In [167]:
import ast
import pandas as pd
import datetime
from keras.layers import Input, Dense, Embedding, merge, Flatten, Merge, BatchNormalization
from keras.models import Model, load_model
from keras.regularizers import l2
import keras.backend as K
from keras.optimizers import SGD
import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
import utils
import data
from sklearn.model_selection import train_test_split
from bcolz_array_iterator import BcolzArrayIterator
import bcolz
from keras_tqdm import TQDMNotebookCallback
from keras.callbacks import ModelCheckpoint
import pickle
Using Theano backend.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-167-a67a2a859c47> in <module>()
5 import datetime
6
----> 7 from keras.layers import Input, Dense, Embedding, merge, Flatten, Merge, BatchNormalization
8 from keras.models import Model, load_model
9 from keras.regularizers import l2
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/keras/__init__.py in <module>()
1 from __future__ import absolute_import
----> 2 from . import backend
3 from . import datasets
4 from . import engine
5 from . import layers
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/__init__.py in <module>()
62 if _BACKEND == 'theano':
63 sys.stderr.write('Using Theano backend.\n')
---> 64 from .theano_backend import *
65 elif _BACKEND == 'tensorflow':
66 sys.stderr.write('Using TensorFlow backend.\n')
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/theano_backend.py in <module>()
----> 1 import theano
2 from theano import tensor as T
3 from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
4 from theano.tensor.signal import pool
5 from theano.tensor.nnet import conv3d2d
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/theano/__init__.py in <module>()
93 # needed during that phase.
94 import theano.tests
---> 95 if hasattr(theano.tests, "TheanoNoseTester"):
96 test = theano.tests.TheanoNoseTester().test
97 else:
AttributeError: module 'theano' has no attribute 'tests'
Data Path
In [59]:
data_path = "/data/datasets/taxi/"
Need to check process and see if resulting data is the same
This is the author's feature extraction process. I have modified it so that it returns the tuple instead of saving as hdf5 file
In [60]:
import ast
import csv
import os
import sys
import h5py
import numpy
from fuel.converters.base import fill_hdf5_file
import data
taxi_id_dict = {}
origin_call_dict = {0: 0}
def get_unique_taxi_id(val):
if val in taxi_id_dict:
return taxi_id_dict[val]
else:
taxi_id_dict[val] = len(taxi_id_dict)
return len(taxi_id_dict) - 1
def get_unique_origin_call(val):
if val in origin_call_dict:
return origin_call_dict[val]
else:
origin_call_dict[val] = len(origin_call_dict)
return len(origin_call_dict) - 1
def read_stands(input_directory, h5file):
stands_name = numpy.empty(shape=(data.stands_size,), dtype=('a', 24))
stands_latitude = numpy.empty(shape=(data.stands_size,), dtype=numpy.float32)
stands_longitude = numpy.empty(shape=(data.stands_size,), dtype=numpy.float32)
stands_name[0] = 'None'
stands_latitude[0] = stands_longitude[0] = 0
with open(os.path.join(input_directory, 'metaData_taxistandsID_name_GPSlocation.csv'), 'r') as f:
reader = csv.reader(f)
next(reader) # header
for line in reader:
id = int(line[0])
stands_name[id] = line[1].encode('utf-8')
stands_latitude[id] = float(line[2])
stands_longitude[id] = float(line[3])
return (('stands', 'stands_name', stands_name),
('stands', 'stands_latitude', stands_latitude),
('stands', 'stands_longitude', stands_longitude))
def read_taxis(input_directory, h5file, dataset):
size=getattr(data, '%s_size'%dataset)
trip_id = numpy.empty(shape=(size,), dtype='S19')
call_type = numpy.empty(shape=(size,), dtype=numpy.int8)
origin_call = numpy.empty(shape=(size,), dtype=numpy.int32)
origin_stand = numpy.empty(shape=(size,), dtype=numpy.int8)
taxi_id = numpy.empty(shape=(size,), dtype=numpy.int16)
timestamp = numpy.empty(shape=(size,), dtype=numpy.int32)
day_type = numpy.empty(shape=(size,), dtype=numpy.int8)
missing_data = numpy.empty(shape=(size,), dtype=numpy.bool)
latitude = numpy.empty(shape=(size,), dtype=data.Polyline)
longitude = numpy.empty(shape=(size,), dtype=data.Polyline)
with open(os.path.join(input_directory, '%s.csv'%dataset), 'r') as f:
reader = csv.reader(f)
next(reader) # header
id=0
for line in reader:
trip_id[id] = line[0].encode('utf-8')
call_type[id] = ord(line[1][0]) - ord('A')
origin_call[id] = 0 if line[2]=='NA' or line[2]=='' else get_unique_origin_call(int(line[2]))
origin_stand[id] = 0 if line[3]=='NA' or line[3]=='' else int(line[3])
taxi_id[id] = get_unique_taxi_id(int(line[4]))
timestamp[id] = int(line[5])
day_type[id] = ord(line[6][0]) - ord('A')
missing_data[id] = line[7][0] == 'T'
polyline = ast.literal_eval(line[8])
latitude[id] = numpy.array([point[1] for point in polyline], dtype=numpy.float32)
longitude[id] = numpy.array([point[0] for point in polyline], dtype=numpy.float32)
id+=1
splits = ()
for name in ['trip_id', 'call_type', 'origin_call', 'origin_stand', 'taxi_id', 'timestamp', 'day_type', 'missing_data', 'latitude', 'longitude']:
splits += ((dataset, name, locals()[name]),)
return splits
def unique(h5file):
unique_taxi_id = numpy.empty(shape=(data.taxi_id_size,), dtype=numpy.int32)
assert len(taxi_id_dict) == data.taxi_id_size
for k, v in taxi_id_dict.items():
unique_taxi_id[v] = k
unique_origin_call = numpy.empty(shape=(data.origin_call_size,), dtype=numpy.int32)
assert len(origin_call_dict) == data.origin_call_size
for k, v in origin_call_dict.items():
unique_origin_call[v] = k
return (('unique_taxi_id', 'unique_taxi_id', unique_taxi_id),
('unique_origin_call', 'unique_origin_call', unique_origin_call))
def get_data(input_directory):
split = ()
split += read_stands(input_directory, None)
split += read_taxis(input_directory, None, 'train')
split += read_taxis(input_directory, None, 'test')
split += unique(None)
return split
manually go through data collection
In [61]:
taxi_id_dict = {}
origin_call_dict = {0: 0}
In [62]:
split = ()
In [63]:
split += read_stands(data_path+'data', None)
In [ ]:
split += read_taxis(data_path+'data', None, 'train')
In [ ]:
split += read_taxis(data_path+'data', None, 'test')
In [ ]:
split += unique(None)
Data structure: Tuple of tuples. Each sub-tuple: ('dataset', 'column' 'data')
Contains stands: metadata.
Save tulpe
In [80]:
with open(data_path+'/data/data_tuple.pickle', 'wb') as f:
pickle.dump(split, f)
Load tuple
In [ ]:
with open(data_path+'/data/data_tuple.pickle', 'r') as f:
split = pickle.load(f)
Time cuts
In [168]:
cuts = [
1376503200, # 2013-08-14 18:00
1380616200, # 2013-10-01 08:30
1381167900, # 2013-10-07 17:45
1383364800, # 2013-11-02 04:00
1387722600 # 2013-12-22 14:30
]
In [191]:
split[12][2][0]
Out[191]:
array([-8.61864281, -8.6184988 , -8.62032604, -8.62215328, -8.62395287,
-8.62668037, -8.62737274, -8.63022614, -8.63274574, -8.63173771,
-8.62993813, -8.62911034, -8.62912846, -8.62878609, -8.6286869 ,
-8.62875938, -8.63083839, -8.63232327, -8.63114357, -8.63082886,
-8.63082886, -8.63082886, -8.63083839], dtype=float32)
In [192]:
split
Out[192]:
(('stands',
'stands_name',
array([b'None', b'Agra', b'Alameda', b'Aldoar', b'Alf\xc3\xa2ndega',
b'Amial', b'Areosa', b'Av. Boavista', b'Azevedo', b'Batalha',
b'Bolh\xc3\xa3o', b'Bom Pastor', b'Bom Sucesso', b'Bras\xc3\xadlia',
b'C\xc3\xa2mara', b'Campanh\xc3\xa3', b'Campismo', b'Carcereira',
b'Carregal', b'Carvalheiras', b'Carvalhido', b'Casa da Musica',
b'Castelo do Queijo', b'Cl\xc3\xa9rigos', b'Conde Ferreira',
b'Cordoaria', b'Corujeira', b'D. Jo\xc3\xa3o I', b'Drag\xc3\xa3o',
b'Francos', b'Galiza', b'H. Magalh\xc3\xa3es Lemos', b'H. Militar',
b'H. S\xc3\xa3o Jo\xc3\xa3o', b'Infante', b'Lago', b'Lordelo',
b'Marechal', b'Marqu\xc3\xaas', b'Maternidade', b'Mercado da Foz',
b'Nevogilde', b'Nove de Abril', b'Ouro', b'Pal\xc3\xa1cio',
b'Passeio Alegre', b'Pereira de Melo', b'Pereir\xc3\xb3',
b'Pinto de Azevedo', b'P\xc3\xb3lo Universit\xc3\xa1rio',
b'Prelada', b'Ramada Alta', b'R\xc3\xa9publica', b'Ribeira',
b'Rotunda', b'S\xc3\xa3o Roque', b'S\xc3\xa1 Carneiro',
b'S\xc3\xa3o Bento', b'Ter\xc3\xa7o', b'Tenente Valadim',
b'Trindade', b'Vinte e Quatro de Agosto', b'Viso', b'Santa Maria'],
dtype='|S24')),
('stands',
'stands_latitude',
array([ 0. , 41.17714691, 41.15618896, 41.1705246 ,
41.1437645 , 41.18350983, 41.18080521, 41.15998077,
41.15159225, 41.14425278, 41.15071106, 41.16703415,
41.15512085, 41.15708923, 41.14970398, 41.14862823,
41.17547226, 41.1644516 , 41.14831924, 41.15359116,
41.16692734, 41.16088486, 41.1683197 , 41.14601517,
41.16952896, 41.14630127, 41.15956116, 41.14803314,
41.16322327, 41.17102051, 41.15178299, 41.17695236,
41.15766144, 41.18278885, 41.14051819, 41.16743088,
41.15465546, 41.16107941, 41.1607132 , 41.15089035,
41.15423203, 41.16306686, 41.17274857, 41.14860916,
41.14961624, 41.14864731, 41.17864609, 41.17381287,
41.17126083, 41.17955017, 41.16983795, 41.16140366,
41.15496445, 41.14120865, 41.15800476, 41.16600037,
41.16287994, 41.14571762, 41.16159058, 41.16070175,
41.15112305, 41.14931107, 41.17787552, 41.16037369], dtype=float32)),
('stands',
'stands_longitude',
array([ 0. , -8.60967064, -8.59106445, -8.66587639, -8.62180328,
-8.61272621, -8.58278275, -8.64198399, -8.56819534, -8.60571957,
-8.60690975, -8.61326694, -8.63046646, -8.62846756, -8.61107063,
-8.58587646, -8.6277914 , -8.63228607, -8.61960316, -8.60542679,
-8.6267786 , -8.62956524, -8.68918037, -8.61257458, -8.58934784,
-8.61763763, -8.57977581, -8.60924149, -8.58404636, -8.6386652 ,
-8.62775898, -8.66396046, -8.62704372, -8.60012245, -8.61595249,
-8.6495266 , -8.6490736 , -8.657547 , -8.60424614, -8.62279987,
-8.67400646, -8.67598343, -8.61178398, -8.64888668, -8.62578487,
-8.6727953 , -8.64853573, -8.65449619, -8.64505005, -8.60253906,
-8.62890244, -8.62112236, -8.61321735, -8.61401272, -8.63037491,
-8.5703516 , -8.59164524, -8.61070728, -8.60452843, -8.64120483,
-8.60984325, -8.60030842, -8.64655781, -8.60936928], dtype=float32)),
('train', 'trip_id', array([b'1372636858620000589', b'1372637303620000596',
b'1372636951620000320', ..., b'1388745716620000264',
b'1404141826620000248', b'1404157147620000079'],
dtype='|S19')),
('train', 'call_type', array([2, 1, 2, ..., 2, 1, 1], dtype=int8)),
('train', 'origin_call', array([0, 0, 0, ..., 0, 0, 0], dtype=int32)),
('train', 'origin_stand', array([ 0, 7, 0, ..., 0, 12, 34], dtype=int8)),
('train', 'taxi_id', array([ 0, 1, 2, ..., 439, 443, 444], dtype=int16)),
('train',
'timestamp',
array([1372636858, 1372637303, 1372636951, ..., 1388745716, 1404141826,
1404157147], dtype=int32)),
('train', 'day_type', array([0, 0, 0, ..., 0, 0, 0], dtype=int8)),
('train',
'missing_data',
array([False, False, False, ..., False, False, False], dtype=bool)),
('train',
'latitude',
array([ array([ 41.14141083, 41.1413765 , 41.14250946, 41.14381409,
41.14437485, 41.14477921, 41.14469528, 41.14521027,
41.14691925, 41.14822388, 41.15038681, 41.1512146 ,
41.1512413 , 41.15220261, 41.15237427, 41.15251923,
41.15267944, 41.15302277, 41.15448761, 41.15450668,
41.15451431, 41.15449905, 41.15448761], dtype=float32),
array([ 41.15982437, 41.15987015, 41.16011429, 41.16049194,
41.16094971, 41.16149139, 41.16202927, 41.16257858,
41.16319275, 41.16368866, 41.16419983, 41.16470337,
41.16513443, 41.16657639, 41.16796112, 41.16898727,
41.1700058 , 41.17063522, 41.17066956], dtype=float32),
array([ 41.14035797, 41.14035034, 41.14027786, 41.14036942,
41.14044952, 41.14060211, 41.14141083, 41.14278793,
41.14409256, 41.14480591, 41.14473343, 41.14517212,
41.14646149, 41.14731598, 41.14877319, 41.15062714,
41.15207672, 41.15246582, 41.15266037, 41.152771 ,
41.15277863, 41.15256119, 41.15301132, 41.15357971,
41.15402222, 41.15450668, 41.15433502, 41.15430069,
41.15433502, 41.1543541 , 41.15430069, 41.15428162,
41.15430069, 41.15426254, 41.14173508, 41.14056778,
41.15428925, 41.15381241, 41.15383148, 41.15378571,
41.15316772, 41.15257263, 41.15237427, 41.1512146 ,
41.15046692, 41.14906311, 41.14826202, 41.14822388,
41.14840317, 41.14841461, 41.14813614, 41.14802551,
41.14836121, 41.14834976, 41.14796448, 41.14717102,
41.14614487, 41.14503098, 41.14464188, 41.14476776,
41.14446259, 41.1434021 , 41.14196014, 41.14086151, 41.14052963], dtype=float32),
..., array([], dtype=float32),
array([ 41.15488434, 41.15481186, 41.1547966 , 41.1547699 ,
41.15450668, 41.15358734, 41.15363312, 41.15438843,
41.15465164, 41.15465164, 41.15462494, 41.15462494,
41.15465164, 41.15506363, 41.15546036, 41.15562439,
41.15591049, 41.15685654, 41.15704346, 41.15831375,
41.15886307, 41.1593132 , 41.16013336, 41.16038513,
41.16049957, 41.16139221, 41.16226578, 41.16247177,
41.16220093, 41.16209412, 41.16217422, 41.16218567,
41.1619873 , 41.1619606 , 41.1619339 , 41.16188812,
41.16247177, 41.16309357, 41.16423416, 41.16457748,
41.16453171, 41.1644783 , 41.1644783 , 41.16525269,
41.16523361, 41.16524506, 41.16542435, 41.1655159 ,
41.16571045, 41.16665649, 41.16711426, 41.167099 ,
41.1666832 , 41.16611862, 41.16669464, 41.1671257 ,
41.16751099, 41.16854858, 41.16963577, 41.17076874,
41.17213821, 41.17352295], dtype=float32),
array([ 41.14062881, 41.14074707, 41.14095306, 41.14133072,
41.14180756, 41.14292526, 41.14385223, 41.14452744,
41.14488602, 41.14488602, 41.14488602, 41.14488602,
41.14583206, 41.14667511, 41.1472168 , 41.14764023,
41.14784622, 41.14784622, 41.14780045, 41.14809799,
