Watch Full Video Here: https://youtu.be/lUI6VMj43PE
Dataset Link: http://www.cis.fordham.edu/wisdm/dataset.php
The WISDM dataset contains six different labels (Downstairs, Jogging, Sitting, Standing, Upstairs, Walking).
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPool2D
from tensorflow.keras.optimizers import Adam
print(tf.__version__)2.0.0-rc0
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoderpd.read_csv('WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt')---------------------------------------------------------------------------
ParserError Traceback (most recent call last)
<ipython-input-5-da09ba528f48> in <module>
----> 1 pd.read_csv('WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt')
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
700 skip_blank_lines=skip_blank_lines)
701
--> 702 return _read(filepath_or_buffer, kwds)
703
704 parser_f.__name__ = name
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
433
434 try:
--> 435 data = parser.read(nrows)
436 finally:
437 parser.close()
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1137 def read(self, nrows=None):
1138 nrows = _validate_integer('nrows', nrows)
-> 1139 ret = self._engine.read(nrows)
1140
1141 # May alter columns / col_dict
C:\ProgramData\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1993 def read(self, nrows=None):
1994 try:
-> 1995 data = self._reader.read(nrows)
1996 except StopIteration:
1997 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 6 fields in line 134634, saw 11
file = open('WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt')
lines = file.readlines()
processedList = []
for i, line in enumerate(lines):
try:
line = line.split(',')
last = line[5].split(';')[0]
last = last.strip()
if last == '':
break;
temp = [line[0], line[1], line[2], line[3], line[4], last]
processedList.append(temp)
except:
print('Error at line number: ', i)Error at line number: 281873
Error at line number: 281874
Error at line number: 281875
processedList[['33', 'Jogging', '49105962326000', '-0.6946377', '12.680544', '0.50395286'],
['33', 'Jogging', '49106062271000', '5.012288', '11.264028', '0.95342433'],
['33', 'Jogging', '49106112167000', '4.903325', '10.882658', '-0.08172209'],
['33', 'Jogging', '49106222305000', '-0.61291564', '18.496431', '3.0237172'],
['33', 'Jogging', '49106332290000', '-1.1849703', '12.108489', '7.205164'],
['33', 'Jogging', '49106442306000', '1.3756552', '-2.4925237', '-6.510526'],
['33', 'Jogging', '49106542312000', '-0.61291564', '10.56939', '5.706926'],
['33', 'Jogging', '49106652389000', '-0.50395286', '13.947236', '7.0553403'],
['33', 'Jogging', '49106762313000', '-8.430995', '11.413852', '5.134871'],
['33', 'Jogging', '49106872299000', '0.95342433', '1.3756552', '1.6480621'],
['33', 'Jogging', '49106982315000', '-8.19945', '19.57244', '2.7240696'],
['33', 'Jogging', '49107092330000', '1.4165162', '5.7886477', '2.982856'],
['33', 'Jogging', '49107202316000', '-1.879608', '-2.982856', '-0.29964766'],
['33', 'Jogging', '49107312332000', '-6.1291566', '6.851035', '-8.158588'],
['33', 'Jogging', '49107422348000', '5.829509', '18.0061', '8.539958'],
['33', 'Jogging', '49107522293000', '6.2789803', '2.982856', '2.9147544'],
['33', 'Jogging', '49107632339000', '-1.56634', '8.308413', '-1.4573772'],
['33', 'Jogging', '49107742355000', '3.5276701', '13.593107', '9.425281'],
['33', 'Jogging', '49107852340000', '-2.0294318', '-5.706926', '-10.18802'],
['33', 'Jogging', '49107962326000', '2.7649305', '10.337844', '-9.724928'],
['33', 'Jogging', '49108062271000', '3.568531', '13.6748295', '1.5390993'],
['33', 'Jogging', '49108172348000', '-0.50395286', '3.8681788', '3.718355'],
['33', 'Jogging', '49108272262000', '-2.3018389', '1.6889231', '0.08172209'],
['33', 'Jogging', '49108382370000', '-3.568531', '19.57244', '6.510526'],
['33', 'Jogging', '49108492294000', '-0.8036005', '-3.2961242', '-4.630918'],
['33', 'Jogging', '49108602371000', '0.50395286', '10.841797', '13.525005'],
['33', 'Jogging', '49108702285000', '5.706926', '15.595298', '6.1700177'],
['33', 'Jogging', '49108812332000', '-8.662541', '7.273266', '4.0180025'],
['33', 'Jogging', '49108922378000', '-1.334794', '1.2258313', '2.3699405'],
['33', 'Jogging', '49109022293000', '-4.5900574', '19.57244', '4.7126403'],
['33', 'Jogging', '49109132308000', '3.8681788', '3.759216', '0.84446156'],
['33', 'Jogging', '49109242355000', '-1.7978859', '1.5390993', '8.730643'],
['33', 'Jogging', '49109352310000', '7.668256', '11.264028', '-1.3075534'],
['33', 'Jogging', '49109462295000', '-2.3699405', '14.2877445', '8.281172'],
['33', 'Jogging', '49109562271000', '2.7240696', '1.4573772', '0.88532263'],
['33', 'Jogging', '49109672348000', '-3.5957718', '18.659876', '-0.6537767'],
['33', 'Jogging', '49109782333000', '3.9499009', '4.140586', '3.990762'],
['33', 'Jogging', '49109892349000', '0.46309182', '-2.4108016', '2.4108016'],
['33', 'Jogging', '49109992263000', '3.7864566', '14.137921', '-3.1463003'],
['33', 'Jogging', '49110102371000', '3.336985', '19.231932', '6.5513873'],
['33', 'Jogging', '49110212326000', '5.6660647', '3.7864566', '0.53119355'],
['33', 'Jogging', '49110263595000', '0.23154591', '0.7627395', '0.7627395'],
['33', 'Jogging', '49110372299000', '-4.8216033', '19.57244', '8.158588'],
['33', 'Jogging', '49110482345000', '1.8387469', '-1.1168685', '-2.7921712'],
['33', 'Jogging', '49110582290000', '-3.2961242', '10.079058', '13.824653'],
['33', 'Jogging', '49110692306000', '11.604536', '17.079916', '1.334794'],
['33', 'Jogging', '49110802291000', '-3.173541', '14.015338', '5.706926'],
['33', 'Jogging', '49110912307000', '0.61291564', '1.1168685', '2.5606253'],
['33', 'Jogging', '49111012222000', '-7.8861814', '19.57244', '1.9885708'],
['33', 'Jogging', '49111112289000', '3.1463003', '5.243834', '4.671779'],
['33', 'Jogging', '49111222305000', '-3.0237172', '-4.3312707', '-3.336985'],
['33', 'Jogging', '49111332290000', '-0.08172209', '11.917805', '-7.8861814'],
['33', 'Jogging', '49111392257000', '-1.0351465', '14.818938', '4.6036777'],
['33', 'Jogging', '49111502304000', '-2.4516625', '2.5333846', '3.486809'],
['33', 'Jogging', '49111612289000', '-1.3756552', '2.070293', '-0.19068487'],
['33', 'Jogging', '49111722335000', '-2.4925237', '19.57244', '6.469665'],
['33', 'Jogging', '49111832290000', '1.4573772', '-5.243834', '-4.372132'],
['33', 'Jogging', '49111942337000', '-1.4165162', '9.80665', '5.7477865'],
['33', 'Jogging', '49112042282000', '-1.2666923', '14.709975', '6.2108784'],
['33', 'Jogging', '49112152297000', '-3.6774938', '3.173541', '3.7864566'],
['33', 'Jogging', '49112252273000', '1.8387469', '2.7649305', '-1.7570249'],
['33', 'Jogging', '49112362289000', '-1.2666923', '19.313654', '6.3198414'],
['33', 'Jogging', '49112472335000', '2.4108016', '-7.6546354', '-6.1291566'],
['33', 'Jogging', '49112572280000', '-0.61291564', '16.358038', '4.944186'],
['33', 'Jogging', '49112682296000', '0.040861044', '17.502148', '2.5333846'],
['33', 'Jogging', '49112792343000', '-7.6546354', '7.8180795', '4.372132'],
['33', 'Jogging', '49112902328000', '-1.2666923', '0.7218784', '0.8036005'],
['33', 'Jogging', '49113002273000', '-5.012288', '19.57244', '5.5162406'],
