{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest from scratch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from IPython.display import display\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in our data from last lesson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/bulldozers/\"\n",
    "\n",
    "df_raw = pd.read_feather('tmp/bulldozers-raw')\n",
    "df_trn, y_trn, nas = proc_df(df_raw, 'SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_vals(a,n): return a[:n], a[n:]\n",
    "n_valid = 12000\n",
    "n_trn = len(df_trn)-n_valid\n",
    "X_train, X_valid = split_vals(df_trn, n_trn)\n",
    "y_train, y_valid = split_vals(y_trn, n_trn)\n",
    "raw_train, raw_valid = split_vals(df_raw, n_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sub = X_train[['YearMade', 'MachineHoursCurrentMeter']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic data structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TreeEnsemble():\n",
    "    def __init__(self, x, y, n_trees, sample_sz, min_leaf=5):\n",
    "        np.random.seed(42)\n",
    "        self.x,self.y,self.sample_sz,self.min_leaf = x,y,sample_sz,min_leaf\n",
    "        self.trees = [self.create_tree() for i in range(n_trees)]\n",
    "\n",
    "    def create_tree(self):\n",
    "        rnd_idxs = np.random.permutation(len(self.y))[:self.sample_sz]\n",
    "        return DecisionTree(self.x.iloc[rnd_idxs], self.y[rnd_idxs], min_leaf=self.min_leaf)\n",
    "        \n",
    "    def predict(self, x):\n",
    "        return np.mean([t.predict(x) for t in self.trees], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs=None, min_leaf=5):\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = TreeEnsemble(X_train, y_train, n_trees=10, sample_sz=1000, min_leaf=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.DecisionTree at 0x7f645ec22358>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs=None, min_leaf=5):\n",
    "        if idxs is None: idxs=np.arange(len(y))\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf\n",
    "        self.n,self.c = len(idxs), x.shape[1]\n",
    "        self.val = np.mean(y[idxs])\n",
    "        self.score = float('inf')\n",
    "        self.find_varsplit()\n",
    "        \n",
    "    # This just does one decision; we'll make it recursive later\n",
    "    def find_varsplit(self):\n",
    "        for i in range(self.c): self.find_better_split(i)\n",
    "            \n",
    "    # We'll write this later!\n",
    "    def find_better_split(self, var_idx): pass\n",
    "    \n",
    "    @property\n",
    "    def split_name(self): return self.x.columns[self.var_idx]\n",
    "    \n",
    "    @property\n",
    "    def split_col(self): return self.x.values[self.idxs,self.var_idx]\n",
    "\n",
    "    @property\n",
    "    def is_leaf(self): return self.score == float('inf')\n",
    "    \n",
    "    def __repr__(self):\n",
    "        s = f'n: {self.n}; val:{self.val}'\n",
    "        if not self.is_leaf:\n",
    "            s += f'; score:{self.score}; split:{self.split}; var:{self.split_name}'\n",
    "        return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = TreeEnsemble(X_train, y_train, n_trees=10, sample_sz=1000, min_leaf=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,  19,\n",
       "        20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59,\n",
       "        60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,\n",
       "        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99,\n",
       "       100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
       "       120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,\n",
       "       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "       180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199,\n",
       "       200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,\n",
       "       240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n",
       "       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319,\n",
       "       320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339,\n",
       "       340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,\n",
       "       360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n",
       "       380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399,\n",
       "       400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419,\n",
       "       420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439,\n",
       "       440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,\n",
       "       460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479,\n",
       "       480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499,\n",
       "       500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n",
       "       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539,\n",
       "       540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559,\n",
       "       560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,\n",
       "       580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599,\n",
