{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "# RF (Random Forest)\n",
    "\n",
    "We will break timeline a bit and jump to random forests instead of introducing perceptrons. The general method of random decision forests was first proposed by Ho only in 1995. RF became really popular approach for tabular dataset due to their simplicity and quite robust behaviour. They are still widely used and for some problems can outperform neural nets.\n",
    "\n",
    "Before defining random forests we need to explore it's main building block - entropy.\n",
    "\n",
    "## Entropy\n",
    "\n",
    "Coined by Claude Shannon in “A Mathematical Theory of Communication” 1948 ([link](http://www.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf)).\n",
    "Main question: how much useful information are we transmitting?\n",
    "When we transmit one bit of information we reduce recipients uncertainty by the factor of two.\n",
    "\n",
    "P.S.: same paper introduced term bit = **bi**nary uni**t**.\n",
    "\n",
    "Look at the following video:\n",
    "\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ErfnhcEV1O8\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
    "\n",
    "Some key points:\n",
    "- How much information are we getting measured in bits? $- \\log_2 (prob) = bits$\n",
    "- Entropy: $H(p)=-\\sum_i p_i \\log_2 (p_i)$\n",
    "- Cross-Entropy: let $p$ - true distribution, $q$ - predicted distribution, then Cross-entropy is\n",
    "$H(p,q) = -\\sum_i p_i \\log_2(q_i)$\n",
    "- If predictions are perfect Cross-entropy is equal to entropy, but usually it is greater than entropy.\n",
    "- Cross-entropy is often used for ML as a cost function (log-loss) comparing $p$ with $q$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from graphviz import Source\n",
    "from IPython.display import display, SVG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "## Iris dataset\n",
    "\n",
    "Bellow we will work with iris dataset. The idea of the dataset is to find out iris class based on given measurements.\n",
    "\n",
    "<img src=\"https://miro.medium.com/max/1100/0*SHhnoaaIm36pc1bd\" alt=\"Iris dataset\" style=\"width: 60%\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "df['class'] = iris.target_names[iris.target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "         class  \n",
       "0       setosa  \n",
       "1       setosa  \n",
       "2       setosa  \n",
       "3       setosa  \n",
       "4       setosa  \n",
       "..         ...  \n",
       "145  virginica  \n",
       "146  virginica  \n",
       "147  virginica  \n",
       "148  virginica  \n",
       "149  virginica  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 4 dimensions, but we already know that we can visualize that using PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(2)\n",
    "pca.fit(df.drop('class', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "X = pca.transform(df.drop('class', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pc_1', ylabel='pc_2'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = pd.DataFrame(X, columns=['pc_1', 'pc_2'])\n",
    "pc['class_id'] = iris.target\n",
    "\n",
    "pc.plot(kind='scatter', x='pc_1', y='pc_2', c='class_id', cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees\n",
    "\n",
    "Simple decision tree can be constructed using rule: make a split which **reduces entropy** the most. In a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
    "\n",
    "<img src=\"https://paper-attachments.dropbox.com/s_4C6E1420D930BE100F0F1313D7A90868E9FB8CAFCB77E86AC4D0FB805D8FC787_1569515384629_image.png\" alt=\"Space deformation\" style=\"width: 70%\"/>\n",
    "\n",
    "\n",
    "Couple facts about decision trees:\n",
    "\n",
    "- Alternatively you could use Gini impurity as a split criteria, which is defined as $G_i = 1 - \\sum_{k=1}^n p_{i,k}^2$ where $p_{i,k}$ is the ratio of class k instances among the training instances in the ith node.\n",
    "- Finding the optimal tree is known to be an NP-Complete problem $O(\\exp (m))$.\n",
    "- Decision Trees are very sensitive to small variations in the training data.\n",
    "- Decision Trees can easily over-fit if there is high number of columns! \n",
    "\n",
