OverallVersion_FullyLearned_NEW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# === Header ===\n",
    "#\n",
    "# @date: 05 / 07 / 2019 (Xi'an)\n",
    "# @author: ZHE LI\n",
    "# @title: Learned KD-Tree - Fully Learned\n",
    "#\n",
    "import math\n",
    "import time\n",
    "import random\n",
    "#import pysnooper\n",
    "# import pixiedust\n",
    "import import_ipynb\n",
    "# import statistics\n",
    "import numpy as np\n",
    "import import_ipynb # this can only be installed from pip (no conda)\n",
    "from scipy import stats\n",
    "from numpy import genfromtxt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# === Data Loading ===\n",
    "original_domains = [\\\n",
    "    [1,1.20000000e+07],\\\n",
    "    [1,4.00000000e+05],\\\n",
    "    [1,2.00000000e+04],\\\n",
    "    [1,7.00000000e+00],\\\n",
    "    [1,5.00000000e+01],\\\n",
    "    [900.99,1.04899500e+05],\\\n",
    "    [0,1.00000000e-01],\\\n",
    "    [0,8.00000000e-02]\\\n",
    "]\n",
    "original_domains = np.asarray(original_domains)\n",
    "# original_dataset = genfromtxt('/Users/lizhe/Desktop/LearnedKDTree/DataAndWorkload/SyntheticData/TPCH_12M_8Field.csv', delimiter=',')\n",
    "original_dataset = genfromtxt('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/data/TPCH_12M_8Field.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print(np.min(original_dataset[:, 0]))\n",
    "# print(np.max(original_dataset[:, 0]))\n",
    "# print(np.min(original_dataset[:, 1]))\n",
    "# print(np.max(original_dataset[:, 1]))\n",
    "# print(np.min(original_dataset[:, 2]))\n",
    "# print(np.max(original_dataset[:, 2]))\n",
    "# print('variance for the first query dimension: ',np.var(dataset[:,0]))\n",
    "# print('variance for the second query dimension: ',np.var(dataset[:,1]))\n",
    "# print('variance for the third query dimension: ',np.var(dataset[:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# === Global Configuration ===\n",
    "\n",
    "# dimension for query and build learned KD-Tree\n",
    "used_dimensions = [1,2]\n",
    "\n",
    "# testing set percentage\n",
    "testing_set_percentage = 0.2\n",
    "\n",
    "# percentage of each dimension\n",
    "percentage_ = [1, 1]\n",
    "\n",
    "# maximum expected ranges for distribution query\n",
    "ranges_ = [40000, 2000]\n",
    "\n",
    "# maximum expected ranges for random query\n",
    "ranges_random_ = [10000, 500]\n",
    "\n",
    "# maximum expected sigma\n",
    "sigmas_ = [1000, 50]\n",
    "\n",
    "# clusters amount\n",
    "cluster_amount_ = 100\n",
    "\n",
    "# queries in each cluster\n",
    "queries_each_cluster_ = 10\n",
    "\n",
    "# page block size, or the minimum number of records a partition should holds\n",
    "block_size = 5000\n",
    "\n",
    "domains_ = np.copy(original_domains)\n",
    "domains_ = domains_[used_dimensions]\n",
    "dataset = np.copy(original_dataset)\n",
    "dataset = dataset[:,used_dimensions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from QueryGeneration.ipynb\n"
     ]
    }
   ],
   "source": [
    "from QueryGeneration import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # load query from file\n",
    "# training_set = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/query/training.csv')\n",
    "# testing_set = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/query/testing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# random_query, distribution_query, fusion_query= generate_query_with_random(100, 0.5, domains_, ranges_random_, cluster_amount_, ranges_, [0.1,0.1], True, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# training_set = fusion_query[0:int(len(fusion_query)/2)]\n",
    "# testing_set = fusion_query[int(len(fusion_query)/2):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save_query(random_query, 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/random.csv')\n",
    "# save_query(distribution_query, 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/distribution.csv')\n",
    "# save_query(fusion_query, 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/fusion.csv')\n",
    "# save_query(training_set, 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/training.csv')\n",
    "# save_query(testing_set, 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/testing.csv')\n",
    "\n",
    "# random_query = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/random.csv')\n",
    "# distribution_query = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/distribution.csv')\n",
