Blog Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Importing all the neccessary libraries\n",
    "#%matplotlib inline\n",
    "import sqlite3, pandas as pd, numpy as np, nltk, string, matplotlib.pyplot as plt, seaborn as sns\n",
    "import string, math, pickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from xgboost.sklearn import XGBModel, XGBRegressor\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi guys! Today I will give you an intuitive understanding of how ensemble models in Machine Learning work. We will take a look at some of the most common ensemble learning strategies which are deployed by thousands of Kagglers and dive deeper into making more accurate predictions using a particular ensemble strategy called Stacking Classifiers.\n",
    "\n",
    "Ensemble learning is a strategy in which multiple models are used in conjugation to solve a particular computational problem. Ensemble methods strategically combine diverse machine learning models into one single predictive model. In most of the cases, ensemble methods are primarily used to improve the overall performance of a model. Instead of using any one model to solve a problem, what ensemble models do is that it combine several different models to predict the results of a final model. More often than not, ensemble models are used to improve the performance accuracy of a model. The more different these models are, the more powerful will the final model be. In any machine learning model, the generalization error is given by the sum of squares of bias + variance + irreducible error. Irreducible errors are something that is beyond us! We cannot reduce them. However using ensemble techniques we can reduce the bias and variance of a model to an extent, as well as improve the prediction accuracy of the final model. This reduces the overall generalization error. Using several models to predict one outcome reduces the likelihood of a forlorn selection of an inferior model. The bias-variance trade-off is the most important benchmark that differentiates a robust model from an inferior one. In machine learning, the models which have a high bias tends to have a lower variance and vice-versa.  \n",
    "\n",
    "1. Bias is an error which arises due to incorrect assumptions in the learning algorithm, which is the backbone of any machine learning model. A high bias can cause a machine learning algorithm to skip relevant information and miss important correlations between the independent variables and the class labels, thereby underfitting the model.\n",
    "\n",
    "2. The variance in a model means how sensitive the model is to small fluctuations in training data. That is by how much the model changes with small changes in the training samples. High variance in a machine learning algorithm will make the model prone to random noise present in the dataset thereby overfitting the model.  \n",
    "\n",
    "Think of ensemble learning analogous to the board of directors in a company, where the final decision is taken by the CEO. Instead of taking a decision all by himself, the CEO takes inputs from each of the board members before arriving at a final conclusion. The CEO, in this case, is the final model and the board members are the several models which provide different inputs to the CEO in order for him to arrive at a conclusion. This drastically reduces the chance of committing an error when the CEO makes his final decision. We use this approach regularly in our daily lives as well -  for example, we ask for the opinions of several different experts before arriving at a conclusion, we generally consult several doctors before agreeing to particular medical procedure, we read so many reviews written by different customers before setting our mind to buy a particular product. In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).  \n",
    "\n",
    "Typically, ensemble learning can be categorized into four categories:\n",
    "\n",
    "1. Bagging: Bagging is mostly used to reduce the variance in a model. A simple example of bagging is the Random Forest algorithm.\n",
    "\n",
    "2. Boosting: Boosting is mostly used to reduce the bias in a model. Examples of boosting algorithms are Ada-Boost, XGBoost, Gradient Boosted Decision Trees etc.\n",
    "\n",
    "3. Stacking: Stacking is mostly used to increase the prediction accuracy of a model. For implementing stacking we will use the mlextend library provided by sci-kit learn.\n",
    "\n",
    "4. Cascading: This class of models is very very accurate. Cascading is mostly used in scenarios where you cannot afford a mistake. For example, a cascading technique is mostly used to detect fraudulent credit card transactions, or maybe when you want to be absolutely sure that you don't have cancer. \n",
    "\n",
    "In this article, I will mostly explain the ensemble learning strategies called Bagging, Boosting and Stacking with some code samples. I will also try to give an intuitive understanding of what cascading means and for what purpose should we use it. For the purpose of this blog, I have chosen to work with the Iris dataset mainly because of its simplicity. You can try all these techniques and apply them to any real-world dataset you want. However, the performance will vary across datasets of different types. The Iris dataset can be downloaded from https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv. For those of you who aren't aware of what Iris database is, it's basically a dataset which contains information about three species of flowers - setosa, versiclor, and virginica. The four features that distinguish these flowers are petal length, septal length, petal width, and sepal width. The main aim of a classification model is to learn the relationships between the features and the class label and thereby classifying the three species of flowers.\n",
    "\n",
    "The first thing that we are going to do is draw the pair plots for all the independent variables in the Iris dataset. In this way, we can visually see how each of the features helps to classify the data with respect to one another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Iris dataset + pair plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 948.75x864 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load Iris.csv into a pandas dataFrame.\n",
    "iris_dataset = pd.read_csv(\"iris.csv\")\n",
    "X, y = iris_dataset.iloc[:,0:4], iris_dataset.iloc[:,4]\n",
    "\n",
    "#Pair plots for iris dataset\n",
    "import seaborn as sns\n",
    "plt.close();\n",
    "sns.set_style(\"whitegrid\");\n",
