{"cells":[{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/gdrive')\n# Establecer ruta de acceso en dr\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/gdrive/My Drive\")","metadata":{"id":"pZFJDVKS-uY8","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4db4035be9944fda9766828acbc16511","outputId":"c5e91778-74b9-44c1-e24b-707c72f8d648","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":69807,"user_tz":240,"timestamp":1650844293663},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Mounted at /content/gdrive\n/content\n"}],"execution_count":1},{"cell_type":"markdown","source":"# Analisis Factorial (FA)\n\nEl análisis factorial es un enfoque útil para encontrar variables latentes que no se miden directamente en una sola variable, sino que se infieren de otras variables en el conjunto de datos. Estas variables latentes se denominan factores. Entonces, el análisis factorial es un modelo de medición de variables latentes. Por ejemplo, si encontramos dos variables latentes en nuestro modelo, se llama modelo de dos factores. La suposición principal del FA es que existen tales variables latentes en nuestros datos.\n\nEn la actualidad, realizamos el análisis factorial utilizando el método de componentes principales, que es muy similar al análisis de componentes principales. Utilizaremos datos de registros nacionales de mujeres que representan a 55 países en siete eventos diferentes.\n\nLas variables son:\n\n- X1: 100m (s)\n- X2: 200m (s)\n- X3: 400m (s)\n- X4: 800m (min)\n- X5: 1500m (min)\n- X6: 3000m (min)\n- X7: Marathon (min)\n","metadata":{"id":"CNwZkkYE_AVb","cell_id":"1bbdfd076e7f4c989aa64223fbe3dc4c","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"import pandas as pd\ndf = pd.read_csv(\"women_track_records.csv\")\ndf.head()","metadata":{"id":"_NzEl43G_jT6","colab":{"height":207,"base_uri":"https://localhost:8080/"},"cell_id":"a06c138da9044e0ca671d51032cd725f","outputId":"690194c4-18a7-4534-ab25-f4f9066e1fd6","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1088,"user_tz":240,"timestamp":1650844423437},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     COUNTRY     X1     X2     X3    X4    X5    X6      X7\n0  Argentina  11.61  22.94  54.50  2.15  4.43  9.79  178.52\n1  Australia  11.20  22.35  51.80  1.98  4.13  9.08  152.37\n2    Austria  11.43  23.09  50.62  1.99  4.22  9.34  159.37\n3    Belgium  11.41  23.04  52.00  2.00  4.14  8.88  157.85\n4    Bermuda  11.46  23.05  53.30  2.16  4.58  9.81  169.98","text/html":"\n  <div id=\"df-7990ddf5-1068-4484-bb8d-d4a1a7d52bbf\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>COUNTRY</th>\n      <th>X1</th>\n      <th>X2</th>\n      <th>X3</th>\n      <th>X4</th>\n      <th>X5</th>\n      <th>X6</th>\n      <th>X7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>11.61</td>\n      <td>22.94</td>\n      <td>54.50</td>\n      <td>2.15</td>\n      <td>4.43</td>\n      <td>9.79</td>\n      <td>178.52</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Australia</td>\n      <td>11.20</td>\n      <td>22.35</td>\n      <td>51.80</td>\n      <td>1.98</td>\n      <td>4.13</td>\n      <td>9.08</td>\n      <td>152.37</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Austria</td>\n      <td>11.43</td>\n      <td>23.09</td>\n      <td>50.62</td>\n      <td>1.99</td>\n      <td>4.22</td>\n      <td>9.34</td>\n      <td>159.37</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Belgium</td>\n      <td>11.41</td>\n      <td>23.04</td>\n      <td>52.00</td>\n      <td>2.00</td>\n      <td>4.14</td>\n      <td>8.88</td>\n      <td>157.85</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bermuda</td>\n      <td>11.46</td>\n      <td>23.05</td>\n      <td>53.30</td>\n      <td>2.16</td>\n      <td>4.58</td>\n      <td>9.81</td>\n      <td>169.98</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7990ddf5-1068-4484-bb8d-d4a1a7d52bbf')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-7990ddf5-1068-4484-bb8d-d4a1a7d52bbf button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-7990ddf5-1068-4484-bb8d-d4a1a7d52bbf');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\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            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":2}],"execution_count":2},{"cell_type":"code","source":"X = df.iloc[:, 1:8]\nX.head()","metadata":{"id":"gLeslioR_tBt","colab":{"height":207,"base_uri":"https://localhost:8080/"},"cell_id":"d252eca9609e40159962aaa0109cf759","outputId":"52974a22-af79-4de4-f278-65102a154486","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":380,"user_tz":240,"timestamp":1650844437043},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"      X1     X2     X3    X4    X5    X6      X7\n0  11.61  22.94  54.50  2.15  4.43  9.79  178.52\n1  11.20  22.35  51.80  1.98  4.13  9.08  152.37\n2  11.43  23.09  50.62  1.99  4.22  9.34  159.37\n3  11.41  23.04  52.00  2.00  4.14  8.88  157.85\n4  11.46  23.05  53.30  2.16  4.58  9.81  169.98","text/html":"\n  <div id=\"df-52cbc3a8-f4e4-431c-a770-9afed7398f03\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>X1</th>\n      <th>X2</th>\n      <th>X3</th>\n      <th>X4</th>\n      <th>X5</th>\n      <th>X6</th>\n      <th>X7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>11.61</td>\n      <td>22.94</td>\n      <td>54.50</td>\n      <td>2.15</td>\n      <td>4.43</td>\n      <td>9.79</td>\n      <td>178.52</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11.20</td>\n      <td>22.35</td>\n      <td>51.80</td>\n      <td>1.98</td>\n      <td>4.13</td>\n      <td>9.08</td>\n      <td>152.37</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>11.43</td>\n      <td>23.09</td>\n      <td>50.62</td>\n      <td>1.99</td>\n      <td>4.22</td>\n      <td>9.34</td>\n      <td>159.37</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.41</td>\n      <td>23.04</td>\n      <td>52.00</td>\n      <td>2.00</td>\n      <td>4.14</td>\n      <td>8.88</td>\n      <td>157.85</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11.46</td>\n      <td>23.05</td>\n      <td>53.30</td>\n      <td>2.16</td>\n      <td>4.58</td>\n      <td>9.81</td>\n      <td>169.98</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52cbc3a8-f4e4-431c-a770-9afed7398f03')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-52cbc3a8-f4e4-431c-a770-9afed7398f03 button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-52cbc3a8-f4e4-431c-a770-9afed7398f03');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\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            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":5}],"execution_count":5},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_scaled = sc.fit_transform(X)\nX_scaled","metadata":{"id":"nbsc3dMZ_yY0","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"103c5fa5b6a04730881cf7ef9cae1e99","outputId":"343e7a53-a8c8-485a-8cbf-17016535f317","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":404,"user_tz":240,"timestamp":1650844459967},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[-2.24428486e-02, -5.62237236e-01,  3.00209295e-01,\n         6.86677436e-01,  8.06867242e-02,  3.93983736e-01,\n         1.74496205e-01],\n       [-9.42599641e-01, -1.08128697e+00, -6.11096516e-01,\n        -8.98614916e-01, -6.80073818e-01, -2.51633758e-01,\n        -6.91870707e-01],\n       [-4.26414124e-01, -4.30275438e-01, -1.00937091e+00,\n        -8.05362425e-01, -4.51845656e-01, -1.52104506e-02,\n        -4.59956046e-01],\n       [-4.71299821e-01, -4.74262704e-01, -5.43592382e-01,\n        -7.12109934e-01, -6.54715134e-01, -4.33497841e-01,\n        -5.10314658e-01],\n       [-3.59085578e-01, -4.65465251e-01, -1.04815510e-01,\n         7.79929927e-01,  4.61066996e-01,  4.12170144e-01,\n        -1.08439681e-01],\n       [-6.95728307e-01, -3.59895812e-01, -2.73575845e-01,\n         2.20414979e-01,  2.32838833e-01,  3.75797327e-01,\n        -1.49190400e-01],\n       [ 1.16702813e+00,  7.83773102e-01,  4.68969630e-01,\n         9.66434910e-01,  1.31404094e-01,  1.39374020e-01,\n         5.88629529e-01],\n       [-1.39145661e+00, -1.16926151e+00, -1.01612132e+00,\n        -7.12109934e-01, -8.57584612e-01, -4.97150270e-01,\n        -7.88612251e-01],\n       [ 8.52828247e-01, -1.81147559e+00,  4.35217563e-01,\n        -2.45847477e-01, -4.26486971e-01,  1.20691619e-02,\n        -6.20567490e-02],\n       [ 7.40614004e-01,  7.30988383e-01,  4.58844010e-01,\n         3.39099968e-02, -1.72900123e-01, -4.24900630e-02,\n        -1.58135680e-01],\n       [-4.48856972e-02,  3.70292802e-01, -1.18316337e-01,\n         3.13667471e-01, -1.22182754e-01,  9.39079991e-02,\n        -2.59515518e-01],\n       [ 2.87268462e+00,  3.09750329e+00,  2.29158125e+00,\n         2.08546481e+00,  1.12039280e+00,  1.58519348e+00,\n         1.98674363e+00],\n       [ 7.63056853e-01,  8.98139994e-01,  1.56591181e+00,\n         1.24619238e+00,  7.14653843e-01,  9.75948801e-01,\n        -4.81418694e-02],\n       [-1.18947098e+00, -1.41559019e+00, -1.89705027e+00,\n        -1.73788734e+00, -6.54715134e-01, -4.94372709e+00,\n        -4.77183992e-01],\n       [-4.48856972e-01, -5.19849507e-02,  3.37164740e+00,\n        -4.32352460e-01, -5.53280395e-01, -5.88082311e-01,\n        -7.12411720e-01],\n       [ 3.81528426e-01,  4.14280068e-01,  8.23366334e-01,\n         1.52594986e+00,  8.66805952e-01,  4.84915777e-01,\n         1.01468989e+00],\n       [-1.09969958e+00, -1.04609716e+00, -1.17138083e+00,\n        -4.32352460e-01, -7.56149873e-01, -3.97125024e-01,\n        -6.30247669e-01],\n       [-1.05481388e+00, -1.22204622e+00, -6.34722963e-01,\n        -7.12109934e-01, -6.54715134e-01, -3.42565800e-01,\n        -6.28922442e-01],\n       [-1.63832795e+00, -1.64432398e+00, -1.83967176e+00,\n        -1.36487737e+00, -1.11117146e+00, -5.51709495e-01,\n        -5.15946871e-01],\n       [-1.36901376e+00, -1.04609716e+00, -1.30301389e+00,\n        -1.17837239e+00, -9.33660666e-01, -6.97200761e-01,\n        -8.19092464e-01],\n       [-1.39145661e+00, -1.27483094e+00, -1.06337421e+00,\n        -8.98614916e-01, -9.33660666e-01, -6.69921149e-01,\n        -7.79666972e-01],\n       [ 3.81528426e-01,  4.40672428e-01,  4.45343183e-01,\n        -5.93424945e-02, -1.22182754e-01,  4.66729369e-01,\n         2.96417056e-01],\n       [ 4.93742669e-01,  8.45355275e-01,  8.36867160e-01,\n         1.89895982e+00,  1.17111017e+00,  1.07597405e+00,\n         1.40960743e+00],\n       [-3.81528426e-01, -4.56667797e-01, -7.12352717e-01,\n        -6.18857442e-01, -6.54715134e-01, -3.42565800e-01,\n        -5.59348044e-01],\n       [ 7.40614004e-01,  6.16621492e-01, -3.55930889e-03,\n         2.20414979e-01, -1.98258808e-01,  5.66754614e-01,\n         4.98514117e-01],\n       [ 5.16185518e-01,  5.81431679e-01,  5.83726658e-01,\n         1.33944488e+00,  5.37143050e-01,  6.03127431e-01,\n         9.28550160e-01],\n       [-4.26414124e-01, -6.07824039e-02, -1.25066750e-01,\n        -2.45847477e-01, -7.30791188e-01, -4.24404637e-01,\n        -7.90931398e-01],\n       [-3.81528426e-01, -7.99768472e-03,  4.35217563e-01,\n         2.20414979e-01, -3.75769601e-01,  1.20691619e-02,\n        -4.23181007e-01],\n       [-7.40614004e-01, -5.09452517e-01, -5.40217175e-01,\n        -1.08511990e+00, -1.06045409e+00, -6.60827944e-01,\n        -7.10092573e-01],\n       [ 2.46871335e-01,  3.70292802e-01,  4.03183783e-02,\n         1.27162488e-01,  2.41368572e+00, -1.42515309e-01,\n        -7.53825052e-01],\n       [ 2.46871335e-01,  2.64723364e-01, -3.07327912e-01,\n        -7.12109934e-01,  1.90651203e+00, -1.42515309e-01,\n         2.58316790e-01],\n       [ 7.63056853e-01,  8.01368009e-01,  7.05234099e-01,\n         6.86677436e-01,  5.53280395e-02,  2.39399265e-01,\n        -2.85026130e-01],\n       [ 1.41389946e+00,  1.93623947e+00, -8.13608918e-01,\n        -9.91867408e-01, -3.75769601e-01, -6.11724642e-03,\n         1.96031138e-01],\n       [ 9.20156793e-01,  1.21484831e+00,  8.40242367e-01,\n        -5.93424945e-02, -4.61066996e-02,  2.57585674e-01,\n         4.72744483e-02],\n       [ 1.36901376e+00,  5.55039319e-01,  4.99346490e-01,\n         1.05968740e+00,  7.40012528e-01,  1.00322841e+00,\n         2.95423136e-01],\n       [ 3.14199880e-01,  1.32041775e+00,  1.51528371e+00,\n         1.80570733e+00,  9.93599375e-01,  1.40332940e+00,\n         2.91142051e+00],\n       [ 6.05956912e-01,  3.59895812e-02,  5.04439984e-02,\n        -3.39099968e-01, -3.75769601e-01,  2.12119653e-01,\n        -4.87785805e-01],\n       [-8.30385398e-01, -6.76604127e-01, -4.15334527e-01,\n        -8.05362425e-01,  1.67828386e+00, -3.15286187e-01,\n        -6.88226334e-01],\n       [-1.57099940e-01, -3.95085625e-01, -6.78600650e-01,\n        -5.25604951e-01,  1.98258808e+00, -5.42616291e-01,\n        -9.20140995e-01],\n       [-8.97713944e-02, -2.36731468e-01, -1.65569231e-01,\n        -4.32352460e-01, -9.84378036e-01, -7.51759986e-01,\n        -9.20140995e-01],\n       [ 1.41389946e+00,  1.27643048e+00,  1.13051014e+00,\n         1.52594986e+00,  1.12039280e+00,  1.21237211e+00,\n         1.97945488e+00],\n       [ 3.14199880e-01, -3.43900443e-02,  3.33961362e-01,\n         1.05968740e+00,  5.11784365e-01,  7.30432289e-01,\n         8.98401254e-01],\n       [-1.09969958e+00, -1.20445132e+00, -1.45827340e+00,\n        -1.17837239e+00, -1.03509541e+00, -3.51659004e-01,\n        -4.11916581e-01],\n       [ 4.26414124e-01,  5.63836773e-01,  2.32705160e-01,\n         1.27162488e-01, -6.03997764e-01, -4.69870658e-01,\n        -7.30633586e-01],\n       [-4.03971275e-01, -1.04769670e-01, -8.13608918e-01,\n        -1.45812986e+00, -1.11117146e+00, -7.51759986e-01,\n        -2.58521598e-01],\n       [ 1.52611371e+00,  1.25003812e+00,  4.95971283e-01,\n         4.06919962e-01,  3.08914887e-01,  5.30381798e-01,\n         3.15301535e-01],\n       [ 4.03971275e-01,  3.52697896e-01, -6.93451559e-03,\n        -2.45847477e-01, -6.54715134e-01, -3.06192983e-01,\n        -3.52943995e-01],\n       [-1.03237104e+00, -6.67806674e-01, -6.14471722e-01,\n        -5.25604951e-01, -7.05432503e-01, -4.69870658e-01,\n        -6.21965002e-01],\n       [-3.81528426e-01, -2.36731468e-01, -1.68944437e-01,\n        -5.25604951e-01, -8.32225927e-01, -5.33523087e-01,\n        -6.57083508e-01],\n       [-8.97713944e-01, -8.43755738e-01, -3.74832046e-01,\n         2.20414979e-01, -4.61066996e-02,  2.48492469e-01,\n         1.52961272e-01],\n       [ 2.91757032e-01,  7.74975649e-01,  7.38986166e-01,\n         1.15293989e+00,  8.16088582e-01,  8.39550739e-01,\n        -1.59129600e-01],\n       [ 8.07942550e-01,  7.57380743e-01,  9.58374602e-01,\n         6.86677436e-01, -7.14653843e-02,  2.11623660e-02,\n         9.21924027e-01],\n       [-1.86275643e+00, -1.53875454e+00, -1.00937091e+00,\n        -1.08511990e+00, -1.13653014e+00, -7.79039598e-01,\n        -1.01158163e+00],\n       [-1.25679952e+00, -1.22204622e+00, -1.49202547e+00,\n        -1.73788734e+00, -1.33939962e+00, -8.24505619e-01,\n        -7.29970973e-01],\n       [ 2.51359904e+00,  1.99782164e+00,  1.72792173e+00,\n         2.36522228e+00,  3.58018522e+00,  3.34927508e+00,\n         4.39799349e+00]])"},"metadata":{},"execution_count":7}],"execution_count":7},{"cell_type":"code","source":"!pip install factor_analyzer","metadata":{"id":"RTH3D02l_67g","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"41ba8f89890c4ed391f6640bb269f1f0","outputId":"690098fb-17e6-4a1a-ae77-4c07c2673da6","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5849,"user_tz":240,"timestamp":1650844494790},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Collecting factor_analyzer\n  Downloading factor_analyzer-0.4.0.tar.gz (41 kB)\n\u001b[?25l\r\u001b[K     |███████▉                        | 10 kB 19.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 20 kB 25.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▋        | 30 kB 14.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 40 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 41 kB 486 kB/s \n\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.3.5)\nRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.4.1)\nRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.21.6)\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from factor_analyzer) (1.0.2)\nRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->factor_analyzer) (2.8.2)\nRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->factor_analyzer) (2022.1)\nRequirement 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)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->factor_analyzer) (3.1.0)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->factor_analyzer) (1.1.0)\nBuilding 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-0.4.0-py3-none-any.whl size=41455 sha256=3d1d93c5107dbc5248504614853c4452dfb77e7b27e85ae0941bc08f1d68247e\n  Stored in directory: /root/.cache/pip/wheels/ac/00/37/1f0e8a5039f9e9f207c4405bbce0796f07701eb377bfc6cc76\nSuccessfully built factor-analyzer\nInstalling collected packages: factor-analyzer\nSuccessfully installed factor-analyzer-0.4.0\n"}],"execution_count":8},{"cell_type":"markdown","source":"Realicemos FA nuevamente con nfactors = 2 (anteriormente, nfactors = 4). Esto se debe a que hemos decidido mantener solo dos factores para nuestros datos","metadata":{"id":"YSR-h9tzAgnA","cell_id":"138f0040fa2b4e83a2ffb5275927e23d","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from factor_analyzer import FactorAnalyzer\nfa = FactorAnalyzer(n_factors=2, rotation=\"varimax\", method=\"principal\", \n                    is_corr_matrix=False)\nfa.fit(X_scaled)","metadata":{"id":"1qxo5ZOn_3Fn","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"00af22449965440ebdb6b8cf9b8c5516","outputId":"05a928f5-7030-4746-c31d-72323a859dde","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":411,"user_tz":240,"timestamp":1650844497870},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/utils/extmath.py:376: FutureWarning: If 'random_state' is not supplied, the current default is to use 0 as a fixed seed. This will change to  None in version 1.2 leading to non-deterministic results that better reflect nature of the randomized_svd solver. If you want to silence this warning, set 'random_state' to an integer seed or to None explicitly depending if you want your code to be deterministic or not.