40_ex_0314.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahdid-lilia/SIC_Samsung-Innovation-Campus-AI-Course/blob/main/40_ex_0314.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-t7mKpeU6oGw"
      },
      "source": [
        "## Coding Exercise #0314"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvj8qILo6oG1"
      },
      "source": [
        "### 1. Bagging ensemble:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Yh3mNJkI6oG1"
      },
      "outputs": [],
      "source": [
        "# Install once.\n",
        "# !pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "mw38mMSZ6oG4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier, GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "warnings.filterwarnings(action='ignore')                  # Turn off the warnings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTEiX4Vh6oG4"
      },
      "source": [
        "#### 1.1. Read in data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Lxdlsbur6oG5"
      },
      "outputs": [],
      "source": [
        "# Load data.\n",
        "data = load_breast_cancer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pp2czNxS6oG5",
        "outputId": "1f07d16a-29f8-4514-beaa-47348b7a47fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
          ]
        }
      ],
      "source": [
        "# Explanatory variables.\n",
        "X = data['data']\n",
        "variable_names = data['feature_names']\n",
        "print(variable_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2z6FIS4a6oG6",
        "outputId": "df56f6a0-f90a-463a-ef05-c422be75c5ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiih7-cE6oG6",
        "outputId": "abf2c182-58a5-4395-b22e-643b8671f71a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['benign', 'malignant']\n"
          ]
        }
      ],
      "source": [
        "# Response variable.\n",
        "# Relabel such that 0 = 'benign' and 1 = malignant.\n",
        "Y = 1 - data['target']\n",
        "label = list(data['target_names'])\n",
        "label.reverse()\n",
        "print(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iXXUJdii6oG7"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1234)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSDw5_9b6oG8"
      },
      "source": [
        "#### 1.2. Random Forest:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMGH_q2q6oG8",
        "outputId": "3f46a9d1-abad-4885-ebdf-ffe92f34d38a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest accuracy : 0.924\n"
          ]
        }
      ],
      "source": [
        "# Classification by Random Forest.\n",
        "RFC = RandomForestClassifier(n_estimators=100,max_depth=5,min_samples_leaf=2,random_state=123)\n",
        "RFC.fit(X_train, Y_train)\n",
        "Y_pred = RFC.predict(X_test)\n",
        "print( \"Random Forest accuracy : \" + str(np.round(metrics.accuracy_score(Y_test,Y_pred),3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aMtn_WNY6oG8",
        "outputId": "5703b3a6-a518-453d-b068-280a58cd44ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Display the top 10 most important variables.\n",
        "variable_importance = pd.Series(RFC.feature_importances_, index = variable_names)\n",
        "top_variables_10 = variable_importance.sort_values(ascending=False)[:10]\n",
        "sns.barplot(x=top_variables_10.values, y = top_variables_10.index, ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEvHI21v6oG9"
      },
      "source": [
        "### 2. Boosting Ensemble:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CnEgIFY6oG9"
      },
      "source": [
        "#### 2.1.  AdaBoost:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "YG38-WOu6oG9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d69e7c64-8e16-49e8-a9c7-b9df709f1f2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoost accuracy : 0.924\n"
          ]
        }
      ],
      "source": [
        "# Classification by AdaBoost.\n",
        "ABC = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=10),n_estimators=100,learning_rate=0.01,random_state=123)\n",
        "ABC.fit(X_train, Y_train)\n",
        "Y_pred = ABC.predict(X_test)\n",
        "print( \"AdaBoost accuracy : \" + str(np.round(metrics.accuracy_score(Y_test,Y_pred),3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "WNBNMaQe6oG9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2290b45e-a328-43b0-e705-8846c17f5524"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Display the top 10 most important variables.\n",
        "variable_importance = pd.Series(ABC.feature_importances_, index = variable_names)\n",
        "top_variables_10 = variable_importance.sort_values(ascending=False)[:10]\n",
        "sns.barplot(x=top_variables_10.values, y = top_variables_10.index, ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDggvyZB6oG-"
      },
      "source": [
        "#### 2.2.  Gradient Boosting:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "DPD4Rl5i6oG-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6aab67a4-b257-43b8-c776-8b96d68934a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting accuracy : 0.906\n"
          ]
        }
      ],
      "source": [
        "# Classification by Gradient Boosting.\n",
        "GBC = GradientBoostingClassifier(n_estimators=100,learning_rate=0.01,min_samples_leaf=2,max_leaf_nodes=30,random_state=123)\n",
        "GBC.fit(X_train, Y_train)\n",
        "Y_pred = GBC.predict(X_test)\n",
        "print( \"Gradient Boosting accuracy : \" + str(np.round(metrics.accuracy_score(Y_test,Y_pred),3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "sqmAzD116oG-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "364c6d4d-0123-4372-a43c-8178b387d82d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Display the top 10 most important variables.\n",
        "variable_importance = pd.Series(GBC.feature_importances_, index = variable_names)\n",
        "top_variables_10 = variable_importance.sort_values(ascending=False)[:10]\n",
        "sns.barplot(x=top_variables_10.values, y = top_variables_10.index, ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1UQdeE36oG_"
      },
      "source": [
        "#### 2.3. XGBoost:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "-2d81LvD6oG_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13ff2075-7763-4ad0-9ac0-8ffda355d219"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost accuracy : 0.942\n"
          ]
        }
      ],
      "source": [
        "# Classification by XGBoost.\n",
        "XGBC = XGBClassifier(n_estimator = 500, learning_Rate = 0.1, max_depth = 4, random_state=123)\n",
        "XGBC.fit(X_train, Y_train)\n",
        "Y_pred = XGBC.predict(X_test)\n",
        "print( \"XGBoost accuracy : \" + str(np.round(metrics.accuracy_score(Y_test,Y_pred),3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Zi3kDzlJ6oG_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "33743ca1-e56e-403f-8017-9e886b4b7abb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Display the top 10 most important variables.\n",
        "variable_importance = pd.Series(XGBC.feature_importances_, index = variable_names)\n",
        "top_variables_10 = variable_importance.sort_values(ascending=False)[:10]\n",
        "sns.barplot(x=top_variables_10.values, y = top_variables_10.index, ci=None)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8p0GUn26oG_"
      },
      "source": [
        "NOTE: The hyperparameters of XGBClassifier can be optimized using GridSearchCV() and RandomSearchCV(). Students are encouraged to explore. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mTIjCKwG6oHA"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}