{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 0.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 183 built in 0.11041831970214844 seconds ---\n",
      "[[1.         1.         0.33333333 ... 0.33333333 0.33333333 0.33333333]\n",
      " [1.         1.         0.33333333 ... 0.33333333 0.33333333 0.33333333]\n",
      " [0.33333333 0.33333333 1.         ... 1.         1.         1.        ]\n",
      " ...\n",
      " [0.33333333 0.33333333 1.         ... 1.         1.         1.        ]\n",
      " [0.33333333 0.33333333 1.         ... 1.         1.         1.        ]\n",
      " [0.33333333 0.33333333 1.         ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb8e93f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 0.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 183 built in 0.38330578804016113 seconds ---\n",
      "[[1.         0.75       0.5        ... 0.16666667 0.16666667 0.16666667]\n",
      " [0.75       1.         0.4        ... 0.15384615 0.15384615 0.15384615]\n",
      " [0.5        0.4        1.         ... 0.27272727 0.27272727 0.27272727]\n",
      " ...\n",
      " [0.16666667 0.15384615 0.27272727 ... 1.         1.         1.        ]\n",
      " [0.16666667 0.15384615 0.27272727 ... 1.         1.         1.        ]\n",
      " [0.16666667 0.15384615 0.27272727 ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6dedd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 1.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 183 built in 0.1993114948272705 seconds ---\n",
      "[[1.         0.8        0.14285714 ... 0.125      0.125      0.125     ]\n",
      " [0.8        1.         0.125      ... 0.11111111 0.11111111 0.11111111]\n",
      " [0.14285714 0.125      1.         ... 0.8        0.8        0.8       ]\n",
      " ...\n",
      " [0.125      0.11111111 0.8        ... 1.         1.         1.        ]\n",
      " [0.125      0.11111111 0.8        ... 1.         1.         1.        ]\n",
      " [0.125      0.11111111 0.8        ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6d5bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 1.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 183 built in 0.5098922252655029 seconds ---\n",
      "[[1.         0.7        0.16666667 ... 0.05555556 0.05555556 0.05555556]\n",
      " [0.7        1.         0.13333333 ... 0.05128205 0.05128205 0.05128205]\n",
      " [0.16666667 0.13333333 1.         ... 0.22580645 0.22580645 0.22580645]\n",
      " ...\n",
      " [0.05555556 0.05128205 0.22580645 ... 1.         1.         1.        ]\n",
      " [0.05555556 0.05128205 0.22580645 ... 1.         1.         1.        ]\n",
      " [0.05555556 0.05128205 0.22580645 ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQgAAAD1CAYAAACsjWuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXmcFNXV93+ne3o2ZgCRYUdEQFFAAVERTdSgBjFuSV4DmsREjUmUaB4fnzfmk7xqfBMTzeuTPSZoXLKo0TwuGImIiIILCKhBVmEYBobZGWBmYLbuPu8fVdVd03Wr6tYsVg9zvp9Pf6bn1q17b3V1nb7LOb9LzAxBEAQVkbAbIAhC9iIGQhAEV8RACILgihgIQRBcEQMhCIIrYiAEQXBFDIQgHAUQ0aNEVEtEm1yOExH9moh2EtFGIpqpU64YCEE4OngcwDyP45cAmGS+bgLwkE6hYiAE4SiAmVcBaPDIcgWAP7PBGgCDiWikX7liIAShfzAawF7b/xVmmic5vdYcQRA8+ewFA3h/Q0Ir74aNbZsBtNqSFjPz4l5pmA0xEIIQEvUNCaxdNkYrb2xkaSszz+pGdfsAjLX9P8ZM80SGGIIQGowEJ7VePcASAF81VzNmAzjEzFV+J0kPQhBCggEk0TPR1ET0FIDzAQwlogoAdwOIAQAz/wHAUgDzAewEcATA13XKFQMhCCGSRI/0DsDMC32OM4BbgpYrBkIQQoLBSGS5HkvocxBENI+ItpseXneG3R5diGg3EX1ERB8S0XozbQgRLSeiHebfY8Jupx2Vt51bm7vqedfbuFzDPUS0z7wXHxLRfNux75vXsJ2IPhtOq91JgrVeYRGqgSCiKIDfwfDyOgXAQiI6Jcw2BeQCZp5um12+E8AKZp4EYIX5fzbxOJzedm5t7pLn3SfA41B7DP7CvBfTmXkpAJjfpQUAppjn/N78zmUFDCAB1nqFRdg9iDMB7GTmXczcDuBpGB5ffZUrADxhvn8CwJUhtsWBi7edW5u75HnX22h4DNq5AsDTzNzGzGUwJujO7LXGBYQBdHBS6xUWYRuILnl3ZQkM4FUi2kBEN5lpw21LR9UAhofTtEC4tbmv3ZtF5lDoUdvQLuuvIan5CouwDURf5lxmngmjK34LEX3aftCcNc7uGagM+mKbTR4CMAHAdABVAB4Mtzl6sObwoj8PMbrk3ZUNMPM+828tgOdhdF1rrG64+bc2vBZq49bmPnNvmLmGmRPMnATwMNLDiOy+BgYSmq+wCNtArAMwiYjGE1EujAmlJSG3yRciGkBExdZ7ABcD2ASj7deZ2a4D8GI4LQyEW5u75HkXBhlzI1fBuBeAcQ0LiCiPiMbDmHB975NunxuGo1R2DzFC9YNg5jgRLQKwDEAUwKPMvDnMNmkyHMDzRAQYn+GTzPwKEa0D8AwR3QCgHMDVIbbRgYu33c+gbnOXPO96G5drOJ+IpsN45nYD+CYAMPNmInoGwBYAcQC3MLNedNQnAiEBCrsRnpBsnCMI4TD11Fz+n5eHauWdfFzVhm4Ga3UJ8aQUhJBgAO2hj/K9EQMhCCGS5OweYoiBEISQMDwpxUAIgqCAQUhk+RCj11oXNAjL5o3YJ+nr7QfkGsIgyaT1CoteMRBdDMLqUzdWQV9vPyDX8IliDTF0XmHRW0OMVBAWABCRFYS1pZfqE4Q+CCHB2T3E6C0DoQqSOcst89AhUc4bVoxBBSMZySSS4wjtrTEAQH5NO5A0fDVaR+alzol0dC4jt74t/Y/Nt6O9JF9ZJ8WdabEDrc5EW1kdQwrcLgGxomNQWDK2k1MJ+bjA5Rxs8c6gaEMm8WMK9cqwEXHx3c0rGIziwWMYACKN/m1jADzQ/TNR4vKZULNPfRmfARerrzsvfzCKB45RXiC5fY6ZdRdpXpOizU04UM/MJTqnM4AOZE30uZLQJinNseJNAJA3rBhH/j0cl559Gbi1DdtuHY8LztyMlWunYvJ9u4A24+Ev/8qU1Pkdpx4GAMQ+GgAAGPf7tAMmd6Sf/torTlXWz4r7MvzPG52JibTj3cGLp2tenUEyx7trOOTZD7TK4YS7pWk5N1ibACCR699lLVri3zZOJNBxZrD6G8floqjKaZ3zXnnfp7LOn0FyRvDrbh6bh8LqDkd6zkqzbsuATJmmV+B7HzmSXuN/lOu2h7n/9iB8g2RMTf/FADCoYCRfevZlePndl3D+pitx8jcqUdVaiMnYhSN/KcSebeMBAJN/sjNdQFubvThsu3dy6n1BVfrpH/eQ2nPbbkQs9tx6miMtf3/6V2fY3xQGxIuEt1dv7VdnaBWT2+Tegxj8/IeBmgR4GxyLQ1/0F5DKbU6i4JVg9R/r8pm0Xnq653nRls7nxV4Pft0DXXQVOuYa1xptM45H3tIrO/kpxf1b9Y9AbUr202XOVBAWDMOwAMA1rrmTSXBrG87fdCXemPoC5rdenDIAe7alexN2o5D5gNuNQseph1M9C5UhAKB8eO3GwOLgyUbaoI/J94HPxO9B9Hrw7Rya4PyVKS5nrTrU7fK/jtxm/3ITuaRVVufK1eVmGoBMGsflpt4XVcVdy/GuW/15W4YhGTM+54hm+EGkzeW7pdscIOuXOXvFQAQNwkqOI2y7dTxO/kYl5rdejKUfvIob9pyLlWunomQdYefyUzB5aw1GvpL+Eq0um9ipjJFPpm9WbE0U1iZEpY90zmeRqHaOM8cvaXOkDd2YLvfjnwbr1ubt9775Y19r1ipn8Fb3L2L594K758ea/POMevOgbx5qj2Pfd1ynltR1N6sfvmHv7vc8L7+882dQe/3sQPUCQKxFXfcx64wIdzJ/TBqu0St7yDuVgdvQmf47xICpC7hUJ297awwXnLkZVa2FQFsbbthzLv503FuYsGYq8g8mEGuOg9o6sLpsQuqchaesBwD8bZMR+h9rTn+Boi3p94lql0k8xXfFfp5FpDWd5vfAO/DpPdrL9iym3T2fzsPurLh7daboiLs+8G4cHkMorFKc49bTs9qTcdztYfeieVQEhTXO81Jlx40foJxWzbLj3QsMNcK9+6mBCMrKtVMxGbtS7yesmYrSq/+A+b+9CtRmTCxZwwYAeHbTeQCAa69+EwCwfuf4dGEd6YmoQR+foKwv97I6R1rObufTxvH0F7O4fJDu5QAAaj/T7nmcKmr0Coq7PzzFFcGFs6vnaIx7K/3bxokkiqqODVR3YT2hbrrza8fVzvvRua7OD2NhtdZCQSfy9xPqp+U60pO19Z3qyK/VKztRVx+4DXYYhHbVbHkWkRXh3oPyhvPZQ75oTkgOR8m6CPIPJjBgax2Wrno+NdwY+1p63GnvMQDAuJ9+nHpv72kct1h9A1S9hbLbnA+OfSgy8e9H9C8K/j2Ej68bqFWOV89l3D/9hwKZ6PQOdn/ePww51gSMXh7wIXHpKeybP8K7royeyrBVmsbVRmYvxKJmriFTafVKdIcODXNGOdLe+9sd2mHZE6YN4Pue1xNxXzBpfT8O904y0NaWmpDcufyU1LDCPtxwG0YAcAw/rKGHyhAA6oc3Ue18YEtOMh6Aum1DtYcEFn4Pou6QpWWCc26kYFeeVh1KfLrzgObQJaJXlh23h9RvqHJ4TNp4F1axazmeuAwJLMOQjJJnvky0hyIu9NtJyu6wcu1UTN5akxpW2Icblz74uXTGjs7r2bGP0gbi2U3npYceu9W7J7Oi2z7oY+cQoq10GACgZH4tqOJAsIvxGBoAQHG5Xve8qMLZLa47zzQaGkOBTHRWPoor/LvZ1XPId2jgrFv98BVVeQuAF9anDUTd9JzUsKAn6i6sNnovtTMNo6s7dNAdiri2B4RElod7Z8UQI++4sXzCV27HuEd3Am1tGPlKAqvLJiD20QAM/SiOWHMceTtr8fLaf6bOubvOcJqyegp5m9NDgbwDjEFlxsP56fvfUda5bN/JjrT2l5w3PGZbaKCFwR6GmhrvOYthrzsffBWxI+4P9L7PBf8ljTbEfPOMeMf/e5HTkkTF3GBj6Fij+hdz+DqnA5OdaGvnz6DyU3kuOd3JOaxOH/a+YWwj7YYBqZ6t5506Yo1zyPn66h9qDwXGTyvie55TO/Jl8rUT3+3HQwxB6Icwo/8ucwqC4Af1W0/KQEQ6zNiKtjZwRxyryyZi4Snr8eym8xBrjhsTjR0dqWEFAPyoxPC7+kvCcNTJO5DuEseagRxzclI1lACA9rizaxxT+C3Zu/f7fIYMDpLeN99r6GAnp8U9n85woSt41ZmquzWJWGOw+ltHdyC/0nlO5hAiE6v7n2qfy3DBi5YRSRTUOH+xrbKtvzmai1WZbQoKA2jnrHgEXcnu1gnCUQwjXDEYHbLGQNidoABj8vHaq9/E2++c1SnNwuo57LrwUQDABU9+I3Usx7a0WbdNvZ7/3c/+y5G2ZM9nHGkRW1kFu4o9ryGTE+aWeR7v2Ksnee71SzWgYnCgNgFA+xz/NcyCSv9fR+pIoLBKb6I1VW5NDg7MdE5I5tX4uJ1nLGsW1gQMMwdQUEdomObsqcRqjc+DzOXNwjq9Scqc+q64sXZGljkFQVDCAJJZPkmZFcucg2LD+Oziy7Ht3skoqIpi6Kb00uaIZw+mljztWHMO1nLmykcfTh27u25Kqrcx8C21YIxqvmHeHascafY5DH4q2Lq33xxD/AbvACULr+XS0f8MbuN15hfK/5fGHERDDGNWBBuHu801lF/qPZeRuTw6arXTecwPt55YxVyjx2DNa6iWL1WolkO3/Px27eXIsVMH8W3P6gWG/dcpr8oypyD0J/pCD0IMhCCEiOyLoQMzuCOOgqqoIfayJppa2lxdNiG15Jnf0HkpE0hPSGYugVqTmKqhBKDu/quWRK8dtw4A8FjpbBRqLkta+HXldZdNp4x3Bg9tLh2tVYcKvyVFQH/5VKcsO27d/Fijt2dk6+j0xGZ+ZaxLS4xu56SWTMk7n+O8YLF7DpgJHcnseATdyLrWGXMNncVjrRWNVd+bk0rLyQjCylzhsFY3LvqTelPqiCKIa6dixeNXWy8BANw271/4595P612EVYfPF61gl94KRGnZeEfalM8YKySJyuDh3tTh/wAMqPCPE2mf04S8Gu+QdgcuQVaFVd6rEgU16a/qgZkdqZWHIJBLEFZhTREAYP9pxg+Q7uqE7mqHG4YeRM/1IIhoHoBfwRBpeoSZf5Zx/DgATwAYbOa509RtcSXrDIQg9B96TlHKthfNRTBU5NcR0RJmtm818UMAzzDzQ+Y+NUsBHO9VblYYiPaSfNRecSrGPbQZ3BFH6SMTkaguxKCPT8Bxi9sQbYlj/e4x+PSydOCVNRyw/BwGvZW2xLHmdM9h+VOPKeucuf5LzsSdAxxJuQeNG/iXn8/HVx7TEshKt7HOJ9Z/hV4PIlfxg7b3RaNXMfaX3r4WKkrr/P0vctUxbp0oXF6Esnv0ZPMsWg+oNTCGrPc+L2bznBy2Ogdb7xgSqF4AiDaqA8uO/bfxd/haowex/WZvbQoLK39XMSYpe6wHobMXDQOwbsAgAL7CF1lhIAShv9KDjlI6e9HcA+BVIvoOgAEALvQrNLvXWAThKMZytdbcm3MoEa23vbqyxeBCAI8z8xgA8wH8hYg8bYD0IAQhRAKI1tb7OEr57kUD4AYA8wCAmd8lonwAQwHUuhUqPQhBCAlmoCMZ0XppkNqLhohyYexFsyQjzx4AcwGAiE4GkA/AUwVJehCCEBLGEKNnfqPd9qIhonsBrGfmJQD+E8DDRPQfMCYsv8Y+sRZZYSAobuyVyR1xIJEwlKTZkKaPflCISGscHI93cmSy9BysqMy/fjA/dSx2JJnyc1CuVgB4f9bfHWlnPvdtR5rdoeq/11wU6Lp+dM4Lnsf/uH2cVjnWzk8qtm3QK8POnDn+m6zv3jXZN0+0NYkjm4NFuOYDKD7D+aM14H+8ncai7Z0/gyM71DE2nhDQcppzw93il42VhIhZR/FuPf+GAZXd9JRCz3pSqvaiYea7bO+3ADgnSJlZYSAEoT/Sw8ucvYIYCEEIjZ4bYvQWYiAEIUREk1IQBCWGqnV2G4jsEIzJKeHZBZdiz62nIX8/45gdhnt1zu4a7PjFCCSqCzDo484fpBWlOXCPEdg159fvpY4t23eyq9ScxbEbnTfmvfsecqT95oAxCfhY6Wy0rgu2D6VbJKnFjTe+rFWOl8u25XIdhJzD/vd85DW7ffOU1g1F4fKiYHU75wgBAI2Xe39YrQc6T0oOW90FoRyXyNPKi4x0yxVb14W65iznd6jsP/W33jv25BKe//gVWnX9dfafRDBGEPobMsQQBEGJrGIIguCJrGIIgqCGZV8MPZiBRAL5+xkHT2YM3RhPeU8mqgtQclI92kqHIfeQTXLO9HCMKHbQunbcupQSlKXnkIlKcs6akLTznWPKAQCLE+f4Tjpm4jcZ6KsXYXL1iHWOtCcq5mjVoWyXy0ShHR3NiGSStMrqVLfLRGHmJGQmJaMPpt7XVQx2LceLaLv6s8rUiXDLl0nO4WAbF2fS04pSvUF2GAhB6KdIDyIgmcuZgKEaVTK/VntfisdKZ+O2eUaMxl9+Pt8nd+fzMlmcMFzXPzrrSZzz1Le0ywK8YyiAtPCsH3fvGONI+9GnngcAPFLz+UBtAvSEZvfvcaprZTJnzhbsrT0xWN3t6rrzK717EE2V6XtfckYdCuqDxYAA6ViLTApqjNiLlulGdyi/Xq8HUVDXvceHAcT1IjVDIyv8IApLxvK0i7+LwUs+AhIJfPzT6cjbH0FxOWPwjsOItMZBFTVo+HNaoNXaTKZgl6GGXLy78/BjwF4jkOZzjzk3wwHUgVcFu53byFnDiqJ9Sbz9yz8Euq65Wy73PL6rfJhWOdGDzi9i4T7ji3XHTc8EahMAvHbAf2jzzjv+eWJNEZzx2U2B6t7WMFyZ3rTO2/hnbtZbcL5nlLKShoNqn42CDw3BXGu41HS23rip+F2n0O7G3+hvnDNo8nA+d7E6mDCTpef9RvwgBKG/IXMQgiCoYZmDEATBhaPeUYqIdgNoApAAEGfmWUQ0BMDfYejt7wZwNTMf6F4zBeHoJNsNRE9MoV7AzNNtEyh3AljBzJMArDD/FwQhAwYhkYxovcKiN2q+Asb2XjD/XtkLdQjCUUESpPUKi+4aCIaxEccGm07/cGauMt9XA1CvawlCP4fNSUrNfTFCobuTlOcy8z4iGgZgORFtsx9kZiYipaOFaVBuAoBYUfANaAXhaICP5jkIZt5n/q0F8DyM/QFriGgkAJh/lZtyMPNiZp7FzLNy8v299gTh6CPQzlqh0GUDQUQDiKjYeg/gYgCbYGzWcZ2Z7ToAL3a3kYJwtMJMWq+w6M4QYziA54nIKudJZn6FiNYBeIaIbgBQDuDq7jdTEI4+jmo/CHOb8dMU6fthbu+lCyWBZA4BiQQ4kUTe/ghAQO1n2jHkozioPQ7E46n4CwBA0vhgT5hbBgA4+JvjUodyWpKItCcAuIdUqza1eXCT05ZZ4dTRtqRvbEUmK07J3PmsM6cuvVmrnJwjqmkcI+0n/9YPRrO4b4b3hj4AsGnHFN88OS2M1RtPClY5Ey6b9YEjeU2Zd3h5Tlvnz6B2azB9UKvucTMyt6sEWvaOApAO827Z6YyxUFFUmQjehk7tyX7RWvGkFISQYGT/JKUYCEEIDVGUEgTBgyxQW/BEDIQghIgMMQRBUMIsBkIQBA9kDkIQBFeSyew2EFmhSTkoOpRn512C2q/OQG4TY2DZkZQO5fYfTErpU9qxZOst7clD96R1BGtqBqW0Kt04ZrtTwHTB3f9ypFl+FLoCs3aKNzs1Lu1svOP3WuX8udHpI2DJ3pf/e1TgdsWa/B1ov7/AX+vytQOnYN2yqYHqztSWtJjxeW9ty0wty5Y39ASMO9XtIjUZu8TQt7Q0K1VakypU2pVl1/xAWzsyf+JoPv6Bb2rVtf0Ld4smpSD0N8L/efZGDIQghIVMUgqC4EmWdyHEQAhCiEgPQhAEV7JgjcCT7N73SxCOYpgBTka0XjoQ0Twi2k5EO4lIKRZNRFcT0RYi2kxET/qVKT0IQQiRnupBEFEUwO8AXASgAsA6IlrCzFtseSYB+D6Ac5j5gCkV6Yn0IAQhTFjz5c+ZAHYy8y5mbgfwNAyFeTvfAPA7a58aUyrSEzEQghAaenJzmhOZowHstf1fYabZORHAiUT0NhGtIaJ5foVmxxCDGZxIIreJcWhCBIO3plWk8vZH0DKhDUUVuchtSns/5rQY7y3lKLva1JTxlSgtGw8AyG1SVxltc3pSqtSnrh6xDgBw944xiDZGA12WWgkqjcpDUsVXB9Y70v4YN26djleko10u3ox2dHYAPxLP1SqrU90u3oxuu35bzB6+O/X+rcoTXMvxItqqvh+WByUnvfNlkmiMBW9EJvpDjKFEtN72/2JmXhywthwAkwCcD2AMgFVENI2ZD3qdIAhCGARzlKr3cbXeB2Cs7f8xZpqdCgBrmbkDQBkRfQzDYKxzK1SGGIIQJj03B7EOwCQiGk9EuQAWwFCYt/MCjN4DiGgojCHHLq9CxUAIQpgw6b38imGOA1gEYBmArQCeYebNRHQvEVlqy8sA7CeiLQBWAvgvU2TaFRliCEKY9KCjFDMvBbA0I+0u23sGcLv50iLrDERmWDcAFOzKQ915bRj9T73mbi4djSmfMeTw9744vlvtscKqf/Sp5/H/Fgfd4sP77ltl+2FNSNp5+9TnAAAzX/h2wDa5TxTaWbvneN889814AffXTgpWd5v6M6mqGOx53kt7Z6TeXzbrA6yvD75doyVrn8nBKkMa4PjplQCA9oaRWuXl1Xbz8WFo9Q7CJOsMhCD0J7Ld1TorBGMGHDuWZ557KwqXbwQnktjzvVmINQHFFQkUlzYZS56VNdj6i4mpc6INxhLTgApjGqV4T3oTk5yWJAoqjfW36C8PKOvctmGcsx0VzikZa+OcATUJ3Hj/c4Guy29Tm0RFoVY5qqXMgmqjXe/f9VCgNgHAF0sv9M3zUaW/EE1kaxF+fM1fA9X9ZqN6o52XNkz3PC/a1HmJef75GwLVCwCbD6p7BuUfGteac9j4nEefU6FV3r63xzjSdtx1u7awS97xY3jE/7lVq649N35PBGMEod8hQwxBEJSwse1kNiMGQhBCQ28JM0zEQAhCmIQ/BeiJGAhBCBMxEIIguCIGQhAEJeIoJQiCFyQ9CEEQXBEDIQiCG9KDEATBHZmDEARBib4YTGhkhYGIJBiJXAInkuBEArEmABGgeg5h4NY40BEHJ5KpAC077XMM0cmc7fmptGhrEtRhBG+V1ql1H+fM2eJI2/zYFEeaFRodbU1q6TTauW/GC57Hf7j1y1rlqHQfrXbpBF5l8o8Jr/nmOXnFzb55co4AP9l2SaC648kIHpjqDHpbWXqG53nRjBD1l7cG21UcMOTdFkxb70jfX24EXeWYWpS7dozQKu/Yih54usVACILghsxBCILgTpYbCF9NSiJ6lIhqiWiTLW0IES0noh3m32PMdCKiX5tbf20kopm92XhB6MuQGc2p8woLHdHaxwFkbrBxJ4AVzDwJwArzfwC4BIaM9iQANwEIrmYiCP2JHhKt7S18DQQzrwLQkJF8BYAnzPdPALjSlv5nNlgDYDAR6Qn8CUJ/pOdk73uFrsreD2fmKvN9NQBrWySd7b8AAER0ExGtJ6L1He0Bt2cShKMEYr1XWHR7XwxTSjvwJTDzYmaexcyzYrkDutsMQeibHKU9iBpr6GD+tXYJ1tn+SxAEwJCcy/IeRFeXOZcAuA7Az8y/L9rSFxHR0wDOAnDINhRxJdLYgqIlH+DQF2citzmJUW8eTClZ7140xVS4LsGId9KflLV5b0Gl4RC17bb0VG+0IYYBFccCAHLfUde5e9dkR9rI/9jtSLMcrfbvGYC97wRzlNq0w+l4Zef7331GqxyVg5a1b4WO+nQmOk5QW2/6vW+eL5ZeiA1bgu07Em2KYtHOrzvS77/RWx07Uw176RunB6oXAGKHI3iu7FxH+sXXGVtTWqrXzQq1ahWDvqRQvw66nW6WL3P6GggiegrGfn5DiagCwN0wDMMzRHQDgHIA1o4ySwHMB7ATwBEAzm+CIAgp+rxoLTMvdDk0V5GXAdzS3UYJgpAdiCelIIRJXx9iCILQS4Q8AamDGAhBCBMxEIIguCIGQhAEFQQZYgiC4IbszakHA+BEArnNSSRyyXCSMlWk7OpSY1YkUudEW41P1lKOylSbspSmCpcXKeu0zrejUp9KJo1IujlztmDdsmAqRjkt3j8PugpVR+K5jjRLreqHT+qpUnVq1xH/PDpKVf+Y8BqmveTvdGUnUxnKwk+ZKp5MO/0+MPU5rCr3VqBSYSlGZWKpU31p6gYAwKsVyvAhB7rKU55keQ+CDNeFcCkePIZPP3MRct/8CJxIoPo7ZyHWzCiqiqNw9yHDWFTXYcc9ac/EWKPxhSmsMtpfXBFPHYu2JpFX0wwAKLvH+XABADYXO5IKK52fhSXtVljbgbH3fBzoulZvPMnzeH6VU0JPhUpyrqDWaOv3fvC3QG0C9GTiDlQN9M1TVBrDR9/197i088qRPDxb73y4P6j1figPHSpMvafqfPz2qkcD1QsAq5rU9+PvmwyvzGSzcT8Wzl6jVd5Ta2Y70vZ8639vYOZZOucXjBzL479+u1ZdW396u3a5PUlW9CAEob8icxCCILgjBkIQBCV9QPa+23oQgiB0nZ7UpCSieUS03dSEvdMj3xeIiInId05DDIQghEhP6UEQURTA72Dowp4CYCEROZbJiKgYwG0A1uq0TwyEIIRJzylKnQlgJzPvYuZ2AE/D0IjN5P8CuB9Aq06hYiAEISx0jYOegfDVgzW3oRjLzC/rNlEmKQUhJMh8aTKUiOz7Bi5mZm39KiKKAPhvAF/Tr1IMhCCEi/4qRr2Po5SfHmwxgKkA3iAiABgBYAkRXc7Mzg1LTcRACEKI9KCj1DoAk4hoPAzDsADANdZBZj4EIBVLQERvALjDyzgAMgchCOGS1Hz5wMxxAIsALAOwFcAzzLyZiO4losu72rzs6EEkgcZxuTg2kQA4iVgz4/AYQmE9AR1xUEccnEhVpIK6AAALkElEQVSk4i8AoHV0BwCgoMa4BHvwVaQ9AXQYsRmtB9QxBfmKtBxFIFGOWW60PYltDcOdGbzw2TJNFWOhzKdqV5vx05Op9qyDPfDJjWhT1D9PixFbEYR5hW14POGMj7HHWqiw4iQAIO8IucZVeDF34Ga8eMC5XaxVdrTZuOYtjXqbwVn5u0wPK0ox81IYwtH2tLtc8p6vU2Z2GAgARVXxTv8XVjHqpuegaIs6f36lcVMPzDQMxTHB4qhQfEadM7HUGc3ZHS6b9YHn8ZWlwSMSe4IHpj7nm0clTa9CFXjlxeOJXDw9/nVH+sR3vu15Xt6Rzsb2rdoJgeoFgJXVk/DjE19wpL9aa1xD1Fz42147TKu8vIYe2DMzyz0ps8ZACEJ/RIK1BEFwRwyEIAhuSA9CEAQ1fSCaMysUpQZGjuWzIheh9dLTEW1JIL+yKaUiVXn9tJS6lB1r1cJSjvr4+mNSx2KNkZTSlGud5R2OtOob2xxprQeM9Q5rUjQIxWXebZj8rc1a5ahWT+oqBgduj0VRqf+13OOzVyagp0yVyaFDhaBq5xrSzmsf8jzPvlrybP0ZeGPHpMB121dC7Dx00RMA0opTKqUoFSrlqfunP6et/DSgZCxPvkpPUer9h0VRShD6H+H/PnsiBkIQQoSyoAfvhRgIQQiLPjAHIQZCEEJEVjEEQXBHDIQgCG5ID0IQBDWy9Z4mzAAnEW1JoHFcLvLLbRGctsjOWFN6671Iu/nejNrMjPS0ojxjLhGT0XbnnbF8HuyUjD4IAGiqLNGOvrSwIi7d0I0OnT18tyPtpb0zAADR5uAR+27b39nRiRKNJyM43KSKi3Un2RxzBF4B/lGh8wrTPiqPJ3JdfRq8cIu+tPwfmhN5nvky0Y369ER6EIIgqJDdvQVB8Eb8IIKR6VINpLUhRq1OKM5wkl8ZS+lEDFvdvUu0XJpLzqhDyxsl3Sqrq7xVeYIjzdKaWPrG6Z90c1I8MPU5LHr++kDnqIYXgL+uhF1k5unxr2Pya5MD1Quk9R4ysbQl7p64BACwokFP40JXN8IL6UFowMWFSM6YjtjrHwKcRO31sxFrYRRWl2DYqhpQRxzJ2nrs/q/pqXOs+YDCmgIAwKjV6TFqpD2BWG0TAGDrHUOUdR7Z4Rw7D1ut2N3bjPkoqC8G7lCIzHhQu/VYz+MFmgZHpSi1vt6IPZl/x4ZAbQLS2917oWN4VpWfgd/e9qdAda9qOkkp9uIXW2Gfc5j82mRs+0awXcUBYEVLVKko9eH+MQCAG5fdAAB45MY/apW36P1r/DN5IY5SgiB4ke2rGL5T4ET0KBHVEtEmW9o9RLSPiD40X/Ntx75v7g24nYg+21sNF4SjgZ7cm7M30FkjexzAPEX6L5h5uvlaCgDmXoALAEwxz/m9uWegIAiZMMwlfo1XSPgaCGZeBaBBs7wrADzNzG3MXAZgJ4w9AwVBUNBTm/f2Ft3ZF2MREW00hyCWWovv/oCCINjoub05e4WuGoiHAEwAMB1AFYAHgxZARDcR0XoiWt/REdBFURCOAixHqaOuB8HMNcycYOYkgIeRHkb47Q9oL2MxM89i5lmx2ICuNEMQ+ja68w/ZPAehgojsTuhXAbBWOJYAWEBEeeYegZMAvNe9JgrC0Uu2r2L4+kEQ0VMAzoex/XgFgLsBnE9E02GMjnYD+CYAmHsBPgNgC4A4gFuYWc/9URD6IX3ek5KZFyqSXd3nmPknAH7SnUYJQr+AASSz20KIJ6UghEl22wcxEIIQJn1+iPFJQMxoHpuHgZwEmBFrYTSPiiB/P4E64kA8AU4kOgm2tIwwg6jqjOjAlICM+Z7ixv/RRhdHTkVQoRWYZSfazmaZSTQcLAp2Yey9+7MqCEtFtNX5LbLatflgcNES9mkXAMQO+89f57RySmxFl7kDN2NltTMwy08Axi7iEm01Aq+CMrcggafrcx3pdY3GfY0eNspce3iiVnmtzc6yAiPh3noUVnfe6aqwhlE/LRfHbVfnL6gxvsAN04yHelBpsPpaTnM+nQPLeuCG2xg3Q7nCm6KhfEyP1qfLgmnrffM8V3Zur9T94oGZ+PGJLzjSb9x9Q+BygvJ0fS4eHvu2I338hikAAMvkvLXfGW2qIqowNkGRHoQgCEqIAZJJSkEQXMnycG8xEIIQIrL1niAIavqAohRxFliwgTSEz4pciI65MxFtSyJWfSglM1e38DRTfq7zJKa1amFJy5UtTEvI5xw2Jjm9KK5oc6SVLnTOjFurINakaBCK93r3H+Nf2a9VjnL1pMpbJt6LonL/a/n0det88+hI12WSbI4hr9b5u/TLa72l6+yrJSrJOh3qGovQWlfgSC+7cjEA4NZKQ4tyeZme3uVvZz7pSLv4hO0bmHmWzvkDi0fzGaffolXX62/+QLvcnkR6EIIQIrKKoQszom1JJGORTr4PsRZGMkqonZmHEWuOpLJbPQjL36HTpjYE7D/N+OSHr1XfgYhi45xoo/uyVcv0FhS/6/z18cLyVXCjVtOvghUdkeOnVwIA9r0dfKk0R+FXkYmOf8WXpm7AU2tmB6o72hxVqkv7+VNYm9oAhvq0JTAbqO7DUai8J6yew69HGb2miSv1fqgfHKpSVHRZl3ejB3vwRDQPwK9grNg+wsw/yzh+O4AbYcRJ1QG4npnLvcrsjmCMIAjdgQFKsNbLD1Pa8XcALgFwCoCFpgSknQ8AzGLmUwH8A8ADfuVmRw+iqACYMg2Rtz5EhBkN18xGTisjv7YEQ96pBOIJJOrqse87aSn2HLMzUVhXCACO3kVOvTE3sf3mEcoqi3cXOtKGr3UGnlq9gPx6xq6bNV0fTVp2evc4dHskKk/K9gbjF370zRWB2gQAu3aoPxM7zRo9k1crRmPhzU7HIy+2NI5U7ifh1xOxe1KuaDhDW5reztrDE5VOUNacg9Vz2HntQ1rlzd8+3z+THz3XgTgTwE5m3gUARPQ0DAnILamqmFfa8q8B8GW/QqUHIQghQsxaLw2Cyj3eAOBffoVmRw9CEPor+nMQQ4nI7iO/mJkXd6VKIvoygFkAzvPLKwZCEMKCEcSTst5nmVNL7pGILgTwAwDnMbNzrT8DMRCCEBIE7eGDDusATDKlHvfB2J+m096ARDQDwB8BzGPmWp1CxUAIQpj0kIFg5jgRLQKwDMYy56OmBOS9ANYz8xIAPwdQBOBZIgKAPcx8uVe5YiAEISwYgMYSpnZxxg53SzPS7rK9vzBomWIgBCFEJFhLEAR3xEAIgqAm3E1xdMieaE6ai+SnZiDSFkdO7aGU9+ShL8wwvSo7r8hYsRiWx+Sez49KHcs5AhTWeW/HMaDSucKz66p8R1qOqc1YUBfsmgCgqNK7DVVfaNcqJ9Ho1GtURUTqUlThf88HfclbLg/Q88jMJNocRV6DUxPziqvf8jxvS2M6NkTlialDa3OuUibuj5833Ake3KOKrXBn6UlLHWnRkTu1oy4HFYzksyder1XXsk33STSnIPQ7RFFKEAQ3ZJJSEAQ1DCCR3V0IMRCCEBoySanXCKI6AIcB1Ifdlm4wFH27/YBcQ08wjplLdDIOyh/Bc8Z+VavQV3b+vP9OUjJzCRGtD+MD6Cn6evsBuYZQyIIfaC+ywkAIQr9EdvcWBMEdVguOZhHZZCC6JH6RRfT19gNyDZ8ssoqhT1fVcbKFvt5+QK4hFGQOQhAEV8RACIKgJvv9IMRACEJYMICkzEEIguCG9CAEQXBFDIQgCEqYwQlvzZCwEQMhCGEinpSCILgiQwxBEJQwyyqGIAgeSA9CEAQ3WHoQgiCoEU9KQRDcYACyzCkIggoGwLLMKQiCEhbBGEEQPMj2HkRWqFoLQn+EiF6BocKtQz0zz+vN9qgQAyEIgiuRsBsgCEL2IgZCEARXxEAIguCKGAhBEFwRAyEIgitiIARBcEUMhCAIroiBEATBFTEQgiC48v8Bj97U9/eYNGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6ceae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 2.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 183 built in 0.33618664741516113 seconds ---\n",
      "[[1.         0.5        0.11111111 ... 0.07692308 0.07692308 0.07692308]\n",
      " [0.5        1.         0.09090909 ... 0.06666667 0.06666667 0.06666667]\n",
      " [0.11111111 0.09090909 1.         ... 0.55555556 0.55555556 0.55555556]\n",
      " ...\n",
      " [0.07692308 0.06666667 0.55555556 ... 1.         1.         1.        ]\n",
      " [0.07692308 0.06666667 0.55555556 ... 1.         1.         1.        ]\n",
      " [0.07692308 0.06666667 0.55555556 ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6cef0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 2.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 183 built in 0.56915283203125 seconds ---\n",
      "[[1.         0.4375     0.125      ... 0.03333333 0.03333333 0.03571429]\n",
      " [0.4375     1.         0.0952381  ... 0.03076923 0.03076923 0.03278689]\n",
      " [0.125      0.0952381  1.         ... 0.16981132 0.16981132 0.18367347]\n",
      " ...\n",
      " [0.03333333 0.03076923 0.16981132 ... 1.         1.         0.9245283 ]\n",
      " [0.03333333 0.03076923 0.16981132 ... 1.         1.         0.9245283 ]\n",
      " [0.03571429 0.03278689 0.18367347 ... 0.9245283  0.9245283  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6e2ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 3.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 183 built in 0.5237185955047607 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.05555556 0.05555556 0.05555556]\n",
      " [0.44444444 1.         0.08333333 ... 0.04761905 0.04761905 0.04761905]\n",
      " [0.11111111 0.08333333 1.         ... 0.35714286 0.35714286 0.35714286]\n",
      " ...\n",
      " [0.05555556 0.04761905 0.35714286 ... 1.         1.         1.        ]\n",
      " [0.05555556 0.04761905 0.35714286 ... 1.         1.         1.        ]\n",
      " [0.05555556 0.04761905 0.35714286 ... 1.         1.         1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6dbeda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 3.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 183 built in 0.6988370418548584 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.02631579 0.02631579 0.02777778]\n",
      " [0.38888889 1.         0.08695652 ... 0.02409639 0.02409639 0.02531646]\n",
      " [0.125      0.08695652 1.         ... 0.13043478 0.13043478 0.13846154]\n",
      " ...\n",
      " [0.02631579 0.02409639 0.13043478 ... 1.         0.94366197 0.83561644]\n",
      " [0.02631579 0.02409639 0.13043478 ... 0.94366197 1.         0.78666667]\n",
      " [0.02777778 0.02531646 0.13846154 ... 0.83561644 0.78666667 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6d4b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 4.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 183 built in 0.7394986152648926 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.04347826 0.04166667 0.04347826]\n",
      " [0.44444444 1.         0.08333333 ... 0.03846154 0.03703704 0.03846154]\n",
      " [0.11111111 0.08333333 1.         ... 0.26315789 0.25       0.26315789]\n",
      " ...\n",
      " [0.04347826 0.03846154 0.26315789 ... 1.         0.95       0.9       ]\n",
      " [0.04166667 0.03703704 0.25       ... 0.95       1.         0.95      ]\n",
      " [0.04347826 0.03846154 0.26315789 ... 0.9        0.95       1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6cc18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 4.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 183 built in 0.870086669921875 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.02222222 0.02222222 0.02325581]\n",
      " [0.38888889 1.         0.08695652 ... 0.02061856 0.02061856 0.02150538]\n",
      " [0.125      0.08695652 1.         ... 0.10843373 0.10843373 0.11392405]\n",
      " ...\n",
      " [0.02222222 0.02061856 0.10843373 ... 1.         0.82417582 0.67010309]\n",
      " [0.02222222 0.02061856 0.10843373 ... 0.82417582 1.         0.70526316]\n",
      " [0.02325581 0.02150538 0.11392405 ... 0.67010309 0.70526316 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb53e0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 5.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 183 built in 1.0799925327301025 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03703704 0.03333333 0.03571429]\n",
      " [0.44444444 1.         0.08333333 ... 0.03333333 0.03030303 0.03225806]\n",
      " [0.11111111 0.08333333 1.         ... 0.2173913  0.19230769 0.20833333]\n",
      " ...\n",
      " [0.03703704 0.03333333 0.2173913  ... 1.         0.88461538 0.74074074]\n",
      " [0.03333333 0.03030303 0.19230769 ... 0.88461538 1.         0.85185185]\n",
      " [0.03571429 0.03225806 0.20833333 ... 0.74074074 0.85185185 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb53bdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 5.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 183 built in 1.0829052925109863 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.01960784 0.01960784 0.02040816]\n",
      " [0.38888889 1.         0.08695652 ... 0.01834862 0.01834862 0.01904762]\n",
      " [0.125      0.08695652 1.         ... 0.09473684 0.09473684 0.0989011 ]\n",
      " ...\n",
      " [0.01960784 0.01834862 0.09473684 ... 1.         0.74311927 0.56302521]\n",
      " [0.01960784 0.01834862 0.09473684 ... 0.74311927 1.         0.6173913 ]\n",
      " [0.02040816 0.01904762 0.0989011  ... 0.56302521 0.6173913  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb531f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 6.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 183 built in 1.2987947463989258 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03333333 0.02857143 0.03030303]\n",
      " [0.44444444 1.         0.08333333 ... 0.03030303 0.02631579 0.02777778]\n",
      " [0.11111111 0.08333333 1.         ... 0.19230769 0.16129032 0.17241379]\n",
