fetch_and_preprocess() - Code Metrics - Inspection of "minor fix in requirements files" - NLeSC/mcfly - Measure and Improve Code Quality continuously with Scrutinizer

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created 2016-09-07 11:42 UTC

fetch_and_preprocess() F

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Tests	1
CRAP Score	520.2811

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Changes	6
Bugs	0	Features	0

Metric	Value
cc	23
c	6
b	0
f	0
dl	0
loc	74
ccs	1
cts	49
cp	0.0204
crap	520.2811
rs	2.4584

How to fix Long Method Complexity

"""
 Summary:
 Function fetch_and_preprocess from tutorial_pamap2.py helps to fetch and
 preproces the data.
 Example function calls in 'Tutorial mcfly on PAMAP2.ipynb'
"""
import numpy as np
from numpy import genfromtxt
import pandas as pd
import matplotlib.pyplot as plt
from os import listdir
import os.path
import zipfile
import keras
from keras.utils.np_utils import to_categorical
import sys
if sys.version_info <= (3,): #python2
    import urllib
else: #python3
    import urllib.request

def split_activities(labels, X, borders=10*100):

    """
    Splits up the data per activity and exclude activity=0.
    Also remove borders for each activity.
    Returns lists with subdatasets
    """
    tot_len = len(labels)
    startpoints = np.where([1] + [labels[i] != labels[i-1] \
        for i in range(1, tot_len)])[0]
    endpoints = np.append(startpoints[1:]-1, tot_len-1)
    acts = [labels[s] for s, e in zip(startpoints, endpoints)]
    #Also split up the data, and only keep the non-zero activities
    xysplit = [(X[s+borders:e-borders+1, :], a) \
        for s, e, a in zip(startpoints, endpoints, acts) if a != 0]
    xysplit = [(X, y) for X, y in xysplit if len(X) > 0]
    Xlist = [X for X, y in xysplit]

    ylist = [y for X, y in xysplit]
    return Xlist, ylist

def sliding_window(frame_length, step, Xsamples,\

    ysamples, ysampleslist, Xsampleslist):
    """
    Splits time series in ysampleslist and Xsampleslist
    into segments by applying a sliding overlapping window
    of size equal to frame_length with steps equal to step
    it does this for all the samples and appends all the output together.
    So, the participant distinction is not kept
    """
    for j in range(len(Xsampleslist)):
        X = Xsampleslist[j]

        ybinary = ysampleslist[j]
        for i in range(0, X.shape[0]-frame_length, step):
            xsub = X[i:i+frame_length, :]
            ysub = ybinary
            Xsamples.append(xsub)
            ysamples.append(ysub)

def transform_y(y, mapclasses, nr_classes):

    """
    Transforms y, a list with one sequence of A timesteps
    and B unique classes into a binary Numpy matrix of
    shape (A, B)
    """
    ymapped = np.array([mapclasses[c] for c in y], dtype='int')
    ybinary = to_categorical(ymapped, nr_classes)
    return ybinary

def addheader(datasets):
    """
    The columns of the pandas data frame are numbers
    this function adds the column labels
    """
    axes = ['x', 'y', 'z']
    IMUsensor_columns = ['temperature'] + \

                    ['acc_16g_' + i for i in axes] + \
                    ['acc_6g_' + i for i in axes] + \
                    ['gyroscope_'+ i for i in axes] + \
                    ['magnometer_'+ i for i in axes] + \
                    ['orientation_' + str(i) for i in range(4)]
    header = ["timestamp", "activityID", "heartrate"] + ["hand_"+s \
        for s in IMUsensor_columns] \
        + ["chest_"+s for s in IMUsensor_columns]+ ["ankle_"+s \
            for s in IMUsensor_columns]
    for i in range(0, len(datasets)):
            datasets[i].columns = header

    return datasets

def numpify_and_store(X, y, xname, yname, outdatapath, shuffle=False):

