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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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import six.moves.urllib as urllib |
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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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Parameters |
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---------- |
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labels : numpy array |
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Activity labels |
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X : numpy array |
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Data points |
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borders : int |
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Nr of timesteps to remove from the borders of an activity |
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Returns |
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------- |
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X_list, y_list |
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""" |
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tot_len = len(labels) |
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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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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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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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Xlist = [X for X, y in xysplit] |
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ylist = [y for X, y in xysplit] |
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return Xlist, ylist |
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def sliding_window(frame_length, step, Xsamples, |
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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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Parameters |
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---------- |
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frame_length : int |
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Length of sliding window |
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step : int |
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Stepsize between windows |
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Xsamples : list |
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Existing list of window fragments |
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ysamples : list |
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Existing list of window fragments |
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Xsampleslist : list |
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Samples to take sliding windows from |
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ysampleslist |
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Samples to take sliding windows from |
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""" |
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for j in range(len(Xsampleslist)): |
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X = Xsampleslist[j] |
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ybinary = ysampleslist[j] |
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for i in range(0, X.shape[0] - frame_length, step): |
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xsub = X[i:i + frame_length, :] |
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ysub = ybinary |
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Xsamples.append(xsub) |
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ysamples.append(ysub) |
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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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Parameters |
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---------- |
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y : list or array |
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List of classes |
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mapclasses : dict |
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dictionary that maps the classes to numbers |
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nr_classes : int |
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total number of classes |
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""" |
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ymapped = np.array([mapclasses[c] for c in y], dtype='int') |
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ybinary = to_categorical(ymapped, nr_classes) |
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return ybinary |
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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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Parameters |
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---------- |
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datasets : list |
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List of pandas dataframes |
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""" |
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axes = ['x', 'y', 'z'] |
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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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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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for i in range(0, len(datasets)): |
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datasets[i].columns = header |
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return datasets |
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def numpify_and_store(X, y, xname, yname, outdatapath, shuffle=False): |
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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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Parameters |
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---------- |
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X : list |
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list with data |
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y : list |
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list with data |
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xname : str |
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name to store the x arrays |
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yname : str |
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name to store the y arrays |
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outdatapath : str |
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path to the directory to store the data |
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shuffle : bool |
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whether to shuffle the data before storing |
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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 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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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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Parameters |
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---------- |
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directory_to_extract_to : str |
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directory to create subfolder 'PAMAP2' |
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Returns |
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------- |
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targetdir: str |
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directory where the data is extracted |