      " ...\n",
      " [0.03333333 0.03030303 0.19230769 ... 1.         0.83870968 0.57142857]\n",
      " [0.02857143 0.02631579 0.16129032 ... 0.83870968 1.         0.71428571]\n",
      " [0.03030303 0.02777778 0.17241379 ... 0.57142857 0.71428571 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb52dbb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 6.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 183 built in 1.317399024963379 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.01785714 0.01785714 0.01851852]\n",
      " [0.38888889 1.         0.08695652 ... 0.01680672 0.01680672 0.0173913 ]\n",
      " [0.125      0.08695652 1.         ... 0.08571429 0.08571429 0.08910891]\n",
      " ...\n",
      " [0.01785714 0.01680672 0.08571429 ... 1.         0.68       0.48201439]\n",
      " [0.01785714 0.01680672 0.08571429 ... 0.68       1.         0.54887218]\n",
      " [0.01851852 0.0173913  0.08910891 ... 0.48201439 0.54887218 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb529da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 7.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 183 built in 1.5710387229919434 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03125    0.02564103 0.02631579]\n",
      " [0.44444444 1.         0.08333333 ... 0.02857143 0.02380952 0.02439024]\n",
      " [0.11111111 0.08333333 1.         ... 0.17857143 0.14285714 0.14705882]\n",
      " ...\n",
      " [0.03125    0.02857143 0.17857143 ... 1.         0.8        0.47619048]\n",
      " [0.02564103 0.02380952 0.14285714 ... 0.8        1.         0.56818182]\n",
      " [0.02631579 0.02439024 0.14705882 ... 0.47619048 0.56818182 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb5228208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 7.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 183 built in 1.4747083187103271 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.01666667 0.01666667 0.01724138]\n",
      " [0.38888889 1.         0.08695652 ... 0.01574803 0.01574803 0.01626016]\n",
      " [0.125      0.08695652 1.         ... 0.07964602 0.07964602 0.08256881]\n",
      " ...\n",
      " [0.01666667 0.01574803 0.07964602 ... 1.         0.64963504 0.43225806]\n",
      " [0.01666667 0.01574803 0.07964602 ... 0.64963504 1.         0.48993289]\n",
      " [0.01724138 0.01626016 0.08256881 ... 0.43225806 0.48993289 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb523aac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 8.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 183 built in 1.8554940223693848 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03030303 0.02439024 0.02325581]\n",
      " [0.44444444 1.         0.08333333 ... 0.02777778 0.02272727 0.02173913]\n",
      " [0.11111111 0.08333333 1.         ... 0.17241379 0.13513514 0.12820513]\n",
      " ...\n",
      " [0.03030303 0.02777778 0.17241379 ... 1.         0.73684211 0.41666667]\n",
      " [0.02439024 0.02272727 0.13513514 ... 0.73684211 1.         0.49019608]\n",
      " [0.02325581 0.02173913 0.12820513 ... 0.41666667 0.49019608 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb5186be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 8.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 183 built in 1.733067274093628 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.015625   0.015625   0.01639344]\n",
      " [0.38888889 1.         0.08695652 ... 0.01481481 0.01481481 0.01550388]\n",
      " [0.125      0.08695652 1.         ... 0.07438017 0.07438017 0.07826087]\n",
      " ...\n",
      " [0.015625   0.01481481 0.07438017 ... 1.         0.58169935 0.3964497 ]\n",
      " [0.015625   0.01481481 0.07438017 ... 0.58169935 1.         0.44785276]\n",
      " [0.01639344 0.01550388 0.07826087 ... 0.3964497  0.44785276 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6dd4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 9.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 183 built in 2.201777219772339 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03030303 0.02439024 0.0212766 ]\n",
      " [0.44444444 1.         0.08333333 ... 0.02777778 0.02272727 0.02      ]\n",
      " [0.11111111 0.08333333 1.         ... 0.17241379 0.13513514 0.11627907]\n",
      " ...\n",
      " [0.03030303 0.02777778 0.17241379 ... 1.         0.73684211 0.38461538]\n",
      " [0.02439024 0.02272727 0.13513514 ... 0.73684211 1.         0.45454545]\n",
      " [0.0212766  0.02       0.11627907 ... 0.38461538 0.45454545 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6d83908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 9.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 183 built in 2.023785352706909 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.015625   0.015625   0.01587302]\n",
      " [0.38888889 1.         0.08695652 ... 0.01481481 0.01481481 0.01503759]\n",
      " [0.125      0.08695652 1.         ... 0.07438017 0.07438017 0.07563025]\n",
      " ...\n",
      " [0.015625   0.01481481 0.07438017 ... 1.         0.58169935 0.38728324]\n",
      " [0.015625   0.01481481 0.07438017 ... 0.58169935 1.         0.43712575]\n",
      " [0.01587302 0.01503759 0.07563025 ... 0.38728324 0.43712575 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb5204c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'tanimoto', 'depth': 10.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 183 built in 2.3982086181640625 seconds ---\n",
      "[[1.         0.44444444 0.11111111 ... 0.03030303 0.02439024 0.02040816]\n",
      " [0.44444444 1.         0.08333333 ... 0.02777778 0.02272727 0.01923077]\n",
      " [0.11111111 0.08333333 1.         ... 0.17241379 0.13513514 0.11111111]\n",
      " ...\n",
      " [0.03030303 0.02777778 0.17241379 ... 1.         0.73684211 0.37037037]\n",
      " [0.02439024 0.02272727 0.13513514 ... 0.73684211 1.         0.43859649]\n",
      " [0.02040816 0.01923077 0.11111111 ... 0.37037037 0.43859649 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6d0d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'k_func': 'MinMax', 'depth': 10.0} is: \n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 183 built in 2.323643684387207 seconds ---\n",
      "[[1.         0.38888889 0.125      ... 0.015625   0.015625   0.015625  ]\n",
      " [0.38888889 1.         0.08695652 ... 0.01481481 0.01481481 0.01481481]\n",
      " [0.125      0.08695652 1.         ... 0.07438017 0.07438017 0.07438017]\n",
      " ...\n",
      " [0.015625   0.01481481 0.07438017 ... 1.         0.58169935 0.38285714]\n",
      " [0.015625   0.01481481 0.07438017 ... 0.58169935 1.         0.43195266]\n",
      " [0.015625   0.01481481 0.07438017 ... 0.38285714 0.43195266 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb6d36828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "22 gram matrices are calculated, 0 of which are ignored.\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "                                                                        \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                             \n",
      "4. Getting final performances...\n",
      "best_params_out:  [{'k_func': 'MinMax', 'depth': 2.0}]\n",
      "best_params_in:  [{'alpha': 0.1}]\n",
      "\n",
      "best_val_perf:  10.231761388304456\n",
      "best_val_std:  0.6348164522571891\n",
      "final_performance:  10.670706158962634\n",
      "final_confidence:  5.197078510391227\n",
      "train_performance:  5.8060420677138325\n",
      "train_std:  0.20976768885688923\n",
      "\n",
      "time to calculate gram matrix with different hyperpapams: 1.15±0.72\n",
      "time to calculate best gram matrix:  0.56915283203125 s\n",
      "\n",
      "params                                                      train_perf            valid_perf                 test_perf                     gram_matrix_time\n",
      "----------------------------------------------------------  --------------------  -------------------------  --------------------------  ------------------\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 0.0}     14.11±10.64           139133319.10±365663824.06  99055450.57±317003668.09                  0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 1.0}   38.58±0.59            62860473.93±180853211.35   32879292.40±101016177.80                  0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 1.0}     67.11±56.60           399999424.07±671923390.29  527445721.25±2397198784.07                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 2.0}   35.78±5.60            407096840.42±320863867.81  983575254.36±3283901647.61                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 2.0}     84.51±42.76           4326.62±4203.90            4040.78±5729.75                           0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 3.0}   239.67±320.71         243507173.25±926668056.15  2311116.12±5071531.44                     0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 3.0}     37.71±20.67           660.44±704.05              789.72±1245.10                            0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 4.0}   197.46±182.95         190334.76±208196.44        125991.01±400573.85                       0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 4.0}     36.87±18.91           394.59±249.67              565.68±663.02                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 5.0}   295.40±216.74         137292.31±155473.18        91513.51±259348.95                        1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 5.0}     58.57±37.35           745.46±539.44              533.25±777.44                             1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 6.0}   451.98±319.19         444796.76±823919.52        561350.67±1446400.89                      1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 6.0}     68.71±44.97           1185.26±2372.80            896.51±999.89                             1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 7.0}   456.63±248.34         258796.95±239154.10        236236.28±810839.92                       1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 7.0}     79.48±45.14           1087.94±1399.03            832.64±983.87                             1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 8.0}   427.36±250.68         221143.69±284987.91        147686.31±309681.37                       1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 8.0}     58.65±39.20           793.76±658.68              882.20±1618.34                            1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 9.0}   547.63±227.51         203225.54±303562.50        436442.47±868829.71                       2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 9.0}     60.80±36.01           658.32±429.68              650.40±932.75                             2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-10', 'depth': 10.0}  544.79±253.00         165113.61±188235.59        120967.35±271939.84                       2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-10', 'depth': 10.0}    67.11±35.10           860.62±622.56              877.21±1141.87                            2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 0.0}     20.06±34.31           44186628.33±115547999.28   31432348.98±100205168.98                  0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 1.0}   38.58±0.59            19871895.43±57176048.58    10392508.79±31927715.50                   0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 1.0}     194.66±183.15         128112896.20±211665627.97  167478838.67±758097839.19                 0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 2.0}   35.78±5.57            128743791.99±101466948.21  310997086.42±1038886001.47                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 2.0}     258.77±274.25         12910.89±15722.92          19970.23±50615.88                         0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 3.0}   692.54±1006.56        82432103.88±291833083.63   7121746.14±15784016.40                    0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 3.0}     47.91±33.93           774.96±745.32              905.61±1351.33                            0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 4.0}   539.36±556.84         518135.72±475726.55        394947.11±1316697.87                      0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 4.0}     46.24±31.29           455.73±303.12              663.63±790.89                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 5.0}   918.51±734.51         442067.00±492171.57        290044.73±811738.71                       1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 5.0}     76.18±62.50           882.85±737.91              705.70±1204.70                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 6.0}   1441.22±1049.07       1055267.83±1446222.62      1514669.22±3553701.50                     1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 6.0}     88.62±72.21           1414.71±2808.62            1070.97±1129.70                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 7.0}   1367.03±714.38        760181.60±718719.26        714209.59±2096712.58                      1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 7.0}     125.37±110.26         2112.55±4108.33            1253.69±1605.93                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 8.0}   1293.62±719.60        472077.20±420465.93        356731.54±656998.77                       1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 8.0}     75.03±71.52           914.18±919.34              1064.00±2086.22                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 9.0}   1687.72±772.89        571837.71±532420.77        947459.22±1563830.97                      2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 9.0}     73.44±55.61           725.41±442.50              767.22±1133.57                            2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-10', 'depth': 10.0}  1457.18±709.18        487251.75±562207.65        405863.73±993871.47                       2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-10', 'depth': 10.0}    95.24±69.56           1175.82±990.01             1092.84±1347.50                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 0.0}     38.92±109.21          14586454.16±36475856.90    10289150.96±31639224.01                   0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 1.0}   38.58±0.59            6284340.52±18081554.60     3286550.65±10096955.95                    0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 1.0}     597.68±583.29         45609051.52±64869750.00    55067222.49±239525308.81                  0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 2.0}   35.77±5.50            40734846.86±32052772.39    98313928.72±328386256.01                  0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 2.0}     660.78±477.19         38460.50±42974.91          38730.55±58312.02                         0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 3.0}   2115.33±3169.73       43219664.24±95827049.53    22356678.82±49911051.39                   0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 3.0}     66.19±92.42           881.52±877.16              1087.03±1784.96                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 4.0}   1706.02±1866.54       2228992.25±3666044.38      1315487.74±4258398.15                     0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 4.0}     51.32±38.30           496.72±374.32              686.12±803.21                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 5.0}   2899.19±2317.34       1442141.97±1568927.95      927670.69±2928901.46                      1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 5.0}     91.88±109.71          908.69±747.68              807.01±1385.34                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 6.0}   4400.95±3414.80       3778222.54±6304179.91      5658347.07±14690380.03                    1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 6.0}     99.59±97.36           1460.77±2888.27            1125.71±1178.28                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 7.0}   4887.55±2895.85       5724566.40±9960547.50      2617788.52±8341716.17                     1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 7.0}     182.40±229.65         2929.96±5361.16            1633.72±2154.83                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 8.0}   3535.99±2266.77       1808859.19±1817802.52      1433690.49±3313059.83                     1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 8.0}     79.29±78.37           932.70±958.88              1101.33±2090.06                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 9.0}   4989.94±2269.98       1669601.93±1698312.42      3804177.84±6054425.03                     2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 9.0}     99.12±120.63          869.85±728.55              1036.82±2030.71                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-09', 'depth': 10.0}  4794.03±2696.58       3023074.96±6321243.28      1189752.38±2894973.51                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-09', 'depth': 10.0}    126.48±136.79         1371.51±1286.93            1238.78±1468.32                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 0.0}     67.56±188.71          5499069.70±12494742.28     3766867.51±10324052.21                    0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 1.0}   38.58±0.59            1987278.26±5717748.36      1039325.22±3192948.09                     0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 1.0}     1655.54±1552.02       26483644.47±21839814.90    20492979.66±75891337.38                   0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 2.0}   35.73±5.30            12898579.80±10117964.47    31088449.67±103836855.91                  0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 2.0}     2015.84±1702.68       162673.90±337073.64        171058.51±458394.84                       0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 3.0}   7159.00±11811.67      51059967.24±45469692.35    65824387.28±161042478.52                  0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 3.0}     88.56±174.71          980.05±1032.66             1164.64±1856.14                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 4.0}   6631.47±12179.35      7221868.64±10951832.81     6929409.19±25921319.75                    0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 4.0}     56.93±53.63           517.33±407.69              699.37±799.44                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 5.0}   8694.20±6956.39       5053285.16±5383589.17      2734044.40±8959603.89                     1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 5.0}     100.82±128.73         939.39±764.30              870.24±1438.28                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 6.0}   22137.04±19770.99     28197340.13±42281242.19    22946187.79±54501546.50                   1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 6.0}     105.96±121.31         1469.36±2886.78            1149.08±1195.46                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 7.0}   18481.58±16861.99     14940307.30±24595280.70    10298025.04±24186799.15                   1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 7.0}     258.84±497.26         4125.06±9134.89            1906.86±2644.32                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 8.0}   15588.37±14193.37     8966885.24±17005064.04     5412566.74±9050863.67                     1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 8.0}     80.50±77.98           939.82±955.26              1118.70±2090.94                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 9.0}   31988.99±37835.18     21477332.19±48090896.48    48860111.20±114004280.94                  2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 9.0}     109.15±148.12         943.27±928.09              1218.60±2881.84                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-09', 'depth': 10.0}  20460.38±16007.72     12557581.84±25942533.43    4531280.61±8193305.04                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-09', 'depth': 10.0}    180.20±324.52         1495.68±1444.98            1505.18±2180.20                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 0.0}     49.71±121.08          1777725.88±3939524.24      1228429.72±3256744.36                     0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 1.0}   38.58±0.59            628463.23±1808115.18       328691.85±1009706.65                      0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 1.0}     2351.95±2845.46       11750316.81±7446939.87     7179043.93±23948557.55                    0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 2.0}   35.63±4.75            4093265.23±3182700.72      9831080.78±32836011.26                    0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 2.0}     10624.08±19482.89     706438.98±1361151.29       877671.95±2514280.87                      0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 3.0}   7738.02±12323.72      29864604.86±16183312.89    52476898.46±134399538.92                  0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 3.0}     106.68±222.57         1024.96±1160.67            1259.16±2143.10                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 4.0}   17655.05±22844.98     15146949.66±11355908.20    13292349.30±44462910.62                   0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 4.0}     57.49±55.90           519.39±412.64              700.71±799.48                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 5.0}   42363.51±46166.44     37475264.80±73466828.95    12990930.96±30953275.67                   1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 5.0}     106.85±141.84         968.17±812.22              919.30±1514.80                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 6.0}   52105.19±50501.55     19343582.62±15244364.26    29478265.51±54321962.94                   1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 6.0}     105.92±121.15         1469.29±2886.71            1148.95±1195.33                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 7.0}   100615.25±120799.84   47941296.71±80307033.13    53473794.34±159668695.03                  1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 7.0}     296.79±673.35         4459.90±10038.00           1993.09±2804.86                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 8.0}   80078.61±83548.11     18062527.52±17891619.50    16561214.28±35828170.08                   1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 8.0}     84.34±80.41           962.53±954.58              1173.50±2121.95                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 9.0}   105801.92±85784.07    20758369.78±23833194.18    36065001.33±57475693.71                   2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 9.0}     109.12±148.02         943.05±927.33              1217.78±2877.73                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-08', 'depth': 10.0}  96819.43±90227.16     24705971.84±37375951.55    12786588.68±24608055.67                   2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-08', 