    "sns.pairplot(iris_dataset, hue=\"species\", size=3);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pair plots above, we can see that petal length and petal width are the two most important features that visually separates the three classes of flower most accurately. I mean we can visually differentiate them just by looking at the pair plots. We don't even need machine learning models to predict the class labels! Now let us build a model and try out different classifiers and different ensemble techniques to find out how they behave on the Iris dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging.\n",
    "\n",
    "The first ensemble technique that I will discuss is Bagging. Bagging stands for bootstrap aggregation. It's a very very interesting idea in ensemble learning! Bagging is one of the earliest yet powerful ensemble algorithm that exists even today. The core idea of bagging is to use bootstrapped replicas of the original dataset and use them to train different classifiers. Suppose we are given a dataset, we will create subsets by randomly sampling a bunch of points from the original data, with replacement. Now we will train individual classifiers on each of these bootstrapped subsets. Each of these base classifiers will predict the class label for a given problem. This is where we combine the predictions of all the base models. This part is called the aggregation stage. Typically a simple majority vote is used in a classification system and taking the mean of all predictions for regression models, to combine all the base classifiers into one single model to provide the final output of the ensemble model. A simple example of such an approach is the Random Forest algorithm. Bagging reduces the high variance of a model while keeping the bias constant, thereby reducing the generalization error. Bagging is a very efficient method especially when you have very limited data. By using bootstrapped samples we are able to get an estimate by aggregating the scores over many samples. Please take a look at the below diagrams to get an intuitive understanding of how bagging works at different stages. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tr>\n",
    "<td> <img src=\"BaggingFINAL.jpg\"> </td>\n",
    "<td> <img src=\"BaggingAlgo.png\"> </td>\n",
    "</tr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us understand the above diagram with the help of a simple example. Let's say we have a training dataset which contains 100K data points. We will create N subsets by randomly sampling 50K data points for each subset. So we have a total of N datasets now. Each of these N datasets will be used to train N different classifiers. At the aggregation stage, all these N predictions will be combined into one single model also called the meta-classifier. Out of the 100K points present originally present in the dataset, if we remove 1000 points, the impact it will have on the sampled datasets will be very less. If you think intuitively, some of these 1000 points might not be present in all the sampled datasets at all and thus the number of points that will be removed from each sampled dataset will be very less. Even zero in some cases! Thus, by using the bootstrap aggregation strategy, the impact of removing 1000 points from the original training set will be very less on the sampled datasets. To sum it up, the impact of removing 1000 such points will be actually less on the base learners, thereby reducing the variance in a model and making it more robust. Variance is nothing but sensitivity to noise as we have discussed earlier.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code sample below, first, what we do is initialize 8 different base learners. We will fit each of these 8 base learners to our training set and compare it's accuracy to the bagging versions of each of the classifiers. In most of the cases below, we see that there is a slight increase in the model's accuracy when we use the bagging version of each classifier as compared to the normal ones. For this classification task, I will use a 3 fold cross-validation to obtain the accuracy scores across different folds. You can increase the number of folds if you have a large real-world training set. For each of the base learners selected below, we see that bagging actually works! Using bagging has actually improved the accuracy by a certain margin (most notably in the AdaBoostClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9538 (+/- 0.0367) [Normal RandomForestClassifier]\n",
      "Accuracy: 0.9604 (+/- 0.0155) [Bagging RandomForestClassifier]\n",
      "\n",
      "Accuracy: 0.9408 (+/- 0.0420) [Normal ExtraTreesClassifier]\n",
      "Accuracy: 0.9412 (+/- 0.0577) [Bagging ExtraTreesClassifier]\n",
      "\n",
      "Accuracy: 0.9534 (+/- 0.0087) [Normal KNeighborsClassifier]\n",
      "Accuracy: 0.9869 (+/- 0.0092) [Bagging KNeighborsClassifier]\n",
      "\n",
      "Accuracy: 0.9400 (+/- 0.0321) [Normal SVC]\n",
      "Accuracy: 0.9604 (+/- 0.0422) [Bagging SVC]\n",
      "\n",
      "Accuracy: 0.7998 (+/- 0.0058) [Normal RidgeClassifier]\n",
      "Accuracy: 0.8607 (+/- 0.0248) [Bagging RidgeClassifier]\n",
      "\n",
      "Accuracy: 0.9542 (+/- 0.0515) [Normal LogisticRegression]\n",
      "Accuracy: 0.9600 (+/- 0.0320) [Bagging LogisticRegression]\n",
      "\n",
      "Accuracy: 0.9473 (+/- 0.0329) [Normal DecisionTreeClassifier]\n",
      "Accuracy: 0.9673 (+/- 0.0245) [Bagging DecisionTreeClassifier]\n",
      "\n",
      "Accuracy: 0.6667 (+/- 0.0000) [Normal AdaBoostClassifier]\n",
      "Accuracy: 0.9604 (+/- 0.0274) [Bagging AdaBoostClassifier]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder_object = LabelEncoder()\n",
    "y = encoder_object.fit_transform(y)\n",
    "\n",
    "iris_dataset = pd.read_csv(\"iris.csv\")\n",
    "X, y = iris_dataset.iloc[:,0:4], iris_dataset.iloc[:,4]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder_object = LabelEncoder()\n",
    "y = encoder_object.fit_transform(y)\n",
    "\n",
    "RANDOM_SEED = 0\n",
    "\n",
    "#Base Learners\n",
    "rf_clf = RandomForestClassifier(n_estimators=10, random_state=RANDOM_SEED)\n",
    "et_clf = ExtraTreesClassifier(n_estimators=5, random_state=RANDOM_SEED)\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=2)\n",
    "svc_clf = SVC(C=10000.0, kernel='rbf', random_state=RANDOM_SEED)\n",
    "rg_clf = RidgeClassifier(alpha=0.1, random_state=RANDOM_SEED)\n",
    "lr_clf = LogisticRegression(C=20000, penalty='l2', random_state=RANDOM_SEED)\n",
    "dt_clf = DecisionTreeClassifier(criterion='gini', max_depth=2, random_state=RANDOM_SEED)\n",
    "adab_clf = AdaBoostClassifier(n_estimators=5,learning_rate=0.001)\n",
    "\n",
    "classifier_array = [rf_clf, et_clf, knn_clf, svc_clf, rg_clf, lr_clf, dt_clf, adab_clf]\n",
    "labels = [clf.__class__.__name__ for clf in classifier_array]\n",
    "\n",
    "normal_accuracy = []\n",
    "normal_std = []\n",
    "\n",
    "bagging_accuracy = []\n",
    "bagging_std = []\n",
    "\n",
    "for clf in classifier_array:\n",
    "    cv_scores = cross_val_score(clf, X, y, cv=3, n_jobs=-1)\n",
