RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Algorithm \n",
    "from random import seed\n",
    "from random import randrange\n",
    "from csv import reader\n",
    "from math import sqrt\n",
    "\n",
    "# Load CSV file\n",
    "def load_csv(filename):\n",
    "    dataset = list()\n",
    "    with open(filename, 'r') as file:\n",
    "        csv_reader = reader(file)\n",
    "        \n",
    "        #remove header\n",
    "        next(csv_reader)\n",
    "        \n",
    "        for row in csv_reader:\n",
    "            if not row:\n",
    "                continue\n",
    "            dataset.append(row)\n",
    "    print(dataset)\n",
    "    return dataset\n",
    "\n",
    "# Convert string column to float\n",
    "def str_column_to_float(dataset, column):\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = float(row[column].strip())\n",
    "\n",
    "\n",
    "# Split a dataset into k folds\n",
    "def cross_validation_split(dataset, n_folds):\n",
    "\tdataset_split = list()\n",
    "\tdataset_copy = list(dataset)\n",
    "\tfold_size = int(len(dataset) / n_folds)\n",
    "\tfor i in range(n_folds):\n",
    "\t\tfold = list()\n",
    "\t\twhile len(fold) < fold_size:\n",
    "\t\t\tindex = randrange(len(dataset_copy))\n",
    "\t\t\tfold.append(dataset_copy.pop(index))\n",
    "\t\tdataset_split.append(fold)\n",
    "\treturn dataset_split\n",
    "\n",
    "# Calculate accuracy percentage\n",
    "def accuracy_metric(actual, predicted):\n",
    "\tcorrect = 0\n",
    "\tfor i in range(len(actual)):\n",
    "\t\tif actual[i] == predicted[i]:\n",
    "\t\t\tcorrect += 1\n",
    "\treturn correct / float(len(actual)) * 100.0\n",
    "\n",
    "\n",
    "# Split a dataset based on an attribute and an attribute value\n",
    "def test_split(index, value, dataset):\n",
    "\tleft, right = list(), list()\n",
    "\tfor row in dataset:\n",
    "\t\tif row[index] < value:\n",
    "\t\t\tleft.append(row)\n",
    "\t\telse:\n",
    "\t\t\tright.append(row)\n",
    "\treturn left, right\n",
    "\n",
    "# Calculate the Gini index for a split dataset\n",
    "def gini_index(groups, classes):\n",
    "\t# count all samples at split point\n",
    "\tn_instances = float(sum([len(group) for group in groups]))\n",
    "\t# sum weighted Gini index for each group\n",
    "\tgini = 0.0\n",
    "\tfor group in groups:\n",
    "\t\tsize = float(len(group))\n",
    "\t\t# avoid divide by zero\n",
    "\t\tif size == 0:\n",
    "\t\t\tcontinue\n",
    "\t\tscore = 0.0\n",
    "\t\t# score the group based on the score for each class\n",
    "\t\tfor class_val in classes:\n",
    "\t\t\tp = [row[-1] for row in group].count(class_val) / size\n",
    "\t\t\tscore += p * p\n",
    "\t\t# weight the group score by its relative size\n",
    "\t\tgini += (1.0 - score) * (size / n_instances)\n",
    "\treturn gini\n",
    "\n",
    "# Select the best split point for a dataset\n",
    "def get_split(dataset, n_features):\n",
    "\tclass_values = list(set(row[-1] for row in dataset))\n",
    "\tb_index, b_value, b_score, b_groups = 999, 999, 999, None\n",
    "\tfeatures = list()\n",
    "\twhile len(features) < n_features:\n",
    "\t\tindex = randrange(len(dataset[0])-1)\n",
    "\t\tif index not in features:\n",