    "# fusion_query = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/fusion.csv')\n",
    "# training_set = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/training.csv')\n",
    "# testing_set = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/testing.csv')\n",
    "\n",
    "# used in old NORA introduction\n",
    "random_query = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/intro_random.csv')\n",
    "distribution_query = load_query('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/intro_distribution.csv')\n",
    "training_set = np.concatenate((random_query[0:10], distribution_query[0:40]), axis=0)\n",
    "testing_set = np.concatenate((random_query[10:], distribution_query[40:]), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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m0urnfN/jMWZmc+o2uRxPXV2kxxOpfBpYWzhvDXA0la9pUT6jjqQLgEs4vxtucIaHzx+X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\nuZiZWemcXMzMrHT/DyKhvPLQZzbyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_queries_2d(training_set, domains_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VOGKO8rmOmVkYsQfYA1CTYtb5vcqY96t+tJj7ZMzMmtZuzeVkauoiPZ5K5VPA6sJ+q4Dj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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_queries_2d(testing_set, domains_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot_queries_2d(fusion_query, domains_)\n",
    "# plot_queries_2d_distribution_and_random(distribution_query, random_query, domains_)\n",
    "# plot_queries_2d(training_set, domains_)\n",
    "# plot_queries_2d(testing_set, domains_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from Query.ipynb\n"
     ]
    }
   ],
   "source": [
    "from Query import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from Utils.ipynb\n"
     ]
    }
   ],
   "source": [
    "from Utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from Qd_tree.ipynb\n",
      "current kdnodes:  1\n",
      "current inner loop i:  0\n",
      "current kdnodes:  2\n",
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      "current kdnodes:  16\n",
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      "current kdnodes:  32\n",
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      "current inner loop i:  21\n",
      "training time for Qd_tree (s):  346.352187871933\n",
      "Qd_tree leaf nodes:  53\n",
      "average partitions each query overlap(average):  1.48\n",
      "average records each query retrieve(average):  287521.42\n",
      "total records that all the queries retrieve:  14376071\n",
      "average partitions each query overlap(average):  1.04\n",
      "average records each query retrieve(average):  152713.96\n",
      "total records that all the queries retrieve:  7635698\n"
     ]
    }
   ],
   "source": [
    "# = = = Qd_tree construction = = =\n",
    "# from Qd_tree import *\n",
    "# start_time = time.time()\n",
    "# Qd_tree_kdnodes = Qd_tree_greedy_optimized_approximate(dataset, training_set, domains_, block_size, bins=(40000,10000))\n",
    "# end_time = time.time()\n",
    "# print(\"training time for Qd_tree (s): \", end_time-start_time)\n",
    "# print('Qd_tree leaf nodes: ',len(Qd_tree_kdnodes))\n",
    "# # Qd_tree query\n",
    "# Query(testing_set, Qd_tree_kdnodes)\n",
    "# Query(training_set, Qd_tree_kdnodes)\n",
    "\n",
    "from Qd_tree import *\n",
    "start_time = time.time()\n",
    "Qd_tree_kdnodes = Qd_tree_greedy_optimized_approximate(dataset, training_set, domains_, block_size, bins=(40000,10000))\n",
    "end_time = time.time()\n",
    "print(\"training time for Qd_tree (s): \", end_time-start_time)\n",
    "print('Qd_tree leaf nodes: ',len(Qd_tree_kdnodes))\n",
    "# Qd_tree query\n",
    "Query(testing_set, Qd_tree_kdnodes)\n",
    "Query(training_set, Qd_tree_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n"
     ]
    }
   ],
   "source": [
    "print(len(Qd_tree_kdnodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save partitions\n",
    "qd_path = 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/HDFSExperiment/generated_partitions/qd_tree_partitions'\n",
    "saved_qd_tree_partitions = save_partitions_to_file(Qd_tree_kdnodes, qd_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "insert 164 at initialization\n",
      "training time for Qd_tree (s):  13.121346712112427\n",
      "Qd_tree leaf nodes:  1593\n",
      "average partitions each query overlap(average):  9.26\n",
      "average records each query retrieve(average):  60526.44\n",
