RFE_Practice.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/AngelGonzs/ColabML/blob/main/RFE_Practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **We will be first working with the RFE Method in two different manners:**\n",
        "\n",
        "*   Random Forest Classifier\n",
        "*   RFE Base\n",
        "\n",
        "**We will commence with the Random Tree method**"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "Use the code below to get all of our imports"
      ],
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        "id": "l55XmRe2SDP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "78-RZAPESH2c"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "Scikit Learn, or sklearn, will do most of the heavy lifting, we just import the RFE from `sklearn.feature_selection` and pass any classifier model to the RFE() method with the number of features to select. \n",
        "\n",
        "Using familiar Scikit Learn syntax, the .fit() method must then be called.\n",
        "\n",
        "Below we will import our sklearn modules:"
      ],
      "metadata": {
        "id": "qevuYVqvK1Jo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "C0Uui648K239"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "RFE recursively removes the features and builds a model with the remaining features. With this we can calculate the model accuracy as well. \n",
        "\n",
        "It trains the model with all the features initially and then it will remove the features that seemingly impact our model the least. Once these features are removed it will train the model again and we can compare what has been done.\n",
        "\n"
      ],
      "metadata": {
        "id": "g1IGOWzyLJPs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will work with the Breast Cancer Data Set to train our model"
      ],
      "metadata": {
        "id": "MAupj81wMTIv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "data = load_breast_cancer()\n",
        "data.keys()\n",
        "#We print the keys of our data"
      ],
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          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We can also use the following method to get the description of our data and gain some insight\n",
        "print(data.DESCR)\n"
      ],
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          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will load the dataset into \n",
        "\n",
        "*  **X (vector)** - This will be all of our features and their values, we can see this with the following.\n",
        "\n",
        "> `data = data.data` is... well, our data\n",
        "> `columns = data.feature_names` here we extract the columns, which we refer to as our features and pass it to the **Pandas DataFrame** as our argument\n",
        "\n",
        "\n",
        "*  **Y (vector)** - will be simply our results, whether it be the possibility or a Y/N, the Y-Vector will contain what we have as results. We can see this as:\n",
        "\n",
        "> `data.target` this is quite intuitive, considering that the target will simply be what we aim for as the result.\n",
        "\n"
      ],
      "metadata": {
        "id": "phtd4CwiMwny"
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    {
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(data = data.data, columns = data.feature_names)\n",
        "X.head()\n",
        "# We have an X.shape of (569, 30)"
      ],
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = data.target\n",
        "print(Y)\n",
        "# We can see with the above print, that Y is a boolean, it dictates whether or not the patient has cancer"
      ],
      "metadata": {
        "id": "K0HiC1mUODNj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now be using the `train_test_split()` method to train four variables\n",
        "\n",
        "- `X_train` \n",
        "- `X_test` \n",
        "- `Y_train` \n",
        "- `Y_test` \n",
        "\n",
        "We will see how these differ, as of now we can say that we will have training and test data to later determine the \n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "As for the variables in our `train_test_split()` , we will be using the following:\n",
        "\n",
        "\n",
        "- **X and Y** will be our arrays, these serve as the allowed input for our training data\n",
        "\n",
        "- `test_size` - this will determine the portion of our data that will be used for testing. We will be using a float e.g. if we have `test_size = 0.2` then our test data will be 20% of our main data\n",
        "\n",
        "- `train_size` - this will represent the proportion of the dataset to include in the train split. If not specified (in our case it won't be) then it will be the remainder of our test split.\n",
        "\n",
        "   e.g. if our `test_size = 0.2` -> %20 then by logic `train_size = 1 - 0.2` -> 80% of our dataset\n",
        "\n",
        "- `random state` - Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "As for what this will be returning, it will be a train-test splitting of `2*length(arrays)`\n",
        "\n"
      ],
      "metadata": {
        "id": "mIfnosKdOVCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,   test_size = 0.2, random_state = 0)\n",
        "\n",
        "X_train.shape, X_test.shape\n",
        "\n",
        "# Considering that we had an initial X.shape of (569, 30)\n",
        "\n",
        "# Our splits should be in 80 to 20 percent ratio, and it seems to match very well.\n",
        "# 9:45"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgwrENDdQV0s",
        "outputId": "3a4cee20-a0c9-4e35-ec53-f2c958386e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((455, 30), (114, 30))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will start working our data in three sections:\n",
        "\n",
        "1. Feature Selection with select_from_model from sklearn library\n",
        "\n",
        "2. RFE with random forest classifier\n",
        "\n",
        "3. Gradient Boosting Algorithm to identify the feature set\n",
        "\n",
        "\n",
        "So let us start with the feature selection "
      ],
      "metadata": {
        "id": "l15a5bz5X4mY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature selection by feature importance of Randome Forest Classifier"
      ],
      "metadata": {
        "id": "bmmJiF6LYUAh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Param and description for `RandomForestClassifier`:\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "> `n_estimators` is the amount of trees that will be in the random forest\n",
        "\n",
