Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207, 61)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from random import seed  # seeding the generated number\n",
    "from random import randrange # provide the value randomly in the limit of the range\n",
    "from csv import reader\n",
    "from math import sqrt\n",
    "\n",
    "data = pd.read_csv('./sonar.all-data.csv')   #with open closes the fie automically after clossing the fie\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.1665, 0.0336, 0.1302, 0.1708, 0.2177, 0.3175, 0.3714, 0.4552, 0.57, 0.7397, 0.8062, 0.8837, 0.9432, 1.0, 0.9375, 0.7603, 0.7123, 0.8358, 0.7622, 0.4567, 0.1715, 0.1549, 0.1641, 0.1869, 0.2655, 0.1713, 0.0959, 0.0768, 0.0847, 0.2076, 0.2505, 0.1862, 0.1439, 0.147, 0.0991, 0.0041, 0.0154, 0.0116, 0.0181, 0.0146, 0.0129, 0.0047, 0.0039, 0.0061, 0.004, 0.0036, 0.0061, 0.0115, 'M']]\n"
     ]
    }
   ],
   "source": [
    "dataset= data.values.tolist()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation_split(dataset, n_folds):\n",
    "    dataset_split =list()\n",
    "    dataset_copy = list(dataset)\n",
    "    fold_size = int(len(dataset)/ n_folds)\n",
    "    \n",
    "    for i in range(n_folds):\n",
    "        fold =list()\n",
    "        while len(fold)< fold_size:\n",
    "            index = randrange(len(dataset_copy))\n",
    "            fold.append(dataset_copy.pop(index))\n",
    "\n",
    "        dataset_split.append(fold)\n",
    "    return dataset_split\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[0.0331,\n",
       "   0.0423,\n",
       "   0.0474,\n",
       "   0.0818,\n",
       "   0.0835,\n",
       "   0.0756,\n",
       "   0.0374,\n",
       "   0.0961,\n",
       "   0.0548,\n",
       "   0.0193,\n",
       "   0.0897,\n",
       "   0.1734,\n",
       "   0.1936,\n",
       "   0.2803,\n",
       "   0.3313,\n",
       "   0.502,\n",
       "   0.636,\n",
       "   0.7096,\n",
       "   0.8333,\n",
       "   0.873,\n",
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       "   'M']]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validation_split(dataset,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_split(index, value, dataset):\n",
    "    \n",
    "    left, right = list(), list()\n",
    "    \n",
    "    for row in dataset:\n",
    "        \n",
    "        if row[index]<value:\n",
    "            \n",
    "            left.append(row)\n",
    "            \n",
    "        else:\n",
    "            right.append(row)\n",
    "            \n",
    "    return left,right\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "l, r =test_split(0,.1,dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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0.0045, 0.0043, 0.0043, 0.0098, 0.0054, 0.0051, 0.0065, 0.0103, 'M'], [0.0187, 0.0346, 0.0168, 0.0177, 0.0393, 0.163, 0.2028, 0.1694, 0.2328, 0.2684, 0.3108, 0.2933, 0.2275, 0.0994, 0.1801, 0.22, 0.2732, 0.2862, 0.2034, 0.174, 0.413, 0.6879, 0.812, 0.8453, 0.8919, 0.93, 0.9987, 1.0, 0.8104, 0.6199, 0.6041, 0.5547, 0.416, 0.1472, 0.0849, 0.0608, 0.0969, 0.1411, 0.1676, 0.12, 0.1201, 0.1036, 0.1977, 0.1339, 0.0902, 0.1085, 0.1521, 0.1363, 0.0858, 0.029, 0.0203, 0.0116, 0.0098, 0.0199, 0.0033, 0.0101, 0.0065, 0.0115, 0.0193, 0.0157, 'M'], [0.0323, 0.0101, 0.0298, 0.0564, 0.076, 0.0958, 0.099, 0.1018, 0.103, 0.2154, 0.3085, 0.3425, 0.299, 0.1402, 0.1235, 0.1534, 0.1901, 0.2429, 0.212, 0.2395, 0.3272, 0.5949, 0.8302, 0.9045, 0.9888, 0.9912, 0.9448, 1.0, 0.9092, 0.7412, 0.7691, 0.7117, 0.5304, 0.2131, 0.0928, 0.1297, 0.1159, 0.1226, 0.1768, 0.0345, 0.1562, 0.0824, 0.1149, 0.1694, 