['33', 'Jogging', '49113112289000', '1.9477097', '2.7921712', '2.070293'],
['33', 'Jogging', '49113222305000', '-5.053149', '1.6480621', '7.6273947'],
['33', 'Jogging', '49113332290000', '9.384419', '13.443283', '1.0351465'],
['33', 'Jogging', '49113442306000', '-5.434519', '13.211738', '6.4424243'],
['33', 'Jogging', '49113560501000', '-0.61291564', '1.879608', '1.4165162'],
['33', 'Jogging', '49113842239000', '4.7126403', '-6.5513873', '-6.0201936'],
['33', 'Jogging', '49113952346000', '-1.7570249', '9.302697', '-6.428804'],
['33', 'Jogging', '49114052230000', '-0.9125633', '10.501288', '-0.27240697'],
['33', 'Jogging', '49114102218000', '2.6014864', '19.381754', '4.440233'],
['33', 'Jogging', '49114212295000', '5.7886477', '3.214402', '1.1441092'],
['33', 'Jogging', '49114312301000', '-1.9885708', '12.4489975', '-2.7240696'],
['33', 'Jogging', '49114422317000', '1.4165162', '16.780268', '8.471856'],
['33', 'Jogging', '49114522293000', '0.42223078', '-8.267551', '-7.3549876'],
['33', 'Jogging', '49114632339000', '-3.568531', '10.95076', '-0.8036005'],
['33', 'Jogging', '49114742355000', '-4.671779', '11.727119', '0.38136974'],
['33', 'Jogging', '49114852340000', '-2.1383946', '1.6889231', '3.5276701'],
['33', 'Jogging', '49114962295000', '-1.334794', '2.4925237', '-0.3405087'],
['33', 'Jogging', '49115072311000', '-2.9147544', '19.57244', '7.5865335'],
['33', 'Jogging', '49115172470000', '3.5276701', '-3.9499009', '-1.920469'],
['33', 'Jogging', '49115272262000', '-4.0588636', '10.038197', '14.2877445'],
['33', 'Jogging', '49115382339000', '7.5865335', '13.33432', '3.8681788'],
['33', 'Jogging', '49115492294000', '-5.175732', '14.1787815', '5.5162406'],
['33', 'Jogging', '49115602310000', '1.1849703', '2.1111538', '2.2201166'],
['33', 'Jogging', '49115712295000', '-3.718355', '19.463476', '-0.8036005'],
['33', 'Jogging', '49115812332000', '3.173541', '6.510526', '4.2086873'],
['33', 'Jogging', '49115912124000', '-1.7297841', '-5.6252036', '-4.3312707'],
['33', 'Jogging', '49115962234000', '0.84446156', '12.980191', '11.563675'],
['33', 'Jogging', '49116072311000', '2.7649305', '17.352324', '7.7363577'],
['33', 'Jogging', '49116122238000', '2.3426998', '15.894946', '3.6774938'],
['33', 'Jogging', '49116232315000', '4.7943625', '3.8273177', '0.9942854'],
['33', 'Jogging', '49116342330000', '-1.8387469', '12.830367', '-1.56634'],
['33', 'Jogging', '49116452316000', '5.366417', '14.410328', '6.742072'],
['33', 'Jogging', '49116502212000', '2.1111538', '0.27240697', '2.6423476'],
['33', 'Jogging', '49116642318000', '4.2086873', '11.454713', '7.6273947'],
['33', 'Jogging', '49116693038000', '-5.134871', '9.00305', '-3.173541'],
['33', 'Jogging', '49116782241000', '3.0237172', '7.164303', '0.08172209'],
['33', 'Jogging', '49116893203000', '2.070293', '3.486809', '1.7570249'],
['33', 'Jogging', '49116992202000', '-3.1054392', '4.862464', '-0.040861044'],
['33', 'Jogging', '49117102310000', '-4.399372', '19.531578', '5.053149'],
['33', 'Jogging', '49117202224000', '4.7535014', '-3.2961242', '-2.1383946'],
['33', 'Jogging', '49117252243000', '-0.6946377', '-7.6273947', '-7.8180795'],
['33', 'Jogging', '49117352279000', '-2.8330324', '6.5513873', '-7.9679036'],
['33', 'Jogging', '49117462722000', '-0.8036005', '10.460427', '0.23154591'],
['33', 'Jogging', '49117572311000', '-3.214402', '3.0645783', '3.255263'],
['33', 'Jogging', '49117682327000', '-2.8738933', '3.5957718', '0.10896278'],
['33', 'Jogging', '49117782302000', '-4.4810944', '19.504337', '5.9793324'],