41.14764786, 41.14908981, 41.14985275, 41.14987946,
41.14987946, 41.14987946, 41.14987946], dtype=float32)], dtype=object)),
('train',
'longitude',
array([ array([-8.61864281, -8.6184988 , -8.62032604, -8.62215328, -8.62395287,
-8.62668037, -8.62737274, -8.63022614, -8.63274574, -8.63173771,
-8.62993813, -8.62911034, -8.62912846, -8.62878609, -8.6286869 ,
-8.62875938, -8.63083839, -8.63232327, -8.63114357, -8.63082886,
-8.63082886, -8.63082886, -8.63083839], dtype=float32),
array([-8.6398468 , -8.6403513 , -8.6421957 , -8.64445496, -8.64692116,
-8.64999866, -8.65316677, -8.65643406, -8.66017818, -8.66311169,
-8.66623497, -8.66916943, -8.67085171, -8.67094231, -8.66961002,
-8.66809845, -8.66664028, -8.66576672, -8.66574001], dtype=float32),
array([-8.61296368, -8.61337757, -8.6142149 , -8.6147728 , -8.61590672,
-8.61660862, -8.6184721 , -8.62062263, -8.62255764, -8.62506008,
-8.62743568, -8.63008213, -8.63189983, -8.63258362, -8.63125229,
-8.62971306, -8.62880421, -8.62857914, -8.62874985, -8.63042355,
-8.6326828 , -8.63513088, -8.63770485, -8.64035988, -8.64220524,
-8.64406776, -8.6464529 , -8.64861298, -8.64950371, -8.64983654,
-8.64983654, -8.64988232, -8.64993572, -8.64990044, -8.59938335,
-8.59652996, -8.65007973, -8.65039539, -8.65037727, -8.65035915,
-8.6498909 , -8.64936924, -8.64919758, -8.64971066, -8.64911747,
-8.64911747, -8.64861298, -8.64842415, -8.64758682, -8.64593983,
-8.64386082, -8.64276314, -8.64091778, -8.63775921, -8.63533783,
-8.63327694, -8.63151264, -8.629776 , -8.62781429, -8.62599564,
-8.62408829, -8.62132454, -8.61944389, -8.61736488, -8.61596966], dtype=float32),
..., array([], dtype=float32),
array([-8.63071156, -8.63072968, -8.6307478 , -8.63076591, -8.63073921,
-8.63049603, -8.62893867, -8.62876797, -8.62751675, -8.62714767,
-8.62705803, -8.62703133, -8.62679672, -8.62676144, -8.62572575,
-8.62489796, -8.62421417, -8.62392616, -8.62385368, -8.62344933,
-8.62380028, -8.62341309, -8.62259388, -8.62220669, -8.62110901,
-8.62080288, -8.6191473 , -8.6173563 , -8.61485386, -8.61451244,
-8.6144762 , -8.6144762 , -8.61381912, -8.61286545, -8.61273003,
-8.61128139, -8.61117268, -8.61105633, -8.61103821, -8.60998535,
-8.60926533, -8.60750103, -8.60642147, -8.60634899, -8.60635757,
-8.60637569, -8.60608768, -8.60408974, -8.60242462, -8.60177708,
-8.60108376, -8.59866333, -8.59742069, -8.59742069, -8.59570217,
-8.59405518, -8.59331703, -8.59143639, -8.58960915, -8.58862782,
-8.58779144, -8.58702564], dtype=float32),
array([-8.61553764, -8.6154213 , -8.61479092, -8.61450291, -8.61443996,
-8.61400795, -8.61250496, -8.61165905, -8.61120892, -8.61120892,
-8.61110973, -8.61110973, -8.61070538, -8.60953522, -8.60875225,
-8.60975075, -8.61071396, -8.61071396, -8.61107445, -8.61273003,
-8.62025356, -8.62075806, -8.62092876, -8.62089252, -8.62089252,
-8.62089252, -8.62089252], dtype=float32)], dtype=object)),
('test',
'trip_id',
array([b'T1', b'T2', b'T3', b'T4', b'T5', b'T6', b'T7', b'T8', b'T9',
b'T10', b'T11', b'T12', b'T13', b'T14', b'T15', b'T16', b'T17',
b'T18', b'T19', b'T20', b'T21', b'T22', b'T23', b'T24', b'T25',
b'T26', b'T27', b'T28', b'T29', b'T30', b'T31', b'T32', b'T33',
b'T34', b'T35', b'T36', b'T37', b'T38', b'T39', b'T40', b'T41',
b'T42', b'T43', b'T44', b'T45', b'T46', b'T47', b'T48', b'T49',
b'T50', b'T51', b'T52', b'T53', b'T54', b'T55', b'T56', b'T57',
b'T58', b'T59', b'T60', b'T61', b'T62', b'T63', b'T64', b'T65',
b'T66', b'T67', b'T68', b'T69', b'T70', b'T71', b'T72', b'T73',
b'T74', b'T75', b'T76', b'T77', b'T78', b'T79', b'T80', b'T81',
b'T82', b'T83', b'T84', b'T85', b'T86', b'T87', b'T88', b'T90',
b'T91', b'T92', b'T93', b'T94', b'T95', b'T96', b'T98', b'T99',
b'T100', b'T101', b'T102', b'T103', b'T104', b'T107', b'T109',
b'T110', b'T111', b'T112', b'T113', b'T114', b'T115', b'T116',
b'T117', b'T118', b'T119', b'T120', b'T121', b'T122', b'T123',
b'T124', b'T125', b'T126', b'T127', b'T128', b'T129', b'T130',
b'T131', b'T132', b'T133', b'T134', b'T135', b'T136', b'T137',
b'T138', b'T139', b'T140', b'T141', b'T142', b'T143', b'T144',
b'T145', b'T146', b'T147', b'T148', b'T149', b'T151', b'T152',
b'T153', b'T154', b'T155', b'T156', b'T157', b'T158', b'T159',
b'T160', b'T161', b'T162', b'T163', b'T164', b'T166', b'T167',
b'T168', b'T169', b'T170', b'T171', b'T172', b'T173', b'T174',
b'T175', b'T176', b'T177', b'T178', b'T179', b'T180', b'T181',
b'T182', b'T183', b'T184', b'T185', b'T186', b'T187', b'T188',
b'T189', b'T190', b'T191', b'T192', b'T193', b'T194', b'T195',
b'T196', b'T197', b'T198', b'T199', b'T200', b'T201', b'T202',
b'T203', b'T204', b'T205', b'T206', b'T207', b'T208', b'T209',
b'T210', b'T211', b'T212', b'T213', b'T214', b'T215', b'T216',
b'T217', b'T218', b'T219', b'T220', b'T221', b'T222', b'T223',
b'T224', b'T225', b'T226', b'T227', b'T228', b'T229', b'T230',
b'T231', b'T232', b'T233', b'T234', b'T235', b'T236', b'T237',
b'T238', b'T239', b'T240', b'T241', b'T242', b'T243', b'T244',
b'T245', b'T246', b'T247', b'T248', b'T249', b'T250', b'T251',
b'T252', b'T253', b'T254', b'T255', b'T256', b'T257', b'T258',
b'T259', b'T260', b'T261', b'T262', b'T263', b'T264', b'T265',
b'T266', b'T267', b'T268', b'T269', b'T270', b'T271', b'T272',
b'T273', b'T274', b'T275', b'T276', b'T277', b'T278', b'T279',
b'T280', b'T281', b'T282', b'T283', b'T284', b'T285', b'T286',
b'T287', b'T288', b'T289', b'T290', b'T291', b'T292', b'T293',
b'T294', b'T295', b'T296', b'T297', b'T298', b'T299', b'T300',
b'T301', b'T302', b'T303', b'T304', b'T305', b'T306', b'T307',
b'T308', b'T309', b'T310', b'T311', b'T312', b'T313', b'T314',
b'T315', b'T316', b'T317', b'T318', b'T319', b'T320', b'T321',
b'T322', b'T323', b'T324', b'T325', b'T326', b'T327'],
dtype='|S19')),
('test',
'call_type',
array([1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1,
2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 1, 2, 2, 0, 1, 1, 1, 0, 2,
1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 0, 1, 0, 0, 1, 0, 2, 2, 1, 0, 0, 2,
2, 0, 1, 2, 0, 0, 0, 2, 1, 1, 2, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,
2, 1, 1, 2, 1, 0, 0, 0, 0, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1,
0, 0, 2, 1, 0, 1, 0, 0, 1, 2, 1, 1, 0, 0, 2, 0, 2, 1, 1, 0, 1, 1, 0,
2, 0, 1, 0, 1, 2, 0, 2, 1, 1, 1, 2, 1, 1, 2, 1, 0, 0, 0, 1, 1, 0, 2,
1, 2, 0, 1, 1, 2, 0, 1, 0, 2, 1, 2, 2, 1, 2, 1, 2, 0, 1, 2, 2, 1, 1,
0, 0, 2, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 2, 2, 0, 2, 1, 1, 2, 0, 1, 2,
1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 0, 2, 2, 1, 2, 2, 1, 1, 2, 0,
2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1,
1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 1, 2, 0, 2, 0, 1, 0, 1, 2,
2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 2, 0, 1, 2, 0, 0], dtype=int8)),
('test',
'origin_call',
array([ 0, 0, 0, 0, 0, 10833, 0, 35708, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 57106, 623, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
7, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 7017, 0, 0, 0,
7, 0, 0, 0, 43989, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 23356,
0, 23356, 16658, 0, 7178, 0, 0, 0, 34224,
7, 0, 0, 7, 0, 0, 41178, 57107, 935,
0, 0, 0, 0, 0, 0, 3520, 0, 0,
0, 7, 47093, 7, 0, 249, 34725, 0, 0,
0, 0, 0, 57108, 11099, 35492, 57109, 0, 0,
0, 3613, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 23831, 22338, 0, 0, 5145, 0,
34117, 57110, 0, 0, 0, 0, 57111, 7, 0,
502, 0, 0, 0, 33467, 0, 0, 7, 0,
57112, 0, 57113, 0, 0, 21655, 0, 0, 0,
0, 0, 0, 0, 0, 0, 23614, 16613, 57114,
0, 0, 57115, 0, 0, 0, 7, 0, 0,
0, 1729, 0, 8326, 0, 0, 0, 0, 0,
0, 0, 0, 20375, 0, 0, 0, 0, 0,
8701, 8316, 0, 0, 0, 0, 0, 0, 57116,
0, 0, 0, 0, 0, 0, 7, 0, 0,
0, 0, 7, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
57117, 0, 0, 0, 0, 0, 0, 0, 0,
7, 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, 57118, 0, 0,
0, 0, 0, 0, 16141, 0, 3476, 0, 9843,
0, 0, 0, 0, 0, 57119, 0, 11134, 57120,
42039, 36905, 0, 2939, 0, 57121, 57122, 0, 0,
57123, 0, 0, 57124, 36140], dtype=int32)),
('test',
'origin_stand',
array([15, 57, 15, 53, 18, 0, 15, 0, 9, 15, 56, 0, 0, 0, 0, 0, 0,
0, 15, 0, 15, 0, 0, 15, 12, 49, 15, 15, 21, 15, 15, 57, 0, 0,
0, 0, 0, 60, 15, 34, 38, 0, 0, 9, 30, 61, 0, 0, 28, 56, 13,
10, 60, 15, 15, 14, 9, 35, 0, 0, 15, 0, 0, 0, 9, 9, 15, 0,
0, 13, 0, 0, 15, 0, 47, 0, 23, 0, 0, 0, 0, 59, 0, 0, 7,
0, 0, 0, 16, 0, 0, 0, 0, 0, 60, 0, 0, 0, 0, 0, 7, 33,
0, 57, 57, 0, 25, 11, 33, 0, 0, 0, 42, 0, 0, 0, 35, 6, 0,
36, 0, 0, 0, 0, 57, 0, 0, 0, 0, 57, 57, 47, 0, 0, 0, 0,
15, 35, 0, 0, 0, 57, 0, 15, 0, 0, 16, 0, 33, 3, 0, 0, 0,
0, 0, 17, 25, 0, 57, 15, 0, 0, 0, 35, 0, 60, 0, 0, 0, 18,
40, 49, 0, 13, 38, 0, 9, 0, 0, 0, 36, 53, 0, 0, 34, 0, 0,
14, 53, 0, 0, 28, 0, 0, 25, 0, 0, 28, 0, 36, 0, 0, 47, 0,
0, 26, 18, 0, 0, 0, 57, 53, 0, 10, 54, 0, 6, 63, 21, 15, 0,
0, 0, 0, 9, 1, 0, 0, 57, 0, 57, 0, 0, 0, 0, 0, 0, 0,
0, 13, 0, 0, 58, 0, 0, 0, 57, 0, 0, 23, 10, 0, 0, 0, 23,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 0, 0, 0, 0, 0, 0,
0, 38, 0, 14, 23, 0, 0, 0, 0, 0, 0, 23, 0, 0, 0, 0, 0,
0, 16, 0, 0, 0, 0, 42, 0, 53, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 22, 0, 0, 0, 0, 0, 53, 0, 0, 0], dtype=int8)),
('test',
'taxi_id',
array([150, 306, 260, 40, 33, 174, 335, 315, 32, 364, 199, 393, 334,
423, 11, 232, 269, 285, 69, 371, 190, 52, 92, 402, 359, 112,
406, 427, 198, 248, 157, 209, 308, 264, 146, 125, 283, 266, 360,
104, 241, 263, 56, 224, 21, 80, 98, 318, 254, 303, 249, 227,
367, 96, 19, 193, 357, 361, 339, 375, 362, 270, 267, 129, 351,
323, 310, 416, 181, 281, 25, 43, 385, 117, 211, 235, 25, 399,
122, 341, 215, 198, 165, 81, 108, 221, 89, 380, 392, 231, 35,
39, 26, 138, 79, 386, 30, 146, 41, 105, 333, 126, 37, 120,
166, 255, 297, 116, 206, 186, 404, 242, 325, 246, 154, 175, 413,
284, 288, 1, 443, 409, 416, 13, 27, 224, 176, 345, 118, 0,
91, 371, 32, 57, 326, 254, 283, 287, 271, 319, 115, 179, 328,
424, 49, 210, 238, 232, 298, 412, 334, 440, 151, 16, 156, 231,
49, 287, 428, 342, 169, 267, 87, 289, 305, 307, 380, 292, 341,
205, 276, 355, 8, 20, 324, 391, 402, 144, 225, 337, 81, 376,
21, 251, 354, 434, 211, 426, 264, 325, 349, 234, 115, 1, 37,
253, 285, 125, 430, 188, 329, 423, 409, 207, 191, 377, 6, 75,
70, 262, 123, 171, 178, 435, 249, 55, 237, 35, 29, 385, 80,
127, 146, 179, 384, 31, 339, 64, 363, 137, 182, 41, 438, 336,
367, 24, 77, 166, 78, 361, 213, 306, 40, 199, 32, 117, 104,
270, 146, 67, 107, 374, 60, 196, 207, 264, 387, 406, 72, 90,
99, 48, 121, 132, 39, 278, 27, 444, 13, 311, 133, 47, 226,
91, 299, 85, 82, 22, 86, 168, 385, 34, 307, 365, 190, 381,
314, 402, 160, 240, 298, 300, 158, 180, 75, 55, 35, 2, 281,
367, 150, 342, 387, 112, 99, 413, 294, 74, 62, 81, 68, 183,
147, 419, 151, 338, 414, 210, 331, 193], dtype=int16)),
('test',
'timestamp',
array([1408039037, 1408038611, 1408038568, 1408039090, 1408039177,
1408037146, 1408038846, 1408038948, 1408038563, 1408038021,
1408038267, 1408038946, 1408039130, 1408036255, 1408038388,
1408037740, 1408038804, 1408038215, 1408038749, 1408036754,
1408039125, 1408039009, 1408038924, 1408039145, 1408039057,
1408039029, 1408038859, 1408039165, 1408038439, 1408038605,
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1412065494, 1412065555, 1412064845, 1412065195, 1412064580,
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1419171485, 1419170802, 1419172121, 1419171980, 1419171420], dtype=int32)),
('test',
'day_type',
array([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,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8)),
('test',
'missing_data',
array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False, False, False], dtype=bool)),
('test',
'latitude',
array([ array([ 41.14852142, 41.14863968, 41.1488533 , 41.14892578,
41.14896393, 41.14895248, 41.14871979, 41.14784622,
41.14746094, 41.14715576, 41.1466217 ], dtype=float32),
array([ 41.14556885, 41.14558029, 41.14576721, 41.14619064,
41.14675903, 41.14711761, 41.14712524, 41.14753342,
41.14811707, 41.14834976, 41.14857483, 41.14926147,
41.14989853, 41.14989853, 41.14996338, 41.15097809,
41.1519165 , 41.15278625, 41.15332031, 41.15434647,
41.15437317, 41.15535355, 41.15677643, 41.1581955 ,
41.15994263, 41.16055679, 41.16059875, 41.16064453,
41.16080856, 41.16117477, 41.16124725, 41.16129303,
41.16126633, 41.16123962, 41.16119385, 41.16127396,
41.16204834, 41.16283035, 41.16311264, 41.16359711], dtype=float32),
array([ 41.14855957, 41.1488266 , 41.14897156, 41.14901733,
41.1485672 , 41.14831543, 41.1479187 , 41.14732361,
41.14683914, 41.14619827, 41.14511108, 41.14485168,
41.14565277, 41.14585876, 41.1463089 , 41.14784622,
41.15024185, 41.15207672, 41.15329361, 41.15383148,
41.15438843, 41.15490341, 41.15557861, 41.15648651,
41.15699005, 41.15777588, 41.15838623, 41.15877151,
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array([-8.57019615, -8.57018661, -8.56946659, -8.56733418, -8.5667944 ,
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array([-8.61387253, -8.61388206, -8.61471939, -8.61583519, -8.61705017,
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-8.6298027 , -8.629776 , -8.62976742, -8.62978458], dtype=float32),
array([-8.6480999 , -8.64746094, -8.64687634, -8.64593124, -8.6453371 ,
-8.64330292], dtype=float32),
array([-8.57169914, -8.57058334, -8.56899929, -8.57006073, -8.57168102,
-8.57124901, -8.57024097, -8.56854916, -8.56672192, -8.56644344,
-8.56541729, -8.56533623, -8.56580353, -8.56668568, -8.56920624], dtype=float32),