    "\t\t\tfeatures.append(index)\n",
    "\tfor index in features:\n",
    "\t\tfor row in dataset:\n",
    "\t\t\tgroups = test_split(index, row[index], dataset)\n",
    "\t\t\tgini = gini_index(groups, class_values)\n",
    "\t\t\tif gini < b_score:\n",
    "\t\t\t\tb_index, b_value, b_score, b_groups = index, row[index], gini, groups\n",
    "\treturn {'index':b_index, 'value':b_value, 'groups':b_groups}\n",
    "\n",
    "# Create a terminal node value\n",
    "def to_terminal(group):\n",
    "\toutcomes = [row[-1] for row in group]\n",
    "\treturn max(set(outcomes), key=outcomes.count)\n",
    "\n",
    "# Create child splits for a node or make terminal\n",
    "def split(node, max_depth, min_size, n_features, depth):\n",
    "\tleft, right = node['groups']\n",
    "\tdel(node['groups'])\n",
    "\t# check for a no split\n",
    "\tif not left or not right:\n",
    "\t\tnode['left'] = node['right'] = to_terminal(left + right)\n",
    "\t\treturn\n",
    "\t# check for max depth\n",
    "\tif depth >= max_depth:\n",
    "\t\tnode['left'], node['right'] = to_terminal(left), to_terminal(right)\n",
    "\t\treturn\n",
    "\t# process left child\n",
    "\tif len(left) <= min_size:\n",
    "\t\tnode['left'] = to_terminal(left)\n",
    "\telse:\n",
    "\t\tnode['left'] = get_split(left, n_features)\n",
    "\t\tsplit(node['left'], max_depth, min_size, n_features, depth+1)\n",
    "\t# process right child\n",
    "\tif len(right) <= min_size:\n",
    "\t\tnode['right'] = to_terminal(right)\n",
    "\telse:\n",
    "\t\tnode['right'] = get_split(right, n_features)\n",
    "\t\tsplit(node['right'], max_depth, min_size, n_features, depth+1)\n",
    "\n",
    "# Build a decision tree\n",
    "def build_tree(train, max_depth, min_size, n_features):\n",
    "\troot = get_split(train, n_features)\n",
    "\tsplit(root, max_depth, min_size, n_features, 1)\n",
    "\treturn root\n",
    "\n",
    "# Make a prediction with a decision tree\n",
    "def predict(node, row):\n",
    "\tif row[node['index']] < node['value']:\n",
    "\t\tif isinstance(node['left'], dict):\n",
    "\t\t\treturn predict(node['left'], row)\n",
    "\t\telse:\n",
    "\t\t\treturn node['left']\n",
    "\telse:\n",
    "\t\tif isinstance(node['right'], dict):\n",
    "\t\t\treturn predict(node['right'], row)\n",
    "\t\telse:\n",
    "\t\t\treturn node['right']\n",
    "\n",
    "# Create a random subsample from the dataset with replacement\n",
    "def subsample(dataset, ratio):\n",
    "\tsample = list()\n",
    "\tn_sample = round(len(dataset) * ratio)\n",
    "\twhile len(sample) < n_sample:\n",
    "\t\tindex = randrange(len(dataset))\n",
    "\t\tsample.append(dataset[index])\n",
    "\treturn sample\n",
    "\n",
    "# Make a prediction with a list of bagged trees\n",
    "def bagging_predict(trees, row):\n",
    "\tpredictions = [predict(tree, row) for tree in trees]\n",
    "\treturn max(set(predictions), key=predictions.count)\n",
    "\n",
    "# Random Forest Algorithm\n",
    "def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):\n",
    "\ttrees = list()\n",
    "\tfor i in range(n_trees):\n",
    "\t\tsample = subsample(train, sample_size)\n",
    "\t\ttree = build_tree(sample, max_depth, min_size, n_features)\n",