    "    bagging_clf = BaggingClassifier(clf, max_samples=0.4, max_features=3, random_state=RANDOM_SEED)\n",
    "    bagging_scores = cross_val_score(bagging_clf, X, y, cv=3, n_jobs=-1)\n",
    "    \n",
    "    normal_accuracy.append(np.round(cv_scores.mean(),4))\n",
    "    normal_std.append(np.round(cv_scores.std(),4))\n",
    "    \n",
    "    bagging_accuracy.append(np.round(bagging_scores.mean(),4))\n",
    "    bagging_std.append(np.round(bagging_scores.std(),4))\n",
    "    \n",
    "    print(\"Accuracy: %0.4f (+/- %0.4f) [Normal %s]\" % (cv_scores.mean(), cv_scores.std(), clf.__class__.__name__))\n",
    "    print(\"Accuracy: %0.4f (+/- %0.4f) [Bagging %s]\\n\" % (bagging_scores.mean(), bagging_scores.std(), clf.__class__.__name__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, in the below code sample what we do is visually see how the accuracy improves on using a bagging classifier as compared to a normal one. The black line present at the top of each bar indicates the variance in the model. As we can see, the variance mostly reduces when we use the bagging version of any classifier. However, for some models, we also have a situation where the variance increases slightly more than the normal version of the classifier. This is mostly because of a lack of training data points. Remember we have only 150 observations in our training dataset. Greater the number of data points in our train set, more robust will the final models be. However, from a simple implementation point of view, this code sample helps us understand how to implement the concept of bootstrap aggregation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Bagging.\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "n_groups = 8\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.35\n",
    "\n",
    "opacity = .7\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "normal_clf = ax.bar(index, normal_accuracy, bar_width, alpha=opacity, color='g', yerr=normal_std, error_kw=error_config, label='Normal Classifier')\n",
    "bagging_clf = ax.bar(index + bar_width, bagging_accuracy, bar_width, alpha=opacity, color='c', yerr=bagging_std, error_kw=error_config, label='Bagging Classifier')\n",
    "\n",
    "ax.set_xlabel('Classifiers')\n",
    "ax.set_ylabel('Accuracy scores with variance')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels((labels))\n",
    "ax.legend()\n",
    "\n",
    "#fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impact of change in bagging accuracies with an increase in the sub-sampling ratio.\n",
    "\n",
    "A very important factor to be kept in mind while building bagging classifiers is that the accuracy of a model doesn't always increase when we increase the sub-sampling ratio. In the Bagging Classifier library, subsampling, i.e. the fraction of data that gets into each of the base learners, is denoted by the parameter \"max_samples\". In the code sample below, we will see the display the bagging scores for each of the base learners at various subsampling ratio. We will also plot the bagging scores for each of the base learners in a line chart to get a more intuitive understanding of this concept - that is bagging scores doesn't necessarily increase when we increase the sub-sampling ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+------------------------+----------------------+----------------------+--------------------+--------------------+--------------------+------------------------+--------------------+\n",
      "| Max Samples | RandomForestClassifier | ExtraTreesClassifier | KNeighborsClassifier |        SVC         |  RidgeClassifier   | LogisticRegression | DecisionTreeClassifier | AdaBoostClassifier |\n",
      "+-------------+------------------------+----------------------+----------------------+--------------------+--------------------+--------------------+------------------------+--------------------+\n",
      "|     0.1     |   0.9011437908496732   |  0.9007352941176471  |  0.9272875816993466  | 0.9272875816993463 | 0.8080065359477123 | 0.9334150326797386 |   0.9538398692810457   | 0.9211601307189542 |\n",
      "|     0.2     |   0.9473039215686274   |  0.9072712418300654  |  0.9738562091503268  | 0.9464869281045751 | 0.8075980392156863 | 0.9464869281045751 |   0.960375816993464    | 0.9468954248366014 |\n",
      "|     0.3     |   0.9473039215686274   |  0.9473039215686274  |  0.9738562091503268  | 0.9538398692810457 | 0.8472222222222222 | 0.9468954248366014 |   0.9538398692810457   | 0.9534313725490197 |\n",
      "|     0.4     |   0.960375816993464    |  0.9411764705882354  |  0.9869281045751634  | 0.960375816993464  | 0.8607026143790849 | 0.9599673202614379 |   0.9673202614379085   | 0.960375816993464  |\n",
      "|     0.5     |   0.960375816993464    |  0.9473039215686274  |  0.9869281045751634  | 0.9669117647058822 | 0.8349673202614379 | 0.9538398692810457 |   0.9673202614379085   | 0.9468954248366014 |\n",
      "|     0.6     |   0.960375816993464    |  0.9473039215686274  |  0.9869281045751634  | 0.9538398692810457 | 0.8410947712418301 | 0.9538398692810457 |   0.9673202614379085   | 0.9538398692810457 |\n",
      "|     0.7     |   0.9669117647058822   |  0.954248366013072   |  0.9803921568627452  | 0.960375816993464  | 0.8276143790849674 | 0.9538398692810457 |   0.9673202614379085   | 0.9468954248366014 |\n",
      "|     0.8     |   0.9538398692810457   |  0.9607843137254902  |  0.9803921568627452  | 0.9607843137254902 | 0.8337418300653594 | 0.9538398692810457 |   0.9607843137254902   | 0.8496732026143791 |\n",
      "|     0.9     |   0.9538398692810457   |  0.9607843137254902  |  0.9738562091503268  | 0.9607843137254902 | 0.8272058823529411 | 0.9473039215686274 |   0.9607843137254902   | 0.9538398692810457 |\n",
      "|     1.0     |   0.9538398692810457   |  0.954248366013072   |  0.9673202614379085  | 0.9738562091503268 | 0.8137254901960785 | 0.9538398692810457 |   0.9607843137254902   | 0.9403594771241831 |\n",
      "+-------------+------------------------+----------------------+----------------------+--------------------+--------------------+--------------------+------------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "### Display the accuracy of different bagging classifiers at various sub sampling ratio in a Pretty table.\n",
    "\n",
    "subsampling_ratio = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n",
    "\n",
    "various_bagging_scores = []\n",
    "\n",
    "for clf in classifier_array:\n",
    "    cv_scores = cross_val_score(clf, X, y, cv=3, n_jobs=-1)\n",
    "    #print(\"\\nAccuracy: %0.4f (+/- %0.4f) [Normal %s]\" % (cv_scores.mean(), cv_scores.std(), clf.__class__.__name__))\n",
    "    \n",
    "    mean_bagging_score = []\n",
    "    for ratio in subsampling_ratio:\n",
    "        bagging_clf = BaggingClassifier(clf, max_samples=ratio, max_features=3, random_state=RANDOM_SEED)\n",
