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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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import zipfile |
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import keras |
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from keras.utils.np_utils import to_categorical |
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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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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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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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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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ylist = [y for X, y in xysplit] |
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1 |
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return Xlist, ylist |
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1 |
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def sliding_window(frame_length, step, Xsamples,\ |
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ysamples, ysampleslist, Xsampleslist): |
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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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""" |
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1 |
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for j in range(len(Xsampleslist)): |
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X = Xsampleslist[j] |
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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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1 |
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xsub = X[i:i+frame_length, :] |
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1 |
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ysub = ybinary |
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1 |
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Xsamples.append(xsub) |
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1 |
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ysamples.append(ysub) |
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1 |
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def transform_y(y, mapclasses, nr_classes): |
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""" |
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Transforms y, a list with one sequence of A timesteps |
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and B unique classes into a binary Numpy matrix of |
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shape (A, B) |
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""" |
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1 |
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ymapped = np.array([mapclasses[c] for c in y], dtype='int') |
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1 |
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ybinary = to_categorical(ymapped, nr_classes) |
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1 |
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return ybinary |
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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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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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1 |
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IMUsensor_columns = ['temperature'] + \ |
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['acc_16g_' + i for i in axes] + \ |
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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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['magnometer_'+ i for i in axes] + \ |
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['orientation_' + str(i) for i in range(4)] |
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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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+ ["chest_"+s for s in IMUsensor_columns]+ ["ankle_"+s \ |
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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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1 |
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datasets[i].columns = header |
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return datasets |
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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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Converts python lists x 3D and y 1D into numpy arrays |
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and stores the numpy array in directory outdatapath |
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shuffle is optional and shuffles the samples |
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""" |
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X = np.array(X) |
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y = np.array(y) |
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#Shuffle around the train set |
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if shuffle is True: |
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np.random.seed(123) |
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neworder = np.random.permutation(X.shape[0]) |
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X = X[neworder, :, :] |
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y = y[neworder, :] |
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# Save binary file |
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np.save(outdatapath+ xname, X) |
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np.save(outdatapath+ yname, y) |
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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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""" |
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Fetch the data and extract the contents of the zip file |
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to the directory_to_extract_to. |
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First check whether this was done before, if yes, then skip |
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""" |
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targetdir = directory_to_extract_to + '/PAMAP2' |
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if os.path.exists(targetdir): |
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print('Data previously downloaded and stored in ' + targetdir) |
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else: |
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os.makedirs(targetdir) # create target directory |
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#download the PAMAP2 data, this is 688 Mb |
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path_to_zip_file = directory_to_extract_to + '/PAMAP2_Dataset.zip' |
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test_file_exist = os.path.isfile(path_to_zip_file) |
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if test_file_exist is False: |
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url = str('https://archive.ics.uci.edu/ml/' + |
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'machine-learning-databases/00231/PAMAP2_Dataset.zip') |
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#retrieve data from url |
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if sys.version_info <= (3,): #python2 |
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local_fn, headers = urllib.urlretrieve(url,\ |
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filename=path_to_zip_file) |
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else: #python3 |
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local_fn, headers = urllib.request.urlretrieve(url,\ |
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filename=path_to_zip_file) |
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print('Download complete and stored in: ' + path_to_zip_file) |
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else: |
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print('The data was previously downloaded and stored in ' + |
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135
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path_to_zip_file) |
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136
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# unzip |