'depth': 10.0}    289.35±879.01         1597.23±1568.34            2230.26±5803.31                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 0.0}     23.48±38.08           562436.20±1245906.61       388671.53±1029890.87                      0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 1.0}   38.58±0.59            198765.14±571774.64        103967.43±319298.32                       0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 1.0}     1356.33±2032.60       4071018.74±2505013.30      2453408.37±7576548.77                     0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 2.0}   35.40±3.54            1304301.26±996825.93       3108951.83±10383989.94                    0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 2.0}     32825.96±38356.29     2167144.66±6237905.52      1482683.23±3218072.65                     0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 3.0}   3527.94±5335.20       10428636.90±4713593.05     17061924.28±43365787.32                   0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 3.0}     118.68±270.12         1053.86±1261.33            1321.53±2376.01                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 4.0}   16008.64±15022.07     8008646.37±5212612.43      6385596.91±20555889.53                    0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 4.0}     57.47±55.82           519.27±412.42              700.70±799.67                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 5.0}   114800.49±145441.11   14547468.93±16587920.64    15555519.66±38390629.33                   1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 5.0}     106.84±141.81         968.14±812.07              919.29±1515.05                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 6.0}   144486.57±192587.85   14612703.47±8568821.66     32786247.78±81959044.86                   1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 6.0}     105.92±121.15         1469.22±2886.51            1148.97±1195.37                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 7.0}   268611.96±343336.70   18460794.67±23298507.31    20417956.06±35135707.64                   1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 7.0}     296.20±671.52         4448.32±9996.95            1990.28±2797.41                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 8.0}   283053.27±283098.78   18812117.37±20870135.69    18252521.71±33733453.92                   1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 8.0}     88.19±87.97           985.28±969.99              1228.46±2194.29                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 9.0}   239098.05±186897.51   15059173.76±7755938.03     23501311.87±28775633.38                   2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 9.0}     109.02±147.73         942.35±924.95              1215.11±2864.07                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-08', 'depth': 10.0}  281485.99±393430.95   14827956.59±10173863.70    32282771.03±65886576.27                   2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-08', 'depth': 10.0}    498.88±2014.53        1791.96±2215.59            3622.48±13329.39                          2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 0.0}     15.19±11.82           178034.48±393917.94        123063.40±325628.55                       0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 1.0}   38.58±0.59            62882.66±180809.71         32903.22±100971.74                        0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 1.0}     434.65±642.72         1287372.87±792206.82       776331.31±2396401.07                      0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 2.0}   35.09±1.90            416851.21±312487.53        983160.67±3283693.32                      0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 2.0}     34174.78±28558.08     1179679.13±1466643.27      763367.56±876496.59                       0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 3.0}   1138.64±1682.69       3298452.07±1490366.61      5395401.89±13711765.59                    0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 3.0}     118.28±268.33         1053.86±1259.78            1320.06±2368.58                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 4.0}   14390.09±22361.88     2605820.02±1695799.23      2104201.92±6493420.11                     0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 4.0}     57.41±55.61           518.89±411.77              700.71±800.29                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 5.0}   116758.94±123742.10   4483198.10±4197612.77      4353706.15±9060406.94                     1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 5.0}     106.82±141.74         968.05±811.63              919.32±1516.08                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 6.0}   168304.70±199780.66   7322859.44±9067250.05      9622427.07±15169513.30                    1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 6.0}     105.93±121.23         1469.01±2885.79            1149.12±1195.57                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 7.0}   215500.20±229311.72   9018650.17±12698557.63     8407335.56±13677944.39                    1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 7.0}     294.46±666.28         4414.15±9876.06            1981.98±2775.49                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 8.0}   213427.29±188423.80   9339596.97±11572405.41     8607197.72±11651408.84                    1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 8.0}     88.23±88.05           985.54±970.11              1229.06±2195.50                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 9.0}   329318.51±233361.76   9907297.21±7971489.87      20905976.69±29737436.99                   2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 9.0}     108.71±146.82         940.20±917.58              1206.86±2821.96                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-07', 'depth': 10.0}  428910.88±537991.85   11832396.10±10215139.07    16226225.42±25833679.17                   2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-07', 'depth': 10.0}    508.21±2066.14        1799.75±2251.19            3685.03±13672.21                          2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 0.0}     12.57±3.55            56473.71±124498.77         39068.62±102923.61                        0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 1.0}   38.58±0.59            19912.87±57175.82          10430.75±31930.85                         0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 1.0}     143.35±203.18         407084.35±250542.76        245980.83±758297.02                       0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 2.0}   34.86±0.86            132921.72±98541.45         310925.87±1038388.08                      0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 2.0}     35727.78±30627.15     1090478.94±1296251.82      697030.88±984455.44                       0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 3.0}   382.66±528.48         1043687.35±471078.96       1706089.31±4334384.34                     0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 3.0}     117.22±263.53         1054.39±1256.91            1316.42±2349.09                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 4.0}   5018.12±7914.25       825781.29±534642.31        667225.90±2052904.55                      0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 4.0}     57.22±54.96           517.73±409.74              700.75±802.28                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 5.0}   119256.87±206769.36   1806896.24±1572692.21      1605305.73±3048081.82                     1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 5.0}     106.76±141.54         967.81±810.39              919.57±1519.67                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 6.0}   124243.85±156915.17   2877265.80±3132732.92      3686225.21±5194704.27                     1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 6.0}     105.95±121.35         1468.35±2883.57            1149.49±1196.07                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 7.0}   175613.03±175436.30   4927949.35±6296166.18      3508754.51±4473816.03                     1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 7.0}     289.19±650.07         4312.60±9523.00            1957.23±2711.50                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 8.0}   188645.92±162319.18   5838368.60±8403391.46      8702714.82±15965427.25                    1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 8.0}     88.33±88.26           986.26±970.39              1230.68±2198.93                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 9.0}   354573.46±309067.36   8117846.32±10350828.69     20605681.88±53741199.57                   2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 9.0}     107.81±144.16         933.81±896.07              1182.46±2698.06                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-07', 'depth': 10.0}  388489.67±443334.64   7649472.05±7910305.16      9594689.41±12431003.36                    2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-07', 'depth': 10.0}    539.88±2241.24        1826.40±2375.60            3897.18±14835.10                          2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 0.0}     11.76±1.01            18024.89±39308.21          12500.15±32504.10                         0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 1.0}   38.58±0.59            6324.63±18079.35           3324.34±10098.20                          0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 1.0}     51.50±64.20           128712.47±79257.74         78262.50±240288.24                        0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 2.0}   34.77±0.69            42221.36±31157.91          98347.15±328363.78                        0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 2.0}     22030.56±29969.07     729804.92±1439749.00       468961.95±1053738.30                      0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 3.0}   142.64±165.84         330550.99±148813.22        539525.63±1369516.10                      0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 3.0}     114.28±250.33         1058.61±1256.77            1307.76±2297.45                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 4.0}   1689.64±2613.28       269859.79±159896.76        211714.35±649028.00                       0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 4.0}     56.67±53.05           514.32±403.72              701.19±809.21                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 5.0}   41644.23±74875.55     610425.44±596884.31        539318.40±1075515.09                      1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 5.0}     106.61±141.00         967.24±806.83              920.65±1532.43                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 6.0}   89966.18±171526.35    1067353.63±1251046.45      1344907.52±1815335.52                     1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 6.0}     106.00±121.70         1466.28±2876.56            1150.71±1197.69                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 7.0}   73407.85±95593.32     1848743.39±2895030.05      1176107.23±1442334.13                     1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 7.0}     275.09±606.00         4051.05±8655.45            1892.52±2553.74                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 8.0}   162203.74±252647.48   3269017.62±5206871.08      10582934.87±26826985.38                   1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 8.0}     88.67±88.99           988.68±971.51              1236.12±2210.59                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 9.0}   381707.78±857569.42   6451659.75±12692709.97     11644098.95±21434296.42                   2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 9.0}     105.42±137.43         917.06±842.13              1119.28±2381.73                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-06', 'depth': 10.0}  370938.66±594951.97   3817072.22±4170134.02      5034518.98±5937319.06                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-06', 'depth': 10.0}    689.73±3066.97        1957.48±3018.17            4898.25±20318.14                          2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 0.0}     11.54±0.41            5845.02±12386.45           4079.74±10250.03                          0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 1.0}   38.58±0.59            2027.65±5715.95            1077.09±3194.11                           0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 1.0}     22.77±20.27           40684.09±25104.22          25255.95±76526.46                         0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 2.0}   34.75±0.70            13394.71±9853.18           31123.87±103834.17                        0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 2.0}     11197.74±20344.44     261985.31±532512.95        219003.25±624610.31                       0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 3.0}   65.18±54.44           104854.27±46970.84         170713.73±432568.51                       0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 3.0}     108.41±222.51         1104.73±1379.05            1307.19±2210.42                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 4.0}   530.80±768.95         83860.14±52477.93          67486.25±205161.09                        0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 4.0}     55.31±48.27           505.57±387.68              705.52±837.63                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 5.0}   13069.39±23584.47     200763.06±221089.55        176193.10±368838.55                       1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 5.0}     106.39±139.87         967.05±799.33              927.96±1589.68                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 6.0}   32536.63±66580.17     348653.38±404557.37        441009.44±582226.29                       1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 6.0}     106.20±122.88         1460.20±2854.81            1155.42±1203.28                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 7.0}   25199.52±39540.49     584045.40±833290.31        390922.88±510240.13                       1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 7.0}     245.41±507.47         3550.66±7178.60            1764.08±2278.80                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 8.0}   114374.73±241436.09   1678943.19±3452178.41      6319465.55±20480515.45                    1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 8.0}     89.98±92.23           997.75±978.04              1256.75±2256.79                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 9.0}   283899.64±873729.65   7892904.46±27824053.77     4844461.05±10972963.86                    2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 9.0}     100.62±124.89         884.25±745.93              999.47±1809.75                            2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-06', 'depth': 10.0}  339175.36±964202.12   2387809.38±4305298.13      2862994.24±4816740.98                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-06', 'depth': 10.0}    3577.30±18897.75      4623.05±17463.09           24097.62±125467.72                        2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 0.0}     11.48±0.35            1950.09±3902.40            1379.07±3235.52                           0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 1.0}   38.58±0.59            668.82±1806.31             366.45±1010.85                            0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 1.0}     14.02±6.41            13005.88±7752.97           8502.28±24859.33                          0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 2.0}   34.75±0.70            4261.84±3114.54            9866.09±32831.66                          0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 2.0}     2510.86±3869.85       76960.08±152099.32         56397.66±143159.76                        0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 3.0}   39.65±19.59           33332.97±14774.73          54091.48±136635.67                        0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 3.0}     100.31±190.67         1186.41±1653.24            1331.08±2217.95                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 4.0}   183.56±238.55         26573.33±16182.03          21576.53±64937.96                         0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 4.0}     53.35±40.76           490.07±354.28              750.54±1022.37                            0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 5.0}   3902.36±6741.38       75112.26±128301.16         65844.22±169461.34                        1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 5.0}     110.53±145.42         990.93±827.20              1042.62±2215.93                           1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 6.0}   9885.67±19977.01      111324.38±124633.32        140450.94±183026.97                       1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 6.0}     107.50±127.01         1446.40±2790.04            1181.41±1231.01                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 7.0}   10261.56±19747.35     544526.27±2279678.53       351031.05±1368888.02                      1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 7.0}     204.02±349.41         2993.93±6033.83            1611.60±2023.34                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 8.0}   26725.56±39713.25     575282.09±1559304.79       734255.91±1738271.07                      1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 8.0}     98.65±123.74          1053.87±1075.23            1387.64±2620.25                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 9.0}   45819.16±114556.07    885163.32±1742465.90       776484.38±1412569.30                      2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 9.0}     94.74±108.97          849.00±643.98              868.94±1270.52                            2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-05', 'depth': 10.0}  125865.76±372111.27   706606.29±1153308.30       1210261.13±2154270.70                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-05', 'depth': 10.0}    265.08±799.77         1504.58±1414.17            2160.43±5388.19                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 0.0}     11.46±0.33            666.30±1239.28             479.89±1030.62                            0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 1.0}   38.58±0.59            239.13±569.97              141.72±320.48                             0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 1.0}     11.47±2.03            4261.88±2278.74            3040.83±8518.75                           0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 2.0}   34.75±0.70            1372.05±983.53             3143.85±10378.68                          0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 2.0}     639.79±939.32         23271.72±43557.62          14262.43±27883.87                         0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 3.0}   31.63±6.75            10687.04±4536.65           17222.01±43140.37                         0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 3.0}     119.60±280.73         1235.08±1886.59            1996.41±5685.95                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 4.0}   79.97±86.26           8761.87±5170.48            6887.56±19858.29                          0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 4.0}     61.86±71.26           501.15±341.66              1423.00±3893.80                           0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 5.0}   1104.02±1763.08       17218.74±13158.44          14356.40±26429.47                         1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 5.0}     105.76±153.92         1046.13±1056.01            897.88±1558.82                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 6.0}   2923.40±5534.15       44831.85±45070.14          50011.38±72115.70                         1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 6.0}     113.66±148.23         1420.36±2640.89            1281.57±1421.66                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 7.0}   3071.63±4704.01       101985.24±197311.12        50131.08±82349.42                         1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 7.0}     211.46±271.95         3678.58±10872.36           1774.72±2441.15                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 8.0}   191952.94±1021308.28  75782.33±106684.52         3388917.85±18102160.22                    1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 8.0}     96.13±100.28          1064.62±1014.12            1380.17±2697.44                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 9.0}   30842.63±147470.80    627669.20±1986141.29       231203.73±563606.50                       2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 9.0}     514.67±2315.54        5719.97±26731.21           1596.83±4350.42                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-05', 'depth': 10.0}  493381.51±2269794.01  929473.25±3967935.90       2204955.97±7898655.66                     2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-05', 'depth': 10.0}    153.58±226.27         1484.92±1794.70            1540.41±2394.35                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 0.0}     11.45±0.33            229.96±396.31              169.55±330.62                             0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 1.0}   38.58±0.59            103.24±179.01              70.66±102.26                              0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 1.0}     10.75±0.64            1417.74±673.66             1114.81±3082.07                           0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 2.0}   34.75±0.70            458.21±309.94              1018.20±3278.40                           0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 2.0}     390.48±675.27         20791.63±56612.21          7030.00±12351.17                          0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 3.0}   29.54±2.12            3720.98±1313.46            5746.10±13557.07                          0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 3.0}     84.42±119.98          1298.22±1824.30            1276.23±1959.47                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 4.0}   53.05±65.72           3121.99±1979.54            3465.67±8125.69                           0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 4.0}     348.56±1680.31        552.57±926.23              1177.13±3241.46                           0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 5.0}   388.28±582.52         7978.45±6631.72            