    "Let's try this out on iris dataset. First we will need to make train/test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.drop('class', axis=1), df['class'],\n",
    "    test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit entropy based tree on iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------\n",
      "Tree for max_dept = 1\n",
      "\n",
      "Correctly identified on train set - 64.76%, on test set - 71.11%\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"272pt\" height=\"195pt\" viewBox=\"0.00 0.00 272.00 195.00\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 191)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-191 268,-191 268,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<polygon fill=\"#ffffff\" stroke=\"black\" points=\"201,-187 52,-187 52,-104 201,-104 201,-187\"/>\n",
       "<text text-anchor=\"middle\" x=\"126.5\" y=\"-171.8\" font-family=\"Times,serif\" font-size=\"14.00\">petal width (cm) &lt;= 0.8</text>\n",
       "<text text-anchor=\"middle\" x=\"126.5\" y=\"-156.8\" font-family=\"Times,serif\" font-size=\"14.00\">entropy = 1.58</text>\n",
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      "----------------------------------------------------------------------\n",
      "Tree for max_dept = 2\n",
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      "----------------------------------------------------------------------\n",
      "Tree for max_dept = 3\n",
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       "<title>8</title>\n",
       "<polygon fill=\"#8139e5\" stroke=\"black\" points=\"581,-68 454,-68 454,0 581,0 581,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"517.5\" y=\"-52.8\" font-family=\"Times,serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"517.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 26</text>\n",
       "<text text-anchor=\"middle\" x=\"517.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 0, 26]</text>\n",
       "<text text-anchor=\"middle\" x=\"517.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = 2 - virginica</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>6-&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M430.89,-103.73C442.84,-94.24 455.55,-84.16 467.45,-74.72\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"469.88,-77.26 475.53,-68.3 465.52,-71.77 469.88,-77.26\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for max_depth in range(1, 4):\n",
    "    print('-' * 70 + '\\nTree for max_dept = {0}\\n'.format(max_depth))\n",
    "    tree = DecisionTreeClassifier(max_depth=max_depth, criterion='entropy')\n",
    "    # Fit on train data\n",
    "    tree.fit(X_train, y_train)\n",
    "\n",
    "    # Make prediction and evaluate performance\n",
    "    pred_train = tree.predict(X_train)\n",
    "    pred_test = tree.predict(X_test)\n",
    "    print('Correctly identified on train set - {0:.02%}, on test set - {1:.02%}'.format(\n",
    "        (pred_train == y_train).mean(),\n",
    "        (pred_test == y_test).mean()))\n",
    "\n",
    "    # Make a nice plot\n",
    "    graph = Source(export_graphviz(tree, out_file=None, filled = True,\n",
    "                                   feature_names=df.drop('class', axis=1).columns,\n",
    "                                   class_names=['0 - setosa', '1 - versicolor', '2 - virginica']))\n",
    "    display(SVG(graph.pipe(format='svg')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see simple decision tree works quite good for iris dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Larger dataset and overfit example\n",
    "\n",
    "Let's load another legendary dataset containing classification problem. If you are interested in dataset details see [UCI](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "# Load dataset\n",
    "cancer = load_breast_cancer()\n",
    "df = pd.DataFrame(cancer.data, columns = cancer.feature_names)\n",
    "df['class'] = cancer.target_names[cancer.target]\n",
    "\n",
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.drop('class', axis=1), df['class'],\n",
    "    test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "benign       357\n",
