text: ###  Model data
  class catboost_model(object):
      float_features_index = [
          0, 1, 2, 3, 4, 10, 11, 14, 15, 16, 17, 18, 20, 22, 23, 24, 26, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 46, 47, 48, 49,
      ]
      float_feature_count = 50
      cat_feature_count = 0
      binary_feature_count = 31
      tree_count = 40
      float_feature_borders = [
          [0.00530660013, 0.116081499, 0.171101987, 0.201321989, 0.323544502, 0.34700349, 0.349058509],
          [0.0542749017, 0.0681224987, 0.0823794976, 0.0930722952, 0.111923002, 0.138787001, 0.140969992, 0.146070987, 0.156479999, 0.577158988],
          [0.0031594648, 0.1276315, 0.243404001, 0.436786503, 0.472510517, 0.521369994, 0.850664496, 0.937812984, 0.93919301],
          [0.5],
          [0.5],
          [0.5],
          [0.5],
          [0.292156994, 0.296078503, 0.311765015, 0.550980508, 0.613725543, 0.660784483, 0.756862521, 0.762745023, 0.860784531, 0.868627489],
          [0.272486508, 0.298029482, 0.550505519, 0.569842517, 0.68501997, 0.745212495, 0.928442955, 0.955504537, 0.967896521, 0.971488953, 0.979140043, 0.99119246, 0.991777003],
          [0.0104166605, 0.0232205, 0.0263671987, 0.0268011987, 0.239619493, 0.295782506, 0.446116984],
          [0.0704592466, 0.0704644024, 0.103073001, 0.119991004, 0.120285496, 0.303927988, 0.357317001, 0.357351482, 0.357426494, 0.370052516, 0.404548526, 0.404646516, 0.404649496],
          [0.5],
          [0.343136996, 0.382353008, 0.460784495, 0.464706004, 0.496078491, 0.629411995, 0.676470518, 0.707843006, 0.747058988, 0.786274493],
          [0.5],
          [0.166666508, 0.416666508, 1.5],
          [0.0224121511, 0.0244276486, 0.188432992, 0.196844995, 0.201371998, 0.215638489, 0.217433989, 0.421199024, 0.494485974, 0.899194479, 0.937721968, 0.93899405, 0.939746976, 0.93988049, 0.954834998],
          [0.5],
          [0.5],
          [0.0697473437, 0.0950040966, 0.10312105, 0.111093499, 0.118925497, 0.16316551, 0.319696009, 0.335482001, 0.544397473, 0.577149987, 0.65397501, 0.721835017, 0.804090977, 0.829556465],
          [0.5],
          [0.0313090011, 0.0418950021, 0.063731499, 0.144879997, 0.175733998, 0.470179021, 0.531372011, 0.601282001, 0.850793004, 0.885619998],
          [0.5],
          [-0.0970558524, -0.0518935025, 0.208198503, 0.307572007, 0.315384001, 0.54995501, 0.601389527, 0.669679046, 1.02302003],
          [0.5],
          [0.000758085982, 0.00261118496, 0.00387466513, 0.00412735995, 0.00496967509, 0.00867587514, 0.0102762505, 0.0214791, 0.0250168499, 0.0603941977, 0.0617945492, 0.08298935, 0.123856999, 0.239182502, 0.285284996],
          [0.6519925, 0.682412982, 0.971971512, 0.979017973],