array([-8.57456112, -8.57224846, -8.57049274, -8.56882763, -8.56724358,
-8.56529999, -8.5627346 , -8.56034088, -8.55880165, -8.55816269,
-8.55764961, -8.55751514, -8.55781174, -8.55823517, -8.55827999,
-8.55827999, -8.55950356, -8.56023312, -8.56050301, -8.5602417 ,
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-8.55858612, -8.55860424, -8.55864906, -8.55847836, -8.55929661,
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-8.5907793 , -8.59234524], dtype=float32)], dtype=object)),
('unique_taxi_id',
'unique_taxi_id',
array([20000589, 20000596, 20000320, 20000520, 20000337, 20000231,
20000456, 20000011, 20000403, 20000233, 20000571, 20000497,
20000570, 20000005, 20000089, 20000423, 20000657, 20000309,
20000161, 20000178, 20000235, 20000653, 20000009, 20000648,
20000424, 20000010, 20000372, 20000686, 20000435, 20000154,
20000060, 20000167, 20000503, 20000621, 20000463, 20000612,
20000360, 20000574, 20000173, 20000560, 20000492, 20000112,
20000305, 20000004, 20000620, 20000671, 20000341, 20000015,
20000307, 20000171, 20000201, 20000007, 20000199, 20000611,
20000398, 20000067, 20000569, 20000473, 20000367, 20000672,
20000190, 20000600, 20000496, 20000013, 20000454, 20000534,
20000195, 20000406, 20000325, 20000101, 20000632, 20000308,
20000333, 20000481, 20000486, 20000450, 20000545, 20000665,
20000012, 20000688, 20000540, 20000436, 20000517, 20000153,
20000458, 20000577, 20000105, 20000128, 20000616, 20000477,
20000197, 20000476, 20000480, 20000451, 20000663, 20000386,
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20000073, 20000397, 20000285, 20000286, 20000024, 20000239,
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20000295, 20000452, 20000634, 20000547, 20000080, 20000903,
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20000250, 20000023, 20000597, 20000606, 20000263, 20000941,
20000610, 20000591, 20000506, 20000072, 20000543, 20000383,
20000020, 20000449, 20000260, 20000003, 20000364, 20000393,
20000002, 20000187, 20000205, 20000312, 20000510, 20000389,
20000340, 20000500, 20000407, 20000980, 20000213, 20000969,
20000609, 20000332, 20000121, 20000362, 20000170, 20000585,
20000185, 20000264, 20000698, 20000670, 20000911, 20000248,
20000079, 20000940, 20000970, 20000931], dtype=int32)),
('unique_origin_call',
'unique_origin_call',
array([ 0, 31508, 33180, ..., 66812, 70885, 76232], dtype=int32)))
In [203]:
split[7][1]
Out[203]:
'taxi_id'
In [224]:
def make_valid(split):
valid = (
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[]
)
for i in range(len(split[5][2])):
trip_id = split[3][2][i]
call_type = split[4][2][i]
origin_call = split[5][2][i]
origin_stand = split[6][2][i]
taxi_id = split[7][2][i]
time = split[8][2][i]
day_type = split[9][2][i]
missing_data = split[10][2][i]
latitude = split[11][2][i]
longitude = split[12][2][i]
if len(latitude) == 0:
continue
for ts in cuts:
if time <= ts and time + 15 * (len(latitude) - 1) >= ts:
# keep it
valid[0].append(trip_id)
valid[1].append(call_type)
valid[2].append(origin_call)
valid[3].append(origin_stand)
valid[4].append(taxi_id)
valid[5].append(time)
valid[6].append(day_type)
valid[7].append(missing_data)
n = (ts - time) / 15 + 1
valid[8].append(latitude[:n])
valid[9].append(longitude[:n])
valid[10].append(latitude[-1])
valid[11].append(longitude[-1])
valid[12].append(15 * (len(latitude)-1))
break
return (
('valid', 'trip_id', np.array(valid[0])),
('valid', 'call_type', np.array(valid[1])),
('valid', 'origin_call', np.array(valid[2])),
('valid', 'origin_stand', np.array(valid[3])),
('valid', 'taxi_id', np.array(valid[4])),
('valid', 'timestamp', np.array(valid[5])),
('valid', 'day_type', np.array(valid[6])),
('valid', 'missing_data', np.array(valid[7])),
('valid', 'latitude', np.array(valid[8])),
('valid', 'longitude', np.array(valid[9])),
('valid', 'destination_latitude', np.array(valid[10])),
('valid', 'destination_longitude', np.array(valid[11])),
('valid', 'travel_time', np.array(valid[12]))
)
In [225]:
valid_split = make_valid(split)
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:46: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:47: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
In [226]:
valid_split
Out[226]:
(('valid', 'trip_id', array([b'1376502576620000126', b'1376503146620000161',
b'1376502942620000500', b'1376502604620000105',
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b'1387708463620000329', b'1387722594620000900',
b'1387722519620000482', b'1387722463620000481',
b'1387721783620000030', b'1387721846620000247'],
dtype='|S22')),
('valid',
'call_type',
array([1, 1, 1, 2, 2, 0, 0, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 0, 0, 1, 2, 1, 1,
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('valid',
'origin_call',
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('valid',
'origin_stand',
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('valid',
'taxi_id',
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('valid',
'timestamp',
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('valid',
'day_type',
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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-8.58950996, -8.66248226, -8.71010971, -8.59846497, -8.59076118,
-8.59382057, -8.61361217, -8.60703278, -8.61561012, -8.61403465,
-8.63087368, -8.63424873, -8.59783459, -8.53081226, -8.64966583,
-8.58598232, -8.60097599, -8.63894653, -8.60730267, -8.5810051 ,
-8.59092331, -8.64455414, -8.64054012, -8.61617661, -8.6214056 ,
-8.61416054, -8.61487198, -8.61631203, -8.64459896, -8.64936924,
-8.60493565, -8.59889698, -8.64666939, -8.58947372, -8.60365772,
-8.61673546, -8.68183231, -8.53608608, -8.62794876, -8.61437702,
-8.53686047, -8.66829586, -8.61607838, -8.66756725, -8.63560772,
-8.58496475, -8.66408443, -8.64082813, -8.66420078, -8.66613579,
-8.61261272, -8.60713196, -8.57224846, -8.67704391, -8.60280323,
-8.66126728, -8.6702404 , -8.65421104, -8.6488924 , -8.62491608,
-8.61251354, -8.66674805, -8.67024899, -8.58682823, -8.67028522,
-8.50113869, -8.67039299, -8.67578411, -8.68574715, -8.6754961 ,
-8.70092106, -8.63965797, -8.67006016, -8.67008686, -8.66438961,
-8.59885216, -8.63688564, -8.61772537, -8.63192654, -8.64587688,
-8.66501999, -8.63185501, -8.68158913, -8.67622471, -8.69559288,
-8.56703663, -8.66973591, -8.61359406, -8.59892368, -8.65874672,
-8.588727 , -8.67010498, -8.58490181, -8.61400795, -8.59616089,
-8.61371136, -8.62228775, -8.65423775, -8.58221054, -8.64675045,
-8.55625534, -8.65103436, -8.49114895, -8.60529613, -8.60068798,
-8.58771896, -8.55832481, -8.51997566, -8.59326267], dtype=float32)),
('valid',
'travel_time',
array([ 675, 285, 705, 885, 615, 960, 2430, 1470, 720,
900, 960, 1080, 4185, 1170, 1020, 1470, 2655, 1605,
2085, 255, 1365, 330, 450, 945, 375, 2625, 1200,
1170, 750, 690, 480, 5535, 1425, 750, 1620, 1860,
3030, 1755, 630, 780, 735, 600, 435, 345, 915,
795, 960, 1230, 1485, 990, 690, 405, 1380, 1905,
9255, 1170, 285, 855, 720, 615, 120, 660, 450,
960, 2070, 180, 1290, 450, 660, 930, 3765, 120,
420, 720, 480, 1095, 1020, 1095, 1020, 915, 975,
1125, 1260, 1245, 1485, 1260, 1890, 1005, 1155, 2160,
825, 1050, 1155, 1005, 1200, 1995, 525, 1050, 1485,
855, 3705, 2070, 1890, 1275, 870, 510, 1485, 570,
915, 1140, 2085, 1110, 720, 915, 1455, 1620, 630,
735, 1455, 840, 1440, 975, 1935, 1560, 975, 1320,
1185, 1515, 1425, 1350, 1215, 900, 1320, 7035, 570,
390, 840, 285, 930, 705, 1770, 1695, 360, 4005,
2865, 3285, 780, 585, 975, 5265, 660, 615, 1110,
720, 795, 1275, 1140, 1020, 810, 705, 885, 900,
720, 1035, 1110, 1905, 435, 1410, 1410, 705, 690,
1440, 1170, 2055, 1845, 1020, 660, 1170, 675, 930,
930, 3045, 1020, 1920, 525, 1410, 750, 1095, 795,
2535, 870, 2760, 1515, 825, 300, 3315, 1800, 1425,
1395, 1065, 975, 1050, 1770, 525, 1095, 1110, 765,
1695, 1950, 1275, 555, 1830, 1215, 1380, 780, 3645,
930, 1845, 825, 510, 390, 285, 585, 1020, 630,
405, 780, 810, 600, 420, 555, 1275, 705, 255,
1800, 1080, 780, 945, 525, 405, 915, 1170, 660,
840, 1170, 1530, 255, 615, 825, 720, 945, 1005,
870, 1395, 750, 930, 885, 1065, 930, 795, 1095,
780, 1155, 1005, 915, 1005, 945, 900, 1080, 840,
330, 2010, 825, 645, 375, 870, 690, 525, 585,
570, 1140, 750, 390, 600, 420, 1710, 840, 1245,
945, 780, 3105, 645, 645, 1080, 735, 1305, 540,
2010, 25860, 915, 840, 795, 1335, 3480])))
In [95]:
stands = pd.read_csv(data_path+'/data/metaData_taxistandsID_name_GPSlocation.csv', header=0)
In [96]:
stands.head()
Out[96]:
ID
Descricao
Latitude
Longitude
0
1
Agra
41.177146
-8.609670
1
2
Alameda
41.156190
-8.591064
2
3
Aldoar
41.170525
-8.665876
3
4
Alfândega
41.143764
-8.621803
4
5
Amial
41.183510
-8.612726
Compare columns
In [97]:
stands['Descricao']
Out[97]:
0 Agra
1 Alameda
2 Aldoar
3 Alfândega
4 Amial
5 Areosa
6 Av. Boavista
7 Azevedo
8 Batalha
9 Bolhão
10 Bom Pastor
11 Bom Sucesso
12 Brasília
13 Câmara
14 Campanhã
15 Campismo
16 Carcereira
17 Carregal
18 Carvalheiras
19 Carvalhido
20 Casa da Musica
21 Castelo do Queijo
22 Clérigos
23 Conde Ferreira
24 Cordoaria
25 Corujeira
26 D. João I
27 Dragão
28 Francos
29 Galiza
...
33 Infante
34 Lago
35 Lordelo
36 Marechal
37 Marquês
38 Maternidade
39 Mercado da Foz
40 Nevogilde
41 Nove de Abril
42 Ouro
43 Palácio
44 Passeio Alegre
45 Pereira de Melo
46 Pereiró
47 Pinto de Azevedo
48 Pólo Universitário
49 Prelada
50 Ramada Alta
51 Républica
52 Ribeira
53 Rotunda
54 São Roque
55 Sá Carneiro
56 São Bento
57 Terço
58 Tenente Valadim
59 Trindade
60 Vinte e Quatro de Agosto
61 Viso
62 Santa Maria
Name: Descricao, dtype: object
In [98]:
len(stands['Descricao'].as_matrix())
Out[98]:
63
In [99]:
split[2][2]
Out[99]:
array([ 0. , -8.60967064, -8.59106445, -8.66587639, -8.62180328,
-8.61272621, -8.58278275, -8.64198399, -8.56819534, -8.60571957,
-8.60690975, -8.61326694, -8.63046646, -8.62846756, -8.61107063,
-8.58587646, -8.6277914 , -8.63228607, -8.61960316, -8.60542679,
-8.6267786 , -8.62956524, -8.68918037, -8.61257458, -8.58934784,
-8.61763763, -8.57977581, -8.60924149, -8.58404636, -8.6386652 ,
-8.62775898, -8.66396046, -8.62704372, -8.60012245, -8.61595249,
-8.6495266 , -8.6490736 , -8.657547 , -8.60424614, -8.62279987,
-8.67400646, -8.67598343, -8.61178398, -8.64888668, -8.62578487,
-8.6727953 , -8.64853573, -8.65449619, -8.64505005, -8.60253906,
-8.62890244, -8.62112236, -8.61321735, -8.61401272, -8.63037491,
-8.5703516 , -8.59164524, -8.61070728, -8.60452843, -8.64120483,
-8.60984325, -8.60030842, -8.64655781, -8.60936928], dtype=float32)
In [100]:
len(split[0][2])
Out[100]:
64
Author's data has a "None" row w/ zeros. Will add to series
In [101]:
d={'col1': 1, 'col2': 2}
In [102]:
stands = pd.DataFrame([['None',0.,0.]], columns=['Descricao', 'Latitude', 'Longitude']).append(stands)
In [103]:
np.allclose(stands['Latitude'],split[1][2])
Out[103]:
True
In [104]:
np.allclose(stands['Longitude'],split[2][2])
Out[104]:
True
Longs/Lats check out
In [105]:
data = pd.read_csv(data_path+'data/train.csv', header=0)
In [169]:
data.columns
Out[169]:
Index(['TRIP_ID', 'CALL_TYPE', 'ORIGIN_CALL', 'ORIGIN_STAND', 'TAXI_ID',
'TIMESTAMP', 'DAY_TYPE', 'MISSING_DATA', 'POLYLINE'],
dtype='object')
Check data same size
In [106]:
data.shape
Out[106]:
(1710670, 9)
In [107]:
len(split[4][2])
Out[107]:
1710670
In [245]:
split[4][1]
Out[245]:
'call_type'
In [242]:
call_type_f = lambda x:ord(x) - ord('A')
In [247]:
np.allclose(data['CALL_TYPE'].apply(call_type_f),split[4][2])
Out[247]:
True
In [248]:
data['CALL_TYPE'] = data['CALL_TYPE'].apply(call_type_f)
Turn origin call into categorical variable
Can do using factorize: we want the nulls to be zero. Factorize sets them as -1, add 1 to set as needed
In [110]:
np.allclose(pd.Series(pd.factorize(data['ORIGIN_CALL'])[0])+1,split[5][2])
Out[110]:
True
And it is the same
In [111]:
data['ORIGIN_CALL'] = pd.Series(pd.factorize(data['ORIGIN_CALL'])[0]) + 1
In [113]:
num_origin_call = len(data['ORIGIN_CALL'].unique())
In [79]:
s = set()
In [ ]:
s.i
In [283]:
origin_stand_f = lambda x: 0 if pd.isnull(x) or x=='' else int(x)
In [178]:
split[6]
Out[178]:
('train', 'origin_stand', array([ 0, 7, 0, ..., 0, 12, 34], dtype=int8))
In [284]:
np.allclose(data["ORIGIN_STAND"].apply(origin_stand_f),split[6][2])
Out[284]:
True
In [285]:
data["ORIGIN_STAND"] = data["ORIGIN_STAND"].apply(origin_stand_f)
In [114]:
num_origin_stand = len(data['ORIGIN_STAND'].unique())
In [115]:
num_origin_stand
Out[115]:
64
In [116]:
split[7][2]
Out[116]:
array([ 0, 1, 2, ..., 439, 443, 444], dtype=int16)
In [117]:
pd.factorize(data['TAXI_ID'])[0]
Out[117]:
array([ 0, 1, 2, ..., 439, 443, 444])
In [118]:
np.allclose(pd.Series(pd.factorize(data['TAXI_ID'])[0]),split[7][2])
Out[118]:
True
In [119]:
data['TAXI_ID'] = pd.Series(pd.factorize(data['TAXI_ID'])[0])
In [120]:
num_taxi_id = data['TAXI_ID'].unique()
In [121]:
split[9]
Out[121]:
('train', 'day_type', array([0, 0, 0, ..., 0, 0, 0], dtype=int8))
In [122]:
day_type_f = lambda x: ord(x[0]) - ord('A')
In [123]:
np.allclose(data['DAY_TYPE'].apply(day_type_f),split[9][2])
Out[123]:
True
In [124]:
data['DAY_TYPE'] = data['DAY_TYPE'].apply(day_type_f)
In [203]:
polyline_f = lambda x: ast.literal_eval(x)
In [204]:
polyline = data['POLYLINE'].apply(polyline_f)
In [205]:
polyline.to_pickle(data_path+'data/polylines.pkl')
In [125]:
polyline = pd.read_pickle(data_path+'data/polylines.pkl')
In [126]:
len(polyline)
Out[126]:
1710670
In [208]:
polyline
Out[208]:
0 [[-8.618643, 41.141412], [-8.618499, 41.141376...
