xgb_test/xgb_test.ipynb

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   "source": [
    "# **XGBoost test**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function handles evaluating XGBoost model performance, test one or more classifier models against held-out dataset<br>\n",
    "Using held-out test features, evaluates the peformance of the estimated model.<br>\n",
    "Can be part of a kubeflow pipeline as a test step that is run post EDA and training/validation cycles.<br>\n",
    "This function is part of the [customer-churn-prediction](https://github.com/mlrun/demos/tree/master/customer-churn-prediction) demo. <br>\n",
    "To see how the model is trained or how the data-set is generated, check out `xgb_trainer` function from the function marketplace repository"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Steps\n",
    "1. [Setup function parameters](#Setup-function-parameters)\n",
    "2. [Importing the function](#Importing-the-function)\n",
    "3. [Running the function locally](#Running-the-function-locally)\n",
    "4. [Running the function remotely](#Running-the-function-remotely)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### **Setup function parameters** "
   ]
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    "test_set = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\"\n",
    "models_path = \"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/xgb_test/xgb_model.pkl\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing the function**"
   ]
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   "execution_count": 3,
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "> 2021-10-17 13:24:42,721 [info] loaded project function-marketplace from MLRun DB\n"
     ]
    }
   ],
   "source": [
    "import mlrun\n",
    "mlrun.set_environment(project='function-marketplace')\n",
    "\n",
    "fn = mlrun.import_function('hub://xgb_test')\n",
    "fn.apply(mlrun.auto_mount())\n",
    "fn.image = \"mlrun/ml-models\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function locally**"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "> 2021-10-17 13:24:43,112 [info] starting run tasks_xgb_test uid=1259c7c9bd0e4b0895be4f5e2fbb65c4 DB=http://mlrun-api:8080\n"
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       "      <td>function-marketplace</td>\n",
       "      <td><div title=\"1259c7c9bd0e4b0895be4f5e2fbb65c4\"><a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/1259c7c9bd0e4b0895be4f5e2fbb65c4/overview\" target=\"_blank\" >...2fbb65c4</a></div></td>\n",
       "      <td>0</td>\n",
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       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/1259c7c9bd0e4b0895be4f5e2fbb65c4/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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      "> 2021-10-17 13:24:48,490 [info] run executed, status=completed\n"
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O0KdPH1555RXGjx+f2mGmKnNpyDCMTO/+/fusXr2ajh074u7uzl9//ZWihWHu3LlD27ZtyZs3L76+vmTNmjX1grUDUyMwDCPTmz9/Pm+88QZ79uwBoESJEo/dsyc6OpoePXpw7tw5li1bhoeHR2qGahemRmAYRqZ09+5dLl++TKlSpXjrrbcoWbJksgaHJWTChAmsWbOG6dOn8/LLL6dCpPZnJp0zDCNTatSoERcvXuTo0aOP1RAcn61bt9K0aVPeeOMNFi9enKbGC5hJ5wzDMICgoCBy5syJi4sLo0aNIioqKtWSwIULF+jcuTPlypVj7ty5aSoJJMW0ERiGkSlcuXKFcuXKxU4S98orr6TafD/h4eG0a9eOiIgIVqxYQc6cOVPluI5iagSGYWRo0dHRZMmShUKFCtGxY0dq1qyZ6mUMGjSIffv2sXr1akqXLp3qx7c3UyMwDCPD2rJlCxUqVODGjRuICFOmTKF69eqpWsa8efOYM2cOH3/8Ma1atUrVYzuKSQSGYWRYRYoUoUCBAgQFBdnl+AcOHKB///40bNiQzz//3C5lOILpNWQYRoYyd+5cLl68yNixY+1azs2bN6lWrRpRUVHs37+fp59+2q7lpZTpNWQYRqbx119/cfbsWZuWjXxcUVFRdO3alStXrrBjx440nwSSYhKBYRjpWlRUFNOmTaNFixa88MILTJ06lWzZstm1++bYsWP5+eefmT17dqq3OTiDXdsIRKSpiJwSkbMi8lE8r+cVkXUickhEjolIL3vGYxhGxhMQEMB///tfFi9eDED27NntmgQ2bNjA2LFj6dWrF3369LFbOY5ktzYCEXEBTgONgEvAn0BnVT0eZ5uRQF5V/VBEngZOAR6qej+h45o2AsMw7t27x6pVq+jUqRMA58+f55lnnrH7IK5//vmHqlWrUqJECXbu3Imbm5tdy0tNibUR2LNGUB04q6r/WL/YfwQe7lulQG6xfHq5gFtApB1jMgwjA5g/fz6dO3eOnSTu2WeftXsSuHv3Lm3btkVEWLFiRbpKAkmxZxtBEeBinMeXgIdndJoBrAWuALmBN1Q1+uEDicg7wDsAzzzzjF2CNQwjbQsNDeXy5cuULl2a3r17U7p06VSZJM4Wqkq/fv04fPgwGzZsoESJEg4p11HsWSOILz0/fB2qCXAQKAxUBmaIyCMLeqrqHFWtpqrV0nvrvGEYj6d169a8/vrrsb2BGjRo4LCyZ8+ejbe3N2PGjOG1115zWLmOYs8awSWgWJzHRbGc+cfVCxivloaKsyLyL1AG2GvHuAzDSCfu3LlDrly5cHFxYfTo0aiq3bqEJmTPnj0MHDiQZs2a8cknnzi0bEexZ43gT6CUiJQQkWxAJyyXgeK6ALwKICLuwAvAP3aMyTCMdOLKlSuULVuW6dOnA1CnTh1eeeUVh8Zw/fp12rdvT7FixVi0aNFjLVmZHtgttapqpIgMAH4GXIB5qnpMRPpaX58FfA4sEJEjWC4lfaiqAfaKyTCMtC8qKgoXFxcKFSpE165dqVOnjlPiiIyMpFOnTgQEBLBr1y6efPJJp8ThCHatY6nqRmDjQ8/NinP/CtDYnjEYhpF+bN68mcGDB8eO1p00aZLTYvnkk0/Ytm0bCxYsoHLlyk6LwxEyZj3HMIx0qVixYnh4eBASEuLUOFauXMmECRPo27cvPXr0cGosjmAmnTMMw6lmz57NpUuX0szsnadOncLLy4uyZcuyfft2smfP7uyQUoWZdM4wjDTr8OHD/P3333adJM5WISEhtG3bluzZs7N8+fIMkwSSYhKBYRgOFRkZyZQpU2jVqhUvvPACU6ZMsfskcbZQVXr37s3JkyfZsmULxYoVS3qnDMK0ERiG4VA3b95k/PjxLF26FLD/JHG2+vrrr/H19eXLL7/k1VdfdXY4DpVkIhCRl0Ukp/X+myIyRUSetX9ohmFkFPfu3WPx4sWoKu7u7hw6dIgxY8Y4O6xYO3bs4IMPPqBNmzaMGDHC2eE4nC01gu+AuyJSCRgBnAe87RqVYRgZyvz583nzzTf5888/AdLUZZerV6/SsWNHnn/+eebPn58maieOZksiiLROAdEK+FpVv8YyQZxhGEaCQkJCOHnyJAC9e/fm119/TXOLuERERNChQweCgoJYuXIlefPmdXZITmFLY3GwiHwMdAPqWNcZyGrfsAzDSO9at27NxYsXOXbsGK6urtStW9fZIT1ixIgR7Ny5k6VLl1K+fHlnh+M0SY4jEBEPoAvwp6ruEJFngHqq6pTLQ2YcgWGkXYGBgeTOnRsXFxd+//13AGrXru3kqP7Hw8MDf3//R553d3fn2rVrTojIcVK0MI2qXgNWADEdagOAVakXnmEYGcHly5cpW7YsX3/9NWBJAGkpCQDxJoHEns8sbOk11AdYDsy2PlUEWG3PoAzDSD+ioqIAKFy4MN26daN+/fpOjshILlsai98DXgaCAFT1DFDQnkEZhpE+bNq0iXLlynH9+nVEhIkTJ1KlShVnh2Ukky2J4F7cxeRFxJVHVxozDCMTevbZZylatCh37951dihJCgoKcnYIaZYtieA3ERkJuIlII2AZsM6+YRmGkVbNnDmT//znPwCUK1eOX375heLFizs3qCTcvXuXFi1aODuMNMuWRPARcAM4AryLZX2BjLlem2EYSTp+/DgHDx6MbRtI68LDw2ndujW///57guME3N3dHRxV2mLLOIJWgLeqzrV3MIZhpD0RERF89dVXtGnThjJlyjBlyhSyZs2aLkbgRkRE0LFjR7Zu3cq8efPo1auXs0NKk2ypEbwOnBYRHxFpbm0jMAwjk7h9+zaTJk3C19cXIE3MFGqLqKgounXrxrp16/j2229NEkiELeMIegElsbQNdAH+FpHv7R2YYRjOEx4ejre3N6pKwYIFOXz4MJ9++qmzw7JZdHQ0vXv3xtfXl4kTJ9K/f39nh5Sm2TQNtapGAJuAH4H9WC4XGYaRQS1YsIAePXrEThJXtGhRJ0dkO1Xl/fffZ8GCBXz66acMHz7c2SGlebYMKGsqIguAs0B74HugkJ3jMgzDwYKDgzlx4gRgmSRu+/btaW6SuKSoKh9++CEzZ87kgw8+SFe1GGey5Xp/Tyw1gXdV9Z59wzEMw1lat27NpUuXOH78OK6urtSpU8fZISXb2LFjmTRpEv3792fixInpoi0jLUgyEahqJ0cEYhiG4926dYs8efLg6urK559/jojg4uLi7LAey1dffcWYMWPo2bMn33zzjUkCyZDgpSER+d36M1hEguLcgkXEDNEzjHQuZpK4adOmAVCrVi1q1qzp5Kgez8yZMxk+fDgdO3bk+++/J0sWswpvciRYI1DV2tafZhEaw8hAIiMjcXV1pXDhwrz11ls0atTI2SGlyIIFC3jvvfdo2bIlixYtSrc1GmeypbHYx5bnDMNI+zZs2ECZMmViJ4kbN24clSpVcnZYj83X15e3336bRo0a8dNPP5E1q1kz63HYUn96YNke64CyqvYJxzAMe3ruuecoUaIEYWFhzg4lxdauXcubb77Jyy+/zKpVq8iRI4ezQ0q3Emsj+FhEggHPuO0DgD+wxmERGoaRIjNmzODjjz8GoGzZsmzdupVnn33WyVGlzJYtW+jQoQMvvvgi69evJ2fOnM4OKV1LMBGo6jhr+8AkVc1jveVW1fyq+rEDYzQMIwVOnz7N0aNH080kcUnZvn07rVu3pkyZMmzatIk8efI4O6R0L8E1i0WkjKqeFJEX43tdVQ/YNbIEmDWLDSNx9+/fZ+LEibRr146yZcsSERGBq6trhuhOuXfvXl599VWKFi3Kb7/9RsGCZo0sWyW2ZnFi4wiGAu8Ak+N5TYEGNhTcFPgacAG+V9Xx8WxTD5gGZAUCVLVuUsc1DCNhgYGBTJ06FVVl1KhRGaYB9dChQzRp0oSCBQvi5+dnkkAqSqz76DvWn4+1AKmIuADfAo2AS8CfIrJWVY/H2SYfMBNoqqoXRMR8sobxGMLCwvD19aVHjx4ULFiQI0eOULhwYWeHlWpOnDhBo0aNyJUrF7/88gtFihRxdkgZii3dRzuISG7r/U9EZKWI2LIoaXXgrKr+Y13q8kcenayuC7BSVS8AqOr15IVvGAbAwoUL6dWrFzGXTTNSEvj7779p2LAhWbJkSReroaVHtnQfHaWqwSJSG2gCLARm2bBfEeBinMeXrM/FVRp4UkR+FZH9ItI9vgOJyDsisk9E9t24ccOGog0j4wsKCuL4cUsFu3fv3uzcuRMvLy8nR5W6Lly4wKuvvkp4eDh+fn6ULl3a2SFlSLYkgpiuBs2B71R1DZDNhv3ia5l6uGU6ZkxCcyxJZpSIPPJJq+ocVa2mqtWefvppG4o2jIyvdevWtGnThqioKFxdXalVq5azQ0pV165do2HDhty+fZstW7ZQoUIFZ4eUYdky++hlEZkNNAQmiEh2bEsgl4BicR4XBa7Es02AqoYCoSKyHagEnLbh+IaR6dy8eZO8efPi6urKF198ka4niUtMQEAADRs25PLly2zZsoWqVc0YVnuy5Qu9I/AzlgbdQOApwJaVHv4ESolICRHJBnQC1j60zRqgjoi4isgTQA3ghM3RG0YmEjNJ3NSpUwGoWbMmL730kpOjSn2BgYE0adKEs2fPsm7dOl5++WVnh5Th2TIN9V0R+RtoIiJNgB2qusWG/SJFZACWJOICzFPVYyLS1/r6LFU9ISKbgcNANJYupkdT8gsZRkYTd5K43r1707RpU2eHZDchISE0a9aMI0eOsHr1aho0SLKXupEKEhxQFruByCCgD7DS+lQbYI6qfmPn2OJlBpQZmcn69esZNGgQf/zxB+7u7s4Ox67CwsJo3rw5v/32Gz/99BPt2rVzdkgZyuMOKIvxNlDDeh0fEZkA7AKckggMIzMpWbIkpUqV4t69jL044P3792nfvj2//vor3t7eJgk4mC2JQPhfzyGs99P/WHXDSKOmTZvG1atXmTBhAmXKlGHz5s3ODsmuIiMj6dy5Mxs3bmT27Nm8+eabzg4p07ElEcwH9ojIKiwJoBXwg12jMoxM7N9//+XcuXNERUVlyB5BcUVFRdGzZ09WrlzJ1KlTeeedd5wdUqaUZBsBgHXiudrWhztU9S+7RpUI00ZgZDT3799nwoQJtG/fPsNNEvcwDw8P/P39H3k+V65cBAcHOyGizCOxNoLkLOwpWAaEZby/TsNwosDAQL7++mtWrrT0x8iaNWuGTAJAvEkALL2FDOexZa6h0VimlXgSKADMF5FP7B2YYWRkd+/eZd68eahq7CRx//nPf5wdlpFJ2VIj6Ax4qeoYVf0UeAnoat+wDCNj8/Hx4e23346dJK5QoUJOjsjIzGxJBOeAuIuBZgf+tks0hpGB3blzh6NHLeMle/fuza5duzLcJHFG+mRLr6F7wDER2YqljaAR8LuITAdQ1YF2jM8wMozWrVtz5coVjh8/jouLS4acHsJIn2xJBKustxi/2icUw8h4AgICyJcvH66urowbNw4XF5cM3yU0MW5uboSFhT3yfEYfNZ3W2TLX0EJHBGIYGc3ly5epVKkSI0aMYMSIEZm+BnDjxg1Uld69ezN37lxnh2PEkZzuo4Zh2CAiIgKwrBLWr18/mjdvbtN+Hh4eiMgjNw8PD3uG6zDfffcd4eHhDB061NmhGA+xaUBZWmIGlBlp2dq1axk0aBC7du1K9hd4YmMH0tv/6cPCw8N55pln8PLyYsOGDc4OJ1N6rAFlIuJj/TnIXoEZRkZTunTp2NHBxv/4+Phw48YNhg0b5uxQjHgkWCMQkePAa1gWk6nHQyOKVfWWvYOLj6kRGGnNlClTuHr1KpMmTUrRcTJqjSA6Opry5cuTI0cODhw4kGFHTad1jzsN9SxgM/AcsJ8HE4FanzeMTO/ChQucP38+U0wS9zg2bdrEyZMn8fHxMUkgjbJlYZrvVLWfg+JJkqkRGM527949xo0bR4cOHShfvjyRkZG4uLik+Esuo9YIGjRowOnTp/n333/JmjWrs8PJtFK0MI2q9hORSkAd61PbVfVwagZoGOlJUFAQ3377LdmyZaN8+fK4utoyHCdpBQsW5Pr16488n5772P/1119s27aNiZEyB7wAACAASURBVBMnmiSQhtky6dxAYDFQ0HpbLCLv2zsww0hLQkNDmTt3LqrK008/zdGjRxk5cmSqljFmzBgA9u/fT3R0NJ6enpQvX56rV6+majmONHnyZHLlykWfPn2cHYqRCFvGEfTGslTlaFUdjWXSOfOpGpmKj48P77zzDvv37wfsc5bu7e1NhQoVqFKlCiLC0KFDOXbsGFu3bk31shzh0qVL+Pr60rt3b/Lly+fscIxE2JIIzFKVRqYUGBjIkSNHAOjTpw979uyhWrV4L7Gm2KlTp9i9ezc9evSIbSvo3LkzhQoVYvLkyXYp096mT59OdHQ0gwaZHuhpnS2JIGapyjEiMgbYjVmq0sgE2rRpQ7t27WJ7A1WvXt1uZXl7e5MlSxa6dv3fDO/ZsmVjwIABbNmyJXbW0vQiODiYOXPm0L59e4oXL+7scIwkJJkIVHUK0Au4BdwGeqnqNHsHZhjOcP36dSIjIwEYP348S5cutXuX0OjoaHx8fGjcuPEj6xK8++67uLm5MXXqVLvGkNp++OEH7ty5YwaQpRM2zTWkqgdUdbqqfu3M9YoNw54uXbpE2bJlmTJlCgA1atSgatWqdi9327ZtXLx4kR49ejzyWv78+enVqxeLFi3i2rVrdo8lNURGRjJt2jRq165t11qUkXrMpHNGphczHUSRIkUYMGAALVu2dGj53t7e5M2bl1atWsX7+qBBg4iIiGDmzJkOjetxrVy5kvPnz5vaQDpiJp0zMrU1a9YwcOBA9uzZ45RZPkNCQvDw8KBLly7MmTMnwe1atWrFzp07uXDhAk888YQDI0weVeWll17i1q1bnDx50oy0TkMea9I5w8gMypQpg6enZ2y7gKOtWLGC0NDQeC8LxTVs2DBu3ryJj4+PgyJ7PDt37mTv3r0MGTLEJIF0xJYpJtoCE7AMJhPrTVU1j/3De5SpERgp9dVXX3H16tU00S2zQYMGXLhwgTNnziQ5xYSXlxchISEcP36cLFnS5jlcmzZt2L59OxcvXkzTNZfMKKU1gonA66qaV1XzqGpuZyUBw0gNly5dip0kzpnOnz/Ptm3b6N69e5LzFMUMMDt16hQbN250UITJc+bMGdasWUO/fv1MEkhnbEkE/qp6wu6RGIadhIeHM2rUKI4dOwZYagTLly93+qWLmMs83bt3t2n7Dh06ULRo0dheTWnNtGnTyJo1KwMGDHB2KEYy2ZII9omIr4h0FpG2MTe7R2YYqSQ4OJhZs2axbt06gFSbJC4lVBVvb2/q1q1r84CrrFmzMnDgQLZt28Zff6WtXtw3b95k/vz5dO3aNcMsrZmZ2JII8gB3gcZAS+uthS0HF5GmInJKRM6KyEeJbOclIlEi0t6W4xpGUkJCQpg9e3bsJHHHjx/no48S/BN0uN27d3PmzJkkG4kf1qdPH3LlypXmagWzZs0iLCzMrEecXqmqXW6AC/A3lgVssgGHgHIJbPd/wEagfVLHrVq1qhpGUmbNmqUion/++aezQ4nXu+++q25ubhoUFJTsfQcNGqSurq566dIlO0SWfGFhYeru7q5NmzZ1dihGIoB9msD3qi3TUBcVkVUicl1E/EVkhYgUtSHHVAfOquo/qnof+BGIb8TM+8AK4NGJ2A0jGW7dusXhw5alMnr37s3evXvtNklcSoSHh+Pr60vbtm3JnTt3svcfNGgQ0dHRzJgxww7RJd+SJUvw9/c3A8jSMVsnnVsLFAaKAOuszyWlCHAxzuNL1udiiUgRoA2WZTETJCLviMg+Edl348YNG4o2MqM2bdrQoUOH2Eni0mISAFi7di2BgYHJviwUo0SJErRt25ZZs2YREhKSytElj6oyZcoUPD09efXVV50ai/H4bEkET6vqfFWNtN4WAE/bsF98/eEeHrQwDfhQVRPtx6eqc1S1mqpWe/ppW4o2Mgt/f//YKSImTZqEr6+v03sDJcXb25siRYrQoEGDxz7G0KFDCQwMZMGCBakX2GP4+eefOXbsGMOGDTPrEadjtiSCABF5U0RcrLc3gZs27HcJKBbncVHgykPbVAN+FJFzQHtgpoi0tuHYhhE7SVzMwLDq1atTuXJlJ0eVOH9/fzZv3ky3bt1SlLBq1qzJSy+9xLRp05w6HmLy5MkULlyYTp06OS0GI+VsSQRvAR2Ba8BVLF/Yb9mw359AKREpISLZgE5YLjHFUtUSqlpcVYsDy4H+qro6GfEbmdD9+/cBKFq0KIMGDaJNmzZOjsh2ixcvJioqyuaxA4kZNmwYf//9N2vXrk16Yzs4dOgQfn5+vP/++2TLls0pMRipw66TzolIMyyXf1yAear6hYj0BVDVWQ9tuwBYr6rLEzummWIic1u1ahWDBg1iz549j8zdnx5UqlSJ7Nmzs3fv3hQfKzIyklKlSlG0aFF27NiRCtElT48ePVixYgUXL17kySefdHj5RvIkNsVEgiNrRGSEqk4UkW949No+qjowqYJVdSOWbqFxn4u3YVhVeyZ1PMMoX748VapUITo62tmhJNvBgwc5fPhwqvX2cXV1ZdCgQQwZMoS9e/c6dO7/K1eusHTpUvr27WuSQAaQ2KWhmGkl9gH747kZhkNMmDCBIUOGAFC6dGnWrFlDkSJFktgr7Vm4cCFZs2ZN1evpb7/9Nnny5HH4ALNvvvmGqKgoBg8e7NByDftIsEagquusd++q6rK4r4lIB7tGZRhx+Pv7c+XKldhuoelRREQES5YsoWXLluTPnz/Vjps7d27eeecdpk6dyvnz53n22WdT7dgJCQkJYdasWbRp04bnnnvO7uUZ9mdLY/HHNj5nGKkiLCyMkSNHcuTIESD9dAtNzM8//8z169cfe+xAYt5//33AcpbuCPPnzycwMNAMIMtAEmsjeA1oBhQRkelxXsoDOGcVDyNTCA0N5fvvvydv3rxUrFgxXSeAGAsXLqRAgQI0bdo01Y/9zDPP0LFjR+bOncvo0aPJk8d+s8RHRUUxbdo0atasSc2aNe1WjuFYidUIrmBpHwjnwbaBtUAT+4dmZCbBwcF89913qCoFChTg+PHjfPjhh84OK1XcunWLtWvX0qVLF7t1sxw6dChBQUH88MMPdjl+jNWrV/PPP/+Y2kAGY8sKZXmA0JjRvyLiAmRX1bsOiO8RpvtoxjRr1iz69+/Pn3/+SdWqVZ0dTqr67rvv6N+/P/v37+fFF1+0WzmvvPIKFy5c4OzZs3abartWrVr4+/tz+vTpDFFTy0xSukLZFsAtzmM3wC81AjMyt5s3b3Lo0CHAMr3yvn37MlwSAMuUEhUqVKBKlSp2LWfYsGGcP3+elStX2uX4u3btYteuXWY94gzIlkSQQ1VjZ7ay3jfr0Bkp1rZt2wcmibPn2bKznDp1it27d9OjRw+7z8XTokULSpYsyeTJk7HHQNHJkyfz5JNP0qtXr1Q/tuFctiSCUBGJ/Q8VkapAmP1CMjKya9euxU4S99VXX7Fs2bIMfXbp7e1NlixZ6Nq1q93LcnFxYfDgwezdu5ddu3al6rH/+ecfVq1aRd++fcmZM2eqHttwPlsSwWBgmYjsEJEdgC9gFiU1ku3ixYsPTBLn5eVFpUqVnByV/URHR+Pj40Pjxo0dNh1Gz549efLJJ2Pf49Qybdo0XFxczHrEGVSSiUBV/wTKAP2A/kBZVTUjiw2b3bt3D4BixYoxbNgw2rbNHEteb9u2jYsXL9pl7EBCcubMSd++fVm1ahV///13qhzz1q1bzJs3jy5dulC4cOFUOaaRtthSIwB4ASgHVAE6i0jKp040MoWVK1fy/PPPc+WKZQbyTz75hNKlSzs5Ksfw9vYmb968tGoV38J89jNgwABcXV2ZPn160hvbYPbs2YSGhpr1iDMwW5aq/BT4xnqrD0wEXrdzXEYGUbFiRWrUqOHsMBwuJCSEFStW0LFjR9zc3JLeIRUVLlyYzp0788MPP3D79u0UHev+/ft88803NGrUCE9Pz1SK0EhrbKkRtAdeBa6pai+gEpDdrlEZ6dq4ceMYNGgQAKVKlWLFihWZ7pLCihUrCA0NdehlobiGDBlCaGgoc+fOTdFxli5dytWrV80AsgzOlkQQpqrRQKR1cNl1wMw0ZSQoICCA69evO3XlLGdbuHAhzz//PLVq1XJK+ZUrV6ZBgwZMnz49tpdWcqkqkydPpkKFCjRu3DiVIzTSElsSwT4RyQfMxTLFxAEg5atqGBlGWFgYH330UewkcRMnTmTp0qUZultoYs6fP8+2bdvo3r27U9fxHTZsGJcvX+ann356rP39/Pw4cuQIQ4cONesRZ3CJJgKxfPrjVDXQuqBMI6CH9RKRYQCWSeLmzZvH5s2bATJtAojh4+MDkCrLUaZE06ZNKVOmDFOmTHmsAWaTJ0/Gw8ODLl262CE6Iy1JNBGo5a9ndZzH51T1sN2jMtK8oKAgZsyYETtJ3IkTJxg+fLizw3I6VcXb25u6detSvHhxp8aSJUsWhgwZwoEDB/jtt9+Ste/Ro0f5+eefGTBgANmzmybBjM6WS0O7RcTL7pEY6crSpUsZNGgQBw4cAEjVxVbSs927d3PmzBmnNRI/rFu3bhQoUCDZK5hNmTIFNzc3+vbta6fIjLTElkRQH0sy+FtEDovIERExtYJMKCAggL/++guA3r17c+DAgQw5SVxKLFy4EDc3N9q1a+fsUABwc3Ojf//+rFu3jtOnT9u0z7Vr11i8eDG9evUyCT6TSDARiMgz1ruvYekl1ABoCbSw/jQymbZt2/LGG2/EThKXkaeHeBzh4eH4+vrStm1buy4Ok1z9+/cne/bsTJ061abtZ8yYQUREROw60UbGl1iNYDWAqp4Hpqjq+bg3x4RnONuVK1diux9OnTqVFStWZPrG4ISsXbuWwMDANHNZKIa7uztvvvkmCxcuJCAgINFtQ0ND+e6772jdujUlS5Z0UISGsyWWCOL2FzPjBjKhmEnivvrqKwCqVq1KxYoVnRxV2uXt7U2RIkVo0KCBs0N5xJAhQwgLC2P27NmJbrdw4UJu3bplBpBlMoklAk3gvpHBhYeHA5ZJ4j788EM6dOjg5IjSPn9/fzZv3ky3bt3SZI2pfPnyNGnShBkzZsROAviwqKgopk6dSo0aNZw2EM5wjsQSQSURCRKRYMDTej9IRIJFJMhRARqOtXz58gcmiRs5cqRdLxF4eHggIo/cPDw87FamPSxevJioqCinjx1IzLBhw7h27RpLly6N9/V169Zx9uxZhg0bZgaQZTJJrlmc1pg1i+3rzJkzjBw5kunTpztkDv3EvnDS099mpUqVyJ49O3v3pt1B96qKp6cnIsKhQ4ceee/r1KnDpUuXOHPmjN3WPDacJ6VrFhsZ3H//+18GDhwIWCaJW7ZsmcMWUskIDh48yOHDh9NcI/HDRIShQ4dy5MgR/PweXHZ87969/P777wwePNgkgUzIJAKDwMBAbt++nakniUsJb29vsmbNSqdOnZwdSpK6dOmCu7v7IwPMJk+eTN68eXnrrbecFJnhTCYRZEJ3795l+PDhHD5sGRc4ceJEfHx8HNrIqaqsXr066Q3TuIiICBYvXkzLli3TxeCr7NmzM2DAADZv3syxY8cAOHfuHMuXL+fdd98ld+7cTo7QcAaTCDKhsLAwfHx82Lp1K2CZk8aRjh07RqNGjWjTpk2i2+3Zs8dBET2+n3/+mevXr6f5y0Jx9e3bFzc3t9gBZl9//TVZsmTh/fffd3JkhtOoqt1uQFPgFHAW+Cie17sCh623P4BKSR2zatWqaiRfYGCgTp8+XaOjo1VV9datWw6P4datW/r++++ri4uL5suXT6dPn67u7u6KpXvyAzcXFxfNkSOH/vTTTw6PMznat2+vBQoU0Hv37jk7lGRxc3OL9313d3d3dmiGnQD7NKHv6oReSOkNcAH+xjIYLRtwCCj30Da1gCet918D9iR1XJMIHs93332nWbJk0f379zu87MjISJ05c6bmz59fs2TJov369dMbN24kus+NGzf05ZdfVkC//PLL2ASWlty8eVOzZcumAwcOdHYoyRZfEoi5GRmTsxJBTeDnOI8/Bj5OZPsngctJHdckAttdv3499os/MjJSDx8+7PAYtm3bpp6engpo3bp19eDBgzbvGx4erl27dlVAe/bsmebOumfOnKmAU5JrSplEkPkklgjseXG4CHAxzuNL1ucS8jawKb4XROQdEdknIvtu3LiRiiFmbO3ataNTp06xk8Q5cnqI8+fP06FDB+rXr8+dO3dYtmwZ27ZtS9ZEddmzZ8fHx4cxY8awYMECGjduzK1bt+wYdfJ4e3tToUIFqlSp4uxQDCNlEsoQKb0BHYDv4zzuBnyTwLb1gRNA/qSOa2oEibt06ZLev39fVVX379+vR48edWj5ISEhOmrUKM2RI4e6ubnp2LFj9e7duyk+7qJFizRbtmxaunRpPXPmTCpEmjInT55UQCdNmuTsUB4LpkaQ6eCkGsEloFicx0WBKw9vJCKewPdAK1W9acd4MryYSeImTZoEwIsvvkj58uUdUraq8uOPP1KmTBk+//xz2rRpw6lTpxg1ahRubm4pPn7Xrl35v//7P27dukWNGjXYsWNHKkT9+Ly9vcmSJQtdu3Z1ahyGkSoSyhApvQGuwD9ACf7XWFz+oW2ewdKjqJatxzU1gkfFPeMeN26c/v333w4tf//+/Vq7dm0FtEqVKrpjxw67lXX27Fl94YUXNGvWrOrt7W23chITFRWlxYoV06ZNmzql/NSQUG8t02so48IZjcWWcmkGnMbSe+g/1uf6An2t978HbgMHrbcEA425mUTwoJ9++kk9PDz00qVLDi/b399fe/furSKiBQoU0Dlz5mhkZKTdy71165bWr19fAR01apTDexT5+fkpoEuXLnVouYaREk5LBPa4mURgEfPld+bMGX3jjTf06tWrDiv7/v37OmXKFM2bN6+6urrqkCFD9Pbt2w4rX1X13r17+vbbbyugnTt31rCwMIeV3b17d82bN2+qtH0YhqOYRJDBfPbZZ/ree+85pexNmzZpmTJlFNAmTZro8ePHnRKHqiUZTpgwQQGtVauWXr9+3e5lBgcHa86cObVPnz52L8swUlNiicBMMZEOhYSEEBwc7NBJ4s6cOUPLli157bXXiIyMZN26dWzatImyZcs6LIaHiQgjRoxg+fLlHDhwgBo1anDixAm7lrlixQpCQ0PT9LoDhpFsCWWItHrLjDWCkJAQHTJkiB46dEhVLY2V9pJQIyKguXPn1okTJ2p4eLjdyn9ce/bsUXd3d82bN6/6+fnZrZz69evr888/nyZHOhtGYjA1gvQtPDycJUuWxM4hb89J4vz9/RN87fTp0wwfPpzs2bPbrfzHVb16dfbs2cMzzzxD06ZN+f7771O9jPPnz7Nt2za6d+9uVvAyMhSTCNKowMBApk2bhqqSP39+Tp48ydChQ+1a5p07dxJ9Pa0vH/nss8/y+++/07BhQ/r06cOIESOIjo5OteP7+PgAmMtCRoZjEkEa5evrywcffMBff/0FQL58+exSzpkzZ5g6dSqvvvoqBQoUsEsZjpQnTx7WrVtH//79mTRpEu3bt+fu3bspPq6q4u3tTd26dSlevHjKAzWMNMSsSZeG+Pv7c/HiRapVq0afPn2oXbt2qo8MjoiIYOfOnaxfv57169dz6tQpAMqXL8+wYcOYMGFCqpbnDK6ursyYMYPSpUszZMgQ6taty9q1a1O0/Obu3bs5c+YMH3/8cSpGmjoiIiK4dOkS4eHhzg7FSANy5MhB0aJFyZo1q837mMXr05A6derg7+/PiRMnUnW1sJs3b7Jp0ybWr1/P5s2buXPnDtmyZaN+/fq0aNGC5s2bU6JECSDjLCYfY926dXTu3JmnnnqKdevWJWvSu7j69u2Lt7c3165dI0+ePKkcZcr8+++/5M6dm/z585u2i0xOVbl58ybBwcGx/9MxElu83tQInOzixYu4u7uTLVs2pk+fTo4cOVKcBFSV48ePs379etatW8euXbuIjo7G3d2ddu3a0aJFCxo2bBjvsoTu7u7xNhi7u7unKCZnadmyJb///jstWrSgdu3a+Pr60qxZs2QdIzw8HF9fX9q2bZvmkgBY4itevLhJAgYiQv78+Un2LM0JdSdKq7eM1H30woULmitXLv3vf/+b4mOFhYXp5s2bdcCAAVq8ePHYLp8vvviijh49Wvfu3WvXbqdp3eXLl/XFF1/ULFmy6DfffJOsfX19fRXQLVu22Cm6lHHmoD4jbYrvb4JEuo+aGoEThIWF4ebmRrFixRg1ahQdO3Z8rONcvXqVjRs3sn79erZu3UpoaChubm40bNiQjz/+mObNm1OkSGJLQGQehQsXZvv27XTp0oX333+f06dPM3XqVJtqX97e3hQpUoQGDRo4IFLDcIKEMkRavaX3GoGvr6+6u7vrxYsXk71vdHS07t+/Xz/77DP18vKKPesvVqyY9uvXTzds2GDmv0lCZGSkDh06VAFt3ry5BgUFJbr9tWvX1MXFRT/66CMHRZh8aaFGkCVLFq1UqZKWL19e27dvr6Ghocna/4MPPtBy5crpBx98kOyyv/jiiwce58yZM9nHsNWnn34auwbFqFGjdOvWraqq+uyzzya5/Gpi/vrrL92wYUOy96tbt67++eefjzxvagRplKoiIlStWpUGDRrE26Lv4eER7/X5fPny0aFDBzZs2MCVK1cQEV566SW++OILWrRoQcWKFc31YRu5uLgwefJkSpcuzXvvvUft2rVZv349xYoVi3f7xYsXExUVZcYOJMHNzY2DBw8ClrUjZs2aZdO4l8jISFxdXZk9ezY3btx4rMGKX375JSNHjkz2fik1duzYZG0f87vG5+DBg+zbty/Z7VepxYwjcIBPP/2U9957D4Dnn3+eJUuWxNv4mtCo3sDAQH788Udq1arFggULuHbtGn/88QcjR47E09PTJIHH8O6777Jx40bOnTtH9erVSagn2sKFC/Hy8nLqnErJVa9ePRYsWABYupbWq1ePRYsWAXD37l3q1auHr68vYBlEWK9ePVauXAlAQEAA9erVY926dQBcu3Yt2eXXqVOHs2fPEhoayltvvYWXlxdVqlRhzZo1ACxYsIAOHTrQsmVLGjduzOuvv05oaCg1atTA19eXGzdu0K5dO7y8vPDy8mLnzp2AZY6tXr16UbFiRTw9PVmxYgUfffQRYWFhVK5c+ZFFgrp16xZbJlgS1Nq1ax+Jd+LEiVSsWJFKlSrx0UcfATB37ly8vLyoVKkS7dq1i3csSs+ePVm+fHns40mTJlG9enWqV6/O2bNnY7cZOnQo9evX58MPP2Tv3r3UqlWLKlWqUKtWLU6dOsX9+/cZPXo0vr6+VK5cGV9f3wTfu7CwMDp16oSnpydvvPEGYWFhyf584mNqBA4QHh5OeHh47NrBD4vp25+YgIAAsmXLZq8QM6XGjRvzxx9/0Lx5c1555RWWLFlC69atY18/ePAghw8fZsaMGU6MMn2JjIxk06ZNNG3alC+++IIGDRowb948AgMDqV69Og0bNgRg165dHD58mKeeegqAXLlyxdYounTpwpAhQ6hduzYXLlygSZMmnDhxgs8//5y8efNy5MgRAG7fvk27du2YMWNG7L5x9e7dm6lTp9KqVSvu3LnDH3/8wcKFCx/YZtOmTaxevZo9e/bwxBNPxK6J3bZtW/r06QPAJ598wg8//MD777+f6O+eJ08e9u7di7e3N4MHD2b9+vWAZWoWPz8/XFxcCAoKYvv27bi6uuLn58fIkSNZsWIFY8eOZd++fbF/ayNHjoz3vZs9ezZPPPEEhw8f5vDhw7z44ouP9Tk9zCQCOwgJCeE///kPvXr1onLlyowfP/6Rs/YrV66wefNmNm7cyJYtWwgODk70mCYJ2Ef58uXZs2cPrVq1om3btkycOJFhw4YhInh7e5M1a1Y6derk7DCT5ddff429nzVr1gceP/HEEw88zps37wOPCxQo8MBjW6cViTkrB0uN4O2336ZWrVqsXbuWr776CrCcEF24cAGARo0axSaBh/n5+XH8+PHYx0FBQQQHB+Pn58ePP/4Y+/yTTz6ZaEx169blvffe4/r166xcuZJ27do9cmnGz8+PXr168cQTTwDExnT06FE++eQTAgMDCQkJoUmTJkm+B507d479OWTIkNjnO3ToEHsCeOfOHXr06MGZM2cQESIiIuI91pYtW+J977Zv387AgQMB8PT0xNPTM8m4bGESgR3cv3+fZcuWUaJECSpXroyIEBkZyZ49e9i4cSMbN26MPYMpUqQInTp1olmzZrRp08bJkWdO7u7ubNu2jZ49ezJ8+HCGDx/+wOsFChTA3d39sS6TZBZx2whiqCorVqzghRdeeOD5PXv2kDNnzgSPFR0dza5dux5Z6zqmnS05unXrxuLFi/nxxx+ZN2/eI68ndMyePXuyevVqKlWqxIIFCx5IjgmJe5y49+P+rqNGjaJ+/fqsWrWKc+fOUa9evXiPldB79/CxU4tpI0glt27dYvLkyagqTz31FCdOnKBr1674+PjQuXNnChYsSO3atZkwYQK5c+dm3LhxHDp0iIsXLzJnzpwHLkkYjufm5sbSpUsTfD2xWVmN+DVp0oRvvvkmdkR6zLxZSWncuPEDl+NiEszDz9++fRuw1HoSOrPu2bMn06ZNA4h3upbGjRszb9682DaAmEtDwcHBFCpUiIiICBYvXmxT3DHtLr6+vtSsWTPebe7cuRPbpTumHQcgd+7cD1wVSOi9e+WVV2LjOXr0KIcPH7YptqSYRJBKli1bxogRI1i0aBGfffYZjRs3xt3dne7du7Nt2zZatWoV2xC2fft2Pvroo0caehMavZteR/WmN/ac3jszGjVqFBEREXh6elKhQgVGjRpl037Tp09n3759eHp6Uq5cOWbNmgVYrtXfvn2bChUqUKlSJbZtzXUmbAAAD9tJREFU2wbAO++8g6en5yONxWD53ylbtiy9evWKt6ymTZvy+uuvU61aNSpXrhx7Kebzzz+nRo0aNGrUiDJlytgU971796hRowZff/01U6dOjXebESNG8PHHH/Pyyy8/sLBU/fr1OX78eGxjcULvXb9+/QgJCcHT05OJEydSvXp1m2JLiplrKAWuXbvGsWPHCAgIYMOGDWzYsIFbt24hItSoUYNmzZrRrFkzqlSpYr5k0on0ONfSiRMn0lWvJke6e/cuFStW5MCBA+TNm9fZ4ThMfH8TZq6hVKSqHDp0iE2bNjF+/HiCgoIASyNT06ZNadasGU2aNMkQUzobRnrm5+fHW2+9xdChQzNVEngcJhHYICgoCD8/v9jpHGKuF5ctW5auXbvSrVs3qlevnqozhhqGkTINGzaM7aVkJM4kgnioKidOnIjt4bNjxw4iIyPJnTs3d+/epU2bNnz77bcpmt/eSJsy2uyrhmGLTJcIEprGoWDBgvzwww+xX/7nz58HoGLFigwcOJBWrVpRs2ZNpk+fTvv27U0SyKBMF1EjM8p0jcVJ9cHNmTMnDRs2pFmzZrz22mvs3LmTgQMHcuDAAYoWLfrY5RqGvZjGYuNhprE4BbZu3UqdOnXInj177EATLy8vmjRpYkb2GoaRYZk+jXE0bNiQ7NmzM2rUKPr16wdYJonz8fGhYMGCTo7OMFLOw8MDEXnkZutUEs5SvHhxAgICUuVYa9euZfz48QDcuHGDGjVqUKVKFXbs2EGzZs0IDAxMlXLSE1MjiEdERAQREREJThJnGOlVQiOkM9PI6ddff53XX38dgF9++YUyZcrETkZXp06dZB0ro3xHmBpBHDFD2ceNG8cPP/yQIT5gI3MZPHgw9erVS/CWmIT2GTx4cKL7nTt3jjJlytC7d28qVKhA165d8fPz4+WXX6ZUqVLs3buX6OhoSpUqFbuWbnR0NCVLlnzkLD++qaYf1rp1a6pWrUr58uWZM2cOYPlC7tmzJxUqVKBixYqxI3unT59OuXLl8PT0jJ08cMGCBQwYMICDBw8yYsQINm7cSOXKlQkLC3ug5rFo0SKqV69O5cqVeffdd2NHAufKlYvRo0dTo0YNdu3alcQnkj5kuhpBQt0Ds2TJwvbt22MniTMMw3Znz55l2bJlzJkzBy8vL5YsWcLvv//O2rVr+fLLL1m9ejVvvvkmixcvZvDgwfj5+VGpUqVHBl7GN9X0w+bNm8dTTz1FWFgYXl5etGvXjnPnznH58mWOHj0KEHt5Z/z48fz7779kz579kUs+lStXfmT65xgnTpzA19eXnTt3kjVrVvr378/ixYvp3r07oaGhVKhQIdkL06RlmS4RxHQPvHnzJvPmzeODDz7g/9s74yCrqjqOf76LS8squRuQUwGxFVHUIhugaGhLBLEwIxYOZoGSMI2TUjLTJMOMReEkWn8UA0nWkOmYNhEWYYYJQ1ACCbSLkJBEKGsYRkaGUe7y649zHj0fb9m7y773eO/9PjN39pxzz73397v75v7uOffe708Sr7zyCn379i2wdY5zdqQE1jriTDc5SRQ2O6Kuro76+nogiLtNmDABSdTX13Pw4EEAbrzxRqZNm8att97KypUrs+r/JJGaXrp0KY888ggAhw4d4tlnn2XYsGEcOHCAefPmMXXqVCZNmgRwSoPo6quv7pKw4/r169mxYwdjxowBgsx26jlhr169mD59euJ9FQM5nRqSNFnSPkn7JS3Isl6Slsb1uyT1TJaFBKxevZqFCxeemg7yIOA43Sc9xWRFRcWpekVFBW1tbQAMGjSIiy66iA0bNrBt2zaamppO209nUtMbN27kiSeeYMuWLbS0tNDQ0MCJEyeora2lpaWFxsZGli9fzty5cwF49NFHufnmm9mxYwejRo06ZUtnmBk33HADzc3NNDc3s2/fPhYtWgRAVVVVyU0b5ywQSOoFLAeagOHAdZKGZ3RrAobG5TPAPbmyB0IymG3btgEwZ84cdu/eTUNDQy4P6TjnFIVWuJ07dy4zZ85kxowZWS+mHUlNpzh27Bi1tbVUV1ezd+9etm7dCoQMfidPnmT69OksXryYnTt3cvLkSQ4dOsT48eO5++67TyWZScKECRNYtWoVR44cAYI8deoj01IklyOCS4D9ZnbAzP4LPAxMy+gzDbjfAluBGkk5+2R3xowZzJo1i/b2dioqKrImfXCcUubFF1/EzE5b8vVF9VVXXXXqgXA2OpKaTjF58mTa2toYMWIEt99+O2PHjgXghRdeoLGxkZEjRzJ79mzuvPNO2tvbmTlzJvX19TQ0NDB//nxqamoS2Tl8+HDuuOMOJk2axIgRI5g4cSKHDx8+O+fPYXL2ZbGka4DJZjY31mcBl5rZLWl91gJLzOw3sb4euM3Mtmfs6zOEEQODBw8e1d3I3NLSQnV1NUOHDu3W9o5zLlJMXxZv376d+fPns3nz5kKbUtKcS18WZ5voy4w6SfpgZvcC90KQmOiuQRdffHF3N3Uc5yxZsmQJ99xzT+KMX07+yOXUUCswKK0+EPhLN/o4jlMCLFiwgOeee45x48YV2hQng1wGgqeAoZLqJPUGPgGsyeizBrg+vj00FjhmZqU7Eec4OaLYxCOd3NGd30LOpobMrE3SLcA6oBew0sz2SLoprl8B/AKYAuwHXgWyP0FyHKdDqqqqOHr0KP369fOPIcscM+Po0aNUVVV1abuyk6F2nFLjtddeo7W1lRMnThTaFOccoKqqioEDB1JZWfm6dpehdpwSprKykrq6ukKb4RQxLjrnOI5T5nggcBzHKXM8EDiO45Q5RfewWNJLQHdFP/oDPZPmqHhwn8sD97k8OBuf325mA7KtKLpAcDZI2t7RU/NSxX0uD9zn8iBXPvvUkOM4TpnjgcBxHKfMKbdAcG+hDSgA7nN54D6XBznxuayeETiO4zinU24jAsdxHCcDDwSO4zhlTkkGAkmTJe2TtF/SgizrJWlpXL9L0gcKYWdPksDnT0Vfd0l6UlLRZ+npzOe0fmMktceseUVNEp8lNUpqlrRH0q/zbWNPk+C3faGkn0tqiT4XtYqxpJWSjkja3cH6nr9+ZctfWswLQfL6T8A7gN5ACzA8o88U4DFChrSxwLZC250Hny8HamO5qRx8Tuu3gSB5fk2h7c7D/7kG+AMwONbfXGi78+DzQuCuWB4A/B3oXWjbz8LnK4EPALs7WN/j169SHBFcAuw3swNm9l/gYWBaRp9pwP0W2ArUSHpLvg3tQTr12cyeNLOXY3UrIRtcMZPk/wwwD/gJcCSfxuWIJD5/ElhtZs8DmFmx+53EZwP6KiRjuIAQCNrya2bPYWabCD50RI9fv0oxELwNOJRWb41tXe1TTHTVnzmEO4piplOfJb0N+BiwIo925ZIk/+d3A7WSNkraIen6vFmXG5L4vAx4LyHN7dPA583sZH7MKwg9fv0qxXwE2VI0Zb4jm6RPMZHYH0njCYGg2BPHJvH5m8BtZtZeIpm7kvh8HjAKmAD0AbZI2mpmf8y1cTkiic8fBZqBDwPvBH4labOZ/TPXxhWIHr9+lWIgaAUGpdUHEu4UutqnmEjkj6QRwPeAJjM7mifbckUSn0cDD8cg0B+YIqnNzH6aHxN7nKS/7b+Z2XHguKRNwMVAsQaCJD5/GlhiYQJ9v6Q/A+8BfpcfE/NOj1+/SnFq6ClgqKQ6Sb2BTwBrMvqsAa6PT9/HAsfM7HC+De1BOvVZ0mBgNTCriO8O0+nUZzOrM7MhZjYEWAV8toiDACT7bf8MuELSeZKqgUuBZ/JsZ0+SxOfnCSMgJF0EDAMO5NXK/NLj16+SGxGYWZukW4B1hDcOVprZHkk3xfUrCG+QTAH2A68S7iiKloQ+fwnoB3w73iG3WRErNyb0uaRI4rOZPSPpl8Au4CTwPTPL+hpiMZDw/7wYuE/S04Rpk9vMrGjlqSU9BDQC/SW1Al8GKiF31y+XmHAcxylzSnFqyHEcx+kCHggcx3HKHA8EjuM4ZY4HAsdxnDLHA4HjOE6Z44HAKQiSTNIDafXzJL0kaW0h7eoqkg5K6h/LT3bSd7akt3Zx/0M6UqEsxH6c0sQDgVMojgPvl9Qn1icCLxTQnlNI6tb3NWZ2eSddZgNdCgSOkw88EDiF5DFgaixfBzyUWiHp/KjL/pSk30uaFtuHSNosaWdcLo/tjVFobZWkvZIeVBaBodjnmwo5GXZLuiS2L5J0r6THgfslDZD0k3j8pyR9MPbrJ+nxaNN3SNN9kfSvtPIXJT0dNfKXKORCGA08qJAroI+kUZJ+HcXh1qUUJGN7i6QtwM3ZTpykH0makla/T9L0js5PxrazJS1Lq6+V1BjLkyRtidv+WNIFZ/wPOqVBobW3fSnPBfgXMIIg/VBFEA1rBNbG9V8DZsZyDUEr53ygGqiK7UOB7bHcCBwj6K5UAFuAcVmOuxH4bixfSdR8BxYBO4A+sf7D1PbAYOCZWF4KfCmWpxLEvvqnfIp/m4AngepYf1PasUfHcmXsMyDWryV8NQvhq+APxfLXyaJLT1BV/UEs9yaoUfY5w/kZkubrbGBZ2r7WxvPXH9gEnB/bb0v56ktpLyUnMeEUD2a2S9IQwmjgFxmrJwFXSfpCrFcRLsh/AZZJGgm0E2SXU/zOzFoBJDUTLn6/yXLoh+LxN0l6o6Sa2L7GzP4dyx8BhqcNKt4oqS8heHw8bv+opJc5nY8A3zezV2O/bNryw4D3E5QyIcgnHJZ0IVBjZqnMYg8QAksmjwFLJb0BmAxsMrN/x+07Oj+dMRYYDvw22tSbEFCdEscDgVNo1gDfINyR9ktrFzDdzPald5a0CPgrQVGzAjiRtvo/aeV2Ov59Z+qqpOrH09oqgMvSAkPq+Nm2z0QJ++wxs8sy9l+TYFvM7ISkjQQJ5mv5/7TafDo+PynaeP20cFWaTb8ys+s6O75TWvgzAqfQrAS+amZPZ7SvA+al5vklNcT2C4HDFhKPzCLcSXeVa+M+xxGUG49l6fM4cEuqEu+wIUydfCq2NQG1HWx7o4L6J5LeFNtfAfrG8j5ggKTLYp9KSe8zs38Ax6JtpI7VAQ8TBMeuIJwvSHZ+DgIjJVVIGkTIAgYhc90HJb0r2lQtqSsjCqdI8UDgFBQzazWzb2VZtZgwj74rvva4OLZ/G7hB0lbCtMfxLNt2xsvxVc8VhCQ92fgcMFohOfgfgJti+1eAKyXtJExfPZ/Fp18SRjrb4xRVanrrPmBFbOsFXAPcJamF8Iwk9WD308Dy+LD4dSOSDB4nTFU9YSGNIyQ7P78F/kzI5vUNYGe0+yXC84OHJO0iBIb3nOH4Tong6qNOWRGnU75gZtsLbYvjnCv4iMBxHKfM8RGB4zhOmeMjAsdxnDLHA4HjOE6Z44HAcRynzPFA4DiOU+Z4IHAcxylz/gfEyGfax8umpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn.run(name='tasks_xgb_test',\n",
    "       params = {\"label_column\"  : \"labels\",\n",
    "                 \"plots_dest\"    : \"plots/xgb_test\"},\n",
    "       inputs = {\"test_set\"      : test_set,\n",
    "                 \"models_path\"   : models_path},\n",
    "       local=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function remotely**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-17 13:24:48,946 [info] starting run tasks_xgb_test uid=550a773aeb7e4754b4652772d205365a DB=http://mlrun-api:8080\n",
      "> 2021-10-17 13:24:49,084 [info] Job is running in the background, pod: tasks-xgb-test-gj7q4\n",
      "> 2021-10-17 13:24:59,214 [info] run executed, status=completed\n",
      "final state: completed\n"
