xplainproj/xplain_shap_values.ipynb

{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:47.886085Z",
     "start_time": "2025-02-08T20:58:47.884049Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning)\n",
    "warnings.filterwarnings(action='ignore', message='.*No further splits with positive gain.*')"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:47.890517Z",
     "start_time": "2025-02-08T20:58:47.888773Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from os import path\n",
    "\n",
    "PROJECT_ROOT = path.abspath(path.join(globals()['_dh'][0], '..'))\n",
    "DATALAKE_PATH = path.abspath(path.join(PROJECT_ROOT, '..', '..', 'datalake', 'xplainproj'))\n",
    "FQFN_PROCESSED_DF = path.join(DATALAKE_PATH, 'processed', f'processed_20250207.jsonl')\n",
    "MODEL_DIR = path.abspath(path.join(PROJECT_ROOT, 'xplainproj', 'classifier'))"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:48.950589Z",
     "start_time": "2025-02-08T20:58:47.932801Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from os import path\n",
    "\n",
    "from xplainproj.classifier.onnx_exporter import WEIGHTS_FILE_NAME\n",
    "from xplainproj.classifier.xplain_configuration import ModelConf\n",
    "from xplainproj.classifier.xplain_classifier import XplainClassifier\n",
    "\n",
    "model_conf = ModelConf()\n",
    "\n",
    "print(f'Model Class: {XplainClassifier.__name__}')\n",
    "fqfn_model_save = path.join(MODEL_DIR, WEIGHTS_FILE_NAME)\n",
    "trained_model = XplainClassifier(model_conf=model_conf)\n",
    "trained_model.load_model_weights(fqfn_model_save)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Class: XplainClassifier\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:48.975344Z",
     "start_time": "2025-02-08T20:58:48.954671Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json(f'file://{FQFN_PROCESSED_DF}', orient='records', lines=True)\n",
    "print(f'df shape={df.shape}')\n",
    "print(f'df columns={df.columns}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape=(704, 11)\n",
      "df columns=Index(['file_name', 'fqfn', 'text_body', 'label', 'longest_code_line_length',\n",
      "       'median_code_line_length', 'lines_of_code', 'code_size_in_bytes',\n",
      "       'ratio_of_comments_to_code', 'is_64base_content_present',\n",
      "       'file_name_embedding'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Shapley Permutation Explainer\n"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:48.983563Z",
     "start_time": "2025-02-08T20:58:48.981836Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import sys\n",
    "\n",
    "if (module_name := 'utils.shapley_permutation_explainer') in sys.modules:\n",
    "    del sys.modules[module_name]"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:54.758692Z",
     "start_time": "2025-02-08T20:58:49.000229Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from utils.shapley_permutation_explainer import compute_shap_values_for_high_dim\n",
    "from xplainproj.trainer.xplain_dataset_pt import XplainPtDataset\n",
    "\n",
    "# Compute SHAP values\n",
    "dataset = XplainPtDataset(df)\n",
    "feature_names: list[str] = dataset.feature_names\n",
    "\n",
    "shap_values = compute_shap_values_for_high_dim(trained_model, dataset, input_features=ModelConf.input_features)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:54.764040Z",
     "start_time": "2025-02-08T20:58:54.761719Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f'feature_names={feature_names}')\n",
    "print(f'type = {type(shap_values)}')\n",
    "print(f'shape = {shap_values.shape}')\n",
    "# print(f'data = {shap_values.data}')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_names=['longest_code_line_length', 'median_code_line_length', 'lines_of_code', 'code_size_in_bytes', 'ratio_of_comments_to_code', 'is_64base_content_present', 'file_name_embedding']\n",
      "type = <class 'shap._explanation.Explanation'>\n",
      "shape = (100, 7)\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "\n",
    "shap_values_class = shap_values.values\n",
    "\n",
    "# Plot SHAP values for the class {category}\n",
    "shap.summary_plot(shap_values_class, feature_names=feature_names, show=False)\n",
    "plt.title(f'SHAP Values for LightGBM')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:54.838570Z",
     "start_time": "2025-02-08T20:58:54.773161Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x430 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "\n",
    "# Extract SHAP values\n",
    "shap_values_class = shap_values.values\n",
    "\n",
    "# Create a new Explanation object\n",
    "shap_values_class_explanation = shap.Explanation(\n",
    "    values=shap_values_class,\n",
    "    data=shap_values.data,\n",
    "    feature_names=feature_names\n",
    ")\n",
    "\n",
    "# Plot SHAP values for the class {category}\n",
    "shap.plots.bar(shap_values_class_explanation, show=False)\n",
    "\n",
    "plt.title(f'SHAP Values for LightGBM')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:54.891063Z",
     "start_time": "2025-02-08T20:58:54.847569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "\n",
    "for instance_index in random.sample(range(shap_values.shape[0]), k=3):\n",
    "    # instance_index is an identificator for a row in shap_values result set\n",
    "\n",
    "    # Extract SHAP values for the specified class and instance\n",
    "    shap_values_instance_class = shap_values.values[instance_index, :]\n",
    "\n",
    "    # Create a new Explanation object for this specific instance and class\n",
    "    shap_values_instance_class_explanation = shap.Explanation(\n",
    "        values=shap_values_instance_class,\n",
    "        base_values=shap_values.base_values[instance_index, ],\n",
    "        data=shap_values.data[instance_index],\n",
    "        feature_names=feature_names\n",
    "    )\n",
    "\n",
    "    # Plot SHAP values for the specific instance and class\n",
    "    shap.plots.waterfall(shap_values_instance_class_explanation, show=False)\n",
    "\n",
    "    plt.title(f'SHAP Values for LightGBM - Instance {instance_index}')\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:55.136465Z",
     "start_time": "2025-02-08T20:58:54.903360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ],
      "image/png": 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+r25y6PwoFVoPXbMqWKapx9Qjr0yBtM6rCpWpGJjS50N7o9NLxc6IPh8Z9RBrHWUYKKjPTkFBtdlvtz67kydPDustV/CsG3DRZhRQUTXdMNHNLyAvMMYaAIA4pi/I0XqT1CMl6rXLCqVhKnjW+GallEc+lO69adOmqOO4s0Np30oTVXVfpf+GpoH7vWmRx+tX6c6IvuxH9mCqonhkj7V6xZU2q57oSPpyr4BNIqf4UjCsCteS0ZRC0ahnfvny5S691ad9aaol9cz7Pbd5RWP19R6oKrVPQX+0Gy2x8gP0yBscopslei+U8hstIM5Mr6nGVquXV4FVWhTwPf744y6jQ1OKZYbGfevGi/+INS07KyKvM93MUe+6zofeH/GHW0Se12ifGaV/K7MiqxTY+kNOIvn70WdIN0hCr53Qvy2qTZBZuomgz4Mq6vvUhsiHbj6IpglTrzyQV+ixBgAgjl122WWuR1GFhJTuqd4spUorONMcs0oHzwoFzAqcVRQtGhUMU0qmerX9L7I5QV/a1eurNFClix555JEpz6mAmX5XgTYFFOrV/PLLL9OdIzvU+eef74praR9K9VZwoODZn57Ip/RSv2iaP9WQAgFNF6TpkJRyqtdoexrzrYJMSj3WmGQFwUpR98dFZ4amJVJvt/apnjm9f9qfxsjq5kG0uZ0zQz3Nd955Z6rlKhqnOcojaT5qjevVlGB6ra4vnRcds2SlJ1I9uwoKdX0qpV4FxDRNnB4qmvXkk0+6a1rjo3VTxb+mFSjrWlNQ6c9R7VOw9eqrr6YE/np/lEmhf2c0DEI1AfTIzzTcQses4F2FBDV2WedJ75l/TegalZtvvtl69+7txpAri0Kv9bMqdLNKNywU7CoTJavZLPp86LpUCram29K+dU3o2tB5180M3ahQ/QF93nTzT23XTRNlX2i5Pnd+Vk009957r+sVVz0HDQFQvQV91nX9hk7zp2s0kt9DrdoHkZ9tIFflSe1xAACQK8aOHesNGjTIa968uZuSqVixYl7jxo29yy67zFuxYkXMUy6FTkUkJ510kpvmasuWLWnue8CAAV7RokVTpr7R6y+99NJsH1OvXr3ctjR1WKTFixd7p512mlehQgWvfPnybt2lS5emmsIp2nRbmirqf//7n1elShWvVKlSbsqh2bNnp5puSzZt2uSm/tK51DnVaw499FDvwQcfTJnK7J133vG6devmVatWza1Tr14976KLLvKWLVuW4TGm9V7oPRs4cKDbn7bZunXrsGmtQqfb0hRLsUpvqjV/WqPI6bZEUxb17dvXK1u2rDvfes81VZJe98Ybb6Ssp9dqirJI/tRHoSZOnOim4NLxRZt6a+rUqV6/fv3c+dQ62m6bNm3clE96v0JFm25L05Bpmrevvvoq3Ws8LXk53Va0/US+DyNGjPCOPPJIr3Llym7qq/32289ND+ZPQRc6TZymGdM0V6HX/kcffeTOnz7PDRo08O677z7vxRdfTPX5SOuaVDv1iJwCbciQIW5/eo80BZ/aHToNlj4n2peOUe2uWLGie9+HDRuWqu2RNB1ax44d3XWnz2qnTp28t956y4sF020hKEn6T+6G7gAAACgo1HuoDInx48fnago0AMQTAmsAAABEpfGwoYW5lM6r9OJJkya58eD5rTI5AASFMdYAAACISuOdFVwfcsghriCbCtppDP/dd99NUA0AIeixBgAAQFSaxuihhx5yxcu2b9/uphC7+OKLXVV4AMA+BNYAAAAAAGQD81gDAAAAAJANBNYAAAAAAGQDgTUAAAAAANlAYA0AAAAAQDYQWAMAAAAAkA0E1gAAAACQD+3du9fuvPNO22+//axo0aLu5/3332/Nmzd3z2XWs88+a/Xq1XPz0iNnMd0WAAAAAORDTz75pF1++eV2zTXXWJs2bax8+fI2cOBAe/DBB93PzNJ89A0aNLCbbrrJbTcv7Nq1y37//XdbuXKlrVq1ygX1nTt3tmbNmllBQo81AAAAAORDo0aNsmOPPdYeeOABO/fcc23u3Lm2e/du69OnT5a2V6JECevfv789/PDDllf9q9u3b7cpU6bY+vXrrVKlSlZQEVgDAAAAQD6jgFQ9vUceeWRYoH3yySe7ADmrzjzzTFuwYIF9++23lhdKlSpl55xzjvXt29c6depkBRWBNQAAAADkI+edd56VLFnS9uzZY7fccoslJSVZzZo1bfr06XbMMcekWn/JkiUu2B40aFDY8q+++sqNzb7qqqtSlrVv3971HH/44Yd5ciyFCxd2wXVBR2ANAAAAAPnI2WefbRdddJH792OPPWavvPKKDR482P3erl27VOvXrl3bzj//fHv11Vddb7T8888/1qtXLzv++OPtoYceCltf25gwYUKG7VCBNPWcx/LwErx0V5GgGwAAAAAA2Kdr16729ddfW+nSpW3IkCFWqFAhu/XWW91zDRs2jPqaG2+80Z5//nm77777bPjw4XbiiSe6QmWvv/66e32oRo0auWA9I8uXL7dPPvkkpjb36dPHypYta4mKwBoAAAAA8hmlfbds2TIlKF6zZo0VKVLEypQpE3V99VpfcMEFNnLkSFcsbNu2bfb999+74DxSxYoV3fNbt25NN027cuXKdsIJJ8TU3pIlS1oiI7AGAAAAgHxGhcu6d++eqddce+21boouBeU//vijC7aj8dO2NXY7PcWLF7c6depkqg2JisAaAAAAAPIRTU21aNEia926dVjvsaba2rRpU5op13fddZf7qfXSm9pq3bp1rqc6o15