      "total records that all the queries retrieve:  3026322\n",
      "average partitions each query overlap(average):  8.66\n",
      "average records each query retrieve(average):  58391.3\n",
      "total records that all the queries retrieve:  2919565\n"
     ]
    }
   ],
   "source": [
    "from LKD_Robust import *\n",
    "start_time = time.time()\n",
    "\n",
    "data_threshold = 5000\n",
    "query_threshold = 5\n",
    "hist_m, hist_n = 1000, 1000\n",
    "\n",
    "LKD_Robust_kdnodes, kdnode_dict, accu_count_list, kdnode_idx, query_idx, query_hist = LKD_Robust_Initialization(\n",
    "    dataset, training_set, domains_, data_threshold, query_threshold, hist_m, hist_n, random_query_percentage = 0.5)\n",
    "\n",
    "end_time = time.time()\n",
    "print(\"training time for Qd_tree (s): \", end_time-start_time)\n",
    "print('Qd_tree leaf nodes: ',len(LKD_Robust_kdnodes))\n",
    "# Qd_tree query\n",
    "# Query(random_query, LKD_Robust_kdnodes)\n",
    "Query(testing_set, LKD_Robust_kdnodes)\n",
    "Query(training_set, LKD_Robust_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save partitions\n",
    "nora_path = 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/HDFSExperiment/generated_partitions/nora_partitions'\n",
    "saved_nora_tree_partitions = save_partitions_to_file(LKD_Robust_kdnodes, nora_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average partitions each query overlap(average):  9.24\n",
      "average records each query retrieve(average):  54136.08\n",
      "total records that all the queries retrieve:  2706804\n",
      "average partitions each query overlap(average):  10.3\n",
      "average records each query retrieve(average):  60342.34\n",
      "total records that all the queries retrieve:  3017117\n"
     ]
    }
   ],
   "source": [
    "# KDT partition\n",
    "root_node = [domains_, len(dataset), 0, -1, -1, -1]\n",
    "kdt_kdnodes = KDPartition(dataset, 0, data_threshold, root_node, {}, [0])\n",
    "Query(testing_set, kdt_kdnodes)\n",
    "Query(training_set, kdt_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from LKD import *\n",
    "start_time = time.time()\n",
    "LKD_kdnodes = FastLearnedResuriveDivide(dataset, training_set, domains_, block_size, 0)\n",
    "end_time = time.time()\n",
    "print(\"training time for LKD Advanced(s): \", end_time-start_time)\n",
    "print('LKD leaf nodes: ',len(LKD_kdnodes))\n",
    "# LKD Query:\n",
    "Query(testing_set, LKD_kdnodes)\n",
    "Query(training_set, LKD_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import import_ipynb # this can only be installed from pip (no conda)\n",
    "from QueryTree import *\n",
    "from LKDAdvanced1 import *\n",
    "training_set_file_path = 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/query/training.csv'\n",
    "query_trees = CreateQueryTree(training_set_file_path, len(domains_))\n",
    "start_time = time.time()\n",
    "LKD_Advanced_kdnodes = LKDAdvanced(dataset, query_trees, domains_, block_size, 0)\n",
    "end_time = time.time()\n",
    "print(\"training time for LKD Advanced(s): \", end_time-start_time)\n",
    "print('LKD Advanced leaf nodes: ',len(LKD_Advanced_kdnodes))\n",
    "# LKD Advanced Query:\n",
    "Query(testing_set, LKD_Advanced_kdnodes)\n",
    "Query(training_set, LKD_Advanced_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from LKDAdvanced2 import *\n",
    "start_time = time.time()\n",
    "LKD_Advanced2_kdnodes = LKDAdvanced2(dataset, np.array(training_set), domains_, block_size, 0)\n",
    "end_time = time.time()\n",
    "print(\"training time for LKD Advanced(s): \", end_time-start_time)\n",
    "print('LKD Advanced leaf nodes: ',len(LKD_Advanced2_kdnodes))\n",
    "# LKD Advanced 2 Query:\n",
    "Query(testing_set, LKD_Advanced2_kdnodes)\n",
    "Query(training_set, LKD_Advanced2_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from LKDAdvanced3 import *\n",
    "start_time = time.time()\n",
    "LKD_Advanced3_kdnodes = LKDAdvanced3(dataset, np.array(training_set), domains_, block_size, 0)\n",
    "end_time = time.time()\n",
    "print(\"training time for LKD Advanced(s): \", end_time-start_time)\n",
    "print('LKD Advanced leaf nodes: ',len(LKD_Advanced3_kdnodes))\n",
    "# LKD Advanced 3 Query:\n",
    "Query(testing_set, LKD_Advanced3_kdnodes)\n",
    "Query(training_set, LKD_Advanced3_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from RtreeBulkloading.ipynb\n"
     ]
    }
   ],
   "source": [
    "from RtreeBulkloading import *\n",