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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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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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path_to_zip_file) |
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# unzip |
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with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref: |
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zip_ref.extractall(targetdir) |
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return targetdir |
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207
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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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209
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""" |
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210
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Take sliding-window frames. Target is label of last time step |
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Data is 100 Hz |
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212
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213
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Parameters |
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214
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---------- |
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215
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y_list : list |
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216
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list of arrays with classes |
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217
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x_list : list |
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218
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list of numpy arrays with data |
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X_name : str |
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Name for X file |
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y_name : str |
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Name for y file |
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outdatapath : str |
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directory to store the data |
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shuffle : bool |
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226
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whether to shuffle the data |
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227
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""" |
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228
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frame_length = int(5.12 * 100) |
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229
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step = 1 * 100 |
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230
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x_set = [] |
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231
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y_set = [] |
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232
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sliding_window(frame_length, step, x_set, y_set, x_list, y_list) |
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numpify_and_store(x_set, y_set, X_name, y_name, |
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outdatapath, shuffle) |
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235
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236
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237
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1 |
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def map_class(datasets_filled): |
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ysetall = [set(np.array(data.activityID)) - set([0]) |
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239
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for data in datasets_filled] |
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240
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classlabels = list(set.union(*[set(y) for y in ysetall])) |
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241
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nr_classes = len(classlabels) |
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242
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mapclasses = {classlabels[i]: i for i in range(len(classlabels))} |
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243
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return classlabels, nr_classes, mapclasses |
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244
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245
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246
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1 |
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def split_data(Xlists, ybinarylists, indices): |
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247
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""" Function takes subset from list given indices |
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248
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249
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Parameters |
|
250
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---------- |
|
251
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Xlists: tuple |
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252
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tuple (samples) of lists (windows) of numpy-arrays (time, variable) |
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253
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ybinarylist : |
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254
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list (samples) of numpy-arrays (window, class) |
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255
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indices : |
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256
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indices of the slice of data (samples) to be taken |
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257
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258
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Returns |
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259
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------- |
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260
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x_setlist : |
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261
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list (windows across samples) of numpy-arrays (time, variable) |
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262
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y_setlist: |
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263
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list (windows across samples) of numpy-arrays (class, ) |
|
264
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""" |
|
265
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1 |
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tty = str(type(indices)) |
|
266
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# or statement in next line is to account for python2 and python3 |
|
267
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# difference |
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268
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1 |
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if tty == "<class 'slice'>" or tty == "<type 'slice'>": |
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269