\n  FutureWarning,\n"},{"output_type":"execute_result","data":{"text/plain":"FactorAnalyzer(method='principal', n_factors=2, rotation='varimax',\n               rotation_kwargs={})"},"metadata":{},"execution_count":9}],"execution_count":9},{"cell_type":"code","source":"print(\"Valores propios:\")\nprint(fa.get_eigenvalues()[0])\nprint()\nprint(\"Communalities:\")\nprint(fa.get_communalities())\nprint()\nprint(\"Varianzas especificas:\")\nprint(fa.get_uniquenesses())\nprint()\nprint(\"Cargas de los factores:\")\nprint(fa.loadings_)","metadata":{"id":"HewcX635ABP2","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"f09d59cbcc18435e8ffde0d37d840bbc","outputId":"89e4a613-e560-4d77-88c7-b4d943e344c9","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":432,"user_tz":240,"timestamp":1650844532070},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Valores propios:\n[5.06759677 0.6020256  0.44429295 0.36590389 0.26931274 0.13929091\n 0.11157713]\n\nCommunalities:\n[0.8632044  0.86854473 0.77623794 0.84827979 0.79619776 0.73826138\n 0.77889637]\n\nVarianzas especificas:\n[0.1367956  0.13145527 0.22376206 0.15172021 0.20380224 0.26173862\n 0.22110363]\n\nCargas de los factores:\n[[0.8399412  0.3971186 ]\n [0.86109019 0.35646657]\n [0.81415209 0.33674071]\n [0.61543129 0.6852183 ]\n [0.22614824 0.86316553]\n [0.48965453 0.7060452 ]\n [0.46668107 0.74906952]]\n"}],"execution_count":10},{"cell_type":"markdown","source":"**Comunalidades**\n\n- X1: Alrededor del 86% de la variabilidad de X1 se explica por los dos factores que seleccionamos.\n- X2: Alrededor del 87% de la variabilidad de X2 se explica por los dos factores que seleccionamos.\n- X3: Alrededor del 78% de la variabilidad de X3 se explica por los dos factores que seleccionamos.\n- X4: Alrededor del 85% de la variabilidad de X4 se explica por los dos factores que seleccionamos.\n- X5: Alrededor del 80% de la variabilidad de X5 se explica por los dos factores que seleccionamos.\n- X6: Alrededor del 74% de la variabilidad de X6 se explica por los dos factores que seleccionamos.\n- X7: Alrededor del 78% de la variabilidad de X7 se explica por los dos factores que seleccionamos.\n\n**variaciones específicas**\n\n- El efecto del factor específico sobre X1 es de alrededor del 14%.\n- El efecto del factor específico sobre X2 es de alrededor del 13%.\n- El efecto del factor específico sobre X3 es de alrededor del 22%.\n- El efecto del factor específico sobre X4 es de alrededor del 15%.\n- El efecto del factor específico sobre X5 es de alrededor del 20%.\n- El efecto del factor específico sobre X6 es de alrededor del 26%.\n- El efecto del factor específico en X7 es de alrededor del 22%","metadata":{"id":"eLBTaoe3A3gm","cell_id":"edbfe5f7834449028f1c22b5b8d77944","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.style.use(\"ggplot\")\n\nplt.plot(fa.get_eigenvalues()[0], marker='o')\nplt.xlabel(\"Eigenvalue \")\nplt.ylabel(\"Eigenvalue tamaño\")\nplt.title(\"Scree Plot\")","metadata":{"id":"tDapTdXZAKDs","colab":{"height":316,"base_uri":"https://localhost:8080/"},"cell_id":"f72199ecd755473199e9f60d4b2359b8","outputId":"fd36df8b-8ed1-4628-b04c-260733aba596","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":973,"user_tz":240,"timestamp":1650844553712},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Scree Plot')"},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":11},{"cell_type":"markdown","source":"Está claro que las **variables X1, X2, X3 definen el factor 1** (cargas altas en el factor 1, cargas relativamente pequeñas en el factor 2) mientras que las **variables X4, X5, X6 y X7 definen el factor 2** (cargas altas en el factor 2, cargas relativamente pequeñas en el factor 2). factor 1). Pero la variable X4 tiene aspectos de atributos representados por ambos factores (cargas aproximadamente iguales en ambos factores).\n\nPara dar nombres a los dos factores, centrémonos en el dominio del conocimiento del campo.\n\nEl conjunto de datos proporcionado representa los registros nacionales de mujeres que representan a 55 países en siete eventos diferentes. Generalmente, en carreras de corta distancia (por ejemplo, 100 m, 200 m, 400 m), los atletas deben centrarse principalmente en la velocidad. En carreras de larga distancia (por ejemplo, 1500 m, 3000 m, maratón), los atletas deben centrarse principalmente en la tolerancia o la resistencia. En nuestro análisis, el **factor 1 representa los antecedentes de corta distancia (ya que X1, X2 y X3)** y el **factor 2 representa los antecedentes de larga distancia (ya que X4, X5, X6 y X7)**. Por lo tanto, podemos dar nombres relevantes para los dos factores de la siguiente manera.\n\n- Factor 1 → factor de velocidad\n- Factor 2 → tolerancia o factor de resistencia","metadata":{"id":"vpdcp6LOBXwh","cell_id":"8b7edf59c7934460a3a353451fdf50a0","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"transformed_df = pd.DataFrame(fa.transform(X_scaled), columns=['RF1', 'RF2'])\ntransformed_df","metadata":{"id":"B-8PjoYOAWOL","colab":{"height":1000,"base_uri":"https://localhost:8080/"},"cell_id":"5fadd406a70b4fed80eaf2afd2bbd6e8","outputId":"96f15348-a32d-4b90-f8bd-fc2ecbd3a376","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":277,"user_tz":240,"timestamp":1650844600934},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"         RF1       RF2\n0  -0.213747  0.492749\n1  -0.864122 -0.351732\n2  -0.645847 -0.195162\n3  -0.335540 -0.573830\n4  -0.628536  0.822508\n5  -0.719778  0.603115\n6   1.014360 -0.016134\n7  -1.178504 -0.320728\n8  -0.129174 -0.167418\n9   1.007324 -0.676723\n10  0.200318 -0.146080\n11  3.068299  0.444342\n12  1.177901  0.243695\n13 -1.407983 -1.528352\n14  1.721239 -1.727718\n15  0.262662  1.080618\n16 -1.123549 -0.150988\n17 -0.999261 -0.227916\n18 -1.856963 -0.146468\n19 -1.205515 -0.520131\n20 -1.213444 -0.427182\n21  0.565193 -0.147662\n22  0.309477  1.578275\n23 -0.363157 -0.522204\n24  0.609653 -0.034210\n25  0.430094  0.805991\n26  0.173307 -0.864563\n27  0.257576 -0.397847\n28 -0.263473 -1.020930\n29 -0.609446  1.162281\n30 -0.777327  1.148980\n31  1.087221 -0.419528\n32  1.304007 -1.043131\n33  1.392007 -0.704095\n34  0.712123  0.605590\n35  0.618732  1.868953\n36  0.509618 -0.612468\n37 -1.532721  1.070597\n38 -1.285372  1.013866\n39  0.365952 -1.256078\n40  0.986941  1.328728\n41 -0.087738  1.039127\n42 -1.278473 -0.287339\n43  0.984739 -1.144108\n44 -0.088259 -1.106852\n45  1.322910 -0.213729\n46  0.699542 -0.931063\n47 -0.666742 -0.425431\n48  0.109772 -0.929756\n49 -1.006417  