Chapter_5_Factor_Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Chapter 5 Factor Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMoIGr/V0o7OZLNL+yP4qGe",
      "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/github/tohnperfect/multivariate/blob/main/Chapter_5_Factor_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paGuFRdZEbiq"
      },
      "source": [
        "https://www.datacamp.com/community/tutorials/introduction-factor-analysis\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ati_OmprDAN0"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eCzUN4fsGRnR",
        "outputId": "64b38739-01aa-4f66-9cdd-25d59d5d7ce6"
      },
      "source": [
        "BFI_data = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/psych/bfi.csv')\n",
        "BFI_data.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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            "tags": []
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      "cell_type": "markdown",
      "metadata": {
        "id": "6_QllhoJHoCp"
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      "source": [
        "## Factor Analysis'\n",
        "\n",
        "## Assumptions: \n",
        "\n",
        "ในข้อมูล multivariate ที่มีตัวแปร Observe Variables จำนวน n ตัว\n",
        "\n",
        "มีตัวแปร Factors (Latent Variables) จำนวน k ตัว ที่สามารถอธิบายข้อมูลทั้งหมดได้\n",
        "\n",
        "โดย k < n\n",
        "\n",
        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)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_y7DZ3hKxug"
      },
      "source": [
        "Assumptions ในการทำ Factor Analysis:\n",
        "\n",
        "* There are no outliers in data.\n",
        "* Sample size should be greater than the factor.\n",
        "* There should not be ***perfect multicollinearity***.\n",
        "* There should not be ***homoscedasticity*** between the variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQ3yJ_GVQmce"
      },
      "source": [
        "การทำ Factor Analysis มีประโยชน์หลัก 2 อันคือ\n",
        "1. Confirmatory\n",
        "2. Exploratory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74OT_UwMOrSa"
      },
      "source": [
        "## การทำ Factor Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "fDhq_X1ROqsD",
        "outputId": "6799ed35-be89-4e23-91e0-9f695f7d677e"
      },
      "source": [
        "from factor_analyzer import FactorAnalyzer\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-bda6d7cd1b63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfactor_analyzer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFactorAnalyzer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'factor_analyzer'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGUXudnCPnBl"
      },
      "source": [
        "แก้ error ```ModuleNotFoundError: No module named 'factor_analyzer'``` โดย https://stackoverflow.com/questions/61830329/modulenotfounderror-no-module-named-factor-analyzer-python-notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3DBAv08P7pD"
      },
      "source": [
        "วิธี install package อื่นๆ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fH4aukMnGi5E",
        "outputId": "82c53f8b-6bba-44cf-922e-fa1f3264e69f"
      },
      "source": [
        "!pip install factor_analyzer"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting factor_analyzer\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/44/b5/cbd83484ca6dd4c6562c6d66a6a3a0ecf526e79b2b575b9fb4bf5ad172dd/factor_analyzer-0.3.2.tar.gz (40kB)\n",