    "\t\ttrees.append(tree)\n",
    "\tpredictions = [bagging_predict(trees, row) for row in test]\n",
    "\treturn(predictions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate an algorithm using a cross validation split\n",
    "\n",
    "def evaluate_algorithm(dataset, algorithm, n_folds, *args):\n",
    "\tfolds = cross_validation_split(dataset, n_folds)\n",
    "\tscores = list()\n",
    "\tfor fold in folds:\n",
    "\t\ttrain_set = list(folds)\n",
    "\t\ttrain_set.remove(fold)\n",
    "\t\ttrain_set = sum(train_set, [])\n",
    "\t\ttest_set = list()\n",
    "\t\tfor row in fold:\n",
    "\t\t\trow_copy = list(row)\n",
    "\t\t\ttest_set.append(row_copy)\n",
    "\t\t\trow_copy[-1] = None\n",
    "\t\tpredicted = algorithm(train_set, test_set, *args)\n",
    "\t\tactual = [row[-1] for row in fold]\n",
    "\t\taccuracy = accuracy_metric(actual, predicted)\n",
    "\t\tscores.append(accuracy)\n",
    "\treturn scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'-0.29917', '-0.85833', '0.2105', '-0.53005', '0.51855', '-0.30319', '0.020018', '-0.020915', '0.66394', '1'], ['0.29877', '-0.17962', '0.032859', '-0.89901', '0.5403', '-0.36184', '0.1779', '0.71325', '-0.098148', '-0.41113', '0.24683', '-0.38916', '2'], ['-0.37812', '-0.52287', '0.21541', '-0.24359', '0.63784', '-0.49221', '-0.50693', '-0.25811', '-0.32899', '-0.039117', '0.007591', '0.47603', '1'], ['0.37913', '-0.046562', '-0.12336', '-0.063958', '-0.10983', '-0.045824', '0.1238', '0.49624', '-0.64157', '-0.24229', '0.17369', '0.49063', '2']]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trees: 10\n",
      "Scores: [58.333333333333336, 72.22222222222221, 61.111111111111114, 75.0, 63.888888888888886]\n",
      "Mean Accuracy: 66.111%\n"
     ]
    }
   ],
   "source": [
    "# Test the random forest algorithm\n",
    "seed(2)\n",
    "\n",
    "# load and prepare data\n",
    "filename = 'planning-relax.csv'\n",
    "dataset = load_csv(filename)\n",
    "\n",
    "# convert string attributes to integers\n",
    "for i in range(0, len(dataset[0])):\n",
    "\tstr_column_to_float(dataset, i)\n",
    "\n",
    "\n",
    "# evaluate algorithm\n",
    "n_folds = 5\n",
    "max_depth = 10\n",
    "min_size = 1\n",
    "sample_size = 1.0\n",
    "n_features = int(sqrt(len(dataset[0])-1))\n",
    "for n_trees in [10]:\n",
    "\tscores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)\n",
    "\tprint('Trees: %d' % n_trees)\n",
    "\tprint('Scores: %s' % scores)\n",
    "\tprint('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.27804, 0.31011, 0.20663, 0.055255, 0.16288, 0.17356, 0.60732, -0.72565, -0.12646, 0.33921, -0.29753], [-0.028981, 0.14378, 0.5059, 0.63189, 0.42136, 0.16252, -0.00334, -0.11127, 0.2157, -0.38415], [0.74227, 0.48285, -0.16571, 0.26462, -0.49121, 0.32778, 0.70852, 0.8351, 0.6366, 0.63527], [-0.81333, -0.89625, -0.016994, -0.44328, 0.65821, -0.76347, -0.96419, -0.096731, 0.054158, 0.18133], [-0.44622, -0.16735, -0.3337, 0.037943, -0.41659, -0.038114, -0.62625, 0.33394, -0.8427, -0.15465], [-0.12502, -0.88804, 0.040125, -0.72326, 0.79727, -0.86209, -0.33343, 0.41008, -0.24276, 0.12511, -0.06684], [-0.39085, 0.35579, -0.18178, -0.027863, -0.48236, 0.38834, -0.35438, 0.17493, 0.66536, 0.23541, -0.20351], [0.54404, 0.20994, 