    """
    Converts python lists x 3D and y 1D into numpy arrays
    and stores the numpy array in directory outdatapath
    shuffle is optional and shuffles the samples
    """
    X = np.array(X)
    y = np.array(y)
    #Shuffle around the train set
    if shuffle is True:
        np.random.seed(123)
        neworder = np.random.permutation(X.shape[0])
        X = X[neworder, :, :]
        y = y[neworder, :]
    # Save binary file
    np.save(outdatapath+ xname, X)
    np.save(outdatapath+ yname, y)


def fetch_data(directory_to_extract_to):
    """
    Fetch the data and extract the contents of the zip file
    to the directory_to_extract_to.
    First check whether this was done before, if yes, then skip
    """
    targetdir = directory_to_extract_to + '/PAMAP2'
    if os.path.exists(targetdir):
        print('Data previously downloaded and stored in ' + targetdir)
    else:
        os.makedirs(targetdir) # create target directory
        #download the PAMAP2 data, this is 688 Mb
        path_to_zip_file = directory_to_extract_to + '/PAMAP2_Dataset.zip'
        test_file_exist = os.path.isfile(path_to_zip_file)
        if test_file_exist is False:
            url = str('https://archive.ics.uci.edu/ml/' +
                'machine-learning-databases/00231/PAMAP2_Dataset.zip')
            #retrieve data from url
            if sys.version_info <= (3,): #python2
                local_fn, headers = urllib.urlretrieve(url,\
                    filename=path_to_zip_file)
            else: #python3
                local_fn, headers = urllib.request.urlretrieve(url,\
                    filename=path_to_zip_file)
            print('Download complete and stored in: ' + path_to_zip_file)
        else:
            print('The data was previously downloaded and stored in ' +
                path_to_zip_file)
        # unzip
        with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref:

            zip_ref.extractall(targetdir)
    return targetdir


def fetch_and_preprocess(directory_to_extract_to, columns_to_use=None):
    """
    High level function to fetch_and_preprocess the PAMAP2 dataset
    directory_to_extract_to: the directory where the data will be stored
    columns_to_use: the columns to use
    """
    if columns_to_use is None:
        columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z',
                     'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z',
                     'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z']
    targetdir = fetch_data(directory_to_extract_to)
    outdatapath = targetdir + '/PAMAP2_Dataset' + '/slidingwindow512cleaned/'
    if not os.path.exists(outdatapath):
        os.makedirs(outdatapath)
    if os.path.isfile(outdatapath+'x_train.npy'):
        print('Data previously pre-processed and np-files saved to ' +
            outdatapath)
    else:
        datadir = targetdir + '/PAMAP2_Dataset/Protocol'
        filenames = listdir(datadir)
        print('Start pre-processing all ' + str(len(filenames)) + ' files...')
        # load the files and put them in a list of pandas dataframes:
        datasets = [pd.read_csv(datadir+'/'+fn, header=None, sep=' ') \
            for fn in filenames]
        datasets = addheader(datasets) # add headers to the datasets
        #Interpolate dataset to get same sample rate between channels
        datasets_filled = [d.interpolate() for d in datasets]
        # Create mapping for class labels
        ysetall = [set(np.array(data.activityID)) - set([0]) \
            for data in datasets_filled]
        classlabels = list(set.union(*[set(y) for y in ysetall]))
        nr_classes = len(classlabels)
        mapclasses = {classlabels[i] : i for i in range(len(classlabels))}
        #Create input (x) and output (y) sets
        xall = [np.array(data[columns_to_use]) for data in datasets_filled]
        yall = [np.array(data.activityID) for data in datasets_filled]

        xylists = [split_activities(y, x) for x, y in zip(xall, yall)]
        Xlists, ylists = zip(*xylists)

        ybinarylists = [transform_y(y, mapclasses, nr_classes) for y in ylists]
        # Split in train, test and val
        train_range = slice(0, 6)
        val_range = 6
        test_range = slice(7, len(datasets_filled))
        x_trainlist = [X for Xlist in Xlists[train_range] for X in Xlist]
        x_vallist = [X for X in Xlists[val_range]]
        x_testlist = [X for Xlist in Xlists[test_range] for X in Xlist]
        y_trainlist = [y for ylist in ybinarylists[train_range] for y in ylist]
        y_vallist = [y for y in ybinarylists[val_range]]
        y_testlist = [y for ylist in ybinarylists[test_range] for y in ylist]

        # Take sliding-window frames. Target is label of last time step
        # Data is 100 Hz
        frame_length = int(5.12 * 100)
        step = 1 * 100
        x_train = []
        y_train = []
        x_val = []
        y_val = []
        x_test = []
        y_test = []
        sliding_window(frame_length, step, x_train, y_train, y_trainlist, \
            x_trainlist)
        sliding_window(frame_length, step, x_val, y_val, y_vallist, x_vallist)
        sliding_window(frame_length, step, x_test, y_test, \
            y_testlist, x_testlist)
        numpify_and_store(x_train, y_train, 'X_train', 'y_train', \
        outdatapath, shuffle=True)
        numpify_and_store(x_val, y_val, 'X_val', 'y_val', outdatapath, \
            shuffle=False)
        numpify_and_store(x_test, y_test, 'X_test', 'y_test', outdatapath, \
            shuffle=False)
        print('Processed data succesfully stored in ' + outdatapath)
    return outdatapath