            "\r\u001b[K     |████████▏                       | 10kB 13.3MB/s eta 0:00:01\r\u001b[K     |████████████████▍               | 20kB 18.6MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 30kB 22.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 40kB 4.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.1.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (0.22.2.post1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->factor_analyzer) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->factor_analyzer) (2.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->factor_analyzer) (1.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->factor_analyzer) (1.15.0)\n",
            "Building wheels for collected packages: factor-analyzer\n",
            "  Building wheel for factor-analyzer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for factor-analyzer: filename=factor_analyzer-0.3.2-cp37-none-any.whl size=40383 sha256=1603bacfd5aa2442a19c69f7a467732f6084bb84bcdd8655d36675391b4d461b\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/d0/57/f1330cb9c80e82d8d05391c74c94ed61ce3f03bf6157f3d6db\n",
            "Successfully built factor-analyzer\n",
            "Installing collected packages: factor-analyzer\n",
            "Successfully installed factor-analyzer-0.3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNZ6s6d7P6fg"
      },
      "source": [
        "from factor_analyzer import FactorAnalyzer\n",
        "import matplotlib.pyplot as plt  # = from matplotlib import pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qob7AoYJSqw0"
      },
      "source": [
        "## Quiz 5 เลือกข้อมูลมาเฉพาะ a1,a2,a3,...,o3,o4,o5 ด้วยคำสั่ง ```.iloc[]```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHj2aVbJQVOf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "968521a4-5f6e-44c7-c2a8-a2bd4fe130ad"
      },
      "source": [
        "BFI_data_dropped = BFI_data.iloc[:,1:-3]\n",
        "BFI_data_dropped"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2799</th>\n",
              "      <td>2.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2800 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       A1   A2   A3   A4   A5   C1   C2  ...   N4   N5   O1  O2   O3   O4   O5\n",
              "0     2.0  4.0  3.0  4.0  4.0  2.0  3.0  ...  2.0  3.0  3.0   6  3.0  4.0  3.0\n",
              "1     2.0  4.0  5.0  2.0  5.0  5.0  4.0  ...  5.0  5.0  4.0   2  4.0  3.0  3.0\n",
              "2     5.0  4.0  5.0  4.0  4.0  4.0  5.0  ...  2.0  3.0  4.0   2  5.0  5.0  2.0\n",
              "3     4.0  4.0  6.0  5.0  5.0  4.0  4.0  ...  4.0  1.0  3.0   3  4.0  3.0  5.0\n",
              "4     2.0  3.0  3.0  4.0  5.0  4.0  4.0  ...  4.0  3.0  3.0   3  4.0  3.0  3.0\n",
              "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ..  ...  ...  ...\n",
              "2795  6.0  1.0  3.0  3.0  3.0  6.0  6.0  ...  NaN  1.0  6.0   1  6.0  6.0  1.0\n",
              "2796  2.0  4.0  4.0  3.0  5.0  2.0  3.0  ...  3.0  3.0  6.0   3  5.0  4.0  2.0\n",
              "2797  2.0  3.0  5.0  2.0  5.0  5.0  5.0  ...  3.0  1.0  5.0   1  6.0  4.0  3.0\n",
              "2798  5.0  2.0  2.0  4.0  4.0  5.0  5.0  ...  4.0  1.0  5.0   2  5.0  5.0  1.0\n",
              "2799  2.0  3.0  1.0  4.0  2.0  5.0  5.0  ...  1.0  1.0  3.0   1  3.0  5.0  1.0\n",
              "\n",
              "[2800 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbDb5e5yygwT"
      },
      "source": [
        "BFI_data_dropped = BFI_data_dropped.dropna()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tslCUQpn0WJI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "607be4e3-9023-46e3-e1a0-001bb9f9af0e"
      },
      "source": [