    "        bagging_scores = cross_val_score(bagging_clf, X, y, cv=3, n_jobs=-1)\n",
    "        mean_bagging_score.append(bagging_scores.mean())\n",
    "        #print(\"Bagging accuracy: %0.4f [max_samples %0.2f]\" % (bagging_scores.mean(), ratio))\n",
    "    various_bagging_scores.append(mean_bagging_score)\n",
    "\n",
    "various_bagging_scores.insert(0,subsampling_ratio)\n",
    "    \n",
    "#Compare performance and display it in a pretty table.\n",
    "from prettytable import PrettyTable\n",
    "table = PrettyTable()\n",
    "labels.insert(0,\"Max Samples\")\n",
    "#table.field_names = label_models\n",
    "\n",
    "index=0\n",
    "\n",
    "for value in various_bagging_scores:\n",
    "    table.add_column(labels[index],value)\n",
    "    index += 1\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code sample helps us understand that the bagging accuracy doesn't always increase when we increase the sub-sampling ratio. As we can clearly see for Ada Boost classifier (denoted by the maroon line), that the bagging accuracy drops to a significantly lower value when we increase the sampling ration from 0.7 to 0.8. For the Decision Tree classifier, the bagging accuracy falls beyond the sampling ratio of 0.4. This behavior is almost the same for all the different base learners. There is absolutely no evidence of the fact that a higher sub-sampling ratio means a higher bagging accuracy. I have taken my sampling ratio to be 0.4. You can try and experiment with different values on a different dataset and see if you find any correlation between the sub-sampling ratio and the bagging accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the bagging scores using a line chart.\n",
    "labels.remove(\"Max Samples\")\n",
    "various_bagging_scores.remove(various_bagging_scores[0])\n",
    "\n",
    "x_axes = subsampling_ratio\n",
    "\n",
    "color_map = ['blue','g','r','c','grey','y','black','m']\n",
    "plt.figure(figsize=(20,10))\n",
    "for index in range(0,len(labels)):\n",
    "    plt.plot(x_axes, various_bagging_scores[index], color=color_map[index], label=labels[index])\n",
    "plt.xlabel('Sub sampling Ratio')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title(\"Comparison b/w accuracy of different classifiers at various sub sampling ratio\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting:\n",
    "\n",
    "The second ensemble technique that we are going to discuss today is called Boosting. Boosting, in general, is used to convert weak learners to strong ones. Weak learners are basically classifier which has a very weak correlation with the true class labels and strong learners are classifiers that have a very high correlation between the model and the true class labels. Boosting involves training the weak learners iteratively, each trying to correct the error made by the previous model. This is achieved by training a weak model on the whole training data, then building a second model which aims at correcting the errors made by the first model. Then we build a third model which will correct the errors made by the second model and so on. Models are added iteratively until the final model has corrected all the errors made by all the previous models. When the models are added at each stage, some weights are assigned to the model which is related to the accuracy of the previous model. After a weak classifier is added, the weights are re adjusted. The misclassified points are given a higher weight and correctly classified points are assigned lower weights. Such an approach will make the next weak classifier to focus on the errors made by the previous model. Boosting reduces generalization error by taking a high-bias & low-variance model and reducing the bias by a significant level. Remember, bagging reduces variance. Similar to bagging, boosting also lets us work with both classification and regression models. Please take a look at the below diagrams to intuitively understand how boosting works at each of the stages. The diagram at the left shows the different stages in a boosting algorithm. On the right hand side you can see the pseudo code for the boosting algorithm. The pseudo code is taken from the link mentioned at the bottom of the pseudo code. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tr>\n",
    "<td> <img src=\"BoostingFINAL.jpg\"> </td>\n",
    "<td> <img src=\"BoostingAlgo.png\"> </td>\n",
    "</tr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's understand the above diagram with an example. Suppose we are given a dataset D, the first thing that we will do at stage 0 is train a model on the whole dataset. The model may be either a classification or a regression model. Let's name this model M_0. Let us assume this model M_0 is trying to fit a function h_0(x). Thus the prediction function for this model is given by y_pred=h_0(x). Model 0 is designed to have a high bias. Generally boosting is applied for a high bias and low variance model. High bias in a model basically refers to a high training error. High bias arises mostly due to some incorrect assumptions made at the training stage. Now, after building the first model we will, at stage 0, get the error in prediction for each data points made by the model M_0. So, the error in the prediction for any class label is given by y-y_pred. Remember, there are lots of lots of error functions out there - for example, the squared error, the hinge loss error, the logistic loss error etc. But, for simplicity, I have taken a simple difference error for this example. Now that we have done these things in stage 0, what we will do in stage 1 is as follows. I will try fit a model M_1 on the errors produced by the model at stage 0. Remember, M_1 is not training on the actual class labels. M_1 is training on the errors we have got at the end of stage 0. Let's say we get a function h_1(x), which has trained on the errors generated by model M_0. Thus at end of stage 1, my final model will actually be the weighted sum of the previous two prediction functions (as shown in the diagram). We will assign weights a_0 and a_1 to h_0(x) and h_1(x) respectively. Thus at the end of stage 1, the model looks like this : F_1(x) = a_0 * h_0(x) + a_1 * h_1(x), where a_0 and a_1 are weights assigned to the prediction functions. Remember, the weights will always be higher for functions which has a high misclassification error. In this way, we can make the next model in the sequence to focus more on the errors made by the previous model. Similarly, the model at the end of stage 2 will have the function F_2(x) = a_0 * h_0(x) + a_1 * h_1(x) + a_2 * h_2(x). Thus at the end of all stages, the final model that we have is given by summation of a_i * h_x(i), where the value of i ranges from 1 to N. Thus, intuitively if we think we are actually reducing the training error which means in other words we are actually reducing the