{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Multi-class AdaBoosted Decision Trees\n",
    "\n",
    "This example shows how boosting can improve the prediction accuracy on a\n",
    "multi-label classification problem. It reproduces a similar experiment as\n",
    "depicted by Figure 1 in Zhu et al [1]_.\n",
    "\n",
    "The core principle of AdaBoost (Adaptive Boosting) is to fit a sequence of weak\n",
    "learners (e.g. Decision Trees) on repeatedly re-sampled versions of the data.\n",
    "Each sample carries a weight that is adjusted after each training step, such\n",
    "that misclassified samples will be assigned higher weights. The re-sampling\n",
    "process with replacement takes into account the weights assigned to each sample.\n",
    "Samples with higher weights have a greater chance of being selected multiple\n",
    "times in the new data set, while samples with lower weights are less likely to\n",
    "be selected. This ensures that subsequent iterations of the algorithm focus on\n",
    "the difficult-to-classify samples.\n",
    "\n",
    ".. rubric:: References\n",
    "\n",
    ".. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, \"Multi-class adaboost.\"\n",
    "    Statistics and its Interface 2.3 (2009): 349-360.\n",
    "    <10.4310/SII.2009.v2.n3.a8>`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Authors: The scikit-learn developers\n",
    "# SPDX-License-Identifier: BSD-3-Clause"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the dataset\n",
    "The classification dataset is constructed by taking a ten-dimensional standard\n",
    "normal distribution ($x$ in $R^{10}$) and defining three classes\n",
    "separated by nested concentric ten-dimensional spheres such that roughly equal\n",
    "numbers of samples are in each class (quantiles of the $\\chi^2$\n",
    "distribution).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "\n",
    "X, y = make_gaussian_quantiles(\n",
    "    n_samples=2_000, n_features=10, n_classes=3, random_state=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the dataset into 2 sets: 70 percent of the samples are used for\n",
    "training and the remaining 30 percent for testing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, train_size=0.7, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the `AdaBoostClassifier`\n",
    "We train the :class:`~sklearn.ensemble.AdaBoostClassifier`. The estimator\n",
    "utilizes boosting to improve the classification accuracy. Boosting is a method\n",
    "designed to train weak learners (i.e. `estimator`) that learn from their\n",
    "predecessor's mistakes.\n",
    "\n",
    "Here, we define the weak learner as a\n",
    ":class:`~sklearn.tree.DecisionTreeClassifier` and set the maximum number of\n",
    "leaves to 8. In a real setting, this parameter should be tuned. We set it to a\n",
    "rather low value to limit the runtime of the example.\n",
    "\n",
    "The `SAMME` algorithm build into the\n",
    ":class:`~sklearn.ensemble.AdaBoostClassifier` then uses the correct or\n",
    "incorrect predictions made be the current weak learner to update the sample\n",
    "weights used for training the consecutive weak learners. Also, the weight of\n",
    "the weak learner itself is calculated based on its accuracy in classifying the\n",
    "training examples. The weight of the weak learner determines its influence on\n",
    "the final ensemble prediction.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "weak_learner = DecisionTreeClassifier(max_leaf_nodes=8)\n",
    "n_estimators = 300\n",
    "\n",
    "adaboost_clf = AdaBoostClassifier(\n",
    "    estimator=weak_learner,\n",
    "    n_estimators=n_estimators,\n",
    "    random_state=42,\n",
    ").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "Convergence of the `AdaBoostClassifier`\n",
    "***************************************\n",
    "To demonstrate the effectiveness of boosting in improving accuracy, we\n",
    "evaluate the misclassification error of the boosted trees in comparison to two\n",
    "baseline scores. The first baseline score is the `misclassification_error`\n",
    "obtained from a single weak-learner (i.e.\n",
    ":class:`~sklearn.tree.DecisionTreeClassifier`), which serves as a reference\n",
    "point. The second baseline score is obtained from the\n",
    ":class:`~sklearn.dummy.DummyClassifier`, which predicts the most prevalent\n",
    "class in a dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier's misclassification_error: 0.475\n",
      "DummyClassifier's misclassification_error: 0.692\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "dummy_clf = DummyClassifier()\n",
    "\n",
    "\n",
    "def misclassification_error(y_true, y_pred):\n",
    "    return 1 - accuracy_score(y_true, y_pred)\n",
    "\n",
    "\n",
    "weak_learners_misclassification_error = misclassification_error(\n",
    "    y_test, weak_learner.fit(X_train, y_train).predict(X_test)\n",
    ")\n",
    "\n",
    "dummy_classifiers_misclassification_error = misclassification_error(\n",
    "    y_test, dummy_clf.fit(X_train, y_train).predict(X_test)\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"DecisionTreeClassifier's misclassification_error: \"\n",
    "    f\"{weak_learners_misclassification_error:.3f}\"\n",
    ")\n",
    "print(\n",
    "    \"DummyClassifier's misclassification_error: \"\n",
    "    f\"{dummy_classifiers_misclassification_error:.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training the :class:`~sklearn.tree.DecisionTreeClassifier` model, the\n",
    "achieved error surpasses the expected value that would have been obtained by\n",
    "guessing the most frequent class label, as the\n",
    ":class:`~sklearn.dummy.DummyClassifier` does.\n",
    "\n",
    "Now, we calculate the `misclassification_error`, i.e. `1 - accuracy`, of the\n",
    "additive model (:class:`~sklearn.tree.DecisionTreeClassifier`) at each\n",
    "boosting iteration on the test set to assess its performance.\n",