0.695615\n50  0.500099  0.559547\n51  1.110912 -0.166870\n52 -1.406942 -0.571562\n53 -1.178561 -0.856376\n54  0.564617  4.267786","text/html":"\n  <div id=\"df-e9a1f416-7119-43b7-b5fd-c875b5b55ef5\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RF1</th>\n      <th>RF2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.213747</td>\n      <td>0.492749</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.864122</td>\n      <td>-0.351732</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.645847</td>\n      <td>-0.195162</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.335540</td>\n      <td>-0.573830</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.628536</td>\n      <td>0.822508</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-0.719778</td>\n      <td>0.603115</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1.014360</td>\n      <td>-0.016134</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-1.178504</td>\n      <td>-0.320728</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-0.129174</td>\n      <td>-0.167418</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1.007324</td>\n      <td>-0.676723</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.200318</td>\n      <td>-0.146080</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3.068299</td>\n      <td>0.444342</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1.177901</td>\n      <td>0.243695</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>-1.407983</td>\n      <td>-1.528352</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1.721239</td>\n      <td>-1.727718</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0.262662</td>\n      <td>1.080618</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>-1.123549</td>\n      <td>-0.150988</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>-0.999261</td>\n      <td>-0.227916</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>-1.856963</td>\n      <td>-0.146468</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>-1.205515</td>\n      <td>-0.520131</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>-1.213444</td>\n      <td>-0.427182</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>0.565193</td>\n      <td>-0.147662</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>0.309477</td>\n      <td>1.578275</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>-0.363157</td>\n      <td>-0.522204</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.609653</td>\n      <td>-0.034210</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>0.430094</td>\n      <td>0.805991</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>0.173307</td>\n      <td>-0.864563</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>0.257576</td>\n      <td>-0.397847</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>-0.263473</td>\n      <td>-1.020930</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>-0.609446</td>\n      <td>1.162281</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>-0.777327</td>\n      <td>1.148980</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1.087221</td>\n      <td>-0.419528</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1.304007</td>\n      <td>-1.043131</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>1.392007</td>\n      <td>-0.704095</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>0.712123</td>\n      <td>0.605590</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>0.618732</td>\n      <td>1.868953</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>0.509618</td>\n      <td>-0.612468</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>-1.532721</td>\n      <td>1.070597</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>-1.285372</td>\n      <td>1.013866</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>0.365952</td>\n      <td>-1.256078</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>0.986941</td>\n      <td>1.328728</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>-0.087738</td>\n      <td>1.039127</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>-1.278473</td>\n      <td>-0.287339</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>0.984739</td>\n      <td>-1.144108</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>-0.088259</td>\n      <td>-1.106852</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>1.322910</td>\n      <td>-0.213729</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>0.699542</td>\n      <td>-0.931063</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>-0.666742</td>\n      <td>-0.425431</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>0.109772</td>\n      <td>-0.929756</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>-1.006417</td>\n      <td>0.695615</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>0.500099</td>\n      <td>0.559547</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>1.110912</td>\n      <td>-0.166870</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>-1.406942</td>\n      <td>-0.571562</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>-1.178561</td>\n      <td>-0.856376</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>0.564617</td>\n      <td>4.267786</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9a1f416-7119-43b7-b5fd-c875b5b55ef5')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-e9a1f416-7119-43b7-b5fd-c875b5b55ef5 button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-e9a1f416-7119-43b7-b5fd-c875b5b55ef5');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\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            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":12}],"execution_count":12},{"cell_type":"markdown","source":"# Analisis Discriminante","metadata":{"id":"cPpT8EN7HRtJ","cell_id":"b1e4536eebec4c7b86311508b5f1c404","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.datasets import load_iris\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n\niris = load_iris()\nX = iris.data\ny = iris.target\nX","metadata":{"id":"xx8r0DV2HTZR","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"55d02deefff943efa1351c498bb0c362","outputId":"5f622459-9ad5-412d-f0a4-2390d85594c9","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":859,"user_tz":240,"timestamp":1650846423202},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n       [5. , 3.6, 1.4, 0.2],\n       [5.4, 3.9, 1.7, 0.4],\n       [4.6, 3.4, 1.4, 0.3],\n       [5. , 3.4, 1.5, 0.2],\n       [4.4, 2.9, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.1],\n       [5.4, 3.7, 1.5, 0.2],\n       [4.8, 3.4, 1.6, 0.2],\n       [4.8, 3. , 1.4, 0.1],\n       [4.3, 3. , 1.1, 0.1],\n       [5.8, 4. , 1.2, 0.2],\n       [5.7, 4.4, 1.5, 0.4],\n       [5.4, 3.9, 1.3, 0.4],\n       [5.1, 3.5, 1.4, 0.3],\n       [5.7, 3.8, 1.7, 0.3],\n       [5.1, 3.8, 1.5, 0.3],\n       [5.4, 3.4, 1.7, 0.2],\n       [5.1, 3.7, 1.5, 0.4],\n       [4.6, 3.6, 1. , 0.2],\n       [5.1, 3.3, 1.7, 0.5],\n       [4.8, 3.4, 1.9, 0.2],\n       [5. , 3. , 1.6, 0.2],\n       [5. , 3.4, 1.6, 0.4],\n       [5.2, 3.5, 1.5, 0.2],\n       [5.2, 3.4, 1.4, 0.2],\n       [4.7, 3.2, 1.6, 0.2],\n       [4.8, 3.1, 1.6, 0.2],\n       [5.4, 3.4, 1.5, 0.4],\n       [5.2, 4.1, 1.5, 0.1],\n       [5.5, 4.2, 1.4, 0.2],\n       [4.9, 3.1, 1.5, 0.2],\n       [5. , 3.2, 1.2, 0.2],\n       [5.5, 3.5, 1.3, 0.2],\n       [4.9, 3.6, 1.4, 0.1],\n       [4.4, 3. , 1.3, 0.2],\n       [5.1, 3.4, 1.5, 0.2],\n       [5. , 3.5, 1.3, 0.3],\n       [4.5, 2.3, 1.3, 0.3],\n       [4.4, 3.2, 1.3, 0.2],\n       [5. , 3.5, 1.6, 0.6],\n       [5.1, 3.8, 1.9, 0.4],\n       [4.8, 3. , 1.4, 0.3],\n       [5.1, 3.8, 1.6, 0.2],\n       [4.6, 3.2, 1.4, 0.2],\n       [5.3, 3.7, 1.5, 0.2],\n       [5. , 3.3, 1.4, 0.2],\n       [7. , 3.2, 4.7, 1.4],\n       [6.4, 3.2, 4.5, 1.5],\n       [6.9, 3.1, 4.9, 1.5],\n       [5.5, 2.3, 4. , 1.3],\n       [6.5, 2.8, 4.6, 1.5],\n       [5.7, 2.8, 4.5, 1.3],\n       [6.3, 3.3, 4.7, 1.6],\n       [4.9, 2.4, 3.3, 1. ],\n       [6.6, 2.9, 4.6, 1.3],\n       [5.2, 2.7, 3.9, 1.4],\n       [5. , 2. , 3.5, 1. ],\n       [5.9, 3. , 4.2, 1.5],\n       [6. , 2.2, 4. , 1. ],\n       [6.1, 2.9, 4.7, 1.4],\n       [5.6, 2.9, 3.6, 1.3],\n       [6.7, 3.1, 4.4, 1.4],\n       [5.6, 3. , 4.5, 1.5],\n       [5.8, 2.7, 4.1, 1. ],\n       [6.2, 2.2, 4.5, 1.5],\n       [5.6, 2.5, 3.9, 1.1],\n       [5.9, 3.2, 4.8, 1.8],\n       [6.1, 2.8, 4. , 1.3],\n       [6.3, 2.5, 4.9, 1.5],\n       [6.1, 2.8, 4.7, 1.2],\n       [6.4, 2.9, 4.3, 1.3],\n       [6.6, 3. , 4.4, 1.4],\n       [6.8, 2.8, 4.8, 1.4],\n       [6.7, 3. , 5. , 1.7],\n       [6. , 2.9, 4.5, 1.5],\n       [5.7, 2.6, 3.5, 1. ],\n       [5.5, 2.4, 3.8, 1.1],\n       [5.5, 2.4, 3.7, 1. ],\n       [5.8, 2.7, 3.9, 1.2],\n       [6. , 2.7, 5.1, 1.6],\n       [5.4, 3. , 4.5, 1.5],\n       [6. , 3.4, 4.5, 1.6],\n       [6.7, 3.1, 4.7, 1.5],\n       [6.3, 2.3, 4.4, 1.3],\n       [5.6, 3. , 4.1, 1.3],\n       [5.5, 2.5, 4. , 1.3],\n       [5.5, 2.6, 4.4, 1.2],\n       [6.1, 3. , 4.6, 1.4],\n       [5.8, 2.6, 4. , 1.2],\n       [5. , 2.3, 3.3, 1. ],\n       [5.6, 2.7, 4.2, 1.3],\n       [5.7, 3. , 4.2, 1.2],\n       [5.7, 2.9, 4.2, 1.3],\n       [6.2, 2.9, 4.3, 1.3],\n       [5.1, 2.5, 3. , 1.1],\n       [5.7, 2.8, 4.1, 1.3],\n       [6.3, 3.3, 6. , 2.5],\n       [5.8, 2.7, 5.1, 1.9],\n       [7.1, 3. , 5.9, 2.1],\n       [6.3, 2.9, 5.6, 1.8],\n       [6.5, 3. , 5.8, 2.2],\n       [7.6, 3. , 6.6, 2.1],\n       [4.9, 2.5, 4.5, 1.7],\n       [7.3, 2.9, 6.3, 1.8],\n       [6.7, 2.5, 5.8, 1.8],\n       [7.2, 3.6, 6.1, 2.5],\n       [6.5, 3.2, 5.1, 2. ],\n       [6.4, 2.7, 5.3, 1.9],\n       [6.8, 3. , 5.5, 2.1],\n       [5.7, 2.5, 5. , 2. ],\n       [5.8, 2.8, 5.1, 2.4],\n       [6.4, 3.2, 5.3, 2.3],\n       [6.5, 3. , 5.5, 1.8],\n       [7.7, 3.8, 6.7, 2.2],\n       [7.7, 2.6, 6.9, 2.3],\n       [6. , 2.2, 5. , 1.5],\n       [6.9, 3.2, 5.7, 2.3],\n       [5.6, 2.8, 4.9, 2. ],\n       [7.7, 2.8, 6.7, 2. ],\n       [6.3, 2.7, 4.9, 1.8],\n       [6.7, 3.3, 5.7, 2.1],\n       [7.2, 3.2, 6. , 1.8],\n       [6.2, 2.8, 4.8, 1.8],\n       [6.1, 3. , 4.9, 1.8],\n       [6.4, 2.8, 5.6, 2.1],\n       [7.2, 3. , 5.8, 1.6],\n       [7.4, 2.8, 6.1, 1.9],\n       [7.9, 3.8, 6.4, 2. ],\n       [6.4, 2.8, 5.6, 2.2],\n       [6.3, 2.8, 5.1, 1.5],\n       [6.1, 2.6, 5.6, 1.4],\n       [7.7, 3. , 6.1, 2.3],\n       [6.3, 3.4, 5.6, 2.4],\n       [6.4, 3.1, 5.5, 1.8],\n       [6. , 3. , 4.8, 1.8],\n       [6.9, 3.1, 5.4, 2.1],\n       [6.7, 3.1, 5.6, 2.4],\n       [6.9, 3.1, 5.1, 2.3],\n       [5.8, 2.7, 5.1, 1.9],\n       [6.8, 3.2, 5.9, 2.3],\n       [6.7, 3.3, 5.7, 2.5],\n       [6.7, 3. , 5.2, 2.3],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]])"},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"code","source":"y","metadata":{"id":"JRUVs84THVYB","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4cd9c0bb02964b89836c463509ef02f7","outputId":"2c47117f-ae31-445a-9f4d-0afc00690e4c","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1650846425438},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"},"metadata":{},"execution_count":14}],"execution_count":14},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_scaled = sc.fit_transform(X)\nX_scaled","metadata":{"id":"hCMOPXwOHWGV","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"64a226aa46e04fafb80748ab9d9840e6","outputId":"de232919-f644-499b-dcbe-fbf3e7a0b9fc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":288,"user_tz":240,"timestamp":1650846441899},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,\n        -1.31544430e+00],\n       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,\n        -1.31544430e+00],\n       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,\n        -1.31544430e+00],\n       [-1.50652052e+00,  9.82172869e-02, -1.28338910e+00,\n        -1.31544430e+00],\n       [-1.02184904e+00,  1.24920112e+00, -1.34022653e+00,\n        -1.31544430e+00],\n       [-5.37177559e-01,  1.93979142e+00, -1.16971425e+00,\n        -1.05217993e+00],\n       [-1.50652052e+00,  7.88807586e-01, -1.34022653e+00,\n        -1.18381211e+00],\n       [-1.02184904e+00,  7.88807586e-01, -1.28338910e+00,\n        -1.31544430e+00],\n       [-1.74885626e+00, -3.62176246e-01, -1.34022653e+00,\n        -1.31544430e+00],\n       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,\n        -1.44707648e+00],\n       [-5.37177559e-01,  1.47939788e+00, -1.28338910e+00,\n        -1.31544430e+00],\n       [-1.26418478e+00,  