1 [[-8.639847, 41.159826], [-8.640351, 41.159871...
2 [[-8.612964, 41.140359], [-8.613378, 41.14035]...
3 [[-8.574678, 41.151951], [-8.574705, 41.151942...
4 [[-8.645994, 41.18049], [-8.645949, 41.180517]...
5 [[-8.615502, 41.140674], [-8.614854, 41.140926...
6 [[-8.57952, 41.145948], [-8.580942, 41.145039]...
7 [[-8.617563, 41.146182], [-8.617527, 41.145849...
8 [[-8.611794, 41.140557], [-8.611785, 41.140575...
9 [[-8.615907, 41.140557], [-8.614449, 41.141088...
10 [[-8.619894, 41.148009], [-8.620164, 41.14773]...
11 [[-8.56242, 41.168403], [-8.562429, 41.168358]...
12 [[-8.618868, 41.155101], [-8.6175, 41.154912],...
13 [[-8.608716, 41.153499], [-8.607627, 41.153481...
14 [[-8.612208, 41.14053], [-8.612235, 41.140521]...
15 [[-8.585145, 41.164857], [-8.584146, 41.164704...
16 [[-8.584263, 41.163156], [-8.584695, 41.163003...
17 [[-8.666757, 41.174055], [-8.666784, 41.174064...
18 [[-8.599239, 41.149188], [-8.598681, 41.149296...
19 [[-8.643807, 41.168979], [-8.642529, 41.170113...
20 [[-8.609706, 41.151303], [-8.609562, 41.151474...
21 [[-8.660646, 41.168574], [-8.661087, 41.167926...
22 [[-8.612955, 41.140377], [-8.612991, 41.140359...
23 [[-8.60418, 41.160969], [-8.603874, 41.1615], ...
24 [[-8.665686, 41.170626], [-8.665677, 41.170653...
25 [[-8.632737, 41.168295], [-8.6328, 41.16825], ...
26 [[-8.648226, 41.148333], [-8.648514, 41.148297...
27 [[-8.638227, 41.159592], [-8.637543, 41.15925]...
28 [[-8.613243, 41.154444], [-8.612811, 41.153733...
29 [[-8.611065, 41.149431], [-8.611209, 41.149368...
...
1710640 [[-8.668845, 41.152725], [-8.668179, 41.15313]...
1710641 [[-8.625726, 41.166864], [-8.625717, 41.166873...
1710642 [[-8.610903, 41.149449], [-8.610885, 41.149503...
1710643 []
1710644 [[-8.604171, 41.161077], [-8.604036, 41.161473...
1710645 [[-8.628777, 41.169807], [-8.628615, 41.169807...
1710646 [[-8.580267, 41.159232], [-8.581743, 41.159142...
1710647 [[-8.628111, 41.157423], [-8.628156, 41.157378...
1710648 [[-8.614323, 41.123385], [-8.614152, 41.123385...
1710649 [[-8.58564, 41.148585], [-8.585658, 41.148567]...
1710650 [[-8.63568, 41.171886], [-8.634924, 41.171859]...
1710651 [[-8.574714, 41.142681], [-8.574714, 41.142681...
1710652 [[-8.66754, 41.238549], [-8.669502, 41.239287]...
1710653 [[-8.66349, 41.177466], [-8.663436, 41.177502]...
1710654 [[-8.585712, 41.148828], [-8.586009, 41.148972...
1710655 [[-8.645364, 41.148324], [-8.642538, 41.148036...
1710656 [[-8.630973, 41.152653], [-8.631423, 41.152662...
1710657 [[-8.689959, 41.240241]]
1710658 []
1710659 [[-8.574732, 41.157099], [-8.574849, 41.156775...
1710660 [[-8.643303, 41.13648]]
1710661 [[-8.585703, 41.148567], [-8.585739, 41.148855...
1710662 [[-8.606385, 41.144742], [-8.606466, 41.144742...
1710663 [[-8.60697, 41.162283], [-8.60697, 41.16231], ...
1710664 []
1710665 [[-8.612469, 41.14602], [-8.612487, 41.145993]...
1710666 [[-8.610138, 41.140845], [-8.610174, 41.140935...
1710667 []
1710668 [[-8.630712, 41.154885], [-8.63073, 41.154813]...
1710669 [[-8.615538, 41.140629], [-8.615421, 41.140746...
Name: POLYLINE, dtype: object
In [127]:
lats = pd.Series([np.array([point[1] for point in poly],dtype=np.float32) for poly in polyline])
In [233]:
split[11][2]
Out[233]:
array([ array([ 41.1414, 41.1414, 41.1425, 41.1438, 41.1444, 41.1448, 41.1447, 41.1452, 41.1469,
41.1482, 41.1504, 41.1512, 41.1512, 41.1522, 41.1524, 41.1525, 41.1527, 41.153 ,
41.1545, 41.1545, 41.1545, 41.1545, 41.1545], dtype=float32),
array([ 41.1598, 41.1599, 41.1601, 41.1605, 41.1609, 41.1615, 41.162 , 41.1626, 41.1632,
41.1637, 41.1642, 41.1647, 41.1651, 41.1666, 41.168 , 41.169 , 41.17 , 41.1706,
41.1707], dtype=float32),
array([ 41.1404, 41.1404, 41.1403, 41.1404, 41.1404, 41.1406, 41.1414, 41.1428, 41.1441,
41.1448, 41.1447, 41.1452, 41.1465, 41.1473, 41.1488, 41.1506, 41.1521, 41.1525,
41.1527, 41.1528, 41.1528, 41.1526, 41.153 , 41.1536, 41.154 , 41.1545, 41.1543,
41.1543, 41.1543, 41.1544, 41.1543, 41.1543, 41.1543, 41.1543, 41.1417, 41.1406,
41.1543, 41.1538, 41.1538, 41.1538, 41.1532, 41.1526, 41.1524, 41.1512, 41.1505,
41.1491, 41.1483, 41.1482, 41.1484, 41.1484, 41.1481, 41.148 , 41.1484, 41.1483,
41.148 , 41.1472, 41.1461, 41.145 , 41.1446, 41.1448, 41.1445, 41.1434, 41.142 ,
41.1409, 41.1405], dtype=float32),
..., array([], dtype=float32),
array([ 41.1549, 41.1548, 41.1548, 41.1548, 41.1545, 41.1536, 41.1536, 41.1544, 41.1547,
41.1547, 41.1546, 41.1546, 41.1547, 41.1551, 41.1555, 41.1556, 41.1559, 41.1569,
41.157 , 41.1583, 41.1589, 41.1593, 41.1601, 41.1604, 41.1605, 41.1614, 41.1623,
41.1625, 41.1622, 41.1621, 41.1622, 41.1622, 41.162 , 41.162 , 41.1619, 41.1619,
41.1625, 41.1631, 41.1642, 41.1646, 41.1645, 41.1645, 41.1645, 41.1653, 41.1652,
41.1652, 41.1654, 41.1655, 41.1657, 41.1667, 41.1671, 41.1671, 41.1667, 41.1661,
41.1667, 41.1671, 41.1675, 41.1685, 41.1696, 41.1708, 41.1721, 41.1735], dtype=float32),
array([ 41.1406, 41.1407, 41.141 , 41.1413, 41.1418, 41.1429, 41.1439, 41.1445, 41.1449,
41.1449, 41.1449, 41.1449, 41.1458, 41.1467, 41.1472, 41.1476, 41.1478, 41.1478,
41.1478, 41.1481, 41.1476, 41.1491, 41.1499, 41.1499, 41.1499, 41.1499, 41.1499], dtype=float32)], dtype=object)
In [128]:
np.alltrue([np.allclose(lats[i],split[11][2][i]) for i in range(len(lats))])
Out[128]:
True
Latitudes check out
In [129]:
longs = pd.Series([np.array([point[0] for point in poly],dtype=np.float32) for poly in polyline])
In [241]:
split[12]
Out[241]:
('train',
'longitude',
array([ array([-8.6186, -8.6185, -8.6203, -8.6222, -8.624 , -8.6267, -8.6274, -8.6302, -8.6327, -8.6317,
-8.6299, -8.6291, -8.6291, -8.6288, -8.6287, -8.6288, -8.6308, -8.6323, -8.6311, -8.6308,
-8.6308, -8.6308, -8.6308], dtype=float32),
array([-8.6398, -8.6404, -8.6422, -8.6445, -8.6469, -8.65 , -8.6532, -8.6564, -8.6602, -8.6631,
-8.6662, -8.6692, -8.6709, -8.6709, -8.6696, -8.6681, -8.6666, -8.6658, -8.6657], dtype=float32),
array([-8.613 , -8.6134, -8.6142, -8.6148, -8.6159, -8.6166, -8.6185, -8.6206, -8.6226, -8.6251,
-8.6274, -8.6301, -8.6319, -8.6326, -8.6313, -8.6297, -8.6288, -8.6286, -8.6287, -8.6304,
-8.6327, -8.6351, -8.6377, -8.6404, -8.6422, -8.6441, -8.6465, -8.6486, -8.6495, -8.6498,
-8.6498, -8.6499, -8.6499, -8.6499, -8.5994, -8.5965, -8.6501, -8.6504, -8.6504, -8.6504,
-8.6499, -8.6494, -8.6492, -8.6497, -8.6491, -8.6491, -8.6486, -8.6484, -8.6476, -8.6459,
-8.6439, -8.6428, -8.6409, -8.6378, -8.6353, -8.6333, -8.6315, -8.6298, -8.6278, -8.626 ,
-8.6241, -8.6213, -8.6194, -8.6174, -8.616 ], dtype=float32),
..., array([], dtype=float32),
array([-8.6307, -8.6307, -8.6307, -8.6308, -8.6307, -8.6305, -8.6289, -8.6288, -8.6275, -8.6271,
-8.6271, -8.627 , -8.6268, -8.6268, -8.6257, -8.6249, -8.6242, -8.6239, -8.6239, -8.6234,
-8.6238, -8.6234, -8.6226, -8.6222, -8.6211, -8.6208, -8.6191, -8.6174, -8.6149, -8.6145,
-8.6145, -8.6145, -8.6138, -8.6129, -8.6127, -8.6113, -8.6112, -8.6111, -8.611 , -8.61 ,
-8.6093, -8.6075, -8.6064, -8.6063, -8.6064, -8.6064, -8.6061, -8.6041, -8.6024, -8.6018,
-8.6011, -8.5987, -8.5974, -8.5974, -8.5957, -8.5941, -8.5933, -8.5914, -8.5896, -8.5886,
-8.5878, -8.587 ], dtype=float32),
array([-8.6155, -8.6154, -8.6148, -8.6145, -8.6144, -8.614 , -8.6125, -8.6117, -8.6112, -8.6112,
-8.6111, -8.6111, -8.6107, -8.6095, -8.6088, -8.6098, -8.6107, -8.6107, -8.6111, -8.6127,
-8.6203, -8.6208, -8.6209, -8.6209, -8.6209, -8.6209, -8.6209], dtype=float32)], dtype=object))
In [130]:
np.alltrue([np.allclose(longs[i],split[12][2][i]) for i in range(len(longs))])
Out[130]:
True
Longitudes check out
In [131]:
data['LATITUDE'] = lats
In [132]:
data['LONGITUDE'] = longs
In [134]:
data
Out[134]:
TRIP_ID
CALL_TYPE
ORIGIN_CALL
ORIGIN_STAND
TAXI_ID
TIMESTAMP
DAY_TYPE
MISSING_DATA
POLYLINE
LATITUDE
LONGITUDE
0
1372636858620000589
C
0
NaN
0
1372636858
0
False
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[41.1414, 41.1414, 41.1425, 41.1438, 41.1444, ...
[-8.61864, -8.6185, -8.62033, -8.62215, -8.623...
1
1372637303620000596
B
0
7.0
1
1372637303
0
False
[[-8.639847,41.159826],[-8.640351,41.159871],[...
[41.1598, 41.1599, 41.1601, 41.1605, 41.1609, ...
[-8.63985, -8.64035, -8.6422, -8.64445, -8.646...
2
1372636951620000320
C
0
NaN
2
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0
False
[[-8.612964,41.140359],[-8.613378,41.14035],[-...
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[-8.61296, -8.61338, -8.61421, -8.61477, -8.61...
3
1372636854620000520
C
0
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False
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[-8.57468, -8.57471, -8.5747, -8.57466, -8.574...
4
1372637091620000337
C
0
NaN
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0
False
[[-8.645994,41.18049],[-8.645949,41.180517],[-...
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[-8.64599, -8.64595, -8.64605, -8.6468, -8.649...
5
1372636965620000231
C
0
NaN
5
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0
False
[[-8.615502,41.140674],[-8.614854,41.140926],[...
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[-8.6155, -8.61485, -8.61335, -8.60998, -8.607...
6
1372637210620000456
C
0
NaN
6
1372637210
0
False
[[-8.57952,41.145948],[-8.580942,41.145039],[-...
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[-8.57952, -8.58094, -8.58271, -8.58409, -8.58...
7
1372637299620000011
C
0
NaN
7
1372637299
0
False
[[-8.617563,41.146182],[-8.617527,41.145849],[...
[41.1462, 41.1458, 41.1448, 41.1454, 41.1454, ...
[-8.61756, -8.61753, -8.61698, -8.61575, -8.61...
8
1372637274620000403
C
0
NaN
8
1372637274
0
False
[[-8.611794,41.140557],[-8.611785,41.140575],[...
[41.1406, 41.1406, 41.1406, 41.1405, 41.1403, ...
[-8.61179, -8.61178, -8.612, -8.61262, -8.6137...
9
1372637905620000320
C
0
NaN
2
1372637905
0
False
[[-8.615907,41.140557],[-8.614449,41.141088],[...
[41.1406, 41.1411, 41.1414, 41.1408, 41.1395, ...
[-8.61591, -8.61445, -8.61352, -8.6099, -8.609...
10
1372636875620000233
C
0
NaN
9
1372636875
0
False
[[-8.619894,41.148009],[-8.620164,41.14773],[-...
[41.148, 41.1477, 41.1485, 41.1503, 41.152, 41...
[-8.61989, -8.62016, -8.62065, -8.62092, -8.62...
11
1372637984620000520
C
0
NaN
3
1372637984
0
False
[[-8.56242,41.168403],[-8.562429,41.168358],[-...
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[-8.56242, -8.56243, -8.56235, -8.56457, -8.56...
12
1372637343620000571
A
1
NaN
10
1372637343
0
False
[[-8.618868,41.155101],[-8.6175,41.154912],[-8...
[41.1551, 41.1549, 41.1545, 41.1542, 41.1541, ...
[-8.61887, -8.6175, -8.61508, -8.61347, -8.613...
13
1372638595620000233
C
0
NaN
9
1372638595
0
False
[[-8.608716,41.153499],[-8.607627,41.153481],[...
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14
1372638151620000231
C
0
NaN
5
1372638151
0
False
[[-8.612208,41.14053],[-8.612235,41.140521],[-...
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[-8.61221, -8.61224, -8.61403, -8.61481, -8.61...
15
1372637610620000497
B
0
13.0
11
1372637610
0
False
[[-8.585145,41.164857],[-8.584146,41.164704],[...
[41.1649, 41.1647, 41.1648, 41.158, 41.1591, 4...
[-8.58514, -8.58415, -8.58315, -8.62793, -8.62...
16
1372638481620000403
B
0
28.0
8
1372638481
0
False
[[-8.584263,41.163156],[-8.584695,41.163003],[...
[41.1632, 41.163, 41.1627, 41.1614, 41.1603, 4...
[-8.58426, -8.58469, -8.5856, -8.58549, -8.583...
17
1372639135620000570
A
2
NaN
12
1372639135
0
False
[[-8.666757,41.174055],[-8.666784,41.174064],[...
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18
1372637482620000005
C
0
NaN
13
1372637482
0
False
[[-8.599239,41.149188],[-8.598681,41.149296],[...