    "BulkLoadMBRs = GenerateRtreePartitionUsingBulkLoading(dataset, maximum_capacity = 10000)\n",
    "print(BulkLoadMBRs)\n",
    "np.savetxt('C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/data/RtreeBulkLoadPartitions_1W.csv', BulkLoadMBRs, delimiter=',')\n",
    "rtree_kdnodes = PartitionFormalization(BulkLoadMBRs)\n",
    "# print(rtree_partitions.shape)\n",
    "print(len(rtree_kdnodes))\n",
    "# RTree Query:\n",
    "Query(testing_set, rtree_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "traditional KD-Tree leaf nodes:  2048\n",
      "average partitions each query overlap(average):  9.24\n",
      "average records each query retrieve(average):  54122.42\n",
      "total records that all the queries retrieve:  2706121\n"
     ]
    }
   ],
   "source": [
    "from TraditionalKDTree import *\n",
    "traditional_kdnodes = TraditionalKDTree(dataset, 0, domains_, block_size, 0)\n",
    "# traditional_variance_kdnodes = TraditionalVarianceKDTree(dataset, domains_, block_size, 0)\n",
    "print('traditional KD-Tree leaf nodes: ', len(traditional_kdnodes))\n",
    "# print('traditional variance KD-Tree leaf nodes: ', len(traditional_variance_kdnodes))\n",
    "Query(testing_set, traditional_kdnodes)\n",
    "\n",
    "# dense_traditional_kdnodes = DenseKDNodes(traditional_kdnodes, block_size)\n",
    "# QueryDense(testing_set, dense_traditional_kdnodes)\n",
    "# print(\"=========\")\n",
    "# Query(testing_set, traditional_variance_kdnodes)\n",
    "# dense_traditional_variance_kdnodes = DenseKDNodes(traditional_variance_kdnodes, block_size)\n",
    "# QueryDense(testing_set, dense_traditional_variance_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from AQWA import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imported_kdnodes = ImportKDNodes() # the kdnodes that have been KD partitioned and insert remaining data into"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "(before) perform split at query:  315  for kdnodes:  689  node info:  [[[363151.0, 374940.0], [2504.0, 3242.0]], 11679.0]\n",
      "(after) perform split at query:  315  for kdnodes:  689  node info:  [[[363151.0, 374940.0], [2504.0, 2900.855]], 5903]\n",
      "split dimension:  1  split position:  2900.855\n",
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      "(before) perform split at query:  329  for kdnodes:  13  node info:  [[[22483.0, 36857.0], [1768.0, 2491.0]], 11609.0]\n",
      "(after) perform split at query:  329  for kdnodes:  13  node info:  [[[22483.0, 36857.0], [1768.0, 2110.8945]], 5815]\n",
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      "(before) perform split at query:  330  for kdnodes:  241  node info:  [[[150052.0, 163095.0], [8009.0, 8739.0]], 11623.0]\n",
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      "(before) perform split at query:  481  for kdnodes:  892  node info:  [[[274891.0, 289295.0], [19287.0, 20000.0]], 11643.0]\n",
      "(after) perform split at query:  481  for kdnodes:  892  node info:  [[[274891.0, 281340.29665], [19287.0, 20000.0]], 6066]\n",
      "split dimension:  0  split position:  281340.29665\n",
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    },
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      "Processing queries No.  571\n",
      "(before) perform split at query:  571  for kdnodes:  130  node info:  [[[110648.0, 124998.0], [1.0, 734.0]], 11694.0]\n",
      "(after) perform split at query:  571  for kdnodes:  130  node info:  [[[110648.0, 115660.71084999999], [1.0, 734.0]], 5511]\n",
      "split dimension:  0  split position:  115660.71084999999\n",
      "new partition:  [[[115660.71084999999, 124998.0], [1.0, 734.0]], 6183.0]\n",
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      "(before) perform split at query:  574  for kdnodes:  692  node info:  [[[349844.0, 364332.0], [4299.0, 5006.0]], 11747.0]\n",
      "(after) perform split at query:  574  for kdnodes:  692  node info:  [[[349844.0, 354880.11279999994], [4299.0, 5006.0]], 5492]\n",
      "split dimension:  0  split position:  354880.11279999994\n",
      "new partition:  [[[354880.11279999994, 364332.0], [4299.0, 5006.0]], 6255.0]\n",
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      "(before) perform split at query:  587  for kdnodes:  201  node info:  [[[125061.0, 135668.0], [5537.0, 6245.0]], 11735.0]\n",
      "(after) perform split at query:  587  for kdnodes:  201  node info:  [[[125061.0, 130690.673275], [5537.0, 6245.0]], 6675]\n",
      "split dimension:  0  split position:  130690.673275\n",