0.0954, 0.008, 0.079, 0.1255, 0.0647, 0.0179, 0.0051, 0.0061, 0.0093, 0.0135, 0.0063, 0.0063, 0.0034, 0.0032, 0.0062, 0.0067, 'M'], [0.0522, 0.0437, 0.018, 0.0292, 0.0351, 0.1171, 0.1257, 0.1178, 0.1258, 0.2529, 0.2716, 0.2374, 0.1878, 0.0983, 0.0683, 0.1503, 0.1723, 0.2339, 0.1962, 0.1395, 0.3164, 0.5888, 0.7631, 0.8473, 0.9424, 0.9986, 0.9699, 1.0, 0.863, 0.6979, 0.7717, 0.7305, 0.5197, 0.1786, 0.1098, 0.1446, 0.1066, 0.144, 0.1929, 0.0325, 0.149, 0.0328, 0.0537, 0.1309, 0.091, 0.0757, 0.1059, 0.1005, 0.0535, 0.0235, 0.0155, 0.016, 0.0029, 0.0051, 0.0062, 0.0089, 0.014, 0.0138, 0.0077, 0.0031, 'M'], [0.0303, 0.0353, 0.049, 0.0608, 0.0167, 0.1354, 0.1465, 0.1123, 0.1945, 0.2354, 0.2898, 0.2812, 0.1578, 0.0273, 0.0673, 0.1444, 0.207, 0.2645, 0.2828, 0.4293, 0.5685, 0.699, 0.7246, 0.7622, 0.9242, 1.0, 0.9979, 0.8297, 0.7032, 0.7141, 0.6893, 0.4961, 0.2584, 0.0969, 0.0776, 0.0364, 0.1572, 0.1823, 0.1349, 0.0849, 0.0492, 0.1367, 0.1552, 0.1548, 0.1319, 0.0985, 0.1258, 0.0954, 0.0489, 0.0241, 0.0042, 0.0086, 0.0046, 0.0126, 0.0036, 0.0035, 0.0034, 0.0079, 0.0036, 0.0048, 'M'], [0.026, 0.0363, 0.0136, 0.0272, 0.0214, 0.0338, 0.0655, 0.14, 0.1843, 0.2354, 0.272, 0.2442, 0.1665, 0.0336, 0.1302, 0.1708, 0.2177, 0.3175, 0.3714, 0.4552, 0.57, 0.7397, 0.8062, 0.8837, 0.9432, 1.0, 0.9375, 0.7603, 0.7123, 0.8358, 0.7622, 0.4567, 0.1715, 0.1549, 0.1641, 0.1869, 0.2655, 0.1713, 0.0959, 0.0768, 0.0847, 0.2076, 0.2505, 0.1862, 0.1439, 0.147, 0.0991, 0.0041, 0.0154, 0.0116, 0.0181, 0.0146, 0.0129, 0.0047, 0.0039, 0.0061, 0.004, 0.0036, 0.0061, 0.0115, 'M']]\n"
     ]
    }
   ],
   "source": [
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1313, 0.2339, 0.3059, 0.4264, 0.401, 0.1791, 0.1853, 0.0055, 0.1929, 0.2231, 0.2907, 0.2259, 0.3136, 0.3302, 0.366, 0.3956, 0.4386, 0.467, 0.5255, 0.3735, 0.2243, 0.1973, 0.4337, 0.6532, 0.507, 0.2796, 0.4163, 0.595, 0.5242, 0.4178, 0.3714, 0.2375, 0.0863, 0.1437, 0.2896, 0.4577, 0.3725, 0.3372, 0.3803, 0.4181, 0.3603, 0.2711, 0.1653, 0.1951, 0.2811, 0.2246, 0.1921, 0.15, 0.0665, 0.0193, 0.0156, 0.0362, 0.021, 0.0154, 0.018, 0.0013, 0.0106, 0.0127, 0.0178, 0.0231, 'M'], [0.1371, 0.1226, 0.1385, 0.1484, 0.1776, 0.1428, 0.1773, 0.2161, 0.163, 0.2067, 0.4257, 0.5484, 0.7131, 0.7003, 0.6777, 0.7939, 0.9382, 0.8925, 0.9146, 0.7832, 0.796, 0.7983, 0.7716, 0.6615, 0.486, 0.5572, 0.4697, 0.564, 0.4517, 0.3369, 0.2684, 0.2339, 0.3052, 0.3016, 0.2753, 0.1041, 0.1757, 0.3156, 0.3603, 0.2736, 0.1301, 0.2458, 0.3404, 0.1753, 0.0679, 0.1062, 0.0643, 0.0532, 0.0531, 0.0272, 0.0171, 0.0118, 0.0129, 0.0344, 0.0065, 0.0067, 0.0022, 0.0079, 0.0146, 0.0051, 'M'], [0.115, 0.1163, 0.0866, 0.0358, 0.0232, 0.1267, 0.2417, 0.2661, 0.4346, 0.5378, 0.3816, 0.0991, 0.0616, 0.1795, 0.3907, 0.3602, 0.3041, 0.2428, 0.406, 0.8395, 0.9777, 0.468, 0.061, 0.2143, 0.1348, 0.2854, 0.1617, 0.2649, 0.4565, 0.6502, 0.2848, 0.3296, 0.537, 0.6627, 0.8626, 0.8547, 0.7848, 0.9016, 0.8827, 0.6086, 0.281, 0.0906, 0.1177, 0.2694, 0.5214, 0.4232, 0.234, 0.1928, 0.1092, 0.0507, 0.0228, 0.0099, 0.0065, 0.0085, 0.0166, 0.011, 0.019, 0.0141, 0.0068, 0.0086, 'M'], [0.1083, 0.107, 0.0257, 0.0837, 0.0748, 0.1125, 0.3322, 0.459, 0.5526, 0.5966, 0.5304, 0.2251, 0.2402, 0.2689, 0.6646, 0.6632, 0.1674, 0.0837, 0.4331, 0.8718, 0.7992, 0.3712, 0.1703, 0.1611, 0.2086, 0.2847, 0.2211, 0.6134, 0.5807, 0.6925, 0.3825, 0.4303, 0.7791, 0.8703, 1.0, 0.9212, 0.9386, 0.9303, 0.7314, 0.4791, 0.2087, 0.2016, 0.1669, 0.2872, 0.4374, 0.3097, 0.1578, 0.0553, 0.0334, 0.0209, 0.0172, 0.018, 0.011, 0.0234, 0.0276, 0.0032, 0.0084, 0.0122, 0.0082, 0.0143, 'M'], [0.1088, 0.1278, 0.0926, 0.1234, 0.1276, 0.1731, 0.1948, 0.4262, 0.6828, 0.5761, 