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1 |
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x_setlist = [X for Xlist in Xlists[indices] for X in Xlist] |
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270
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1 |
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y_setlist = [y for ylist in ybinarylists[indices] for y in ylist] |
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271
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else: |
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272
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x_setlist = [X for X in Xlists[indices]] |
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273
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y_setlist = [y for y in ybinarylists[indices]] |
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274
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1 |
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return x_setlist, y_setlist |
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275
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276
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|
277
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1 |
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def preprocess(targetdir, outdatapath, columns_to_use): |
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278
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""" Function to preprocess the PAMAP2 data after it is fetched |
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279
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|
280
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Parameters |
|
281
|
|
|
---------- |
|
282
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|
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targetdir : str |
|
283
|
|
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subdirectory of directory_to_extract_to, targetdir |
|
284
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|
|
is defined by function fetch_data |
|
285
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|
|
outdatapath : str |
|
286
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a subdirectory of directory_to_extract_to, outdatapath |
|
287
|
|
|
is the direcotry where the Numpy output will be stored. |
|
288
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columns_to_use : list |
|
289
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|
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list of column names to use |
|
290
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|
291
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Returns |
|
292
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|
|
------- |
|
293
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|
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None |
|
294
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|
|
""" |
|
295
|
|
|
datadir = targetdir + '/PAMAP2_Dataset/Protocol' |
|
296
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|
|
filenames = listdir(datadir) |
|
297
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print('Start pre-processing all ' + str(len(filenames)) + ' files...') |
|
298
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# load the files and put them in a list of pandas dataframes: |
|
299
|
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datasets = [pd.read_csv(datadir + '/' + fn, header=None, sep=' ') |
|
300
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|
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for fn in filenames] |
|
301
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|
datasets = addheader(datasets) # add headers to the datasets |
|
302
|
|
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# Interpolate dataset to get same sample rate between channels |
|
303
|
|
|
datasets_filled = [d.interpolate() for d in datasets] |
|
304
|
|
|
# Create mapping for class labels |
|
305
|
|
|
classlabels, nr_classes, mapclasses = map_class(datasets_filled) |
|
306
|
|
|
# Create input (x) and output (y) sets |
|
307
|
|
|
xall = [np.array(data[columns_to_use]) for data in datasets_filled] |
|
308
|
|
|
yall = [np.array(data.activityID) for data in datasets_filled] |
|
309
|
|
|
xylists = [split_activities(y, x) for x, y in zip(xall, yall)] |
|
310
|
|
|
Xlists, ylists = zip(*xylists) |
|
|
|
|
|
|
311
|
|
|
ybinarylists = [transform_y(y, mapclasses, nr_classes) for y in ylists] |
|
312
|
|
|
# Split in train, test and val |
|
313
|
|
|
x_vallist, y_vallist = split_data(Xlists, ybinarylists, indices=6) |
|
314
|
|
|
test_range = slice(7, len(datasets_filled)) |
|
315
|
|
|
x_testlist, y_testlist = split_data(Xlists, ybinarylists, test_range) |
|
316
|
|
|
x_trainlist, y_trainlist = split_data(Xlists, ybinarylists, |
|
317
|
|
|
indices=slice(0, 6)) |
|
318
|
|
|
# Take sliding-window frames, target is label of last time step, |
|
319
|
|
|
# and store as numpy file |
|
320
|
|
|
slidingwindow_store(y_list=y_trainlist, x_list=x_trainlist, |
|
321
|
|
|
X_name='X_train', y_name='y_train', |
|
322
|
|
|
outdatapath=outdatapath, shuffle=True) |
|
323
|
|
|
slidingwindow_store(y_list=y_vallist, x_list=x_vallist, |
|
324
|
|
|
X_name='X_val', y_name='y_val', |
|
325
|
|
|
outdatapath=outdatapath, shuffle=False) |
|
326
|
|
|
slidingwindow_store(y_list=y_testlist, x_list=x_testlist, |
|
327
|
|
|
X_name='X_test', y_name='y_test', |
|
328
|
|
|
outdatapath=outdatapath, shuffle=False) |
|
329
|
|
|
print('Processed data succesfully stored in ' + outdatapath) |
|
330
|
|
|
return None |
|
331
|
|
|
|
|
332
|
|
|
|
|
333
|
1 |
|
def fetch_and_preprocess(directory_to_extract_to, columns_to_use=None): |
|
334
|
|
|
""" |
|
335
|
|
|
High level function to fetch_and_preprocess the PAMAP2 dataset |
|
336
|
|
|
|
|
337
|
|
|
Parameters |
|
338
|
|
|
---------- |
|
339
|
|
|
directory_to_extract_to : str |
|
340
|
|
|
the directory where the data will be stored |
|
341
|
|
|
columns_to_use : list |
|
342
|
|
|
the columns to use |
|
343
|
|
|
|
|
344
|
|
|
Returns |
|
345
|
|
|
------- |
|
346
|
|
|
outdatapath: The directory in which the numpy files are stored |
|
347
|
|
|
""" |
|
348
|
|
|
if columns_to_use is None: |
|
349
|
|
|
columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z', |
|
350
|
|
|
'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z', |
|
|
|
|
|
|
351
|
|
|
'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z'] |
|
|
|
|
|
|
352
|
|
|
targetdir = fetch_data(directory_to_extract_to) |
|
353
|
|
|
outdatapath = targetdir + '/PAMAP2_Dataset/slidingwindow512cleaned/' |
|
354
|
|
|
if not os.path.exists(outdatapath): |
|
355
|
|
|
os.makedirs(outdatapath) |
|
356
|
|
|
if os.path.isfile(outdatapath + 'x_train.npy'): |
|
357
|
|
|
print('Data previously pre-processed and np-files saved to ' + |
|
358
|
|
|
outdatapath) |
|
359
|
|
|
else: |
|
360
|
|
|
preprocess(targetdir, outdatapath, columns_to_use) |
|
361
|
|
|
return outdatapath |
|
362
|
|
|
|
|
363
|
|
|
|
|
364
|
1 |
|
def load_data(outputpath): |
|
365
|
|
|
""" Function to load the numpy data as stored in directory |
|
366
|
|
|
outputpath. |
|
367
|
|
|
|
|
368
|
|
|
Parameters |
|
369
|
|
|
---------- |
|
370
|
|
|
outputpath : str |
|
371
|
|
|
directory where the numpy files are stored |
|
372
|
|
|
""" |
|
373
|
|
|
ext = '.npy' |
|
374
|
|
|
x_train = np.load(outputpath + 'X_train' + ext) |
|
375
|
|
|
y_train_binary = np.load(outputpath + 'y_train' + ext) |
|
376
|
|
|
x_val = np.load(outputpath + 'X_val' + ext) |
|
377
|
|
|
y_val_binary = np.load(outputpath + 'y_val' + ext) |
|
378
|
|
|
x_test = np.load(outputpath + 'X_test' + ext) |
|
379
|
|
|
y_test_binary = np.load(outputpath + 'y_test' + ext) |
|
380
|
|
|
return x_train, y_train_binary, x_val, y_val_binary, x_test, y_test_binary |
|
381
|
|
|
|
NLeSC /
mcfly
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