      "new partition:  [[[130690.673275, 135668.0], [5537.0, 6245.0]], 5060.0]\n",
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      "(before) perform split at query:  594  for kdnodes:  198  node info:  [[[111912.0, 125061.0], [6252.0, 6990.0]], 11743.0]\n",
      "(after) perform split at query:  594  for kdnodes:  198  node info:  [[[111912.0, 116820.70795], [6252.0, 6990.0]], 4988]\n",
      "split dimension:  0  split position:  116820.70795\n",
      "new partition:  [[[116820.70795, 125061.0], [6252.0, 6990.0]], 6755.0]\n",
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      "(before) perform split at query:  623  for kdnodes:  193  node info:  [[[100058.0, 110662.0], [5539.0, 6252.0]], 11713.0]\n",
      "(after) perform split at query:  623  for kdnodes:  193  node info:  [[[100058.0, 110662.0], [5539.0, 5920.704]], 6766]\n",
      "split dimension:  1  split position:  5920.704\n",
      "new partition:  [[[100058.0, 110662.0], [5920.704, 6252.0]], 4947.0]\n",
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      "Processing queries No.  717\n",
      "Processing queries No.  718\n",
      "Processing queries No.  719\n",
      "Processing queries No.  720\n",
      "Processing queries No.  721\n",
      "Processing queries No.  722\n",
      "Processing queries No.  723\n",
      "Processing queries No.  724\n",
      "Processing queries No.  725\n",
      "Processing queries No.  726\n",
      "Processing queries No.  727\n",
      "Processing queries No.  728\n",
      "Processing queries No.  729\n",
      "Processing queries No.  730\n",
      "Processing queries No.  731\n",
      "Processing queries No.  732\n",
      "Processing queries No.  733\n",
      "Processing queries No.  734\n",
      "Processing queries No.  735\n",
      "Processing queries No.  736\n",
      "Processing queries No.  737\n",
      "Processing queries No.  738\n",
      "Processing queries No.  739\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing queries No.  740\n",
      "Processing queries No.  741\n",
      "Processing queries No.  742\n",
      "Processing queries No.  743\n",
      "Processing queries No.  744\n",
      "Processing queries No.  745\n",
      "Processing queries No.  746\n",
      "Processing queries No.  747\n",
      "Processing queries No.  748\n",
      "Processing queries No.  749\n",
      "Processing queries No.  750\n",
      "Processing queries No.  751\n",
      "Processing queries No.  752\n",
      "Processing queries No.  753\n",
      "Processing queries No.  754\n",
      "Processing queries No.  755\n",
      "Processing queries No.  756\n",
      "Processing queries No.  757\n",
      "Processing queries No.  758\n",
      "Processing queries No.  759\n",
      "Processing queries No.  760\n",
      "Processing queries No.  761\n",
      "Processing queries No.  762\n",
      "Processing queries No.  763\n",
      "Processing queries No.  764\n",
      "Processing queries No.  765\n",
      "Processing queries No.  766\n",
      "Processing queries No.  767\n",
      "Processing queries No.  768\n",
      "Processing queries No.  769\n",
      "Processing queries No.  770\n",
      "Processing queries No.  771\n",
      "Processing queries No.  772\n",
      "Processing queries No.  773\n",
      "Processing queries No.  774\n",
      "Processing queries No.  775\n",
      "Processing queries No.  776\n",
      "Processing queries No.  777\n",
      "Processing queries No.  778\n",
      "Processing queries No.  779\n",
      "Processing queries No.  780\n",
      "Processing queries No.  781\n",
      "Processing queries No.  782\n",
      "Processing queries No.  783\n",
      "Processing queries No.  784\n",
      "Processing queries No.  785\n",
      "Processing queries No.  786\n",
      "Processing queries No.  787\n",
      "Processing queries No.  788\n",
      "Processing queries No.  789\n",
      "Processing queries No.  790\n",
      "Processing queries No.  791\n",
      "Processing queries No.  792\n",
      "Processing queries No.  793\n",
      "Processing queries No.  794\n",
      "Processing queries No.  795\n",
      "Processing queries No.  796\n",
      "Processing queries No.  797\n",
      "Processing queries No.  798\n",
      "Processing queries No.  799\n",
      "training time for fast Learned KD-Tree(s):  2134.4680318832397\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "AQWA_kdnodes = AQWAPartition(imported_kdnodes, dataset, domains_, block_size, training_set, m=40000, n=2000)\n",