        "> `random_state` Controls both the randomness of the bootstrapping of the samples used when building trees (`if bootstrap=True`) and the sampling of the features to consider when looking for the best split at each node (if `max_features < n_features`). \n",
        "\n",
        "> `n_jobs` is the number of jobs to run in parallel. fit, predict, decision_path and apply are all parallelized over the trees.\n",
        "\n",
        "\n",
        "\n",
        "*   `None` means only one job ran at a time\n",
        "*   `-1` indicates us that all the processors will be used\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Param and description for `.fit()`\n",
        "\n",
        "the fit method takes the training data as arguments, which can be **one array** in the case of **unsupervised learning**, or **two arrays** in the case of **supervised learning**.\n",
        "\n",
        "As we can see, in our case we will be using two arrays, with `X_train` being the training features and `Y_train` being what should be our results from the split method above.\n",
        "\n",
        "> `X` -\tarray-like of shape (n_samples, n_features)\n",
        "\n",
        "> `Y` - array-like of shape (n_samples)"
      ],
      "metadata": {
        "id": "-orUBdVZWCt6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selector = SelectFromModel(RandomForestClassifier(n_estimators = 100, random_state = 0 , n_jobs = -1))\n",
        "selector.fit(X_train, Y_train)\n",
        "\n",
        "selector.get_support()\n",
        "#tells us which features are going to be selected from our model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfrQJzeRYZA6",
        "outputId": "6893fd63-7231-4613-acc1-bb432bc9d783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False,  True,  True, False, False,  True,  True, False,\n",
              "       False, False, False, False,  True, False, False, False, False,\n",
              "       False, False,  True, False,  True,  True, False, False, False,\n",
              "        True, False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(selector.get_support())\n",
        "len(X_train.columns) #these are the features stored in X_train\n",
        "\n",
        "\n",
        "#these will be the same, at 30"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKr7jIfmYlSq",
        "outputId": "3d3c5493-638c-4bb4-f41e-884a6e971f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = X_train.columns[selector.get_support()]\n",
        "#with this, we will only select the features marked as True, which will be a total of 10\n",
        "len(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAjoimncYwuD",
        "outputId": "7f0fabc0-0ce4-4371-c20e-f890bf54d699"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(selector.estimator_.feature_importances_)\n",
        "#the mean iw what will be set as the threshold for which features we are going to select\n",
        "#if a feature has a value greater than the mean, it will be selected. We will see this in the next\n",
        "#code block"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2wuHlFrY9TO",
        "outputId": "2972d4c9-3c70-49ef-8363-f40e309d5d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selector.estimator_.feature_importances_\n",
        "#we can see and compare this with the output in block[10] and note\n",
        "#that all the features selected are those greater than our mean 0.0333"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16zX18OjZKbJ",
        "outputId": "4a41bb31-1910-4252-f4f3-0f44674a5924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.03699612, 0.01561296, 0.06016409, 0.0371452 , 0.0063401 ,\n",
              "       0.00965994, 0.0798662 , 0.08669071, 0.00474992, 0.00417092,\n",
              "       0.02407355, 0.00548033, 0.01254423, 0.03880038, 0.00379521,\n",
              "       0.00435162, 0.00452503, 0.00556905, 0.00610635, 0.00528878,\n",
              "       0.09556258, 0.01859305, 0.17205401, 0.05065305, 0.00943096,\n",
              "       0.01565491, 0.02443166, 0.14202709, 0.00964898, 0.01001304])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Now we will be get the training datasets for our first section\n",
        "\n",
        "It is also important to point out the functionality of the `transform()` method\n",
        "\n",
        "This method takes `(X)` ,an array of shape [n_samples, n_features] as it's parameter and will reduce it to the selected features. It will return `X` with only the selected features"
      ],
      "metadata": {
        "id": "imTcwblFZkj1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rfc = selector.transform(X_train)\n",
        "X_test_rfc = selector.transform(X_test)\n",
        "\n",
        "#make sure to transform from respective X value i.e. X_train_rfc -> X_train\n",
        "#If these values don't add up with the Y, we will have an error\n",
        "\n",
        "# That is\n",
        "# X.shape[0] must == Y.shape[0]"
      ],
      "metadata": {
        "id": "jRlATl9oZpVk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will create our **RandomForest** algorithm"
      ],
      "metadata": {
        "id": "a0tCVAFmadl_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def run_randomForest(X_train, X_test, Y_train, Y_test):\n",
        "\n",
        "    #clean fill method\n",
        "    clf = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=-1)\n",
        "\n",
        "    clf.fit(X_train, Y_train)\n",
        "\n",
        "    Y_pred = clf.predict(X_test)\n",
        "    print('Accuracy: ', accuracy_score(Y_test, Y_pred))"
      ],
      "metadata": {
        "id": "Sy8Nxak1ajYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "run_randomForest(X_train_rfc , X_test_rfc,  Y_train , Y_test)\n",
        "#Accuracy on new DataSet with reduced features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8771NBQobNQ4",
        "outputId": "81344b60-2c00-4e58-bbe2-fb68e08b83df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9473684210526315\n",
            "CPU times: user 321 ms, sys: 12.6 ms, total: 334 ms\n",
            "Wall time: 490 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "run_randomForest(X_train , X_test,  Y_train , Y_test)\n",
        "#Accuracy on original dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87bZr4njdVx6",
        "outputId": "5962c6bd-b681-4f02-da23-b66c546a14b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9649122807017544\n",
            "CPU times: user 378 ms, sys: 10.3 ms, total: 388 ms\n",
            "Wall time: 502 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###RFE\n",
        "19:30"
      ],
      "metadata": {
        "id": "9XjlRWWDfOoV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8McRhvHgfYHz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}