5290.97±8144.48                           1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 5.0}     183.14±450.63         1474.14±1755.32            1887.71±5248.97                           1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 6.0}   746.90±1369.15        14057.93±21553.13          16442.79±22352.19                         1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 6.0}     189.11±552.00         1375.48±2317.50            1480.88±2291.01                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 7.0}   727.47±1169.12        19715.00±32069.60          23603.76±57065.69                         1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 7.0}     198.26±340.58         2179.44±3962.19            2271.97±4733.76                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 8.0}   1664.14±3435.79       15866.23±20018.54          19977.96±33516.31                         1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 8.0}     106.72±92.76          1443.27±1689.37            1946.87±5647.27                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 9.0}   2132.72±7627.62       22538.80±41734.61          28245.20±46843.37                         2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 9.0}     133.50±281.90         1312.90±3033.16            1192.29±2016.33                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-04', 'depth': 10.0}  13051.43±34624.99     124343.63±456898.63        126985.45±428940.61                       2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-04', 'depth': 10.0}    129.38±233.89         1353.14±2157.71            1083.44±1249.92                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 0.0}     11.44±0.33            83.49±126.15               64.23±105.82                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 1.0}   38.58±0.59            60.28±55.40                48.19±33.54                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 1.0}     10.58±0.35            471.30±210.58              394.10±1084.48                            0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 2.0}   34.75±0.70            169.45±97.24               346.16±1033.08                            0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 2.0}     229.58±442.91         7492.94±16974.83           4313.17±9725.45                           0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 3.0}   29.39±2.92            1528.24±2321.02            1753.93±4288.05                           0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 3.0}     91.33±109.57          1203.66±1786.66            1531.67±2675.38                           0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 4.0}   26.37±12.18           929.86±495.93              744.93±2009.33                            0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 4.0}     62.28±81.44           629.15±1057.48             696.63±772.79                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 5.0}   91.71±95.57           2497.41±4089.17            2618.76±4455.21                           1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 5.0}     82.41±92.56           808.17±574.67              772.00±1388.33                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 6.0}   182.93±291.98         3155.84±4235.62            3824.10±5010.13                           1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 6.0}     162.79±208.31         1568.42±2018.40            1944.28±2744.73                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 7.0}   212.86±329.93         6617.34±17966.26           3340.23±3333.63                           1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 7.0}     120.17±105.86         1288.23±1264.35            1310.51±1776.64                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 8.0}   248.63±339.48         4822.92±10848.15           6058.56±19581.12                          1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 8.0}     160.76±248.19         2237.25±4617.49            2556.71±6531.43                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 9.0}   463.84±1212.10        6544.81±11783.73           8545.11±20534.29                          2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 9.0}     138.95±239.56         1705.46±4151.55            1428.28±3652.01                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-04', 'depth': 10.0}  1324.91±2654.32       12635.15±21465.83          14019.48±40664.23                         2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-04', 'depth': 10.0}    118.73±123.23         3362.06±10154.45           1547.72±2144.62                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.75                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 0.0}     11.43±0.33            35.80±39.78                29.77±33.94                               0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 1.0}   38.58±0.59            46.70±16.36                41.09±12.57                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 1.0}     10.55±0.32            159.09±67.10               136.79±360.11                             0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 2.0}   34.75±0.70            78.39±30.07                133.82±323.10                             0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 2.0}     45.86±77.93           885.07±921.87              826.16±1066.10                            0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 3.0}   29.15±2.11            413.15±273.17              653.51±1590.25                            0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 3.0}     1149.66±5949.93       32406.58±173126.08         38514.39±203148.90                        0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 4.0}   22.69±7.57            385.51±415.41              247.84±620.40                             0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 4.0}     48.92±41.82           659.94±683.91              720.45±839.27                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 5.0}   34.55±22.12           453.52±174.27              419.18±707.83                             1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 5.0}     71.05±68.08           1152.32±1874.31            781.47±1485.79                            1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 6.0}   51.39±56.89           856.63±1180.92             1068.12±1333.94                           1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 6.0}     113.67±140.85         1632.50±3699.16            1520.00±2107.57                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 7.0}   59.34±79.65           739.36±523.67              818.29±960.19                             1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 7.0}     129.16±138.39         1740.25±2937.72            1589.73±2682.74                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 8.0}   59.05±44.84           1188.18±1668.04            1029.04±1571.61                           1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 8.0}     70.43±51.20           1182.80±2211.01            862.65±1076.67                            1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 9.0}   65.62±73.03           842.48±377.57              922.20±1156.40                            2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 9.0}     85.34±69.72           1107.82±1269.14            972.09±1386.55                            2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-03', 'depth': 10.0}  171.68±419.99         2768.29±10276.06           1047.81±1534.16                           2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-03', 'depth': 10.0}    126.62±135.93         1298.72±1459.88            1612.57±2299.04                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.74                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 0.0}     11.42±0.32            20.52±12.36                18.70±11.21                               0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 1.0}   38.58±0.59            42.42±4.18                 38.85±7.12                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 1.0}     10.54±0.31            58.55±21.24                51.19±115.44                              0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 2.0}   34.75±0.70            49.85±8.87                 66.83±98.81                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 2.0}     28.95±85.83           176.62±295.25              242.57±541.09                             0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 3.0}   28.56±0.47            138.09±45.02               200.35±424.79                             0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 3.0}     197.54±774.72         2504.09±7453.45            3527.62±11169.11                          0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 4.0}   19.82±1.64            110.08±45.69               94.72±204.32                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 4.0}     58.86±111.45          934.09±2080.38             731.36±1444.56                            0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 5.0}   21.30±4.94            153.48±54.28               141.23±228.54                             1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 5.0}     175.45±416.92         1157.90±1796.23            1277.95±2518.65                           1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 6.0}   20.72±11.02           180.12±61.64               282.22±406.93                             1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 6.0}     105.33±104.13         1831.45±2805.32            1170.03±1290.20                           1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 7.0}   22.35±14.49           210.51±71.34               242.18±291.31                             1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 7.0}     207.34±330.88         1773.43±2335.57            2655.35±4827.20                           1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 8.0}   21.69±8.12            234.31±176.54              214.55±280.03                             1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 8.0}     64.53±60.75           887.21±863.41              1264.22±2736.52                           1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 9.0}   20.45±7.55            224.83±68.23               264.80±327.61                             2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 9.0}     329.54±1395.22        1555.62±2892.05            1996.65±5963.63                           2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-03', 'depth': 10.0}  28.38±18.50           251.94±115.90              216.33±281.27                             2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-03', 'depth': 10.0}    152.11±185.65         1376.86±1277.94            1875.64±3105.96                           2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 0.0}   42.37±0.73            42.31±0.79                 42.94±5.74                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 0.0}     11.39±0.31            15.58±3.74                 15.05±4.30                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 1.0}   38.57±0.59            41.09±0.87                 38.16±6.07                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 1.0}     10.51±0.30            26.77±6.66                 24.05±36.19                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 2.0}   34.76±0.70            41.06±2.45                 45.81±28.50                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 2.0}     6.42±2.53             27.75±8.95                 33.52±41.69                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 3.0}   28.41±0.46            62.81±13.46                80.92±126.86                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 3.0}     17.39±18.28           80.99±74.13                103.81±121.33                             0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 4.0}   19.24±0.69            49.49±13.79                45.01±59.47                               0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 4.0}     20.79±25.05           102.70±82.91               116.94±143.15                             0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 5.0}   18.07±0.91            60.83±15.88                55.34±65.69                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 5.0}     65.54±109.48          351.08±441.91              333.41±561.88                             1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 6.0}   14.84±2.20            64.89±16.45                97.04±124.42                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 6.0}     74.50±94.34           503.91±607.95              730.98±1199.49                            1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 7.0}   14.55±1.79            72.00±11.67                83.05±90.18                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 7.0}     72.06±103.82          384.73±436.56              474.43±727.46                             1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 8.0}   14.22±1.79            77.09±22.90                72.86±79.84                               1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 8.0}     52.09±53.91           475.76±850.91              341.69±356.28                             1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 9.0}   13.92±1.12            79.93±20.48                91.85±99.30                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 9.0}     35.49±25.07           250.38±165.67              237.70±302.25                             2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-02', 'depth': 10.0}  15.67±4.77            84.84±28.36                81.07±93.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-02', 'depth': 10.0}    131.65±447.41         1192.76±4964.59            527.77±836.69                             2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 0.0}   42.37±0.73            42.30±0.79                 42.94±5.74                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 0.0}     11.37±0.30            13.82±1.14                 13.66±2.51                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 1.0}   38.57±0.59            40.59±0.70                 37.88±5.85                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 1.0}     10.48±0.28            16.81±2.09                 15.73±11.09                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 2.0}   34.80±0.70            38.40±1.10                 39.23±8.11                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 2.0}     4.89±0.24             12.52±1.78                 13.68±11.99                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 3.0}   28.21±0.45            39.00±3.66                 43.13±33.30                               0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 3.0}     5.04±0.51             15.62±2.27                 16.44±11.05                               0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 4.0}   19.23±0.61            30.98±3.79                 30.07±15.39                               0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 4.0}     9.58±16.63            26.62±18.44                36.56±80.43                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 5.0}   17.38±0.39            33.66±4.49                 30.65±17.47                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 5.0}     13.41±17.65           57.93±101.84               53.31±67.95                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 6.0}   13.90±0.73            33.00±4.41                 42.53±36.07                               1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 6.0}     8.85±2.94             37.96±13.90                34.38±16.41                               1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 7.0}   13.40±0.59            34.60±3.20                 37.89±25.67                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 7.0}     9.71±3.69             43.04±14.11                43.54±34.93                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 8.0}   12.89±0.62            36.58±5.25                 34.99±21.05                               1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 8.0}     24.73±73.50           72.42±118.04               179.53±711.71                             1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 9.0}   12.99±0.57            37.96±5.90                 40.43±28.03                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 9.0}     15.68±23.07           84.08±189.67               62.92±73.43                               2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-02', 'depth': 10.0}  13.41±1.30            39.47±6.70                 40.86±31.46                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-02', 'depth': 10.0}    12.18±7.57            55.18±30.96                59.04±50.04                               2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 0.0}   42.36±0.73            42.30±0.79                 42.94±5.73                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 0.0}     11.41±0.28            13.07±0.48                 13.02±2.17                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 1.0}   38.57±0.59            40.18±0.70                 37.62±5.77                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 1.0}     10.54±0.24            13.63±0.72                 13.11±3.68                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 2.0}   34.92±0.70            37.48±0.95                 37.17±5.32                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 2.0}     5.81±0.21             10.23±0.63                 10.67±5.20                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 3.0}   28.16±0.45            32.09±1.01                 31.87±6.91                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 3.0}     5.58±0.20             11.33±0.81                 11.48±4.04                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 4.0}   19.89±0.61            25.86±1.06                 26.17±5.23                                0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 4.0}     5.72±0.33             12.59±0.81                 12.08±3.91                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 5.0}   17.64±0.36            25.83±1.23                 23.73±3.96                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 5.0}     6.36±0.42             15.42±1.17                 16.03±6.09                                1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 6.0}   14.55±0.47            24.51±1.10                 26.86±9.65                                1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 6.0}     6.65±0.46             16.68±1.44                 15.98±4.24                                1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 7.0}   13.94±0.51            24.76±1.17                 25.57±6.80                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 7.0}     6.82±0.40             17.59±1.24                 17.77±6.39                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 8.0}   13.38±0.39            25.55±1.12                 24.52±4.57                                1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 8.0}     7.01±0.54             18.32±1.40                 19.52±6.57                                1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 9.0}   13.51±0.49            25.87±1.82                 25.24±6.73                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 9.0}     7.18±0.57             18.82±1.30                 18.21±9.61                                2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e-01', 'depth': 10.0}  13.62±0.48            26.63±1.58                 28.22±8.70                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e-01', 'depth': 10.0}    7.22±0.92             18.88±1.53                 19.04±9.02                                2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 0.0}   42.36±0.73            42.29±0.79                 42.94±5.70                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 0.0}     11.80±0.26            13.03±0.35                 13.04±2.20                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 1.0}   38.61±0.60            39.79±0.64                 37.41±5.73                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 1.0}     11.10±0.21            13.10±0.40                 12.57±2.12                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 2.0}   35.18±0.70            37.10±0.88                 36.59±5.35                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 2.0}     8.57±0.19             11.80±0.33                 11.96±3.26                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 3.0}   28.77±0.46            31.41±0.73                 30.32±4.72                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 3.0}     8.35±0.19             12.62±0.47                 12.66±3.11                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 4.0}   21.78±0.61            26.06±0.78                 26.69±5.23                                0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 4.0}     8.39±0.20             13.53±0.43                 12.72±2.92                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 5.0}   19.46±0.31            25.32±0.66                 23.66±3.59                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 5.0}     8.78±0.15             15.01±0.53                 15.24±5.51                                1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 6.0}   17.17±0.36            24.15±0.74                 24.94±5.78                                1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 6.0}     9.03±0.24             15.85±0.58                 15.93±4.64                                1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 7.0}   16.50±0.39            24.14±0.75                 24.39±5.05                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 7.0}     9.16±0.26             16.40±0.58                 16.38±4.80                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 8.0}   16.15±0.37            24.76±0.92                 24.29±4.70                                1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 8.0}     9.36±0.35             16.73±0.69                 17.81±5.16                                1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 9.0}   16.25±0.41            24.79±1.01                 22.91±4.70                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 9.0}     9.49±0.31             17.09±0.54                 16.14±4.40                                