mFU/TvNOxKFGiRKqU80RCYA0AAAAA+Yh6nKVNmzYpy5o3b+5+zps3L2y5T3Nda4y1eqyvu+46F2Tr92i0jRYtWmTYjhUrVjDGOkYE1gAAAACQz9LAJTSAPuSQQ9zPSZMmpQqsP/jgA7vhhhtc0bJLL73UZs2aZU8//bTdfPPNUYudaQy2Ko9nhDHWsUvyEr0uOgAAAADkIypCNnbsWFu8eHHYcqWG6zFmzJiUZZMnT7YjjzzSzjjjDBs9erRbtnTpUlf5+5xzzknVa631DzroIDfH9dFHH50nxzNjxgzbuXOnK5b2119/uWrlVapUcc+1atXKihUrZvEucZPgAQAAACCfpoJHS/ceNGiQffzxx66ityjwPumkk+zAAw901cB9tWrVcusq0Fbad6i3337b6tWr56b0ysvjmTRpkguqZf78+e53PWIdw53f0WMNAAAAAHFgw4YNrif6/vvvt/POOy/Tr1cQq95ipY1fccUVudLGREWPNQAAAADEgfLly9v111/vCpXt3bs3068fNWqUFS1a1AYPHpwr7Utk9FgDAAAAAJAN9FgDAAAAAJANBNYAAAAAAGQDgTUAAAAAANlAYA0AAAAAQDYQWAMAAAAAkA0E1gAAAAAAZAOBNQAAAADEqY0bN1qXLl3cTwSHwBoAAAAA4pQC6u+//57AOmAE1gAAAAAAZAOBNQAAAAAA2UBgDQAAAABANhBYAwAAAECcKleunB1yyCHuJ4KT5HmeF+D+AQAAAADZMG3aNGvbtm3QzUho9FgDAAAAQBwrW7Zs0E1IePRYAwAAAEAc2759u5UoUSLoZiQ0eqwBAAAAII79888/QTch4RFYAwAAAACQDQTWAAAAABDH6tSpE3QTEh6BNQAAAADEsd27dwfdhIRHYA0AAAAAcWz58uVBNyHhEVgDAAAAAJANTLcFAAAAAHGeCl6kSJGgm5HQ6LEGAAAAgDg2Z86coJuQ8AisAQAAACCObdu2LegmJDwCawAAAACIY6VLlw66CQmPMdYAAAAAEMd27NhhxYsXD7oZCY0eawAAAACIY3///XfQTUh4lI4DACCv7d1r9uRYs7Wbgm4JACAe1Klsdv6xQbcC6SCwBgAgr302xeyKF8yKFDZLCroxAIB8TSN3NXj3pA5m1StEXaVWrVp53iyEI7AGACCv7d4T/hMAgPQUSjIb/Z3ZdacG3RKkgTHWAAAAAJCf7fXMRnyR3HsdxdKlS/O8SQhHYA0AAAAA+d2cFWYT/gm6FUgDgTUAAAAA5HdFCpk9/1XUp1q0aJHnzUE4AmsAAAAAyO927zV7c7zZxq2pnlq4cGEgTcI+BNYAAAAAEA927DJ7Y3yqxVu2bAmkOdiHwBoAAAAA4kKS2bNfpFpasmTJQFqDfQisAQAAACAeqCr41Hlm0+eHLd5vv/0CaxKSEVgDAAAAQBwXMZsxY0ZgzUEyAmsAAAAAiKciZi9/lzzeGvkGgTUAAAAAxBNVBv/gl5Rfa9SoEWhzQGANAAAAAPGlcCGzEV+m/FqkSJFAmwMCawAAAAShfCmzEYPNVo4y2zzG7JthZgc2yvh1SUlm/Y8y+/BGs4XPJb/2j0fNbu5pVrxo6vUHdzd761qzBSPMvPfMRg3JlcMB8tSevWbfzjCbt8L9unjx4qBblPAIrIGA3H777ZaUlGRvvPFGyrKXXnrJLbv77rutIJs3b54dffTRVq5cOXe8hx56qG3atMnym7PPPtu1b9KkSUE3BQAKFgXHn95i1vcIsyfHml0/2qxaebPv7jBrXDP915YqbvbSZWZVyyVPO3Tli2a/zjIbdpbZ2FtSr/+/08y6tjb7c5HZrt25dkhAIL3Wo74JuhX4DzkDSNPUqVNt9erV7qGgp0yZMta3b9+o6+oumYIlrbt27Vrbs2ePnXjiiVarVq2Y9zd9+nRbsGCBrV+/3nbs2GHFixe3ChUqWKtWraxhw4ap1t+1a5ergDh79mzbvHmzFSpUyK3fvHlza9q0qQuIQi1cuNDtY926de61pUuXtvr161ubNm2sVKlSWThDyCpdR3ov9FPvld6HEiVKBN0sAEBO+fYOs/krzQY+Gf35noeYHdbcrOcDZu/+lLzsrYlmM59MDpDPfjTtbe/cbXbojWY//btvmSoka3939DE7uo3Z19P3Pdf5VrOFq5L/vem1HDk8IN/0Wo8cZ3bbme77L4JFYI00/fbbby64rVKliu3cuTPddRXc6lGxYkUX3K5ZsybT+1u5cqWVLVvW6tat64IsBddz5861cePG2UEHHWTt2rVLWdfzPBs7dqytWLHCmjRp4oLv3bt3uzZ8//33Ljg/+OCDU9b/+++/7ccff3THcsABB1jRokVt1apV9scff7gbAj179nTLgnbuuedar169rFixYlZQ6X395ZdfrEuXLvbEE08QUANAIlJgvXyd2Xs/71u2emNycH3OkWbFiiQH0NGo1zk0qPa9/0tyYN2iTnhg7QfVQEG0fL3Zl7/bkqblmcs6YATWSFPv3r1dqq68/fbbrpc3LR06dLAjjjjCChcubL///nuWAutjjjkm1bLWrVvbe++957bZtm1b1yvtB+HLly93AbXSiH3777+/vfXWWy6QDg2s1TuqXumTTz45pbhDixYtrGTJkq5nfsmSJdagQQMLms6fetILMt0M0Y0RHSdBNQAkqAMbmk2Zqzvl4cuV0n1RN7OmtcxmLMzcNmtU3BegA4k0p/XIcbZpaPegW5LwGGONNPlBdSwUJCkozGkKpLVtBfV79+5NWe73oEcGoWqDgrXIyohaX73vkcv9FPD8Ukkx2hhrf9mdd95pw4cPt5o1a7redU2rcN1119nWrVtTbUe9+bqxoPOjdZUFcOWVV9rGjeFfNn744Qd3Y0JZBlpPGQfKDHj++ecz3XYNA1CPu7ICdD4rVapkJ5xwgrtxETpmWWnf8sknn7jjysqY8i+//NIOO+wwl+GgdlerVs3dmPn1119T1lEGw2233eb2p3U0lKFjx47uJlHotSTbt2+3Sy65xLVZ2QK64/vwww+nWs83c+ZMd5NG50vHqmM+/fTTXcYEACAGNSuaLVuXerm/rFalzG/z+lPNNmwxGzsl++0D4mlO649+s1KbmdM6aPkjmgAighz1aOqnUsEXLVrkxmqHBr8KpBQAqSdbwZV+VyClgEcB3uGHHx62TQWWeu6nn35yY1D8VPApU6a4QDWWseAK7jV2PBYK8HM6tfzll192adSnnnqqC4THjBljDz74oDt2Bdi+p59+2oYMGWKNGjWyAQMGWOXKlV0a/GOPPebOwZtvvunO2b///mvdu3d3AafSzxWA6twpOFWxrvPPPz/mtm3YsMGl62scu1K8O3XqZP/884998MEHLrD++OOP3fODBw92mQEKpJWBoEBbVMgsVk8++aRdfvnl7hycdNJJboz20qVL7ZtvvrFp06a54FmU3v/hhx9ay5Yt7YorrnA3FXTOFPyrDsBFF12Usk0FxboZoTYde+yxLhvi5ptvduc20uTJk61z587uRo32r/OsY3333Xfd9fj555+74QkAkDCKFE6u8B2qaJHkCt2Vy4YvX7s5uZe6ZDGzHVFSvbf/N/RMz2fGjWeYHXuA2cUjzDakvuEMFGieZ41/WmJ2eIegW5LQCKyR7yjwUwAp6s1U4bLIQFlBjYJC9bh+9dVXKcsVzCowikzrVq+sAm8VO9O4ap+CsiOPPDIlxTw9EyZMcIFpLLRdBZg5Sefkiy++cCnsouBZ50Y9sBdccIELNJctW2ZXXXWVS83/7LPPXFDtu/jii23EiBEuyFXRMG1LNy/uuece15udHQqUVXhu4MCB9uyzz6aMEVcwr20PGzbM3nnnHTdcQDc5tH6dOnXs2muvzdR+VCTv6quvdr3177//fli6v/g3PjQuX0G1gnkFzOpRFvVKt2/f3t2QUHG92rVru95vrXPIIYe4c6bzKLqBccYZZ6RqwznnnONuRqjHXdv3KbBWMP/QQw/ZM888k6p4HgAUWCpC9t3w6Mv7HBG+rMFFZgtWmW3baVY8ytfQEv8F1Ho+VmceZnZnn+QCZqoSDiSavZ7tfPpTK3HdqUG3JKERWCPf6datmwuQtmzZ4nqs9W/1Fms8dCg/dVk9rdWrV3eB559//mlff/21C7oVuPkUOCsYUsCt9dX7rSBNvbZ6TsF1RlT0rHHjxjEdQ26Mk9YNAz+oFgWFCqw1Zlk9xgoIFWQr7f2UU06xbdu2hc1pqN5VBb26EaHAunz58m75d999Z2eeeWamKrhHUlCpXnD1JIcWXrv00ktdUK3MAI1jV+9udmj8vK4FtTcyqBZ/OILOg5x33nkpQbWoR/q4445zAbTapHOogF90U8APqv1ebLVX16BPN2XUO62gW9dc6PlVT7muUW3Xfz9iod5zXS+6WSSqcK+MDZ1P0fupqvyhN0l0A0WZFmn9rh53tc8P7tlH/ttH2V27LPhyiUAO+X2+2TG3hy97aEBycbIHPkxdaMlP+VY6eCR/2dK1se37mAPMRl9u9ulks8HPZqn5QNwrXMi2N6pqheLo/4Ol43AfGSGwRr4TegE3a9bMBcrqfVQw5X849GHRMvUyqmCZT4Gvgir1ZKv4moJmfYgUSOmnxsX6HzgFTdqe0nf179BAPBoF8XoEJVpxNf0xUC+638OvHnlRGrMe0fiF5XR+Ro4c6c6jzo/Sl5UZoFRpjV/OTI+rAkz1RFetWjVsuW5g6D1Rinbk+O6sUFArBx54YLrrzZkzx7VfN0MiaZmOV+uIxkVrXRXCi6RhA6GBtYriiVLc9YhGx6mbGrEG1hrXHUo3gELpRkXoH32J/CMf+bt69NlHPt9HPpiFAMgx67eEV+GWdZuTg+fI5b5p882OaJE8n3VoAbODm5ht2W42c2nG++3YxOz9680mzTE786HkqYeARLRnr+0Z1DW+/j8Yh/vICIE18j2lVSsI0rRY/hx9qvKtnuzIHlAFcvXq1XM917ozpQJsuoOlh8b9RgaLer0Ca92Ryiiw1p0tpZPHQu3I6Smz0isOp5sGoT+VLh2aphxK50d0U2H8+PFu/LV6nPXvF1980UaNGmXXXHONK5aWGwXp4pl/ftXr3a9fv6jrKBMg8o85ACDCOz+Z9TrU7PRO++ax1nhsLft4UvhUW42qJ/+cu2Lfsua1zT692Wz+KrMT79o3NhtIRDUrWlK3tkG3IuERWCPf84NZv1dW/ErYfqATyq/k7P9USnno7+kFpOmZOHFioGOsY+HfeFC18z59+sT0Go171kNUfKxNmzauUJrGbceauq1gXTcvVBBO6dWh751uiqgnOzNV5tPip8KrB7x///5prqdechUz000TZTWE0k0Z8ed61Lrffvut6+2PXNfvIff5Rcl0veSXuc8BIG4Da81FPWqI2f51zFZvMrvkOJfSare9Eb7u18OSfzYcnPyzTAmzL4aaVSydnGreo334+nOWm/0c8v/rEw8yO6DBvqJqbRqY3dwz+fePfjP7Y0HuHSeQ2/SZueBYW7hksVWqum/4G/Ie020hz6nnd/369a5wlk/jZqPNk61g+K+//nL/Dq3Q7KfZaox0KAXfKqKl3lg/kPPTt5XyGxlc+6+PTGGORinEmj4qlke0FOS8cNZZZ7me8ldffTXlvIXS2F+de38u8NCbFaJ0bqXF6GZE6PuTkdNOO82NU3niiSdSpkITFfFS2r6m8MooIyAWql6uYFYF7lS5PJL//iroFfXAh86prjHSKlSmYFptEr9AmXrq/XMjmj89NA3cT0HXTZPvv//eFU+LpBsJoeOuAQBp0N/rE+40e3OC2eU9zB7olzz/dNfbMk4DV892vapK5TK771yzV68Mf2ge7FBndDK7s2/yQ5XK2zXa97v+DcT7Z2lg16BbAXqskR71ziqdWhRkKfVahZn8cQoKMHwKXhTQioppyaxZs1wvpmj8qp8arZRuBSYKbPx0ZQV8qlatHlKl0mouagV3Cob1nPYVOs6hdevWbvuaGkqBm8ZVKEhUD6N6szVG2K/0rUBRRb60XwVL6nVUqram8VIPrQJ2f27l/DzGOhYKXjUdlaa1UkE2pSwriFTAqPHBKlSm3miNr9ac2EoBV2+1em91TlTYTMGkbg7Eck58N910kwt2FZzqOlAhL920UPCpQhKaTzonUuN1fKq6remzVKDu+OOPd++nbhKoEriqjF944YWu0JsKuGn8uDIHdB50HWm6LQXmWs/vWdd2VDBP1cGPOuoo929dtyqUpp54XSM+DSXQcercKhV89OjR7iaKPhvqmVc9ANUCUJE4AEhoRw2NbWz2BU8nP9Lj91T7VFU86fTY2zLwyeQHUBB7qzu3NGtQzZpuDR9DjLxHYI00KTDS2ONQfi+hgtzQwFrzH0f2IIb2Jiv4SS+wUqCudRTQzJ8/3/V6an1VdFYAHlmNWxX91EuqOYU1h7GCGgWGCqI1llqBdKiuXbu63koF6mqnXxVQVaLVCxnLdFvxQincem9uv/12d7NCNyh0fhVIam5qTT0mqmytGxFKg1YArIBT76uCThU+y0xlc90M+e2339xUX5rHWQG8MgYU0Gqstt87nBMuu+wy9/7ecccd7kaJMh10w0PvZeh+VO1bNw8U7D/66KPuelKhu+uvvz7VNFoqRKa2K2h+5JFHXM/9XXfd5Y4pNLAW7UfX0v/+9z937nS8uhGkrAcF5f7c3AAAALlKBfv+y9BQx1bk91/krSQvlsGlAAAg53zwi9lp9wXdCgBAPCtfymzFKDfEQfVndPMfwSk43XQAAAAAkAiKFDIb0DW5boDq8lFQNXCkggOISun4SrOPVk09lNLHS5Ysma19aciB5n5Oj9LZQwvYAQAAJKzde83OPzrl15YtWwbaHBBYA0hnejEV88rI66+/7oqhZYeK2CmIT8+JJ57oxowDAAAktKSk5Ir2rfYVmiUVPHgE1gCiUrXrt99+O+o0aKE6d+6c7X2purYqe6dHFboBAADgpZ5WDoGjeBkAAHmN4mUAgKwqUTS5aFm5UimLFi9e7KYlRXAoXgYAAAAA8VK0rPfhYUG1X4sGwSKwBgAAAIC4KVp2TKrF8+fPD6Q52IfAGgAAAADiQeMaZoc2D7oViILAGgAAAADyu0JJyUXLVBU8QuPGjQNpEvYhsAYAAACA/E4Bdb8uUZ9as2ZNnjcH4QisAQAAACA/K1zI7KSDzKpViPr0unXr8rxJCEdgDQAAAAD52Z69ZhemPXd14cKF87Q5SI3AGgCAvNakpu0tXiToVgAA4kXNimbdDkjz6datW+dpc5Bakud5XpTlAAAgN23ZbrZrT9CtAADEy/zVZUqm+fT06dOtTZs2edokhON2OQAAAZgxb7a1atUq6GYAAAqAvXv3Bt2EhEcqOAAAAdi9e3fQTQAAFBCVKlUKugkJj8AaAIAAVKgQvbIrAACZxf9TgkdgDQBAAKpUqRJ0EwAABcTcuXODbkLCI7AGACAAs2fPDroJAAAghxBYAwAAAEAca9iwYdBNSHgE1gAABKBBgwZBNwEAUEBs3Lgx6CYkPKbbAoD8xPPMduwKuhXICUlJZsWLpvn0li1bKDYDAMgRa9assbp16wbdjIRGYA0A+cld75jd+nrQrUBOKFLIbPHzZtWjB8+rVq2y2rVr53mzAAAFT5Ju5iJQpIIDQH7yy8ygW4Ccstcze+W7oFsBAEgABxxwQNBNSHgE1gAA5FZg/ewXyen9UfAlCACQU2bMmBF0ExIegTUAALllzgqzif9Efervv//O8+YAAAqm3bt3B92EhEdgDQBAbo6zfv6rqE/t3Lkzz5sDACiYKIYZPAJrAAByy+69Zm+MN9u4NdVT5cqVC6RJAICCp0qVKkE3IeERWAMAkJs0fdqbE1ItrlGjRiDNAQAUPLNnzw66CQmPwBoAgNykKVBUxCzCzJlUgAcAoKAgsAYAILerg0+Za/bHgqBbAgAooBo0aBB0ExIegTUAALmtcOoiZvXq1QusOQCAgmXLli1BNyHhEVgDAJDb9uw1e/nb5PHW/9mxY0egTQIAFByrVq0KugkJj8AaAIC8sGGr2Qe/pPy6YsWKQJsDAAByDoE1AAB5lQ7+3LigWwEAKIAOOOCAoJuQ8AisAQDIq3Twb/4wm7/S/dq6deugWwQAKCD+/vvvoJuQ8AisAQC5q3wpsxGDzVaOMts8xuybYWYHNor99c1rm4291WzTa2ZrXjYbfblZlXLR121U3ey1K81WjDLb+rrZzCfN7uxr+arXetQ37p+zZs0KujUAgAJi586dQTch4RFYF1BLly61KlWqWMeOHS2/2L17t915551Wv359K1asmCUlJdl3330XdLOANP3777/uOj3zzDODbkp8z+H86S1mfY8we3Ks2fWjzaqVN/vuDrPGNTN+fe3KZj/cada4htlNr5k9+JFZj/Zm424zK1okfN0DGphNfjD550MfmV32gtnr481qVbJ81Ws9cpzZnj22ffv2oFsDACggypVL44Yz8kzEt5LcN3PmTPvjjz9s/fr1LrjSdCMK/kqWLJnj2/A8z6VF6KF1CxcubNWqVbP27dtb9erVLd6tXr3abrrpJjvqqKOsT58+lt99+OGHdtttt1mbNm3swgsvtFKlSlmLFi2CblaBllfXyIsvvmhTpkxxN04qVKiQa/tBPvTtHcmpzQOfjP58z0PMDmtu1vMBs3d/Sl721sTknuRhZ5md/Wj627/pDLPSJczaX2e2aHXysl9nmX11u9mAo5KDVD+Af+UKs3+WmB011Gx7Pr5zv2yd2bjfrWyT8kG3BABQQNSoUSPoJiS8PO2xnj59uuuhVDB86KGHuqBqzpw59sknn9iuXbtyfBvjx493D6178MEHW9u2bW3Dhg328ccfux7deLdmzRobOXKkvf/++6meq1mzpi1YsMB++OEHyy++/vpr27t3r73wwgt2880321VXXVUgbnDE6zWSkz799FN76qmn3A0sIFVgvXyd2Xs/71u2emNycH1KR7NiGdzfPaOT2SeT9gXV8vV0s3+XmJ156L5l3dqata5vNuzN5KC6ZDGzQvk0KatIIbORX1nt2rWDbgkAoIBQxyOClWffOpTyNmnSJKtatar16NHDBcQHHXSQHX300bZu3TqbMWNGjm5DPXXqqa5bt66deOKJ1qpVKxdYn3766Va8eHH78ccfXY92fqGAX6nSOUXpq6VLl7