    "end_time = time.time()\n",
    "print(\"training time for fast Learned KD-Tree(s): \", end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AQWA_kdnodes_copy = copy.deepcopy(AQWA_kdnodes)\n",
    "ExportKDNodes(AQWA_kdnodes_copy, path='C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Legacy/data/AQWA_partitions.csv')\n",
    "exported_AQWA_kdnodes = ExportKDNodes(AQWA_kdnodes_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1035\n"
     ]
    }
   ],
   "source": [
    "print(len(AQWA_kdnodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imported_AQWA_kdnodes = ImportKDNodes()\n",
    "Query(testing_set, imported_AQWA_kdnodes)\n",
    "Query(training_set, imported_AQWA_kdnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# === KDTree Visualization ===\n",
    "#\n",
    "# restricted to 2D\n",
    "#\n",
    "# @kdnodes: the generated kdnodes, domains in them are ordered according to the learned split dimension order,\n",
    "#   should only generated on the domains for visualization?\n",
    "# @query: the query in the query dimension order, should be the same as the order of 'kdnode domains'\n",
    "# @domains: the domains for visualization, should be in the origin order\n",
    "# @realtive_order: whether to adjust the order of domains in kdnodes to corresponding the 'domains' parameteer\n",
    "#\n",
    "def visualize_kdnodes_and_query(kdnodes, query, domains):\n",
    "    \n",
    "    fig, ax = plt.subplots(1)\n",
    "    \n",
    "    plt.xlim(domains[0][0],domains[0][1])\n",
    "    plt.ylim(domains[1][0],domains[1][1])\n",
    "    # transform the queries into boxes\n",
    "    \n",
    "    # first plot all the kdnodes\n",
    "    for i in range(len(kdnodes)):\n",
    "        \n",
    "        kd_domains = kdnodes[i][0]\n",
    "        kd_domains = np.asarray(kd_domains)\n",
    "        \n",
    "        lower1 = kd_domains[0][0]\n",
    "        upper1 = kd_domains[0][1]\n",
    "        lower2 = kd_domains[1][0]\n",
    "        upper2 = kd_domains[1][1]\n",
    "        \n",
    "        rect = Rectangle((lower1,lower2),upper1-lower1,upper2-lower2,fill=False,edgecolor='g',linewidth=1)\n",
    "        ax.add_patch(rect)\n",
    "    \n",
    "    # plot the queries\n",
    "    for i in range(len(query)):\n",
    "        \n",
    "        lower1 = query[i][0][0]\n",
    "        upper1 = query[i][0][1]\n",
    "        lower2 = query[i][1][0]\n",
    "        upper2 = query[i][1][1]\n",
    "        \n",
    "        rect = Rectangle((lower1,lower2),upper1-lower1,upper2-lower2,fill=False,edgecolor='r',linewidth=3)\n",
    "        ax.add_patch(rect)\n",
    "        \n",
    "    plt.show()\n",
    "    \n",
    "def visualize_kdnodes_and_query_2(kdnodes, d_query, r_query, domains, path=None):\n",
    "    \n",
    "    fig, ax = plt.subplots(1)\n",
    "    \n",
    "    plt.xlim(domains[0][0],domains[0][1])\n",
    "    plt.ylim(domains[1][0],domains[1][1])\n",
    "    # transform the queries into boxes\n",
    "    \n",
    "    # first plot all the kdnodes\n",
    "    for i in range(len(kdnodes)):\n",
    "        \n",
    "        kd_domains = kdnodes[i][0]\n",
    "        kd_domains = np.asarray(kd_domains)\n",
    "        \n",
    "        lower1 = kd_domains[0][0]\n",
    "        upper1 = kd_domains[0][1]\n",
    "        lower2 = kd_domains[1][0]\n",
    "        upper2 = kd_domains[1][1]\n",
    "        \n",
    "        rect = Rectangle((lower1,lower2),upper1-lower1,upper2-lower2,fill=False,edgecolor='g',linewidth=1)\n",
    "        ax.add_patch(rect)\n",
    "    \n",
    "    # plot the queries\n",
    "    for i in range(len(d_query)):\n",
    "        \n",
    "        lower1 = d_query[i][0][0]\n",
    "        upper1 = d_query[i][0][1]\n",
    "        lower2 = d_query[i][1][0]\n",
    "        upper2 = d_query[i][1][1]\n",
    "        \n",
    "        rect = Rectangle((lower1,lower2),upper1-lower1,upper2-lower2,fill=False,edgecolor='r',linewidth=2)\n",
    "        ax.add_patch(rect)\n",
    "        \n",
    "    for i in range(len(r_query)):\n",
    "        \n",
    "        lower1 = r_query[i][0][0]\n",
    "        upper1 = r_query[i][0][1]\n",
    "        lower2 = r_query[i][1][0]\n",
    "        upper2 = r_query[i][1][1]\n",
    "        \n",
    "        rect = Rectangle((lower1,lower2),upper1-lower1,upper2-lower2,fill=False,edgecolor='b',linewidth=2)\n",
    "        ax.add_patch(rect)\n",
    "    \n",
    "    ax.set_xlabel('dim 1', fontsize=15)\n",