bias of a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will look at some of the most popular boosting classifiers in the below code sample. We will also see how using boosting increases the overall accuracy in prediction using an EnsembleVoteClassifier. EnsembleVoteClassifier is a very powerful class available in the MLEXTEND package, which is used to combine the predictions of different machine learning models by using the concept of majority voting. The EnsembleVoteClassifier implements two types of voting approach - \"hard\" and \"soft\". In a \"hard\" voting approach we will predict the class label of the final model based on the majority vote obtained from all the base classifiers. For example, if 7 out of 10 base learners predicts the class label to be \"Yes\" in a binary classification problem, we will take \"Yes\" to be the final class label of the final ensemble model. Anyway, in the below example we see that the accuracy increases to 0.967 (when we use boosted classifiers with majority voting) as compared to the highest accuracy of an individual model which was 0.961, although the increase is very slight. Still, you got my point!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy: 0.967, Variance: (+/-) 0.018 [Ada Boost]\n",
      "Acuuracy: 0.960, Variance: (+/-) 0.027 [Gradient Boost]\n",
      "Acuuracy: 0.961, Variance: (+/-) 0.042 [XG Boost]\n",
      "Acuuracy: 0.967, Variance: (+/-) 0.033 [Ensemble]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "ada_boost = AdaBoostClassifier(n_estimators=5)\n",
    "grad_boost = GradientBoostingClassifier(n_estimators=10)\n",
    "xgb_boost = XGBClassifier(max_depth=5, learning_rate=0.001)\n",
    "\n",
    "ensemble_clf = EnsembleVoteClassifier(clfs=[ada_boost, grad_boost, xgb_boost], voting='hard')\n",
    "boosting_labels = ['Ada Boost', 'Gradient Boost', 'XG Boost', 'Ensemble']\n",
    "\n",
    "for clf, label in zip([ada_boost, grad_boost, xgb_boost, ensemble_clf], boosting_labels):\n",
    "    scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')\n",
    "    print(\"Accuracy: {0:.3f}, Variance: (+/-) {1:.3f} [{2}]\".format(scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we look at the decision boundaries by each boosted classifiers, and how all the three base models classify the data over a region. We will also take a look at the decision region generated by the voting classifier. In the MLEXTEND package, there is a very powerful library called \"plot_decision_regions\" which can be used to visually see the decision regions for different classifiers. You can check their GitHub profile for more techniques in this link: http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/. Anyway, in the below example I have trained and fitted the model to two of the most important features, i.e. \"petal_length\" and \"petal_width\". Remember, the pair plots above? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decision Regions for all the boosting algorithms.\n",
    "X = np.array(iris_dataset[['petal_length','petal_width']])\n",
    "y = np.array(y)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import matplotlib.gridspec as gridspec\n",
    "import itertools\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "fig = plt.figure(figsize=(20,16))\n",
    "\n",
    "for clf, label, grd in zip([ada_boost, grad_boost, xgb_boost, ensemble_clf], boosting_labels, itertools.product([0, 1], repeat=2)):\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking.\n",
    "\n",
    "Stacking is an ensemble learning technique which is used to combine the predictions of diverse classification models into one single model also known as the meta-classifier. All the individual models are trained separately on the complete training data set and fine-tuned to achieve a greater accuracy. The bias and variance trade-off is taken care off for each model. The final model, also known as the meta-classifier is fed either the class labels predicted by the base models or the predicted probabilities for each class label. The meta-classifier is then trained based on the outputs given by the ensemble models. In stacking, a new model is trained based on the predictions made by the previous models. This process takes place sequentially. This means several models are trained at stage 1 and are fine-tuned. The predicted probabilities of each model from stage 1 are fed as an input to all the models at stage 2. The models at stage 2 are then fine-tuned and the corresponding outputs are fed to models at stage 3 and so on. This process occurs multiple times based on how many layers of stacking one would like to use. The final stage consists of one single powerful model, which gives us the final output by combining the output of all the models present in the previous layers. This single powerful model at the end of a stacking pipeline is called the meta-classifier. Often times, using stacking classifiers increases the prediction accuracy of a model. But in no way can there be a guarantee that using stacking will increase the prediction accuracy at all times! Take a look at the below diagrams to understand how stacking works. You can refer to the MLEXTEND GitHub page highlighted in bold to get more ideas on how to implement stacking in different scenarios.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tr>\n",
    "<td> <img src=\"StackingFINAL.jpg\"> </td>\n",
    "<td> <img src=\"StackingAlgo.png\"> </td>\n",
    "</tr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the below code sample, we will use eight different base learners and train each of them on the whole dataset. Each of these models can be fine-tuned using grid search cross-validation. Each of these N models will predict eight class labels. At the final stage, the predictions of all the base models are combined using majority voting (for classification tasks), to create a final model called the meta-classifier. The meta-classifier in our case is the logistic regression model. As we can see from the outputs below, stacking has indeed managed to increase the accuracy of the final model although the increase is very less. But you get the idea! Like the previous examples, we will use a 3 fold cross-validation. Again, you can experiment with this value when you work with some real-world datasets. Further down, in a separate example, we will try grid search cross-validation on the base learners and see if the overall accuracy increases (well, it should actually)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9538 (+/- 0.0367) [RandomForestClassifier]\n",
      "Accuracy: 0.9408 (+/- 0.0420) [ExtraTreesClassifier]\n",
      "Accuracy: 0.9534 (+/- 0.0087) [KNeighborsClassifier]\n",
      "Accuracy: 0.9400 (+/- 0.0321) [SVC]\n",
      "Accuracy: 0.7998 (+/- 0.0058) [RidgeClassifier]\n",
      "Accuracy: 0.9542 (+/- 0.0515) [LogisticRegression]\n",