0.32438, -0.13544, 0.37796, 0.080315, 0.77345, -0.22166, 0.2341, 0.37484], [-0.19505, 0.37878, -0.094639, 0.12086, -0.44253, 0.40391, 0.11906, -0.24731, 0.67528, 1.0], [0.96875, 0.08624, -0.12021, 0.26143, -0.19613, -0.009844, 0.79458, 0.2492, 0.26205, 0.46033], [-0.71551, 0.23102, 0.24042, 0.22922, -0.039318, 0.39194, -0.61702, -0.47685, 0.34128, 0.092268, -0.1117], [-0.2096, 0.097956, -0.29864, -0.001306, -0.47853, 0.10043, -0.11274, 0.047922, -0.40933, -0.75552], [0.003044, -0.10676, -0.32792, -0.3156, -0.16313, -0.18827, 0.056916, 0.12192, -0.88249, -0.10043, 0.12099], [0.62466, 0.045108, -0.25712, 0.017442, -0.27089, -0.078603, 0.57986, 0.32923, 0.07863, -0.025246], [-0.3516, -0.27364, -0.3483, -0.79662, -0.15937, -0.19144, -0.52412, 0.14331, 0.42387, 0.19384], [0.45178, 0.34555, -0.19567, 0.19613, -0.56848, 0.30438, 0.45634, 0.24498, 0.93069, 1.0, -0.88277], [-0.78399, 0.2546, -0.70099, -0.29917, -0.85833, 0.2105, -0.53005, 0.51855, -0.30319, 0.020018], [0.55356, -0.06379, -0.26414, 0.11277, -0.33695, -0.064323, 0.18942, 0.99771, 0.2226, -0.10899, 0.069032], [-0.35143, 0.096283, 0.25055, 0.21386, 0.079608, 0.27, -0.62198, 0.3287, 0.5533, 0.019974], [-0.23609, -0.19751, -0.36531, -0.41347, -0.18178, -0.22667, -0.21286, 0.030467, 0.2041, 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-0.53557, -0.3026, -0.39181], [0.10995, 0.043399, -0.40768, -0.4793, -0.30018, -0.052069, 0.31102, -0.53557, -0.3026, -0.39181, 0.25246], [0.7049, -0.5306, -0.084001, -0.36863, 0.49063, -0.67493, 0.63424, -0.16896, 0.12623, 0.22158], [0.46108, 0.4653, 0.93117, 0.88999, 0.68771, 0.45577, 0.48605, -0.14595, -0.47858, -0.4528, 0.39988], [-0.006252, -0.90522, -0.55939, -1.015, 0.32723, -1.0508, -0.064957, 0.245, 0.37, -0.068241], [-0.45771, -0.24003, -0.33706, -0.31257, -0.32192, -0.10749, -0.61389, 0.067457, -0.79716, -0.8837, 0.74126], [0.56325, 0.048384, 0.090836, 0.21251, 0.13027, -0.067413, 0.57934, 0.17318, 0.57083, 0.12186], [-0.3014, -0.13387, -0.29681, -0.52241, -0.058147, -0.26256, 0.041177, -0.20758, 0.20368, 0.21374, -0.20313], [0.37913, -0.046562, -0.12336, -0.063958, -0.10983, -0.045824, 0.1238, 0.49624, -0.64157, -0.24229], [0.5435, 0.39541, 0.048231, -0.16127, -0.28744, 0.4885, 0.18915, 0.6029, 0.49797, -0.19786, 0.11255], [-0.014658, -0.53032, -0.039628, -0.84633, 0.67986, 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-0.54814, -1.0859, 0.21302, -0.94204, -0.67525, 0.64821, -0.7455, -0.41391], [0.49053, -0.35245, -0.20838, -0.1247, 0.089263, -0.41729, 0.37017, -0.31768, 0.63013, 0.13456, -0.096455], [-0.28879, -0.047979, 0.64954, 0.20301, 0.82824, -0.031554, -0.13786, -0.18661, 0.5622, 0.60462], [0.58217, -0.045015, -0.19187, -0.012795, -0.37745, 0.042399, 0.29406, 0.017011, 0.28906, 0.49668], [0.28511, 0.33513, -0.063525, -0.30297, -0.085579, 0.21858, 0.28942, 0.02513, 0.39539, 0.22997], [-0.48175, 0.34358, -0.62436, -0.75435, -0.80838, 0.28018, -0.23729, -0.10163, 0.88105, 0.77408], [-0.49114, 0.14523, -0.26203, 0.15395, -0.58218, 0.2917, -0.51303, -0.12608, 0.099311, 0.090028, -0.053935], [0.81876, 0.33239, -0.59413, -0.76517, -0.65296, 0.13226, 0.72883, 0.45269, 0.25206, -0.12098, 0.10376], [0.49777, -0.18688, 0.1742, -0.26179, 0.56263, -0.32682, 0.58529, -0.46772, -0.15347, 0.16883], [0.70053, -0.24519, 