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19
1372639181620000089
C
0
NaN
14
1372639181
0
False
[[-8.643807,41.168979],[-8.642529,41.170113],[...
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[-8.64381, -8.64253, -8.64213, -8.64324, -8.64...
20
1372638161620000423
C
0
NaN
15
1372638161
0
False
[[-8.609706,41.151303],[-8.609562,41.151474],[...
[41.1513, 41.1515, 41.1525, 41.1534, 41.1535, ...
[-8.60971, -8.60956, -8.61002, -8.60977, -8.60...
21
1372637254620000657
A
3
NaN
16
1372637254
0
False
[[-8.660646,41.168574],[-8.661087,41.167926],[...
[41.1686, 41.1679, 41.1666, 41.1664, 41.1668, ...
[-8.66065, -8.66109, -8.66123, -8.66064, -8.66...
22
1372638502620000320
C
0
NaN
2
1372638502
0
False
[[-8.612955,41.140377],[-8.612991,41.140359],[...
[41.1404, 41.1404, 41.1404, 41.1404, 41.1403, ...
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23
1372639960620000309
B
0
38.0
17
1372639960
0
False
[[-8.60418,41.160969],[-8.603874,41.1615],[-8....
[41.161, 41.1615, 41.1615, 41.162, 41.163, 41....
[-8.60418, -8.60387, -8.60391, -8.60392, -8.60...
24
1372637658620000596
A
4
NaN
1
1372637658
0
False
[[-8.665686,41.170626],[-8.665677,41.170653],[...
[41.1706, 41.1707, 41.1713, 41.1725, 41.1739, ...
[-8.66569, -8.66568, -8.66479, -8.66324, -8.66...
25
1372639092620000233
C
0
NaN
9
1372639092
0
False
[[-8.632737,41.168295],[-8.6328,41.16825],[-8....
[41.1683, 41.1683, 41.1682, 41.1682, 41.1682, ...
[-8.63274, -8.6328, -8.63284, -8.63284, -8.632...
26
1372639535620000161
A
5
NaN
18
1372639535
0
False
[[-8.648226,41.148333],[-8.648514,41.148297],[...
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27
1372640499620000596
C
0
NaN
1
1372640499
0
False
[[-8.638227,41.159592],[-8.637543,41.15925],[-...
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[-8.63823, -8.63754, -8.63516, -8.6332, -8.632...
28
1372639635620000178
B
0
52.0
19
1372639635
0
False
[[-8.613243,41.154444],[-8.612811,41.153733],[...
[41.1544, 41.1537, 41.1546, 41.1567, 41.1573, ...
[-8.61324, -8.61281, -8.61197, -8.61192, -8.61...
29
1372640555620000235
C
0
NaN
20
1372640555
0
False
[[-8.611065,41.149431],[-8.611209,41.149368],[...
[41.1494, 41.1494, 41.1482, 41.1479, 41.1482, ...
[-8.61106, -8.61121, -8.61124, -8.61179, -8.61...
...
...
...
...
...
...
...
...
...
...
...
...
1710640
1404151621620000543
C
0
NaN
412
1404151621
0
False
[[-8.668845,41.152725],[-8.668179,41.15313],[-...
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[-8.66885, -8.66818, -8.66858, -8.66997, -8.67...
1710641
1404152121620000383
C
0
NaN
413
1404152121
0
False
[[-8.625726,41.166864],[-8.625717,41.166873],[...
[41.1669, 41.1669, 41.1669, 41.1668, 41.1669, ...
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1710642
1404170192620000020
B
0
14.0
414
1404170192
0
False
[[-8.610903,41.149449],[-8.610885,41.149503],[...
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1710643
1386603894620000970
C
0
NaN
446
1386603894
0
False
[]
[]
[]
1710644
1401596832620000609
B
0
38.0
432
1401596832
0
False
[[-8.604171,41.161077],[-8.604036,41.161473],[...
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1710645
1404151410620000185
B
0
NaN
438
1404151410
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False
[[-8.628777,41.169807],[-8.628615,41.169807],[...
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1710646
1404172198620000260
B
0
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1404172198
0
False
[[-8.580267,41.159232],[-8.581743,41.159142],[...
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1710647
1404155241620000340
B
0
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1404155241
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False
[[-8.628111,41.157423],[-8.628156,41.157378],[...
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1710648
1404171548620000364
C
0
NaN
418
1404171548
0
False
[[-8.614323,41.123385],[-8.614152,41.123385],[...
[41.1234, 41.1234, 41.1237, 41.1238]
[-8.61432, -8.61415, -8.61453, -8.61476]
1710649
1404151498620000393
B
0
15.0
419
1404151498
0
False
[[-8.58564,41.148585],[-8.585658,41.148567],[-...
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[-8.58564, -8.58566, -8.58572, -8.58655, -8.58...
1710650
1404168899620000002
A
55949
NaN
420
1404168899
0
False
[[-8.63568,41.171886],[-8.634924,41.171859],[-...
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1710651
1404153627620000187
A
7
NaN
421
1404153627
0
False
[[-8.574714,41.142681],[-8.574714,41.142681],[...
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1710652
1401475142620000312
C
0
NaN
423
1401475142
0
False
[[-8.66754,41.238549],[-8.669502,41.239287],[-...
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[-8.66754, -8.6695, -8.67093, -8.6704, -8.6698...
1710653
1403935197620000205
A
8714
NaN
422
1403935197
0
False
[[-8.66349,41.177466],[-8.663436,41.177502],[-...
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[-8.66349, -8.66344, -8.66322, -8.66223, -8.65...
1710654
1404166892620000500
B
0
15.0
427
1404166892
0
False
[[-8.585712,41.148828],[-8.586009,41.148972],[...
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[-8.58571, -8.58601, -8.587, -8.58915, -8.5915...
1710655
1404143157620000389
C
0
NaN
425
1404143157
0
False
[[-8.645364,41.148324],[-8.642538,41.148036],[...
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[-8.64536, -8.64254, -8.6404, -8.63895, -8.637...
1710656
1404014448620000407
A
5941
NaN
428
1404014448
0
False
[[-8.630973,41.152653],[-8.631423,41.152662],[...
[41.1527, 41.1527, 41.1526, 41.1526, 41.1533, ...
[-8.63097, -8.63142, -8.63379, -8.63679, -8.63...
1710657
1380123541620000980
C
0
NaN
429
1380123541
0
False
[[-8.689959,41.240241]]
[41.2402]
[-8.68996]
1710658
1373986578620000931
A
7
NaN
447
1373986578
0
False
[]
[]
[]
1710659
1403941536620000213
A
32002
NaN
430
1403941536
0
False
[[-8.574732,41.157099],[-8.574849,41.156775],[...
[41.1571, 41.1568, 41.157, 41.1571, 41.1571, 4...
[-8.57473, -8.57485, -8.57565, -8.57658, -8.57...
1710660
1384165182620000170
C
0
NaN
436
1384165182
0
False
[[-8.643303,41.13648]]
[41.1365]
[-8.6433]
1710661
1404164723620000332
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0
15.0
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1404164723
0
False
[[-8.585703,41.148567],[-8.585739,41.148855],[...
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1404155105620000121
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0
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False
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1710663
1388660427620000585
C
0
NaN
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1388660427
0
False
[[-8.60697,41.162283],[-8.60697,41.16231],[-8....
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[-8.60697, -8.60697, -8.60698, -8.60699, -8.60...
1710664
1390403767620000911
C
0
NaN
442
1390403767
0
False
[]
[]
[]
1710665
1404171463620000698
C
0
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1404171463
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False
[[-8.612469,41.14602],[-8.612487,41.145993],[-...
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[-8.61247, -8.61249, -8.61248, -8.6116, -8.611...
1710666
1404171367620000670
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1388745716620000264
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[]
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[]
1710668
1404141826620000248
B
0
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1710669
1404157147620000079
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1710670 rows × 11 columns
SAVE DICTS
In [308]:
np.save(data_path+'data/origin_call_dict.npy', origin_call_dict)
In [309]:
np.save(data_path+'data/taxi_id_dict.npy', taxi_id_dict)
In [135]:
test_data = pd.read_csv(data_path+'data/test.csv', header=0)
In [136]:
test_data.columns
Out[136]:
Index(['TRIP_ID', 'CALL_TYPE', 'ORIGIN_CALL', 'ORIGIN_STAND', 'TAXI_ID',
'TIMESTAMP', 'DAY_TYPE', 'MISSING_DATA', 'POLYLINE'],
dtype='object')
Check data same size
In [137]:
test_data.shape
Out[137]:
(320, 9)
In [138]:
len(split[13][2])
Out[138]:
320
In [242]:
call_type_f = lambda x:ord(x) - ord('A')
In [257]:
np.allclose(test_data['CALL_TYPE'].apply(call_type_f),split[14][2])
Out[257]:
True
In [258]:
test_data['CALL_TYPE'] = test_data['CALL_TYPE'].apply(call_type_f)
Turn origin call into categorical variable
Can do using factorize: we want the nulls to be zero. Factorize sets them as -1, add 1 to set as needed
In [75]:
np.unique(split[15][2])
Out[75]:
array([ 0, 7, 249, 502, 623, 935, 1729, 2939, 3476,
3520, 3613, 5145, 7017, 7178, 8316, 8326, 8701, 9843,
10833, 11099, 11134, 16141, 16613, 16658, 20375, 21655, 22338,
23356, 23614, 23831, 33467, 34117, 34224, 34725, 35492, 35708,
36140, 36905, 41178, 42039, 43989, 47093, 57106, 57107, 57108,
57109, 57110, 57111, 57112, 57113, 57114, 57115, 57116, 57117,
57118, 57119, 57120, 57121, 57122, 57123, 57124], dtype=int32)
Hold up! We need to use our previous mapping
In [9]:
import numpy as np
In [47]:
taxi_id_dict = np.load(data_path+'data/taxi_id_dict.npy').item()
In [139]:
len(origin_call_dict)
Out[139]:
57125
In [140]:
test_origin_call_f = lambda x: 0 if (np.isnan(x) or x=='' or x >= num_origin_call) else origin_call_dict[x]
In [141]:
np.allclose(test_data['ORIGIN_CALL'].apply(test_origin_call_f),split[15][2])
Out[141]:
False
In [142]:
test_data['ORIGIN_CALL'] = test_data['ORIGIN_CALL'].apply(test_origin_call_f)
In [144]:
origin_stand_f = lambda x: 0 if pd.isnull(x) or x=='' else int(x)
In [269]:
split[16]
Out[269]:
('test',
'origin_stand',
array([15, 57, 15, 53, 18, 0, 15, 0, 9, 15, 56, 0, 0, 0, 0, 0, 0, 0, 15, 0, 15, 0, 0,
15, 12, 49, 15, 15, 21, 15, 15, 57, 0, 0, 0, 0, 0, 60, 15, 34, 38, 0, 0, 9, 30, 61,
0, 0, 28, 56, 13, 10, 60, 15, 15, 14, 9, 35, 0, 0, 15, 0, 0, 0, 9, 9, 15, 0, 0,
13, 0, 0, 15, 0, 47, 0, 23, 0, 0, 0, 0, 59, 0, 0, 7, 0, 0, 0, 16, 0, 0, 0,
0, 0, 60, 0, 0, 0, 0, 0, 7, 33, 0, 57, 57, 0, 25, 11, 33, 0, 0, 0, 42, 0, 0,
0, 35, 6, 0, 36, 0, 0, 0, 0, 57, 0, 0, 0, 0, 57, 57, 47, 0, 0, 0, 0, 15, 35,
0, 0, 0, 57, 0, 15, 0, 0, 16, 0, 33, 3, 0, 0, 0, 0, 0, 17, 25, 0, 57, 15, 0,
0, 0, 35, 0, 60, 0, 0, 0, 18, 40, 49, 0, 13, 38, 0, 9, 0, 0, 0, 36, 53, 0, 0,
34, 0, 0, 14, 53, 0, 0, 28, 0, 0, 25, 0, 0, 28, 0, 36, 0, 0, 47, 0, 0, 26, 18,
0, 0, 0, 57, 53, 0, 10, 54, 0, 6, 63, 21, 15, 0, 0, 0, 0, 9, 1, 0, 0, 57, 0,
57, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 58, 0, 0, 0, 57, 0, 0, 23, 10, 0, 0,
0, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 0, 0, 0, 0, 0, 0, 0, 38, 0, 14,
23, 0, 0, 0, 0, 0, 0, 23, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 42, 0, 53, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 0, 0, 0, 0, 53, 0, 0, 0], dtype=int8))
In [145]:
np.allclose(test_data["ORIGIN_STAND"].apply(origin_stand_f),split[16][2])
Out[145]:
True
In [146]:
test_data["ORIGIN_STAND"] = test_data["ORIGIN_STAND"].apply(origin_stand_f)
In [147]:
split[17][2]
Out[147]:
array([150, 306, 260, 40, 33, 174, 335, 315, 32, 364, 199, 393, 334,
423, 11, 232, 269, 285, 69, 371, 190, 52, 92, 402, 359, 112,
406, 427, 198, 248, 157, 209, 308, 264, 146, 125, 283, 266, 360,
104, 241, 263, 56, 224, 21, 80, 98, 318, 254, 303, 249, 227,
367, 96, 19, 193, 357, 361, 339, 375, 362, 270, 267, 129, 351,
323, 310, 416, 181, 281, 25, 43, 385, 117, 211, 235, 25, 399,
122, 341, 215, 198, 165, 81, 108, 221, 89, 380, 392, 231, 35,
39, 26, 138, 79, 386, 30, 146, 41, 105, 333, 126, 37, 120,
166, 255, 297, 116, 206, 186, 404, 242, 325, 246, 154, 175, 413,
284, 288, 1, 443, 409, 416, 13, 27, 224, 176, 345, 118, 0,
91, 371, 32, 57, 326, 254, 283, 287, 271, 319, 115, 179, 328,
424, 49, 210, 238, 232, 298, 412, 334, 440, 151, 16, 156, 231,
49, 287, 428, 342, 169, 267, 87, 289, 305, 307, 380, 292, 341,
205, 276, 355, 8, 20, 324, 391, 402, 144, 225, 337, 81, 376,
21, 251, 354, 434, 211, 426, 264, 325, 349, 234, 115, 1, 37,
253, 285, 125, 430, 188, 329, 423, 409, 207, 191, 377, 6, 75,
70, 262, 123, 171, 178, 435, 249, 55, 237, 35, 29, 385, 80,
127, 146, 179, 384, 31, 339, 64, 363, 137, 182, 41, 438, 336,
367, 24, 77, 166, 78, 361, 213, 306, 40, 199, 32, 117, 104,
270, 146, 67, 107, 374, 60, 196, 207, 264, 387, 406, 72, 90,
99, 48, 121, 132, 39, 278, 27, 444, 13, 311, 133, 47, 226,
91, 299, 85, 82, 22, 86, 168, 385, 34, 307, 365, 190, 381,
314, 402, 160, 240, 298, 300, 158, 180, 75, 55, 35, 2, 281,
367, 150, 342, 387, 112, 99, 413, 294, 74, 62, 81, 68, 183,
147, 419, 151, 338, 414, 210, 331, 193], dtype=int16)
In [148]:
test_taxi_id_f = lambda x: taxi_id_dict[x]
In [149]:
np.allclose(test_data['TAXI_ID'].apply(test_taxi_id_f),split[17][2])
Out[149]:
True
In [150]:
test_data['TAXI_ID'] = test_data['TAXI_ID'].apply(test_taxi_id_f)
In [152]:
test_data['ORIGIN_CALL'].unique()
Out[152]:
array([ 0, 10833, 35708, 623, 7, 7017, 23356, 16658, 7178,
34224, 41178, 935, 3520, 47093, 249, 34725, 11099, 35492,
3613, 23831, 22338, 5145, 34117, 502, 33467, 21655, 23614,
16613, 1729, 8326, 20375, 8316, 16141, 3476, 11134, 42039,
36905, 2939, 36140])
In [153]:
test_data['TAXI_ID'].as_matrix()
Out[153]:
array([150, 306, 260, 40, 33, 174, 335, 315, 32, 364, 199, 393, 334,
423, 11, 232, 269, 285, 69, 371, 190, 52, 92, 402, 359, 112,
406, 427, 198, 248, 157, 209, 308, 264, 146, 125, 283, 266, 360,
104, 241, 263, 56, 224, 21, 80, 98, 318, 254, 303, 249, 227,
367, 96, 19, 193, 357, 361, 339, 375, 362, 270, 267, 129, 351,
323, 310, 416, 181, 281, 25, 43, 385, 117, 211, 235, 25, 399,
122, 341, 215, 198, 165, 81, 108, 221, 89, 380, 392, 231, 35,
39, 26, 138, 79, 386, 30, 146, 41, 105, 333, 126, 37, 120,
166, 255, 297, 116, 206, 186, 404, 242, 325, 246, 154, 175, 413,
284, 288, 1, 443, 409, 416, 13, 27, 224, 176, 345, 118, 0,
91, 371, 32, 57, 326, 254, 283, 287, 271, 319, 115, 179, 328,
424, 49, 210, 238, 232, 298, 412, 334, 440, 151, 16, 156, 231,
49, 287, 428, 342, 169, 267, 87, 289, 305, 307, 380, 292, 341,
205, 276, 355, 8, 20, 324, 391, 402, 144, 225, 337, 81, 376,
21, 251, 354, 434, 211, 426, 264, 325, 349, 234, 115, 1, 37,
253, 285, 125, 430, 188, 329, 423, 409, 207, 191, 377, 6, 75,
70, 262, 123, 171, 178, 435, 249, 55, 237, 35, 29, 385, 80,
127, 146, 179, 384, 31, 339, 64, 363, 137, 182, 41, 438, 336,
367, 24, 77, 166, 78, 361, 213, 306, 40, 199, 32, 117, 104,
270, 146, 67, 107, 374, 60, 196, 207, 264, 387, 406, 72, 90,
99, 48, 121, 132, 39, 278, 27, 444, 13, 311, 133, 47, 226,
91, 299, 85, 82, 22, 86, 168, 385, 34, 307, 365, 190, 381,
314, 402, 160, 240, 298, 300, 158, 180, 75, 55, 35, 2, 281,
367, 150, 342, 387, 112, 99, 413, 294, 74, 62, 81, 68, 183,
147, 419, 151, 338, 414, 210, 331, 193])
In [154]:
split[19]
Out[154]:
('test',
'day_type',
array([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, 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], dtype=int8))
In [155]:
day_type_f = lambda x: ord(x[0]) - ord('A')
In [156]:
np.allclose(test_data['DAY_TYPE'].apply(day_type_f),split[19][2])
Out[156]:
True
In [157]:
test_data['DAY_TYPE'] = test_data['DAY_TYPE'].apply(day_type_f)
In [158]:
polyline_f = lambda x: ast.literal_eval(x)
In [159]:
test_polyline = test_data['POLYLINE'].apply(polyline_f)
In [160]:
test_polyline.to_pickle(data_path+'data/test_polylines.pkl')
In [208]:
polyline
Out[208]:
0 [[-8.618643, 41.141412], [-8.618499, 41.141376...