          [0.000513223466, 0.000716489973, 0.00207644491, 0.00234788517, 0.00290592015, 0.00859374553, 0.00921617076, 0.0110566001, 0.0213984996, 0.0258278511, 0.0313712507],
          [0.0951329023, 0.211134493, 0.256389499, 0.262685001, 0.277090013, 0.287964523, 0.344983518, 0.588838518, 0.619901478],
          [0.238694996, 0.327475488, 0.397475511, 0.509687006, 0.566181004, 0.683337986, 0.708531499, 0.711776018, 0.7370345, 0.739754021, 0.753007531, 0.8853935, 0.958191514],
          [0.0170545503, 0.0981363058, 0.164981991, 0.177221507, 0.244706005, 0.261609018, 0.280121505, 0.300293505, 0.328040004, 0.40584451, 0.422665507, 0.440944016, 0.447151482, 0.472499996, 0.644344985, 0.714405537, 0.89823401],
          [0.5],
      ]
      tree_depth = [6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
      tree_split_border = [6, 1, 2, 11, 14, 13, 1, 1, 1, 4, 6, 5, 1, 6, 8, 7, 1, 10, 15, 9, 11, 7, 1, 2, 8, 8, 1, 9, 2, 5, 7, 6, 4, 1, 2, 1, 11, 7, 14, 1, 8, 7, 1, 3, 7, 5, 4, 9, 7, 7, 9, 1, 3, 5, 4, 1, 9, 4, 1, 5, 3, 1, 6, 8, 3, 1, 7, 1, 14, 1, 5, 1, 1, 2, 10, 9, 1, 3, 6, 6, 2, 9, 6, 7, 2, 8, 3, 10, 8, 8, 1, 13, 12, 1, 15, 5, 11, 12, 1, 10, 13, 6, 5, 2, 13, 2, 5, 1, 5, 1, 12, 1, 4, 3, 10, 1, 4, 4, 9, 5, 1, 14, 5, 1, 3, 1, 3, 9, 7, 4, 10, 8, 6, 12, 16, 2, 1, 1, 7, 3, 11, 2, 1, 1, 3, 7, 1, 1, 6, 2, 2, 12, 1, 7, 7, 2, 9, 7, 1, 6, 15, 11, 1, 13, 1, 11, 4, 10, 9, 1, 5, 6, 6, 7, 3, 9, 1, 1, 4, 4, 2, 9, 4, 2, 4, 8, 4, 12, 3, 17, 8, 10, 13, 10, 10, 1, 8, 3, 3, 12, 3, 3, 10, 11, 3, 1, 3, 1, 1, 8, 5, 6, 9, 2, 4, 3, 4, 8, 8, 8, 13, 3, 1, 6, 5, 10, 1, 6, 14, 9, 4, 2, 3, 1, 7, 2, 5, 5]
      tree_split_feature_index = [2, 2, 18, 8, 24, 29, 19, 3, 28, 7, 27, 0, 19, 1, 7, 7, 4, 12, 29, 7, 15, 20, 17, 2, 10, 28, 30, 18, 26, 24, 27, 26, 15, 8, 22, 14, 29, 22, 29, 9, 12, 18, 26, 2, 24, 15, 0, 24, 0, 1, 10, 23, 0, 29, 8, 3, 27, 25, 12, 28, 15, 15, 22, 20, 8, 19, 29, 21, 15, 30, 20, 19, 11, 14, 7, 12, 5, 14, 10, 29, 0, 15, 0, 28, 1, 29, 27, 18, 27, 1, 19, 10, 24, 27, 24, 1, 28, 15, 4, 1, 24, 28, 27, 10, 8, 24, 12, 19, 18, 7, 10, 25, 22, 7, 28, 22, 12, 29, 8, 2, 21, 18, 26, 30, 1, 29, 26, 22, 12, 9, 24, 26, 24, 28, 29, 29, 19, 0, 1, 14, 26, 7, 16, 18, 14, 10, 6, 13, 18, 15, 25, 29, 24, 8, 15, 20, 2, 2, 30, 9, 15, 18, 20, 18, 3, 10, 18, 15, 28, 3, 22, 12, 8, 26, 9, 1, 6, 10, 27, 28, 9, 26, 1, 28, 24, 22, 26, 18, 14, 29, 15, 29, 28, 8, 20, 6, 2, 22, 10, 8, 29, 25, 10, 24, 12, 23, 28, 13, 1, 8, 7, 15, 20, 27, 2, 20, 20, 24, 18, 8, 15, 24, 17, 20, 8, 26, 18, 7, 18, 29, 10, 12, 18, 16, 9, 8, 9, 10]
      tree_split_xor_mask = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
      cat_features_index = []
      one_hot_cat_feature_index = []
      one_hot_hash_values = [
      ]
      ctr_feature_borders = [
      ]
  