       "malignant    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before we will fit multiple trees with different max_depth's. This time let's plot performance on train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Accuracy for different max_depth values'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = []\n",
    "for max_depth in range(1, 10):\n",
    "    tree = DecisionTreeClassifier(max_depth=max_depth, criterion='entropy', random_state=42)\n",
    "    # Fit on train data\n",
    "    tree.fit(X_train, y_train)\n",
    "\n",
    "    # Make prediction and evaluate performance\n",
    "    pred_train = tree.predict(X_train)\n",
    "    pred_test = tree.predict(X_test)\n",
    "    L.append([(pred_train == y_train).mean(),\n",
    "              (pred_test == y_test).mean()])\n",
    "\n",
    "pd.DataFrame(L, columns=['train', 'test']).plot(\n",
    "    title='Accuracy for different max_depth values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that as the depth increases, we tend to get very strangely shaped classification regions. Such overfitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from. Another way to see this overfitting is to look at models trained on different subsets of the data.\n",
    "\n",
    "How can we prevent it in this case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembles\n",
    "\n",
    "The notion that multiple overfitting estimators can be combined to reduce the effect of overfitting is what underlies an ensemble method called bagging. Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which overfits the data, and averages the results to find a better classification. An ensemble of randomized decision trees is known as a random forest.\n",
    "\n",
    "<img src=\"https://paper-attachments.dropbox.com/s_4C6E1420D930BE100F0F1313D7A90868E9FB8CAFCB77E86AC4D0FB805D8FC787_1569583935206_image.png\"  style=\"width: 50%\"/>\n",
    "\n",
    "<img src=\"https://paper-attachments.dropbox.com/s_4C6E1420D930BE100F0F1313D7A90868E9FB8CAFCB77E86AC4D0FB805D8FC787_1569583987571_image.png\"  style=\"width: 50%\"/>\n",
    "\n",
    "Ensemble methods work best when the predictors are as independent from one another as possible. One way to get diverse classifiers is to train them using very different algorithms. This increases the chance that they will make very different types of errors, improving the ensemble’s accuracy.\n",
    "\n",
    "Ensemble methods lead to similar bias but a lower variance.\n",
    "\n",
    "<img src=\"https://nvsyashwanth.github.io/machinelearningmaster/assets/images/bias_variance.jpg\" style=\"width: 40%\"/>\n",
    "\n",
    "### Random forests\n",
    "\n",
    "You can make multiple trees by choosing sub-sample of features (usually $\\sqrt{m}$) and taking bootstrapped samples. Then just let trees vote for decision.\n",
    "\n",
    "<img src=\"https://victorzhou.com/media/random-forest-post/random-forest.png\" style=\"width: 60%\"/>\n",
    "\n",
    "Hard voting averages predictions, soft voting averages predicted probabilities instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Defining the base estimator\n",
    "base = DecisionTreeClassifier(max_depth=5, splitter='best',\n",
    "                              max_features='sqrt', criterion='entropy')\n",
    "\n",
    "# Create Random Forest \n",
    "ensemble = BaggingClassifier(base_estimator=base, n_estimators=1000,\n",
    "                             bootstrap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly identified on train set - 99.71%, on test set - 96.93%\n",
      "\n",
      "CPU times: user 1.19 s, sys: 311 ms, total: 1.5 s\n",
      "Wall time: 830 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "ensemble.fit(X_train, y_train)\n",
    "pred_train = ensemble.predict(X_train)\n",
    "pred_test = ensemble.predict(X_test)\n",
    "    \n",
    "print('Correctly identified on train set - {0:.02%}, on test set - {1:.02%}\\n'.format(\n",
    "    (pred_train == y_train).mean(),        # train set\n",
    "    (pred_test == y_test).mean()))         # test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In realiy you should use `RandomForestClassifier` implementation instead. Lines above are only for illustation purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "RandomForestClassifier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correctly identified on train set - 100.00%, on test set - 97.81%\n",