0.14019, -0.017159, 0.47359, -0.39034, 0.70116, 0.0149, -0.43974, -0.48201, 0.38535, 0.062836], [0.37767, -0.2747, -0.14337, -0.33985, 0.16217, -0.34524, 0.2431, 0.16193, 0.75394, 0.30457, -0.27613], [0.49941, -0.080415, 0.31429, 0.11138, 0.53456, -0.19192, 0.6081, -0.402, -0.061734, -0.087725], [-0.93788, 0.10974, 0.62036, 0.33672, 0.67177, 0.15943, -0.75701, 0.21452, 0.55672, -0.10568, 0.050288], [0.96405, -0.003361, -0.86876, -0.76013, -0.78482, -0.21668, 0.84748, 0.49843, 0.094435, 0.14669, -0.12851], [0.00975, -0.43543, -0.037979, -0.30924, 0.33629, -0.4155, -0.1002, -0.27457, -0.6707, -0.32523], [0.12099, -0.012491, 0.23769, 0.01501, 0.24272, 0.060151, -0.038016, -0.13711, -0.066597, 0.015968], [0.72065, 0.30374, -0.12744, 0.070148, -0.38114, 0.21582, 0.78517, -0.12819, 0.24596, 0.26766, -0.26945], [-0.42711, -0.45887, -0.23525, 0.11849, -0.20632, -0.27183, -0.71013, 0.51291, -0.60181, -0.11146], [0.55846, 0.11956, 0.35626, 0.62037, 0.15961, 0.16768, 0.27651, 0.54844, -0.25772, -0.37108], [0.73128, 0.47436, 0.16796, -0.006515, -0.032151, 0.39054, 0.66198, -0.097159, -0.031626, 0.22667, -0.18468], [0.1119, 1.0, -0.24805, 0.10381, -1.0777, 0.979, 0.27342, -0.0129, -1.8637, -0.73233, 0.70367], [-0.74731, -0.68034, -0.1477, 0.000797, 0.21018, -0.56256, -0.70805, -0.36788, -0.70741, -0.10145], [-0.068616, 0.003195, 0.34194, 0.2964, 0.374, 0.018357, -0.26298, 0.46411, -0.96973, -1.6044], [-1.667, -1.0049, -0.089985, -0.54556, 0.63478, -0.84512, -1.651, 0.43848, -0.15217, 0.080638, -0.032242], [-0.2658, 0.58079, 0.2547, 0.37903, -0.1631, 0.55455, 0.21399, -0.88275, -0.72675, 0.22396, -0.18088], [-0.11658, -0.081404, -0.37479, 0.003842, -0.53532, 0.020666, -0.29141, 0.15555, 0.17182, 0.11565, -0.1041], [-0.25543, -0.049659, 0.074905, 0.026031, 0.19394, -0.095382, 0.026131, -0.66914, -0.36642, -0.012091], [0.38925, 0.22424, -0.91208, -1.1074, -0.80474, -0.04202, 0.51315, 0.20566, 0.59533, 0.47762, -0.39719, -0.11988], [-0.13131, 0.51816, 0.43281, 0.39789, 0.043127, 0.5632, -0.013246, -0.062833, -0.88339, -0.040303, 0.088057], [0.080397, -0.63074, -0.15661, -0.17755, 0.31831, -0.62741, -0.1577, 0.35696, 0.1662, -0.20586, 0.14678], [-0.39808, 0.16055, 1.0, 0.63696, 0.96583, 0.27422, -0.42293, 0.1319, 0.34348, 0.055806], [-0.22818, -0.042154, 0.56279, 0.29118, 0.65198, -0.014788, -0.08006, -0.32186, -0.21905, -0.48214, 0.41296], [-0.28724, 0.41281, -0.66205, -0.80608, -0.88434, 0.32156, -0.064677, -0.033885, 0.34148, 0.35127, -0.285], [0.45926, -0.59109, 0.25277, -0.26535, 1.0, -0.76061, 0.55313, -0.40006, -0.8536, -0.23317], [0.11676, 0.73167, 0.36396, 0.44228, -0.19801, 0.75373, 0.46036, -0.14066, 0.82845, 0.87067], [0.7363, -0.3396, -0.21098, 0.048958, -0.056706, -0.38819, 0.59417, -0.058747, -0.14397, -0.21605], [0.057925, -0.23926, 0.10415, -0.018956, 0.43645, -0.35041, 0.27003, -0.30977, 0.46696, 0.67257, -0.62995], [-0.46684, -0.062034, -0.36059, -0.2627, -0.4015, 0.003603, -0.4092, -0.39695, -0.27627, 0.074153], [-0.28839, 0.15463, 0.73964, 0.23138, 0.91331, 0.083101, -0.093932, 0.26179, 0.16296, 0.38338], [-0.14648, -0.10456, 0.27055, 0.42322, 0.21375, -0.053184, -0.078576, -0.27821, 0.24185, 0.19105], [-0.31338, -0.003663, 0.085749, 0.071735, 0.046584, 0.04056, -0.34241, 0.17207, -0.11802, -0.39248, 