=0.05, multi_class='ovr', random_state=0)\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.05, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='ovr', n_jobs=None, penalty='l2', random_state=0,\n",
              "                   solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpUSrQEf_k8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make a prediction\n",
        "y_pred = model.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCrUb4i7A4Qd",
        "colab_type": "code",
        "outputId": "79f19256-1524-45ba-ba03-644e541c12ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Evaluate the Model\n",
        "print(model.score(x_train, y_train))\n",
        "print(model.score(x_test, y_test))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.964509394572025\n",
            "0.9416666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLnIv05NBuDO",
        "colab_type": "code",
        "outputId": "b0610cf5-7191-49ba-8fbb-8a35c6ff71e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "# Get a confusion matrix\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[27  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 32  0  0  0  0  1  0  1  1]\n",
            " [ 1  1 33  1  0  0  0  0  0  0]\n",
            " [ 0  0  1 28  0  0  0  0  0  0]\n",
            " [ 0  0  0  0 29  0  0  1  0  0]\n",
            " [ 0  0  0  0  0 39  0  0  0  1]\n",
            " [ 0  1  0  0  0  0 43  0  0  0]\n",
            " [ 0  0  0  0  0  0  0 39  0  0]\n",
            " [ 0  2  1  2  0  0  0  1 33  0]\n",
            " [ 0  0  0  1  0  1  0  2  1 36]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyT5utTGCCwm",
        "colab_type": "code",
        "outputId": "5c08cd02-89a3-4030-c71b-fd443fc2bbe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        }
      },
      "source": [
        "# Visualize the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "font_size = 18\n",
        "ax.imshow(cm)\n",
        "ax.grid(False)\n",
        "ax.set_xlabel('Predicted outputs', fontsize=font_size, color='black')\n",
        "ax.set_ylabel('Actual outputs', fontsize=font_size, color='black')\n",
        "ax.xaxis.set(ticks=range(10))\n",
        "ax.yaxis.set(ticks=range(10))\n",
        "ax.set_ylim(9.5, -0.5)\n",
        "for i in range(10):\n",
        "    for j in range(10):\n",
        "        ax.text(j, i, cm[i, j], ha='center', va='center',\n",
        "                color='white', fontsize=font_size)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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05+uB/7HWpkalOoxoDWRpLVoDORYiHchei9ZAFhH3tTeQIzlCZQdwPIAxZjBw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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3w5uMV7Cs5m",
        "colab_type": "code",
        "outputId": "4afe9d72-2d7f-4724-f788-ab6ef52a9917",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "# Get a report on classification as a string or dictionary\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98        27\n",
            "           1       0.89      0.91      0.90        35\n",
            "           2       0.94      0.92      0.93        36\n",
            "           3       0.88      0.97      0.92        29\n",
            "           4       1.00      0.97      0.98        30\n",
            "           5       0.97      0.97      0.97        40\n",
            "           6       0.98      0.98      0.98        44\n",
            "           7       0.91      1.00      0.95        39\n",
            "           8       0.94      0.85      0.89        39\n",
            "           9       0.95      0.88      0.91        41\n",
            "\n",
            "    accuracy                           0.94       360\n",
            "   macro avg       0.94      0.94      0.94       360\n",
            "weighted avg       0.94      0.94      0.94       360\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dK0acqX3Fkw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}