Random_Forest_Intro.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random_Forest_Intro.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNOfGBtsIE4OAJTy6LBQYYt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tawfiqam/MI564/blob/main/Random_Forest_Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsvmNXcxjAWP"
      },
      "source": [
        "#Random Forest Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYrT-XrpjJQM"
      },
      "source": [
        "Frome our earlier example fosugin on Decision Trees, we identified a tree as a flowchart-like tree structure where an internal node represents feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. \n",
        "\n",
        "A random forest classifier uses a number of trees, thus forming a forest!! The thicker the forest (the more the trees), the more robust the random forest classifier. Random forests create decision trees aon randomly selected data sampels, get predictions from all the trees, and determing the solution through voting. That is why random forest are under the heading of ensemble models in SKlearn. They are an ensemble of decision trees, with the final decision being the vote from all the decision trees.\n",
        "\n",
        "Random forests is a supervised learning algorithm. It can be used both for classification and regression.   \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGlqIgxZ4BZU"
      },
      "source": [
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxVNXiaDqlyK"
      },
      "source": [
        "##Example: Where is your wine from?!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PR9uewIlqp_y"
      },
      "source": [
        "We will use the Wine dataset from sklearn.\n",
        "\n",
        "The details of the dataset can be found here: https://archive.ics.uci.edu/ml/datasets/wine"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uz7gN5ewrMuK"
      },
      "source": [
        "These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.\n",
        "\n",
        "I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPtJ12GPbZu8"
      },
      "source": [
        "import sklearn\n",
        "import numpy as np\n",
        "\n",
        "#getting the wine quality dataset\n",
        "from sklearn.datasets import load_wine\n",
        "dataset = load_wine()\n",
        "\n",
        "from  sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.30, random_state = 45)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfHQGxv2rwIl"
      },
      "source": [
        "The **features (attributes)** are: \n",
        "\n",
        "1. Alcohol level\n",
        "2. Malic acid\n",
        "3. Ash\n",
        "4. Alcalinity of ash\n",
        "5. Magnesium\n",
        "6. Total phenols\n",
        "7. Flavanoids\n",
        "8. Nonflavanoid phenols\n",
        "9. Proanthocyanins\n",
        "10. Color intensity\n",
        "11. Hue\n",
        "12. OD280/OD315 of diluted wines\n",
        "13. Proline \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_pC2K8EkVJv",
        "outputId": "1dc50f49-4629-4d06-9393-7b51a29b66ca"
      },
      "source": [
        "dataset.feature_names"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['alcohol',\n",
              " 'malic_acid',\n",
              " 'ash',\n",
              " 'alcalinity_of_ash',\n",
              " 'magnesium',\n",
              " 'total_phenols',\n",
              " 'flavanoids',\n",
              " 'nonflavanoid_phenols',\n",
              " 'proanthocyanins',\n",
              " 'color_intensity',\n",
              " 'hue',\n",
              " 'od280/od315_of_diluted_wines',\n",
              " 'proline']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VVZMyR-ryOd"
      },
      "source": [
        "\n",
        "There are three **classes** for the different areas in Italy where the wine is cultivated: \n",
        "Class0, Class1, Class 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzuOaaqbkjjA",
        "outputId": "80f85cdc-86aa-4c66-93df-342c65d68b17"
      },
      "source": [
        "dataset.target_names"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONNHPLfSr280"
      },
      "source": [
        "Note that this is not a binary classifier because there is more than just two choices when it comes to the predicted classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdFiXxlRr04t"
      },
      "source": [
        "Next, we build the classifier..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEU0PIL_0q1z"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Create the model with 100 trees\n",
        "classifier = RandomForestClassifier(n_estimators=100, \n",
        "                               bootstrap = True,\n",
        "                               max_features = 'sqrt')\n",
        "# Fit on training data\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ODKwVpQr__9"
      },
      "source": [
        "Let's take a look at the confusion matrix for three classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtB6lsJlbqXV",
        "outputId": "29e3d960-878a-4379-b9a2-b9976ffbe9db"
      },
      "source": [
        "#import the metrics class\n",
        "from sklearn import metrics\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16,  0,  0],\n",
              "       [ 0, 18,  0],\n",
              "       [ 0,  0, 20]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        },
        "id": "mniNAif5b4Vw",
        "outputId": "71785e25-7c83-4332-a654-b30f665d6c11"
      },
      "source": [
        "# import required modules\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "#this is a different plotting package\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size':20,'figure.figsize':(15,12)})\n",