      "\n",
      "CPU times: user 736 ms, sys: 11.6 ms, total: 747 ms\n",
      "Wall time: 753 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "rf = RandomForestClassifier(1000, criterion='entropy', max_depth=10)\n",
    "rf.fit(X_train, y_train)\n",
    "pred_train = rf.predict(X_train)\n",
    "pred_test = rf.predict(X_test)\n",
    "\n",
    "print('Correctly identified on train set - {0:.02%}, on test set - {1:.02%}\\n'.format(\n",
    "    (pred_train == y_train).mean(),\n",
    "    (pred_test == y_test).mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** there are other variations of boosted trees, for example ExtraTrees can be constructed by setting splitter to random and bootstrap to False. ExtraTrees are usually faster to train, but Random Forests are more well known, just try them both and see which works better for problem at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task\n",
    "\n",
    "Try to use `sklearn.ensemble.RandomForestClassifier` for iris dataset. Use sklearn implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "df['class'] = iris.target_names[iris.target]\n",
    "\n",
    "# df['sepal area'] = df['sepal length (cm)'] * df['sepal width (cm)']\n",
    "# df['petal area'] = df['petal length (cm)'] * df['petal width (cm)']\n",
    "# df['visual pleasure'] = df['sepal area'] / df['petal area']\n",
    "\n",
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.drop(['class', 'sepal length (cm)', 'sepal width (cm)',\n",
    "             'petal length (cm)', 'petal width (cm)'], axis=1), df['class'],\n",
    "    test_size=0.5, random_state=42)\n",
    "\n",
    "# TODO:\n",
    "# 1. train RF and compare results to the ones we had above\n",
    "# 2. add more features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "at least one array or dtype is required",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/71/2t6j6ytn1t3dfp5dxb9zhh6w0000gn/T/ipykernel_22584/4229513911.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'entropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpred_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpred_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    301\u001b[0m                 \u001b[0;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m             )\n\u001b[0;32m--> 303\u001b[0;31m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[0m\u001b[1;32m    304\u001b[0m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[1;32m    305\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    793\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ai_primer/lib/python3.9/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m             \u001b[0mdtype_orig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mresult_type\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: at least one array or dtype is required"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(1000, criterion='entropy', max_depth=10)\n",
    "rf.fit(X_train, y_train)\n",
    "pred_train = rf.predict(X_train)\n",
    "pred_test = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(pred_train == y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(pred_test == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(rf.feature_importances_[np.newaxis],\n",
    "             columns=df.drop(['class', 'sepal length (cm)', 'sepal width (cm)',\n",
    "             'petal length (cm)', 'petal width (cm)'], axis=1).columns).T.sort_values(0).style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I suggest to try it out on your own before looking at the answer given bellow.\n",
    "\n",
    "---\n",
    "\n",
    "#### Possible answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "df['sepal area'] = df['sepal length (cm)'] * df['sepal width (cm)']\n",
    "df['petal area'] = df['petal length (cm)'] * df['petal width (cm)']\n",
    "df['simetry'] = df['sepal area'] / df['petal area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.drop('class', axis=1), df['class'],\n",
    "    test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=1000, max_depth=2)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pred = rf.predict(X_test)\n",