def load_data(outputpath):
    ext = '.npy'
    x_train = np.load(outputpath+'X_train'+ext)
    y_train_binary = np.load(outputpath+'y_train'+ext)
    x_val = np.load(outputpath+'X_val'+ext)
    y_val_binary = np.load(outputpath+'y_val'+ext)
    x_test = np.load(outputpath+'X_test'+ext)
    y_test_binary = np.load(outputpath+'y_test'+ext)
    return x_train, y_train_binary, x_val, y_val_binary, x_test, y_test_binary


1		"""
2		Summary:
3		Function fetch_and_preprocess from tutorial_pamap2.py helps to fetch and
4		preproces the data.
5		Example function calls in 'Tutorial mcfly on PAMAP2.ipynb'
6		"""
7	1	import numpy as np
8	1	from numpy import genfromtxt
9	1	import pandas as pd
10	1	import matplotlib.pyplot as plt
11	1	from os import listdir
12	1	import os.path
13	1	import zipfile
14	1	import keras
15	1	from keras.utils.np_utils import to_categorical
16	1	import sys
17	1	if sys.version_info <= (3,): #python2
18	1	import urllib
19		else: #python3
20		import urllib.request
21
22	1	def split_activities(labels, X, borders=10*100):
		0 ignored issues – show Coding Style Naming introduced 2016-09-01 14:23 UTC by Report Bug Copy Issue Report The name `X` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
23		"""
24		Splits up the data per activity and exclude activity=0.
25		Also remove borders for each activity.
26		Returns lists with subdatasets
27		"""
28		tot_len = len(labels)
29		startpoints = np.where([1] + [labels[i] != labels[i-1] \
30		for i in range(1, tot_len)])[0]
31		endpoints = np.append(startpoints[1:]-1, tot_len-1)
32		acts = [labels[s] for s, e in zip(startpoints, endpoints)]
33		#Also split up the data, and only keep the non-zero activities
34		xysplit = [(X[s+borders:e-borders+1, :], a) \
35		for s, e, a in zip(startpoints, endpoints, acts) if a != 0]
36		xysplit = [(X, y) for X, y in xysplit if len(X) > 0]
37		Xlist = [X for X, y in xysplit]
		0 ignored issues – show Coding Style Naming introduced 2016-09-02 09:06 UTC by Report Bug Copy Issue Report The name `Xlist` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
38		ylist = [y for X, y in xysplit]
39		return Xlist, ylist
40
41	1	def sliding_window(frame_length, step, Xsamples,\
		0 ignored issues – show Coding Style Naming introduced 2016-09-02 09:06 UTC by Report Bug Copy Issue Report The name `Xsamples` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style Naming introduced 2016-09-02 09:06 UTC by Report Bug Copy Issue Report The name `Xsampleslist` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
42		ysamples, ysampleslist, Xsampleslist):
43		"""
44		Splits time series in ysampleslist and Xsampleslist
45		into segments by applying a sliding overlapping window
46		of size equal to frame_length with steps equal to step
47		it does this for all the samples and appends all the output together.
48		So, the participant distinction is not kept
49		"""
50		for j in range(len(Xsampleslist)):
51		X = Xsampleslist[j]
		0 ignored issues – show Coding Style Naming introduced 2016-09-01 15:34 UTC by Report Bug Copy Issue Report The name `X` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
52		ybinary = ysampleslist[j]
53		for i in range(0, X.shape[0]-frame_length, step):
54		xsub = X[i:i+frame_length, :]
55		ysub = ybinary
56		Xsamples.append(xsub)
57		ysamples.append(ysub)
58
59	1	def transform_y(y, mapclasses, nr_classes):
		0 ignored issues – show Coding Style Naming introduced 2016-09-01 15:34 UTC by Report Bug Copy Issue Report The name `y` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
60		"""
61		Transforms y, a list with one sequence of A timesteps
62		and B unique classes into a binary Numpy matrix of
63		shape (A, B)
64		"""
65		ymapped = np.array([mapclasses[c] for c in y], dtype='int')
66		ybinary = to_categorical(ymapped, nr_classes)
67		return ybinary
68
69	1	def addheader(datasets):
70		"""
71		The columns of the pandas data frame are numbers
72		this function adds the column labels
73		"""
74		axes = ['x', 'y', 'z']
75		IMUsensor_columns = ['temperature'] + \
		0 ignored issues – show Coding Style Naming introduced 2016-09-01 14:23 UTC by Report Bug Copy Issue Report The name `IMUsensor_columns` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