       "       600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619,\n",
       "       620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639,\n",
       "       640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659,\n",
       "       660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679,\n",
       "       680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699,\n",
       "       700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719,\n",
       "       720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739,\n",
       "       740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759,\n",
       "       760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,\n",
       "       780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799,\n",
       "       800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819,\n",
       "       820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839,\n",
       "       840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859,\n",
       "       860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879,\n",
       "       880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899,\n",
       "       900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919,\n",
       "       920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939,\n",
       "       940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959,\n",
       "       960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979,\n",
       "       980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.trees[0].idxs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single branch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best split given variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YearMade', 'MachineHoursCurrentMeter'], dtype='object')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens = TreeEnsemble(x_sub, y_train, 1, 1000)\n",
    "tree = ens.trees[0]\n",
    "x_samp,y_samp = tree.x, tree.y\n",
    "x_samp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<title>Tree</title>\n",
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       "<text text-anchor=\"start\" x=\"8\" y=\"-96.2669\" font-family=\"Times,serif\" font-size=\"14.00\">YearMade ≤ 1974.5</text>\n",
       "<text text-anchor=\"start\" x=\"32.5\" y=\"-81.2669\" font-family=\"Times,serif\" font-size=\"14.00\">mse = 0.47</text>\n",
       "<text text-anchor=\"start\" x=\"20\" y=\"-66.2669\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 1000</text>\n",
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       "<text text-anchor=\"start\" x=\"178\" y=\"-104.767\" font-family=\"Times,serif\" font-size=\"14.00\">value = 9.66</text>\n",
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       "<text text-anchor=\"start\" x=\"172.5\" y=\"-29.7669\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 841</text>\n",
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       "</g>\n",
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       "<g id=\"edge2\" class=\"edge\"><title>0&#45;&gt;2</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7f645ec7c080>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=1, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_better_split(self, var_idx):\n",
    "    x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "\n",
    "    for i in range(self.n):\n",
    "        lhs = x<=x[i]\n",
    "        rhs = x>x[i]\n",
    "        if rhs.sum()<self.min_leaf or lhs.sum()<self.min_leaf: continue\n",
    "        lhs_std = y[lhs].std()\n",
    "        rhs_std = y[rhs].std()\n",
    "        curr_score = lhs_std*lhs.sum() + rhs_std*rhs.sum()\n",
    "        if curr_score<self.score: \n",
    "            self.var_idx,self.score,self.split = var_idx,curr_score,x[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.9 ms ± 2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:681.0184057251435; split:3744.0; var:MachineHoursCurrentMeter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit find_better_split(tree,1)\n",
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055949; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_better_split(tree,0); tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speeding things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def std_agg(cnt, s1, s2): return math.sqrt((s2/cnt) - (s1/cnt)**2)\n",
    "\n",
    "def find_better_split(self, var_idx):\n",
    "    x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "    \n",
    "    sort_idx = np.argsort(x)\n",
    "    sort_y,sort_x = y[sort_idx], x[sort_idx]\n",
    "    rhs_cnt,rhs_sum,rhs_sum2 = self.n, sort_y.sum(), (sort_y**2).sum()\n",
    "    lhs_cnt,lhs_sum,lhs_sum2 = 0,0.,0.\n",
    "\n",
    "    for i in range(0,self.n-self.min_leaf):\n",
    "        xi,yi = sort_x[i],sort_y[i]\n",
    "        lhs_cnt += 1; rhs_cnt -= 1\n",
    "        lhs_sum += yi; rhs_sum -= yi\n",
    "        lhs_sum2 += yi**2; rhs_sum2 -= yi**2\n",
    "        if i<self.min_leaf-1 or xi==sort_x[i+1]:\n",