      ## Aggregated array of leaf values for trees. Each tree is represented by a separate line:
      leaf_values = [
          0.001259011842534326, 0, 0.001474009916287763, 0.00064166661547497, 0, 0, 0.002863636311820962, 0.0009333333164453506, 0.0002386363593184135, 0, 0.002116666806116696, 0, 0, 0, 0, 0.001679999969601631, 0.0005249999905005097, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.002088370528948082, 0, 0.001851857674803305, 0.001553571389056742, 0, 0, 0.002822325645650539, 0.004138979825677001, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.001799999967430319, 0, 0, 0, 0, 0, 0, 0, 0,
          0.0003675624400133544, 0, 0.001059092784733128, 0, 0, 0, 0.002066946665087761, 0.001192459419076518, 0.001103175683534166, 0, 0.0004773354283567961, 0, 0, 0, 0.001827927523090045, 0, 0, 0, 0.001934663401212227, 0, 0, 0, 0.002079562911984692, 0.002016393416732761, 0, 0, 0.00153485204725672, 0, 0, 0, 0.001034478467845754, 0, 0.0006763936415426848, 0, 0.0005583088061050648, 0, 0, 0, 0.00178538384126724, 0.003265464248219695, 0.0004448876734695067, 0, 0.0005178430285843198, 0, 0, 0, 0.00121787682371597, 0.003195419440159142, 0, 0, 0.002190701743713123, 0, 0, 0, 0.00230819225408413, 0.003807514519097499, 0, 0, 0.00167323412960814, 0, 0, 0, 0.001315321241806467, 0.003950911460742678,
          0.0006713525754710141, 0.002743616539224415, 0.001408130668629405, 0.002730909875149311, 0, 0, 0, 0, 0, 0, 0, 0, -1.771640604952115e-05, 0, 0.001028008136701507, 0, 0.001233014979262325, 0.003496442848789191, 0.002169239572695188, 0.003407024687013412, 0, 0, 0, 0, 0.0005528444855849338, 0, 0.0007686862953506046, 0, 0.0003454888241153968, 0, 0.001341340799600677, 0, -2.95801344266585e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.302260424060552e-05, 0, 0, 0, 0.0007891084545405152, 0.0002992009679615992, 0.0002840800672516245, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.001114235179996917, 0.002001825815311041, 0.001005785750902519, 0,
          0.001247271030258472, 0.0024367670519546, 0.001013941957750236, 0, 0.001620674119751847, 0.003470147730588041, 0, 0, 0.001256032119324776, 0.001094133184019391, 0, 0, 0.001497695432335084, 0.002183694031674648, 0, 0, 0.001462538384527338, 0.002024478959640045, 0.0007932258564564424, 0, 0.001459708716005046, 0.00410276266641921, 0, 0, 0.002892385768820297, 0.001095933008101081, 0, 0, 0.001200722104144041, 0.002100162968582172, 0, 0,
          0.0006261723644569373, 0.0008637599141720322, 0.0002786990507308698, 0, 0, 0.0005118615751778726, 0, 0, 0.0006445336059794616, 0.002284807938861288, -8.064215898006237e-05, 0, 0.0009469512756610591, 0.001624056946325842, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.57544487765812e-05, 0.004221336689877834, 0, 0, 0.001474750759473612, 0.0005504932792037034, 0.001674408014257176, -4.632036522173779e-05, 0, 0.0008597578509021077, 0, 0, 0.001624198596521712, 0.001667390092688491, 0.0001935170419209184, -7.636931416561795e-05, 0.002616532248255767, 0.003029054813313845, 0, 0, 0, 0, 0, 0, 0, 0.003489369002781199, 0, 0, 0, 0.0009529452384761211, 0, 0, 0, 0.003670409124562618, 0, 0,
          0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0001337965847613156, 0, 0, 0, 0, 0, 0, 0, -5.851963259214481e-05, 0, 0, 0, 0, 0, 0, 0.0005914167927075687, 0.001616681447251949, 0, 0.000939850530076848, 0, 0.00229996295147978, 0, 0.002703932782502181, -8.097388146735455e-05, 0, 0, 0, 0, 0, 0.00058689199283601, 0, 0.000653871927347388, 0.002490454230716794, 0.0004607211761809621, 0, 0.00423077926483619, 0, 0.0006047519903006006, 0, 0.0009279210261868157, 0.0003380735870666396, 0.001268235309684023, 0.0006739173378845086, 0.0009994685369282825, 0, 0.001576000078038965, 0.000920674835551659, 0.0008536040422672071, 0.001691615410406881, 0.001513312890623431, 0.003213984614666313, 0.002467794288450969, 0.003168295944074068, 0.00212736062347172, 0.003449532333534402,