2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e-01', 'depth': 10.0}  16.27±0.39            25.27±0.77                 26.39±5.01                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e-01', 'depth': 10.0}    9.52±0.38             17.19±0.64                 15.96±4.67                                2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 0.0}   42.35±0.73            42.29±0.78                 42.95±5.61                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 0.0}     13.78±0.22            14.80±0.31                 14.89±2.27                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 1.0}   38.91±0.60            39.69±0.62                 37.53±5.72                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 1.0}     13.79±0.18            15.40±0.32                 14.60±2.58                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 2.0}   35.94±0.67            37.31±0.81                 36.81±5.14                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 2.0}     13.71±0.16            16.26±0.25                 16.25±3.45                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 3.0}   30.52±0.48            32.62±0.64                 31.72±4.97                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 3.0}     13.68±0.20            16.98±0.39                 17.23±3.67                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 4.0}   25.54±0.58            28.86±0.73                 29.80±5.67                                0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 4.0}     14.05±0.16            18.02±0.34                 16.97±3.57                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 5.0}   23.90±0.31            28.28±0.56                 27.39±4.48                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 5.0}     14.63±0.25            19.30±0.49                 19.30±6.16                                1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 6.0}   22.61±0.37            27.51±0.64                 28.45±5.94                                1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 6.0}     15.08±0.19            20.14±0.46                 20.24±5.54                                1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 7.0}   22.34±0.29            27.80±0.73                 27.75±5.55                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 7.0}     15.40±0.21            20.79±0.48                 20.65±5.54                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 8.0}   22.35±0.42            28.54±0.73                 28.84±5.50                                1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 8.0}     15.69±0.28            21.18±0.67                 22.28±5.98                                1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 9.0}   22.59±0.36            28.68±0.69                 26.52±5.30                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 9.0}     15.89±0.20            21.59±0.48                 20.35±4.65                                2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+00', 'depth': 10.0}  22.56±0.31            28.98±0.65                 30.12±5.97                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+00', 'depth': 10.0}    15.97±0.24            21.70±0.66                 20.10±4.70                                2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 0.0}   42.51±0.71            42.44±0.75                 43.16±5.40                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 0.0}     35.85±28.67           36.61±28.91                37.23±29.27                               0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 1.0}   39.86±0.61            40.33±0.64                 38.49±5.74                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 1.0}     25.55±21.56           27.44±25.24                25.19±18.77                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 2.0}   37.74±0.64            38.62±0.76                 38.03±4.53                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 2.0}     23.76±7.38            25.83±8.54                 25.68±8.83                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 3.0}   34.28±0.51            35.71±0.62                 35.23±5.36                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 3.0}     23.45±0.97            25.80±0.95                 26.44±5.07                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 4.0}   31.87±0.53            34.04±0.74                 35.54±6.87                                0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 4.0}     24.70±1.82            27.49±2.21                 26.09±5.54                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 5.0}   31.59±0.43            34.27±0.57                 34.81±5.23                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 5.0}     29.47±13.88           32.35±12.91                31.69±15.21                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 6.0}   31.66±0.44            34.59±0.64                 35.66±7.10                                1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 6.0}     31.06±19.05           34.82±21.67                33.98±20.28                               1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 7.0}   32.31±0.38            35.64±0.71                 34.88±6.66                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 7.0}     29.36±7.33            32.70±6.81                 32.21±9.70                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 8.0}   32.83±0.42            36.55±0.55                 37.40±6.93                                1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 8.0}     38.22±38.52           40.44±32.20                43.32±44.36                               1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 9.0}   33.39±0.34            37.02±0.51                 35.23±6.39                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 9.0}     34.75±17.11           38.26±16.78                36.43±16.55                               2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+00', 'depth': 10.0}  33.35±0.34            37.27±0.58                 38.83±7.28                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+00', 'depth': 10.0}    50.55±53.97           54.60±56.70                55.18±66.28                               2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 0.0}   76.35±74.48           75.55±73.76                76.03±72.42                               0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 0.0}     31.08±0.31            31.37±0.36                 31.66±5.60                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 1.0}   73.04±59.35           73.80±60.84                71.72±59.53                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 1.0}     33.09±0.34            33.62±0.35                 33.13±6.09                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 2.0}   103.91±277.36         97.82±243.23               104.49±283.44                             0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 2.0}     36.12±0.35            36.95±0.52                 38.07±6.53                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 3.0}   149.56±435.93         150.65±444.17              149.70±432.27                             0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 3.0}     38.56±0.46            39.75±0.59                 40.84±7.55                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 4.0}   59.24±53.79           58.94±48.43                59.96±54.14                               0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 4.0}     41.20±0.48            42.53±0.58                 40.77±7.40                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 5.0}   45.92±2.00            46.92±1.68                 47.95±5.94                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 5.0}     43.25±0.62            44.78±0.77                 44.05±8.67                                1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 6.0}   47.29±0.58            48.63±0.68                 49.77±9.22                                1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 6.0}     44.63±0.61            46.29±0.70                 45.32±9.48                                1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 7.0}   49.02±0.64            50.46±0.76                 49.12±8.29                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 7.0}     45.69±0.53            47.49±0.67                 46.37±8.82                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 8.0}   49.94±0.55            51.59±0.59                 52.22±9.56                                1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 8.0}     46.31±0.45            48.03±0.61                 48.96±9.69                                1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 9.0}   50.75±0.44            52.26±0.54                 51.60±8.16                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 9.0}     46.84±0.47            48.65±0.57                 47.29±8.42                                2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+01', 'depth': 10.0}  50.71±0.64            52.54±0.69                 54.61±9.20                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+01', 'depth': 10.0}    46.84±0.54            48.72±0.68                 46.59±8.52                                2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 0.0}   55.08±0.53            54.88±0.55                 55.28±6.91                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 0.0}     50.34±0.40            50.18±0.42                 50.35±8.72                                0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 1.0}   56.03±0.51            55.89±0.50                 55.37±6.70                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 1.0}     54.10±0.46            54.07±0.52                 54.03±8.16                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 2.0}   58.38±0.38            58.44±0.43                 56.10±6.08                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 2.0}     58.93±0.45            59.07±0.62                 60.90±8.52                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 3.0}   62.07±0.60            62.12±0.69                 63.08±8.16                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 3.0}     63.56±0.52            63.96±0.60                 65.35±9.63                                0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 4.0}   66.00±0.75            66.27±0.83                 67.98±11.07                               0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 4.0}     67.83±0.56            68.34±0.67                 66.68±9.42                                0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 5.0}   69.92±0.60            70.33±0.66                 72.12±7.52                                1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 5.0}     70.80±0.68            71.41±0.83                 70.46±9.99                                1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 6.0}   73.03±0.82            73.57±0.87                 75.34±11.95                               1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 6.0}     72.70±0.72            73.37±0.75                 72.05±11.11                               1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 7.0}   75.24±0.67            75.77±0.68                 73.99±9.76                                1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 7.0}     73.87±0.62            74.64±0.75                 72.98±9.45                                1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 8.0}   76.22±0.73            76.82±0.78                 77.37±11.33                               1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 8.0}     74.42±0.74            75.07±0.78                 76.19±11.49                               1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 9.0}   76.84±0.60            77.45±0.71                 77.09±9.84                                2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 9.0}     74.89±0.59            75.56±0.72                 74.39±10.06                               2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+01', 'depth': 10.0}  76.70±0.78            77.49±0.76                 80.65±9.97                                2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+01', 'depth': 10.0}    75.01±0.68            75.77±0.75                 73.38±10.53                               2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 0.0}   81.92±0.57            81.61±0.58                 81.28±9.79                                0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 0.0}     80.91±0.64            80.57±0.62                 80.41±10.38                               0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 1.0}   84.66±0.58            84.38±0.58                 84.26±7.81                                0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 1.0}     86.11±0.64            85.86±0.69                 86.01±9.12                                0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 2.0}   88.57±0.45            88.42±0.48                 85.09±8.50                                0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 2.0}     91.65±0.64            91.52±0.74                 93.01±9.40                                0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 3.0}   93.16±0.68            92.95±0.72                 94.42±9.27                                0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 3.0}     96.55±0.81            96.53±0.83                 98.09±10.42                               0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 4.0}   97.80±1.00            97.77±1.03                 99.33±11.49                               0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 4.0}     100.72±0.81           100.77±0.87                99.36±10.00                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 5.0}   101.68±0.73           101.71±0.81                103.36±8.24                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 5.0}     103.31±0.87           103.43±0.96                102.52±10.09                              1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 6.0}   104.32±1.12           104.38±1.13                106.61±12.28                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 6.0}     104.93±0.97           105.05±0.98                103.74±11.03                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 7.0}   106.22±0.87           106.27±0.86                104.24±9.82                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 7.0}     105.84±0.80           106.02±0.88                104.04±9.05                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 8.0}   106.77±1.01           106.82±1.02                107.61±11.37                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 8.0}     106.05±1.04           106.15±1.04                107.38±11.62                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 9.0}   107.16±0.90           107.27±1.00                106.95±10.09                              2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 9.0}     106.43±0.87           106.50±0.94                105.51±10.38                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+02', 'depth': 10.0}  106.91±0.93           107.13±0.88                110.60±9.38                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+02', 'depth': 10.0}    106.58±0.94           106.73±1.01                104.38±11.09                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 0.0}   112.17±0.96           111.82±0.94                111.10±10.96                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 0.0}     112.18±0.92           111.81±0.90                111.41±10.67                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 1.0}   114.31±0.75           113.99±0.77                113.98±7.98                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 1.0}     115.82±0.86           115.48±0.86                115.67±9.34                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 2.0}   117.18±0.79           116.95±0.77                113.20±9.18                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 2.0}     119.30±0.88           119.03±0.94                120.04±9.60                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 3.0}   119.79±0.91           119.45±0.92                121.01±9.91                               0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 3.0}     122.10±1.06           121.83±1.09                123.50±10.66                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 4.0}   122.57±1.17           122.38±1.20                123.72±11.23                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 4.0}     124.53±1.01           124.32±1.04                123.02±9.97                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 5.0}   124.74±0.87           124.52±0.94                126.00±8.40                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 5.0}     125.86±1.01           125.70±1.06                124.80±9.90                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 6.0}   126.04±1.29           125.83±1.28                128.12±12.03                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 6.0}     126.70±1.11           126.51±1.11                125.29±10.65                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 7.0}   127.23±0.99           127.02±1.00                124.97±9.57                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 7.0}     127.20±0.91           127.06±0.98                124.92±8.74                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 8.0}   127.30±1.17           127.07±1.16                128.07±11.10                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 8.0}     127.08±1.19           126.90±1.18                128.10±11.33                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 9.0}   127.56±1.04           127.38±1.12                126.97±9.85                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 9.0}     127.38±1.05           127.14±1.10                126.22±10.26                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+02', 'depth': 10.0}  127.22±0.98           127.13±0.93                130.65±8.76                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+02', 'depth': 10.0}    127.52±1.12           127.36±1.16                125.04±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 0.0}   130.22±1.18           129.85±1.17                129.01±11.19                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 0.0}     130.31±1.10           129.91±1.07                129.41±10.66                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 1.0}   131.15±0.84           130.80±0.87                130.84±7.89                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 1.0}     131.86±1.00           131.48±0.98                131.68±9.38                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 2.0}   132.62±0.95           132.34±0.92                128.47±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 2.0}     133.30±1.02           132.96±1.07                133.71±9.63                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 3.0}   133.38±1.07           132.98±1.07                134.52±10.19                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 3.0}     134.35±1.19           133.97±1.23                135.68±10.75                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 4.0}   134.52±1.24           134.25±1.27                135.47±11.03                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 4.0}     135.49±1.10           135.16±1.12                133.90±9.91                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 5.0}   135.38±0.95           135.05±1.00                136.44±8.44                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 5.0}     135.97±1.07           135.69±1.12                134.78±9.79                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 6.0}   135.80±1.35           135.48±1.34                137.74±11.89                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 6.0}     136.31±1.16           135.99±1.16                134.81±10.46                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 7.0}   136.55±1.03           136.21±1.06                134.19±9.43                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 7.0}     136.57±0.97           136.30±1.03                134.08±8.62                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 8.0}   136.36±1.23           136.01±1.22                137.09±10.95                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 8.0}     136.29±1.25           135.98±1.23                137.15±11.16                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 9.0}   136.56±1.09           136.25±1.15                135.78±9.70                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 9.0}     136.54±1.13           136.17±1.17                135.27±10.18                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+03', 'depth': 10.0}  136.17±0.98           135.94±0.95                139.48±8.48                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+03', 'depth': 10.0}    136.68±1.19           136.38±1.23                134.07±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 0.0}   137.59±1.27           137.21±1.26                136.34±11.23                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 0.0}     137.62±1.17           137.21±1.14                136.67±10.64                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 1.0}   137.88±0.87           137.51±0.90                137.58±7.83                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 1.0}     138.13±1.06           137.73±1.03                137.92±9.38                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 2.0}   138.65±1.01           138.35±0.98                134.