    "acc = np.mean(pred == y_test)\n",
    "print(f'Accuracy {acc:.02%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neat tricks\n",
    "\n",
    "### Confusion matrix\n",
    "\n",
    "Thisis a neat way to see where your model is making mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, pred),\n",
    "             columns=rf.classes_, index=rf.classes_).style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance\n",
    "\n",
    "It is easy to check which features are most important - ones which were used in most trees or/and gave highest information gain overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(rf.feature_importances_,\n",
    "             index=df.drop('class', axis=1).columns).style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A note on decision boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate some data blobs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4,\n",
    "                  random_state=0, cluster_std=1.0)\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X, y)\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=500, max_depth=2)\n",
    "rf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at overfitting here, make decision boundary plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "mesh = np.transpose([np.tile(np.linspace(-5, 5, 100), 100),\n",
    "                     np.repeat(np.linspace(-3, 12, 100), 100)])\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.subplot(121)\n",
    "plt.scatter(mesh[:, 0], mesh[:, 1], c=tree.predict(mesh), marker='s', s=20, cmap='inferno')\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='coolwarm')\n",
    "plt.title('Simple decision tree')\n",
    "plt.subplot(122)\n",
    "plt.scatter(mesh[:, 0], mesh[:, 1], c=rf.predict(mesh), marker='s', s=20, cmap='inferno')\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='coolwarm')\n",
    "plt.title('Random forest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you understand why those decision boundaries look this way.\n",
    "\n",
    "By the way, RF with standard param set will not look so nice, play around."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST\n",
    "\n",
    "The [MNIST database](http://yann.lecun.com/exdb/mnist/) contains handwritten digits and has a training set of 60,000 examples, and a test set of 10,000 examples. It is legendary dataset crated by LeCun group in 1999."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
    "# Normalize\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "for i in range(30):\n",
    "    plt.subplot(3, 10, i + 1)\n",
    "    plt.imshow(X_train[i], cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "X_train = X_train.reshape((60000, 28*28))\n",
    "X_test = X_test.reshape((10000, 28*28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_711c9_row0_col0, #T_711c9_row1_col1, #T_711c9_row2_col2, #T_711c9_row3_col3, #T_711c9_row4_col4, #T_711c9_row5_col5, #T_711c9_row6_col6, #T_711c9_row7_col7, #T_711c9_row8_col8, #T_711c9_row9_col9 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_711c9_row0_col1, #T_711c9_row0_col2, #T_711c9_row0_col3, #T_711c9_row0_col4, #T_711c9_row0_col5, #T_711c9_row0_col6, #T_711c9_row0_col7, #T_711c9_row0_col8, #T_711c9_row0_col9, #T_711c9_row1_col0, #T_711c9_row1_col2, #T_711c9_row1_col4, #T_711c9_row1_col5, #T_711c9_row1_col6, #T_711c9_row1_col7, #T_711c9_row1_col8, #T_711c9_row1_col9, #T_711c9_row2_col1, #T_711c9_row2_col4, #T_711c9_row2_col5, #T_711c9_row2_col6, #T_711c9_row2_col9, #T_711c9_row3_col0, #T_711c9_row3_col1, #T_711c9_row3_col4, #T_711c9_row3_col6, #T_711c9_row3_col9, #T_711c9_row4_col0, #T_711c9_row4_col1, #T_711c9_row4_col2, #T_711c9_row4_col3, #T_711c9_row4_col5, #T_711c9_row4_col7, #T_711c9_row4_col8, #T_711c9_row5_col0, #T_711c9_row5_col1, #T_711c9_row5_col2, #T_711c9_row5_col4, #T_711c9_row5_col7, #T_711c9_row5_col9, #T_711c9_row6_col1, #T_711c9_row6_col2, #T_711c9_row6_col3, #T_711c9_row6_col7, #T_711c9_row6_col8, #T_711c9_row6_col9, #T_711c9_row7_col0, #T_711c9_row7_col1, #T_711c9_row7_col3, #T_711c9_row7_col4, #T_711c9_row7_col5, #T_711c9_row7_col6, #T_711c9_row7_col8, #T_711c9_row8_col1, #T_711c9_row9_col2, #T_711c9_row9_col5, #T_711c9_row9_col6, #T_711c9_row9_col8 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_711c9_row1_col3, #T_711c9_row2_col0, #T_711c9_row2_col3, #T_711c9_row2_col8, #T_711c9_row4_col6, #T_711c9_row5_col6, #T_711c9_row5_col8, #T_711c9_row6_col0, #T_711c9_row6_col4, #T_711c9_row6_col5, #T_711c9_row8_col0, #T_711c9_row8_col2, #T_711c9_row8_col3, #T_711c9_row8_col4, #T_711c9_row8_col6, #T_711c9_row8_col7, #T_711c9_row9_col0, #T_711c9_row9_col1, #T_711c9_row9_col7 {\n",