    "\n",
    "We use :meth:`~sklearn.ensemble.AdaBoostClassifier.staged_predict` that makes\n",
    "as many iterations as the number of fitted estimator (i.e. corresponding to\n",
    "`n_estimators`). At iteration `n`, the predictions of AdaBoost only use the\n",
    "`n` first weak learners. We compare these predictions with the true\n",
    "predictions `y_test` and we, therefore, conclude on the benefit (or not) of adding a\n",
    "new weak learner into the chain.\n",
    "\n",
    "We plot the misclassification error for the different stages:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "boosting_errors = pd.DataFrame(\n",
    "    {\n",
    "        \"Number of trees\": range(1, n_estimators + 1),\n",
    "        \"AdaBoost\": [\n",
    "            misclassification_error(y_test, y_pred)\n",
    "            for y_pred in adaboost_clf.staged_predict(X_test)\n",
    "        ],\n",
    "    }\n",
    ").set_index(\"Number of trees\")\n",
    "ax = boosting_errors.plot()\n",
    "ax.set_ylabel(\"Misclassification error on test set\")\n",
    "ax.set_title(\"Convergence of AdaBoost algorithm\")\n",
    "\n",
    "plt.plot(\n",
    "    [boosting_errors.index.min(), boosting_errors.index.max()],\n",
    "    [weak_learners_misclassification_error, weak_learners_misclassification_error],\n",
    "    color=\"tab:orange\",\n",
    "    linestyle=\"dashed\",\n",
    ")\n",
    "plt.plot(\n",
    "    [boosting_errors.index.min(), boosting_errors.index.max()],\n",
    "    [\n",
    "        dummy_classifiers_misclassification_error,\n",
    "        dummy_classifiers_misclassification_error,\n",
    "    ],\n",
    "    color=\"c\",\n",
    "    linestyle=\"dotted\",\n",
    ")\n",
    "plt.legend([\"AdaBoost\", \"DecisionTreeClassifier\", \"DummyClassifier\"], loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows the missclassification error on the test set after each\n",
    "boosting iteration. We see that the error of the boosted trees converges to an\n",
    "error of around 0.3 after 50 iterations, indicating a significantly higher\n",
    "accuracy compared to a single tree, as illustrated by the dashed line in the\n",
    "plot.\n",
    "\n",
    "The misclassification error jitters because the `SAMME` algorithm uses the\n",
    "discrete outputs of the weak learners to train the boosted model.\n",
    "\n",
    "The convergence of :class:`~sklearn.ensemble.AdaBoostClassifier` is mainly\n",
    "influenced by the learning rate (i.e. `learning_rate`), the number of weak\n",
    "learners used (`n_estimators`), and the expressivity of the weak learners\n",
    "(e.g. `max_leaf_nodes`).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Errors and weights of the Weak Learners\n",
    "As previously mentioned, AdaBoost is a forward stagewise additive model. We\n",
    "now focus on understanding the relationship between the attributed weights of\n",
    "the weak learners and their statistical performance.\n",
    "\n",
    "We use the fitted :class:`~sklearn.ensemble.AdaBoostClassifier`'s attributes\n",
    "`estimator_errors_` and `estimator_weights_` to investigate this link.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weak_learners_info = pd.DataFrame(\n",
    "    {\n",
    "        \"Number of trees\": range(1, n_estimators + 1),\n",
    "        \"Errors\": adaboost_clf.estimator_errors_,\n",
    "        \"Weights\": adaboost_clf.estimator_weights_,\n",
    "    }\n",
    ").set_index(\"Number of trees\")\n",
    "\n",
    "axs = weak_learners_info.plot(\n",
    "    subplots=True, layout=(1, 2), figsize=(10, 4), legend=False, color=\"tab:blue\"\n",
    ")\n",
    "axs[0, 0].set_ylabel(\"Train error\")\n",
    "axs[0, 0].set_title(\"Weak learner's training error\")\n",
    "axs[0, 1].set_ylabel(\"Weight\")\n",
    "axs[0, 1].set_title(\"Weak learner's weight\")\n",
    "fig = axs[0, 0].get_figure()\n",
    "fig.suptitle(\"Weak learner's errors and weights for the AdaBoostClassifier\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the left plot, we show the weighted error of each weak learner on the\n",
    "reweighted training set at each boosting iteration. On the right plot, we show\n",
    "the weights associated with each weak learner later used to make the\n",
    "predictions of the final additive model.\n",
    "\n",
    "We see that the error of the weak learner is the inverse of the weights. It\n",
    "means that our additive model will trust more a weak learner that makes\n",
    "smaller errors (on the training set) by increasing its impact on the final\n",
    "decision. Indeed, this exactly is the formulation of updating the base\n",
    "estimators' weights after each iteration in AdaBoost.\n",
    "\n",
    ".. dropdown:: Mathematical details\n",
    "\n",
    "   The weight associated with a weak learner trained at the stage $m$ is\n",
    "   inversely associated with its misclassification error such that:\n",
    "\n",
    "   .. math:: \\alpha^{(m)} = \\log \\frac{1 - err^{(m)}}{err^{(m)}} + \\log (K - 1),\n",
    "\n",
    "   where $\\alpha^{(m)}$ and $err^{(m)}$ are the weight and the error\n",
    "   of the $m$ th weak learner, respectively, and $K$ is the number of\n",
    "   classes in our classification problem.\n",
    "\n",
    "Another interesting observation boils down to the fact that the first weak\n",
    "learners of the model make fewer errors than later weak learners of the\n",
    "boosting chain.\n",
    "\n",
    "The intuition behind this observation is the following: due to the sample\n",
    "reweighting, later classifiers are forced to try to classify more difficult or\n",
    "noisy samples and to ignore already well classified samples. Therefore, the\n",
    "overall error on the training set will increase. That's why the weak learner's\n",
    "weights are built to counter-balance the worse performing weak learners.\n",
    "\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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