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 2.0}     138.59±1.07           138.23±1.12                138.89±9.64                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 3.0}   138.55±1.14           138.13±1.14                139.65±10.28                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 3.0}     138.87±1.24           138.45±1.28                140.18±10.77                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 4.0}   138.96±1.26           138.65±1.29                139.83±10.95                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 4.0}     139.47±1.13           139.10±1.15                137.84±9.89                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 5.0}   139.26±0.98           138.88±1.03                140.24±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 5.0}     139.61±1.10           139.28±1.14                138.37±9.75                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 6.0}   139.32±1.38           138.95±1.36                141.19±11.84                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 6.0}     139.73±1.18           139.37±1.17                138.21±10.39                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 7.0}   139.88±1.05           139.50±1.08                137.49±9.37                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 7.0}     139.90±0.99           139.59±1.05                137.35±8.58                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 8.0}   139.59±1.25           139.20±1.24                140.32±10.90                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 8.0}     139.57±1.26           139.21±1.25                140.37±11.10                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 9.0}   139.78±1.10           139.42±1.16                138.93±9.64                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 9.0}     139.80±1.15           139.38±1.20                138.48±10.15                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+03', 'depth': 10.0}  139.37±0.99           139.08±0.95                142.63±8.38                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+03', 'depth': 10.0}    139.93±1.22           139.59±1.25                137.28±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 0.0}   140.14±1.30           139.76±1.29                138.87±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 0.0}     140.13±1.19           139.72±1.17                139.17±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 1.0}   140.18±0.89           139.82±0.91                139.89±7.81                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 1.0}     140.25±1.08           139.85±1.05                140.05±9.38                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 2.0}   140.70±1.02           140.39±0.99                136.48±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 2.0}     140.37±1.09           140.00±1.14                140.63±9.64                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 3.0}   140.30±1.16           139.86±1.16                141.38±10.32                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 3.0}     140.38±1.26           139.95±1.30                141.69±10.78                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 4.0}   140.45±1.27           140.12±1.30                141.28±10.92                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 4.0}     140.79±1.14           140.41±1.16                139.15±9.88                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 5.0}   140.55±0.99           140.15±1.04                141.50±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 5.0}     140.81±1.11           140.47±1.15                139.55±9.74                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 6.0}   140.48±1.38           140.10±1.37                142.33±11.82                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 6.0}     140.86±1.19           140.49±1.18                139.33±10.36                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 7.0}   140.98±1.05           140.58±1.08                138.57±9.36                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 7.0}     141.00±0.99           140.67±1.06                138.42±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 8.0}   140.66±1.25           140.25±1.24                141.38±10.88                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 8.0}     140.64±1.27           140.27±1.26                141.43±11.08                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 9.0}   140.84±1.11           140.46±1.16                139.97±9.62                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 9.0}     140.87±1.16           140.43±1.21                139.54±10.14                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+04', 'depth': 10.0}  140.42±0.99           140.12±0.95                143.66±8.35                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+04', 'depth': 10.0}    141.00±1.23           140.64±1.26                138.33±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 0.0}   140.97±1.30           140.59±1.30                139.70±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 0.0}     140.95±1.20           140.54±1.18                139.98±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 1.0}   140.93±0.89           140.56±0.92                140.64±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 1.0}     140.94±1.08           140.53±1.05                140.73±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 2.0}   141.37±1.03           141.05±1.00                137.14±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 2.0}     140.95±1.09           140.57±1.14                141.19±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 3.0}   140.86±1.17           140.42±1.17                141.94±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 3.0}     140.87±1.27           140.43±1.30                142.17±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 4.0}   140.92±1.27           140.60±1.30                141.75±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 4.0}     141.21±1.14           140.82±1.16                139.57±9.88                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 5.0}   140.96±0.99           140.56±1.04                141.90±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 5.0}     141.19±1.11           140.85±1.15                139.93±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 6.0}   140.86±1.39           140.47±1.37                142.70±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 6.0}     141.23±1.19           140.85±1.18                139.69±10.36                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 7.0}   141.33±1.06           140.93±1.09                138.92±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 7.0}     141.35±0.99           141.01±1.06                138.77±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 8.0}   141.00±1.25           140.58±1.24                141.72±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 8.0}     140.99±1.27           140.61±1.26                141.77±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 9.0}   141.17±1.11           140.79±1.16                140.30±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 9.0}     141.21±1.16           140.77±1.21                139.88±10.14                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+04', 'depth': 10.0}  140.75±0.99           140.45±0.95                143.99±8.34                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+04', 'depth': 10.0}    141.34±1.24           140.98±1.26                138.67±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 0.0}   141.24±1.31           140.85±1.30                139.96±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 0.0}     141.21±1.20           140.80±1.18                140.24±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 1.0}   141.17±0.89           140.80±0.92                140.88±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 1.0}     141.16±1.09           140.75±1.05                140.95±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 2.0}   141.58±1.03           141.26±1.00                137.35±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 2.0}     141.13±1.10           140.75±1.14                141.37±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 3.0}   141.04±1.17           140.60±1.17                142.12±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 3.0}     141.02±1.27           140.58±1.31                142.32±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 4.0}   141.07±1.27           140.75±1.30                141.90±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 4.0}     141.35±1.14           140.96±1.16                139.70±9.88                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 5.0}   141.09±0.99           140.69±1.04                142.03±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 5.0}     141.31±1.11           140.97±1.15                140.05±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 6.0}   140.97±1.39           140.58±1.37                142.82±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 6.0}     141.34±1.19           140.96±1.18                139.80±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 7.0}   141.44±1.06           141.04±1.09                139.03±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 7.0}     141.46±1.00           141.12±1.06                138.87±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 8.0}   141.10±1.26           140.69±1.25                141.83±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 8.0}     141.10±1.27           140.72±1.26                141.88±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 9.0}   141.28±1.11           140.90±1.17                140.40±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 9.0}     141.32±1.17           140.88±1.21                139.99±10.14                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+05', 'depth': 10.0}  140.86±0.99           140.56±0.95                144.10±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+05', 'depth': 10.0}    141.45±1.24           141.09±1.27                138.78±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 0.0}   141.32±1.31           140.94±1.30                140.05±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 0.0}     141.29±1.20           140.88±1.18                140.32±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 1.0}   141.25±0.89           140.88±0.92                140.95±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 1.0}     141.23±1.09           140.82±1.05                141.02±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 2.0}   141.65±1.03           141.33±1.00                137.42±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 2.0}     141.19±1.10           140.81±1.15                141.42±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 3.0}   141.10±1.17           140.66±1.17                142.18±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 3.0}     141.07±1.27           140.63±1.31                142.37±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 4.0}   141.12±1.27           140.79±1.30                141.95±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 4.0}     141.39±1.14           141.00±1.16                139.75±9.88                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 5.0}   141.13±0.99           140.73±1.04                142.07±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 5.0}     141.35±1.11           141.01±1.15                140.09±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 6.0}   141.01±1.39           140.62±1.37                142.85±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 6.0}     141.38±1.19           140.99±1.18                139.84±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 7.0}   141.47±1.06           141.07±1.09                139.07±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 7.0}     141.50±1.00           141.16±1.06                138.91±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 8.0}   141.14±1.26           140.72±1.25                141.86±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 8.0}     141.13±1.27           140.76±1.26                141.91±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 9.0}   141.32±1.11           140.93±1.17                140.44±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 9.0}     141.36±1.17           140.91±1.21                140.02±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+05', 'depth': 10.0}  140.90±0.99           140.59±0.95                144.13±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+05', 'depth': 10.0}    141.49±1.24           141.12±1.27                138.81±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 0.0}   141.35±1.31           140.96±1.31                140.07±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 0.0}     141.32±1.20           140.91±1.18                140.35±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 1.0}   141.27±0.89           140.90±0.92                140.98±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 1.0}     141.25±1.09           140.84±1.05                141.04±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 2.0}   141.67±1.03           141.35±1.00                137.44±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 2.0}     141.21±1.10           140.83±1.15                141.44±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 3.0}   141.11±1.17           140.68±1.17                142.19±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 4.0}   141.14±1.27           140.81±1.30                141.96±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 4.0}     141.40±1.14           141.01±1.16                139.76±9.88                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 5.0}   141.14±0.99           140.75±1.04                142.08±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 5.0}     141.36±1.11           141.02±1.15                140.10±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 6.0}   141.02±1.39           140.63±1.37                142.86±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.85±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 7.0}   141.49±1.06           141.08±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 7.0}     141.51±1.00           141.17±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 8.0}   141.15±1.26           140.73±1.25                141.87±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 8.0}     141.14±1.27           140.77±1.26                141.92±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 9.0}   141.33±1.11           140.94±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 9.0}     141.37±1.17           140.92±1.21                140.03±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+06', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.14±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+06', 'depth': 10.0}    141.50±1.24           141.13±1.27                138.82±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 0.0}   141.35±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 0.0}     141.33±1.20           140.91±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.98±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 2.0}   141.67±1.03           141.36±1.00                137.44±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 2.0}     141.21±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 4.0}   141.14±1.27           140.81±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 7.0}     141.51±1.00           141.17±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.87±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.92±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 9.0}     141.37±1.17           140.92±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+06', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+06', 'depth': 10.0}    141.50±1.24           141.13±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 4.0}   141.14±1.27           140.81±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 7.0}     141.51±1.00           141.18±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+07', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+07', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+07', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+07', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+08', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+08', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.92±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+08', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+08', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.93±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+09', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+09', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.93±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '3.16e+09', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '3.16e+09', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 0.0}   141.36±1.31           140.97±1.31                140.08±11.24                              0.11\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 0.0}     141.33±1.20           140.92±1.18                140.36±10.63                              0.38\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 1.0}   141.28±0.89           140.91±0.92                140.99±7.80                               0.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 1.0}     141.26±1.09           140.85±1.06                141.05±9.39                               0.51\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 2.0}   141.68±1.03           141.36±1.00                137.45±9.27                               0.34\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 2.0}     141.22±1.10           140.83±1.15                141.45±9.65                               0.57\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 3.0}   141.12±1.17           140.68±1.17                142.20±10.33                              0.52\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 3.0}     141.09±1.27           140.65±1.31                142.39±10.79                              0.7\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 4.0}   141.14±1.27           140.82±1.31                141.97±10.91                              0.74\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 4.0}     141.41±1.14           141.02±1.16                139.76±9.87                               0.87\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 5.0}   141.15±0.99           140.75±1.04                142.09±8.46                               1.08\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 5.0}     141.37±1.11           141.02±1.15                140.11±9.73                               1.08\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 6.0}   141.03±1.39           140.64±1.37                142.87±11.81                              1.3\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 6.0}     141.39±1.19           141.01±1.18                139.86±10.35                              1.32\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 7.0}   141.49±1.06           141.09±1.09                139.08±9.35                               1.57\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 7.0}     141.52±1.00           141.18±1.06                138.93±8.56                               1.47\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 8.0}   141.15±1.26           140.74±1.25                141.88±10.87                              1.86\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 8.0}     141.15±1.27           140.77±1.26                141.93±11.07                              1.73\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 9.0}   141.33±1.11           140.95±1.17                140.45±9.61                               2.2\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 9.0}     141.37±1.17           140.93±1.21                140.04±10.13                              2.02\n",
      "{'k_func': 'tanimoto', 'alpha': '1.00e+10', 'depth': 10.0}  140.91±0.99           140.60±0.95                144.15±8.33                               2.4\n",
      "{'k_func': 'MinMax', 'alpha': '1.00e+10', 'depth': 10.0}    141.50±1.24           141.14±1.27                138.83±11.15                              2.32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 27060/27060 [24:52<00:00, 23.39it/s]"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.untildPathKernel import untildpathkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = untildpathkernel\n",
    "param_grid_precomputed = {'depth': np.linspace(0, 10, 11), 'k_func': ['tanimoto', 'MinMax']}\n",
    "param_grid = {'alpha': np.logspace(-10, 10, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 185 built in 0.09047365188598633 seconds ---\n",
      "[[ 1.          1.          0.33333333 ...,  0.33333333  0.33333333\n",
      "   0.33333333]\n",
      " [ 1.          1.          0.33333333 ...,  0.33333333  0.33333333\n",
      "   0.33333333]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " ..., \n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 43.616902\n",
      "With standard deviation: 2.132120\n",
      "\n",
      " Mean performance on test set: 41.620214\n",
      "With standard deviation: 6.453003\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 185 built in 0.1754138469696045 seconds ---\n",
      "[[ 1.          0.8         0.14285714 ...,  0.125       0.125       0.125     ]\n",
      " [ 0.8         1.          0.125      ...,  0.11111111  0.11111111\n",
      "   0.11111111]\n",
      " [ 0.14285714  0.125       1.         ...,  0.8         0.8         0.8       ]\n",
      " ..., \n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]\n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]\n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 40.832861\n",
      "With standard deviation: 3.441465\n",
      "\n",
      " Mean performance on test set: 38.844613\n",
      "With standard deviation: 6.446482\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 185 built in 0.3448960781097412 seconds ---\n",
      "[[ 1.          0.5         0.11111111 ...,  0.07692308  0.07692308\n",
      "   0.07692308]\n",
      " [ 0.5         1.          0.09090909 ...,  0.06666667  0.06666667\n",
      "   0.06666667]\n",
      " [ 0.11111111  0.09090909  1.         ...,  0.55555556  0.55555556\n",
      "   0.55555556]\n",
      " ..., \n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]\n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]\n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 35.746142\n",
      "With standard deviation: 1.611340\n",
      "\n",
      " Mean performance on test set: 35.291451\n",
      "With standard deviation: 4.781298\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 185 built in 0.5539388656616211 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.05555556  0.05555556\n",
      "   0.05555556]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.04761905  0.04761905\n",
      "   0.04761905]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.35714286  0.35714286\n",
      "   0.35714286]\n",
      " ..., \n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 28.464581\n",
      "With standard deviation: 3.001371\n",
      "\n",
      " Mean performance on test set: 29.484499\n",
      "With standard deviation: 3.903507\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 185 built in 0.7706489562988281 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.04347826  0.04166667\n",