        "\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 764.32, 'Predicted label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvQoUXP0sJJv"
      },
      "source": [
        "The metrics are a little different here. Notice that instead of the average saying 'binar' as it did in the other two examples (binary classifiers) is set to macro. This will count the true positives, true negatives, false positives, and false negatives across all three classifiers. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Stkc6gQAcBci",
        "outputId": "9d4ecacf-8107-4378-ea88-37988c9add22"
      },
      "source": [
        "print('{:<15s}{:<20s}'.format('Accuracy:',str(metrics.accuracy_score(y_test,y_pred))))\n",
        "print('{:<15s}{:<20s}'.format('Precision:',str(metrics.precision_score(y_test,y_pred,average='macro'))))\n",
        "print('{:<15s}{:<20s}'.format('Recall:',str(metrics.recall_score(y_test,y_pred, average='macro'))))\n",
        "print('{:<15s}{:<20s}'.format('f1 score:',str(metrics.f1_score(y_test,y_pred, average='macro'))))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:      1.0                 \n",
            "Precision:     1.0                 \n",
            "Recall:        1.0                 \n",
            "f1 score:      1.0                 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joucQTvVserm"
      },
      "source": [
        "##Seeing the most important predictors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNiR4cePsi-6"
      },
      "source": [
        "Much like we did with earlier examples, we want to try and explain our classifier. Why does it work the way it does? The cool thing about random forest classifiers is that the model provides you with a chance to get the top predictors (the featuers that are more important in determining how the classifier makes a prediction).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3JqIj0akL1p"
      },
      "source": [
        "importances = classifier.feature_importances_"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwyURCYnrbfS",
        "outputId": "e8883f15-15fa-4c58-a946-8eedbeee1e30"
      },
      "source": [
        "#testing how to get importances sorted inversely\n",
        "a = list(range(10))\n",
        "a[::-1]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GI-hSwNQrHr-"
      },
      "source": [
        "'''Returns the indices that would sort an array.\n",
        "Perform an indirect sort along the given axis using \n",
        "the algorithm specified by the kind keyword. \n",
        "It returns an array of indices of the same shape\n",
        " as a that index data along the given axis in sorted order.'''\n",
        "indices = np.argsort(importances)[::-1]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "CtbrV5V-uUNZ",
        "outputId": "e584b33c-4b00-4c0b-82db-9f309f5a2cde"
      },
      "source": [
        "# Plot the impurity-based feature importances of the forest\n",
        "plt.figure()\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(len(Features)), importances[indices],\n",
        "        color=\"b\", align=\"center\")\n",
        "plt.xticks(range(X_test.shape[1]), indices)\n",
        "plt.xlim([-1, X_test.shape[1]])\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9DLdKnsLsk89",
        "outputId": "61e47f19-913f-4efc-809a-e2cf717c3af9"
      },
      "source": [
        "Features = dataset.feature_names\n",
        "predict_score_RF = list(zip(Features, classifier.feature_importances_))\n",
        "predict_score_sorted_RF = sorted(predict_score_RF, key=lambda tup: tup[1])\n",
        "print(predict_score_sorted_RF)\n",
        "predictions = pd.DataFrame(predict_score_sorted_RF, columns=['predictor', 'importance'])\n",
        "predictions = predictions.sort_values(['importance'], ascending=False)\n",
        "predictions.plot(kind='bar', x='predictor', y='importance',title='Feature Importances')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('ash', 0.009631291920457941), ('nonflavanoid_phenols', 0.014464390958803941), ('magnesium', 0.020552262441992084), ('proanthocyanins', 0.029538177858659875), ('malic_acid', 0.02968120529751509), ('total_phenols', 0.042362416037799284), ('alcalinity_of_ash', 0.04845498965830053), ('hue', 0.06581745207866667), ('od280/od315_of_diluted_wines', 0.07774108560764421), ('alcohol', 0.15769704918393546), ('flavanoids', 0.15817944233023967), ('proline', 0.16936022449621838), ('color_intensity', 0.17652001212976678)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a8b34a910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvLTYr8mlMkv"
      },
      "source": [
        "##Advantages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pezg8YoQlSnP"
      },
      "source": [
        "1. Random forests is considered as a highly accurate and robust method because of the number of decision trees participating in the process.\n",
        "2. Less probability of over-fitting when compared with decision tree classifiers\n",
        "3. Easy to file relative feature importance (as we saw in this example!), which helps in selecting the most contributing features for the classifier. \n",
        "\n",
        "References:\n",
        "https://www.datacamp.com/community/tutorials/random-forests-classifier-python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmkvnjgwldWe"
      },
      "source": [
        "##Disadvantages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEf8K4ynlfbQ"
      },
      "source": [
        "\n",
        "1. Random forest classifiers are less easy to interpret than decision tree classifiers\n",
        "2. Random forest classifiers can be slower than decision tree classifiers since they are building a bunch of trees\n",
        "\n",
        "References:\n",
        "https://www.datacamp.com/community/tutorials/random-forests-classifier-python"
      ]
    }
  ]
}