1 [[-8.639847, 41.159826], [-8.640351, 41.159871...
2 [[-8.612964, 41.140359], [-8.613378, 41.14035]...
3 [[-8.574678, 41.151951], [-8.574705, 41.151942...
4 [[-8.645994, 41.18049], [-8.645949, 41.180517]...
5 [[-8.615502, 41.140674], [-8.614854, 41.140926...
6 [[-8.57952, 41.145948], [-8.580942, 41.145039]...
7 [[-8.617563, 41.146182], [-8.617527, 41.145849...
8 [[-8.611794, 41.140557], [-8.611785, 41.140575...
9 [[-8.615907, 41.140557], [-8.614449, 41.141088...
10 [[-8.619894, 41.148009], [-8.620164, 41.14773]...
11 [[-8.56242, 41.168403], [-8.562429, 41.168358]...
12 [[-8.618868, 41.155101], [-8.6175, 41.154912],...
13 [[-8.608716, 41.153499], [-8.607627, 41.153481...
14 [[-8.612208, 41.14053], [-8.612235, 41.140521]...
15 [[-8.585145, 41.164857], [-8.584146, 41.164704...
16 [[-8.584263, 41.163156], [-8.584695, 41.163003...
17 [[-8.666757, 41.174055], [-8.666784, 41.174064...
18 [[-8.599239, 41.149188], [-8.598681, 41.149296...
19 [[-8.643807, 41.168979], [-8.642529, 41.170113...
20 [[-8.609706, 41.151303], [-8.609562, 41.151474...
21 [[-8.660646, 41.168574], [-8.661087, 41.167926...
22 [[-8.612955, 41.140377], [-8.612991, 41.140359...
23 [[-8.60418, 41.160969], [-8.603874, 41.1615], ...
24 [[-8.665686, 41.170626], [-8.665677, 41.170653...
25 [[-8.632737, 41.168295], [-8.6328, 41.16825], ...
26 [[-8.648226, 41.148333], [-8.648514, 41.148297...
27 [[-8.638227, 41.159592], [-8.637543, 41.15925]...
28 [[-8.613243, 41.154444], [-8.612811, 41.153733...
29 [[-8.611065, 41.149431], [-8.611209, 41.149368...
...
1710640 [[-8.668845, 41.152725], [-8.668179, 41.15313]...
1710641 [[-8.625726, 41.166864], [-8.625717, 41.166873...
1710642 [[-8.610903, 41.149449], [-8.610885, 41.149503...
1710643 []
1710644 [[-8.604171, 41.161077], [-8.604036, 41.161473...
1710645 [[-8.628777, 41.169807], [-8.628615, 41.169807...
1710646 [[-8.580267, 41.159232], [-8.581743, 41.159142...
1710647 [[-8.628111, 41.157423], [-8.628156, 41.157378...
1710648 [[-8.614323, 41.123385], [-8.614152, 41.123385...
1710649 [[-8.58564, 41.148585], [-8.585658, 41.148567]...
1710650 [[-8.63568, 41.171886], [-8.634924, 41.171859]...
1710651 [[-8.574714, 41.142681], [-8.574714, 41.142681...
1710652 [[-8.66754, 41.238549], [-8.669502, 41.239287]...
1710653 [[-8.66349, 41.177466], [-8.663436, 41.177502]...
1710654 [[-8.585712, 41.148828], [-8.586009, 41.148972...
1710655 [[-8.645364, 41.148324], [-8.642538, 41.148036...
1710656 [[-8.630973, 41.152653], [-8.631423, 41.152662...
1710657 [[-8.689959, 41.240241]]
1710658 []
1710659 [[-8.574732, 41.157099], [-8.574849, 41.156775...
1710660 [[-8.643303, 41.13648]]
1710661 [[-8.585703, 41.148567], [-8.585739, 41.148855...
1710662 [[-8.606385, 41.144742], [-8.606466, 41.144742...
1710663 [[-8.60697, 41.162283], [-8.60697, 41.16231], ...
1710664 []
1710665 [[-8.612469, 41.14602], [-8.612487, 41.145993]...
1710666 [[-8.610138, 41.140845], [-8.610174, 41.140935...
1710667 []
1710668 [[-8.630712, 41.154885], [-8.63073, 41.154813]...
1710669 [[-8.615538, 41.140629], [-8.615421, 41.140746...
Name: POLYLINE, dtype: object
In [161]:
lats = pd.Series([np.array([point[1] for point in poly],dtype=np.float32) for poly in test_polyline])
In [233]:
split[11][2]
Out[233]:
array([ array([ 41.1414, 41.1414, 41.1425, 41.1438, 41.1444, 41.1448, 41.1447, 41.1452, 41.1469,
41.1482, 41.1504, 41.1512, 41.1512, 41.1522, 41.1524, 41.1525, 41.1527, 41.153 ,
41.1545, 41.1545, 41.1545, 41.1545, 41.1545], dtype=float32),
array([ 41.1598, 41.1599, 41.1601, 41.1605, 41.1609, 41.1615, 41.162 , 41.1626, 41.1632,
41.1637, 41.1642, 41.1647, 41.1651, 41.1666, 41.168 , 41.169 , 41.17 , 41.1706,
41.1707], dtype=float32),
array([ 41.1404, 41.1404, 41.1403, 41.1404, 41.1404, 41.1406, 41.1414, 41.1428, 41.1441,
41.1448, 41.1447, 41.1452, 41.1465, 41.1473, 41.1488, 41.1506, 41.1521, 41.1525,
41.1527, 41.1528, 41.1528, 41.1526, 41.153 , 41.1536, 41.154 , 41.1545, 41.1543,
41.1543, 41.1543, 41.1544, 41.1543, 41.1543, 41.1543, 41.1543, 41.1417, 41.1406,
41.1543, 41.1538, 41.1538, 41.1538, 41.1532, 41.1526, 41.1524, 41.1512, 41.1505,
41.1491, 41.1483, 41.1482, 41.1484, 41.1484, 41.1481, 41.148 , 41.1484, 41.1483,
41.148 , 41.1472, 41.1461, 41.145 , 41.1446, 41.1448, 41.1445, 41.1434, 41.142 ,
41.1409, 41.1405], dtype=float32),
..., array([], dtype=float32),
array([ 41.1549, 41.1548, 41.1548, 41.1548, 41.1545, 41.1536, 41.1536, 41.1544, 41.1547,
41.1547, 41.1546, 41.1546, 41.1547, 41.1551, 41.1555, 41.1556, 41.1559, 41.1569,
41.157 , 41.1583, 41.1589, 41.1593, 41.1601, 41.1604, 41.1605, 41.1614, 41.1623,
41.1625, 41.1622, 41.1621, 41.1622, 41.1622, 41.162 , 41.162 , 41.1619, 41.1619,
41.1625, 41.1631, 41.1642, 41.1646, 41.1645, 41.1645, 41.1645, 41.1653, 41.1652,
41.1652, 41.1654, 41.1655, 41.1657, 41.1667, 41.1671, 41.1671, 41.1667, 41.1661,
41.1667, 41.1671, 41.1675, 41.1685, 41.1696, 41.1708, 41.1721, 41.1735], dtype=float32),
array([ 41.1406, 41.1407, 41.141 , 41.1413, 41.1418, 41.1429, 41.1439, 41.1445, 41.1449,
41.1449, 41.1449, 41.1449, 41.1458, 41.1467, 41.1472, 41.1476, 41.1478, 41.1478,
41.1478, 41.1481, 41.1476, 41.1491, 41.1499, 41.1499, 41.1499, 41.1499, 41.1499], dtype=float32)], dtype=object)
In [162]:
np.alltrue([np.allclose(lats[i],split[21][2][i]) for i in range(len(lats))])
Out[162]:
True
Latitudes check out
In [163]:
longs = pd.Series([np.array([point[0] for point in poly],dtype=np.float32) for poly in test_polyline])
In [241]:
split[12]
Out[241]:
('train',
'longitude',
array([ array([-8.6186, -8.6185, -8.6203, -8.6222, -8.624 , -8.6267, -8.6274, -8.6302, -8.6327, -8.6317,
-8.6299, -8.6291, -8.6291, -8.6288, -8.6287, -8.6288, -8.6308, -8.6323, -8.6311, -8.6308,
-8.6308, -8.6308, -8.6308], dtype=float32),
array([-8.6398, -8.6404, -8.6422, -8.6445, -8.6469, -8.65 , -8.6532, -8.6564, -8.6602, -8.6631,
-8.6662, -8.6692, -8.6709, -8.6709, -8.6696, -8.6681, -8.6666, -8.6658, -8.6657], dtype=float32),
array([-8.613 , -8.6134, -8.6142, -8.6148, -8.6159, -8.6166, -8.6185, -8.6206, -8.6226, -8.6251,
-8.6274, -8.6301, -8.6319, -8.6326, -8.6313, -8.6297, -8.6288, -8.6286, -8.6287, -8.6304,
-8.6327, -8.6351, -8.6377, -8.6404, -8.6422, -8.6441, -8.6465, -8.6486, -8.6495, -8.6498,
-8.6498, -8.6499, -8.6499, -8.6499, -8.5994, -8.5965, -8.6501, -8.6504, -8.6504, -8.6504,
-8.6499, -8.6494, -8.6492, -8.6497, -8.6491, -8.6491, -8.6486, -8.6484, -8.6476, -8.6459,
-8.6439, -8.6428, -8.6409, -8.6378, -8.6353, -8.6333, -8.6315, -8.6298, -8.6278, -8.626 ,
-8.6241, -8.6213, -8.6194, -8.6174, -8.616 ], dtype=float32),
..., array([], dtype=float32),
array([-8.6307, -8.6307, -8.6307, -8.6308, -8.6307, -8.6305, -8.6289, -8.6288, -8.6275, -8.6271,
-8.6271, -8.627 , -8.6268, -8.6268, -8.6257, -8.6249, -8.6242, -8.6239, -8.6239, -8.6234,
-8.6238, -8.6234, -8.6226, -8.6222, -8.6211, -8.6208, -8.6191, -8.6174, -8.6149, -8.6145,
-8.6145, -8.6145, -8.6138, -8.6129, -8.6127, -8.6113, -8.6112, -8.6111, -8.611 , -8.61 ,
-8.6093, -8.6075, -8.6064, -8.6063, -8.6064, -8.6064, -8.6061, -8.6041, -8.6024, -8.6018,
-8.6011, -8.5987, -8.5974, -8.5974, -8.5957, -8.5941, -8.5933, -8.5914, -8.5896, -8.5886,
-8.5878, -8.587 ], dtype=float32),
array([-8.6155, -8.6154, -8.6148, -8.6145, -8.6144, -8.614 , -8.6125, -8.6117, -8.6112, -8.6112,
-8.6111, -8.6111, -8.6107, -8.6095, -8.6088, -8.6098, -8.6107, -8.6107, -8.6111, -8.6127,
-8.6203, -8.6208, -8.6209, -8.6209, -8.6209, -8.6209, -8.6209], dtype=float32)], dtype=object))
In [164]:
np.alltrue([np.allclose(longs[i],split[22][2][i]) for i in range(len(longs))])
Out[164]:
True
Longitudes check out
In [165]:
test_data['LATITUDE'] = lats
In [166]:
test_data['LONGITUDE'] = longs
In [227]:
data.head()
Out[227]:
TRIP_ID
CALL_TYPE
ORIGIN_CALL
ORIGIN_STAND
TAXI_ID
TIMESTAMP
DAY_TYPE
MISSING_DATA
POLYLINE
LATITUDE
LONGITUDE
0
1372636858620000589
C
0
NaN
0
1372636858
0
False
[[-8.618643,41.141412],[-8.618499,41.141376],[...
[41.1414, 41.1414, 41.1425, 41.1438, 41.1444, ...
[-8.61864, -8.6185, -8.62033, -8.62215, -8.623...
1
1372637303620000596
B
0
7.0
1
1372637303
0
False
[[-8.639847,41.159826],[-8.640351,41.159871],[...
[41.1598, 41.1599, 41.1601, 41.1605, 41.1609, ...
[-8.63985, -8.64035, -8.6422, -8.64445, -8.646...
2
1372636951620000320
C
0
NaN
2
1372636951
0
False
[[-8.612964,41.140359],[-8.613378,41.14035],[-...
[41.1404, 41.1404, 41.1403, 41.1404, 41.1404, ...
[-8.61296, -8.61338, -8.61421, -8.61477, -8.61...
3
1372636854620000520
C
0
NaN
3
1372636854
0
False
[[-8.574678,41.151951],[-8.574705,41.151942],[...
[41.152, 41.1519, 41.1519, 41.152, 41.1519, 41...
[-8.57468, -8.57471, -8.5747, -8.57466, -8.574...
4
1372637091620000337
C
0
NaN
4
1372637091
0
False
[[-8.645994,41.18049],[-8.645949,41.180517],[-...
[41.1805, 41.1805, 41.18, 41.1789, 41.1785, 41...
[-8.64599, -8.64595, -8.64605, -8.6468, -8.649...