YSJUpYfptfr1KlfJSWCSB3HdjQbMpcpRCFL1evs3qim9ZK+7VK4a5ewWzSnNTP6fWh47SPaZP8U/NE/3a/2dY3ksdYv361WcUylq/s3mv20a82a+LkoFsCAADiLbCeP3++CxxbtmxphUJ6ETTetmzZsjEVccnMNvwe6SZNmrgg06egWusrkF2+fLkFQT3tapN634855hgrX768VaxY0RYvXmxbtmyxa6+91pXM17IiRYq4n7qRMHfu3JRtfPTRR9a8eXP377ffftttz3+kN8ZavfoPPPCANWvWzJ0LBd666fDGG29k6Vj8Hmildyu1W9kBOuePP/54qnGq77zzjvu9YcOG7vdatdL5Qp1GYH7RRRe5909t1/6aNm1q9957b9h6ylLo0qWLO69FixZ1qTGXXnqpbd26NWy9M844w7Vj4sSJdsopp7gUZm1XN2t0U0bXmrbdoEEDd1zqXX/kkUfSHIP77LPPumPXNipXrmy33HKLW+evv/5y77PGvui5I444ImrGhDIvtB3tR+3W+scdd5zNnj07bL0rr7zS7VO9xDofGt6g9dX79cwzz8R8jej4Hn30Udt///3d50fHqGtG5y4zdz11Pb/33nth760eN954Y6bfk6CuBf986eabtlemTBn3mVOPf1oFQpT2rvOrz5Beoxt9o0ePztLxFGg1KyanPkfyl6U3/lmvDV038vWVy+7r8W7y33jtt65NTgc/436z+95P7vH++CbLd/Z6VunT6UG3AgBQQGhoLBJkjPXKlcnTi0RL/dUyBQ8K+vTlNye2oYBPFJhG8pdpe0qZTo+Cj1h7khXsKziJlXrSFVxedtlltm3bNvdlfu3ate7Lub7gqydeAc+0adPs888/d0GyAjkFbXpe6dR33XWX+/fZZ5/ttpneWHWdkyFDhthzzz3nevIVYCgLQGnCGn+r83H55ZdbZmj/t99+uwv+Bw4c6I5fAcoVV1xh8+bNc4GozrGCeQXvkydPdmN+tX5m0sCXLFnigtN//vnHnQcFoDrfujmh9t9www1uvXHjxrmAWefzrLPOcn9kvv32W3v66aft119/tZ9++inVNXHOOee4oFrnQzdvdAOga9eubrluGujcqPdf7b/66qtd4K3gOJS2PXbs2JTAWG3SudG4/pdfftkFnDonqg2goQg9e/Z0Ab1P2RbdunVzmRennXaaCxKVyv/uu++6/f3+++8uiIwMsPWennvuue6n2nfJJZe4GyVqX0bXiFK3dTxq28UXX+yuv0WLFrl26bOkNsRC7VCgGfreisZ1Z/U9yetrQe+d3uc9e/ZY37593c0KLdM1EEnHqnV1M6FTp07u/dLfCN3o6N+/vwv6r7nmGiuQihROrvAdSgXEihdNDnJDrd2c3EutlOwdUf6G+mOg9Xxa/OfUC53q9bv2rbNzt1mZ/7Jzfpttdu5jyf9W+vnWHWb3nmt2dJvkFPL8Yq9nNT743exBL3l8OAAA2bBjx46gmwAvj4wdO9YbMWKEt2vXrlTP/fTTT+65devW5dg25s+f736fMGFC2Hp79+713nnnHffc+PHjM2z3b7/95taN5fHRRx/FcCY875BDDlFOpHfaaaelek7HtmnTplTLH3jgAfea4cOHpyz7559/3LJevXqlWn/JkiVe5cqVvQ4dOoSdo+LFi3u1a9f2NmzYkLJ8xowZXpUqVbySJUtm+B6E0jmuVKmSV7p0aW/x4sUpy5cuXertt99+XqFChbyZM2emLD/99NNde+fNm+dlht6zs88+2732yiuvTPX87t273c/t27d7Bx98sFvvgw8+SHl+x44dXo8ePdzyZ555JlV7unbt6vbhGzRokFteqlQpb8GCBSnLv//+e69IkSLeySefnOo9SEpKCrvW/v33X69cuXJu+fnnnx/W1u7du7vX6LX+e96tWzevWLFi3pQpU1Jd8yVKlAh7j6+44gr3+mbNmrlj833xxRdpti/aNeK3Y8WKFV52pfXeZvY9CeJaCF133LhxKetu3LjRO/DAA1OdvxdffDHq/v31dd3o37Fas2aNa4Nv13HDPM9Oy5+PzrfEfFxe/QuTX7Npq+c9Py71to7/729Zt3SOt/21yeuc82jq5+57L/m5Yr2Sf//4t+Tfz30sfL26FyQvv3VM8Ocv4rG3aE/P27zN/c0PvWZ0na5evTrsdOrvanq/L1u2LOzvWOR1xT7YB/tgH+yjYO9j6tSpBeI48vM+MlIks3dC1OMWK/Wc+WN8/V5f9eBF8nuNMuoZzsw21COrnjOl4SpNVL1yek7tV69gLPsT9drFWmVP6aCZMXTo0KjHoZ5DUe+7erD1Uz2Q6mmcMGGCZacqt95D9VKGluRXav1JJ51ko0aNcr18vXr1iml7X3zxhWvf4MGDw4rwqIdaPX3Dhg1zvb+hKcFZoXTc77//3vXe33PPPame96+HhQsXut59peQqtdunXnT1Yn722Wf21ltvufaG0nOhwwV0rlXlunv37mFpNUqZ1rUQmZot6hFWSrRPvcu65tTT/L///S+srepl1bnT0AWl5GsIgN7XDh06uPoB+t2n1HJtRz2t0XqKQzMkDjzwQNfTGq190fjXmTIktK3M9BrHKqvvSV5eC/66+hyoJ9ynfZx//vkuk8Gnugxvvvmmu16UoRH6XvnXztSpU11vuDIQYhFZc6CIeoXzq9/nmx1ze/iyhwYkFyd74MPw5cvX70vZ9lO6Q/nLlq5Ne39+Cnhar1+zKbm3OnQ7KyIK6K3ckPwzv42zLlLI1h3d3CqVLmGRLdN1qsykUJHZVZG/R/5/KvK68j/v7IN9sA/2wT7YB/uwLO0jI5n6Jq0USE2pEysFBX5g7X9pV6pl5Bd4P8DN6It9Zrah1NDjjz/eBSRK+9TDP8kKbH7++eeY0rYVgObWvHDRUm2V0qtUbY1R9lPbQ/k3BbJCAYRo/HYkf9qrzIyt9cd8t2vXLtVzGnOd2e2lRePhV6xY4QKf9Iqx6fh04yDaFF4aJ61xtkr1jtSoUaOoH8TGjRuHLffH8karfK3gN9q60Z7zt++P39V51Nh6Bde6IRRNaE2B0M9XKJ0b/dGItTK30sR/++03u+6669xNHr1nupmgQFIBek7I6nuSl9eCUu61rm5yRLthEkpDJ5SKrgA72ufIpzYWSOu3pE6nXrc5OQBOK8162nyzI1okpzuHFjA7uInZlu1mM9OZoUHBsgLjg/ZL/VzHJmbT5u37ffJ/NShqR4zZ9sdwr9po+cruvbb29HZGKUcAQE5o3bp10E1IeJkKrNWDo/mHs8IPMhRA6EttKL+QkMax5uQ2FGSoJ3bz5s22adMmF+woqPnzzz/d87HMt6vANtapwBT8ZKYKt388odRTqsBGPcAKeBT0qad648aNrqiZP3YcOSdaBkR6y6NVk09r3Vi24//UlHAahx1NtOsqM+2LRj3cyujQuGT1oCvIHj58uD300ENuTP/hhx8e03YSic6tHuqxVk9/Wu+BCqbhP+/8ZNbrULPTO+2bx1rjsbXs40n7epyl0X91F+aG3JjQa/ofZVanstni/4rJdW1t1qy22SMf71vvw1/NHhtkNrCr2Uvf7gviz/8vC2Hc75av1Kti9fudEHQrAAAFhDIh/aK1KODFy9QDpmJD6smJDIq1TEFueoXLsrMNBdih3f8q0KQvxnXq1Mmw3UrljbWXXukCCuSzQ9WbFTwr3TW0p1PTjEVWMg5NX46FX/xKx6TCaKFUBVtiLVgl++2X3IsU7fz4QwYys7206L1WQTBVzVbPYlop9zo+BaD+sYRSr6R6OxVM5jfqQdVNFr2/KoqVkzK6RnQjSmn7eihgfO2111wBrltvvTVq+nlm95PT70luXAtK99d2VBgwklLEQ+kmlz7nujnXvn37qD3iiBJY//Sv2aghZvvXMVu9yeyS48wKFzK7LWI2gq+HJf9sGDI04O53k4Pwb+8we+zT5CJl151iNn2+2ahv9q2nFPC73jUb3sfs81vNPvjV7IAGZhccYzbmB7NJsQ2RyBOFkswu6mbzFizIkb+RAAAoqw4JMt2Wvuiqd0dfSEN7XfUlV73JkWm36mVWSmvoupndRjRK/1SaqNJo1QOfEX3pOeGEE2J6qEJwdvkpv6G9juoxV2XnyJ5Ipb0rINA451horKnWV6VunS+fei1V0VhBQ6zjQkXrKgPg1VdftWXLloXd5NAyBVuqfp1dGv+gHkC1WenLkfxrQWnUSs9VMKXK26FDGO677z53/lQdOr9RYKfx2boZMWbMmFTPa5hD5FjeWKV1jeicKaU5lN4vpfVriERmhxz42ReR+8np9yQ3rgX9XdG6+rvy1VdfpayrfTz//PNh2/enVhNlF2hYSqTMpLYnBL0nJ9xp9uYEs8t7mD3Qz2z1RrOut6WfBu5TL3XnW83mLDe79xyz6081+2yK2bHDwnu75c63zYaMNKtf1ezRgWbHH5gcbPd/wvIV/Snvf1SWp5sDACBSLHENCkiPtYI2FWfS2GYFcQqCldKtKXLU0xw5LkC9ZQrW1IPnXyiZ3YZ6ffUFWnPzKiDXvNUat6wCUaGFpoIaYx2NeryVlqvgwe+9/Prrr10ve+Sge/XgK1X8xx9/dNP7qAdeAY7m941G5069kRrDrcJyCnr96bY01dZjjz2WKhMgPQpIVPRK021pfK4CFX+6LfUo6rnIccBZoWBGhao0Tl5pyjpenR+9p3rvV69e7a4JBZBKZdYUS3ooAFLQ+t1337liUpq26rzzzrP8RlkWmk9aNQHUc6wicmqrjltB2g8//OCuVxXbyqy0rpEBAwa4a02fHxXc0nlSL67eO10Tej4zlMb+yiuvuGm7Tj/9dPdedO7c2fUK5+R7khvXgtbVGHMV7dMUeJpuS38jNN2WbvBF0mdIAbjeD/0N0k01nWfNTa7sDfVyxzp8pEA4KnURxqhjsy94OvmRntCe6lB/LTI7bnhs7XlqbPIjv1JPffe2ZrUrW+mtsd0UBQAgI6GFhFHAA2tR8KUvseqZ01y5Cij0pV9fyjNKA8/KNvTlWKnjmk9ZPVkKkPWFWgF4blRAzgkaw66esmeffdYFWwpUFVRp7l19gQ+lQFvraCy2nvdTQNIKrNUb/uSTT7oUbvXE6d+i4EBBSlbSkNVrqNRYFVvTnM/qXVXArSA9s3Nip0cBoW6UaH+68fDwww+7c6M/Iqrc7Dv22GPtk08+cYGSgkT1COnGiuZ31lza+fV9VzEuXc9KwVbQpmNVsKi264aI5h/PirSuEZ0z3QhR1XbNx6xrTjeuNPxA827369cvU/sZNGiQq4atc665rPV5U/VtBdY5/Z7kxrXQo0cPly2gdZUOr78lujGgfRx22GFh+9e+tI4+l7oJ8tJLL7mecN2U0mfr7rvvztTxIMHs2Wt2wbHun5Fz0wMAkFWKeSKLriJvJWnOrTzeJwAgLSfdZfbJ5KBbgdyiwm3LXjArWsRlN/AlCACQE/h/SgKNsQYAIKEpDfy8o11QDQBAToqlKDNyF/93Ryoa275t27YMU4yzO9exxq9qbHd6lL6uQlTpTWeF3KECZhkVMVM6tcYuZxfXAhImDXzQvhkZatWqFWhzAAAFR7SCqshbBNZIRcWeVOApPRr3qwJS2aFx3ldddVWG62mMvKakQt4aNmyYGyufkZwYTcK1gAJPU2x1apo8/3YWp0wEACC9jjFNSYrgEFgjlTvvvNNNmZWenOil1PRfGf0BUBGpGjVqZHtfyDwVAlNRwPRoruicwLWAAm+vZ3Zh+HSGmvJORTYBAED8o3gZAOQnFC8rmEoXN1sxyqz0vptRFJoBAOQUTfUZ6yxLyB0ULwMAIDcVKWR2duewoFpatGgRWJMAAAWLhsshWATWAADkpt2au/qYVIsXLVoUSHMAAAXP1q1bg25CwmOMNQAAuUX1yVrUMWu/X9Rq+AAA5ITSpUsH3YSER481AAC5JslscHeVAM+14n8AANSvXz/oJiQ8AmsAAHJ1fPWRUZ9q3LhxnjcHAFAw/fXXX0E3IeERWAMAkFtB9RmdzCqVjfr0jBkz8rxJAAAgdzDGGgDyk8KFzEsySypSOOiWILt27TE7/9igWwEASAC1atUKugkJj8AaAPKT+/tbUrPaQbcCOaFYEbOjWqX5dI0aNfK0OQCAgispSi0P5K0kz/O8PN4nACAdShFu1SrtgAwAACDUtGnTrG3btkE3I6ExxhoA8pndu3cH3QQAAABkAoE1AOQzFSpUCLoJAAAgjrRo0SLoJiQ8AmsAyGeqVKkSdBMAAEAcWbRoUdBNSHgE1gCQz8yePTvoJgAAgDiyefPmoJuQ8AisAQAAACCOlShRIugmJDyqggNAPrN+/XrGWQMAgEwVPi1ShJmUg0SPNQDkM1u2bAm6CQAAIM6m6kSwCKwBIJ9ZtWpV0E0AAABAJhBYA0B+MmWOtT3wDvcTAAAgFjVq1Ai6CQmPwBoA8pMpc8N/AgAAZKBo0aJBNyHhEVgDAAAAQBxjHuvgEVgDAAAAAJANBNYAAAAAEMeaNWsWdBMSHoE1AAAAAMSxZcuWBd2EhEdgDQAAAABxbOPGjUE3IeERWAMAAABAHCtevHjQTUh4BNYAAAAAEMcYYx08AmsAAAAAiGPTp08PugkJj8AaAAAAAIBsILAGAAAAgDhWrVq1oJuQ8AisAQAAACCOlSxZMugmJDwCawBIJOu3mF34jFnVAWal+5gdNdRsypygWwUAALJhwYIFQTch4RFYF0BDhgyxpKQk++WXX4JuCiK0bdvWqlSpkufv/yOPPOKWjR492vKLE0880bVp06ZNFk/y4j3MNXv3mvW402zMj2ZDjje7v5/Zyg1mXYaazVoadOsAAADiVpGgGxDvpk6daqtXr3YPBQhlypSxvn37Bt2shPHvv//aQw89ZD179rRu3boF3RwUEA8++KAtXbrUHn74YYsrXW41a1DN7KXLoj//zk9mE/81e/tas56HJi8781CzpkPMbnvTbMxVedpcAACQM5o0aRJ0ExIePdbZ9Ntvv7kv4OXKlWNi9gDMmTPHRo4caRMmTAi6KfnaZZdd5m78cNMnNq+++mq+6t3PMQqsq1cwO73TvmVVyycH1x/+arZjV5CtAwAAWbRq1aqgm5Dw6LHOpt69e7ugWt5++23btYsvpsh/ihQp4rIpkOCmzjNr18isUMQ91Y5NzJ4bZzZzqVnr+kG1DgAAZNH69euDbkLCo8c6m/ygOh4obbp79+5Wvnx5K1q0qFWvXt3OPfdcW7t2bdQxuj/88IOdd955VrlyZbd+/fr1Xe9wpM2bN9s555xjFStWtGLFilnTpk3tlVdeSXMMrSawVzsqVKjgAr6qVatanz59XDp9qFmzZrlt6HntX+e6efPmNnz48JRxwz169HD/vuOOO9y+9Nh///0zfW50XG3atLFSpUq5zINatWrZGWecYRs3bkxZR8cxaNAgq1GjhmuPzuMxxxxjv//+e9S7hqeccoqVLVvWba9Vq1b22Wefpbn/7777zg4//HC3vrat/Q8ePNi2bdtmOSHaGGt/2csvv2zXX399ynHVrFnTnc9o3nnnHWvfvr07T/41cdNNN9lejd3NweIbp59+uhvHrOtD15XeZ2Un5MV1qs+F3tM1a9akXFPRxqfPnz/fvf+lS5d27/GBBx5oP/30k+Vry9aZ1ayYerm/bGn43wIAABAf9B0IwaLHOh/Yvn17zOsqICgU2dsUAwWpHTt2tK1bt9qpp57qAtTx48e7lNfJkyfbpEmTXLAU6oILLnD7UjC5e/duF4BdfPHFrnhThw4dUtY79thj7eeff7ZOnTpZ165dXWB04YUXugAl0o8//uiCau1L46Lr1atnf/zxhwvY1A6NWVegov0dddRRLthWgNuiRQvbsGGDzZgxw7VbNKb6/PPPt+eff95OOOEEt75E2296LrroInvuueesdu3aLvDSTwVx48aNcwGyAnplIhxyyCH2559/2mGHHeb2q3XU7iOPPNIdlwJz0boKkmfOnGlHH320e51uJvTq1csFzpHGjBlj/fv3d+3u16+fu5GgwmNqk86Ntp2V9zxWd999t7sGzzrrLDdVg66J2267zZo1a+aW+e69914XRGsMj95fHYtuCNxzzz3u+lLGRnbNnj3bXac6h7pOtS+d57feesudd12nderUydXr9K677rJhw4a5O786D75DD/1vTLKZ7dixw72vui6vvvpqt63XX3/dXatqb55MebFrt9mGramXKZ179b4bQk6lMsm91Nt2mhWP8me/RLHkn3oeAADEnZYtWwbdBHjIMW+99Zb32muvZfp1I0aMiPmxZMmSDLd36aWXenprf/7555Rl3bt3d8uefvrpsHUHDBjglt98882pXt+uXTtv165dKcu///57t7x3794py9544w23TNvfu3dvquV6bNy4MWV5w4YNvZo1a3pLly4Na8eoUaPcukOHDnW/T5o0yf0+ePDgdI/1008/DXtdZn377bfu9S1btvTWr18f9pyOZ8+ePe7f9913n1vvzDPPDDvOMWPGuOVdunRJWXbvvfe6Zf369Qvb3q233uqWV65cOWXZtm3bvPLly3vNmzf3tmzZEra+3hOt//LLL2fqmKK9/w8//HCqbfnL6tev723atCll+fz5870iRYp4hx9+eMqyhQsXekWLFvWOPPLIlHPi69u3r5eUlORNnDgxU+3s0aNHqutD+yxbtqw3ffr0sHW/++47r1ChQl7//v3z5Do94IADwt6nUHou2rX5v//9zy3X5zRbRn7peXZa8s/0fPtH8nqxPOatSH5N6T6eN+jJ1Nv6dFLyep9PyV7bAQBAIKZNmxZ0ExIeqeD5gHpbY30o3TWzlKb7/fffuxRZ9c6GeuCBB1ya69ixY1O97pJLLnGpuD71zJYoUcKlwPrUYyvXXXed245PPZ1169YN296UKVNs3rx5dtxxx9mePXts8eLFKQ/17Cqd9ttvv3XrKl1X1HM7d+5cyy1+yrB6KJXaHUrH4/cUv//+++539e6GHqdS2Pfbbz/XE7plyxa37IMPPnCv0zZD3Xzzzal6Mt977z3XE3/mmWe6lPzQc6Iebvn8888tN6mgWej4a10n6hVWG3wvvvii60UeOHCgK9YX2U7P8+yjjz7KVjvUQ6widOoJ1vsfug+dY6XHRytSl9PXaSy0jdtvvz1s2cknn5wy5CIz9L6rB9y3ffu+f8vOnTtdWnqoZcuWmR3QwGzcbe6x5o3Lkv/dpr5Zt7a29s3LzftyaMrza4t5yftQyveydS4tPnSIxu5F/xU8qVUpfB+R+wyxfPly976ndRyR+0jzONgH+2Af7IN9sA/2ke19aL2CcBz5eR8ZCjqyL0iy2mOd0yJ7LJcvX56qVzWUeubUixz5+vHjx6dat1q1at7++++f8nvHjh1db+Xq1atTravezdCewOeeey6ldzCth3qOfRdeeKHbth7q6VbP6CeffJKjPdadOnVy21+2bFm669WtW9erWLFiqt5aOe6441wbZsyY4X6vU6dOmuvut99+YT2h119/fYbn5Nhjj83VHusXX3wx1TYie2x79eqVYTsHDRqUrR5rv6c5vUeVKlVy/TqNdvyR5yba+ztv3jy3HWWB5EmPdTSdb/G8/o+n/XzP+z2v+kDPi7w2L3ja80r19rztOzO/TwAAELhFixYF3YSExxjrfEDjnmOlXt3ChQtbXgjtBQwVejfIF9oLmBb/derhPO2006KuU6nSvh6zESNG2OWXX+7Grmpc9YcffujGI6t3980337ScFEv7c4N/TlSIK3QMb6is9KhmRizXk9/OO++80xo1ahR1HY3bzw5/Hxorr3HT0agnOrev01goIyGtce/R9ptv9Dwkecqt937eN4+1xmO/PdHspIPMilP4BACAeBStjg/yFoF1PqBiUbFSBWOlxGaGimEpBVlp2EoLDw0IlAKhVAlVNM4KBX2//vqrKzqmdO5Q2l+o1q1bpwQ3SqGOtRCDgjn/BoTShFXIaujQoe657AZKfhq3UoxViTotKmimtHSlF0cGliq4pYCvQYMG7nelUeucLFy4MGWZKB1FadShReL8CuZ6f2I9J0FQBW1Rpe7caqf/fuo85fQ+MnOdBnmjJU8C605NzQY+afbXYrMqZc2e/txsz16zYb2Dbh0AAMgifZ9R4VYEhzHWCTDGWoG0xp2qcnHkNESaZkk9bMcff3yW2q7K3v5Y7dCeOvUoL1q0KGxdVWNWoKmxuApyImkM75IlS1IC/sipphSQqkq0qFq3+OOiI6cMi5Wqe4vGy2rsRSR/Gin1sOv4VBU79DgV5Cuw1rGpmrk/1lavC60o7VebjjwmnT8dg8Ywh44J9mnctn+sQdJ50jQO999/v61bty7V83q/IqdVyywF7TqPutHx7rvvpnpe5103K3L7OvWvNZ37nJruLN9QdsJnt5iddZjZ45+aXTfarEo5s2+GmTWrHXTrAAAA4hY91tmkKZX8gExTFqkol4p0iQpC+T196YmcPig3PPHEE3bQQQe5lOOvv/7aTaU0ceJE++abb9yUQQoYs0LFnzQf8hdffOGmQ/KnMVJgpCJY+ndoL+Brr73mpslSoK+5iTW/s4IXTVGktij1W0W/Pv30U9dWpQXrHGrKK/U2Kh28cePGbjorUU+7ensV4GpebN140PRJsfZ4dunSxc2B/MILL7jzoDYpI0BBroqGaaor9WpfddVVbh5jBWIqZHDEEUe4omqaYkpte/TRR1O2ec0117hAWevrRoE/3dZXX33l2qYpoXy6RjStlqb5Uo++gnIdrwqa6dpSMbdnnnnGTcMVpIYNG7pptVT8S+076aST3DIF/X/99Zc7T5pP+uCDD87WfjSvtIJrXVeaHqtdu3YuENZdWF0fugH00ksv5ep1KjoOfT70Ol2rSjfXMetayNe+S57jPV0Vy5g9f2nyAwAAFAj5/jtKAiCwziZVAI6sGKe5dqVmzZoxBdZ5QT296gm88sorXXChatQaz6yA7rHHHks1h3WsFCwrYFS1cQXDOnYFKgoWFVwq9Tl0DKzGEWu+6htuuMEFYgqUlUZdrVo1l+bu9yxqLmMFP9rel19+6XqA1aOpAFOVuf1tKqjWPNa33nqrm2dZQasC5MykEuv1Sp15+umnXdCmQE4BugIwpdGLemt/+uknu+KKK9xxKnVc50xB18MPP2wHHHBA2Fzjel4BuwJjBZ06/wr+VRk8tNq2aMy4zpmOQdXZFbyr91vXj94fBf/5gW4YKHVdPe+qsq3UfI3n0Y0hVebWzZqc+J+C5u5WJoWuKz107vXe62aGzmleXKfDhw93c3Ormv4nn3zirgl9XvifFgAAyI+UUcg462AlqYJZwG1AAaUeTfXiq9c2rUJPQNDy3XX6/DizC54xG3mx2fnHBt0aAAAQB6ZNm8YY64Dlg2+RiHfRxtaqkrfSqTt06JA/ghUkPK5TAABQUOXVrEFIG6ngyLarr77apXdr3HPFihXdeOKPP/7YjR/2K3rnNY3JzSgZQ+nFGh8dD9avX59hgTb9QVV6c5BWr15tGzduzHDKOFVZz2v58ToFAADICf7sOwgOgTWyTQXGNP541KhRbtytAhUFL/fdd5+1adMmkDalNddyKE3ZpUJp8eCWW26xp556Kt11NC5cgW2QBgwY4MYwp0dj4FXwLK/lx+sUAAAgJ6hGDcF1sBhjjQJJhcJUoT097du3zzfF5TLy559/uh7W9KgInKYFC5KmUVOF9/SoCFj37t3zrE1xhzHWAAAgkxhjHTx6rFEgqdJ2QdKyZUv3yO9UzV0PAAAA5B11XCBYVOsBAAAAgDim+jEIFoE1AAAAAMSxjIbiIfcRWAMAAAAAkA0E1gAAAAAQxxo2bBh0ExIegTUAAAAAxLFNmzYF3YSER2ANAAAAAHFs9erVQTch4RFYAwAAAEAcS0pKCroJCY/AGgDyk3aNwn8CAABk4IADDgi6CQkvyfM8L+hGAAAAAACy5s8//7SWLVsG3YyERo81AOQzM2bMCLoJAAAgjuzatSvoJiQ8AmsAyGd2794ddBMAAEAcqVChQtBNSHgE1gCQz/A/RwAAkBlVq1YNugkJj8AaAPKZKlWqBN0EAAAQR2bNmhV0ExIegTUA5DOzZ88OugkAAADIBAJrAAAAAIhj9evXD7oJCY/AGgDymQYNGgTdBAAAEEe2bdsWdBMSHoE1AOQzW7ZsCboJAAAgjqxcuTLoJiQ8AmsAyGdWrVoVdBMAAACQCUme53mZeQEAIBdNmWPW/jqzyQ+Ytdsv6NYAAIA4sHfvXitUiD7TIHH2ASA/mTI3/CcAAEAG/v3336CbkPAIrAEAAAAgju3YsSPoJiQ8AmsAAAAAiGPlypULugkJj8AaAAAAAOJYzZo1g25CwiOwBgAAAIA4xhjr4BFYAwAAAACQDQTWAAAAABDH6tatG3QTEh6BNQAAAADEsV27dgXdhIRHYA0AAAAAcWz58uVBNyHhEVgDAAAAAJANBNYAAAAAEMdatWoVdBMSHoE1AAAAAMSx2bNnB92EhEdgDQCJZP0WswufMas6wKx0H7OjhppNmRN0qwAAQDZs37496CYkPAJr5JohQ4ZYUlKS/fLLL+kuSyR5cfyfffaZ28dtt92Wsmz+/Plu2YABAyy/eOSRR1ybRo8ebfEkrq/hvXvNetxpNuZHsyHHm93fz2zlBrMuQ81mLQ26dQAAIIvKlCkTdBMSXpGgGxDPpk6daqtXr3aPTZs2uQu6b9++QTcLQDZ9+OGH9umnn9qtt94aX/NCdrnVrEE1s5cui/78Oz+ZTfzX7O1rzXoemrzszEPNmg4xu+1NszFX5WlzAQBAzoir7ysFFD3W2fDbb7/Z0qVLrVy5cla8ePGgmxMXHn74YXcTokOHDkE3JaHUr1/fnffnnnsu6KbEhXHjxtnIkSPd57tAUWBdvYLZ6Z32LataPjm4/vBXsx3MgQkAQDz6+++/g25CwqPHOht69+7tgmp5++23mZg9BsWKFXMP5C2lLpMiBJs6z6xdI7NCEfdUOzYxe26c2cylZq3rB9U6AACAuEWPdTb4QXV+cuKJJ7ogas6cOXbCCSdY2bJlrUSJEtapUyf7999/3Tp333231alTx4oWLWq1atWyJ598Muq2nn76aWvZsqWVLFnS9cg3adLEjYuNtHfvXrvqqqusWrVqbptKRbn33ntjHp+q8b9nn322NWzY0EqXLu22ofZdeumltmPHjqjjcl9++WW7/vrrrUaNGm79mjVr2h133JHl8/bDDz9Yly5drHz58m57lStXdr//+uuvYevpuPbbbz93c6BUqVLWtm1bd1MlO+dEFixYYKeffrpVqVLFihQpYhUrVrQePXq49zEnRBtjHbrslVdece+v2lqhQgV302jbtm2ptjN9+nTr3r27W0ftrFq1qvXp08cNh8jJ4htXXnmlO2dqj85zx44d7csvv0xzLPkDDzxg9erVc+vrHOo603sQafjw4e6a13q6dnQNKYsidKy3PkNPPfWU+7c+N3ou2vh0XZvnnXeeu1a0PWUFqJc7X1u2zqxmxdTL/WVL1+Z5kwAAQPbVrl076CYkPHqs46iCn4K5QpE9TWlQ8KOg54orrrB58+bZG2+84QKG0047zQUQvXr1cgHLSy+95NZR4KKHb/DgwTZixAhr166dC1IURCmQufrqq23JkiX24IMPpqzbr18/e+2112z//fe3gQMH2po1a1wAU7169ZjaOmnSJBc0de3a1Ro3bmx79uyxr7/+2gX2c+fOtbFjx6Z6jW4O6NydddZZLvB/9dVXXYDVrFkztywz1HYFTTq/p556qgswV6xYYd9//739/PPPKedFQdSLL77o2nj55Zfbli1b7K233nJBqI5Z5ywr50TTI2gfynjw96+AWts+7LDD3PnRjYbcMmHCBHvvvfesZ8+eLkjW+X7zzTfdTZnQQPHHH39015WuG62rQPaPP/6wd955xyZPnuxqDujGSHboHCiY/fPPP+2YY45x78vGjRtde3T9al8nn3xy2Gtef/11W7dunWtTpUqV7N1333WBsQLom266KWU9BdEKwHUDR++fAuNRo0a5mymhdL1rnzpeBfj+/6jat28ftt4FF1zgPo+DBg2y3bt3u5s9F198sbvZkidDHXbtNtuwNfUypXOv3hi+vFKZ5F7qbTvNikf5s1/ivywSPQ8AAOKO53lBNwEecsRbb73lvfbaa5l+3YgRI2J+LFmyJMPt9ejRQ58q78QTT/T27t2bsvzcc891yytVqhS2nV9++cUt7927d8qy8ePHu2V9+vRJtf0jjjjCK1GihLd48WL3+/Tp072kpCSvVatW3vbt21PW+/HHH91ybefnn39OWX7ppZemWrZp0yZv9+7dqfZ1/PHHu21oH76HH37Yvb5+/frudb758+d7RYoU8Q4//HAvMzZv3uyVLVvWPf74449Uz/vt8o+zWbNmYfudN2+eV6pUKa9y5creli1bsnRO1GbtP/Q45bvvvvMKFSrk9e/fP1PH9Omnn7p9DB06NKydWha6LX9ZsWLFvN9++y1lua4bnd9y5cp5O3fuTFnesGFDr2bNmt7SpUvD9jdq1KhU+4uF/16+/PLLKctuvvlmt+y5554LW3fdunVelSpVvBYtWqRc1/5xli9f3lu0aFGq93S//fZLWbZq1SqvaNGiXt26db3169enLF+4cKFXsmTJVO2Idp1GPteuXTtv165dKcu///77VJ+lWKxZsybsOtn2xMeeZ6d53sgv3e87duzwVq9eHfYa9x58+0fyejE81k39N3kfpft43qAn3TW8cePGlO3t/ODn5HU/nxK+j8h9hli2bFnY35jI44jcR5rHwT7YB/tgH+yDfbCPbO9j6tSpBeI48vM+MkJgHXBgrYAg1kfoxZFRYP3DDz+ELX/ppZfc8rPPPjvVaxRYdOrUKeX3fv36uQBQgUJkGx544AG3nZEjR7p1r7/+evf7U089lWq7HTp0iCmwDrVt2zZ3EWtfjz/+uFv3iSeeSBWM3Xjjjale26BBA/fIDP+8XHTRRemu5x+n2hTpnHPOcc+9//77mT4nChh1rrt16xb1Pa9Tp47XuHHjXA2sjzzyyFTbUHCo5xYsWOB+nzx5svt94MCBqdqo4LR48eLupkt2A2sdq4J3bTNyPyeffLK70eD/kfOPs2/fvqm2feihh3qlS5dOuTGgG1Na97rrrku1bq9evbIUWD///POpntNNp9DPUpYooA4JrNO0dpPnjZsW/mhzped1G5Z6+bYdya9pfInnHT889baeH5e8z+nzs9d2AAAQCAXWCBap4AHLrRTfVq1ahf2utHBp0KBBqnWV8qvUV9/MmTNdOknnzp3T3P6yZcvcT38M8AEHHJBqHaVlq3J6RpRGqxRzpfkuX748VSqL0qgjKV06klJ6Fy9ebJnx119/uZ8HHXRQuuspJV2UGh+pTZs2Kects+dEY5Z1vEqFT2uaBI0Zzk1K6Y6kccOi90PPK9VblDqtRzRr12Z/fO7ChQtt586dUdvk01AEjan3KbU7ksaoK1VfwwU0/nnWrFluuVLzI7Vo0SJLbW3evHnUuguhn6VcVbGM2TEHpF6m8dKRy31tG5j9+HfyfNahw0p+mWVWqrhZ01q522YAAJAron3HQd4isA7Y1q0RYyTToQJihQsXjmldjYmOJq3Xhwaz+reKNWl8rcbTRhM53jQ7NB5Z42SPPPJIN/ZVQZPGO0+ZMsWN5Y5WhCrW85Df+ef9qKOOcmN2o1HxudyU3rn02+f/1Nh8jdOPRuObs0v70c2m+++/P811Im+qBHUtpPUZy9djnHoekjzl1ns/75vHWuOx355odtJBZsWLBt1CAACQBSqEG63jCXmHwDpgKroVKxVvUkGm3NaoUSNXtVu920cffXS666pCtvz++++u0FYovwp5Rj766CPX+/fdd9+5gN6nomu5TVXPRT2y559/fobHqWA/8jhVwEuaNm2a6XOi/euYVUhLhcPyq9atW7ufamtutlOFwtTzrQrpOTk3vArOhWYoZDTvY6xFAuOOAutOTc0GPmn212KzKmXNnv7cbM9es2G9g24dAADIImXqIVgF9Ntj/NCUWLE+/PTc3HbRRRe5n6qoHDndlSxatMhV7hZNk6Vg65lnnglbd/z48a6adSz8ICa0p2/Tpk2uKnhuO+OMM1wqvCp4Rwu6/N5y/zhVbTr0D5fuDqqitt4bVczO7DlRmreqYKv6uKpZR9I5UXp00NRG3WjRTZDIKcj8at5K0c4uVVhXKrWGBkST1ZstCtSVEq7q+Bs2bAi7lj/55JNU6/tzfq9cudIKFPXuf3aL2VmHmT3+qdl1o82qlDP7ZphZM6bpAAAgXqWVZYq8Q491NmhM7ebNm92/NZZTwaZ6NP0v5n4PZnpycxqlrNLYak05pPmt1dOnoF7t1Lhq9cKqN1vTGykg1fhiTW+lgEXjj9WrrjHRSu1WIBZLINStWzcXVGreaPWQK5jReGttP7dpeigdp6ZM0pRXp5xyihsHvWrVKteDrvRspaerx1bTZmm6rQMPPNCt50+3pfmelbKuab8ks+dEc0grcNVrjj32WPcaBdRa75tvvrHjjz/eTYsWJN0o0M0HvVdK2dcc2xrHr2PXmHK1U+dp2LBh2dqP5iL/6quv3E0V3WzQvjRntm4uaFowpX1rKq7MUo0Bte+hhx5y758CbX1mNY2XskB0DKG91ErNv+eee+yWW26xGTNmuPdWr0uv7kC+8N3wjNfROOznL01+AACAAiFazRnkLQLrbFBar1/Ey+f3SGqccCyBdX71xBNP2MEHH2yPPvqoC6jU86rCTAoMb7jhhpQgUvR8tWrVbMyYMfbwww9bjRo17NZbb3WFxNTDmxEFjbrLpvmTf/rpJ9f7q55kBXFpjefNSRrjrRTkoUOHuh5ZBVwqhKYA+dBD/xuHamYvvPCCu9Ggsec6LxpjqyBcQeCZZ54Zts3MnBOljiudXPMsK6jUQ72r6s0+4ogj3PzZ+YHOhVLm9f7/8MMP9uGHH7rx3zpO3TzQPNLZpePWNXD77be7oNfPWlAxMhXl0HuVVbr5oZs1mp/9sccec9eZbpbo2tMNgdA7vbrBce2117o53/W+6aZZ//79839gDQAAEpI6Htq2bRt0MxJakkqDB90IAAiKxowru0AV2v2x5IF6fpzZBc+YjbzY7Pxjg24NAACIA9OmTSOwDhhjrAEkBKXuR1aYV4q5et411IFpKgAAQLwKnYoUwSAVHAWS5l/OaCozja+uXr26xQOlpy9dujTD9RQgaqqyoKjmQCwFvzSkIK8rb2uogYqiaYiB5smeP3++G9uvc6tCfQVlCjcAAJB4+B4TPAJrFEjHHXecK7SWnq5du9rXX39t8UDFwVQwLCMq+KWx8UHR+PO0KnqHUuXvvChOF6pFixZWt25dF0zrBoDGc2vMvMZSZ2fsNgAAQNBUx0f1eRAcAmsUSI888ojrtU6Pgqx40aFDB1cILSOaDzxIKjanQm0ZCS1+l1c0Z7gqiwMAAAA5jeJlAJCfULwMAABkkoa2abYWBIfiZQAAAAAQx5YsWRJ0ExIegTUAAAAAxLFNmzYF3YSER2ANAAAAAHGMNPDgEVgDAAAAQBxr0qRJ0E1IeATWAAAAABDH/vjjj6CbkPAIrAEAAAAAyAYCawAAAACIY9WrVw+6CQmPwBoA8pN2jcJ/AgAAZKB48eJBNyHhJXme5wXdCAAAAABA1kybNs3atm0bdDMSGj3WAAAAAABkAz3WAAAAABDHtm7daqVKlQq6GQmNHmsAAAAAiGPLly8PugkJj8AaAAAAAOLYxo0bg25CwiOwBgAAAIA4VqxYsaCbkPAYYw0AAAAAcUwhXVJSUtDNSGj0WAMAAABAHPv999+DbkLCI7AGAAAAACAbCKwBID+ZMscs6fTknwAAADGoWrVq0E1IeATWAJCfTJkb/hMAACADpUuXDroJCY/AGgAAAADi2Pz584NuQsIjsAYAAAAAIBsIrAEAAAAgjjVu3DjoJiQ8AmsAAAAAiGOrV68OugkJj8AaAAAAAOLY+vXrg25CwiOwBgAAAIA4VqRIkaCbkPAIrAEAAAAgjrVq1SroJiQ8AmsAAAAAiGO///570E1IeATWAAAAABDHPM8LugkJj8AaAAAAAOJY5cqVg25CwiOwBgAAAIA4Vq5cuaCbkPAIrAEgkazfYnbhM2ZVB5iV7mN21FCzKXOCbhUAAMiGefPmBd2EhEdgDaThu+++s6SkJLvooouCboqdffbZri2TJk0KuilWu3Zta9asmS1dujTX9vHSSy+547377rtzbR8Jae9esx53mo350WzI8Wb39zNbucGsy1CzWbn3fgIAABR0THiWw6ZOnWqrV692j02bNlmZMmWsb9++UdddvHixu7ukddeuXWt79uyxE0880WrVqhXz/qZPn24LFixwk8Lv2LHDihcvbhUqVHAl9xs2bJhq/V27dtmMGTNs9uzZtnnzZitUqJBbv3nz5ta0aVMXzIRauHCh28e6devca0uXLm3169e3Nm3aWKlSpbJwhoDgzJ8/3x588EHr3Lmz9erVywqcLreaNahm9tJl0Z9/5yezif+avX2tWc9Dk5edeahZ0yFmt71pNuaqPG0uAADIGY0aNQq6CQmPwDqH/fbbby64rVKliu3cuTPddRXc6lGxYkUX3K5ZsybT+1u5cqWVLVvW6tatayVKlHDB9dy5c23cuHF20EEHWbt27cKqBY4dO9ZWrFhhTZo0ccH37t27XRu+//57F5wffPDBKev//fff9uOPP7pjOeCAA6xo0aK2atUq++OPP9wNgZ49e7plyH2jRo2yESNG5IubGbpedC2VLFnS4jGwfuqpp9xNogIZWGdEgXX1Cmand9q3rGr55OD61R/MduwyK85nGgCAeKPv8YyzDhaBdQ7r3bt3ykX99ttvuy/waenQoYMdccQRVrhwYTf3XFYC62OOOSbVstatW9t7773nttm2bVvXK+0H4cuXL3cB9aGH/tdbZWb777+/vfXWWy6QDg2s1VOtQO7kk0+2IkWSL5UWLVq4gEo980uWLLEGDRpkus3IvGLFirlHfhCPATX+M3WeWbtGZv/9TUjRsYnZc+PMZi41a10/qNYBAIAsUvZrvXr1gm5GQmOMdQ7LzJ0ipVUrqM5pCqS1bQX1ezWm8j9+D7qeC6U2qLfbD55D11fve+Ryv9c0cnle0c2B/v37W40aNVyPuc65UtMfffRRl07v++abb9yNArVXQaluAlx33XUuRT/SO++843rxtb1KlSrZWWed5Xrno1FWwM033+xS4rW+tt++fXsbM2ZM2PmO1XPPPeduWOh90fmuVq2aS1X++eef0xxj7Y//TusROi5cvcsPP/ywS/XX9vVe62aK33ObE2Os/WXKfNDNIgXfOi86/xMmTLCs0vt5/fXXu3Oic63MjFtvvdW2bduWso7Ond6zP//8M9XrdXNL5+OSSy5xvf5HHXVUyjn3z1XosWTmXMXyvuU7y9aZ1ayYerm/bOnaPG8SAADIPr8jDcGhx7qA2L59uwsK9FOp4IsWLXJjtUODX33xV4Cpnmylj+t3pYLPnDnTjfM+/PDDw7apIEbP/fTTT24Mtp8KPmXKFKtZs2ZMY8EVjIQGu+lRgJ9RarlSeTt27Oh694888kgXuOkYJk+e7ALpiy++2G3nww8/tNNPP90F3Ur5VRD+ySefuPG1//77rwuCNf5d3n33XTvzzDPd/H8K2MuXL+8C7WgBko6nS5cuLsDV/hXwKshTj/+AAQPcWPRLL73UYvXyyy+7IFiBnH5qSIAyARQ4//PPP9apU0jKbggFdI899liq4QYjR45075neW5+OXxkMOm+nnnqqW/bBBx/Y5Zdf7gLK4cOH58gfY6UgafvKxLj66qtt1qxZ7jxecMEF7r3Re5BZCoZ1fk877TQXwGp7d955p7srq+PX9a2gWcei92DYsGFhr3/mmWdckH/uueda9erV3bpPP/20ew979Ojh1tFQB533zJyrrL5vOWrXbrMNW1MvUzr36o3hyyuVSe6l3rbTrHiUP/sl/suG0PMAACDuqJMJwSKwLiDefPNN15Mq6oVT4bLIQFm9at27d7cffvjBvvrqq5TlCmaPPfbYVGndShdX0KpiZxpX7VMwoaAylmBMvZUK9GKh7SrgSc/555/vgnsFUDfddFPYjQMF8GqTfirA1vGOHj3aTjrpJPe8giIdkwJsBU/9+vVz6w4ZMsT1rr7++uspqfVDhw51wWskBXMKuG+77Ta3jn8O1B71fD7++OMuCIy1AJ16VNUr+tprr7kx8bFSkKhgL9QTTzzhzrXeS/XMy/vvv+9uHOi8qdfVTydXte0DDzzQjdtW0KkbJ9mloQYKqO+///6UTIwLL7zQBfuff/65u/GQWRs2bLCPP/44ZeiCzrveF1UNV7sVwOqnjvfTTz+1K6+80tUsEN1c8nvQ9T8bBeYKnBVY61q79tprw/aVmXOV1fctR034J3mqrEgqTvbG+PBl855NLmpWspjZjt2pX7P9v4BazwMAgLij7+oaDorgkDNQQHTr1s1OOOEEl4pap04dFzBGS/NVEK3AQ4GGAjAFyOrV/frrr12V8lAKGtWrq4BbAa+CTgUV6okcPz7ii3saVPRM7YrloXXTo15K9Xy2bNnSBg8enCoVXcGcbiqo93rZsmV22GGHufPiU6CkNGL17CsI00+tq9Ryratz4VOvdbSeZ/VUquf1uOOOcz2YOmd6bNy40Z0fvyBdrNTTqV7njz76yLZujeh9zITPPvvMBZV6X9XL6w9JePHFF10AqB55Bb5+e3XMp5xyiuv5/+WXX7K838hjUVAaOrxBN3Ik1psrkZS6HTru339fdK70Hvr7PeOMM1wmRuixPP/88y41X89FDn+IJjPnKqfeN/+69m+Kyfbt+/4t2k9k/QVd33ZAA7Nxt7nHmjcuS/53m/pm3dra2jcvN+/LoSnPry3mJe9DKd/L1rkZAUKHROxe9N+wh1qVwvcRuc8QOi/6DKV1HJH7SPM42Af7YB/sg32wD/aR7X3ou39BOI78vI8Mecg1b731lvfaa6/FtO60adO8ESNGeEuWLMmRfX/11Vfe6NGjve3bt6csW7Nmjff88897f/75Z9i6u3bt8saMGePaumfPHrds79693ocffuh98MEH7t+hfv75Z9fWRYsWeXnpl19+0afFO+6441yb0/LGG2+49c4///xUz82aNcs917lzZ2/r1q3e66+/nua6OnY9d+GFF6YsK1GihFuW3kPve6xmzpzp1a5d272udOnS3sEHH+zdeOON3pw5c8LW69u3r1vnt99+S7WN6dOnu9fWqVPHmzJlSthz++23X4btvffee73MqFWrlte0adOwa1XLGjdu7C1evDhs3W+//dbtY/DgwZnax6hRo9zrrr/++jTfl3PPPTdl2YQJE9wynSddw7pmdV7r1q3rzZs3L1V7Qt/TrJyrWN+3LBn5pefZack/M6vzLZ7X//G0n+95v+dVH+h5/33OU1zwtOeV6u1523dmfp8AACBw8+fPD7oJCY9U8AJKqa5z5sxx02L5ab6q8q27WZHz3KnnV1UEVfxJd3PU26m7Pnoo1TZybmu9Xr2Duouj3vH06G6Q0sljoXbEUvnaLzoVBN0JU6Eu9XynVaguMgU/PSqYpvdJ6c56aDz7Pffc48YGq8dVva3p0XukXnmdO71GKcuRNJ5e45KrVq0adRs5lcqsDIe03pesFHXLDKWK65rXGGeNw1edAY15Vrp/Zipkxnqusvu+BabnIclTbr338755rDUe++2JZicdxFRbAADEKdUKQrAIrAsoP5gNTYHwU1ZD0yQiAx//55YtW8J+D+W/Ptp2Ik2cODHHxlg3btzYBW4aO6vUjdACXaH8GwcKsHT8Gmvt++uvv1LGKCvt11934cKF7iZAaGCvseWRlBbvp5lr2rKcoPZpTnA9RIGapjjTmGmlyKc1vZXeTwXVSnV55JFHUopxRZ4zBZkac+NXxI4nCl51Myg0vdx/X1SVPZQKkykdXunZmn9dwx40Jjq0FkB6N2Qye66y+r4FHlh3amo28EmzvxabVSlr9vTnZnv2mg3rHXTrAABAFmkooqbZRXAYYx0nFPSp6rKqfvs0hjraOGoFw34AGRp8+pWPVRU7lILPBQsWuEDB74X1C0DpQxoZXPuvT6tXL7fGWGtKpaOPPtoFViqIFVltXIG+Hu3atXNVyxXUa+x46PlSb6SCKwWh+qlpsjRmWkXWVNTNpzHTKmAVaeDAge459U6Gvhc+9ZJGVupOj8ZpR55fVdX2A+fQGyORx6pCXDoXV111lSsSFi1oPO+889y6d911l2t3JN0kiHYc+cW3334bNm5axcz0vqjY3PHHHx+2rnqndQ2/8sorLshV771/Ln1+JXhtJ/LGUGbOVVbft8DpBsVnt5iddZjZ45+aXTfarEo5s2+GmTWrHXTrAAAA4hY91jlMvbNKpxZ9CVfwp+mp/C/16pX1qddVAa2sWLHC/VRhMKX3inpE/R5UpXSrwrGCRj8d1a+YrF5XFXVSD6x6mhUM6zntSwGmTz1x2v6vv/7qejkVUCoA0PRACgbUC+v37imdRJXFtV9V0Fbqq9KN1Vus3l0F7JE9htEoQPeD9JyguYMVwKgitwqZaVokBTg6x+ohVLVmBVdKydV0W+ecc47rRfSn21K6u6qE6zlRT6gqfffu3dv69OnjpljSudR2ok39parXKhSm6boU1KpYnM6VilwpkFdbFMzHWhVcPc56jQp06XzqfdCUUgqSVfjLvxkS6dlnn3Xt0HusmyGawzuUzouKsSn41nGp4rluIujGhK4JBYk6Z+rVV++uKprnR3ovlFat99Cfbktt1xRXkUGzrjMVGdO0W6J/6/WhNCe1tqNrR5XEldav6bbU65yZc5XV9y3XfTc843UqljF7/tLkBwAAKBAiZ/dB3iOwzmHqzY2sIKc5j0Vf0kMDa80d7T8X+nqfgtn0xhwrUNc6CsT1pd9PZVagoABcqa2R40c1FZQqYavHTWm2CpYVGGostQLpUF27dnWl+xWoq53qzdM2lGai3sAgJqJXGzW+W1Mlacow3WxQoKTgRhWp/UrhCqq+/PJLNyWXAi31VivYveaaa9yUTX7PpagKtIKkW265xU3jpGNUxXQFW3oulIJtBc4PPPCAqxCuKZj8oE4Bl9ZXz3qs1NOsHlZN9aQbMmqXjlFTQg0aNCjN1/k3YnQj5+abb466Xb/KuW4CKMVePb1qs4YJKODU9XHFFVe44DK/8jME9L4oY0Mp/HpPdczRbnxo6jS937rxE22cs26+KHDWdaCp0XRd6DOpIFrPxXqusvq+AQAA5AZ9Hwnsxj6cJFUwS/4nAMQ3ZWOoF1lB9RtvvJFqSra48Pw4swueMRt5sdn5xwbdGgAAEAemTZvGGOuAMcYaQIHx8MMPu/R+jbeOy6AaAAAAcYlvnkAu0Fj0aIXlQildJzNp47lJwxe2bduW7jpKd06rEnssNFQhWtGvSBoykZmK2qor8Oqrr7ohAkoDVwp8PFZABwAAyCp6q4NHYA3kgljmTtY4XX+MdtBUEE9Bb3pOPPFEVywvq1TcLZaAV2OgVUwuVqtWrbLBgwe7onUq2qax0xonDwAAkChUoLdly5ZBNyOhEVgDueCDDz5ImTc8LapQnV+MHj3aVq5cme2bBenRdGqqtp5RT74qrWe2CialIgAAQCLL6PsVch+BNZALVJU8nqgqdm5T5XRVWgcAAEDOipxiFHmP4mUAAAAAEMc0JSmCRWANAAAAAHFs5syZQTch4RFYAwAAAACQDQTWAAAAABDHsltkFtlHYA0AAAAAcWz79u1BNyHhEVgDAAAAQBzLaNpU5D4CawAAAAAAsoHAGgDyk3aNwn8CAABkoHXr1kE3IeEleZ7nBd0IAAAAAEDW/P3339aiRYugm5HQ6LEGAAAAgDi2Y8eOoJuQ8AisAQAAACCOlS1bNugmJDxSwQEAAAAgzqfbKlGiRNDNSGj0WAMAAABAHPvnn3+CbkLCI7AGAAAAACAbCKwBAAAAII7VqVMn6CYkPAJrAAAAAIhju3fvDroJCY/AGgAAAADi2PLly4NuQsIjsAYAAAAAIBsIrAEgP5kyxyzp9OSfAAAAMWjVqlXQTUh4BNYAkJ9MmRv+EwAAIANz5nBDPmgE1gAAAAAQx7Zt2xZ0ExIegTUAAAAAxLHSpUsH3YSER2ANAAAAAHGsXr16QTch4RFYAwAAAEAc+/vvv4NuQsIjsAYAAAAAIBsIrAEAAAAgjtWqVSvoJiQ8AmsAAAAAALKBwBoAAAAA4tjSpUuDbkLCI7AGAAAAACAbCKwBAAAAII61aNEi6CYkPAJrAEgk67eYXfiMWdUBZqX7mB011GzKnKBbBQAAsmHhwoVBNyHhEVgDQKLYu9esx51mY340G3K82f39zFZuMOsy1GwWY7MAAIhXW7ZsCboJCY/AOh+58MILLSkpyWbMmGHxRO1Vu9V+AAHqcqvZgCfSfv6dn8wm/mv20hCz284yu/R4s+/uMCtcyOy2N/OypQAAIAeVLFky6CYkvIQNrD3Ps+nTp9ubb75pL7zwgr322mv2008/2a5du6ygu//++904DH0AS5QoYfXq1bOLL7443df88MMPVrhwYRdAP/XUU3nW1kT13HPP5fmNihUrVrh9vvPOO3m633j37bffuvMWFzfEFFhXr2B2eqd9y6qWNzvzULMPfzXbUfD//gEAUBDtt99+QTch4SVsYK0g+ueff7aKFSvaoYceao0aNXJfjL/44gsXdAfhySeftE2bNtn++++fa/s4/vjj7YYbbrBq1arZdddd5/7dpUuXdMdl7Nmzx84//3wrWrRorrUL4d577z0bOXJknu5z1apVbp9ffvllnu433unviM7bP//8Y/ne1Hlm7RqZFYr409+xidnWHWYzSQcHACAexcUN/gKuiCWgtWvXuouvQYMG1q1bt5TlZcuWtYkTJ9qcOXOscePGed6uYsWKuUduufPOO+3zzz+3++67z66//vqYX3fTTTe5wHvAgAE2YsSIXGsfsGPHDpc1UqZMmaCbUjAtW2d2ZJQbdzUrJv9cutasdf08bxYAAEC8S8geawXO0rp167DlzZs3tyJFitisWbPyzRhrTfZ+1llnWfXq1V2PsQIOpXpcddVVmdr23r17XY+4eub9oHrNmjVueXrUC/fYY4/ZJZdcYvXrZ/yF+5FHHnHrqa1VqlRxx7Rz586wdX799Vc77bTTrG7dui4VvXjx4u6Yhg8fnmp7mTn+xx9/3PX2+9ts2rRpttLWv/nmGzvyyCOtfPnybt+VK1e2rl272rRp08LWU7t1XnVTpFSpUtauXTv78MMPU21P7+1xxx1nn376qbVq1cq1UcfTo0cPW7duXcp6ui6VOeG/xn/cc889Ydfwqaee6s6xrlllXpx00km2YMGCqNfUb7/95m6MVKpUyR2Lbiq99NJLKesp/dv/PKj31d9n1apVM3XO/GN8/fXXrVmzZu4YK1SoYGeeeWbYMYa2TTezevfu7dqm4Qm6+SNbt261IUOGuOtEbda5Pfjgg937EplRoWta153ee22jVq1a7rwqUA81btw4O+yww9xNNG1T61166aWprlGdCx377Nmz3XteunRpdyzt27e3SZMmhR2DbjxJr169Us6bzkGu27XbbPXG8IeWKZ07crn/Od+206x4lPupJYrtex4AAMSdGjVqBN2EhJeQPdZKedWXX6VDh1KAouBJz2dE6eKRX9rToy/l2mdWUrcVaCsQbdOmjW3bts3+/vtvmzBhQqa2o2BAY2jPOOMMN576lVdecdUDFYQce+yxLshScBapf//+7oN677332kMPPZTuPr766it79dVXXSCs1yhAUpC2aNEiGzt2bMp6CiwnT57sUtAVkG7evNk++eQTGzp0qK1cudKeeOKJTB//oEGDbNSoUS7wufzyy914cO1HgdmyZctcb31mvPjii3bRRRe5900BbJMmTdx2vv/+e3djoG3btm69c8891x2znr/iiitcKv9bb71lp59+uhu7r2A21L///uvOz8knn2w9e/Z0Y9c/++wz69evn3388cdunRtvvNHuuusu++uvv+yBBx5Iea3eJ9HxKzjcvXu3a5uyK3QzSMFxp06dXO2AyID4nHPOcde3UvoVRL788svu3wcddJAL8nXeNDRA+9O2tV0pV65cps6b376BAwfaKaec4gJqnbO3337bHfuUKVPcexPq7LPPdjclzjvvPPcZUYCsz5aORdtSVom2t379elcTQdeErhf/fOg9fvbZZ13QrfOo7c+dO9cF4ArO9R6Krg8Fwro29b7os6407meeecb++OMP916EUhsOP/xwd36uueYat03tX8c1f/58F5jrvC5fvty9d9pmy5Yt824uyQn/JE+VFUnFyd4YH75s3rNmDaqZlSxmtmN36tds/y+g1vMAACDu6HseAuYloLfeessbPXp01OfGjRvnjRgxwtu9e3e629i4caNbL9aH1s/IBRdcoMHd3h9//OF+X7lypfv91FNP9bJLbdC2ypYt65UsWdK76qqrvKeeeso77rjj3PLWrVt7e/bsCXvNY4895iUlJXmffPKJ+/3uu+926z755JNh66m9Wq51v/rqq5Tl2t6hhx7qnvv0009Tlq9bty5V+3S+W7Vq5dq2ffv2TB2/9qn1+vfvn+q5Qw45xCtRooS3evXqmM/Vhg0b3HnSY+bMmVHbKr/++qs75hYtWnhbt25NeX7WrFnuOKpUqeLt3LkzZbl/jj777LOw7XXo0MErVKiQt3bt2pRl3bt3d+tH06lTJ9e2f/75J2z5l19+6baj6yjymurYsWPY+/v555+75f369Uv1Poa+PrP0ej10vYXq1auXW/7II4+kapve9x07doStf+2117rnXnnllbDlq1at8ipXruxe42vYsKFXu3btdNu1adMmr3z58t7++++f5r7efvvtlGXavpZdccUVYeteeeWVqdrlfy5CX58Za9asSbnmZdsTH3ueneZ5I790v6u9kdfv0qVLPW/tJs8bN809Vr/xTfK/21zped2GeWve/Nbb++XUlOfXLFmevI/Gl3je8cPd+Qj9m7Tr2bHJ+5w+P3wfkfsMsWzZMm/v3r1pHkfkPtI8DvbBPtgH+2Af7IN9ZHsfU6dOLRDHkZ/3kZGEDKzHjBnjvfrqq1Gf++abb1xQEPpGRLNr1y5v0aJFMT+0fmYDawVrRYoU8Ro1apSyLKseeOCBlKBHxx/KD+JeeumlsItTgchJJ52UsiyjwLpdu3ap9qugXM+dc845Uduli3zx4sXuHF166aVu3R9//DFTx9+7d28XsCrQjTzvd955p9tmWu93NM8884x7zeWXX57uegq6tN5zzz2X6rkzzzzTPadg16ffmzdvnmrdq6++2j03fvz4DANr3WzQsZ5wwglRr7NatWp5zZo1S3VNvfbaa6m2Vbx4ce/www/P8cBabYg0e/Zs91znzp1TtS0yCBe959pOtGPUsesGgv/HUTeFdCPjgw8+SLNdzz//vNvXXXfdlWp7um4ib8wosNZ51vUZeeNN69544405FlinooA6JLDOlM63eF7/x9N+vuf9nld9oO56hS+/4GnPK9Xb87bvuxEEAADihwJrBKtIoqZKbN++PepzGq/pr5PRNurUqWO5SWnaGr959913uzGf2p/SXTWWU+nEmaHxqaJ07z59+oQ9pxRcjelVKrdSv/1lipM07VOsohV869ixo/sZOvZXY201rlXVpzXOO5Kfih/r8WscrNrq7yuaJUuWxHwcfnXn9LYnSgeWDh06pHpO6cNKCVcqs5+yLNGuGY2TFqUUZ0Qp9DpWpY9r7HE0kUMcJFqleY0z3rBhg+U0jd+OpHHxugYXL16c6rnIWgei9ZSyntYx+u+p6iJo7Hnfvn1d+rqub41xP+GEE9yQB39OR6V6y8033+we0UQOAdG2Iouo+eOXol23caHnIclTbr33s1nPQ5OXaQz22xPNTjrIrDiV/wEAiEf6ToRgJWRgrUJEGq+pIDpyvKfGHasAUuTySCr6lVZwHo22WShyipsYDBs2zI0bHTNmjBsDquD33XffdWNKNX9urBo2bOh+qkBUJL8omc6JfP31125MtMYYa1yxHqIx2n5AM3XqVFecyg/YM0NjZDVOWcW2Onfu7AJBnW8V/NIYVv/mRqzHr0BTY3NHjx6d5vuWUZCcV9K7rmKZ5s1f55hjjnHjytO6viOldaMoqKnlIgP8aO3S/Ooa258W/yaFipTpJocKpuna1bWlnw8//LD7twqU+ceponfRboREuyGQ3uc1P5y3LAfWnZqaDXzS7K/FZlXKmj39udmevWbDegfdOgAAkEX6fs5c1sFKyMBahZ3UI6ZCWTVr1kxZrmJQ6okKXZYWBeD6Ih8r9RJHCyBioQ/Jrbfe6v6tqYi6d+/ugkr1MuvfsVBBKhVbWr16dZpV0v2CV/pdgYOCVz0iqYdQD/U4h/bGquc4kgKb0OBdwbmW6XWRlbNDC5xl5vh100A9ueoxV8Gr7PJ7d1VJW4W1MrpZofX8Yma+P//8M1tFrNIqdKcCbnpO5yEy8yC7slJcL72e/FC6plRILNYsD30G1Zuu4mcZ3eTye5dVuV4P0fWignUqxqZK9f5dXPVA5/R5y8oNs8DoXH52i9l1L5s9/mlyFfAOjc1eusysWe2gWwcAALJIBXQRrDj6Rphz/Ls5fnpoaAqwgutY5rBWiqnSTWN9+CmpmbFx40b3CKXgWGnGEkv1cp+qO2vqKAUrkcGyPyWVKm+LphfS1FyRD1W6FgUm+j0yhVe92OopDO3VVxq3aDql0J7TyB4/BV7vv/9+lo5f1a1FlZsVcEaaN29ezOfJb6tugqiKtH/TIZQ/RZnSjxWMPvroo2EV4vUaVYlWircqn2eF3+us6cZC1a5d2/W4/vjjjy4dPFrbNOd4VmhaLImcFiuz1ObIIQSqdC6qqB0LVa/XtapK5dGEvqfR0ssPOeSQlDnr/WtWn4Gnn3461Tn1/2eU1fRu/4ZZZj6Puea74clBcnoqljF7/lKz1S+bbXk9+TUHZfw3DwAA5F/+LCgITkL2WCsdWtPiqFdRva5KOVUwoWmd1FMWS2CdF2Os1QurNFcFxOpFVbs1BZNSodW7rCmbMkPTWCng0HRU48ePdz2uOn71IGtKI6Vmi44/2jlQkPvee++53m+NkY6WSqttaPyzzqN6oDXns3qndRyiKY4OPPBAl9Kt9itIVA+npmPSXNWaeiuzx69ea02jpGBON010I0MBqAKo33//3R2fbpjESgGYphbTGN0DDjjA3XDQdFrKcPjuu+/c+dP+1Hb1aGu6LfUkK2j0p9vSMAHdsNCNgKzQ+6Tj1PaVOq/pqHTDQ/vRVFmaBkrHr2PX+VT6vKaDUk++xhpnZmy8T+dMY4iVCfC///3PvYcKGjXePjP0edI50k0WzSWu6bZ0I0A3RLQ8FsqI0OvU26zXHnHEEW4+cY3V11Rr+p+HphUTXRvKDNCUYf77riEF+oz66fK6aaD3Q7+r91rvld5Tfe5nzpzp3ledV02BlllHHXWUu8Hy4IMPuuBcveIaJqH3DQAAIC/oOxcC5iUoTT30+++/e2+88YY3cuRIN33OxIkTw6ZHymuRVcFVLVvVpRs0aOCqHhctWtSrXr26m7pIVZazQts++uij3XRNhQsXdpWXVZU6o+nFYqkKrvY//PDDXr169Vw170qVKnnnnXdeqgrrfmXnChUquPXq1q3rDRs2LFV15cwe/4svvugqk5cuXTpl/+3bt/eGDh2apXM1duxYN11XmTJl3PY0zZPO3bRp08LWU9vVRq2jqb3atm3rvf/++6m2p2NTte+0zmtoVWm9H3369PEqVqzoqlPrea0Xeg779u3r1ahRw+1X50fnUedG13Fa11QoTQcWOm2Vf8yqXF6sWDH3Oq2TGf4xqvJ8kyZN3HtWrlw574wzzkg1pUF6bfOnQbjhhhu8/fbbz7VHDx2v3oPQKu+XXXaZm0ZL17T/vh9xxBFhU7/5VHldr9d51fWvtqmK+uDBg70lS5akrKfzEu3Y06qcfu+997opv7TNtN7nPKkKDgAAEhJVwYOXpP8EHdwDKBjUc6te9M8//zzopsSv58eZXfCM2ciLzc7fV8MAAAAgLcoSjaz5g7yVkGOsAQAAAKCgiDbdKvJWQo6xLkhUqCqj8cOqmKxHotPY24ymSNOYYr86OpJxjQEAAORvmtoXwSKwjnMq2BRtCq1QF1xwQZaKWRU0SlFWgbqM1iGNORzXGAAAQP7vCFGhXwSHwDrOvfDCC25O7VjmZU50qjCd0ZRIqmyOrF9jlGwAAABAIiKwjnOZnXIrkR1zzDFBNyEucY0BAADkb0y3FTyKlwEAAABAHFuxYkXQTUh4BNYAAAAAEMc2bNgQdBMSHoE1AAAAAMSxokWLBt2EhEdgDQAAAABxrGXLlkE3IeERWAMAAABAHJs2bVrQTUh4BNYAAAAAAGQDgTUAAAAAxLEqVaoE3YSER2ANAPlJu0bhPwEAADJQpkyZoJuQ8JI8z/OCbgQAAAAAIOtjrNu2bRt0MxIaPdYAAAAAAGQDPdYAAAAAEMc2b95MOnjA6LEGAAAAgDi2Zs2aoJuQ8AisAQAAACCOrVu3LugmJDwCawAAAACIY4ULFw66CQmPMdYAAAAAAGQDPdYAAAAAEMemT58edBMSHoE1AAAAAMSxvXv3Bt2EhEdgDQD5yZQ5ZkmnJ/8EAACIQaVKlYJuQsIjsAaA/GTK3PCfAAAAGahQoULQTUh4BNYAAAAAEMfmzuWGfNAIrAEAAAAAyAYCawAAAACIYw0bNgy6CQmPwBoAAAAA4tjGjRuDbkLCI7AGAAAAgDi2Zs2aoJuQ8AisAQAAACCOJSUlBd2EhEdgDQAAAABx7IADDgi6CQmPwBoAAAAA4tiMGTOCbkLCI7AGAAAAgDi2e/fuoJuQ8AisAQAAACCOVahQIegmJDwCawAAAACIY1WqVAm6CQmPwBoAEsn6LWYXPmNWdYBZ6T5mRw01mzIn6FYBAIBsmD17dtBNSHgE1sgRH3zwgTVv3txKlizpyv3fdttt9tlnn6X82zd//ny3bMCAAYG2F9mn9/Hoo4/O031Wr17d9t9//5jWHTJkiGvjL7/8ku6yhLJ3r1mPO83G/Gg25Hiz+/uZrdxg1mWo2aylQbcOAAAgbhUJugH50cyZM+2PP/6w9evXW7FixaxevXrWsWNHFzTm9Da+++47t240xxxzjDVq1Chs2caNG23SpEm2ZMkS27Fjh5UpU8YaN25sbdu2tSJFigQ2If3ZZ59tlSpVsuuvv97Kli1rRx11lK1YsSKQ9gAJq8utZg2qmb10WfTn3/nJbOK/Zm9fa9bz0ORlZx5q1nSI2W1vmo25Kk+bCwAAckaDBg2CbkLCI7COMH36dPv555+tZs2aduihh9qWLVvcspUrV9qpp55qRYsWzZVtKBCNVK1atbDfFaSrZ9jzPNdrpwBW25wyZYr7efzxxwcyOfz3339vW7duteHDh9vVV1+dsnzPnj22adMmK168eJ63CYjm4YcftnvvvddKlSplCUmBdfUKZqd32resavnk4PrVH8x27DIrnvHfOAAAkL8o3qCAWbAIrENs377d9QZXrVrVevToYYUKJWfK6/cvvvjCzQ934IEH5so2mjRpkmH7lL66c+dOO/nkk61GjRpumQLs8uXL22+//ebGVsSynZy2ePHiqEUTChcu7HrUgfxC2SN6JKyp88zaNTL77+9Sio5NzJ4bZzZzqVnr+kG1DgAAZNGqVausdu3aQTcjoTHGOoTG/2oOuJYtW6YExFK/fn3XOzxr1qxc24Z6oRU062dali5d6oJoP6j2NWvWzP38999/La9pzOsVV1zh/t2/f3/XY66HeqqjjbFOz9NPP+3Om9Ll1cutmwSPPPJIltulmw7qzT/44IPdNtVLefjhh6c6T3rPlMresGFDK126tMsoqFOnjl166aUu3T6U2qNjeumll9x4Xd0wUaCmtr7//vtunY8//tgdh45Bdw4vuOAC26uxrRE0DEDt0XWhfdaqVcsGDx5s27Zty9IxL1iwwE4//XR3g0PDAipWrOhu7syZE16Yyh9n/O2331rfvn3demprmzZtbMKECW6d559/3vbbbz93bDrGW265Jc39vvvuu258vbah6/OMM85wf9wjrV271o2t1/Wr49Vxd+nSxd2IiqSbRMri0Hum907DKH799deo+9e5veqqq1yGh7Zbt25d1yud2XHXP/zwg5133nlWuXJltx19ZkeOHJlqG5s3b7ZzzjnHnTedn6ZNm9orr7xiJ554Ysq1n28tW2dWs2Lq5f6ypWvzvEkAAAAFAT3WIZRO7QdlkbRMX/Z37dqVbjp4VrehQE3LFYwrhbxDhw6pUsEVQEQbR+0vUzCjwDyjdHD1qsdKgUPoDYJId911l3366acuRV3BxgEHHOCWlyhRwjJDAeWIESOsXbt2LtDRMSkwV2q5xpM/+OCDllkK5Lp27WpHHnmkG/utMe9q55lnnmlTp05NOS4Fdl9++aVbV+PVlcL+9ddfu0B/7ty5Nnbs2FTbvvPOO916/fr1cz9ffPFFF6Q+9thjrs2nnXaaCzA//PBDF6QqSAsNTseMGeNuROia0DYUvCrYe+6551w7f/zxx3TPeyRdVwo+dQ1puIECfQXUb731lh122GHuGHWzIJQCfr1PF110kTtXL7/8sp1yyil200032R133OHOk9r1+uuvu/dZNwv69OkTtg3dKNJNiZNOOsl69erl2v3ee++5ugGTJ09O6R1et26dqwOgz4cC0FatWrkx+DoPCqA1nEDvvf++qc2rV692223durUL+DXUQccXSefvtddeczdSBg4c6Mb8a1hCtM9genQ+dM4HDRrkbo7pfFx88cWu3fo8+o499lg31KNTp07umtENjQsvvDDT+8u2XbvNNmxNvUzp3Ks3hi+vVCa5l3rbTrPiUf7sl/ivF1/PAwCAuON/B0dwCKxDaJywqNcykj8mM6PxC5ndhnrjFDiol1HBtoICBVYfffSRHXfccWHBkHrIFKBoH6FjRNWTLQo61MOaUVA7evRoi5WCIPWkpuX88893PXQKWBVwKMjJLAVNCqoVtCnQ8t1zzz0uKH7qqadcj2Rm01sUuN13330uqPadddZZLthU76R6S0Xnefny5S51PdQJJ5xgn3/+uXs/9B6FUuCltH4/1f2ggw6yc8891wViCiwVoIqCaQVcunHiB9a6sXHJJZe4IF7BZ+h7qXUUxL766quZOpcKKNWmiRMnhrVVQaKCP21XbQila1ABon9jRjd0FFDffPPN9tNPP7mAUi677DJXfE83GiID60WLFtmjjz6akrUgCrT1Pmo88w033OCW6WaJzrF687t3756yrm5CqKf8uuuuczczRK9RAH7rrbe69vh040YBdCi9N9qXAnXdPPDH86tnXNdOZui91M0N/3zo5kjnzp3dcejmgrz55pvunOkYdMPFv4mlGwC9e/e2PDXhn+SpsiKpONkb48OXzXs2uahZyWJmO3anfs32/wJqPQ8AAOLO33//HfPMKcgdBS4VXIGlvmDH+gjtvVVgIpEBlvhftv110pLZbShN+ZBDDnE9jKrm1759e/eFXj1n48eHfzlWAKLeUY3VVjCtgFa9kqG9mxm1zw8YY30oLTa3qZdWAYp6rTVeO/Sh8eR6j6L1GmdEgWNoMTXxg7o///wzLKDy3y/ta9myZW7f6iFVBoB6UyOpdzp0/LhSrkXBsh9Ui3psFfQpqPR7WxV4b9iwwfUIq3c29HjV6ysK6GOlona6OaHrSDdfQrendG7dGPFTvEPpJkBoBoTeb/+a9INqUeq2bvD4Y+lD6TkFzaF0M0MURIvOof7dokUL1+sd2j7dWNL50Q0G/9rVsZcrV85uvPHGsO1GS+/WDQhtX8cSWiRPKfa62ZEZutkRej4UmOsmlYYK+N555x33UzcCQjNDdMNGKehZoWsgdMjB9u3hww80REQ33ELpGrUDGpiNu8091rxxWfK/29Q369bW1r55uXlfDk15fm0xL3kfSvlets6ls4emrO9e9F/qfq1K4fuI3GcIXdOhQ1cijyNyH2keB/tgH+yDfbAP9sE+sr0Pbb8gHEd+3kfC9VjrJGlcbawU0Po9vP6XagWvkSnX/pf+jKa0yoltaJyqptlSOq2CJr93W0GbXxztk08+ccsUUKsY2sKFC10qeCyFmSJTgoOm49QHQb2DacnshS1KpY881/74dKUZh74vCsAVNEV+KCXyQyeRReIU0Eq03n29n8pS0HunrITff//dLVdvbGiPbKjQ9mVEFefVZqWzpxXcRRaWE42LDuWnMkfbhgLdaIG1erIjbyLp+lLGhv+e6brUHzK1M632KUjV+dHNCr0HGu8eOTWdv91Q/vjxaOlPqj2gon6xijwf/nFrijufPmdqa+iNB5/arB78zNI0daFKlAivoq/PdOQNLmUXOMckH3fKsxXLuOC50pldwvfh/6NtA7Mf/7YyypIIGWpQZPI8s1LFzZrWSr2PNH6PrPUQeRyRhQvTPQ72wT7YB/tgH+yDfWRrH/rOUhCOIz/vI+ECaxVE0njHrAhN1VYwFGuKd05vwz+OaOOh1bunnj/dhVHw7heeUg+s9h1LYO23IxbadrTe95zkjwtXoai0pkFST35mpdfu0GJiSrlWqq96KC+//HL3IdJ51A0aje2OVngsrW3HMi7aD9zV06vp2KLJTO+nvz2NVdY44WiiDQ9I6wZPTr/ffvuUoh7ZCx0qs+Pyc1pa5yNaQcEgprXLET0PSZ5y672f981jrfHYb080O+kgptoCACBORQaWyHsFLrDODvVw/vPPP25sbmRQrGXqOc5oHuuc2IYoVViiBZoKfFRUyqceQQXgfnXwjCh9NqfGWOcE9c5rbKtS4Y8++mjLaxrPrt5KVekODZjmzZuXK/vzx7+oRzZyzHJWKL1a7Va6S05sLzPUg6sbPKHBuHq2dWPJv8una1U3k7RM45AzCkr1Pwb1Wqs6emivtb/dUEp1F2UBqOBZqNyokq8bHqpOruJ3kddqbl0vOR5Yd2pqNvBJs78Wm1Upa/b052Z79poNy+Mx4gAAIEczQKNl1CHvFLgx1tmhys0KENT7G9pLqaq/SmVVKnYo5eorVTt03cxsQ2Nuo42JVhqwqlErCFdaR3r8glXaZ6zVAPPbGGtVpRZVo46c3kqUXqvgLbf4vcyhPZN6r1SsKzf07NnT3XRRJfHQ8bs+BY/RpqtKi9K8VaFaRbU09VUkHZcC4NygAPjJJ58MW+YXLNNNGf/86t+6ph966KGo2wk9DxoHr/RrFa+Ltt1QKpSmQP2ZZ54Ju3ZUnyDaNF458d7JAw88EHa9qKhZVtLA85xugHx2i9lZh5k9/qnZdaPNqpQz+2aYWTPmvgQAAMgqeqxDqHdM0+ooQNEUUgqCFeRobKiC3MjK0JoHWONI1Uvop25nZhvqlVZRLvXUKtBSKqpSvNXTpmAhsqqxnlMhLY1rVQ+gevR0d0pBiMYnp1etPD+PsVbblRatAE3nSwG92qhzq55I9WarGrp/jnNat27dXECqKuHqhVRFao23zq39aYyHCrapyrWuBxVo01zIuh70fuq6UqCYmargmkdZwbWKaKk6u6auUuCnXtRvvvnGFWKLrAqeUz24//vf/1wgq15/FdLTNape+dDCcToejXdWhXYVMtPUYPqsKKDW+nrf/argCqhVZV7V0fW5UdE+bV/XQuR7oud0zG+88YY7ZgXwGhOv1H59rnK6F1n70lzmKiCoHnJ/ui1dP7qppn8H6rvhGa+jcdjPX5r8AAAABYLiAwSLwDqCvqhrXLGm8VFPsNK2laqsSsmxpHBnZhtK89YUUqrwrXmI1fusZVpXBckiA2WNQVVArVRzBdUaB6y0WY2tjZzzOt488cQT7vxo6iZNqaTeR/XWKzhST2VkIaucpIBT5103OTTNlHrpNQe1Am5VaM8NqgiuQExTSmm/6vHUe6v0aQXc/lRgsVJKtK43Ba5fffWVe+haU2/2EUccYeedd16uHIeKuCnQVLaB5uzW+6R5tJ999tmwKt2qBaDAWO+lAmudZ/Vk61wrbUkV4X1qs6qYa7y4Alg9dANC50nTWkXS9aLr35/iS58JnVeljmuqtpykG146t8qy0I0z9YrrfdSNEmUg6LOcUXFCAACAnBYt6xN5K8mLVpkHAJApqgquWgdLliyJqYhdmp4fZ3bBM2YjLzY7/9icbCIAACigpk2bxhjrgDHGGgAyIXQORJ9Sz5XWrmEg2QqqAQAAEJfIWUTcUKGsjKYKUzq1Px9zvFNhPI2rT4+K1ikVGXlHY8cnT55shx9+uEtx1zhwpbdr7Pydd94ZdPMAAEACiqwFhbxHYI24cdxxx7lxuulRMSm/CFa8u+WWWzIcI6wxyqoij7yjmgYaIz5q1Ch3o0cBtYLs++67z9VXAAAAyGuzZs1yhWQRHAJrxA0VyVKvdUZVqguKiy++ONXczJFU0A55q2/fvu4BAACQX6jOC4JFYI246ilMJC1btnQPAAAAID25NU0sYkeVHQAAAACIY5rCF8EisAYAAACAOPbPP/8E3YSER2ANAAAAAEA2EFgDAAAAQByrU6dO0E1IeATWAAAAABDH9uzZE3QTEh6BNQAAAADEsWXLlgXdhIRHYA0AAAAAQDYQWANAftKuUfhPAACADLRs2TLoJiS8JM/zvKAbAQDYZ+bMmda0adOgmwEAAOIE3x2CR481AOQzW7duDboJAAAgjvDdIXgE1gCQz5QuXTroJgAAgDjCd4fgkQoOAPnMzp07rVixYkE3AwAAxAm+OwSPHmsAyGf++uuvoJsAAADiCN8dgkdgDQAAAABANhBYA0A+U6tWraCbAAAA4gjfHYJHYA0A+UxSUlLQTQAAAHGE7w7BI7AGgHxmyZIlQTcBAADEEb47BI/AGgAAAACAbGC6LQDIZ3bs2GHFixcPuhkAACBO8N0hePRYA0A+s2jRoqCbAAAA4gjfHYJHYA0A+czmzZuDbgIAAIgjfHcIHoE1AOQzJUqUCLoJAAAgjvDdIXiMsQaAfGb37t1WpEiRoJsBAADiBN8dgkePNQDkMzNmzAi6CQAAII7w3SF43NYAgIDs2bPHZs6cmWr53LlzqewJAABixneH3Ne0aVMrXLhwms8TWANAQBRU77///kE3AwAAABn466+/rEWLFmk+zxhrAMhHPdbLly+3rl272jfffGM1atSwgli1tGPHjvbrr79amTJlgm5OQuHcB4PzHgzOezA478HgvOePHmsCawDIRxYvXmx169Z181HWqVPHCpqNGzda+fLlbcOGDVauXLmgm5NQOPfB4LwHg/MeDM57MDjv+QPFywAAAAAAyAYCawAAAAAAsoHAGgDyEaVwde7cucCmcqli6W233Ubl0gBw7oPBeQ8G5z0YnPdgcN7zB8ZYAwAAAACQDfRYAwAAAACQDQTWAAAAAABkA4E1AAAAAADZQGANAAAAAEA2EFgDAGK2d+9ee+SRR6x58+ZWokQJq1u3rl1zzTW2ZcuWDF97++23W1JSUpqPokWLxrz+gw8+aIkkO+ddNm/ebHfffbe1bt3aypYta1WqVLFDDz3UXnrpJYtWw/SXX36xY445xq2rCvXHHXecTZs2zRJNXp73AQMGpHm9v/POO7l0hAXzvK9YscIGDx7sXlesWDGrV6+eXXHFFbZ+/fqo6//777926qmnWsWKFa106dJ2xBFH2DfffGOJJi/PO3/f97nnnnusV69e1qhRI3f8DRo0yNJ2Ro8ebQceeKCVLFnSqlevbueff76tWrUq6rr8jc8dRXJpuwCAAuiqq66yxx9/3E477TT3hevvv/92v0+dOtW++uorK1Qo7fu1p59+ujVu3DjV8unTp9sDDzxgJ510UtTX6YueApJQ7du3t0SSnfOuL8vHH3+8TZw40fr372+XXXaZbd261V5//XUbOHCg29Z9992Xsv7PP/9sXbp0sdq1a9sdd9zhlj355JMu2NA2FCQmirw8775XXnkl1bKOHTtaIsnOeV+5cqUdfPDBtnTpUrvooousVatWNmPGDHvmmWfshx9+sAkTJlipUqVS1p8zZ4672VGkSBG7/vrrrXz58jZy5Ejr3r27jR071gUfiSIvz7uPv+9mN910k1WqVMnatWuX5s2fjOg8Xn311W66zscee8wWL15sDz/8sP3000/266+/uhtGPv7G5yJNtwUAQEZmzJjhJSUleaeffnrY8scff1xdb95rr72Wpe1eeOGF7vWffPJJ2PLbbrvNLZ83b56XyLJ73idOnOjWu/LKK8OW79ixw2vYsKFXvnz5sOUdOnTwypYt6y1evDhlmf6tZccee6yXKPL6vPfv39+tn+iye96vuOIKt96YMWPClut3LR8+fHjY8l69enmFChXypk6dmrJs06ZNXr169bymTZt6e/fu9RJBXp93/r7vM2fOnJR/t2zZ0qtfv36mXr9q1SqvVKlS7m/37t27U5Z/9NFH7hzfddddYevzNz73kAoOAIiJetqUvnrllVeGLb/gggtcT8Srr76a6W0qxfCNN96wOnXquFS0tGzcuNF2795tiSi7513nTmrVqhW2XKma6ikK7cmYPXu2/fbbby4tUb0ZPv1by9RrtXz5cksEeXneQ2mfeq16vBNRds/7t99+61Jhe/fuHbb8rLPOcunNo0aNCvv789FHH7neu7Zt26YsL1OmjEujnTlzpvs8JIK8PO+REvnvuygFPDs++OADlw2jrJjChQunLFcWmLYd+t7xNz53EVgDAGKi/xkrFTAyLVVfmvSlNCtfQN9++233pUrjS0O/EIRq06aNS8/UfpSyqfTMRJLd867XVahQwe6//353vhcuXGj//POP3XjjjTZ58mQ31jF0X3LIIYek2k6nTp3cF2+9JhHk5XkPpWtdDwUpxx57rBsLmUiye9537Njh1tVY1VDaps7p3LlzbfXq1SnDULR+Wte7355EkJfnPVSi/33PCRn93dbfHdV7iGXdRPobnxsIrAEAMdHYOfW0FS9ePNVzututL007d+7M1DZfeOEF90Vs0KBBqZ5TUHLhhRfaE088YR9++KEr8LJgwQLr0aOHK/6UKLJ73lWQSb1yGsN35plnWv369a1Fixb21FNP2bvvvut6pEL35W832r5kyZIllgjy8rxLjRo13BhXjUl9//333bjLSZMmuXGP6kVKFNk97y1btrR169alKsSk37VcdJPD35e/3Wj7Eq73nD/vwt/3nJPRdaxg2V+Haz53UbwMABATpZpF+9Il6m3w11GqayxUiXf8+PF29NFHW8OGDVM9H5mSKArAVRRHAUjPnj1dymZBlxPnXedJ5+3kk092vUJr1651AV7fvn3dl1r1jPrbkWj7C91XIsjL8y733ntv2GtVpVrrqbfw4osvtlmzZlkiyO55198NpcbqZsajjz7qzv+ff/7plmvmgV27dqVcw1zvwZx3f/1Iifj3PSdk5jrmms9d9FgDAGKicXZK94tm+/btKetkprdaNJYxVpUrV3bTuahyqqqXJoLsnvc//vjDBXUK4lR9XRV/zzvvPHdTQ72k6jnds2dP2Hai7S8r73E8y8vznpYmTZq4QEXjIjXeNxFk97yrh191GzZt2uR6P5UpoLGmRx11lJ144oluHU0vFLodrve8Pe9pScS/7zkhM9cx13zuIrAGAMRERZiUDhjtf8hKHVMaYay91SpUozk39UVKAUdm+HN8RhuvVxBl97xrGhZ9YVJhmlD68qQvwEq/nD9/fsq+/O1G21daKYQFUV6e9/RwvWf+74zOuaYb0jRRmupJ6a/PPvusW6Zptfxp/7jegznv6Um06z0nZHQda7iVvw7XfO4isAYAxKRDhw6uUrHmxAyl4EHj6A466KCYt/Xxxx/bihUr7Jxzzkkz/TAtfkps9erVLRFk97z7X5ai9Y76lXj9n9qXaO7TSJr7VF/QEmWO2bw87+nhes/a3xkVQ1QavXpSq1Wr5iodK+DTPL9+j5zm69Xfn7Sud8nM37V4lpfnPT2Jdr3nhIz+bjdr1iwlrZ6/8bksF6fyAgAUINOnT093ntNXXnklZdns2bO9v//+O81t9ejRw71G24xm165d3vr161MtX7hwoVepUiWvcuXK3tatW71EkN3zrnmUtd59990XtnzdunVezZo1vYoVK4bNfXrQQQe5+UyXLFmSskz/1rKjjz7aSxR5ed43b97sbdu2LVUbpkyZ4hUrVsxr0aKFlyhy8u+Mb8+ePW6+am33m2++CXuuZ8+ebh7radOmpZrHukmTJgkzj3Vennf+vqcto3msFyxY4M79zp07U5atXLnSK1mypNexY8eo81hHziHO3/jcQ2ANAIjZkCFD3P+oTzvtNG/kyJHe1Vdf7RUpUsTr3Lmz+xLl0xeDtO7d6n/ghQsXdl8C0qLgo0KFCt6AAQNcYPLcc89511xzjVe+fHn32rfeestLJNk57/Pnz3dfVvXl9pxzzvGeeeYZ76677vIaNGjg1n3qqafC1p8wYYIL5ho1auQ98sgj7qF/ly5dOiz4SAR5dd6nTp3q1ahRwxs8eLD30EMPec8++6x38cUXe8WLF3dfmH/88UcvkWTnvCso1o2Im266yb32wQcf9Nq3b+/W0/mPNGvWLHeTo1q1at4999zj3pe2bdu6vzOff/65l0jy6rzz9z3c6NGjXfCrh65DnRv/dz0XSu+Fzum8efPClut8a3mXLl28ESNGeEOHDnV/s5s3b+7em1D8jc89BNYAgJjpbrj+B960aVP3P+ZatWp5V111Var/cacXWOtLlp7Tl6m0bN++3TvvvPO8Vq1auS8Z+nKnwOOMM87wfvnlFy/RZPe8q4epX79+Xu3atd25VM/EEUcc4b377rtR9zdx4kSva9eu7otWmTJlvG7dunmTJ0/2Ek1enfdly5a54LtZs2ZuHa1bt25d99pYegYLmuyc9x07dni9e/d2NzB0Y0JBs67f9ILkv/76yzv55JNdYKcbGYcddpg3btw4L9Hk1Xnn77sXNViO9tBzsQTWMmrUKK9Nmzbu/FetWtUbOHCgt2LFiqj75G987kjSf3I73RwAAAAAgIKK4mUAAAAAAGQDgTUAAAAAANlAYA0AAAAAQDYQWAMAAAAAkA0E1gAAAAAAZAOBNQAAAAAA2UBgDQAAAABANhBYA0AOGzBggCUlJblHq1atUj2/d+9eu/POO22//fazokWLup9y//33W/Pmzd3zWfHss89avXr1bMeOHamee/TRR1PapMfq1auztA8AAACkViTKMgBAGr744gs77rjj0nz+5Zdfdj+rVKlijzzyiFWoUCHVOk8//bQNHTrUrrnmGmvTpo3VrFnTNm7caPfdd589+OCDVqhQ+D3PYcOGuceff/5pLVq0CHtu0KBBbp8fffSRC+hvv/12GzFihF1++eVh66nNatN7771n77//vgVhz549NmnSJJs1a5YL/itVqmQdOnSwOnXqpPu67777zmbOnJnm82effbaVLl3aVq5c6dZbtmyZbdq0yYoXL27Vq1e3gw46KOr7AAAAkFOSPM/zcmxrAFDAqVf5f//7nz3++ONWsWLFVM93797drrvuOhcMzp8/P+o22rdv74JcBemhPcq33XabrVixwkqUKBG2vgLG+vXrW79+/VzQ7HviiSdcAK3e75tvvtktU9vefPNNmzdvnuuZjqTAW0H6qlWrXBvy0tdff21z58611q1bW/ny5V0QrGM76aSTrEaNGmm+TudENx5C6X9d48ePt7Jly1qvXr3csnHjxtny5cutUaNGVrlyZdu6dau7GbFr1y479dRTXSAPAACQG+ixBoBMmD59ugsKhwwZEjVwzcj27dvt999/d8FtqFGjRtnJJ5+cKqiWatWquV7ZV155xe666y4XEH///fd29dVX2xlnnJESVMuZZ57pgv9vv/3WunbtavmFAug5c+bYwQcfbAcccIBb1qRJE3vnnXfsl19+sVNOOSXN16rXWY9QCqB3795tjRs3TlmmgF3HXLhw4ZRlSrPXPqZNm5avzgcAAChYGGMNAJmgoPjAAw/MUlB93nnnWcmSJV1K9C233OK2ccghh7jeZQXsxxxzTJqvveqqq2zbtm1uHPWiRYtcAK3x2C+99FKq3nD1zH744YeWn6inWscbmspepEgRa9asmeuR3rx5c6a2N3v2bPczNLBWr3doUC26CaLMgvXr12f7GAAAANJCjzUAxGjnzp3277//2uGHHx61+JeCOBUjS4t6nfW80rkfe+wxFwArxXvixInu+Xbt2qX52pYtW1q3bt3sqaeesg8++MClN+tnmTJlUq2r7UyYMMFyioqp6dhjoXHN0W46rFmzxp2fYsWKpeqN95+PdixptUe93+rFVip4epQyrhsS0dL2AQAAcgqBNQDE6K+//nIBrXqN9YikoLtp06Zpvl6pyBpnrEJbSiX3i5Tdeuut7mfDhg3T3b9Sv1WETGnVn332WUo18UgaY6y08ZyitOtPPvkkpnX79OkTNdjVeOdSpUqlWu4v27JlS8ztUY+9ip8plTyWnm1tWz35AAAAuYXAGgBipHRtUfp17dq1Uz0fS6Cnbaj3ObTyt3prlRadUY/t33//nRI4q0haWtQ7q17atILZzFIhsBNOOCGmdZXqHo3GQ0emaYu/TOnxsVKwrPOn85AepX+rwJl6ttO74QEAAJBdBNYAkInx1QqA1SsbmdKcmW2kFxSn5auvvrJrr73WBe+arurLL790qeHR+JM9ZGUceFrp3RlNiZURnbdowbO/LFrQHY0yBhYsWODaE63Qm083FcaOHeveJ41dj5zCDAAAICfxTQMAYqTeZqVrZzWoVg+q0phVvTqyR1g9upp7Oa3CX2eddZYrmvbbb7+5ccmaIzst69atcz3VafUeZ5aCXwWqsTw0/jkatUfPR/KXKT0+FprCLLIaeCSNB1dQrZ/qaY912wAAAFlFjzUAZCKw7tSpU7ZeL23atAlbrureourgkc+pWramolLRs/fff98VALvkkkvcdF1KDQ+tsu3TdqItzypV7c7uGGvdPFi6dKkLdkNvTGi8uP98rGngOhcNGjSI+ryC7s8//9w2bNhgPXr0oGgZAADIEwTWABBjAS8FgX4QnNU0cIkMnjXllkyaNCnsOaV0n3vuua4omual9tOxFVjfe++99uijj7oK45GmTJniKpDnlJwYY63x0LqxoJsB/jzW6gnXsakH3h9frsBYNxOU5h2Z6q1x44sXL3a91Uotj6TechWH040ApdtHzn0NAACQWwisASATQfGqVavs1VdfTfW8gsXIFO9ICixV9EzTbEUGna1atXLjqAcNGpSy/Pbbb3dTail4Puyww1KWV61a1c455xxX+fvuu+8O6+2dPHmyrV271vVy55ScGGOt4FnH+euvv7oAWT3vM2fOdOnvnTt3TllPNy/UO64pww466KCwbWiKLd1sSCsN/Oeff3bjr+vVq+eqhmssemaLywEAAGQFgTUAZCKNe9SoUe4RafTo0TEF1pG91T4F1EOHDnVBp3p9lfY9fPhwGzx4sF144YWp1r/qqqvshRdecNN+3XzzzSnL3377bRdYamqv/KZLly6uZ1oBr1LCdYNB04fVrFkz5jRwnZtoFdn96uqycOFC94hEYA0AAHJLkueXjwUA5IgBA/7fzr3bRghEUQClBALKoRhLFECGqAF6ogxqIbTuSBusJWstHs7OSYEZ0qv3+eqO42gt2WlZ7vv+4zeZCU5Fd9u2bpqmW/emSpvZ43Vdu3me355d19VarHP+vu+t8j4Mw617AAB4Zys4wD/I9u+0bI/j+Kf30xq9LEsLvb9t1v4klfQs9kqV+6dUtvM/OR8AgGepWAM87DzPtgE70vpc2ST+ZNDPorCXzDUnhAMAUCdYAwAAQIFWcAAAACgQrAEAAKBAsAYAAIACwRoAAAAKBGsAAAAoEKwBAACgQLAGAACAAsEaAAAACgRrAAAAKBCsAQAAoLvvGweUgqiaGoGbAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Kernel Shapley Explainer"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:58:55.149395Z",
     "start_time": "2025-02-08T20:58:55.147422Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import logging\n",
    "for log_name in ['shap', 'matplotlib', 'sklearn']:\n",
    "    logging.getLogger(log_name).setLevel(logging.WARNING)"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:59:04.698495Z",
     "start_time": "2025-02-08T20:58:55.159747Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from os import path\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from txtproj.trainer.txt_dataset import concat_row_elements\n",
    "from xplainproj.classifier.onnx_exporter import WEIGHTS_FILE_NAME\n",
    "from xplainproj.classifier.xplain_configuration import ModelConf, TrainerConf\n",
    "from xplainproj.classifier.xplain_classifier import XplainClassifier\n",
    "\n",
    "from utils.shapley_kernel_explainer import (\n",
    "    compute_kernel_shapley_values_for_high_dim, shap_bar_plot, shap_beeswarm_plot, shap_heatmap_plot, shap_decision_plot, shap_force_plot, shap_waterfall_plot\n",
    ")\n",
    "\n",
    "\n",
    "for outlier_class in [XplainClassifier]:\n",
    "    print(f'Outlier Class: {outlier_class.__name__}')\n",
    "    feature_names=[name for name, _, _ in ModelConf.input_features]\n",
    "\n",
    "    train_df, test_df = train_test_split(\n",
    "        df,\n",
    "        test_size=TrainerConf.dataset_split_ratio,\n",
    "        random_state=TrainerConf.dataset_random_state\n",
    "    )\n",
    "\n",
    "    X: np.ndarray = np.stack(test_df[feature_names].apply(concat_row_elements, axis=1).to_list())\n",
    "\n",
    "    # Sample 1000 rows (vectors) without replacement\n",
    "    background_size: int = 50\n",