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with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref: |
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138
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zip_ref.extractall(targetdir) |
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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 fetch_and_preprocess(directory_to_extract_to, columns_to_use=None): |
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143
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""" |
|
144
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|
High level function to fetch_and_preprocess the PAMAP2 dataset |
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145
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directory_to_extract_to: the directory where the data will be stored |
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146
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columns_to_use: the columns to use |
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147
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""" |
|
148
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if columns_to_use is None: |
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149
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columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z', |
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150
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'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z', |
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151
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'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z'] |
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152
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targetdir = fetch_data(directory_to_extract_to) |
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153
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outdatapath = targetdir + '/PAMAP2_Dataset' + '/slidingwindow512cleaned/' |
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154
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if not os.path.exists(outdatapath): |
|
155
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os.makedirs(outdatapath) |
|
156
|
|
|
if os.path.isfile(outdatapath+'x_train.npy'): |
|
157
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print('Data previously pre-processed and np-files saved to ' + |
|
158
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outdatapath) |
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159
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else: |
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160
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datadir = targetdir + '/PAMAP2_Dataset/Protocol' |
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161
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filenames = listdir(datadir) |
|
162
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print('Start pre-processing all ' + str(len(filenames)) + ' files...') |
|
163
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|
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# load the files and put them in a list of pandas dataframes: |
|
164
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|
datasets = [pd.read_csv(datadir+'/'+fn, header=None, sep=' ') \ |
|
165
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for fn in filenames] |
|
166
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datasets = addheader(datasets) # add headers to the datasets |
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167
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|
#Interpolate dataset to get same sample rate between channels |
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168
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datasets_filled = [d.interpolate() for d in datasets] |
|
169
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# Create mapping for class labels |
|
170
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ysetall = [set(np.array(data.activityID)) - set([0]) \ |
|
171
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for data in datasets_filled] |
|
172
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|
|
classlabels = list(set.union(*[set(y) for y in ysetall])) |
|
173
|
|
|
nr_classes = len(classlabels) |
|
174
|
|
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mapclasses = {classlabels[i] : i for i in range(len(classlabels))} |
|
175
|
|
|
#Create input (x) and output (y) sets |
|
176
|
|
|
xall = [np.array(data[columns_to_use]) for data in datasets_filled] |
|
177
|
|
|
yall = [np.array(data.activityID) for data in datasets_filled] |
|
178
|
|
|
|
|
179
|
|
|
xylists = [split_activities(y, x) for x, y in zip(xall, yall)] |
|
180
|
|
|
Xlists, ylists = zip(*xylists) |
|
|
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|
|
|
181
|
|
|
ybinarylists = [transform_y(y, mapclasses, nr_classes) for y in ylists] |
|
182
|
|
|
# Split in train, test and val |
|
183
|
|
|
train_range = slice(0, 6) |
|
184
|
|
|
val_range = 6 |
|
185
|
|
|
test_range = slice(7, len(datasets_filled)) |
|
186
|
|
|
x_trainlist = [X for Xlist in Xlists[train_range] for X in Xlist] |
|
187
|
|
|
x_vallist = [X for X in Xlists[val_range]] |
|
188
|
|
|
x_testlist = [X for Xlist in Xlists[test_range] for X in Xlist] |
|
189
|
|
|
y_trainlist = [y for ylist in ybinarylists[train_range] for y in ylist] |
|
190
|
|
|
y_vallist = [y for y in ybinarylists[val_range]] |
|
191
|
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|
y_testlist = [y for ylist in ybinarylists[test_range] for y in ylist] |
|
192
|
|
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|
|
193
|
|
|
# Take sliding-window frames. Target is label of last time step |
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194
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|
# Data is 100 Hz |
|
195
|
|
|
frame_length = int(5.12 * 100) |
|
196
|
|
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step = 1 * 100 |
|
197
|
|
|
x_train = [] |
|
198
|
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|
y_train = [] |
|
199
|
|
|
x_val = [] |
|
200
|
|
|
y_val = [] |
|
201
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|
x_test = [] |
|
202
|
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|
y_test = [] |
|
203
|
|
|
sliding_window(frame_length, step, x_train, y_train, y_trainlist, \ |
|
204
|
|
|
x_trainlist) |
|
205
|
|
|
sliding_window(frame_length, step, x_val, y_val, y_vallist, x_vallist) |
|
206
|
|
|
sliding_window(frame_length, step, x_test, y_test, \ |
|
207
|
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y_testlist, x_testlist) |
|
208
|
|
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numpify_and_store(x_train, y_train, 'X_train', 'y_train', \ |
|
209
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outdatapath, shuffle=True) |
|
210
|
|
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numpify_and_store(x_val, y_val, 'X_val', 'y_val', outdatapath, \ |
|
211
|
|
|
shuffle=False) |
|
212
|
|
|
numpify_and_store(x_test, y_test, 'X_test', 'y_test', outdatapath, \ |
|
213
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shuffle=False) |
|
214
|
|
|
print('Processed data succesfully stored in ' + outdatapath) |
|
215
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|
|
return outdatapath |
|
216
|
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|
217
|
1 |
|
def load_data(outputpath): |
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218
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|
ext = '.npy' |
|
219
|
|
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x_train = np.load(outputpath+'X_train'+ext) |
|
220
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y_train_binary = np.load(outputpath+'y_train'+ext) |
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221
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|
x_val = np.load(outputpath+'X_val'+ext) |
|
222
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|
|
y_val_binary = np.load(outputpath+'y_val'+ext) |
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223
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|
x_test = np.load(outputpath+'X_test'+ext) |
|
224
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|
y_test_binary = np.load(outputpath+'y_test'+ext) |
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225
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|
return x_train, y_train_binary, x_val, y_val_binary, x_test, y_test_binary |
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226
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NLeSC /
mcfly
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