      "Accuracy: 0.9473 (+/- 0.0329) [DecisionTreeClassifier]\n",
      "Accuracy: 0.9600 (+/- 0.0161) [AdaBoostClassifier]\n",
      "Accuracy: 0.9608 (+/- 0.0424) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "RANDOM_SEED = 0\n",
    "\n",
    "X, y = iris_dataset.iloc[:,0:4], iris_dataset.iloc[:,4]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder_object = LabelEncoder()\n",
    "y = encoder_object.fit_transform(y)\n",
    "\n",
    "#Base Learners\n",
    "rf_clf = RandomForestClassifier(n_estimators=10, random_state=RANDOM_SEED)\n",
    "et_clf = ExtraTreesClassifier(n_estimators=5, random_state=RANDOM_SEED)\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=2)\n",
    "svc_clf = SVC(C=10000.0, kernel='rbf', random_state=RANDOM_SEED)\n",
    "rg_clf = RidgeClassifier(alpha=0.1, random_state=RANDOM_SEED)\n",
    "lr_clf = LogisticRegression(C=20000, penalty='l2', random_state=RANDOM_SEED)\n",
    "dt_clf = DecisionTreeClassifier(criterion='gini', max_depth=2, random_state=RANDOM_SEED)\n",
    "adab_clf = AdaBoostClassifier(n_estimators=100)\n",
    "lr = LogisticRegression(random_state=RANDOM_SEED) # meta classifier\n",
    "\n",
    "sclf = StackingClassifier(classifiers=[rf_clf, et_clf, knn_clf, svc_clf, rg_clf, lr_clf, dt_clf, adab_clf], meta_classifier=lr)\n",
    "\n",
    "classifier_array = [rf_clf, et_clf, knn_clf, svc_clf, rg_clf, lr_clf, dt_clf, adab_clf, sclf]\n",
    "labels = [clf.__class__.__name__ for clf in classifier_array]\n",
    "\n",
    "acc_list = []\n",
    "var_list = []\n",
    "\n",
    "for clf, label in zip(classifier_array, labels):\n",
    "    cv_scores = model_selection.cross_val_score(clf, X, y, cv=3, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.4f (+/- %0.4f) [%s]\" % (cv_scores.mean(), cv_scores.std(), label))\n",
    "    acc_list.append(np.round(cv_scores.mean(),4))\n",
    "    var_list.append(np.round(cv_scores.std(),4))\n",
    "    #print(\"Accuracy: {} (+/- {}) [{}]\".format(np.round(scores.mean(),4), np.round(scores.std(),4), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A bar graph to see stacking actually increases the accuracy.\n",
    "\n",
    "The below code block will plot the accuracy values for each of the base learners and also the accuracy of the final meta-classifier. We have managed to increase the accuracy value very slightly from 0.9600 (highest accuracy obtained from a single base learner) to 0.9608 (using the stacking classifier)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "n_groups = 9\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.30\n",
    "\n",
    "opacity = .7\n",
    "error_config = {'ecolor': '0.3'}\n",
    "\n",
    "normal_clf = ax.bar(index, acc_list, bar_width, alpha=opacity, color='g', yerr=var_list, error_kw=error_config, label='Classifiers')\n",
    "\n",
    "ax.set_xlabel('Classifiers')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title('Improving accuracy using stacking classifiers')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels((labels))\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we will look at the decision regions obtained using three of our base learners and also the final stacked meta-classifier. The three base learners that we will select for this purpose are RandomForestClassifier, SupportVectorClassifer, and RidgeClassifier. As like before, I will train and fit the model to two of the most important features, i.e. \"petal_length\" and \"petal_width\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decision Regions for 4 algorithms.\n",
    "X = np.array(iris_dataset[['petal_length','petal_width']])\n",
    "y = np.array(y)\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "fig = plt.figure(figsize=(20,16))\n",
    "\n",
    "\n",
    "for clf, label, grd in zip([rf_clf, svc_clf, rg_clf, sclf], [\"Random Forest Classifier\", \"Support Vector Classifer\", \"Ridge Classifier\", \"Stacking Classifier\"], itertools.product([0, 1], repeat=2)):\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using the actual class label of the base learners, we can also use the probability scores of each of the base models to train the meta-classifier. For this, we need to set \"use_probas=True\". Using \"average_probas=True\" the probability scores of each of the base learners are averaged. However, setting \"average_probas=False\" means that the probability scores for each of the base learners are stacked and they are all passed as input to the next level classifiers. Note, that we have actually managed to increase the accuracy of the stacking classifiers by using probability score. 0.9673 as compared to 0.9608, as you would see in the example below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9538 (+/- 0.0367) [RandomForestClassifier]\n",
      "Accuracy: 0.9534 (+/- 0.0087) [KNeighborsClassifier]\n",
      "Accuracy: 0.9342 (+/- 0.0328) [GaussianNB]\n",
      "Accuracy: 0.9542 (+/- 0.0515) [LogisticRegression]\n",
      "Accuracy: 0.9408 (+/- 0.0420) [ExtraTreesClassifier]\n",
      "Accuracy: 0.9473 (+/- 0.0329) [DecisionTreeClassifier]\n",
      "Accuracy: 0.9600 (+/- 0.0161) [AdaBoostClassifier]\n",
      "Accuracy: 0.9673 (+/- 0.0333) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "RANDOM_SEED = 0\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "X, y = iris_dataset.iloc[:,0:4], iris_dataset.iloc[:,4]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder_object = LabelEncoder()\n",
    "y = encoder_object.fit_transform(y)\n",
    "\n",
    "#Base Learners\n",
    "rf_clf = RandomForestClassifier(n_estimators=10, random_state=RANDOM_SEED)\n",
    "et_clf = ExtraTreesClassifier(n_estimators=5, random_state=RANDOM_SEED)\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=2)\n",
    "lr_clf = LogisticRegression(C=20000, penalty='l2', random_state=RANDOM_SEED)\n",
    "dt_clf = DecisionTreeClassifier(criterion='gini', max_depth=2, random_state=RANDOM_SEED)\n",
    "adab_clf = AdaBoostClassifier(n_estimators=100)\n",
    "lr = LogisticRegression(random_state=RANDOM_SEED) # meta classifier\n",
    "gnb_clf = GaussianNB()\n",
    "\n",
    "#sclf = StackingClassifier(classifiers=[rf_clf, et_clf, knn_clf, svc_clf, rg_clf, lr_clf, dt_clf, adab_clf], use_probas=True, average_probas=False, meta_classifier=lr)\n",
    "sclf = StackingClassifier(classifiers=[rf_clf, knn_clf, gnb_clf, lr_clf, et_clf, dt_clf, adab_clf], use_probas=True, average_probas=False, meta_classifier=lr)\n",
    "\n",
    "classifier_array = [rf_clf, knn_clf, gnb_clf, lr_clf, et_clf, dt_clf, adab_clf, sclf]\n",
    "labels = [clf.__class__.__name__ for clf in classifier_array]\n",
    "\n",
    "for clf, label in zip(classifier_array, labels):\n",