7.88807586e-01, -1.22655167e+00,\n        -1.31544430e+00],\n       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,\n        -1.44707648e+00],\n       [-1.87002413e+00, -1.31979479e-01, -1.51073881e+00,\n        -1.44707648e+00],\n       [-5.25060772e-02,  2.16998818e+00, -1.45390138e+00,\n        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9.82172869e-02,  7.62758269e-01,\n         1.44883158e+00],\n       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n         9.22302838e-01],\n       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,\n         1.44883158e+00],\n       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n         1.71209594e+00],\n       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,\n         1.44883158e+00],\n       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,\n         9.22302838e-01],\n       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,\n         1.05393502e+00],\n       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,\n         1.44883158e+00],\n       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,\n         7.90670654e-01]])"},"metadata":{},"execution_count":16}],"execution_count":16},{"cell_type":"code","source":"pca = PCA(n_components=2)\nX_pca = pca.fit_transform(X_scaled)\n\nlda = LinearDiscriminantAnalysis(n_components=2, solver='svd')\nX_lda = lda.fit_transform(X, y)","metadata":{"id":"wzIgxWyXHdaf","cell_id":"271a753cdedc40fba7cb2d38e0d970e9","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":413,"user_tz":240,"timestamp":1650846465990},"deepnote_cell_type":"code"},"outputs":[],"execution_count":17},{"cell_type":"code","source":"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(13.5 ,4))\nsns.scatterplot(X_pca[:,0], X_pca[:,1], hue=y, palette='Set1', ax=ax[0])\nsns.scatterplot(X_lda[:,0], X_lda[:,1], hue=y, palette='Set1', ax=ax[1])\nax[0].set_title(\"PCA de IRIS\", fontsize=15, pad=15)\nax[1].set_title(\"LDA de IRIS\", fontsize=15, pad=15)\nax[0].set_xlabel(\"PC1\", fontsize=12)\nax[0].set_ylabel(\"PC2\", fontsize=12)\nax[1].set_xlabel(\"LD1\", fontsize=12)\nax[1].set_ylabel(\"LD2\", fontsize=12)","metadata":{"id":"Fnhb1n4VHgUk","colab":{"height":416,"base_uri":"https://localhost:8080/"},"cell_id":"f8cd2996b43142fc9b322d407bcd6948","outputId":"54689ba9-2964-489e-d32f-f64789670664","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1317,"user_tz":240,"timestamp":1650846490659},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'LD2')"},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":"<Figure size 972x288 with 2 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\n"},"metadata":{}}],"execution_count":18},{"cell_type":"markdown","source":"# Kernel PCA","metadata":{"id":"m8XJowqJHqFQ","cell_id":"42fed6ea1fba47c88f4f1e02e7d81817","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_moons\n\n# Creamos los datos\nX, y = make_moons(n_samples = 500, random_state=42)\nsns.scatterplot(X[:, 0], X[:, 1], hue=y, palette='Set1')","metadata":{"id":"friE4r0nHrFE","colab":{"height":337,"base_uri":"https://localhost:8080/"},"cell_id":"3b34ae5a8ac4408ea8a529b3149f9fde","outputId":"7fad7052-0967-4fb0-ecbe-d2593baca8c9","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":977,"user_tz":240,"timestamp":1650846533320},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"<matplotlib.axes._subplots.AxesSubplot at 0x7f367c4450d0>"},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"Implementemos PCA y Kernel PCA y veamos las diferencias","metadata":{"id":"ywnUdFlrHyIi","cell_id":"184c2b6a27ee4185b3eb9fe474d6abe7","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.decomposition import PCA\nfrom sklearn.decomposition import KernelPCA\n\npca = PCA(n_components=2)\nX_pca = pca.fit_transform(X)\n\nkpca = KernelPCA(n_components=2, kernel='rbf', gamma=15, random_state=42)\nX_kpca = kpca.fit_transform(X)","metadata":{"id":"7AGxsNDUH0m-","cell_id":"e8a08a148fcd4c349190905a377f170e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":410,"user_tz":240,"timestamp":1650846563285},"deepnote_cell_type":"code"},"outputs":[],"execution_count":20},{"cell_type":"code","source":"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(13.5 ,4))\nsns.scatterplot(X_pca[:, 0], X_pca[:, 1], hue=y, palette='Set1', ax=ax[0])\nsns.scatterplot(X_kpca[:, 0], X_kpca[:, 1], hue=y, palette='Set1', ax=ax[1])\nax[0].set_title(\"PCA\", fontsize=15, pad=15)\nax[1].set_title(\"RBF Kernel PCA\", fontsize=15, pad=15)\nax[0].set_xlabel(\"Componente 1\", fontsize=12)\nax[0].set_ylabel(\"Componente 2\", fontsize=12)\nax[1].set_xlabel(\"Componente 1\", fontsize=12)\nax[1].set_ylabel(\"Componente 2\", fontsize=12)","metadata":{"id":"B6oQjTUWH4Ay","colab":{"height":416,"base_uri":"https://localhost:8080/"},"cell_id":"3472a74ab5c54cd5b19bdd68bf4ec3a1","outputId":"97c934f1-8ee4-41a0-bea8-b78b50ac46cb","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1646,"user_tz":240,"timestamp":1650846584497},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Componente 2')"},"metadata":{},"execution_count":21},{"output_type":"display_data","data":{"text/plain":"<Figure size 972x288 with 2 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\n"},"metadata":{}}],"execution_count":21},{"cell_type":"markdown","source":"El PCA normal no puede transformar datos no lineales en una forma lineal. Después de aplicar Kernel PCA a los mismos datos, las dos clases están linealmente bien separadas (ahora, las clases se pueden dividir dibujando una línea recta vertical).\n\nAquí, los datos originales tienen una dimensión de 2 y los datos trazados también tienen una dimensión de 2. Entonces, el **Kernel PCA realmente redujo la dimensionalidad de los datos?