      "   0.04347826]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03846154  0.03703704\n",
      "   0.03846154]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.26315789  0.25        0.26315789]\n",
      " ..., \n",
      " [ 0.04347826  0.03846154  0.26315789 ...,  1.          0.95        0.9       ]\n",
      " [ 0.04166667  0.03703704  0.25       ...,  0.95        1.          0.95      ]\n",
      " [ 0.04347826  0.03846154  0.26315789 ...,  0.9         0.95        1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 19.251747\n",
      "With standard deviation: 3.428930\n",
      "\n",
      " Mean performance on test set: 22.669312\n",
      "With standard deviation: 6.280526\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 185 built in 1.015580415725708 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03703704  0.03333333\n",
      "   0.03571429]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03333333  0.03030303\n",
      "   0.03225806]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.2173913   0.19230769\n",
      "   0.20833333]\n",
      " ..., \n",
      " [ 0.03703704  0.03333333  0.2173913  ...,  1.          0.88461538\n",
      "   0.74074074]\n",
      " [ 0.03333333  0.03030303  0.19230769 ...,  0.88461538  1.          0.85185185]\n",
      " [ 0.03571429  0.03225806  0.20833333 ...,  0.74074074  0.85185185  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 16.886016\n",
      "With standard deviation: 2.605194\n",
      "\n",
      " Mean performance on test set: 21.795626\n",
      "With standard deviation: 5.522502\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 185 built in 1.3330223560333252 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03333333  0.02857143\n",
      "   0.03030303]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03030303  0.02631579\n",
      "   0.02777778]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.19230769  0.16129032\n",
      "   0.17241379]\n",
      " ..., \n",
      " [ 0.03333333  0.03030303  0.19230769 ...,  1.          0.83870968\n",
      "   0.57142857]\n",
      " [ 0.02857143  0.02631579  0.16129032 ...,  0.83870968  1.          0.71428571]\n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  0.57142857  0.71428571  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 13.109746\n",
      "With standard deviation: 2.584308\n",
      "\n",
      " Mean performance on test set: 20.604920\n",
      "With standard deviation: 5.499831\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 185 built in 1.602663278579712 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03125     0.02564103\n",
      "   0.02631579]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02857143  0.02380952\n",
      "   0.02439024]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17857143  0.14285714\n",
      "   0.14705882]\n",
      " ..., \n",
      " [ 0.03125     0.02857143  0.17857143 ...,  1.          0.8         0.47619048]\n",
      " [ 0.02564103  0.02380952  0.14285714 ...,  0.8         1.          0.56818182]\n",
      " [ 0.02631579  0.02439024  0.14705882 ...,  0.47619048  0.56818182  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.015210\n",
      "With standard deviation: 2.592798\n",
      "\n",
      " Mean performance on test set: 20.347932\n",
      "With standard deviation: 5.176314\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 185 built in 1.8121819496154785 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02325581]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.02173913]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.12820513]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.41666667]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.49019608]\n",
      " [ 0.02325581  0.02173913  0.12820513 ...,  0.41666667  0.49019608  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.798096\n",
      "With standard deviation: 2.130816\n",
      "\n",
      " Mean performance on test set: 19.822797\n",
      "With standard deviation: 5.137687\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 185 built in 2.2172586917877197 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.0212766 ]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727  0.02      ]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11627907]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.38461538]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.45454545]\n",
      " [ 0.0212766   0.02        0.11627907 ...,  0.38461538  0.45454545  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.799656\n",
      "With standard deviation: 2.095494\n",
      "\n",
      " Mean performance on test set: 19.873364\n",
      "With standard deviation: 5.103689\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 185 built in 2.4100613594055176 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 11.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 11 of size 185 built in 2.7440149784088135 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 12.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 12 of size 185 built in 2.723442316055298 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 13.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 13 of size 185 built in 2.6163382530212402 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 14.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 14 of size 185 built in 2.629500389099121 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 15.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 15 of size 185 built in 2.664158821105957 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 16.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 16 of size 185 built in 2.7301340103149414 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 17.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 17 of size 185 built in 2.6328580379486084 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 18.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of path kernel up to 18 of size 185 built in 2.592944383621216 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 19.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 19 of size 185 built in 2.6368520259857178 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 20.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 20 of size 185 built in 2.52734375 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.778685\n",
      "With standard deviation: 2.100015\n",
      "\n",
      " Mean performance on test set: 19.870809\n",
      "With standard deviation: 5.092173\n",
      "\n",
      "\n",
      "  depth    rmse_test    std_test    rmse_train    std_train     k_time\n",
      "-------  -----------  ----------  ------------  -----------  ---------\n",
      "      0      41.6202     6.453         43.6169      2.13212  0.0904737\n",
      "      1      38.8446     6.44648       40.8329      3.44147  0.175414\n",
      "      2      35.2915     4.7813        35.7461      1.61134  0.344896\n",
      "      3      29.4845     3.90351       28.4646      3.00137  0.553939\n",
      "      4      22.6693     6.28053       19.2517      3.42893  0.770649\n",
      "      5      21.7956     5.5225        16.886       2.60519  1.01558\n",
      "      6      20.6049     5.49983       13.1097      2.58431  1.33302\n",
      "      7      20.3479     5.17631       12.0152      2.5928   1.60266\n",
      "      8      19.8228     5.13769       10.7981      2.13082  1.81218\n",
      "      9      19.8734     5.10369       10.7997      2.09549  2.21726\n",
      "     10      19.8708     5.09217       10.7787      2.10002  2.41006\n",
      "     11      19.8708     5.09217       10.7787      2.10002  2.74401\n",
      "     12      19.8708     5.09217       10.7787      2.10002  2.72344\n",
      "     13      19.8708     5.09217       10.7787      2.10002  2.61634\n",
      "     14      19.8708     5.09217       10.7787      2.10002  2.6295\n",
      "     15      19.8708     5.09217       10.7787      2.10002  2.66416\n",
      "     16      19.8708     5.09217       10.7787      2.10002  2.73013\n",
      "     17      19.8708     5.09217       10.7787      2.10002  2.63286\n",
      "     18      19.8708     5.09217       10.7787      2.10002  2.59294\n",
      "     19      19.8708     5.09217       10.7787      2.10002  2.63685\n",
      "     20      19.8708     5.09217       10.7787      2.10002  2.52734\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 185 built in 0.1027534008026123 seconds ---\n",
      "[[ 1.          1.          0.33333333 ...,  0.33333333  0.33333333\n",
      "   0.33333333]\n",
      " [ 1.          1.          0.33333333 ...,  0.33333333  0.33333333\n",
      "   0.33333333]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " ..., \n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]\n",
      " [ 0.33333333  0.33333333  1.         ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 42.787136\n",
      "With standard deviation: 0.675806\n",
      "\n",
      " Mean performance on test set: 42.645892\n",
      "With standard deviation: 6.560629\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 185 built in 0.18301701545715332 seconds ---\n",
      "[[ 1.          0.8         0.14285714 ...,  0.125       0.125       0.125     ]\n",
      " [ 0.8         1.          0.125      ...,  0.11111111  0.11111111\n",
      "   0.11111111]\n",
      " [ 0.14285714  0.125       1.         ...,  0.8         0.8         0.8       ]\n",
      " ..., \n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]\n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]\n",
      " [ 0.125       0.11111111  0.8        ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 38.880117\n",
      "With standard deviation: 0.623999\n",
      "\n",
      " Mean performance on test set: 39.174317\n",
      "With standard deviation: 6.195371\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 185 built in 0.33235955238342285 seconds ---\n",
      "[[ 1.          0.5         0.11111111 ...,  0.07692308  0.07692308\n",
      "   0.07692308]\n",
      " [ 0.5         1.          0.09090909 ...,  0.06666667  0.06666667\n",
      "   0.06666667]\n",
      " [ 0.11111111  0.09090909  1.         ...,  0.55555556  0.55555556\n",
      "   0.55555556]\n",
      " ..., \n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]\n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]\n",
      " [ 0.07692308  0.06666667  0.55555556 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 35.348332\n",
      "With standard deviation: 0.727833\n",
      "\n",
      " Mean performance on test set: 35.604226\n",
      "With standard deviation: 4.539211\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 185 built in 0.5400393009185791 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.05555556  0.05555556\n",
      "   0.05555556]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.04761905  0.04761905\n",
      "   0.04761905]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.35714286  0.35714286\n",
      "   0.35714286]\n",
      " ..., \n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.04761905  0.35714286 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 28.047646\n",
      "With standard deviation: 1.077805\n",
      "\n",
      " Mean performance on test set: 30.192177\n",
      "With standard deviation: 5.110324\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 185 built in 0.8054666519165039 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.04347826  0.04166667\n",
      "   0.04347826]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03846154  0.03703704\n",
      "   0.03846154]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.26315789  0.25        0.26315789]\n",
      " ..., \n",
      " [ 0.04347826  0.03846154  0.26315789 ...,  1.          0.95        0.9       ]\n",
      " [ 0.04166667  0.03703704  0.25       ...,  0.95        1.          0.95      ]\n",
      " [ 0.04347826  0.03846154  0.26315789 ...,  0.9         0.95        1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Mean performance on train set: 18.878595\n",
      "With standard deviation: 1.711897\n",
      "\n",
      " Mean performance on test set: 23.751530\n",
      "With standard deviation: 7.808559\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 185 built in 1.0195980072021484 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03703704  0.03333333\n",
      "   0.03571429]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03333333  0.03030303\n",
      "   0.03225806]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.2173913   0.19230769\n",
      "   0.20833333]\n",
      " ..., \n",
      " [ 0.03703704  0.03333333  0.2173913  ...,  1.          0.88461538\n",
      "   0.74074074]\n",
      " [ 0.03333333  0.03030303  0.19230769 ...,  0.88461538  1.          0.85185185]\n",
      " [ 0.03571429  0.03225806  0.20833333 ...,  0.74074074  0.85185185  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 16.339135\n",
      "With standard deviation: 1.397693\n",
      "\n",
      " Mean performance on test set: 23.482309\n",
      "With standard deviation: 7.727117\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 185 built in 1.2962956428527832 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03333333  0.02857143\n",
      "   0.03030303]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.03030303  0.02631579\n",
      "   0.02777778]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.19230769  0.16129032\n",
      "   0.17241379]\n",
      " ..., \n",
      " [ 0.03333333  0.03030303  0.19230769 ...,  1.          0.83870968\n",
      "   0.57142857]\n",
      " [ 0.02857143  0.02631579  0.16129032 ...,  0.83870968  1.          0.71428571]\n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  0.57142857  0.71428571  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.523830\n",
      "With standard deviation: 1.040404\n",
      "\n",
      " Mean performance on test set: 22.745367\n",
      "With standard deviation: 8.028051\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 185 built in 1.5462064743041992 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03125     0.02564103\n",
      "   0.02631579]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02857143  0.02380952\n",
      "   0.02439024]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17857143  0.14285714\n",
      "   0.14705882]\n",
      " ..., \n",
      " [ 0.03125     0.02857143  0.17857143 ...,  1.          0.8         0.47619048]\n",
      " [ 0.02564103  0.02380952  0.14285714 ...,  0.8         1.          0.56818182]\n",
      " [ 0.02631579  0.02439024  0.14705882 ...,  0.47619048  0.56818182  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 11.371668\n",
      "With standard deviation: 0.925446\n",
      "\n",
      " Mean performance on test set: 22.831602\n",
      "With standard deviation: 7.978369\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 185 built in 1.8658208847045898 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02325581]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.02173913]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.12820513]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.41666667]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.49019608]\n",
      " [ 0.02325581  0.02173913  0.12820513 ...,  0.41666667  0.49019608  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.132106\n",
      "With standard deviation: 0.525580\n",
      "\n",
      " Mean performance on test set: 22.586071\n",
      "With standard deviation: 8.067887\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 185 built in 2.185042381286621 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.0212766 ]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727  0.02      ]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11627907]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.38461538]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.45454545]\n",
      " [ 0.0212766   0.02        0.11627907 ...,  0.38461538  0.45454545  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.078464\n",
      "With standard deviation: 0.518149\n",
      "\n",
      " Mean performance on test set: 22.766801\n",
      "With standard deviation: 8.005709\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 185 built in 2.35276198387146 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 11.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 11 of size 185 built in 2.6274359226226807 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 12.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 12 of size 185 built in 2.7209105491638184 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 13.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 13 of size 185 built in 2.699059247970581 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 14.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 14 of size 185 built in 2.6328344345092773 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 15.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 15 of size 185 built in 2.6556999683380127 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 16.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 16 of size 185 built in 2.621814012527466 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 17.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 17 of size 185 built in 2.5938243865966797 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 18.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 18 of size 185 built in 2.65336275100708 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 19.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 19 of size 185 built in 2.628486156463623 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 20.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 20 of size 185 built in 2.682689666748047 seconds ---\n",
      "[[ 1.          0.44444444  0.11111111 ...,  0.03030303  0.02439024\n",
      "   0.02040816]\n",
      " [ 0.44444444  1.          0.08333333 ...,  0.02777778  0.02272727\n",
      "   0.01923077]\n",
      " [ 0.11111111  0.08333333  1.         ...,  0.17241379  0.13513514\n",
      "   0.11111111]\n",
      " ..., \n",
      " [ 0.03030303  0.02777778  0.17241379 ...,  1.          0.73684211\n",
      "   0.37037037]\n",
      " [ 0.02439024  0.02272727  0.13513514 ...,  0.73684211  1.          0.43859649]\n",
      " [ 0.02040816  0.01923077  0.11111111 ...,  0.37037037  0.43859649  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.075607\n",
      "With standard deviation: 0.672820\n",
      "\n",
      " Mean performance on test set: 22.869720\n",
      "With standard deviation: 7.944560\n",
      "\n",
      "\n",
      "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-------  -----------  ----------  ------------  -----------  --------\n",
      "      0      42.6459     6.56063       42.7871     0.675806  0.102753\n",
      "      1      39.1743     6.19537       38.8801     0.623999  0.183017\n",
      "      2      35.6042     4.53921       35.3483     0.727833  0.33236\n",
      "      3      30.1922     5.11032       28.0476     1.0778    0.540039\n",
      "      4      23.7515     7.80856       18.8786     1.7119    0.805467\n",
      "      5      23.4823     7.72712       16.3391     1.39769   1.0196\n",
      "      6      22.7454     8.02805       12.5238     1.0404    1.2963\n",
      "      7      22.8316     7.97837       11.3717     0.925446  1.54621\n",
      "      8      22.5861     8.06789       10.1321     0.52558   1.86582\n",
      "      9      22.7668     8.00571       10.0785     0.518149  2.18504\n",
      "     10      22.8697     7.94456       10.0756     0.67282   2.35276\n",
      "     11      22.8697     7.94456       10.0756     0.67282   2.62744\n",
      "     12      22.8697     7.94456       10.0756     0.67282   2.72091\n",
      "     13      22.8697     7.94456       10.0756     0.67282   2.69906\n",
      "     14      22.8697     7.94456       10.0756     0.67282   2.63283\n",
      "     15      22.8697     7.94456       10.0756     0.67282   2.6557\n",
      "     16      22.8697     7.94456       10.0756     0.67282   2.62181\n",
      "     17      22.8697     7.94456       10.0756     0.67282   2.59382\n",
      "     18      22.8697     7.94456       10.0756     0.67282   2.65336\n",
      "     19      22.8697     7.94456       10.0756     0.67282   2.62849\n",
      "     20      22.8697     7.94456       10.0756     0.67282   2.68269\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 185 built in 0.3893899917602539 seconds ---\n",
      "[[ 1.          0.75        0.5        ...,  0.16666667  0.16666667\n",
      "   0.16666667]\n",
      " [ 0.75        1.          0.4        ...,  0.15384615  0.15384615\n",
      "   0.15384615]\n",
      " [ 0.5         0.4         1.         ...,  0.27272727  0.27272727\n",
      "   0.27272727]\n",
      " ..., \n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]\n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]\n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.207923\n",
      "With standard deviation: 0.700182\n",
      "\n",
      " Mean performance on test set: 12.682718\n",
      "With standard deviation: 2.748815\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 185 built in 0.4729621410369873 seconds ---\n",
      "[[ 1.          0.7         0.16666667 ...,  0.05555556  0.05555556\n",
      "   0.05555556]\n",
      " [ 0.7         1.          0.13333333 ...,  0.05128205  0.05128205\n",
      "   0.05128205]\n",
      " [ 0.16666667  0.13333333  1.         ...,  0.22580645  0.22580645\n",
      "   0.22580645]\n",
      " ..., \n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.279220\n",
      "With standard deviation: 0.914688\n",
      "\n",
      " Mean performance on test set: 12.609828\n",
      "With standard deviation: 2.372778\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 185 built in 0.576836109161377 seconds ---\n",
      "[[ 1.          0.4375      0.125      ...,  0.03333333  0.03333333\n",
      "   0.03571429]\n",
      " [ 0.4375      1.          0.0952381  ...,  0.03076923  0.03076923\n",
      "   0.03278689]\n",
      " [ 0.125       0.0952381   1.         ...,  0.16981132  0.16981132\n",
      "   0.18367347]\n",
      " ..., \n",
      " [ 0.03333333  0.03076923  0.16981132 ...,  1.          1.          0.9245283 ]\n",
      " [ 0.03333333  0.03076923  0.16981132 ...,  1.          1.          0.9245283 ]\n",
      " [ 0.03571429  0.03278689  0.18367347 ...,  0.9245283   0.9245283   1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 2.588811\n",
      "With standard deviation: 0.557162\n",
      "\n",
      " Mean performance on test set: 8.060609\n",
      "With standard deviation: 2.470450\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 185 built in 0.7169125080108643 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.02631579  0.02631579\n",
      "   0.02777778]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.02409639  0.02409639\n",
      "   0.02531646]\n",
      " [ 0.125       0.08695652  1.         ...,  0.13043478  0.13043478\n",
      "   0.13846154]\n",
      " ..., \n",
      " [ 0.02631579  0.02409639  0.13043478 ...,  1.          0.94366197\n",
      "   0.83561644]\n",
      " [ 0.02631579  0.02409639  0.13043478 ...,  0.94366197  1.          0.78666667]\n",
      " [ 0.02777778  0.02531646  0.13846154 ...,  0.83561644  0.78666667  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.272670\n",
      "With standard deviation: 0.760432\n",
      "\n",
      " Mean performance on test set: 9.755135\n",