       "  background-color: #fef6fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_711c9_row2_col7, #T_711c9_row3_col2, #T_711c9_row3_col5, #T_711c9_row3_col7, #T_711c9_row3_col8, #T_711c9_row7_col9, #T_711c9_row8_col5, #T_711c9_row8_col9, #T_711c9_row9_col3 {\n",
       "  background-color: #fef6fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_711c9_row4_col9, #T_711c9_row5_col3, #T_711c9_row9_col4 {\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_711c9_row7_col2 {\n",
       "  background-color: #fcf4fa;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_711c9_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >0</th>\n",
       "      <th class=\"col_heading level0 col1\" >1</th>\n",
       "      <th class=\"col_heading level0 col2\" >2</th>\n",
       "      <th class=\"col_heading level0 col3\" >3</th>\n",
       "      <th class=\"col_heading level0 col4\" >4</th>\n",
       "      <th class=\"col_heading level0 col5\" >5</th>\n",
       "      <th class=\"col_heading level0 col6\" >6</th>\n",
       "      <th class=\"col_heading level0 col7\" >7</th>\n",
       "      <th class=\"col_heading level0 col8\" >8</th>\n",
       "      <th class=\"col_heading level0 col9\" >9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_711c9_row0_col0\" class=\"data row0 col0\" >970</td>\n",
       "      <td id=\"T_711c9_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "      <td id=\"T_711c9_row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "      <td id=\"T_711c9_row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "      <td id=\"T_711c9_row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "      <td id=\"T_711c9_row0_col5\" class=\"data row0 col5\" >3</td>\n",
       "      <td id=\"T_711c9_row0_col6\" class=\"data row0 col6\" >3</td>\n",
       "      <td id=\"T_711c9_row0_col7\" class=\"data row0 col7\" >1</td>\n",
       "      <td id=\"T_711c9_row0_col8\" class=\"data row0 col8\" >2</td>\n",
       "      <td id=\"T_711c9_row0_col9\" class=\"data row0 col9\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_711c9_row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "      <td id=\"T_711c9_row1_col1\" class=\"data row1 col1\" >1124</td>\n",
       "      <td id=\"T_711c9_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_711c9_row1_col3\" class=\"data row1 col3\" >4</td>\n",
       "      <td id=\"T_711c9_row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "      <td id=\"T_711c9_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_711c9_row1_col6\" class=\"data row1 col6\" >3</td>\n",
       "      <td id=\"T_711c9_row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "      <td id=\"T_711c9_row1_col8\" class=\"data row1 col8\" >1</td>\n",
       "      <td id=\"T_711c9_row1_col9\" class=\"data row1 col9\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_711c9_row2_col0\" class=\"data row2 col0\" >6</td>\n",
       "      <td id=\"T_711c9_row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "      <td id=\"T_711c9_row2_col2\" class=\"data row2 col2\" >1002</td>\n",
       "      <td id=\"T_711c9_row2_col3\" class=\"data row2 col3\" >5</td>\n",
       "      <td id=\"T_711c9_row2_col4\" class=\"data row2 col4\" >2</td>\n",
       "      <td id=\"T_711c9_row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "      <td id=\"T_711c9_row2_col6\" class=\"data row2 col6\" >2</td>\n",
       "      <td id=\"T_711c9_row2_col7\" class=\"data row2 col7\" >10</td>\n",
       "      <td id=\"T_711c9_row2_col8\" class=\"data row2 col8\" >5</td>\n",
       "      <td id=\"T_711c9_row2_col9\" class=\"data row2 col9\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_711c9_row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "      <td id=\"T_711c9_row3_col1\" class=\"data row3 col1\" >0</td>\n",
       "      <td id=\"T_711c9_row3_col2\" class=\"data row3 col2\" >10</td>\n",