['33', 'Jogging', '49117892318000', '2.7921712', '-1.7570249', '0.19068487'],
['33', 'Jogging', '49117992141000', '-4.7535014', '4.2086873', '11.141444'],
['33', 'Jogging', '49118252334000', '-1.56634', '3.759216', '3.255263'],
['33', 'Jogging', '49118362319000', '-3.214402', '2.9556155', '0.7627395'],
['33', 'Jogging', '49118472305000', '-2.6423476', '19.57244', '8.008764'],
['33', 'Jogging', '49118572219000', '0.5720546', '-1.4982382', '-0.38136974'],
['33', 'Jogging', '49118622512000', '-1.525479', '-7.7363577', '-8.580819'],
['33', 'Jogging', '49118732315000', '-4.7535014', '18.537292', '-3.1054392'],
['33', 'Jogging', '49118842300000', '1.7978859', '14.410328', '1.0760075'],
['33', 'Jogging', '49118952316000', '-0.8036005', '3.255263', '3.990762'],
['33', 'Jogging', '49119052291000', '-1.2258313', '3.990762', '0.8036005'],
['33', 'Jogging', '49119162307000', '-4.630918', '19.57244', '6.3607025'],
['33', 'Jogging', '49119262283000', '1.9885708', '-1.3756552', '-0.5720546'],
['33', 'Jogging', '49119372268000', '-4.630918', '1.920469', '8.281172'],
['33', 'Jogging', '49119482315000', '12.299174', '17.883516', '5.325556'],
['33', 'Jogging', '49119592300000', '0.9942854', '15.241169', '5.325556'],
['33', 'Jogging', '49119702316000', '1.0351465', '1.7570249', '1.9885708'],
['33', 'Jogging', '49119802322000', '-9.384419', '19.57244', '0.10896278'],
['33', 'Jogging', '49119912338000', '2.9556155', '5.597963', '4.630918'],
['33', 'Jogging', '49120012252000', '-2.3699405', '-5.366417', '-2.7513103'],
['33', 'Jogging', '49120122299000', '-1.879608', '10.882658', '-7.7772183'],
['33', 'Jogging', '49120232315000', '1.1168685', '18.496431', '3.5957718'],
['33', 'Jogging', '49120342361000', '5.012288', '4.372132', '1.334794'],
['33', 'Jogging', '49120442245000', '-3.336985', '16.589584', '-4.671779'],
['33', 'Jogging', '49120492233000', '-6.891896', '19.57244', '6.238119'],
['33', 'Jogging', '49120602310000', '3.3778462', '-0.6537767', '2.3018389'],
['33', 'Jogging', '49120942214000', '-2.1383946', '15.554437', '4.372132'],
['33', 'Jogging', '49121042221000', '5.284695', '1.6480621', '-0.29964766'],
['33', 'Jogging', '49121152328000', '-9.425281', '18.959524', '-4.903325'],
['33', 'Jogging', '49121202163000', '2.070293', '19.57244', '8.008764'],
['33', 'Jogging', '49121302261000', '1.6480621', '-2.6014864', '-1.4573772'],
['33', 'Jogging', '49121352279000', '-1.56634', '-7.7772183', '-9.112013'],
['33', 'Jogging', '49121462295000', '-6.932757', '13.33432', '-6.3198414'],
['33', 'Jogging', '49121572311000', '-4.140586', '12.871228', '-0.84446156'],
['33', 'Jogging', '49121682296000', '3.0237172', '2.3699405', '2.0294318'],
['33', 'Jogging', '49121792312000', '-1.9885708', '5.9793324', '0.040861044'],
['33', 'Jogging', '49121902297000', '2.9556155', '19.57244', '7.6273947'],
['33', 'Jogging', '49122002273000', '1.7297841', '-5.325556', '-2.7513103'],
['33', 'Jogging', '49122112289000', '-0.42223078', '14.1787815', '9.888372'],
['33', 'Jogging', '49122222305000', '3.1463003', '17.24336', '5.407278'],
['33', 'Jogging', '49122282272000', '-0.5720546', '18.727978', '4.671779'],
['33', 'Jogging', '49122392318000', '5.5162406', '3.7864566', '1.1849703'],
['33', 'Jogging', '49122502304000', '-4.3312707', '15.6634', '-3.445948'],
['33', 'Jogging', '49122612289000', '5.5571017', '14.709975', '7.43671'],
['33', 'Jogging', '49122722305000', '0.50395286', '-7.7772183', '-8.921328'],
['33', 'Jogging', '49122832290000', '-4.7126403', '6.891896', '-11.032481'],
['33', 'Jogging', '49122942337000', '-1.7297841', '8.962189', '-2.1111538'],
['33', 'Jogging', '49123043411000', '-3.214402', '3.486809', '3.6774938'],