     ]
    },
    {
     "data": {
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       "      <th>name</th>\n",
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       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>function-marketplace</td>\n",
       "      <td><div title=\"550a773aeb7e4754b4652772d205365a\"><a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/550a773aeb7e4754b4652772d205365a/overview\" target=\"_blank\" >...d205365a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 17 13:24:55</td>\n",
       "      <td>completed</td>\n",
       "      <td>tasks_xgb_test</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=dani</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=dani</div><div class=\"dictlist\">host=tasks-xgb-test-gj7q4</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_test/test_set.csv\">test_set</div><div title=\"https://s3.wasabisys.com/iguazio/models/function-marketplace-models/xgb_test/xgb_model.pkl\">models_path</div></td>\n",
       "      <td><div class=\"dictlist\">label_column=labels</div><div class=\"dictlist\">plots_dest=plots/xgb_test</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=0.9632</div><div class=\"dictlist\">test-error=0.0368</div><div class=\"dictlist\">rocauc=0.984364949478981</div><div class=\"dictlist\">brier_score=0.03287091841943238</div><div class=\"dictlist\">f1-score=0.9624796084828712</div><div class=\"dictlist\">precision_score=0.9744013212221305</div><div class=\"dictlist\">recall_score=0.9508460918614021</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result2cf17f7d\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/probability-calibration.html\">probability-calibration</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result2cf17f7d\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/confusion-matrix.html\">confusion-matrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result2cf17f7d\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/feature-importances.html\">feature-importances</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result2cf17f7d\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/precision-recall-binary.html\">precision-recall-binary</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result2cf17f7d\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/roc-binary.html\">roc-binary</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"result2cf17f7d-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"result2cf17f7d-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"result2cf17f7d\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"result2cf17f7d-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/550a773aeb7e4754b4652772d205365a/overview\" target=\"_blank\">click here</a> to open in UI</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-17 13:25:08,339 [info] run executed, status=completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.model.RunObject at 0x7f506ca06d50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.deploy(with_mlrun=False, # mlrun is included in our image (mlrun/ml-models) therefore no mlrun installation is needed.\n",
    "          skip_deployed=True) # because no new packages or upgrade is required, we can use the original image and not build another one.\n",
    "\n",
    "fn.run(name='tasks_xgb_test',\n",
    "       params = {\"label_column\"  : \"labels\",\n",
    "                 \"plots_dest\"    : \"plots/xgb_test\"},\n",
    "       inputs = {\"test_set\"      : test_set,\n",
    "                 \"models_path\"   : models_path},\n",
    "       local=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#XGBoost-test)"
   ]
  }
 ],
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