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""" |
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Summary: |
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Function fetch_and_preprocess from tutorial_pamap2.py helps to fetch and |
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preproces the data. |
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Example function calls in 'Tutorial mcfly on PAMAP2.ipynb' |
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""" |
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import numpy as np |
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from numpy import genfromtxt |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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from os import listdir |
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import os.path |
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1 |
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import zipfile |
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1 |
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import keras |
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from keras.utils.np_utils import to_categorical |
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1 |
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import sys |
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1 |
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if sys.version_info <= (3,): #python2 |
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import urllib |
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else: #python3 |
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1 |
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import urllib.request |
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1 |
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def split_activities(labels, X, borders=10*100): |
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""" |
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Splits up the data per activity and exclude activity=0. |
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Also remove borders for each activity. |
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Returns lists with subdatasets |
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""" |
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1 |
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tot_len = len(labels) |
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1 |
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startpoints = np.where([1] + [labels[i] != labels[i-1] \ |
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for i in range(1, tot_len)])[0] |
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1 |
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endpoints = np.append(startpoints[1:]-1, tot_len-1) |
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1 |
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acts = [labels[s] for s, e in zip(startpoints, endpoints)] |
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#Also split up the data, and only keep the non-zero activities |
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1 |
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xysplit = [(X[s+borders:e-borders+1, :], a) \ |
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for s, e, a in zip(startpoints, endpoints, acts) if a != 0] |
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1 |
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xysplit = [(X, y) for X, y in xysplit if len(X) > 0] |
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1 |
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Xlist = [X for X, y in xysplit] |
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38
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1 |
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ylist = [y for X, y in xysplit] |
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39
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1 |
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return Xlist, ylist |
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41
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1 |
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def sliding_window(frame_length, step, Xsamples,\ |
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42
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ysamples, Xsampleslist, ysampleslist): |
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""" |
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Splits time series in ysampleslist and Xsampleslist |
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into segments by applying a sliding overlapping window |
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of size equal to frame_length with steps equal to step |
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it does this for all the samples and appends all the output together. |
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So, the participant distinction is not kept |
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49
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""" |
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50
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1 |
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for j in range(len(Xsampleslist)): |
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1 |
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X = Xsampleslist[j] |
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52
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1 |
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ybinary = ysampleslist[j] |
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1 |
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for i in range(0, X.shape[0]-frame_length, step): |
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54
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xsub = X[i:i+frame_length, :] |
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55
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ysub = ybinary |
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56
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Xsamples.append(xsub) |
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57
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ysamples.append(ysub) |
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58
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59
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1 |
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def transform_y(y, mapclasses, nr_classes): |
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60
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""" |
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61
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Transforms y, a list with one sequence of A timesteps |
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62
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and B unique classes into a binary Numpy matrix of |
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63
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shape (A, B) |
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64
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""" |
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65
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1 |
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ymapped = np.array([mapclasses[c] for c in y], dtype='int') |
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66
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1 |
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ybinary = to_categorical(ymapped, nr_classes) |
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67
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1 |
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return ybinary |
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68
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69
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1 |