       "      <td id=\"T_711c9_row3_col3\" class=\"data row3 col3\" >972</td>\n",
       "      <td id=\"T_711c9_row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "      <td id=\"T_711c9_row3_col5\" class=\"data row3 col5\" >7</td>\n",
       "      <td id=\"T_711c9_row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "      <td id=\"T_711c9_row3_col7\" class=\"data row3 col7\" >10</td>\n",
       "      <td id=\"T_711c9_row3_col8\" class=\"data row3 col8\" >9</td>\n",
       "      <td id=\"T_711c9_row3_col9\" class=\"data row3 col9\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_711c9_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "      <td id=\"T_711c9_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_711c9_row4_col2\" class=\"data row4 col2\" >2</td>\n",
       "      <td id=\"T_711c9_row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "      <td id=\"T_711c9_row4_col4\" class=\"data row4 col4\" >958</td>\n",
       "      <td id=\"T_711c9_row4_col5\" class=\"data row4 col5\" >0</td>\n",
       "      <td id=\"T_711c9_row4_col6\" class=\"data row4 col6\" >5</td>\n",
       "      <td id=\"T_711c9_row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "      <td id=\"T_711c9_row4_col8\" class=\"data row4 col8\" >3</td>\n",
       "      <td id=\"T_711c9_row4_col9\" class=\"data row4 col9\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_711c9_row5_col0\" class=\"data row5 col0\" >2</td>\n",
       "      <td id=\"T_711c9_row5_col1\" class=\"data row5 col1\" >1</td>\n",
       "      <td id=\"T_711c9_row5_col2\" class=\"data row5 col2\" >1</td>\n",
       "      <td id=\"T_711c9_row5_col3\" class=\"data row5 col3\" >17</td>\n",
       "      <td id=\"T_711c9_row5_col4\" class=\"data row5 col4\" >2</td>\n",
       "      <td id=\"T_711c9_row5_col5\" class=\"data row5 col5\" >856</td>\n",
       "      <td id=\"T_711c9_row5_col6\" class=\"data row5 col6\" >4</td>\n",
       "      <td id=\"T_711c9_row5_col7\" class=\"data row5 col7\" >2</td>\n",
       "      <td id=\"T_711c9_row5_col8\" class=\"data row5 col8\" >5</td>\n",
       "      <td id=\"T_711c9_row5_col9\" class=\"data row5 col9\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_711c9_row6_col0\" class=\"data row6 col0\" >5</td>\n",
       "      <td id=\"T_711c9_row6_col1\" class=\"data row6 col1\" >3</td>\n",
       "      <td id=\"T_711c9_row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "      <td id=\"T_711c9_row6_col3\" class=\"data row6 col3\" >0</td>\n",
       "      <td id=\"T_711c9_row6_col4\" class=\"data row6 col4\" >5</td>\n",
       "      <td id=\"T_711c9_row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "      <td id=\"T_711c9_row6_col6\" class=\"data row6 col6\" >936</td>\n",
       "      <td id=\"T_711c9_row6_col7\" class=\"data row6 col7\" >0</td>\n",
       "      <td id=\"T_711c9_row6_col8\" class=\"data row6 col8\" >4</td>\n",
       "      <td id=\"T_711c9_row6_col9\" class=\"data row6 col9\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_711c9_row7_col0\" class=\"data row7 col0\" >1</td>\n",
       "      <td id=\"T_711c9_row7_col1\" class=\"data row7 col1\" >2</td>\n",
       "      <td id=\"T_711c9_row7_col2\" class=\"data row7 col2\" >20</td>\n",
       "      <td id=\"T_711c9_row7_col3\" class=\"data row7 col3\" >3</td>\n",
       "      <td id=\"T_711c9_row7_col4\" class=\"data row7 col4\" >2</td>\n",
       "      <td id=\"T_711c9_row7_col5\" class=\"data row7 col5\" >0</td>\n",
       "      <td id=\"T_711c9_row7_col6\" class=\"data row7 col6\" >0</td>\n",
       "      <td id=\"T_711c9_row7_col7\" class=\"data row7 col7\" >988</td>\n",
       "      <td id=\"T_711c9_row7_col8\" class=\"data row7 col8\" >2</td>\n",
       "      <td id=\"T_711c9_row7_col9\" class=\"data row7 col9\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_711c9_row8_col0\" class=\"data row8 col0\" >4</td>\n",
       "      <td id=\"T_711c9_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_711c9_row8_col2\" class=\"data row8 col2\" >5</td>\n",
       "      <td id=\"T_711c9_row8_col3\" class=\"data row8 col3\" >6</td>\n",
       "      <td id=\"T_711c9_row8_col4\" class=\"data row8 col4\" >6</td>\n",
       "      <td id=\"T_711c9_row8_col5\" class=\"data row8 col5\" >7</td>\n",