['33', 'Jogging', '49123152328000', '-2.3018389', '0.84446156', '1.3075534'],
['33', 'Jogging', '49123212234000', '-4.7943625', '18.428331', '-4.9850473'],
['33', 'Jogging', '49123312240000', '4.0588636', '14.628253', '9.00305'],
['33', 'Jogging', '49123362289000', '1.6889231', '-0.7218784', '1.3075534'],
['33', 'Jogging', '49123612167000', '2.2609777', '15.213928', '7.5865335'],
['33', 'Jogging', '49123680191000', '2.2609777', '18.578154', '4.630918'],
['33', 'Jogging', '49123782333000', '5.597963', '4.630918', '-0.08172209'],
['33', 'Jogging', '49123832321000', '-2.070293', '1.8387469', '1.0351465'],
['33', 'Jogging', '49123942306000', '-5.706926', '19.57244', '6.4015636'],
['33', 'Jogging', '49124042282000', '1.6889231', '-1.9885708', '0.42223078'],
['33', 'Jogging', '49124142288000', '-3.1463003', '0.23154591', '5.597963'],
['33', 'Jogging', '49124252304000', '5.7477865', '15.28203', '1.0351465'],
['33', 'Jogging', '49124352279000', '2.4516625', '19.340895', '5.366417'],
['33', 'Jogging', '49124462295000', '5.706926', '4.2086873', '1.56634'],
['33', 'Jogging', '49124572341000', '-3.5276701', '8.049625', '-0.61291564'],
['33', 'Jogging', '49124682296000', '5.284695', '16.358038', '9.112013'],
['33', 'Jogging', '49124792312000', '0.08172209', '-6.701211', '-7.5456724'],
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['33', 'Walking', '49435942306000', '-2.3426998', '1.920469', '-1.7570249'],
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['33', 'Walking', '49436162307000', '-5.434519', '13.443283', '0.27240697'],
['33', 'Walking', '49436272293000', '-0.5720546', '10.923519', '2.2201166'],
['33', 'Walking', '49436382309000', '0.92618364', '10.460427', '-0.42223078'],
['33', 'Walking', '49436492294000', '2.4516625', '10.691973', '0.27240697'],
['33', 'Walking', '49436602310000', '0.95342433', '15.935807', '1.4573772'],
['33', 'Walking', '49436702316000', '-7.6273947', '15.4046135', '0.84446156'],
['33', 'Walking', '49436812301000', '2.2201166', '3.7864566', '-1.2666923'],
['33', 'Walking', '49436922317000', '-2.5606253', '3.6774938', '0.95342433'],
['33', 'Walking', '49437032303000', '-0.38136974', '7.205164', '8.117727'],
['33', 'Walking', '49437142318000', '0.7627395', '3.0237172', '-4.630918'],
['33', 'Walking', '49437252304000', '-1.7978859', '5.9793324', '-0.9942854'],
['33', 'Walking', '49437362289000', '-1.7978859', '8.430995', '-0.10896278'],
['33', 'Walking', '49437472305000', '4.6036777', '14.137921', '0.46309182'],
...]
columns = ['user', 'activity', 'time', 'x', 'y', 'z']data = pd.DataFrame(data = processedList, columns = columns)
data.head()
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| user | activity | time | x | y | z | |
|---|---|---|---|---|---|---|
| 0 | 33 | Jogging | 49105962326000 | -0.6946377 | 12.680544 | 0.50395286 |
| 1 | 33 | Jogging | 49106062271000 | 5.012288 | 11.264028 | 0.95342433 |
| 2 | 33 | Jogging | 49106112167000 | 4.903325 | 10.882658 | -0.08172209 |
| 3 | 33 | Jogging | 49106222305000 | -0.61291564 | 18.496431 | 3.0237172 |
| 4 | 33 | Jogging | 49106332290000 | -1.1849703 | 12.108489 | 7.205164 |
data.shape(343416, 6)
data.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 343416 entries, 0 to 343415
Data columns (total 6 columns):
user 343416 non-null object
activity 343416 non-null object
time 343416 non-null object
x 343416 non-null object
y 343416 non-null object
z 343416 non-null object
dtypes: object(6)
memory usage: 15.7+ MB
data.isnull().sum()user 0
activity 0
time 0
x 0
y 0
z 0
dtype: int64
data['activity'].value_counts()Walking 137375