0.23859], [-0.17797, -0.33157, -0.22153, -0.43216, 0.075834, -0.3139, -0.35746, 0.29253, -0.17256, -0.42951, 0.38425], [0.11357, -0.051154, 0.7061, 0.7413, 0.70137, 0.032429, 0.004308, 0.056379, 0.26248, 0.49233], [-0.39085, 0.35579, -0.18178, -0.027863, -0.48236, 0.38834, -0.35438, 0.17493, 0.66536, 0.23541, -0.20351], [0.12536, -0.10587, 0.2972, 0.18969, 0.38521, -0.075452, 0.10535, -0.37423, -0.38113, -0.44376], [0.53967, 0.69333, 0.13401, 0.72406, -0.6858, 0.82868, 0.37774, -0.26206, 0.22456, -0.11229, 0.077138], [-0.12807, 0.024672, 0.070916, 0.20642, -0.10484, 0.17115, -0.30479, -0.033722, 0.23421, 0.18663, -0.16351], [-1.3327, 0.47654, 0.25801, 0.31696, -0.1547, 0.63507, -0.96759, -0.71409, 0.62105, 0.63737], [0.35839, 0.34759, 0.0433, 0.070895, -0.18527, 0.30925, 0.42525, -0.28421, 1.0, 0.85432, -0.75554], [0.45717, -0.084912, 0.26939, -0.23486, 0.55998, -0.23241, 0.60834, -0.21876, 0.50563, 0.28007], [-0.45771, -0.4003, -0.14458, -0.4325, 0.086964, -0.27686, -0.66429, 0.27989, -0.062158, -0.078413], [-0.82742, -0.23351, -0.1542, -0.27695, -0.09226, -0.066378, -0.836, -0.51125, 0.30271, 0.19194, -0.17069], [0.11357, -0.051154, 0.7061, 0.7413, 0.70137, 0.032429, 0.004308, 0.056379, 0.26248, 0.49233], [0.58217, -0.045015, -0.19187, -0.012795, -0.37745, 0.042399, 0.29406, 0.017011, 0.28906, 0.49668], [-0.55005, 0.27629, -0.27324, 0.11319, -0.77698, 0.52266, -0.85062, 0.44004, 0.20391, -0.014462], [0.29877, -0.17962, 0.032859, -0.89901, 0.5403, -0.36184, 0.1779, 0.71325, -0.098148, -0.41113, 0.24683], [0.24747, 0.056669, 0.606, 0.45417, 0.65759, 0.019882, 0.22097, 0.51108, -0.19738, -0.15559], [-0.3405, -0.34461, 0.10165, -0.27984, 0.43791, -0.30819, -0.46573, 0.32657, -0.45646, -0.63705], [0.44589, 0.38443, 0.14877, 0.63854, -0.27498, 0.41201, 0.18946, 1.0, 0.78605, 0.65022, -0.57912], [-0.1518, 0.59652, 0.14833, 0.55739, -0.49566, 0.70595, 0.001053, -0.6444, 0.36821, 0.1684], [0.30402, -0.26957, -0.098415, -0.26427, 0.46769, -0.61393, 1.0, -1.0236, 0.043996, 0.3463], [-0.068616, 0.003195, 0.34194, 0.2964, 0.374, 0.018357, -0.26298, 0.46411, -0.96973, -1.6044], [-0.49008, 0.57689, 0.18151, 0.20012, -0.19942, 0.54698, 0.018037, -0.7971, -1.415, -0.026412], [-1.044, 0.11272, 0.071678, 0.35082, -0.099634, 0.17511, -0.59954, -0.67152, 0.034449, 0.21535], [-0.65123, -0.23004, 0.21374, 1.0, -0.049209, -0.003613, -0.81967, -0.11053, -0.73427, -0.057459], [-0.039708, 0.15247, -0.29121, -0.10321, -0.39136, 0.060044, 0.20798, -0.22665, 0.38294, 0.25886, -0.23099], [-0.40572, -0.32364, 0.12453, -0.14796, 0.38672, -0.28487, -0.29948, -0.22222, 0.19134, 0.70321], [-0.51497, 0.08682, -0.38881, -0.64284, -0.38562, 0.091563, -0.4981, 0.26624, -0.31386, -0.32063], [-0.1682, -0.161, 0.032007, 0.040236, 0.13349, -0.11975, -0.29302, 0.30586, -0.49597, -0.44144], [-0.4446, -0.78779, 0.009537, -0.52695, 0.66358, -0.74537, -0.48479, -0.075233, -0.45825, -0.36456, 0.28376], [-0.14659, -0.16494, 0.24987, -0.073985, 0.49494, -0.15633, -0.28891, 0.5008, -0.045553, 0.050759], [-0.32234, 0.1822, 0.074902, -0.12816, 0.091872, 0.12344, -0.15783, 0.16143, 0.22004, 0.07262], [-0.51547, 0.63185, 0.26332, 0.31588, -0.21086, 0.68763, -0.23399, -0.54362, 0.40349, 0.008412, -0.04257], [-0.62366, -0.55094, 0.003511, -0.18136, 0.44797, -0.49882, -0.52377, -0.64549, -0.043079, 