        "BFI_data_dropped.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2436, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwQkUuki06XL"
      },
      "source": [
        "## Bartlett’s test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDKzTgeJIWjz"
      },
      "source": [
        "p value เข้าใกล้ 0 ดี"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAudD-dR0YnQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6c71c9e-8c41-4cb9-892e-6efa2c7d99d2"
      },
      "source": [
        "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
        "\n",
        "chi_square_value,p_value = calculate_bartlett_sphericity(BFI_data_dropped)\n",
        "\n",
        "chi_square_value, p_value"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18170.966350869243, 0.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glVPufQp2QxU"
      },
      "source": [
        "## Kaiser-Meyer-Olkin (KMO) Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzY0HTZl28ru"
      },
      "source": [
        "ถ้าค่า KMO model มีค่ามากกว่า 0.6 แปลว่าข้อมูลเหมาะสมที่นำไปทำ Factor Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_aYnoxh1Iyn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2488b920-44ad-4834-a14e-449e9ff7d409"
      },
      "source": [
        "from factor_analyzer.factor_analyzer import calculate_kmo\n",
        "\n",
        "kmo_all,kmo_model=calculate_kmo(BFI_data_dropped)\n",
        "\n",
        "kmo_model"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8485397221949221"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNr12PVl4uoi"
      },
      "source": [
        "## Factor Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtJGxEye6PN9"
      },
      "source": [
        "### Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiIQ8zut6ROr"
      },
      "source": [
        "from factor_analyzer import FactorAnalyzer"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moQ2bWpo6Uzw"
      },
      "source": [
        "### Define"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFKV5wyi6bgE"
      },
      "source": [
        "fa = FactorAnalyzer(n_factors=20)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8QsXN_l8V_9"
      },
      "source": [
        "## Fit-transform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6eu1THq8tup"
      },
      "source": [
        "data_fa = fa.fit_transform(BFI_data_dropped)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7Ki_fwm87qn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e61ed08-2924-4377-c2fb-0e1c605c9da4"
      },
      "source": [
        "data_fa.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2436, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0XYOQh8-dqR"
      },
      "source": [
        "## วิธีตัดสินว่าเราจะลดเหลือกี่ dimension (มี factors ทั้งหมดกี่ตัว)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbvyZKwP87mV"
      },
      "source": [
        "ev,v = fa.get_eigenvalues()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOfcouP4-uk0"
      },
      "source": [
        "### ใช้ eigen values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RY-dg3JA9plt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2e9871a-67d4-4bf7-b382-f9afdc4c2256"
      },
      "source": [
        "ev"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.13431118, 2.75188667, 2.14270195, 1.85232761, 1.54816285,\n",
              "       1.07358247, 0.83953893, 0.79920618, 0.71898919, 0.68808879,\n",
              "       0.67637336, 0.65179984, 0.62325295, 0.59656284, 0.56309083,\n",
              "       0.54330533, 0.51451752, 0.49450315, 0.48263952, 0.448921  ,\n",