In [273]:
def make_valid_pandas(data):
valid = (
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[]
)
for row in data.itertuples():
trip_id = row[1]
call_type = row[2]
origin_call = row[3]
origin_stand = row[4]
taxi_id = row[5]
time = row[6]
day_type = row[7]
missing_data = row[8]
latitude = row[10]
longitude = row[11]
if len(latitude) == 0:
continue
for ts in cuts:
if time <= ts and time + 15 * (len(latitude) - 1) >= ts:
# keep it
valid[0].append(trip_id)
valid[1].append(call_type)
valid[2].append(origin_call)
valid[3].append(origin_stand)
valid[4].append(taxi_id)
valid[5].append(time)
valid[6].append(day_type)
valid[7].append(missing_data)
n = (ts - time) / 15 + 1
valid[8].append(latitude[:n])
valid[9].append(longitude[:n])
valid[10].append(latitude[-1])
valid[11].append(longitude[-1])
valid[12].append(15 * (len(latitude)-1))
break
return pd.DataFrame({
'TRIP_ID':valid[0],
'CALL_TYPE':valid[1],
'ORIGIN_CALL':valid[2],
'ORIGIN_STAND':valid[3],
'TAXI_ID':valid[4],
'TIMESTAMP':valid[5],
'DAY_TYPE':valid[6],
'MISSING_DATA':valid[7],
'LATITUDE':valid[8],
'LONGITUDE':valid[9],
'DESTINATION_LATITUDE':valid[10],
'DESTINATION_LONGITUDE':valid[11],
'TRAVEL_TIME':valid[12]
}
)
In [287]:
valid_data = make_valid_pandas(data)
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:43: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:44: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
In [288]:
np.allclose(valid_data['CALL_TYPE'],valid_split[1][2])
Out[288]:
True
In [289]:
np.allclose(valid_data['ORIGIN_CALL'],valid_split[2][2])
Out[289]:
True
In [290]:
np.allclose(valid_data['ORIGIN_STAND'],valid_split[3][2])
Out[290]:
True
In [291]:
np.allclose(valid_data['TAXI_ID'],valid_split[4][2])
Out[291]:
True
In [292]:
np.allclose(valid_data['TIMESTAMP'],valid_split[5][2])
Out[292]:
True
In [293]:
np.allclose(valid_data['DAY_TYPE'],valid_split[6][2])
Out[293]:
True
In [294]:
np.allclose(valid_data['MISSING_DATA'],valid_split[7][2])
Out[294]:
True
In [295]:
np.alltrue([np.allclose(valid_data['LATITUDE'][i], valid_split[8][2][i]) for i in range(0,len(valid_data['LATITUDE']))])
Out[295]:
True
In [297]:
np.alltrue([np.allclose(valid_data['LONGITUDE'][i], valid_split[9][2][i]) for i in range(0,len(valid_data['LATITUDE']))])
Out[297]:
True
In [298]:
np.allclose(valid_data['DESTINATION_LATITUDE'],valid_split[10][2])
Out[298]:
True
In [299]:
np.allclose(valid_data['DESTINATION_LONGITUDE'],valid_split[11][2])
Out[299]:
True
In [300]:
np.allclose(valid_data['TRAVEL_TIME'],valid_split[12][2])
Out[300]:
True
Values check out. Yay
In [310]:
from sklearn.cluster import MeanShift, estimate_bandwidth
In [303]:
split[11][1]
Out[303]:
'latitude'
In [335]:
dests = []
for i in range(0, len(split[5][2])):
if len(split[11][2][i]) == 0: continue
dests.append([split[11][2][i][-1], split[12][2][i][-1]])
pts = numpy.array(dests)
In [308]:
pts.shape
Out[308]:
(1704769, 2)
In [309]:
bw = 0.001
In [311]:
ms = MeanShift(bandwidth=bw, bin_seeding=True, min_bin_freq=5)
ms.fit(pts)
cluster_centers = ms.cluster_centers_
In [312]:
cluster_centers.shape
Out[312]:
(3454, 2)
In [342]:
dests = []
for row in data[['LATITUDE','LONGITUDE']].itertuples():
if len(row[1]) == 0: continue
dests.append([row[1][-1], row[2][-1]])
pts2 = numpy.array(dests)
In [344]:
np.allclose(pts, pts2)
Out[344]:
True
The points are the same. Hooray!
In [345]:
np.save(data_path+'data/cluster_centers.npy', cluster_centers)
In [351]:
(1,2)+(3,)
Out[351]:
(1, 2, 3)
In [356]:
split[8][1]
Out[356]:
'timestamp'
In [397]:
valid_split[5][2]
Out[397]:
array([1376502576, 1376503146, 1376502942, 1376502604, 1376502611,
1376503017, 1376501811, 1376502255, 1376502950, 1376502635,
1376502627, 1376502237, 1376500608, 1376502047, 1376502355,
1376502180, 1376502802, 1376502603, 1376501182, 1376503155,
1376502617, 1376502994, 1376502822, 1376502466, 1376503126,
1376501063, 1376502919, 1376503023, 1376502971, 1376503012,
1376503077, 1376500095, 1376502674, 1376503111, 1376502451,
1376501436, 1376500679, 1376502483, 1376502952, 1376502546,
1376503135, 1376502684, 1376502852, 1376502866, 1376502678,
1376503196, 1376502511, 1376502716, 1376501962, 1376503045,
1376503048, 1376503060, 1376501941, 1376502639, 1376498899,
1380615372, 1380616084, 1380615517, 1380615888, 1380615819,
1380616117, 1380615616, 1380615829, 1380616031, 1380614780,
1380616088, 1380616142, 1380615884, 1380615884, 1380615546,
1380612513, 1380616168, 1380616051, 1380615733, 1380615963,
1380616135, 1380615490, 1380615733, 1380615270, 1380615881,
1380615736, 1380615935, 1380615687, 1380615093, 1380615018,
1380614973, 1380614475, 1380615478, 1380615480, 1380615206,
1380615481, 1380615954, 1380615680, 1380615931, 1380615954,
1380615820, 1380616006, 1380615811, 1380615429, 1380615717,
1380612549, 1380615287, 1380614665, 1380615249, 1380615419,
1380616036, 1380615386, 1380615798, 1380615365, 1380616063,
1380614893, 1380615561, 1380615679, 1380615860, 1380616005,
1380615508, 1380615585, 1380616090, 1380615740, 1380615522,
1380615592, 1380615524, 1380615311, 1380615564, 1380615838,
1380616130, 1380615204, 1380616118, 1380615890, 1380615259,
1380615005, 1380615991, 1380616081, 1380609856, 1380615703,
1380616017, 1380615925, 1380615936, 1380615394, 1380616000,
1380615296, 1380615190, 1380616114, 1380615548, 1380615897,
1380613963, 1380615928, 1380615774, 1380616129, 1380611605,
1381167287, 1381167488, 1381167070, 1381167312, 1381167710,
1381167271, 1381167722, 1381167545, 1381167103, 1381167267,
1381167108, 1381167744, 1381167517, 1381166942, 1381167235,
1381166227, 1381167871, 1381167772, 1381166765, 1381167247,
1381167659, 1381167696, 1381167227, 1381167059, 1381167539,
1381166936, 1381167684, 1381167709, 1381167767, 1381167249,
1381167631, 1381166494, 1381167832, 1381166291, 1381167707,
1381167184, 1381167653, 1381167398, 1381167706, 1381165416,
1381167885, 1381166502, 1381166647, 1381167635, 1381167660,
1381167790, 1381166635, 1381167810, 1381166724, 1381167307,
1381167813, 1381167570, 1381167102, 1381167557, 1381167767,
1381167560, 1381167881, 1381166662, 1381165959, 1381167445,
1381167775, 1381166075, 1381167432, 1381167149, 1381167757,
1381166812, 1381167079, 1381167898, 1383364057, 1383364440,
1383364733, 1383364597, 1383364569, 1383364265, 1383364258,
1383364526, 1383364423, 1383364426, 1383364495, 1383364768,
1383364614, 1383364237, 1383364120, 1383364546, 1383363920,
1383363982, 1383364105, 1383364039, 1383364651, 1383364694,
1383364170, 1383363811, 1383364740, 1383364463, 1383364261,
1383363492, 1383364771, 1383364606, 1383364431, 1383364080,
1383364684, 1383364370, 1383364692, 1383364657, 1383364413,
1383364451, 1383364129, 1383363776, 1383364536, 1383364296,
1383364125, 1383364157, 1383364430, 1383363928, 1383363904,
1383364337, 1383364330, 1383364756, 1383364297, 1383364210,
1387722290, 1387721052, 1387721787, 1387722186, 1387722244,
1387721853, 1387722175, 1387722275, 1387722116, 1387722169,
1387722331, 1387722091, 1387722451, 1387722585, 1387722369,
1387721828, 1387722360, 1387722056, 1387722189, 1387722151,
1387722599, 1387722463, 1387722184, 1387722330, 1387721956,
1387722186, 1387722361, 1387721985, 1387708463, 1387722594,
1387722519, 1387722463, 1387721783, 1387721846], dtype=int32)
In [405]:
def get_date_data(split):
tmp = (
[],
[],
[],
[],
[],
[],
[],
[],
[]
)
for ts in split[8][2]:
date = datetime.datetime.utcfromtimestamp(ts)
yearweek = date.isocalendar()[1] - 1
tmp[0].append(numpy.int8(51 if yearweek == 52 else yearweek))
tmp[1].append(numpy.int8(date.weekday()))
tmp[2].append(numpy.int8(date.hour * 4 + date.minute / 15))
for ts in split[18][2]:
date = datetime.datetime.utcfromtimestamp(ts)
yearweek = date.isocalendar()[1] - 1
tmp[3].append(numpy.int8(51 if yearweek == 52 else yearweek))
tmp[4].append(numpy.int8(date.weekday()))
tmp[5].append(numpy.int8(date.hour * 4 + date.minute / 15))
for ts in valid_split[5][2]:
date = datetime.datetime.utcfromtimestamp(ts)
yearweek = date.isocalendar()[1] - 1
tmp[6].append(numpy.int8(51 if yearweek == 52 else yearweek))
tmp[7].append(numpy.int8(date.weekday()))
tmp[8].append(numpy.int8(date.hour * 4 + date.minute / 15))
return (
('train', 'yearweek', np.array(tmp[0])),
('train', 'weekday', np.array(tmp[1])),
('train', 'quarterhour', np.array(tmp[2])),
('test', 'yearweek', np.array(tmp[3])),
('test', 'weekday', np.array(tmp[4])),
('test', 'quarterhour', np.array(tmp[5])),
('valid', 'yearweek', np.array(tmp[6])),
('valid', 'weekday', np.array(tmp[7])),
('valid', 'quarterhour', np.array(tmp[8]))
)
In [406]:
dates = get_date_data(split)
In [408]:
dates[3][2].shape
Out[408]:
(320,)
In [365]:
dates[2][2].shape[0]
Out[365]:
1710670
In [366]:
trn_size = dates[2][2].shape[0]
In [389]:
test_size = dates[4][2].shape[0]
In [409]:
val_size= dates[6][2].shape[0]
In [388]:
_size
Out[388]:
320
In [424]:
train_gps_mean = [np.concatenate([split[11][2][i] for i in range(trn_size)]).mean(),
np.concatenate([split[12][2][i] for i in range(trn_size)]).mean()]
In [425]:
train_gps_std = [np.concatenate([split[11][2][i] for i in range(trn_size)]).std(),
np.concatenate([split[12][2][i] for i in range(trn_size)]).std()]
In [426]:
def at_least_k(k, v, pad_at_begin, is_longitude):
if len(v) == 0:
v = numpy.array([train_gps_mean[1 if is_longitude else 0]])
if len(v) < k:
if pad_at_begin:
v = numpy.concatenate((numpy.full((k - len(v),), v[0]), v))
else:
v = numpy.concatenate((v, numpy.full((k - len(v),), v[-1])))
return v
In [411]:
valid_split[9]
Out[411]:
('valid',
'longitude',
array([ array([-8.64950371, -8.64968395, -8.65029621, -8.64983654, -8.64794731,
-8.64522934, -8.64282608, -8.6416378 , -8.63961315, -8.63767815,
-8.63641834, -8.63382626, -8.63256645, -8.63259315, -8.63254833,
-8.63091946, -8.62826443, -8.62643719, -8.62646389, -8.62608624,
-8.62596893, -8.62595081, -8.62570763, -8.62559128, -8.62558174,
-8.62537479, -8.62343979, -8.62133408, -8.62027168, -8.62012768,
-8.61866093, -8.61792278, -8.61756325, -8.61727524, -8.61649227,
-8.61578083, -8.61471939, -8.61359406, -8.61299992, -8.61210918,
-8.61203671, -8.61202812], dtype=float32),
array([-8.64962101, -8.64962959, -8.64984608, -8.64981937], dtype=float32),
array([-8.58569431, -8.58571243, -8.58622456, -8.58612633, -8.58614445,
-8.58617115, -8.58624268, -8.58703518, -8.5863781 , -8.5848484 ,
-8.58359718, -8.5822649 , -8.58025837, -8.57950211, -8.57837677,
-8.57883644, -8.5811758 , -8.58397484], dtype=float32),
array([-8.61093044, -8.61093903, -8.6107769 , -8.60993099, -8.60937309,
-8.6089592 , -8.60894108, -8.60878754, -8.60952568, -8.61074066,
-8.61178493, -8.61218071, -8.62130737, -8.62336826, -8.62466431,
-8.62576199, -8.62592411, -8.62584305, -8.62587929, -8.62602329,
-8.62613106, -8.62719345, -8.62914562, -8.63061333, -8.63176537,
-8.63193607, -8.6319809 , -8.63279057, -8.63451004, -8.63651657,
-8.63716507, -8.63757896, -8.63859558, -8.63757038, -8.6364994 ,
-8.63591385, -8.63593197, -8.63593197, -8.63813686, -8.63973045], dtype=float32),
array([-8.59130096, -8.59100437, -8.59169674, -8.59191322, -8.59238052,
-8.59276772, -8.59344292, -8.59373093, -8.59381199, -8.59410858,
-8.59467602, -8.59490108, -8.59495544, -8.59502697, -8.59501839,
-8.59501839, -8.59540462, -8.5954771 , -8.59550381, -8.59554005,
-8.59567547, -8.59718704, -8.59939194, -8.59937382, -8.59937382,
-8.59937382, -8.59975243, -8.60013866, -8.60047245, -8.60053539,
-8.60051727, -8.60051727, -8.60150719, -8.60194778, -8.60248756,
-8.60453987, -8.60625935, -8.60686207, -8.60816669, -8.60878754], dtype=float32),
array([-8.6132431 , -8.61220837, -8.61057854, -8.60934639, -8.60929203,
-8.60925579, -8.60899544, -8.60814857, -8.60797787, -8.60616016,
-8.60587215, -8.60589027, -8.60590839], dtype=float32),
array([-8.62436676, -8.62434006, -8.62438488, -8.624403 , -8.62436676,
-8.62433147, -8.62432194, -8.62432194, -8.62433147, -8.62435818,
-8.62434864, -8.62402534, -8.62377262, -8.62456512, -8.62332344,
-8.62158585, -8.62018204, -8.62029934, -8.61983967, -8.61948872,
-8.61984921, -8.61969566, -8.61957932, -8.62073994, -8.62153244,
-8.62224293, -8.62277412, -8.62333202, -8.62462807, -8.6248436 ,
-8.62483501, -8.62483501, -8.62483501, -8.62483501, -8.6248436 ,
-8.62485313, -8.6248436 , -8.62483501, -8.62519455, -8.62497044,
-8.62497044, -8.6249609 , -8.6249609 , -8.6249609 , -8.62497902,
-8.6249876 , -8.62499714, -8.62502384, -8.62503338, -8.62505054,
-8.62506008, -8.62504196, -8.62504196, -8.62506008, -8.62504196,
-8.62504196, -8.62504196, -8.62503338, -8.62502384, -8.62501526,
-8.62501526, -8.62499714, -8.6249876 , -8.62497902, -8.62495232,
-8.62495232, -8.62494278, -8.6249342 , -8.62492466, -8.62492466,
-8.62491608, -8.62491608, -8.62491608, -8.62492466, -8.62489796,
-8.62485313, -8.62495232, -8.6249609 , -8.62499714, -8.62516785,
-8.62541962, -8.62555504, -8.62586117, -8.62708473, -8.62693214,
-8.62647343, -8.62642765, -8.62636471, -8.62636471, -8.62634659,
-8.62598705, -8.62596035, -8.62596893], dtype=float32),
array([-8.61553764, -8.61551952, -8.61553764, -8.61553764, -8.61551094,
-8.6154213 , -8.61519623, -8.61515141, -8.61514187, -8.61460209,
-8.61413383, -8.61383724, -8.61335087, -8.61006641, -8.6089859 ,
-8.60747433, -8.60669136, -8.60577297, -8.60328865, -8.60013866,
-8.59714222, -8.59487438, -8.59207535, -8.58926678, -8.58734131,
-8.58527088, -8.58288574, -8.58086109, -8.57880878, -8.57799911,
-8.57793617, -8.57880878, -8.57887173, -8.5793848 , -8.57989788,
-8.57872772, -8.58007812, -8.58241844, -8.58288574, -8.58159924,
-8.58131981, -8.58287716, -8.58529758, -8.58824062, -8.59085083,
-8.59265995, -8.59462166, -8.59414482, -8.59322739, -8.59291172,
-8.59390163, -8.59470272, -8.59224606, -8.5899601 , -8.58755684,
-8.58634186, -8.59004116, -8.59336185, -8.59795189, -8.60233498,
-8.60685253, -8.61170387, -8.61666298, -8.62159538], dtype=float32),
array([-8.5857029 , -8.58572102, -8.58602715, -8.58628845, -8.58613491,
-8.58617973, -8.58631516, -8.58718777, -8.5858736 , -8.58449745,
-8.58342648, -8.58190536, -8.579916 , -8.57945728, -8.57931328,
-8.57970905, -8.57881832], dtype=float32),
array([-8.57474995, -8.57474136, -8.57538891, -8.57583904, -8.57539845,
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-7.59721518, -7.59720612, -7.59720612, -7.59720612, -7.59719706,
-7.59719706, -7.59720612, -7.59715223, -7.59721518, -7.59768295,
-7.59815121, -7.59830379, -7.59870911, -7.59821415, -7.59735012,
-7.59749413, -7.5979352 , -7.59787178, -7.59766483, -7.59692717,
-7.59600878, -7.59484816, -7.59480286, -7.59490204, -7.59564018,
-7.59631491, -7.59778214, -7.59955502, -7.60120201, -7.60237217,
-7.604352 , -7.60596323, -7.60689878, -7.60712385, -7.60790682,
-7.60949087, -7.611444 , -7.61320782], dtype=float32),
array([-8.60637569], dtype=float32),
array([-8.61551952, -8.61483574, -8.61445808, -8.61411572, -8.61340523,
-8.61318016], dtype=float32),
array([-8.60021114, -8.6005621 , -8.60141659, -8.60218239, -8.60274887,
-8.60319901, -8.60345078, -8.60420704, -8.60486412, -8.60507965], dtype=float32),
array([-8.60018444, -8.60025597, -8.60089493, -8.60147095, -8.60220909,
-8.60272217, -8.60312653, -8.60348701, -8.6042614 , -8.6048727 ,
-8.60488224, -8.60492706, -8.60545826, -8.60631275, -8.60514259,
-8.60388279, -8.60272217, -8.60128212, -8.60028267, -8.59981537,
-8.59922123, -8.59854603, -8.59775352, -8.59736729, -8.59738541,
-8.59737587, -8.59737587, -8.59733963, -8.59733105, -8.59686279,
-8.59517097, -8.59303761, -8.5906353 , -8.58804321, -8.58590984,
-8.58414555, -8.58256245, -8.58024025, -8.5770359 , -8.57444382,
-8.57189655, -8.57003403, -8.56879234, -8.56758595, -8.56567764,
-8.56493092, -8.56357193, -8.56184387, -8.56089878, -8.55961227,
-8.55863953, -8.55729866, -8.55576897, -8.55513859, -8.55416679], dtype=float32),
array([-8.59207535, -8.59329033, -8.59443283, -8.59490108, -8.5953064 ,
-8.59536934, -8.5953598 , -8.5953598 , -8.59544086, -8.5953598 ,
-8.59514427, -8.5946312 , -8.59401894, -8.59325409, -8.59246159,
-8.59205723, -8.59202099, -8.59194946, -8.59108543, -8.59006786,
-8.58909607, -8.58871841, -8.58871841, -8.58830357, -8.5876646 ,
-8.58862782, -8.58967209, -8.59101295, -8.59310055, -8.59565735,
-8.59852791, -8.60113811, -8.60366726, -8.60456657, -8.60569191,
-8.60626793, -8.6078701 , -8.60827541, -8.6078701 , -8.60874271,
-8.60921955, -8.60919285, -8.60931015, -8.60931015, -8.60931873,
-8.60933685, -8.60935497, -8.60935497, -8.60934639, -8.60933685,
-8.60933685], dtype=float32)], dtype=object))
In [418]:
import theano
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-418-3397704bd624> in <module>()
----> 1 import theano
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/theano/__init__.py in <module>()
93 # needed during that phase.