       "      <td id=\"T_711c9_row8_col6\" class=\"data row8 col6\" >4</td>\n",
       "      <td id=\"T_711c9_row8_col7\" class=\"data row8 col7\" >4</td>\n",
       "      <td id=\"T_711c9_row8_col8\" class=\"data row8 col8\" >927</td>\n",
       "      <td id=\"T_711c9_row8_col9\" class=\"data row8 col9\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_711c9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_711c9_row9_col0\" class=\"data row9 col0\" >7</td>\n",
       "      <td id=\"T_711c9_row9_col1\" class=\"data row9 col1\" >5</td>\n",
       "      <td id=\"T_711c9_row9_col2\" class=\"data row9 col2\" >3</td>\n",
       "      <td id=\"T_711c9_row9_col3\" class=\"data row9 col3\" >9</td>\n",
       "      <td id=\"T_711c9_row9_col4\" class=\"data row9 col4\" >13</td>\n",
       "      <td id=\"T_711c9_row9_col5\" class=\"data row9 col5\" >1</td>\n",
       "      <td id=\"T_711c9_row9_col6\" class=\"data row9 col6\" >1</td>\n",
       "      <td id=\"T_711c9_row9_col7\" class=\"data row9 col7\" >5</td>\n",
       "      <td id=\"T_711c9_row9_col8\" class=\"data row9 col8\" >4</td>\n",
       "      <td id=\"T_711c9_row9_col9\" class=\"data row9 col9\" >961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x296bc8b80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "pd.DataFrame(confusion_matrix(y_test, pred), columns=range(10), index=range(10)).style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK:** using PCA compress MNIST to two dimensions and make a plot (color by class label)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reshape the data from 28x28 to 784\n",
    "X_train_flat = X_train.reshape(-1, 28*28)\n",
    "\n",
    "# Apply PCA and reduce the dimensionality to 2\n",
    "pca = PCA(n_components=2)\n",
    "X_train_pca = pca.fit_transform(X_train_flat)\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(10, 8))\n",
    "for i in range(10):  # there are 10 classes (0 to 9) in MNIST\n",
    "    subset = X_train_pca[y_train == i]\n",
    "    plt.scatter(subset[:, 0], subset[:, 1], label=str(i), alpha=0.5, s=10)\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.title(\"PCA of MNIST Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True labels are on the left and predicted ones on the top. Quick glimpse gives you insightes like - RF makes most mistakes while identifying actual 7 as 2 and actual 4 as 9. What other insights can you give?\n",
    "\n",
    "Soon we will see that using neural networks we can avoid some of these mistakes, but key insight here is that RF is good method even for complex tasks as this one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Whats next?\n",
    "\n",
    "There are many variations of Random Forests you can explore, to name a few:\n",
    "- [ExtraTrees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees)\n",
    "- [XGBoost](https://xgboost.readthedocs.io/en/latest/)\n",
    "- [LightGBM](https://lightgbm.readthedocs.io/en/latest/)\n",
    "\n",
    "Up to the day of writing XGboost is still a leading method for tabular problems, but as we will see deep learning will beat them at unstructured data. Basic idea of XGBoost: fit next decision tree on residual errors made by the previous predictor, thus trying to fix them.\n",
    "\n",
    "1. Fit $\\text{tree}_0$ on $X$ and $y_0=y$ and make predictions $y_1$. Set $i=1$.\n",
    "2. Fit $\\text{tree}_i$ to predict residuals $y_{i-1} - y_i$ given $X$ and make predictions $y_{i+1}$.\n",
    "3. Increment $i$ and jump back to step 2.\n",
    "\n",
    "\n",
    "## (Re) Sources:\n",
    "- Short [podcast](https://dataskeptic.com/blog/episodes/2016/random-forest) about random forest that contains simple explanation of ensambles.\n",
    "- Claude Shannon “A Mathematical Theory of Communication” 1948 ([link](http://www.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf)).\n",
    "- Nice [video](https://youtu.be/ErfnhcEV1O8) about Entropy \n",
    "- Medium [post](https://towardsdatascience.com/must-know-information-theory-concepts-in-deep-learning-ai-e54a5da9769d) about Entropy\n",
    "- Logistic regression from scratch ([medium post](https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac))\n"
   ]
  }
 ],
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