    "            continue\n",
    "            \n",
    "        lhs_std = std_agg(lhs_cnt, lhs_sum, lhs_sum2)\n",
    "        rhs_std = std_agg(rhs_cnt, rhs_sum, rhs_sum2)\n",
    "        curr_score = lhs_std*lhs_cnt + rhs_std*rhs_cnt\n",
    "        if curr_score<self.score: \n",
    "            self.var_idx,self.score,self.split = var_idx,curr_score,xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.73 ms ± 3.58 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:681.0184057251353; split:3744.0; var:MachineHoursCurrentMeter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit find_better_split(tree,1)\n",
    "tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_better_split(tree,0); tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTree.find_better_split = find_better_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]; tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full single tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=2, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_varsplit(self):\n",
    "    for i in range(self.c): self.find_better_split(i)\n",
    "    if self.is_leaf: return\n",
    "    x = self.split_col\n",
    "    lhs = np.nonzero(x<=self.split)[0]\n",
    "    rhs = np.nonzero(x>self.split)[0]\n",
    "    self.lhs = DecisionTree(self.x, self.y, self.idxs[lhs])\n",
    "    self.rhs = DecisionTree(self.x, self.y, self.idxs[rhs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTree.find_varsplit = find_varsplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 1000; val:10.079014121552744; score:658.5510186055565; split:1974.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = TreeEnsemble(x_sub, y_train, 1, 1000).trees[0]; tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 159; val:9.660892662981706; score:76.82696888346362; split:2800.0; var:MachineHoursCurrentMeter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 841; val:10.158064432982941; score:571.4803525045031; split:2005.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.rhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 150; val:9.619280538108496; score:71.15906938383463; split:1000.0; var:YearMade"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs.lhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n: 9; val:10.354428077535193"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.lhs.rhs"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['MachineID', 'YearMade', 'MachineHoursCurrentMeter', 'ProductSize', 'Enclosure',\n",
    "        'Coupler_System', 'saleYear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 325 ms, sys: 3.98 ms, total: 329 ms\n",
      "Wall time: 328 ms\n"
     ]
    }
   ],
   "source": [
    "%time tree = TreeEnsemble(X_train[cols], y_train, 1, 1000).trees[0]\n",
    "x_samp,y_samp = tree.x, tree.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<graphviz.files.Source at 0x7f645ec0f208>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=3, bootstrap=False)\n",
    "m.fit(x_samp, y_samp)\n",
    "draw_tree(m.estimators_[0], x_samp, precision=2, ratio=0.9, size=7)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(self, x): return np.array([self.predict_row(xi) for xi in x])\n",
    "DecisionTree.predict = predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if something:\n",
    "    x= do1()\n",
    "else:\n",
    "    x= do2()"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = do1() if something else do2()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = something ? do1() : do2()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_row(self, xi):\n",
    "    if self.is_leaf: return self.val\n",
    "    t = self.lhs if xi[self.var_idx]<=self.split else self.rhs\n",
    "    return t.predict_row(xi)\n",
    "\n",
    "DecisionTree.predict_row = predict_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 94.4 ms, sys: 0 ns, total: 94.4 ms\n",
      "Wall time: 93.4 ms\n"
     ]
    }
   ],
   "source": [
    "%time preds = tree.predict(X_valid[cols].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(preds, y_valid, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4840854669925271"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(preds, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n",
      "Wall time: 8.48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f645dee8860>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSo1BYHUgUgIp4H4vbeWxNed0c/PeQD+N8b5djU2rpl3Zeo8UkkgJTucNjWk3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\nWcn8uU8yencnpb5lP7fZtd9W8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UlZdXz8YsYffHzEedXSaTguW3qRIg8lf/ZkzH/w/Udb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\nazrkDIUw8EFAq+tR0HUcP2JkRYjfaNGk0faQ6UJoEEva3R7DaZueH1G0t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SkXpZRLwJeA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f645df531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, min_samples_leaf=5, bootstrap=False)\n",
    "%time m.fit(x_samp, y_samp)\n",
    "preds = m.predict(X_valid[cols].values)\n",