      "With standard deviation: 3.049170\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 185 built in 0.8342421054840088 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.02222222  0.02222222\n",
      "   0.02325581]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.02061856  0.02061856\n",
      "   0.02150538]\n",
      " [ 0.125       0.08695652  1.         ...,  0.10843373  0.10843373\n",
      "   0.11392405]\n",
      " ..., \n",
      " [ 0.02222222  0.02061856  0.10843373 ...,  1.          0.82417582\n",
      "   0.67010309]\n",
      " [ 0.02222222  0.02061856  0.10843373 ...,  0.82417582  1.          0.70526316]\n",
      " [ 0.02325581  0.02150538  0.11392405 ...,  0.67010309  0.70526316  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.032293\n",
      "With standard deviation: 0.728380\n",
      "\n",
      " Mean performance on test set: 10.319167\n",
      "With standard deviation: 3.616673\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 185 built in 0.9938209056854248 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01960784  0.01960784\n",
      "   0.02040816]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01834862  0.01834862\n",
      "   0.01904762]\n",
      " [ 0.125       0.08695652  1.         ...,  0.09473684  0.09473684\n",
      "   0.0989011 ]\n",
      " ..., \n",
      " [ 0.01960784  0.01834862  0.09473684 ...,  1.          0.74311927\n",
      "   0.56302521]\n",
      " [ 0.01960784  0.01834862  0.09473684 ...,  0.74311927  1.          0.6173913 ]\n",
      " [ 0.02040816  0.01904762  0.0989011  ...,  0.56302521  0.6173913   1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 0.923543\n",
      "With standard deviation: 0.660532\n",
      "\n",
      " Mean performance on test set: 10.659250\n",
      "With standard deviation: 4.120523\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 185 built in 1.1753439903259277 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01785714  0.01785714\n",
      "   0.01851852]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01680672  0.01680672\n",
      "   0.0173913 ]\n",
      " [ 0.125       0.08695652  1.         ...,  0.08571429  0.08571429\n",
      "   0.08910891]\n",
      " ..., \n",
      " [ 0.01785714  0.01680672  0.08571429 ...,  1.          0.68        0.48201439]\n",
      " [ 0.01785714  0.01680672  0.08571429 ...,  0.68        1.          0.54887218]\n",
      " [ 0.01851852  0.0173913   0.08910891 ...,  0.48201439  0.54887218  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 0.878589\n",
      "With standard deviation: 0.603598\n",
      "\n",
      " Mean performance on test set: 11.102521\n",
      "With standard deviation: 4.330554\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 185 built in 1.4358420372009277 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01666667  0.01666667\n",
      "   0.01724138]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01574803  0.01574803\n",
      "   0.01626016]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07964602  0.07964602\n",
      "   0.08256881]\n",
      " ..., \n",
      " [ 0.01666667  0.01574803  0.07964602 ...,  1.          0.64963504\n",
      "   0.43225806]\n",
      " [ 0.01666667  0.01574803  0.07964602 ...,  0.64963504  1.          0.48993289]\n",
      " [ 0.01724138  0.01626016  0.08256881 ...,  0.43225806  0.48993289  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 0.944049\n",
      "With standard deviation: 0.694844\n",
      "\n",
      " Mean performance on test set: 11.352962\n",
      "With standard deviation: 4.305459\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 185 built in 1.7005987167358398 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625\n",
      "   0.01639344]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01550388]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07826087]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.3964497 ]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.44785276]\n",
      " [ 0.01639344  0.01550388  0.07826087 ...,  0.3964497   0.44785276  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.033979\n",
      "With standard deviation: 0.775622\n",
      "\n",
      " Mean performance on test set: 11.298981\n",
      "With standard deviation: 4.349648\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 185 built in 2.0194287300109863 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625\n",
      "   0.01587302]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01503759]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07563025]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.38728324]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.43712575]\n",
      " [ 0.01587302  0.01503759  0.07563025 ...,  0.38728324  0.43712575  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.003187\n",
      "With standard deviation: 0.572070\n",
      "\n",
      " Mean performance on test set: 11.332669\n",
      "With standard deviation: 4.324120\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 185 built in 2.243326187133789 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625    0.015625  ]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01481481]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07438017]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.38285714]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.43195266]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.38285714  0.43195266  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.002272\n",
      "With standard deviation: 0.570937\n",
      "\n",
      " Mean performance on test set: 11.343515\n",
      "With standard deviation: 4.327265\n",
      "\n",
      "\n",
      "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-------  -----------  ----------  ------------  -----------  --------\n",
      "      0     12.6827      2.74882     12.2079       0.700182  0.38939\n",
      "      1     12.6098      2.37278     10.2792       0.914688  0.472962\n",
      "      2      8.06061     2.47045      2.58881      0.557162  0.576836\n",
      "      3      9.75514     3.04917      1.27267      0.760432  0.716913\n",
      "      4     10.3192      3.61667      1.03229      0.72838   0.834242\n",
      "      5     10.6593      4.12052      0.923543     0.660532  0.993821\n",
      "      6     11.1025      4.33055      0.878589     0.603598  1.17534\n",
      "      7     11.353       4.30546      0.944049     0.694844  1.43584\n",
      "      8     11.299       4.34965      1.03398      0.775622  1.7006\n",
      "      9     11.3327      4.32412      1.00319      0.57207   2.01943\n",
      "     10     11.3435      4.32726      1.00227      0.570937  2.24333\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 0 of size 185 built in 0.3775763511657715 seconds ---\n",
      "[[ 1.          0.75        0.5        ...,  0.16666667  0.16666667\n",
      "   0.16666667]\n",
      " [ 0.75        1.          0.4        ...,  0.15384615  0.15384615\n",
      "   0.15384615]\n",
      " [ 0.5         0.4         1.         ...,  0.27272727  0.27272727\n",
      "   0.27272727]\n",
      " ..., \n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]\n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]\n",
      " [ 0.16666667  0.15384615  0.27272727 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.120872\n",
      "With standard deviation: 0.500467\n",
      "\n",
      " Mean performance on test set: 12.579966\n",
      "With standard deviation: 2.732346\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 185 built in 0.4563324451446533 seconds ---\n",
      "[[ 1.          0.7         0.16666667 ...,  0.05555556  0.05555556\n",
      "   0.05555556]\n",
      " [ 0.7         1.          0.13333333 ...,  0.05128205  0.05128205\n",
      "   0.05128205]\n",
      " [ 0.16666667  0.13333333  1.         ...,  0.22580645  0.22580645\n",
      "   0.22580645]\n",
      " ..., \n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]\n",
      " [ 0.05555556  0.05128205  0.22580645 ...,  1.          1.          1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 10.224322\n",
      "With standard deviation: 0.734261\n",
      "\n",
      " Mean performance on test set: 12.621509\n",
      "With standard deviation: 2.188664\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 185 built in 0.5852782726287842 seconds ---\n",
      "[[ 1.          0.4375      0.125      ...,  0.03333333  0.03333333\n",
      "   0.03571429]\n",
      " [ 0.4375      1.          0.0952381  ...,  0.03076923  0.03076923\n",
      "   0.03278689]\n",
      " [ 0.125       0.0952381   1.         ...,  0.16981132  0.16981132\n",
      "   0.18367347]\n",
      " ..., \n",
      " [ 0.03333333  0.03076923  0.16981132 ...,  1.          1.          0.9245283 ]\n",
      " [ 0.03333333  0.03076923  0.16981132 ...,  1.          1.          0.9245283 ]\n",
      " [ 0.03571429  0.03278689  0.18367347 ...,  0.9245283   0.9245283   1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 2.718851\n",
      "With standard deviation: 0.732922\n",
      "\n",
      " Mean performance on test set: 7.429032\n",
      "With standard deviation: 2.693953\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 185 built in 0.7065560817718506 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.02631579  0.02631579\n",
      "   0.02777778]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.02409639  0.02409639\n",
      "   0.02531646]\n",
      " [ 0.125       0.08695652  1.         ...,  0.13043478  0.13043478\n",
      "   0.13846154]\n",
      " ..., \n",
      " [ 0.02631579  0.02409639  0.13043478 ...,  1.          0.94366197\n",
      "   0.83561644]\n",
      " [ 0.02631579  0.02409639  0.13043478 ...,  0.94366197  1.          0.78666667]\n",
      " [ 0.02777778  0.02531646  0.13846154 ...,  0.83561644  0.78666667  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.540000\n",
      "With standard deviation: 1.138134\n",
      "\n",
      " Mean performance on test set: 9.024680\n",
      "With standard deviation: 2.508084\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 185 built in 0.8479568958282471 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.02222222  0.02222222\n",
      "   0.02325581]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.02061856  0.02061856\n",
      "   0.02150538]\n",
      " [ 0.125       0.08695652  1.         ...,  0.10843373  0.10843373\n",
      "   0.11392405]\n",
      " ..., \n",
      " [ 0.02222222  0.02061856  0.10843373 ...,  1.          0.82417582\n",
      "   0.67010309]\n",
      " [ 0.02222222  0.02061856  0.10843373 ...,  0.82417582  1.          0.70526316]\n",
      " [ 0.02325581  0.02150538  0.11392405 ...,  0.67010309  0.70526316  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.360291\n",
      "With standard deviation: 1.423990\n",
      "\n",
      " Mean performance on test set: 10.081112\n",
      "With standard deviation: 3.647700\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 185 built in 1.0008597373962402 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01960784  0.01960784\n",
      "   0.02040816]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01834862  0.01834862\n",
      "   0.01904762]\n",
      " [ 0.125       0.08695652  1.         ...,  0.09473684  0.09473684\n",
      "   0.0989011 ]\n",
      " ..., \n",
      " [ 0.01960784  0.01834862  0.09473684 ...,  1.          0.74311927\n",
      "   0.56302521]\n",
      " [ 0.01960784  0.01834862  0.09473684 ...,  0.74311927  1.          0.6173913 ]\n",
      " [ 0.02040816  0.01904762  0.0989011  ...,  0.56302521  0.6173913   1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.085175\n",
      "With standard deviation: 1.062063\n",
      "\n",
      " Mean performance on test set: 11.300476\n",
      "With standard deviation: 4.441634\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 185 built in 1.1979196071624756 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01785714  0.01785714\n",
      "   0.01851852]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01680672  0.01680672\n",
      "   0.0173913 ]\n",
      " [ 0.125       0.08695652  1.         ...,  0.08571429  0.08571429\n",
      "   0.08910891]\n",
      " ..., \n",
      " [ 0.01785714  0.01680672  0.08571429 ...,  1.          0.68        0.48201439]\n",
      " [ 0.01785714  0.01680672  0.08571429 ...,  0.68        1.          0.54887218]\n",
      " [ 0.01851852  0.0173913   0.08910891 ...,  0.48201439  0.54887218  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.064431\n",
      "With standard deviation: 1.001911\n",
      "\n",
      " Mean performance on test set: 12.186014\n",
      "With standard deviation: 4.888158\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 185 built in 1.4372029304504395 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.01666667  0.01666667\n",
      "   0.01724138]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01574803  0.01574803\n",
      "   0.01626016]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07964602  0.07964602\n",
      "   0.08256881]\n",
      " ..., \n",
      " [ 0.01666667  0.01574803  0.07964602 ...,  1.          0.64963504\n",
      "   0.43225806]\n",
      " [ 0.01666667  0.01574803  0.07964602 ...,  0.64963504  1.          0.48993289]\n",
      " [ 0.01724138  0.01626016  0.08256881 ...,  0.43225806  0.48993289  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.199119\n",
      "With standard deviation: 1.340313\n",
      "\n",
      " Mean performance on test set: 12.753387\n",
      "With standard deviation: 5.145288\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 185 built in 1.68448805809021 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625\n",
      "   0.01639344]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01550388]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07826087]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.3964497 ]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.44785276]\n",
      " [ 0.01639344  0.01550388  0.07826087 ...,  0.3964497   0.44785276  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.358221\n",
      "With standard deviation: 1.843147\n",
      "\n",
      " Mean performance on test set: 13.047098\n",
      "With standard deviation: 5.271835\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 185 built in 1.9654510021209717 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625\n",
      "   0.01587302]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01503759]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07563025]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.38728324]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.43712575]\n",
      " [ 0.01587302  0.01503759  0.07563025 ...,  0.38728324  0.43712575  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.360024\n",
      "With standard deviation: 1.848342\n",
      "\n",
      " Mean performance on test set: 13.178933\n",
      "With standard deviation: 5.277067\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when depth = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 185 built in 2.2494258880615234 seconds ---\n",
      "[[ 1.          0.38888889  0.125      ...,  0.015625    0.015625    0.015625  ]\n",
      " [ 0.38888889  1.          0.08695652 ...,  0.01481481  0.01481481\n",
      "   0.01481481]\n",
      " [ 0.125       0.08695652  1.         ...,  0.07438017  0.07438017\n",
      "   0.07438017]\n",
      " ..., \n",
      " [ 0.015625    0.01481481  0.07438017 ...,  1.          0.58169935\n",
      "   0.38285714]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.58169935  1.          0.43195266]\n",
      " [ 0.015625    0.01481481  0.07438017 ...,  0.38285714  0.43195266  1.        ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 1.362078\n",
      "With standard deviation: 1.854262\n",
      "\n",
      " Mean performance on test set: 13.253773\n",
      "With standard deviation: 5.264247\n",
      "\n",
      "\n",
      "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-------  -----------  ----------  ------------  -----------  --------\n",
      "      0     12.58        2.73235      12.1209      0.500467  0.377576\n",
      "      1     12.6215      2.18866      10.2243      0.734261  0.456332\n",
      "      2      7.42903     2.69395       2.71885     0.732922  0.585278\n",
      "      3      9.02468     2.50808       1.54        1.13813   0.706556\n",
      "      4     10.0811      3.6477        1.36029     1.42399   0.847957\n",
      "      5     11.3005      4.44163       1.08518     1.06206   1.00086\n",
      "      6     12.186       4.88816       1.06443     1.00191   1.19792\n",
      "      7     12.7534      5.14529       1.19912     1.34031   1.4372\n",
      "      8     13.0471      5.27184       1.35822     1.84315   1.68449\n",
      "      9     13.1789      5.27707       1.36002     1.84834   1.96545\n",
      "     10     13.2538      5.26425       1.36208     1.85426   2.24943\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.untildPathKernel import untildpathkernel\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, k_func = 'tanimoto')\n",
    "\n",
    "# kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = False)\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = True)\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = False)\n",
    "\n",
    "kernel_para['k_func'] = 'minmax'\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = True)\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "\n",
    "# # kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)\n",
    "\n",
    "# kernel_para['depth'] = 10\n",
    "# %lprun -f untildpathkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results\n",
    "\n",
    "# kernel Tanimoto with y normalization\n",
    "  depth    rmse_test    std_test    rmse_train    std_train     k_time\n",
    "-------  -----------  ----------  ------------  -----------  ---------\n",
    "      0      41.6202     6.453         43.6169      2.13212  0.0904737\n",
    "      1      38.8446     6.44648       40.8329      3.44147  0.175414\n",
    "      2      35.2915     4.7813        35.7461      1.61134  0.344896\n",
    "      3      29.4845     3.90351       28.4646      3.00137  0.553939\n",
    "      4      22.6693     6.28053       19.2517      3.42893  0.770649\n",
    "      5      21.7956     5.5225        16.886       2.60519  1.01558\n",
    "      6      20.6049     5.49983       13.1097      2.58431  1.33302\n",
    "      7      20.3479     5.17631       12.0152      2.5928   1.60266\n",
    "      8      19.8228     5.13769       10.7981      2.13082  1.81218\n",
    "      9      19.8734     5.10369       10.7997      2.09549  2.21726\n",
    "     10      19.8708     5.09217       10.7787      2.10002  2.41006\n",
    "     11      19.8708     5.09217       10.7787      2.10002  2.74401\n",
    "     12      19.8708     5.09217       10.7787      2.10002  2.72344\n",
    "     13      19.8708     5.09217       10.7787      2.10002  2.61634\n",
    "     14      19.8708     5.09217       10.7787      2.10002  2.6295\n",
    "     15      19.8708     5.09217       10.7787      2.10002  2.66416\n",
    "     16      19.8708     5.09217       10.7787      2.10002  2.73013\n",
    "     17      19.8708     5.09217       10.7787      2.10002  2.63286\n",
    "     18      19.8708     5.09217       10.7787      2.10002  2.59294\n",
    "     19      19.8708     5.09217       10.7787      2.10002  2.63685\n",
    "     20      19.8708     5.09217       10.7787      2.10002  2.52734\n",
    "\n",
    "# kernel Tanimoto without y normalization\n",
    "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
    "-------  -----------  ----------  ------------  -----------  --------\n",
    "      0      42.6459     6.56063       42.7871     0.675806  0.102753\n",
    "      1      39.1743     6.19537       38.8801     0.623999  0.183017\n",
    "      2      35.6042     4.53921       35.3483     0.727833  0.33236\n",
    "      3      30.1922     5.11032       28.0476     1.0778    0.540039\n",
    "      4      23.7515     7.80856       18.8786     1.7119    0.805467\n",
    "      5      23.4823     7.72712       16.3391     1.39769   1.0196\n",
    "      6      22.7454     8.02805       12.5238     1.0404    1.2963\n",
    "      7      22.8316     7.97837       11.3717     0.925446  1.54621\n",
    "      8      22.5861     8.06789       10.1321     0.52558   1.86582\n",
    "      9      22.7668     8.00571       10.0785     0.518149  2.18504\n",
    "     10      22.8697     7.94456       10.0756     0.67282   2.35276\n",
    "     11      22.8697     7.94456       10.0756     0.67282   2.62744\n",
    "     12      22.8697     7.94456       10.0756     0.67282   2.72091\n",
    "     13      22.8697     7.94456       10.0756     0.67282   2.69906\n",
    "     14      22.8697     7.94456       10.0756     0.67282   2.63283\n",
    "     15      22.8697     7.94456       10.0756     0.67282   2.6557\n",
    "     16      22.8697     7.94456       10.0756     0.67282   2.62181\n",
    "     17      22.8697     7.94456       10.0756     0.67282   2.59382\n",
    "     18      22.8697     7.94456       10.0756     0.67282   2.65336\n",
    "     19      22.8697     7.94456       10.0756     0.67282   2.62849\n",
    "     20      22.8697     7.94456       10.0756     0.67282   2.68269\n",
    "    \n",
    "# kernel MinMax with y normalization    \n",
    "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
    "-------  -----------  ----------  ------------  -----------  --------\n",
    "      0     12.6827      2.74882     12.2079       0.700182  0.38939\n",
    "      1     12.6098      2.37278     10.2792       0.914688  0.472962\n",
    "      2      8.06061     2.47045      2.58881      0.557162  0.576836\n",
    "      3      9.75514     3.04917      1.27267      0.760432  0.716913\n",
    "      4     10.3192      3.61667      1.03229      0.72838   0.834242\n",
    "      5     10.6593      4.12052      0.923543     0.660532  0.993821\n",
    "      6     11.1025      4.33055      0.878589     0.603598  1.17534\n",
    "      7     11.353       4.30546      0.944049     0.694844  1.43584\n",
    "      8     11.299       4.34965      1.03398      0.775622  1.7006\n",
    "      9     11.3327      4.32412      1.00319      0.57207   2.01943\n",
    "     10     11.3435      4.32726      1.00227      0.570937  2.24333\n",
    "\n",
    "# kernel MinMax without y normalization\n",
    "  depth    rmse_test    std_test    rmse_train    std_train    k_time\n",
    "-------  -----------  ----------  ------------  -----------  --------\n",
    "      0     12.58        2.73235      12.1209      0.500467  0.377576\n",
    "      1     12.6215      2.18866      10.2243      0.734261  0.456332\n",
    "      2      7.42903     2.69395       2.71885     0.732922  0.585278\n",
    "      3      9.02468     2.50808       1.54        1.13813   0.706556\n",
    "      4     10.0811      3.6477        1.36029     1.42399   0.847957\n",
    "      5     11.3005      4.44163       1.08518     1.06206   1.00086\n",
    "      6     12.186       4.88816       1.06443     1.00191   1.19792\n",
    "      7     12.7534      5.14529       1.19912     1.34031   1.4372\n",
    "      8     13.0471      5.27184       1.35822     1.84315   1.68449\n",
    "      9     13.1789      5.27707       1.36002     1.84834   1.96545\n",
    "     10     13.2538      5.26425       1.36208     1.85426   2.24943"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}