              "       0.42336611, 0.40067145, 0.38780448, 0.38185679, 0.26253902])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgFykNPq_A_R"
      },
      "source": [
        "## scree plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sm6haZqc8tkm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4e0c6f9c-7d62-4757-ad3b-ee438aa9d066"
      },
      "source": [
        "# Create scree plot using matplotlib\n",
        "plt.plot(range(1,BFI_data_dropped.shape[1]+1),ev,'bo-')\n",
        "plt.plot([0,BFI_data_dropped.shape[1]+1],[1,1],'r--')\n",
        "plt.title('Scree Plot')\n",
        "plt.xlabel('Factors')\n",
        "plt.ylabel('Eigenvalue')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vv3Axn5s5gTJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1cdfd4b-57f0-4c71-9658-41bab1cc9d2b"
      },
      "source": [
        "data_fa[:,:6]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.89749661, -0.12809666, -1.22649076,  1.08665889,  0.42476701,\n",
              "         0.03981611],\n",
              "       [ 0.33679356, -0.15018138, -0.04332274, -0.10339328,  0.29665648,\n",
              "        -1.11019718],\n",
              "       [-0.24521029,  0.80409961,  0.39343377, -0.30106518,  0.82703928,\n",
              "        -0.2982022 ],\n",
              "       ...,\n",
              "       [ 0.38724631, -0.02183587,  1.19616826, -1.19357208,  0.79669501,\n",
              "        -0.95295109],\n",
              "       [-0.75695976,  0.94984758,  1.24363484, -1.15230215,  2.2534133 ,\n",
              "        -1.0218667 ],\n",
              "       [-2.35583952, -1.49829856,  0.49436981, -1.64734311,  0.94742323,\n",
              "         0.43462495]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQCo4ZaOCW3w"
      },
      "source": [
        "## รัน Factor Analyzer อีกรอบ ด้วย paremeters จำนวน factor เท่ากับ 6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGV6ctdwCdSJ"
      },
      "source": [
        "from factor_analyzer import FactorAnalyzer\n",
        "fa2 = FactorAnalyzer(n_factors=6)\n",
        "data_fa = fa2.fit_transform(BFI_data_dropped)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogR_EpWyM33s",
        "outputId": "9d427f3d-c381-48ec-c93d-d547ae96a70b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fa2.loadings_.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IRjSUuKqOxNm"
      },
      "source": [
        "#### Plot factor loadings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esNwFLArNLem"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import matplotlib"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iK8lmO3CRpJU"
      },
      "source": [
        "# matrix plot\n",
        "confirmatory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f4lU6LsPTWj",
        "outputId": "83cd2518-ee3f-4011-b45c-3d771e95fef7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        }
      },
      "source": [
        "matplotlib.rcParams['figure.figsize']=[15,15]\n",
        "plt.imshow(np.absolute(fa2.loadings_))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6f8a2c4510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOUAAANOCAYAAADu+sumAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVzklEQVR4nO3da4ymd33e8eu/J6/xicUntj6XGlIohUQbK2poRUtIIY0CVaQKV4l4gepWChUolSrSvuhJqqJKTfMmiuQUGrdNoFEBxU1RwKJWXaQkeE1MsDGOXWPjdXzmYJvU9h7+feFB2nj24OzMPve1zOcjrWaeex7P72d7vnvP88zhHnPOAD22Lb0A8GeJEsqIEsqIEsqIEsrsWOWwXWP3PHvbuascud6Olf4rr7dtLDs/yZErl3/GfRzYvuj8uWP5/w/PPfPIU3POi19+fKUfoWdvOzc/cu5PrXLkOtsu3LPo/HnWrkXnJ8kLv/Li0itk5y9csOj8F/ectej8JPnfn/3IQ8c67tNXKCNKKCNKKCNKKCNKKCNKKCNKKCNKKCNKKCNKKCNKKCNKKLOhKMcY7xpj3DvGuH+M8ZHNWgq2slOOcoyxPcmvJHl3kjcmuX6M8cbNWgy2qo2cKa9Lcv+c84E554tJPpHkPZuzFmxdG4nysiQPH3X7wNqxP2OMccMYY/8YY/+L8/kNjIOt4bQ/0TPnvHHOuW/OuW/X2H26x8EZbyNRPpLkiqNuX752DNiAjUR5e5JrxxjXjDF2JXlfkps3Zy3Yuk75d/TMOQ+NMT6Y5LNJtif52Jzz7k3bDLaoDf3irDnnZ5J8ZpN2AeI7eqCOKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKHMaq+gumtnxuWvXenIlzu451WLzt9x78Mnv9NpduHu5S+Y+uzXvrXo/N1j+f8Gx+NMCWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVECWVWen3K+fwLOXzvA6scuc53f3rfovPP++Izi85PkjsefPPSK+T1h+9ddP7BH3r9ovOTJP/n2IedKaGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKHMSi8aO3Zsz/ZXX7DKkeucf+93Fp3/wtvfsuj8JDn3jrOWXiGHfvgNi87fsf+PF51/Is6UUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUGal16ecu3fl0A9cucqR6zz9prMXnX/xf7pj0flJkjfvW3qD7PzyA4vOf/qn/+qi85MkNx37sDMllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllFnpRWPz3eezbf89Kx35cpfevXvR+Y9/6upF5yfJ5f/4wNIr5MiVexedf+HNX110/ok4U0IZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUKZDf2UyBjjwSTPJjmc5NCcc99mLAVb2Wb86NbfnHM+tQnvB4hPX6HORqOcST43xrhjjHHDse4wxrhhjLF/jLH/4Hx+g+Pg+99GP31925zzkTHGJUluGWN8bc5529F3mHPemOTGJDl/24Vzg/Pg+96GzpRzzkfWXj6R5NNJrtuMpWArO+UoxxjnjDHO+97rSX48yV2btRhsVRv59PXSJJ8eY3zv/fzmnPN3N2Ur2MJOOco55wNJ3rKJuwDxJRGoI0ooI0ooI0ooI0ooI0ooI0ooI0ooI0ooI0oos9KLxo6dO7L9tZescuQ689xXLTr/fdfsX3R+ktz63NVLr5AjD35j0fnbL7pw0fkn4kwJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZVZ6fcp58FAO/8njqxy5zvbLXrvo/E89/NZF5yfJnvmtpVfI9kuXvU5pLjhv2flJ8uSxDztTQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQpmVXjQ2mck8stqRL3PooYcXnf+6Cy5YdH6SPHX1ZUuvkGxb9nxw398/Z9H5SZIPH/uwMyWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUESWUWe1FY191do685U0rHflyOx//zqLzb3/4/EXnJ8k1X/7q0itk2549i87f+4W/uOj8JHnwOMedKaGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKGMKKHMaq9PWeCq//bYovP/3z+/aNH5SbLtdVcvvULGc3+66PwL7nh00fkn4kwJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZU4a5RjjY2OMJ8YYdx117DVjjFvGGPetvdxzeteEreOVnCl/Pcm7XnbsI0k+P+e8Nsnn124Dm+CkUc45b0vyzZcdfk+Sm9ZevynJezd5L9iyTvUx5aVzzu/9lOhjSS493h3HGDeMMfaPMfYfPPjdUxwHW8eGn+iZc84k8wRvv3HOuW/OuW/nznM2Og6+751qlI+PMfYmydrLJzZvJdjaTjXKm5O8f+319yf57c1ZB3glXxL5eJLfS/KGMcaBMcYHkvxikneOMe5L8mNrt4FNcNLfZjfnvP44b3rHJu8CxHf0QB1RQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQpmVXjR2vHAwO+7/k1WOXOfys7616PyHvnJg0flJMr+77AVbk+TIiy8uOn+c2/tjhM6UUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUEaUUGalF42dhw7l8JNPrnLkOr+2/68vOv8Hnrln0flJMl94YekVcuSH37To/APvKLho7L859mFnSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSigjSiiz0utTjp07s+PSv7DKkeu8/qrHFp2fw4eXnZ9k+xWXLb1CDu3evuj8S750cNH5SXLvcY47U0IZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUKZlV40du7akYNXXbzKkevc9/Wdi86//N3LX7D1nN/5w6VXyI6Lzl90/q47v77o/BNxpoQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyooQyK71o7Otf93Q+999vWuXIdf7az/+jReef85nlL9i67dqrl14h48CTi86fF79m0flJkm8e+7AzJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQ5aZRjjI+NMZ4YY9x11LF/OcZ4ZIxx59qfnzi9a8LW8UrOlL+e5F3HOP4f5pxvXfvzmc1dC7auk0Y557wtx/0hE2CzbeQx5QfHGH+09untnuPdaYxxwxhj/xhj/5NPH97AONgaTjXKX03yuiRvTfJokn9/vDvOOW+cc+6bc+67+MLtpzgOto5TinLO+fic8/Cc80iSX0ty3eauBVvXKUU5xth71M2/m+Su490X+PM56e/oGWN8PMnbk1w0xjiQ5F8kefsY461JZpIHk/zD07gjbCknjXLOef0xDn/0NOwCxHf0QB1RQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQpmVXp9yZubwPLLKkeuc9e1lf6Zz7Nq16PwkydPfXnqDHPnOM8vOv+LiReefiDMllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllFnpRWMfPXR2/vVTb17lyHV233b3ovOPPP/CovOTZBTskG1j0fHbH3p80fkn4kwJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZVZ6fcqnnz0v/+W2t61y5Dqvfv+yfw/t/R/fWHR+ksznn196hYwdK/3QW+fgNa9ddH6S5LFjH3amhDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKihDKrvXLnyOJ/DVxy+7OLzm+4YGsuuXDpDfLCJecuOv+s//vEovNPxJkSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyogSyqz0orG7H30xf/nfPrzKkescvOriRedv27590flJkjmX3iAPv/OsRefvfsuVi85PkvzysQ87U0IZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUIZUUKZk0Y5xrhijHHrGOOrY4y7xxgfWjv+mjHGLWOM+9Ze7jn968L3v1dypjyU5J/MOd+Y5EeS/NwY441JPpLk83POa5N8fu02sEEnjXLO+eic80trrz+b5J4klyV5T5Kb1u52U5L3nq4lYSv5c/085Rjj6iQ/mOQPklw653x07U2PJbn0OP/MDUluSJLd28871T1hy3jFT/SMMc5N8skkH55zPnP02+acM8kxf3J2znnjnHPfnHPfrm1nb2hZ2ApeUZRjjJ15KcjfmHN+au3w42OMvWtv35vkidOzImwtr+TZ15Hko0numXP+0lFvujnJ+9def3+S39789WDreSWPKX80yc8m+coY4861Y/8syS8m+a0xxgeSPJTk752eFWFrOWmUc84vJBnHefM7NncdwHf0QBlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQhlRQpmVXp8y27dlnn/OSke+3Pj9uxadnwvOX3Z+kiP3fX3pFfLi3gsWnf+XfvXRk9/pNDveR6IzJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZQRJZRZ6UVj5wsv5sj9D61y5Drf/pnrFp2/5zdvX3R+kmx71auWXiE5PBYd/43rr1p0fpLk3x37sDMllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllBEllFnpRWPHWbuy7eorVzlynZ1/emTR+fPQoUXnJ8m85rKlV8irv7xz0fnnPnJ40fkn4kwJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZUQJZVZ6fcr5/As5fN/XVzlynXMWnp8xlp2fZDx/cOkVsu1vP7Xo/PP/wXcWnX8izpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQRpRQZsw5VzdsjCeTPLSBd3FRkmWvNrr8DkvPt8Pmzb9qznnxyw+uNMqNGmPsn3Pu28o7LD3fDqd/vk9foYwoocyZFuWNSy+Q5XdYen5ih9M6/4x6TAlbwZl2poTve6KEMmdMlGOMd40x7h1j3D/G+MgC8z82xnhijHHXqmevzb9ijHHrGOOrY4y7xxgfWmCH3WOML44xvry2w79a9Q5re2wfY/zhGON3Fpr/4BjjK2OMO8cY+zf9/Z8JjynHGNuT/HGSdyY5kOT2JNfPOb+6wh3+RpLnkvznOedfWdXco+bvTbJ3zvmlMcZ5Se5I8t4V/zcYSc6Zcz43xtiZ5AtJPjTn/P1V7bC2x88n2Zfk/DnnT65y9tr8B5Psm3Oelm9eOFPOlNcluX/O+cCc88Ukn0jynlUuMOe8Lck3VznzZfMfnXN+ae31Z5Pck+SyFe8w55zPrd3cufZnpX+rjzEuT/J3kvzHVc5dpTMlysuSPHzU7QNZ8QdkkzHG1Ul+MMkfLDB7+xjjziRPJLllzrnqHX45yT9NcmTFc482k3xujHHHGOOGzX7nZ0qUrBljnJvkk0k+POd8ZtXz55yH55xvTXJ5kuvGGCv7VH6M8ZNJnphz3rGqmcfxtjnnDyV5d5KfW3tos2nOlCgfSXLFUbcvXzu2paw9jvtkkt+Yc35qyV3mnN9OcmuSd61w7I8m+am1x3SfSPK3xhj/dYXzkyRzzkfWXj6R5NN56eHVpjlTorw9ybVjjGvGGLuSvC/JzQvvtFJrT7J8NMk9c85fWmiHi8cYr157/ey89MTb11Y1f875C3POy+ecV+elj4H/Nef8mVXNT5IxxjlrT7RljHFOkh9PsqnPyJ8RUc45DyX5YJLP5qUnOH5rznn3KncYY3w8ye8lecMY48AY4wOrnJ+XzhI/m5fODneu/fmJFe+wN8mtY4w/ykt/Ud4y51zkyxILujTJF8YYX07yxST/c875u5s54Iz4kghsJWfEmRK2ElFCGVFCGVFCGVFCGVFCGVFCmf8PZJ1Bfys2Ss4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX-Yj2qsRtvO"
      },
      "source": [
        "## กราฟแท่ง\n",
        "Exploratory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7y8c1NmUIGh",
        "outputId": "09dfa509-fc12-4c40-90eb-852990a3d336",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "BFI_data_dropped.columns"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A1', 'A2', 'A3', 'A4', 'A5', 'C1', 'C2', 'C3', 'C4', 'C5', 'E1', 'E2',\n",
              "       'E3', 'E4', 'E5', 'N1', 'N2', 'N3', 'N4', 'N5', 'O1', 'O2', 'O3', 'O4',\n",
              "       'O5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMtY6O_3PqWX",
        "outputId": "962b8944-91eb-4530-b73a-50ae419d61c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        }
      },
      "source": [
        "plt.bar(range(25),np.absolute(fa2.loadings_[:,0]),tick_label=BFI_data_dropped.columns)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 25 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1WEMOxrVMgK"
      },
      "source": [
        "# Exam 3\n",
        "\n",
        "ทำ Factor Analysis ข้อมูล Fifa 2018 \n",
        "https://drive.google.com/file/d/1UORRKWMb8GCN455eJE_n9MvBHupnSVwv/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwydAqqATIcl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}