Jogging 129392
Upstairs 35137
Downstairs 33358
Sitting 4599
Standing 3555
Name: activity, dtype: int64
data['x'] = data['x'].astype('float')
data['y'] = data['y'].astype('float')
data['z'] = data['z'].astype('float')data.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 343416 entries, 0 to 343415
Data columns (total 6 columns):
user 343416 non-null object
activity 343416 non-null object
time 343416 non-null object
x 343416 non-null float64
y 343416 non-null float64
z 343416 non-null float64
dtypes: float64(3), object(3)
memory usage: 15.7+ MB
Fs = 20activities = data['activity'].value_counts().indexactivitiesIndex(['Walking', 'Jogging', 'Upstairs', 'Downstairs', 'Sitting', 'Standing'], dtype='object')
def plot_activity(activity, data):
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(15, 7), sharex=True)
plot_axis(ax0, data['time'], data['x'], 'X-Axis')
plot_axis(ax1, data['time'], data['y'], 'Y-Axis')
plot_axis(ax2, data['time'], data['z'], 'Z-Axis')
plt.subplots_adjust(hspace=0.2)
fig.suptitle(activity)
plt.subplots_adjust(top=0.90)
plt.show()
def plot_axis(ax, x, y, title):
ax.plot(x, y, 'g')
ax.set_title(title)
ax.xaxis.set_visible(False)
ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])
ax.set_xlim([min(x), max(x)])
ax.grid(True)
for activity in activities:
data_for_plot = data[(data['activity'] == activity)][:Fs*10]
plot_activity(activity, data_for_plot)
df = data.drop(['user', 'time'], axis = 1).copy()
df.head()
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| activity | x | y | z | |
|---|---|---|---|---|
| 0 | Jogging | -0.694638 | 12.680544 | 0.503953 |
| 1 | Jogging | 5.012288 | 11.264028 | 0.953424 |
| 2 | Jogging | 4.903325 | 10.882658 | -0.081722 |
| 3 | Jogging | -0.612916 | 18.496431 | 3.023717 |
| 4 | Jogging | -1.184970 | 12.108489 | 7.205164 |
df['activity'].value_counts()Walking 137375
Jogging 129392
Upstairs 35137
Downstairs 33358
Sitting 4599
Standing 3555
Name: activity, dtype: int64
Walking = df[df['activity']=='Walking'].head(3555).copy()
Jogging = df[df['activity']=='Jogging'].head(3555).copy()
Upstairs = df[df['activity']=='Upstairs'].head(3555).copy()
Downstairs = df[df['activity']=='Downstairs'].head(3555).copy()
Sitting = df[df['activity']=='Sitting'].head(3555).copy()
Standing = df[df['activity']=='Standing'].copy()balanced_data = pd.DataFrame()
balanced_data = balanced_data.append([Walking, Jogging, Upstairs, Downstairs, Sitting, Standing])
balanced_data.shape(21330, 4)
balanced_data['activity'].value_counts()Upstairs 3555
Walking 3555
Jogging 3555
Standing 3555
Sitting 3555
Downstairs 3555
Name: activity, dtype: int64
balanced_data.head()
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| activity | x | y | z | |
|---|---|---|---|---|
| 597 | Walking | 0.844462 | 8.008764 | 2.792171 |
| 598 | Walking | 1.116869 | 8.621680 | 3.786457 |
| 599 | Walking | -0.503953 | 16.657684 | 1.307553 |
| 600 | Walking | 4.794363 | 10.760075 | -1.184970 |
| 601 | Walking | -0.040861 | 9.234595 | -0.694638 |
from sklearn.preprocessing import LabelEncoderlabel = LabelEncoder()
balanced_data['label'] = label.fit_transform(balanced_data['activity'])
balanced_data.head()
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| activity | x | y | z | label | |
|---|---|---|---|---|---|
| 597 | Walking | 0.844462 | 8.008764 | 2.792171 | 5 |
| 598 | Walking | 1.116869 | 8.621680 | 3.786457 | 5 |
| 599 | Walking | -0.503953 | 16.657684 | 1.307553 | 5 |
| 600 | Walking | 4.794363 | 10.760075 | -1.184970 | 5 |
| 601 | Walking | -0.040861 | 9.234595 | -0.694638 | 5 |