          0.0004994633061071265, 0.000940617250198437, 0, 0, 0.0007526364755952145, 0, 0, 0, 0, 0, 0.002216902438834851, 0.0002594660701735625, 0, 0, 0.003439659962769849, 0, 0.001107188271297417, 0.0007627671396725976, 0.001512238693965317, 9.238340799175322e-05, 0.001390443588792112, -0.0001101361801509666, 0.002066983310646019, 0, 0, 0, 0.002154114520489098, 0.002563888102324688, 0, 0, 0.001802300330062641, 0.002681692694671999, 0.0001440979390624734, 0, 0, 0, -4.10677687166951e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0007348975247747243, 0, 0.0004817237723861387, 0, 0.0006238563890071828, 0.0009864565041205709, 0.0004233906225266243, 0, 0, 0, 0.00163506189163973, 0, 0, 0, 0.0004939411883112205, 0,
          0.0006130656081750645, 0, 0.001498536304639067, 0, 0, 0, 0.001117901801731573, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0003514884431949286, 0, 0.001430323818162417, 0, 0.001243754646852168, 0, 0.001526581843036115, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.001011769189895441, 0, 0.000864432317359969, 0, 0, 0, 0.001628954582297081, 0, 0, 0, 0.0007067125990129767, 0, 0, 0, 0, 0, 0.0004134020253548067, 0.0001249536196177113, 0.001606933093650728, 0, 0.001309338194172928, -0.0001240575909492471, 0.002238311337132574, 0.001479676300840754, 0.0004724362155667481, 0, 0.001729639588517977, 0.001426587520967144, 0.001025571318721689, 0.004715814795435876, 0.002106083286981046, 0.003533185403160956,
          0.0006713040027249849, 0, 0, 0, 0.001754830378859819, 0, 0, 0, 0.0001888357316642482, 0, 0, 0, 0.0001987550400963919, 0, 0, 0, 0.001265296770830103, 0, 0, 0, 0.001822839344250091, 0, 0, 0, -6.937950925003171e-05, 0, 0, 0, 0, 0, 0, 0, 0.0007176325076491641, 0, 0.0004935609943609756, 0, 0.001679823488051739, 0, 0.001131049507788218, 0, 0.0004867072626472099, 0, 0, 0, 0.001842394692162205, 0, -7.767871869844774e-05, 0, 0.001193651630428944, 0.000954085984623889, 0.00135072580009651, 0.002704125450400669, 0.002116537035483374, 0, 0.002517811146469275, 0.002899319493633974, 0.0005503079270282635, 0, 9.124337951001465e-05, 0, -0.0001519554504186443, 0, 0, 0,
          0.0003430188341006684, 0, 0.0001073900859417958, 0, 0.0002009572970413582, 0.001203260585838676, 0.0009900019440051615, 0.002402135913893199, 0, 0, 0, 0, 0, 0.001519902792691174, 0, 0.002052704756945831, 0, 0, 0, 0, 0, 0.0004031385510654417, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000119481669010383, 0, 0.00083911934373414, 0, 1.403849961867766e-05, 0.00121497456868816, 0.0005691253299986597, 0.001549194502678979, 0, 0, 0, 0, 0, 0.001030765282120077, 0, 0.001836832393041273, 0, 0, 0, 0, 0, 0.0005066561062581063, 0, 0.0002578005326954254, 0, 0, 0, 0, 0, 0.0004000947946809938, 0, 0,
          -0.0002066661195256151, 0, 0, 0, 0.0006132522153288301, 8.169120368247289e-05, 0.0008883175610120763, 0.003563259960394594, -9.509429833243448e-05, 0, 0, 0, 0.0005803610361243575, 0, 0.000704843388694952, 0.004039057824357703, 0, 0, 0, 0, 0, 0, 0, 0.001136788732793222, 0, 0, 0, 0, 0.0006443386534132715, -9.478985008154691e-05, 0.000136143744623063, -0.0002908541579397471, 0.001808560864454995, 0, 0, 0, 0.000904596389116127, 0.001746668434973936, 0.001190335111933643, 0.002683474273109413, 0.004465284296174428, 0, 0, 0, 0.0008810584544450766, 0, 0.001452522422293049, 0.002529281914387054, 0, 0, 0, 0, 0, 0.002075922131165884, 0.0008414936442865748, 0.002085564159965548, 0, 0, 0, 0, 3.378753093775059e-05, 0.0006478973256395021, 0.002187410162293151, 0.001751500865114772,
          0.000583690901793301, 0, 0.0007087919167513874, 0, -0.0001901575147635265, 0, 0.0008491763121492214, 0.0009667308797482187, 0.0003826349156390115, 0, -0.0001014003446959843, 0, -0.0001022293949105037, 0, 0.001150749587644658, 0.0005526474795782473, 0.000590619959791953, 0, 0.001570031479404329, 0, 0.001037019421771918, 0, 0.001135917346158705, 0.001318825936706807, 0.0002099724022436963, 0, 0.002108861092401775, 0, 0.0009360226348111408, 0, 0.001501259455337795, 0.002353523108647582, 0, 0, 0, 0, 0, 0, 0.0006287530800650742, -0.0001034518924654112, 0, 0, 0, 0, 0, 0, 0, 0.001930736507878415, 0, 0, 0, 0, 0.002560029712935392, 0, 0.001475089015454072, 0.001788943516746938, 0, 0, 0, 0, 0.002956651860848169, 0.001409780557262284, 0.0007000991351925702, 0.003887319180620406,
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      ]
  cat_features_hashes = {
  }
  