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def addheader(datasets): |
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""" |
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The columns of the pandas data frame are numbers |
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72
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this function adds the column labels |
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""" |
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1 |
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axes = ['x', 'y', 'z'] |
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75
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1 |
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IMUsensor_columns = ['temperature'] + \ |
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76
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['acc_16g_' + i for i in axes] + \ |
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77
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['acc_6g_' + i for i in axes] + \ |
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['gyroscope_'+ i for i in axes] + \ |
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79
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['magnometer_'+ i for i in axes] + \ |
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80
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['orientation_' + str(i) for i in range(4)] |
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81
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1 |
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header = ["timestamp", "activityID", "heartrate"] + ["hand_"+s \ |
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for s in IMUsensor_columns] \ |
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83
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+ ["chest_"+s for s in IMUsensor_columns]+ ["ankle_"+s \ |
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84
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for s in IMUsensor_columns] |
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1 |
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for i in range(0, len(datasets)): |
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86
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1 |
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datasets[i].columns = header |
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87
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1 |
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return datasets |
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89
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1 |
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def numpify_and_store(X, y, xname, yname, outdatapath, shuffle=False): |
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90
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""" |
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91
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Converts python lists x 3D and y 1D into numpy arrays |
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92
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and stores the numpy array in directory outdatapath |
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93
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shuffle is optional and shuffles the samples |
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94
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""" |
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95
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1 |
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X = np.array(X) |
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96
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1 |
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y = np.array(y) |
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97
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#Shuffle around the train set |
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98
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1 |
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if shuffle is True: |
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99
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1 |
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np.random.seed(123) |
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100
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1 |
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neworder = np.random.permutation(X.shape[0]) |
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101
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1 |
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X = X[neworder, :, :] |
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102
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1 |
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y = y[neworder, :] |
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103
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# Save binary file |
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104
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1 |
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np.save(outdatapath+ xname, X) |
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105
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1 |
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np.save(outdatapath+ yname, y) |
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106
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107
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108
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1 |
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def fetch_data(directory_to_extract_to): |
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109
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""" |
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110
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Fetch the data and extract the contents of the zip file |
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111
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to the directory_to_extract_to. |
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112
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First check whether this was done before, if yes, then skip |
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113
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""" |
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114
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targetdir = directory_to_extract_to + '/PAMAP2' |
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115
|
|
|
if os.path.exists(targetdir): |
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116
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print('Data previously downloaded and stored in ' + targetdir) |
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117
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else: |
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118
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os.makedirs(targetdir) # create target directory |
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119
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#download the PAMAP2 data, this is 688 Mb |
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120
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path_to_zip_file = directory_to_extract_to + '/PAMAP2_Dataset.zip' |
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121
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test_file_exist = os.path.isfile(path_to_zip_file) |
|
122
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|
|
if test_file_exist is False: |
|
123
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|
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url = str('https://archive.ics.uci.edu/ml/' + |
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124
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'machine-learning-databases/00231/PAMAP2_Dataset.zip') |
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125
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#retrieve data from url |
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126
|
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|
if sys.version_info <= (3,): #python2 |
|
127
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|
local_fn, headers = urllib.urlretrieve(url,\ |
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128
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filename=path_to_zip_file) |
|
129
|
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|
else: #python3 |
|
130
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|
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local_fn, headers = urllib.request.urlretrieve(url,\ |
|
131
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filename=path_to_zip_file) |
|
132
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|
print('Download complete and stored in: ' + path_to_zip_file) |
|
133
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else: |
|
134
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print('The data was previously downloaded and stored in ' + |
|
135
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path_to_zip_file) |
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136
|
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|
# unzip |
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137
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with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref: |
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|
|
138
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|
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zip_ref.extractall(targetdir) |
|
139
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return targetdir |
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140
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|
141
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142
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1 |
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def slidingwindow_store(y_list, x_list,X_name, y_name, outdatapath, shuffle): |
|
|
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|
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|
143
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# Take sliding-window frames. Target is label of last time step |
|
144
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# Data is 100 Hz |
|
145
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|
frame_length = int(5.12 * 100) |
|
146
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|
|
step = 1 * 100 |
|
147
|
|
|
x_set = [] |
|
148
|
|
|
y_set = [] |
|
149
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|
|
sliding_window(frame_length, step, x_set, y_set, x_list, y_list) |
|
150
|
|
|
numpify_and_store(x_set, y_set, X_name, y_name, \ |
|
151
|
|
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outdatapath, shuffle) |
|
152
|
|
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|
|
153
|
1 |
|
def map_clas(datasets_filled): |
|
154
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|
|
ysetall = [set(np.array(data.activityID)) - set([0]) \ |
|
155
|
|
|
for data in datasets_filled] |
|
156
|
|
|
classlabels = list(set.union(*[set(y) for y in ysetall])) |
|
157
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|
|
nr_classes = len(classlabels) |
|
158
|
|
|
mapclasses = {classlabels[i] : i for i in range(len(classlabels))} |
|
159
|
|
|
return classlabels, nr_classes, mapclasses |
|
160
|
|
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|
|
161
|
1 |
|
def split_data(Xlist,ylist,indices): |
|
|
|
|
|
|
162
|
|
|
""" Function takes subset from list given indices""" |
|
163
|
|
|
if str(type(indices)) == "<class 'slice'>": |
|
164
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|
|
x_setlist = [X for Xlist in Xlists[indices] for X in Xlist] |
|
165
|
|
|
y_setlist = [y for ylist in ybinarylists[indices] for y in ylist] |
|
166
|
|
|
else: |
|
167
|
|
|
x_setlist = [X for X in Xlists[indices]] |
|
168
|
|
|
y_setlist = [y for y in ybinarylists[indices]] |
|
169
|
|
|
return x_setlist, ysetlist |
|
170
|
|
|
|
|
171
|
1 |
|
def fetch_and_preprocess(directory_to_extract_to, columns_to_use=None): |
|
172
|
|
|
""" |
|
173
|
|
|
High level function to fetch_and_preprocess the PAMAP2 dataset |
|
174
|
|
|
directory_to_extract_to: the directory where the data will be stored |
|
175
|
|
|
columns_to_use: the columns to use |
|
176
|
|
|
""" |
|
177
|
|
|
if columns_to_use is None: |
|
178
|
|
|
columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z', |
|
179
|
|
|
'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z', |
|
180
|
|
|
'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z'] |
|
181
|
|
|
targetdir = fetch_data(directory_to_extract_to) |
|
182
|
|
|
outdatapath = targetdir + '/PAMAP2_Dataset/slidingwindow512cleaned/' |
|
183
|
|
|
if not os.path.exists(outdatapath): |
|
184
|
|
|
os.makedirs(outdatapath) |
|
185
|
|
|
if os.path.isfile(outdatapath+'x_train.npy'): |
|
186
|
|
|
print('Data previously pre-processed and np-files saved to ' + |
|
187
|
|
|
outdatapath) |
|
188
|
|
|
else: |
|
189
|
|
|
datadir = targetdir + '/PAMAP2_Dataset/Protocol' |
|
190
|
|
|
filenames = listdir(datadir) |
|
191
|
|
|
print('Start pre-processing all ' + str(len(filenames)) + ' files...') |
|
192
|
|
|
# load the files and put them in a list of pandas dataframes: |
|
193
|
|
|
datasets = [pd.read_csv(datadir+'/'+fn, header=None, sep=' ') \ |
|
194
|
|
|
for fn in filenames] |
|
195
|
|
|
datasets = addheader(datasets) # add headers to the datasets |
|
196
|
|
|
#Interpolate dataset to get same sample rate between channels |
|
197
|
|
|
datasets_filled = [d.interpolate() for d in datasets] |
|
198
|
|
|
# Create mapping for class labels |
|
199
|
|
|
classlabels, nr_classes, mapclasses = map_clas(datasets_filled) |
|
200
|
|
|
#Create input (x) and output (y) sets |
|
201
|
|
|
xall = [np.array(data[columns_to_use]) for data in datasets_filled] |
|
202
|
|
|
yall = [np.array(data.activityID) for data in datasets_filled] |
|
203
|
|
|
xylists = [split_activities(y, x) for x, y in zip(xall, yall)] |
|
204
|
|
|
Xlists, ylists = zip(*xylists) |
|
|
|
|
|
|
205
|
|
|
ybinarylists = [transform_y(y, mapclasses, nr_classes) for y in ylists] |
|
206
|
|
|
# Split in train, test and val |
|
207
|
|
|
x_trainlist, y_trainlist = split_data(Xlist,ylist,slice(0, 6)) |
|
|
|
|
|
|
208
|
|
|
x_vallist, y_vallist = split_data(Xlist,ylist,indices=6) |
|
|
|
|
|
|
209
|
|
|
test_range = slice(7, len(datasets_filled)) |
|
210
|
|
|
x_testlist, y_testlist = split_data(Xlist,ylist,test_range) |
|
|
|
|
|
|
211
|
|
|
# Take sliding-window frames, target is label of last time step, |
|
212
|
|
|
# and store as numpy file |
|
213
|
|
|
slidingwindow_store(y_list=y_trainlist, x_list=x_trainlist, \ |
|
214
|
|
|
X_name='X_train', y_name='y_train', \ |
|
215
|
|
|
outdatapath=outdatapath,shuffle=True) |
|
|
|
|
|
|
216
|
|
|
slidingwindow_store(y_list=y_vallist, x_list=x_vallist, \ |
|
217
|
|
|
X_name='X_val', y_name='y_val', \ |
|
218
|
|
|
outdatapath=outdatapath,shuffle=False) |
|
|
|
|
|
|
219
|
|
|
slidingwindow_store(y_list=y_testlist, x_list=x_testlist, \ |
|
220
|
|
|
X_name='X_test', y_name='y_test', \ |
|
221
|
|
|
outdatapath=outdatapath,shuffle=False) |
|
|
|
|
|
|
222
|
|
|
print('Processed data succesfully stored in ' + outdatapath) |
|
223
|
|
|
return outdatapath |
|
224
|
|
|
|
|
225
|
1 |
|
def load_data(outputpath): |
|
226
|
|
|
ext = '.npy' |
|
227
|
|
|
x_train = np.load(outputpath+'X_train'+ext) |
|
228
|
|
|
y_train_binary = np.load(outputpath+'y_train'+ext) |
|
229
|
|
|
x_val = np.load(outputpath+'X_val'+ext) |
|
230
|
|
|
y_val_binary = np.load(outputpath+'y_val'+ext) |
|
231
|
|
|
x_test = np.load(outputpath+'X_test'+ext) |
|
232
|
|
|
y_test_binary = np.load(outputpath+'y_test'+ext) |
|
233
|
|
|
return x_train, y_train_binary, x_val, y_val_binary, x_test, y_test_binary |
|
234
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|
NLeSC /
mcfly
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