    "plt.scatter(preds, y_valid, alpha=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47541053100694797"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(preds, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Putting it together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TreeEnsemble():\n",
    "    def __init__(self, x, y, n_trees, sample_sz, min_leaf=5):\n",
    "        np.random.seed(42)\n",
    "        self.x,self.y,self.sample_sz,self.min_leaf = x,y,sample_sz,min_leaf\n",
    "        self.trees = [self.create_tree() for i in range(n_trees)]\n",
    "\n",
    "    def create_tree(self):\n",
    "        idxs = np.random.permutation(len(self.y))[:self.sample_sz]\n",
    "        return DecisionTree(self.x.iloc[idxs], self.y[idxs], \n",
    "                    idxs=np.array(range(self.sample_sz)), min_leaf=self.min_leaf)\n",
    "        \n",
    "    def predict(self, x):\n",
    "        return np.mean([t.predict(x) for t in self.trees], axis=0)\n",
    "\n",
    "def std_agg(cnt, s1, s2): return math.sqrt((s2/cnt) - (s1/cnt)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTree():\n",
    "    def __init__(self, x, y, idxs, min_leaf=5):\n",
    "        self.x,self.y,self.idxs,self.min_leaf = x,y,idxs,min_leaf\n",
    "        self.n,self.c = len(idxs), x.shape[1]\n",
    "        self.val = np.mean(y[idxs])\n",
    "        self.score = float('inf')\n",
    "        self.find_varsplit()\n",
    "        \n",
    "    def find_varsplit(self):\n",
    "        for i in range(self.c): self.find_better_split(i)\n",
    "        if self.score == float('inf'): return\n",
    "        x = self.split_col\n",
    "        lhs = np.nonzero(x<=self.split)[0]\n",
    "        rhs = np.nonzero(x>self.split)[0]\n",
    "        self.lhs = DecisionTree(self.x, self.y, self.idxs[lhs])\n",
    "        self.rhs = DecisionTree(self.x, self.y, self.idxs[rhs])\n",
    "\n",
    "    def find_better_split(self, var_idx):\n",
    "        x,y = self.x.values[self.idxs,var_idx], self.y[self.idxs]\n",
    "        sort_idx = np.argsort(x)\n",
    "        sort_y,sort_x = y[sort_idx], x[sort_idx]\n",
    "        rhs_cnt,rhs_sum,rhs_sum2 = self.n, sort_y.sum(), (sort_y**2).sum()\n",
    "        lhs_cnt,lhs_sum,lhs_sum2 = 0,0.,0.\n",
    "\n",
    "        for i in range(0,self.n-self.min_leaf):\n",
    "            xi,yi = sort_x[i],sort_y[i]\n",
    "            lhs_cnt += 1; rhs_cnt -= 1\n",
    "            lhs_sum += yi; rhs_sum -= yi\n",
    "            lhs_sum2 += yi**2; rhs_sum2 -= yi**2\n",
    "            if i<self.min_leaf-1 or xi==sort_x[i+1]:\n",
    "                continue\n",
    "\n",
    "            lhs_std = std_agg(lhs_cnt, lhs_sum, lhs_sum2)\n",
    "            rhs_std = std_agg(rhs_cnt, rhs_sum, rhs_sum2)\n",
    "            curr_score = lhs_std*lhs_cnt + rhs_std*rhs_cnt\n",
    "            if curr_score<self.score: \n",
    "                self.var_idx,self.score,self.split = var_idx,curr_score,xi\n",
    "\n",
    "    @property\n",
    "    def split_name(self): return self.x.columns[self.var_idx]\n",
    "    \n",
    "    @property\n",
    "    def split_col(self): return self.x.values[self.idxs,self.var_idx]\n",
    "\n",
    "    @property\n",
    "    def is_leaf(self): return self.score == float('inf')\n",
    "    \n",
    "    def __repr__(self):\n",
    "        s = f'n: {self.n}; val:{self.val}'\n",
    "        if not self.is_leaf:\n",
    "            s += f'; score:{self.score}; split:{self.split}; var:{self.split_name}'\n",
    "        return s\n",
    "\n",
    "    def predict(self, x):\n",
    "        return np.array([self.predict_row(xi) for xi in x])\n",
    "\n",
    "    def predict_row(self, xi):\n",
    "        if self.is_leaf: return self.val\n",
    "        t = self.lhs if xi[self.var_idx]<=self.split else self.rhs\n",
    "        return t.predict_row(xi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens = TreeEnsemble(X_train[cols], y_train, 5, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = ens.predict(X_valid[cols].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(y_valid, preds, alpha=0.1, s=6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7025757322910476"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_valid, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fib1(n):\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "def fib2(n):\n",
    "    a, b = 0, 1\n",
    "    while b < n:\n",
    "        a, b = b, a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%cython\n",
    "def fib3(int n):\n",
    "    cdef int b = 1\n",
    "    cdef int a = 0\n",
    "    cdef int t = 0\n",
    "    while b < n:\n",
    "        t = a\n",
    "        a = b\n",
    "        b = t + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "698 ns ± 10.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib1(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291 ns ± 13.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib2(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49 ns ± 1.1 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fib3(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}