label.classes_array(['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs',
'Walking'], dtype=object)
X = balanced_data[['x', 'y', 'z']]
y = balanced_data['label']scaler = StandardScaler()
X = scaler.fit_transform(X)
scaled_X = pd.DataFrame(data = X, columns = ['x', 'y', 'z'])
scaled_X['label'] = y.values
scaled_X
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| x | y | z | label | |
|---|---|---|---|---|
| 0 | 0.000503 | -0.099190 | 0.337933 | 5 |
| 1 | 0.073590 | 0.020386 | 0.633446 | 5 |
| 2 | -0.361275 | 1.588160 | -0.103312 | 5 |
| 3 | 1.060258 | 0.437573 | -0.844119 | 5 |
| 4 | -0.237028 | 0.139962 | -0.698386 | 5 |
| 5 | 0.442677 | 0.041644 | -0.925081 | 5 |
| 6 | -0.083546 | 0.267510 | -0.856263 | 5 |
| 7 | 0.245343 | 0.490718 | -0.686242 | 5 |
| 8 | 0.574233 | 0.708612 | -0.880552 | 5 |
| 9 | -0.441670 | 0.820217 | -0.868408 | 5 |
| 10 | -0.174904 | -0.309113 | -0.334055 | 5 |
| 11 | -0.317423 | -0.428689 | -0.342151 | 5 |
| 12 | -0.101818 | 0.073531 | 1.228521 | 5 |
| 13 | -0.204139 | -0.247996 | 1.260906 | 5 |
| 14 | -0.430707 | -0.763501 | -1.277268 | 5 |
| 15 | -0.657275 | -0.189537 | -0.184275 | 5 |
| 16 | 0.164948 | 0.049616 | -0.629568 | 5 |
| 17 | 0.051664 | 0.296739 | -0.730771 | 5 |
| 18 | 0.544998 | 1.274606 | -0.297622 | 5 |
| 19 | -1.285820 | 0.998252 | -1.176065 | 5 |
| 20 | 0.946974 | -0.205480 | -0.524317 | 5 |
| 21 | -0.708436 | -1.377325 | -0.706483 | 5 |
| 22 | 0.289195 | 1.941574 | 3.082561 | 5 |
| 23 | -1.417375 | 1.752910 | 0.022180 | 5 |
| 24 | 0.164948 | 0.618266 | -0.479788 | 5 |
| 25 | 0.289195 | -0.136392 | -1.006044 | 5 |
| 26 | 0.278232 | 0.169191 | -0.593135 | 5 |
| 27 | 0.515764 | 0.969022 | 0.010035 | 5 |
| 28 | -0.533028 | 1.550959 | -1.629455 | 5 |
| 29 | 0.884851 | -0.481833 | -0.504076 | 5 |
| ... | ... | ... | ... | ... |
| 21300 | -0.617782 | 0.178084 | 0.224586 | 3 |
| 21301 | -0.582903 | 0.168329 | 0.236730 | 3 |
| 21302 | -0.577537 | 0.168329 | 0.236730 | 3 |
| 21303 | -0.582903 | 0.178084 | 0.224586 | 3 |
| 21304 | -0.582903 | 0.178084 | 0.248875 | 3 |
| 21305 | -0.582903 | 0.185888 | 0.236730 | 3 |
| 21306 | -0.577537 | 0.185888 | 0.269115 | 3 |
| 21307 | -0.582903 | 0.185888 | 0.261019 | 3 |
| 21308 | -0.577537 | 0.185888 | 0.261019 | 3 |
| 21309 | -0.582903 | 0.193692 | 0.236730 | 3 |
| 21310 | -0.566805 | 0.185888 | 0.236730 | 3 |
| 21311 | -0.577537 | 0.185888 | 0.248875 | 3 |
| 21312 | -0.582903 | 0.178084 | 0.248875 | 3 |
| 21313 | -0.582903 | 0.185888 | 0.236730 | 3 |
| 21314 | -0.617782 | 0.185888 | 0.236730 | 3 |
| 21315 | -0.596318 | 0.197593 | 0.236730 | 3 |
| 21316 | -0.582903 | 0.178084 | 0.236730 | 3 |
| 21317 | -0.531926 | 0.185888 | 0.248875 | 3 |
| 21318 | -0.556073 | 0.197593 | 0.236730 | 3 |
| 21319 | -0.607050 | 0.168329 | 0.236730 | 3 |
| 21320 | -0.566805 | 0.148820 | 0.269115 | 3 |
| 21321 | -0.658026 | 0.152722 | 0.317693 | 3 |
| 21322 | -0.577537 | 0.178084 | 0.350078 | 3 |
| 21323 | -0.542658 | 0.185888 | 0.317693 | 3 |
| 21324 | -0.470217 | 0.131262 | 0.269115 | 3 |
| 21325 | -0.470217 | 0.178084 | 0.261019 | 3 |
| 21326 | -0.542658 | 0.193692 | 0.248875 | 3 |
| 21327 | -0.628514 | 0.197593 | 0.261019 | 3 |
| 21328 | -0.781444 | 0.049322 | 0.155768 | 3 |
| 21329 | -0.800225 | 0.267827 | 0.475569 | 3 |
21330 rows × 4 columns
import scipy.stats as statsFs = 20
frame_size = Fs*4 # 80
hop_size = Fs*2 # 40def get_frames(df, frame_size, hop_size):
N_FEATURES = 3
frames = []
labels = []
for i in range(0, len(df) - frame_size, hop_size):
x = df['x'].values[i: i + frame_size]
y = df['y'].values[i: i + frame_size]
z = df['z'].values[i: i + frame_size]