  def hash_uint64(string):
      return cat_features_hashes.get(str(string), 0x7fFFffFF)
  
  
  ### Applicator for the CatBoost model
  
  def apply_catboost_model(float_features, cat_features=[], ntree_start=0, ntree_end=catboost_model.tree_count):
      """
      Applies the model built by CatBoost.
  
      Parameters
      ----------
  
      float_features : list of float features
  
      cat_features : list of categorical features
          You need to pass float and categorical features separately in the same order they appeared in train dataset.
          For example if you had features f1,f2,f3,f4, where f2 and f4 were considered categorical, you need to pass here float_features=f1,f3, cat_features=f2,f4
  
  
      Returns
      -------
      prediction : formula value for the model and the features
  
      """
      if ntree_end == 0:
          ntree_end = catboost_model.tree_count
      else:
          ntree_end = min(ntree_end, catboost_model.tree_count)
  
      model = catboost_model
  
      assert len(float_features) >= model.float_feature_count
      assert len(cat_features) >= model.cat_feature_count
  
      # Binarise features
      binary_features = [0] * model.binary_feature_count
      binary_feature_index = 0
  
      for i in range(len(model.float_feature_borders)):
          for border in model.float_feature_borders[i]:
              binary_features[binary_feature_index] += 1 if (float_features[model.float_features_index[i]] > border) else 0
          binary_feature_index += 1
      transposed_hash = [0] * model.cat_feature_count
      for i in range(model.cat_feature_count):
          transposed_hash[i] = hash_uint64(cat_features[i])
  
      if len(model.one_hot_cat_feature_index) > 0:
          cat_feature_packed_indexes = {}
          for i in range(model.cat_feature_count):
              cat_feature_packed_indexes[model.cat_features_index[i]] = i
          for i in range(len(model.one_hot_cat_feature_index)):
              cat_idx = cat_feature_packed_indexes[model.one_hot_cat_feature_index[i]]
              hash = transposed_hash[cat_idx]
              for border_idx in range(len(model.one_hot_hash_values[i])):
                  binary_features[binary_feature_index] |= (1 if hash == model.one_hot_hash_values[i][border_idx] else 0) * (border_idx + 1)
              binary_feature_index += 1
  
      if hasattr(model, 'model_ctrs') and model.model_ctrs.used_model_ctrs_count > 0:
          ctrs = [0.] * model.model_ctrs.used_model_ctrs_count;
          calc_ctrs(model.model_ctrs, binary_features, transposed_hash, ctrs)
          for i in range(len(model.ctr_feature_borders)):
              for border in model.ctr_feature_borders[i]:
                  binary_features[binary_feature_index] += 1 if ctrs[i] > border else 0
              binary_feature_index += 1
  
      # Extract and sum values from trees
      result = 0.
      tree_splits_index = 0
      current_tree_leaf_values_index = 0
      for tree_id in range(ntree_start, ntree_end):
          current_tree_depth = model.tree_depth[tree_id]
          index = 0
          for depth in range(current_tree_depth):
              border_val = model.tree_split_border[tree_splits_index + depth]
              feature_index = model.tree_split_feature_index[tree_splits_index + depth]
              xor_mask = model.tree_split_xor_mask[tree_splits_index + depth]
              index |= ((binary_features[feature_index] ^ xor_mask) >= border_val) << depth
          result += model.leaf_values[current_tree_leaf_values_index + index]
          tree_splits_index += current_tree_depth
          current_tree_leaf_values_index += (1 << current_tree_depth)
      return result
  
  
  
  