    "    cv_scores = model_selection.cross_val_score(clf, X, y, cv=3, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.4f (+/- %0.4f) [%s]\" % (cv_scores.mean(), cv_scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us implement stacking using Grid Search cross-validation to fine tune the base learners. For simplicity, I will use three base learners and one final meta-classifier. Needless to say, you can use as many models as you want as long as you know how the implementation works and what the specific hyperparameters are for each base learners. Please note that I have used the same set of classifiers as shown in the first example of stacking. This time with Grid Search cross-validation. Let's find out if the accuracy increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracies of all classifiers using grid search cross validation.\n",
      "Best parameters: {'decisiontreeclassifier__max_depth': 1, 'kneighborsclassifier__n_neighbors': 7, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__max_depth': 3, 'randomforestclassifier__n_estimators': 15}\n",
      "Accuracy: 0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "RANDOM_SEED = 0\n",
    "\n",
    "X, y = iris_dataset.iloc[:,0:4], iris_dataset.iloc[:,4]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder_object = LabelEncoder()\n",
    "y = encoder_object.fit_transform(y)\n",
    "\n",
    "\n",
    "#Base Learners.\n",
    "rf_clf = RandomForestClassifier(random_state=RANDOM_SEED,n_jobs=-1)\n",
    "knn_clf = KNeighborsClassifier(p=2, metric='minkowski',n_jobs=-1)\n",
    "dt_clf = DecisionTreeClassifier(criterion='gini', random_state=RANDOM_SEED)\n",
    "lr = LogisticRegression(random_state=RANDOM_SEED) # meta classifier\n",
    "\n",
    "#sclf = StackingClassifier(classifiers=[rf_clf, et_clf, knn_clf, svc_clf, rg_clf, lr_clf, dt_clf, adab_clf], meta_classifier=lr)\n",
    "sclf = StackingClassifier(classifiers=[rf_clf, knn_clf, dt_clf], meta_classifier=lr)\n",
    "\n",
    "print(\"\\nAccuracies of all classifiers using grid search cross validation.\")\n",
    "\n",
    "params = {'randomforestclassifier__n_estimators': np.arange(10,20), 'randomforestclassifier__max_depth': np.arange(1,5), \n",
    "          'kneighborsclassifier__n_neighbors': np.arange(1,20,2),\n",
    "          'decisiontreeclassifier__max_depth': np.arange(1,5),\n",
    "          'meta-logisticregression__C': [0.001,0.01,0.1,1,10,100,1000]}\n",
    "\n",
    "\n",
    "gsearch_cv = GridSearchCV(estimator=sclf, param_grid=params, cv=5, refit=True)\n",
    "gsearch_cv.fit(X, y)\n",
    "\n",
    "cv_keys = ('mean_test_score', 'std_test_score', 'params')\n",
    "\n",
    "print('Best parameters: %s' % gsearch_cv.best_params_)\n",
    "print('Accuracy: %.2f' % gsearch_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision boundaries of the above stacking classifiers using the best hyperparameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1152 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import matplotlib.gridspec as gridspec\n",
    "import itertools\n",
    "\n",
    "#Decision Regions for 4 algorithms.\n",
    "X = np.array(iris_dataset[['petal_length','petal_width']])\n",
    "y = np.array(y)\n",
    "\n",
    "#Base Learners.\n",
    "rf_clf = RandomForestClassifier(max_depth=3,n_estimators=15,random_state=RANDOM_SEED,n_jobs=-1)\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=7,p=2, metric='minkowski',n_jobs=-1)\n",
    "dt_clf = DecisionTreeClassifier(max_depth=1,criterion='gini', random_state=RANDOM_SEED)\n",
    "lr = LogisticRegression(C=0.1,random_state=RANDOM_SEED) # meta classifier\n",
    "sclf = StackingClassifier(classifiers=[rf_clf, knn_clf, dt_clf], meta_classifier=lr)\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "fig = plt.figure(figsize=(20,16))\n",
    "\n",
    "\n",
    "for clf, label, grd in zip([rf_clf, knn_clf, dt_clf, sclf], [\"RandomForestClassifier\", \"KNeighborsClassifier\", \"DecisionTreeClassifier\", \"StackingClassifier\"], itertools.product([0, 1], repeat=2)):\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Level stacking.\n",
    "\n",
    "The examples I have covered for Stacking classifiers are all ensemble models which has one single level. But in general multiple models can be stacked across multiple level. I will show you one such Stacking design used by the winners of the Kaggle KDD cup compeition. This image was uploaded in a PPT designed by the SAS team, by author Jeong-Yoon Lee. You can check this wonderful video in youtube at this link:https://www.youtube.com/watch?v=9IyJ4HvubGo&t=1070s, for further elaborations.\n",
    "\n",
    "<img src=\"EnsembleLevels.png\">\n",
    "\n",
    "The above stacking model uses 64 base learners on seven feature sets at level 1. All the 64 models are trained independently on the training set. The predictions of these 64 models are fed to Stage 1 ensemble classifiers which has 15 more models. The predictions of these 15 models are further combined using majority vote and their output is fed to Stage 2 ensemble classifiers, which has 2 models. Finally, the output of these 2 models are fed to the meta-classifier in Stage 3, which gives us the final output of the model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cascading classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cascading, according to Google, in simple English literature means \"a process whereby something, typically information or knowledge, is successively passed on\". Cascading is one of the most powerful ensemble learning algorithm which is used by Machine Learning engineers and scientists when they want to be absolutely dead sure about the accuracy of a result. For example, suppose we want to build a machine learning model which would detect if a credit card transaction is fraudulent or not. If you think about it, it's a binary classification problem where a class label 0 means the transaction is not fraud & a class label 1 means the transaction is fraudulent. In such a model, it's very risky to put our faith completely on just one model. So what we do is build a sequence of models (or a cascade of models) to be absolutely sure about the fact that the transaction is not fraudulent. Cascade models are mostly used when the cost of making a mistake is very very high. I will try to explain cascading with the help of a simple diagram. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "<img src=\"CascadingTESTING.