0.001461, -0.001456], [-0.65959, 0.20072, 0.40465, 0.29754, 0.23683, 0.29286, -0.41675, -0.56583, -0.027265, -0.066083, 0.034435]]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-c9cf6bab47b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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[1;32m     31\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn_trees\u001b[0m \u001b[0;32min\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_algorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_forest_sklearn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\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     33\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Trees: %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Scores: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-44f830b84152>\u001b[0m in \u001b[0;36mevaluate_algorithm\u001b[0;34m(dataset, algorithm, n_folds, *args)\u001b[0m\n\u001b[1;32m     15\u001b[0m                 \u001b[0mpredicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_set\u001b[0m\u001b[0;34m,\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[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0mactual\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                 \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\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     18\u001b[0m                 \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-1-5030d143a9e4>\u001b[0m in \u001b[0;36maccuracy_metric\u001b[0;34m(actual, predicted)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mcorrect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\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---> 46\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mactual\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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[0m\u001b[1;32m     47\u001b[0m                         \u001b[0mcorrect\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcorrect\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "# Evaluate sklearn RF model\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def random_forest_sklearn(train, test, max_depth, min_size, sample_size, n_trees, n_features):\n",
    "    # Instantiate model with 10 decision trees\n",
    "    rf = RandomForestClassifier(n_estimators = 10, random_state = 0)\n",
    "    #print(train[0])\n",
    "    y_train = []\n",
    "    for i in range(len(train)):\n",
    "        y_train.append(train[i][-1])\n",
    "\n",
    "    \n",
    "    x_train = train.copy() \n",
    "    for row in x_train:\n",
    "        del row[-1]\n",
    "\n",
    "        \n",
    "    print(x_train)\n",
    "\n",
    "\n",
    "# evaluate algorithm\n",
    "n_folds = 5\n",
    "max_depth = 10\n",
    "min_size = 1\n",
    "sample_size = 1.0\n",
    "n_features = int(sqrt(len(dataset[0])-1))\n",
    "for n_trees in [10]:\n",
    "\tscores = evaluate_algorithm(dataset, random_forest_sklearn, n_folds, max_depth, min_size, sample_size, n_trees, n_features)\n",
    "\tprint('Trees: %d' % n_trees)\n",
    "\tprint('Scores: %s' % scores)\n",
    "\tprint('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))"
   ]
  }
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