Week10 - Random Forest.ipynb

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   "source": [
    "# Random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The algorithm\n",
    "\n",
    "Steps:\n",
    "1. Sample $q$ datasets each of shape (n,m) say, $D_1, D_2, ......, D_q$ with replacement from $D$\n",
    "2. In each dataset $D_j$, select $u$ out of $m$ where $u \\le m$ features before each split and train a full decision tree $h_j(x)$ \n",
    "3. The final predictor is \n",
    "    * For regression, an average of the outputs from $q$ regressors\n",
    "    $$\n",
    "    h(x) = \\frac{1}{q} \\sum_{j=1}^q h_j(x)\n",
    "    $$\n",
    "    * For classification, a majority vote is taken and the class label with the maximum number of votes is assigned to the new example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bag(X, y):\n",
    "    \n",
    "    n_samples = X.shape[0]\n",
    "\n",
    "    #Generate a random indices for a sample from the input\n",
    "    indices = np.random.choice(n_samples, size = n_samples, replace=True)\n",
    "    \n",
    "    return X[indices], y[indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Majority voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_common_label(y):\n",
    "    counter = Counter(y)\n",
    "    most_common = counter.most_common(1)[0][0]\n",
    "    return most_common"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomForest:\n",
    "    def __init__(self, n_trees=10, min_samples_split=2, max_depth=100, max_features=None):\n",
    "        self.n_trees = n_trees\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.max_depth = max_depth\n",
    "        self.max_features = max_features\n",
    "        self.trees = []\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \n",
    "        for _ in range(self.n_trees):\n",
    "            tree = DecisionTreeClassifier(\n",
    "                    min_samples_split = self.min_samples_split,\n",
    "                    max_depth = self.max_depth,\n",
    "                    max_features= self.max_features)\n",
    "            \n",
    "            X_sample, y_sample = bag(X, y)\n",
    "            tree.fit(X_sample, y_sample)\n",
    "            self.trees.append(tree)\n",
    "\n",
    "    def predict(self, X):\n",
    "        tree_predict = np.array([tree.predict(X) for tree in self.trees])\n",
    "        tree_predict = np.swapaxes(tree_predict, 0, 1)\n",
    "        y_pred = [most_common_label(tree_pred) for tree_pred in tree_predict]\n",
    "        return np.array(y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration on breast cancer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def accuracy(y_true, y_pred):\n",
    "    acc = np.sum(y_true == y_pred) / len(y_true)\n",
    "    return acc\n",
    "\n",
    "X, y = load_breast_cancer(as_frame=True, return_X_y = True)\n",
    "\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "clf = RandomForest(n_trees=10, max_depth=10)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "acc = accuracy(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95        42\n",
      "           1       0.95      1.00      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.97      0.95      0.96       114\n",
      "weighted avg       0.97      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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