    "    sampled_indexes = np.random.choice(X.shape[0], size=background_size * 2, replace=False)\n",
    "    X = X[sampled_indexes]\n",
    "\n",
    "    fqfn_model_weights = path.join(MODEL_DIR, WEIGHTS_FILE_NAME)\n",
    "    trained_model = outlier_class(model_conf=ModelConf())\n",
    "    trained_model.load_model_weights(fqfn_model_weights)\n",
    "\n",
    "    print(f'Shapley values computation for {X.shape=};')\n",
    "    shap_values = compute_kernel_shapley_values_for_high_dim(\n",
    "        model=trained_model,  # model with *decision_function* method\n",
    "        X=X,\n",
    "        input_features=ModelConf.input_features,\n",
    "        num_parallel_calls=7\n",
    "    )\n",
    "\n",
    "    print(f'Global SHAP vizualization for {X.shape=}; {shap_values.shape=}; {len(feature_names)=}')\n",
    "    shap_beeswarm_plot(shap_values=shap_values, input_features=ModelConf.input_features, X=X)\n",
    "    shap_bar_plot(shap_values=shap_values, input_features=ModelConf.input_features, X=X)\n",
    "    shap_heatmap_plot(shap_values=shap_values, input_features=ModelConf.input_features, X=X)\n",
    "\n",
    "    index = 2\n",
    "    print(f'Local {index=} vizualization for {X.shape=}; {shap_values.shape=}; {len(feature_names)=}')\n",
    "    base_value = shap_values.mean(axis=0).sum()  # Use the global mean as an approximation\n",
    "    shap_decision_plot(shap_values=shap_values, base_value=base_value, input_features=ModelConf.input_features, X=X, index=index)\n",
    "    shap_waterfall_plot(shap_values=shap_values, base_value=base_value, input_features=ModelConf.input_features, X=X, index=index)\n",
    "    shap_force_plot(shap_values=shap_values, base_values=base_value, input_features=ModelConf.input_features, X=X, index=index)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier Class: XplainClassifier\n",
      "Shapley values computation for X.shape=(100, 38);\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/14 [00:00<?, ?it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "  0%|          | 0/14 [00:00<?, ?it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "  0%|          | 0/14 [00:00<?, ?it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "  7%|▋         | 1/14 [00:00<00:07,  1.84it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "  7%|▋         | 1/14 [00:00<00:06,  1.87it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "  7%|▋         | 1/14 [00:00<00:07,  1.83it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 14%|█▍        | 2/14 [00:01<00:06,  1.96it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 14%|█▍        | 2/14 [00:01<00:06,  1.89it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 14%|█▍        | 2/14 [00:01<00:06,  1.88it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 14%|█▍        | 2/14 [00:01<00:06,  1.86it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.99it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.98it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.97it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.92it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.93it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 21%|██▏       | 3/14 [00:01<00:05,  1.88it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 29%|██▊       | 4/14 [00:01<00:04,  2.12it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 29%|██▊       | 4/14 [00:02<00:05,  1.94it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 29%|██▊       | 4/14 [00:02<00:05,  1.94it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 29%|██▊       | 4/14 [00:02<00:05,  1.93it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 29%|██▊       | 4/14 [00:02<00:05,  1.90it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 5/14 [00:02<00:04,  2.07it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 5/14 [00:02<00:04,  2.03it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 5/14 [00:02<00:04,  1.97it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 5/14 [00:02<00:04,  1.95it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 36%|███▌      | 5/14 [00:02<00:04,  1.90it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 43%|████▎     | 6/14 [00:02<00:03,  2.04it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 43%|████▎     | 6/14 [00:03<00:03,  2.00it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 43%|████▎     | 6/14 [00:03<00:03,  2.04it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 43%|████▎     | 6/14 [00:03<00:03,  2.02it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 43%|████▎     | 6/14 [00:03<00:04,  1.96it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 7/14 [00:03<00:03,  2.06it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 7/14 [00:03<00:03,  2.01it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 7/14 [00:03<00:03,  2.02it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 7/14 [00:03<00:03,  1.95it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 7/14 [00:03<00:03,  1.98it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 8/14 [00:03<00:02,  2.04it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 8/14 [00:04<00:02,  2.02it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 8/14 [00:04<00:02,  2.01it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 8/14 [00:04<00:03,  1.98it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 8/14 [00:04<00:02,  2.01it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 9/14 [00:04<00:02,  2.07it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 9/14 [00:04<00:02,  2.06it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 9/14 [00:04<00:02,  2.02it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 9/14 [00:04<00:02,  2.05it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 9/14 [00:04<00:02,  2.00it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 71%|███████▏  | 10/14 [00:04<00:01,  2.04it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 71%|███████▏  | 10/14 [00:05<00:01,  2.03it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 71%|███████▏  | 10/14 [00:05<00:02,  2.00it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 71%|███████▏  | 10/14 [00:05<00:02,  1.99it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 79%|███████▊  | 11/14 [00:05<00:01,  2.07it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 79%|███████▊  | 11/14 [00:05<00:01,  2.06it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 79%|███████▊  | 11/14 [00:05<00:01,  2.04it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 79%|███████▊  | 11/14 [00:05<00:01,  1.95it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 79%|███████▊  | 11/14 [00:05<00:01,  1.76it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:05<00:00,  2.07it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:05<00:00,  2.05it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:05<00:00,  2.03it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:06<00:01,  1.92it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:06<00:01,  1.86it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 12/14 [00:06<00:01,  1.85it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  2.02it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  2.15it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  2.07it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  2.01it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  1.86it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 13/14 [00:06<00:00,  1.97it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "100%|██████████| 14/14 [00:06<00:00,  2.06it/s]\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "100%|██████████| 14/14 [00:06<00:00,  2.06it/s]\n",
      "100%|██████████| 14/14 [00:06<00:00,  2.03it/s]\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "100%|██████████| 14/14 [00:06<00:00,  2.03it/s]\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "100%|██████████| 14/14 [00:07<00:00,  1.97it/s]\n",
      "100%|██████████| 14/14 [00:07<00:00,  1.97it/s]\n",
      "100%|██████████| 14/14 [00:07<00:00,  1.97it/s]\n",
      " 50%|█████     | 1/2 [00:00<00:00,  3.53it/s]/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "/Users/shershen/virtualenvs/tensor_craft/lib/python3.12/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global SHAP vizualization for X.shape=(100, 38); shap_values.shape=(100, 7); len(feature_names)=7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00,  3.73it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x430 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local index=2 vizualization for X.shape=(100, 38); shap_values.shape=(100, 7); len(feature_names)=7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x430 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 3 Axes>"
      ],
      "image/png": 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"
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 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       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-08T20:59:04.712728Z",
     "start_time": "2025-02-08T20:59:04.711548Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}