** La respuesta es 'Sí' porque la función kernel RBF proyecta temporalmente los datos bidimensionales en un nuevo espacio de características de dimensiones superiores donde las clases se vuelven linealmente separables y luego el algoritmo proyecta esos datos de dimensiones superiores nuevamente en los datos bidimensionales que se puede trazar en un gráfico 2D. \n\n**Una limitación del uso de Kernel PCA para la reducción de dimensionalidad es que tenemos que especificar un valor para el hiperparámetro gamma antes de ejecutar el algoritmo** y para esto e puede usar una técnica de ajuste de hiperparámetros como Grid Search para encontrar un valor óptimo para la gamma. ","metadata":{"id":"W16NmnOsICPA","cell_id":"75e148321bad4b7bb43f5dd629bb8a3c","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"# t-SNE","metadata":{"id":"gegbb8cmIaZN","cell_id":"1159cf4c446e4ba1ad2bd3e1dee98b45","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_scaled = sc.fit_transform(X)\npca = PCA()\nX_pca = pca.fit_transform(X_scaled)\ntsne = TSNE()\nX_tsne = tsne.fit_transform(X_pca)","metadata":{"id":"ShgTerZZIbhJ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"855f8ebe917d44cb896c8bbc603ae41a","outputId":"4e2b8415-a94d-4db8-de8e-ea381a69145b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":19906,"user_tz":240,"timestamp":1650846749895},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n  FutureWarning,\n"}],"execution_count":24},{"cell_type":"markdown","source":"Ahora creamos un pipeline","metadata":{"id":"Xf9hVXyUIkFQ","cell_id":"3e70d9580cdc465cab1a1913130f0695","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.pipeline import Pipeline\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\npca = PCA()\ntsne = TSNE()\ntsne_after_pca = Pipeline([\n    ('std_scaler', sc),\n    ('pca', pca),\n    ('tsne', tsne)\n])\nX_tsne = tsne_after_pca.fit_transform(X)","metadata":{"id":"W5cuhFbDIhch","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"9c9b7b378c8c4834b2575966160239c1","outputId":"ce5da38b-9717-415e-c3db-e14e576176ed","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7782,"user_tz":240,"timestamp":1650846777288},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n  FutureWarning,\n"}],"execution_count":26},{"cell_type":"markdown","source":"Ahora, aplicamos t-SNE al conjunto de datos Iris. Tiene solo 4 características. Por lo tanto, no necesitamos ejecutar PCA antes de t-SNE","metadata":{"id":"x0eXSntUIuFV","cell_id":"b34da54850814aedb773ce347aec36d4","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.datasets import load_iris\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.manifold import TSNE\nfrom sklearn.preprocessing import StandardScaler\nsns.set_style('darkgrid')\n\niris = load_iris()\nX = iris.data\ny = iris.target\n\nsc = StandardScaler()\nX_scaled = sc.fit_transform(X)\n\ntsne = TSNE(n_components=2, random_state=1)\nX_tsne = tsne.fit_transform(X_scaled)\n\nsns.scatterplot(X_tsne[:,0], X_tsne[:,1], hue=y, palette='Set1')\nplt.title(\"t-SNE de IRIS\", fontsize=15, pad=15)","metadata":{"id":"RVSBnkeuIxTi","colab":{"height":434,"base_uri":"https://localhost:8080/"},"cell_id":"42a76f10c4d14b03a7dd98582eb6882a","outputId":"85e1c9f9-96a6-454c-d05a-f6d8b74061ab","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":5955,"user_tz":240,"timestamp":1650846815092},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 't-SNE de IRIS')"},"metadata":{},"execution_count":27},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":27},{"cell_type":"markdown","source":"# Multidimensional Scaling","metadata":{"id":"LBLYzohZI3qS","cell_id":"5da679383a25426386138738db0ff956","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.datasets import load_iris\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.manifold import MDS\nsns.set_style('darkgrid')\n\niris = load_iris()\nX = iris.data\ny = iris.target","metadata":{"id":"cK_tHWEsI5Hn","cell_id":"000d0720c2cd4b0cae192c44cd9e9434","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1650846839444},"deepnote_cell_type":"code"},"outputs":[],"execution_count":28},{"cell_type":"code","source":"mds = MDS(n_components=2, metric=True, random_state=2)\nX_mds = mds.fit_transform(X)\n\nsns.scatterplot(X_mds[:,0], X_mds[:,1], hue=y, palette='Set1')\nplt.title(\"MDS de IRIS dataset\", fontsize=15, pad=15)","metadata":{"id":"2C2yFRgjI7Gl","colab":{"height":364,"base_uri":"https://localhost:8080/"},"cell_id":"4ead8f3ee6274e3ab9af46e28389a0c2","outputId":"46964a1a-a13a-4d65-cd16-df426e1ebf60","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2044,"user_tz":240,"timestamp":1650846854092},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'MDS de IRIS dataset')"},"metadata":{},"execution_count":30},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}],"execution_count":30},{"cell_type":"markdown","source":"# Isomap","metadata":{"id":"WIcLv39cI_qn","cell_id":"06722eda2a894c699c6ed57f99181d41","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.datasets import load_iris\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.manifold import Isomap\nsns.set_style('darkgrid')\n\niris = load_iris()\nX = iris.data\ny = iris.target","metadata":{"id":"0ENmMTsuJAqq","cell_id":"e454b518bc6b4c4084e0aaadb60083d3","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":428,"user_tz":240,"timestamp":1650846868263},"deepnote_cell_type":"code"},"outputs":[],"execution_count":31},{"cell_type":"code","source":"isomap = Isomap(n_neighbors=5, n_components=2, \n                eigen_solver='auto')\nX_isomap = isomap.fit_transform(X)\n\nsns.scatterplot(X_isomap[:,0], X_isomap[:,1], hue=y, palette='Set1')\nplt.title(\"Isomap de IRIS\", fontsize=15, pad=15)","metadata":{"id":"zkb-lkcSJCTM","colab":{"height":434,"base_uri":"https://localhost:8080/"},"cell_id":"4215319288384abab833a53172dd46e5","outputId":"c456ce95-194a-4934-c192-8741a19233e9","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1440,"user_tz":240,"timestamp":1650846883612},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_isomap.py:324: UserWarning: The number of connected components of the neighbors graph is 2 > 1. Completing the graph to fit Isomap might be slow. Increase the number of neighbors to avoid this issue.\n  self._fit_transform(X)\n/usr/local/lib/python3.7/dist-packages/scipy/sparse/_index.py:84: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_intXint(row, col, x.flat[0])\n/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"},{"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Isomap de IRIS')"},"metadata":{},"execution_count":32},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":32},{"cell_type":"markdown","source":"<a 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