    "    ax.set_ylabel('dim 2', fontsize=15)\n",
    "    plt.xticks(np.arange(0, 400001, 100000), fontsize=10)\n",
    "    plt.yticks(np.arange(0, 20001, 5000), fontsize=10)\n",
    "    \n",
    "    #plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) # no margin at all\n",
    "    plt.tight_layout() # preventing clipping the labels when save to pdf\n",
    "    \n",
    "    if path != None:\n",
    "        fig.savefig(path)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8WWvlYgM3bdYldHwu5to3Y6wHa61chm5qM8YlxD81RSSdYRj9rLVyGQOb3PoZ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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_kdnodes_and_query(LKD_Robust_kdnodes, testing_set, domains_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K2ohHiH1qDk86wzDG2WrhkgKb3MaZ6ro9RAxvMcPYFJbs21sjXGL4n0/VJ7t4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\nMdZH+n775OvSD33LnZJHH74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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_kdnodes_and_query(LKD_Robust_kdnodes, distribution_query, domains_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KtQLTRgdJJJn5irr/pG97G3AZ8GHg/fSWxDgC+FfAGcCvTiqDU1Ht/WKOc3XPZVgpdKss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\n6D+wt6yhTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath1 = 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/intro_training.pdf'\n",
    "visualize_kdnodes_and_query_2(Qd_tree_kdnodes, distribution_query[0:40], random_query[0:9], domains_, savepath1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 97878.09501478, 154935.10187047],\n",
      "       [  4167.14280896,   6475.04571319]]), 196928]\n"
     ]
    }
   ],
   "source": [
    "print(Qd_tree_kdnodes[52])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savepath2 = 'C:/Users/Cloud/iCloudDrive/HUAWEI_LKD/Dataset/Robust/query/intro_testing.pdf'\n",
    "visualize_kdnodes_and_query_2(Qd_tree_kdnodes, distribution_query[40:], random_query[9:], domains_, savepath2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # === Baseline Performance 2D\n",
    "# #\n",
    "# # @dataset: the ordered dataset, contains only the columns to build, and ordered\n",
    "# # @threshold: the block/page size\n",
    "# #\n",
    "# # return @minmaxnodes: contains all the min max info of all the build dimensions (in the build_dim order)\n",
    "# #\n",
    "# def MinMaxOrdering(dataset, threshold):\n",
    "#     minmaxnodes = []\n",
    "    \n",
    "#     # currently sorted by A, B\n",
    "#     dataset = dataset[np.lexsort((dataset[:,1], dataset[:,0]))]\n",
    "    \n",
    "#     count = 0;\n",
    "#     total_size = len(dataset)\n",
    "#     block_amount = math.ceil(total_size / threshold)\n",
    "    \n",
    "#     lower = 0;\n",
    "#     upper = lower + threshold\n",
    "    \n",
    "#     # concat the results of each sorted nodes\n",
    "#     for i in range(block_amount):\n",
    "#         if upper > total_size:\n",
    "#             upper = total_size\n",
    "#         sub_dataset = dataset[lower:upper,:]\n",
    "        \n",
    "#         minmax_dim0 = [min(sub_dataset[:,0]),max(sub_dataset[:,0])]\n",
    "#         minmax_dim1 = [min(sub_dataset[:,1]),max(sub_dataset[:,1])]\n",
    "#         minmaxnodes.append([minmax_dim0, minmax_dim1])\n",
    "        \n",
    "#         lower += threshold\n",
    "#         upper += threshold\n",
    "        \n",
    "#     return minmaxnodes\n",
    "\n",
    "\n",
    "# def QueryMinMaxOrdering(queries, minmaxnodes):\n",
    "    \n",
    "#     counts = []\n",
    "#     count_single_query = 0;\n",
    "    \n",
    "#     # number of dimensions\n",
    "#     dims = int(len(queries[0]))\n",
    "    \n",
    "#     # for each query\n",
    "#     for i in range(len(queries)):\n",
    "        \n",
    "#         count_single_query = 0\n",
    "        \n",
    "#         # check for intersection for each kdnode\n",
    "#         for j in range(len(minmaxnodes)):\n",
    "            \n",
    "#             # for each dimension\n",
    "#             intersection_tag = True\n",
    "#             for k in range(dims):\n",
    "                \n",
    "#                 # an intersection holds if it intersecs in all dimensions\n",
    "#                 if queries[i][k][0] >= minmaxnodes[j][k][1] or queries[i][k][1] <= minmaxnodes[j][k][0]:\n",
    "#                     intersection_tag = False\n",
    "#                     break\n",
    "                \n",