0.4733, 0.2362, 0.1023, 0.2904, 0.4713, 0.4659, 0.1415, 0.0849, 0.3257, 0.9007, 0.9312, 0.4856, 0.1346, 0.1604, 0.2737, 0.5609, 0.3654, 0.6139, 0.547, 0.8474, 0.5638, 0.5443, 0.5086, 0.6253, 0.8497, 0.8406, 0.842, 0.9136, 0.7713, 0.4882, 0.3724, 0.4469, 0.4586, 0.4491, 0.5616, 0.4305, 0.0945, 0.0794, 0.0274, 0.0154, 0.014, 0.0455, 0.0213, 0.0082, 0.0124, 0.0167, 0.0103, 0.0205, 0.0178, 0.0187, 'M'], [0.1021, 0.083, 0.0577, 0.0627, 0.0635, 0.1328, 0.0988, 0.1787, 0.1199, 0.1369, 0.2509, 0.2631, 0.2796, 0.2977, 0.3823, 0.3129, 0.3956, 0.2093, 0.3218, 0.3345, 0.3184, 0.2887, 0.361, 0.2566, 0.4106, 0.4591, 0.4722, 0.7278, 0.7591, 0.6579, 0.7514, 0.6666, 0.4903, 0.5962, 0.6552, 0.4014, 0.1188, 0.3245, 0.3107, 0.1354, 0.5109, 0.7988, 0.7517, 0.5508, 0.5858, 0.7292, 0.5522, 0.3339, 0.1608, 0.0475, 0.1004, 0.0709, 0.0317, 0.0309, 0.0252, 0.0087, 0.0177, 0.0214, 0.0227, 0.0106, 'M']]\n"
     ]
    }
   ],
   "source": [
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_metric(actual, predicted):\n",
    "    correct = 0\n",
    "    \n",
    "    for i in range(len(actual)):\n",
    "        if actual[i] == predicted[i]:\n",
    "            \n",
    "            correct +=1\n",
    "            \n",
    "    return correct / float(len(actual)) * 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_algorithm(dataset, algorithm, n_folds, *args):\n",
    "    fold = cross_validation_split(dataset,n_folds)\n",
    "    scores = list()\n",
    "    \n",
    "    for fold in folds:\n",
    "        train_set = list(folds)\n",
    "        train_set.remove(fold)\n",
    "        train_set= sum(train_set, [])\n",
    "        \n",
    "        test_set = list()\n",
    "        \n",
    "        for row in fold:\n",
    "            row_copy = list(row)\n",
    "            test_set.append(row_copy)\n",
    "            row_copy[-1]=None\n",
    "            \n",
    "        predicted = algorithm(train_set, test_set, *args)\n",
    "        actual= [row[-1] for row in fold]\n",
    "        \n",
    "        accuracy= accuracy_metric(actual,predicted)\n",
    "        \n",
    "        scores.append(accuracy)\n",
    "        \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini_index(groups, class_values):\n",
    "    \n",
    "    gini =0.0\n",
    "    \n",
    "    for class_value in class_values:\n",
    "        for group in groups:\n",
    "            size=len(group)\n",
    "            if size==0:\n",
    "                continue\n",
    "                proportion = [row[-1] for row in group].count(class_value)\n",
    "                gini+=(proportion* (1.0 - proportion))\n",
    "                \n",
    "    return gini\n",
    "                \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_split(dataset,n_features):\n",
    "    class_values = list(set(row[-1] for row in dataset))\n",
    "    b_index, b_value, b_score, b_groups = 999,999,999,999\n",
    "    features = list()\n",
    "    while len(features) < n_features:\n",
    "        index = random.randrange(len(dataset[0])-1)\n",
    "        if index not in features:\n",
    "            features.append(index)\n",
    "    for index in features:\n",
    "        for row in dataset:\n",
    "            groups = test_split(index,row[index],dataset)\n",
    "            gini = gini_index(groups,class_values)\n",
    "            if gini < b_score:\n",
    "                b_index, b_value, b_score, b_groups = index,row[index],gini,groups\n",
    "    return {'index':b_index,'value':b_value,'groups':b_groups}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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