raidenworks/rw_confusion_matrix.ipynb Secret

## rw_confusion_matrix.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPJfkeW434zgosjxLXz/TMR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/raidenworks/2a00ab123f6f927fbb538671dd9a4ff5/rw_confusion_matrix.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQGhhbaOIgAG",
        "outputId": "0ba5750c-b23d-4146-d80a-a5bfd2f162ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [3, 4]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_true = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
        "y_pred = [0, 1, 1, 0, 0, 0, 1, 1, 1, 1]\n",
        "cm_2 = confusion_matrix(y_true, y_pred)\n",
        "cm_2"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the terms in the predicted list `y_pred ` are: \\\n",
        "`[TN, FP, FP, FN, FN, FN, TP, TP, TP, TP]`\n",
        "\n",
        "From the values in the confusion matrix output in the above cell, we can see that the matrix is arranged as such:\n",
        "\n",
        "![cf_matrix.png](data:image/png;base64,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)\n",
        "\n",
        "![cf_matrix_terms.png](data:image/png;base64,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)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "q8zI_BwvJIOk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(cm_2, annot=True, cmap='Blues').set(xlabel='Predicted Class', ylabel='Actual Class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "9iAAyKaXKcPs",
        "outputId": "3699ea12-3541-41f1-b44f-e4a57289619d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 23.52222222222222, 'Predicted Class'),\n",
              " Text(50.722222222222214, 0.5, 'Actual Class')]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at an example with 3 classes:"
      ],
      "metadata": {
        "id": "CyPnCkhrUu3e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
        "y_pred = [0, 1, 1, 2, 2, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
        "cm_3 = confusion_matrix(y_true, y_pred)\n",
        "cm_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZ3gVLD5NV-6",
        "outputId": "ab02aae5-d2f0-4eaf-ef8d-63ce075b0105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the count of the terms in the predicted list `y_pred ` are: \\\n",
        "*Actual_0_Predicted_0* = 1 \\\n",
        "*Actual_0_Predicted_1* = 2 \\\n",
        "*Actual_0_Predicted_2* = 3 \\\n",
        "*Actual_1_Predicted_0* = 4 \\\n",
        "*Actual_1_Predicted_1* = 5 \\\n",
        "*Actual_1_Predicted_2* = 6 \\\n",
        "*Actual_2_Predicted_0* = 7 \\\n",
        "*Actual_2_Predicted_1* = 8 \\\n",
        "*Actual_2_Predicted_2* = 9\n",
        "\n",
        "From the values in the confusion matrix output in the above cell, we can see that the matrix is arranged as such:\n",
        "\n",
        "![cf_matrix_3.png](data:image/png;base64,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)\n",
        "\n",
        "![cf_matrix_3_terms.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "FHJ5u5E2Uokn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm_3, annot=True, cmap='Blues').set(xlabel='Predicted Class', ylabel='Actual Class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "0o5b1jaNUa7B",
        "outputId": "a59586b1-6560-4fdc-db26-1f26dba3b829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 23.52222222222222, 'Predicted Class'),\n",
              " Text(50.722222222222214, 0.5, 'Actual Class')]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}