# Retrieve the most often used label in this segment
label = stats.mode(df['label'][i: i + frame_size])[0][0]
frames.append([x, y, z])
labels.append(label)
# Bring the segments into a better shape
frames = np.asarray(frames).reshape(-1, frame_size, N_FEATURES)
labels = np.asarray(labels)
return frames, labelsX, y = get_frames(scaled_X, frame_size, hop_size)X.shape, y.shape((532, 80, 3), (532,))
(3555*6)/40533.25
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)X_train.shape, X_test.shape((425, 80, 3), (107, 80, 3))
X_train[0].shape, X_test[0].shape((80, 3), (80, 3))
X_train = X_train.reshape(425, 80, 3, 1)
X_test = X_test.reshape(107, 80, 3, 1)X_train[0].shape, X_test[0].shape((80, 3, 1), (80, 3, 1))
model = Sequential()
model.add(Conv2D(16, (2, 2), activation = 'relu', input_shape = X_train[0].shape))
model.add(Dropout(0.1))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(64, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(6, activation='softmax'))model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])history = model.fit(X_train, y_train, epochs = 10, validation_data= (X_test, y_test), verbose=1)Train on 425 samples, validate on 107 samples
Epoch 1/10
425/425 [==============================] - 1s 2ms/sample - loss: 1.6548 - accuracy: 0.2400 - val_loss: 1.3757 - val_accuracy: 0.4206
Epoch 2/10
425/425 [==============================] - 0s 292us/sample - loss: 1.3048 - accuracy: 0.4871 - val_loss: 1.0143 - val_accuracy: 0.7103
Epoch 3/10
425/425 [==============================] - 0s 294us/sample - loss: 0.9848 - accuracy: 0.6659 - val_loss: 0.7149 - val_accuracy: 0.8598
Epoch 4/10
425/425 [==============================] - 0s 273us/sample - loss: 0.7407 - accuracy: 0.7459 - val_loss: 0.4961 - val_accuracy: 0.8411
Epoch 5/10
425/425 [==============================] - 0s 299us/sample - loss: 0.5676 - accuracy: 0.8188 - val_loss: 0.3573 - val_accuracy: 0.9065
Epoch 6/10
425/425 [==============================] - 0s 296us/sample - loss: 0.4372 - accuracy: 0.8494 - val_loss: 0.2836 - val_accuracy: 0.9159
Epoch 7/10
425/425 [==============================] - 0s 301us/sample - loss: 0.3648 - accuracy: 0.8871 - val_loss: 0.2614 - val_accuracy: 0.9065
Epoch 8/10
425/425 [==============================] - 0s 315us/sample - loss: 0.3070 - accuracy: 0.9035 - val_loss: 0.3019 - val_accuracy: 0.8598
Epoch 9/10
425/425 [==============================] - 0s 287us/sample - loss: 0.3254 - accuracy: 0.8918 - val_loss: 0.2392 - val_accuracy: 0.9065
Epoch 10/10
425/425 [==============================] - 0s 303us/sample - loss: 0.2385 - accuracy: 0.9388 - val_loss: 0.2269 - val_accuracy: 0.8972
def plot_learningCurve(history, epochs):
# Plot training & validation accuracy values
epoch_range = range(1, epochs+1)
plt.plot(epoch_range, history.history['accuracy'])
plt.plot(epoch_range, history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(epoch_range, history.history['loss'])
plt.plot(epoch_range, history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()plot_learningCurve(history, 10)
Detailed video is available here: https://youtu.be/SToqP9V9y7Q
pip install mlxtend -> http://rasbt.github.io/mlxtend/installation/
from mlxtend.plotting import plot_confusion_matrix
from sklearn.metrics import confusion_matrixy_pred = model.predict_classes(X_test)mat = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(conf_mat=mat, class_names=label.classes_, show_normed=True, figsize=(7,7))(<Figure size 504x504 with 1 Axes>,
<matplotlib.axes._subplots.AxesSubplot at 0x250748656a0>)
model.save_weights('model.h5')