    "#             # if the query intersect with this kdnode\n",
    "#             if intersection_tag:\n",
    "#                 count_single_query += 1\n",
    "            \n",
    "#         counts.append(count_single_query)\n",
    "    \n",
    "#     #print(\"blocks IO: \", counts)\n",
    "#     print(\"blocks IO(average): \", statistics.mean(counts))\n",
    "    \n",
    "# minmaxnodes = MinMaxOrdering(dataset, block_size)\n",
    "# print('min max nodes amount: ',len(minmaxnodes))\n",
    "# QueryMinMaxOrdering(testing_set, minmaxnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min max nodes amount:  375\n",
      "blocks IO(average):  337.84422110552765\n"
     ]
    }
   ],
   "source": [
    "# === Baseline Performance 3D\n",
    "#\n",
    "# @dataset: the ordered dataset, contains only the columns to build, and ordered\n",
    "# @threshold: the block/page size\n",
    "#\n",
    "# return @minmaxnodes: contains all the min max info of all the build dimensions (in the build_dim order)\n",
    "#\n",
    "def MinMaxOrdering(dataset, threshold):\n",
    "    minmaxnodes = []\n",
    "    \n",
    "    # currently sorted by A, B, C\n",
    "    dataset = dataset[np.lexsort((dataset[:,2], dataset[:,1], dataset[:,0]))]\n",
    "    \n",
    "    count = 0;\n",
    "    total_size = len(dataset)\n",
    "    block_amount = math.ceil(total_size / threshold)\n",
    "    \n",
    "    lower = 0;\n",
    "    upper = lower + threshold\n",
    "    \n",
    "    # concat the results of each sorted nodes\n",
    "    for i in range(block_amount):\n",
    "        if upper > total_size:\n",
    "            upper = total_size\n",
    "        sub_dataset = dataset[lower:upper,:]\n",
    "        \n",
    "        minmax_dim0 = [min(sub_dataset[:,0]),max(sub_dataset[:,0])]\n",
    "        minmax_dim1 = [min(sub_dataset[:,1]),max(sub_dataset[:,1])]\n",
    "        minmax_dim2 = [min(sub_dataset[:,2]),max(sub_dataset[:,2])]\n",
    "        minmaxnodes.append([minmax_dim0, minmax_dim1, minmax_dim2])\n",
    "        \n",
    "        lower += threshold\n",
    "        upper += threshold\n",
    "        \n",
    "    return minmaxnodes\n",
    "\n",
    "\n",
    "def QueryMinMaxOrdering(queries, minmaxnodes):\n",
    "    \n",
    "    counts = []\n",
    "    count_single_query = 0;\n",
    "    \n",
    "    # number of dimensions\n",
    "    dims = int(len(queries[0]))\n",
    "    \n",
    "    # for each query\n",
    "    for i in range(len(queries)):\n",
    "        \n",
    "        count_single_query = 0\n",
    "        \n",
    "        # check for intersection for each kdnode\n",
    "        for j in range(len(minmaxnodes)):\n",
    "            \n",
    "            # for each dimension\n",
    "            intersection_tag = True\n",
    "            for k in range(dims):\n",
    "                \n",
    "                # an intersection holds if it intersecs in all dimensions\n",
    "                if queries[i][k][0] >= minmaxnodes[j][k][1] or queries[i][k][1] <= minmaxnodes[j][k][0]:\n",
    "                    intersection_tag = False\n",
    "                    break\n",
    "                \n",
    "            # if the query intersect with this kdnode\n",
    "            if intersection_tag:\n",
    "                count_single_query += 1\n",
    "            \n",
    "        counts.append(count_single_query)\n",
    "    \n",
    "    #print(\"blocks IO: \", counts)\n",
    "    print(\"blocks IO(average): \", statistics.mean(counts))\n",
    "    \n",
    "minmaxnodes = MinMaxOrdering(dataset, block_size)\n",
    "print('min max nodes amount: ',len(minmaxnodes))\n",
    "QueryMinMaxOrdering(testing_set, minmaxnodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blocks IO(average):  7.517587939698492\n",
      "blocks IO(average):  9.809045226130653\n",
      "blocks IO(average):  9.658291457286433\n",
      "blocks IO(average):  15.864321608040202\n",
      "blocks IO(average):  375\n"
     ]
    }
   ],
   "source": [
    "# === Ignore PK ===¶\n",
    "for i in range(len(testing_set)):\n",
    "    testing_set[i][0][0] = domains_[0][0]\n",
    "    testing_set[i][0][1] = domains_[0][1]\n",
    "Query(testing_set, kdnodes)\n",
    "QueryDense(testing_set, dense_kdnodes)\n",
    "Query(testing_set, traditional_kdnodes)\n",
    "QueryDense(testing_set, dense_traditional_kdnodes)\n",
    "QueryMinMaxOrdering(testing_set, minmaxnodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  }
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