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "Look at the above diagram. Given that we have a transaction query point Xq, we will feed it to Model 1. Model 1 can be anything - a random forest, or a logistic regression model or maybe a support vector machine. It can be anything! Basically what Model 1 does is that it predicts class probabilities to determine to which class do a given query point has higher chances of belonging to. Let's say class label 1 means the transaction is fraudulent, and class label 0 means the transaction is not fraud. Typically, the predicted probabilities is given by this - P(Yq=0) and P(Yq=1), where Yq is our actual class label. Now let's assume that P(Yq=0), i.e. the probability of the transaction to be not fraudulent is very high. If you think carefully, if P(Yq=0) is extremely high, we will say that the transaction is not fraud. Let's assume we have set a threshold of 99%. It means if and only if P(Yq=0) > 0.99, we will declare the final prediction to be not fraudulent. However, if P(Yq=0) < 0.99 we are not very sure if or not it's a fraudulent transaction although though there is a high chance that the transaction is not fraudulent. In such a case, when P(Yq=0) < 0.99, we want to be really really sure that the transaction is not fraudulent. We need to be absolutely careful because if our model fails to detect a fraudulent transaction we might lose millions of dollars! So even when we are slightly unsure, we will train another Model 2. Model 2 does the same thing, it receives the query point and predicts P(Yq=0). Just like in stage 1, if P(Yq=0) > 0.99, we will declare the transaction to be not fraudulent and terminate the loop. But again if we get P(Yq=0) < 0.99, we aren' sure! Hence, we will pass the query point to another Model 3 in the cascade which does the same thing. In a typical cascading system the complexity of models increases as we add more and more models to the cascade.  Please note that all the models in a cascade are super powerful and has a very high accuracy on unseen data. However, it might happen that none of the models can give us a value of P(Yq=0) > 0.99. In such a case, typically there is a human being who sits at the end of a cascade. This person will personally call the customer and ask him whether or not he has done the transaction. Now, we are absolutely certain that the transaction is not a fraud one when the customer says that he is the one who has done the transaction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "<img src=\"CascadingTRAINING.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "The above diagram shows us the different stages of training in a cascade system of classifiers. Here we have four models in a sequence. We will train model 1 on the whole training dataset and evaluate its performance on the test dataset. Now wherever we are sure that the class label is 0, we will categorize all such points in a dataset D'. This is at stage 1 of the training phase. Thus D' contains all such data points for which the class label is 0, i.e. the transaction is not fraudulent. Now, wherever we are not sure about some data points (if they are fraudulent or not) we pass it to the next stage i.e. Model 2 in our case. Hence, model 2 will only train on the dataset which does not contain points from D'. Model 2 can't be exposed to all such points for which we are sure that the class label is 0. It will only train on the points for which we are not sure. After this, we will put all such points in a dataset D'' for which we are sure that they belong to class label 0 at the end of stage 2. We will repeat the same procedure for stage 3 as well unless the training phase reaches the final model. Intuitively, if you think about it, the cascades are designed in such a way that the next model in the sequence is only trained on the data points for which the model isn't sure what the class label is. We must always train our model as per what type of data it would see during runtime. \n",
    "\n",
    "I hope I was able to give an intuition of how cascading in machine learning works! For the above explanations on cascading, I have referred to Mr. Varma's teachings from AAIC. I have not provided any code samples for cascading classifiers as I have not implemented it. Keep watching this space for more. I will update this blog in future with code samples on how to build cascade classifiers after I have implemented it myself.\n",
    "\n",
    "There is a very interesting paper on cascading models written by few Ph.D. folks at Stanford University. These guys have actually build a framework called Cascaded Classification Models (CCM), which aims at improving the accuracy/performance at each level by repeatedly instantiating the classifiers coupled by their input/output variables in a cascade. You can read more about the paper at this link: https://ai.stanford.edu/~koller/Papers/Heitz+al:NIPS08a.pdf\n",
    "\n",
    "There is also a very interesting blog on how to build a face detection model using cascade classifiers. If you are interested you can visit http://scikit-image.org/docs/dev/auto_examples/xx_applications/plot_face_detection.html to see how it works!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "****CLOSING NOTE******"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have come this far, thank you for having the immense patience to read through all the stuffs. I hope I was able to make you guys understand what ensemble techniques are and how do they increase the accuracy in Machine Learning models. In general, ensemble learnings are mostly used by Kaggle participants to extremely fine-tune their models. In industries however, stacking huge models in several levels is a very costly procedure. And a 0.5% increase in accuracy practically wont have the greatest of impact on business problems. However, for Kaggle competetions ensemble techniques are state of the art strategies that can be used to increase the accuracy by maybe 0.001, which might just help you to win the competetion! \n",
    "\n",
    "Here are few solid recommendations as part of some additional readings on ensemble learning algorithms:\n",
    "\n",
    "1. The diagrams are drawn using an awesome tool provdided by this website: https://www.draw.io/. You can use this tool to draw anything you like and it's as simple as drawing using your crayons!\n",
    "2. I would recommend you guys to read through each and every examples given by the MLEXTEND GitHub page at: http://rasbt.github.io/mlxtend/.  \n",
    "3. For additional details about ensemble techniques please read this nice blog written by Dr. Robi Polikar of Rowan University at this link: http://www.scholarpedia.org/article/Ensemble_learning\n",
    "4. You can also visit the Wikipedia page on ensemble learning for further information: https://en.wikipedia.org/wiki/Ensemble_learning\n",
    "5. Please refer to this blogpost by Vadim Smolyakov: https://blog.statsbot.co/ensemble-learning-d1dcd548e936\n",
    "6. Also, there is an extremely beautiful series of videos at MIT OpenCourseWare by Dr. Patrick Winston. Check this link: https://www.youtube.com/watch?v=UHBmv7qCey4\n",
    "7. Read the Kaggle ensemble guide, written by  Vitaly Kuznetsov here: https://mlwave.com/kaggle-ensembling-guide/\n",
    "8. For sklearn, you can visit https://scikit-learn.org/stable/modules/ensemble.html for more information about the built in packages used for ensemble learning."
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