94 import theano.tests
---> 95 if hasattr(theano.tests, "TheanoNoseTester"):
96 test = theano.tests.TheanoNoseTester().test
97 else:
AttributeError: module 'theano' has no attribute 'tests'
In [437]:
def get_first_last_k(split, k):
first_k = (
[],
[],
[],
[],
[],
[]
)
last_k = (
[],
[],
[],
[],
[],
[]
)
for i in range(trn_size):
first_k[0].append(np.array(at_least_k(k, split[11][2][i], False, False)[:k]))
first_k[1].append(np.array(at_least_k(k, split[12][2][i], False, True)[:k]))
last_k[0].append(np.array(at_least_k(k, split[11][2][i], True, False)[-k:]))
last_k[1].append(np.array(at_least_k(k, split[12][2][i], True, True)[-k:]))
for i in range(test_size):
first_k[2].append(np.array(at_least_k(k, split[21][2][i], False, False)[:k]))
first_k[3].append(np.array(at_least_k(k, split[22][2][i], False, True)[:k]))
last_k[2].append(np.array(at_least_k(k, split[21][2][i], True, False)[-k:]))
last_k[3].append(np.array(at_least_k(k, split[22][2][i], True, True)[-k:]))
for i in range(val_size):
first_k[4].append(np.array(at_least_k(k, valid_split[8][2][i], False, False)[:k]))
first_k[5].append(np.array(at_least_k(k, valid_split[9][2][i], False, True)[:k]))
last_k[4].append(np.array(at_least_k(k, valid_split[8][2][i], True, False)[-k:]))
last_k[5].append(np.array(at_least_k(k, valid_split[9][2][i], True, True)[-k:]))
return (
('train', 'first_latitude', np.array(first_k[0])),
('train', 'first_longitude', np.array(first_k[1])),
('train', 'last_latitude', np.array(last_k[0])),
('train', 'last_longitude', np.array(last_k[1])),
('test', 'first_latitude', np.array(first_k[2])),
('test', 'first_longitude', np.array(first_k[3])),
('test', 'last_latitude', np.array(last_k[2])),
('test', 'last_longitude', np.array(last_k[3])),
('valid', 'first_latitude', np.array(first_k[4])),
('valid', 'first_longitude', np.array(first_k[5])),
('valid', 'last_latitude', np.array(last_k[4])),
('valid', 'last_longitude', np.array(last_k[5])))
In [434]:
import warnings
warnings.filterwarnings("ignore")
In [438]:
coords = get_first_last_k(split, 5)
In [440]:
coords[0][2].shape
Out[440]:
(1710670, 5)
In [446]:
coords[4][2].shape
Out[446]:
(320, 5)
In [447]:
coords[4]
Out[447]:
('test',
'first_latitude',
array([[ 41.14852142, 41.14863968, 41.1488533 , 41.14892578,
41.14896393],
[ 41.14556885, 41.14558029, 41.14576721, 41.14619064,
41.14675903],
[ 41.14855957, 41.1488266 , 41.14897156, 41.14901733, 41.1485672 ],
...,
[ 41.15253448, 41.1524086 , 41.15313721, 41.15382385, 41.1543541 ],
[ 41.15607452, 41.15592957, 41.15547943, 41.15605545,
41.15622711],
[ 41.18018341, 41.17995071, 41.17948914, 41.18055344,
41.18162537]]))
In [454]:
valid_split[8][2][0][-1]
Out[454]:
41.145992
In [455]:
valid_split[10][2][0]
Out[455]:
41.146263
In [500]:
np.sum([len(l) for l in data['LATITUDE']])
Out[500]:
83409386
In [ ]:
data.to_pickle(data_path+'data/train_data.pkl')
In [513]:
data['DAY_OF_WEEK'] = data['TIMESTAMP'].apply(lambda t:datetime.datetime.fromtimestamp(t).weekday())
In [515]:
data['QUARTER_HOUR'] = data['TIMESTAMP'].apply(lambda t:int((datetime.datetime.fromtimestamp(t).hour*60 + datetime.datetime.fromtimestamp(t).minute)/15))
In [517]:
data['WEEK_OF_YEAR'] = data['TIMESTAMP'].apply(lambda t:datetime.datetime.fromtimestamp(t).isocalendar()[1])
In [529]:
data['DESTINATION_LATITUDE'] = data['LATITUDE'].apply(lambda l: l[-1] if len(l) > 0 else np.nan)
In [530]:
data['DESTINATION_LONGITUDE'] = data['LONGITUDE'].apply(lambda l: l[-1] if len(l) > 0 else np.nan)
In [534]:
data = data.dropna()
In [535]:
train_gps_mean
Out[535]:
[41.157284, -8.6161261]
In [537]:
data['LATITUDE'] = data['LATITUDE'].apply(lambda l: (l-train_gps_mean[0])/train_gps_std[0])
In [538]:
data['LONGITUDE'] = data['LONGITUDE'].apply(lambda l: (l-train_gps_mean[1])/train_gps_std[1])
In [547]:
data.columns
Out[547]:
Index(['TRIP_ID', 'CALL_TYPE', 'ORIGIN_CALL', 'ORIGIN_STAND', 'TAXI_ID',
'TIMESTAMP', 'DAY_TYPE', 'MISSING_DATA', 'POLYLINE', 'LATITUDE',
'LONGITUDE', 'DAY_OF_WEEK', 'QUARTER_HOUR', 'WEEK_OF_YEAR',
'DESTINATION_LATITUDE', 'DESTINATION_LONGITUDE'],
dtype='object')
In [548]:
data['CALL_TYPE'].unique()
Out[548]:
array([2, 1, 0])
In [518]:
def at_least_k(k, v, pad_at_begin, is_longitude):
if len(v) == 0:
v = numpy.array([train_gps_mean[1 if is_longitude else 0]])
if len(v) < k:
if pad_at_begin:
v = numpy.concatenate((numpy.full((k - len(v),), v[0]), v))
else:
v = numpy.concatenate((v, numpy.full((k - len(v),), v[-1])))
return v
In [608]:
origin_call = []
origin_stand = []
taxi_id = []
day_of_week = []
quarter_hour = []
week_of_year = []
day_type = []
first_latitude = []
first_longitude = []
last_latitude = []
last_longitude = []
destination_latitude = []
destination_longitude = []
In [609]:
k = 5
In [ ]:
def
origin_call = []
origin_stand = []
taxi_id = []
day_of_week = []
quarter_hour = []
week_of_year = []
day_type = []
first_latitude = []
first_longitude = []
last_latitude = []
last_longitude = []
destination_latitude = []
destination_longitude = []
for i in data.index:
latitude = data['LATITUDE'][i][:-1]
longitude = data['LONGITUDE'][i][:-1]
l = len(latitude)
if l==0:
continue
if l < 100:
for j in range(l):
first_latitude.append(np.array(at_least_k(k, latitude[:j+1], False, False)[:k]))
first_longitude.append(np.array(at_least_k(k, longitude[:j+1], False, True)[:k]))
last_latitude.append(np.array(at_least_k(k, latitude[:j+1], False, False)[-k:]))
last_longitude.append(np.array(at_least_k(k, longitude[:j+1], False, True)[-k:]))
origin_call.append(data['ORIGIN_CALL'][i])
origin_stand.append(data['ORIGIN_STAND'][i])
taxi_id.append(data['TAXI_ID'][i])
day_of_week.append(data['DAY_OF_WEEK'][i])
quarter_hour.append(data['QUARTER_HOUR'][i])
week_of_year.append(data['WEEK_OF_YEAR'][i])
day_type.append(data['DAY_TYPE'][i])
destination_latitude.append(data['DESTINATION_LATITUDE'][i])
destination_longitude.append(data['DESTINATION_LONGITUDE'][i])
else:
indices = np.random.choice(range(l), 100, replace=False)
for j in indices:
first_latitude.append(np.array(at_least_k(k, latitude[:j+1], False, False)[:k]))
first_longitude.append(np.array(at_least_k(k, longitude[:j+1], False, True)[:k]))
last_latitude.append(np.array(at_least_k(k, latitude[:j+1], False, False)[-k:]))
last_longitude.append(np.array(at_least_k(k, longitude[:j+1], False, True)[-k:]))
origin_call.append(data['ORIGIN_CALL'][i])
origin_stand.append(data['ORIGIN_STAND'][i])
taxi_id.append(data['TAXI_ID'][i])
day_of_week.append(data['DAY_OF_WEEK'][i])
quarter_hour.append(data['QUARTER_HOUR'][i])
week_of_year.append(data['WEEK_OF_YEAR'][i])
day_type.append(data['DAY_TYPE'][i])
destination_latitude.append(data['DESTINATION_LATITUDE'][i])
destination_longitude.append(data['DESTINATION_LONGITUDE'][i])
In [ ]:
print('I"m finished')
In [ ]:
5
In [613]:
len(origin_stand)
Out[613]:
54538180
In [584]:
data['LATITUDE'][0]
Out[584]:
array([-0.21412922, -0.21459237, -0.19930845, -0.1817088 , -0.17414403,
-0.16868916, -0.16982131, -0.16287406, -0.13981953, -0.12221988,
-0.09304149, -0.08187445, -0.08151422, -0.06854605, -0.0662303 ,
-0.06427479, -0.06211343, -0.05748194, -0.03772091, -0.03746361,
-0.03736069, -0.03756653, -0.03772091], dtype=float32)
In [605]:
first_longitude[:22]
Out[605]:
[array([-0.04360518, -0.04360518, -0.04360518, -0.04360518, -0.04360518]),
array([-0.04360518, -0.04111015, -0.04111015, -0.04111015, -0.04111015]),
array([-0.04360518, -0.04111015, -0.07276894, -0.07276894, -0.07276894]),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.10442773]),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32)]
In [606]:
last_longitude[:22]
Out[606]:
[array([-0.04360518, -0.04360518, -0.04360518, -0.04360518, -0.04360518]),
array([-0.04360518, -0.04111015, -0.04111015, -0.04111015, -0.04111015]),
array([-0.04360518, -0.04111015, -0.07276894, -0.07276894, -0.07276894]),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.10442773]),
array([-0.04360518, -0.04111015, -0.07276894, -0.10442773, -0.13560733], dtype=float32),
array([-0.04111015, -0.07276894, -0.10442773, -0.13560733, -0.18286417], dtype=float32),
array([-0.07276894, -0.10442773, -0.13560733, -0.18286417, -0.19486015], dtype=float32),
array([-0.10442773, -0.13560733, -0.18286417, -0.19486015, -0.24429809], dtype=float32),
array([-0.13560733, -0.18286417, -0.19486015, -0.24429809, -0.28795284], dtype=float32),
array([-0.18286417, -0.19486015, -0.24429809, -0.28795284, -0.27048764], dtype=float32),
array([-0.19486015, -0.24429809, -0.28795284, -0.27048764, -0.23930801], dtype=float32),
array([-0.24429809, -0.28795284, -0.27048764, -0.23930801, -0.22496574], dtype=float32),
array([-0.28795284, -0.27048764, -0.23930801, -0.22496574, -0.22527967], dtype=float32),
array([-0.27048764, -0.23930801, -0.22496574, -0.22527967, -0.21934779], dtype=float32),
array([-0.23930801, -0.22496574, -0.22527967, -0.21934779, -0.21762936], dtype=float32),
array([-0.22496574, -0.22527967, -0.21934779, -0.21762936, -0.21888514], dtype=float32),
array([-0.22527967, -0.21934779, -0.21762936, -0.21888514, -0.25490609], dtype=float32),
array([-0.21934779, -0.21762936, -0.21888514, -0.25490609, -0.28063297], dtype=float32),
array([-0.21762936, -0.21888514, -0.25490609, -0.28063297, -0.26019356], dtype=float32),
array([-0.21888514, -0.25490609, -0.28063297, -0.26019356, -0.25474086], dtype=float32),
array([-0.25490609, -0.28063297, -0.26019356, -0.25474086, -0.25474086], dtype=float32),
array([-0.28063297, -0.26019356, -0.25474086, -0.25474086, -0.25474086], dtype=float32)]
In [ ]:
first_latitude[:22]
In [ ]:
last_latitude[:22]
In [510]:
valid_data.to_pickle(data_path+'data/valid_data.pkl')
In [511]:
test_data.to_pickle(data_path+'data/test_data.pkl')
In [ ]:
n_origin_call = len(data['ORIGIN_CALL'].unique())
n_taxi_id = len(data['TAXI_ID'].unique())
n_origin_stand = len(data['ORIGIN_STAND'].unique())
n_quarter_hour = len(data['QUARTER_HOUR'].unique())
n_day_of_week = len(data['DAY_OF_WEEK'].unique())
n_week_of_year = len(data['WEEK_OF_YEAR'])
n_day_type = 3
In [ ]:
latitude_sum = lambda x: np.dot(x,cluster_centers[0])
longitude_sum = lambda x: np.dot(x, cluster_centers[1])
In [ ]:
def taxi_mlp(k, shp = cluster_centers.shape[0]):
first_lat_in = Input(shape(k,))
last_lat_in = Input(shape(k,))
first_long_in = Input(shape(k,))
last_long_in = Input(shape(k,))
center_lats = Input(shape=(shp,))
center_longs = Input(shape=(shp,))
emb_names = ['origin_call', 'taxi_ID', "origin_stand", "quarter_hour", "day_of_week", "week_of_year", "day_type"]
emb_ins = [n_origin_call + 1, n_taxi_id + 1, n_origin_stand + 1, n_quarter_hour + 1, n_day_of_week + 1, n_week_of_year + 1, n_day_type+1]
emb_outs = [10 for i in range(0,6)]
regs = [0 for i in range(0,6)]
embs = [embedding_input(e[0], e[1]+1, e[2], e[3]) for e in zip(emb_names, emb_ins, emb_outs, regs)]
x = merge([first_lat_in, last_lat_in, first_long_in, last_long_in] + [Flatten()(e[1]) for e in embs], mode='concat')
x = Dense(500, activation='relu')(x)
x = Dense(shp, activation='softmax')(x)
#CHECK ON CLUSTERS!!!!
y_latitude = Lambda(latitude_sum,(1,))(x)
y_longitude = Lambda(longitude_sum,(1,))(x)
return Model(input = [first_lat_in, last_lat_in, first_long_in, last_long_in]+[e[0] for e in embs] + [center_longs, center_lats],
output = [y_latitude,y_longitude])
Content source: kmclaugh/fastai_courses
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