76		['acc_16g_' + i for i in axes] + \
77		['acc_6g_' + i for i in axes] + \
78		['gyroscope_'+ i for i in axes] + \
79		['magnometer_'+ i for i in axes] + \
80		['orientation_' + str(i) for i in range(4)]
81		header = ["timestamp", "activityID", "heartrate"] + ["hand_"+s \
82		for s in IMUsensor_columns] \
83		+ ["chest_"+s for s in IMUsensor_columns]+ ["ankle_"+s \
84		for s in IMUsensor_columns]
85		for i in range(0, len(datasets)):
86		datasets[i].columns = header
		0 ignored issues – show Coding Style introduced 2016-09-01 14:23 UTC by Report Bug Copy Issue Report The indentation here looks off. 8 spaces were expected, but 12 were found. Loading history...
87		return datasets
88
89	1	def numpify_and_store(X, y, xname, yname, outdatapath, shuffle=False):
		0 ignored issues – show Coding Style Naming introduced 2016-09-05 08:07 UTC by Report Bug Copy Issue Report The name `X` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history... Coding Style Naming introduced 2016-09-01 15:34 UTC by Report Bug Copy Issue Report The name `y` does not conform to the argument naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
90		"""
91		Converts python lists x 3D and y 1D into numpy arrays
92		and stores the numpy array in directory outdatapath
93		shuffle is optional and shuffles the samples
94		"""
95		X = np.array(X)
96		y = np.array(y)
97		#Shuffle around the train set
98		if shuffle is True:
99		np.random.seed(123)
100		neworder = np.random.permutation(X.shape[0])
101		X = X[neworder, :, :]
102		y = y[neworder, :]
103		# Save binary file
104		np.save(outdatapath+ xname, X)
105		np.save(outdatapath+ yname, y)
106
107
108	1	def fetch_data(directory_to_extract_to):
109		"""
110		Fetch the data and extract the contents of the zip file
111		to the directory_to_extract_to.
112		First check whether this was done before, if yes, then skip
113		"""
114		targetdir = directory_to_extract_to + '/PAMAP2'
115		if os.path.exists(targetdir):
116		print('Data previously downloaded and stored in ' + targetdir)
117		else:
118		os.makedirs(targetdir) # create target directory
119		#download the PAMAP2 data, this is 688 Mb
120		path_to_zip_file = directory_to_extract_to + '/PAMAP2_Dataset.zip'
121		test_file_exist = os.path.isfile(path_to_zip_file)
122		if test_file_exist is False:
123		url = str('https://archive.ics.uci.edu/ml/' +
124		'machine-learning-databases/00231/PAMAP2_Dataset.zip')
125		#retrieve data from url
126		if sys.version_info <= (3,): #python2
127		local_fn, headers = urllib.urlretrieve(url,\
128		filename=path_to_zip_file)
129		else: #python3
130		local_fn, headers = urllib.request.urlretrieve(url,\
131		filename=path_to_zip_file)
132		print('Download complete and stored in: ' + path_to_zip_file)
133		else:
134		print('The data was previously downloaded and stored in ' +
135		path_to_zip_file)
136		# unzip
137		with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref:
		0 ignored issues – show Coding Style introduced 2016-09-01 14:23 UTC by Report Bug Copy Issue Report No space allowed before comma with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref: ^ Loading history... Coding Style introduced 2016-09-01 14:23 UTC by Report Bug Copy Issue Report Exactly one space required after comma with zipfile.ZipFile(path_to_zip_file ,"r") as zip_ref: ^ Loading history...
138		zip_ref.extractall(targetdir)
139		return targetdir
140
141
142	1	def fetch_and_preprocess(directory_to_extract_to, columns_to_use=None):
143		"""
144		High level function to fetch_and_preprocess the PAMAP2 dataset
145		directory_to_extract_to: the directory where the data will be stored
146		columns_to_use: the columns to use
147		"""
148		if columns_to_use is None:
149		columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z',
150		'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z',
151		'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z']
152		targetdir = fetch_data(directory_to_extract_to)
153		outdatapath = targetdir + '/PAMAP2_Dataset' + '/slidingwindow512cleaned/'
154		if not os.path.exists(outdatapath):
155		os.makedirs(outdatapath)
156		if os.path.isfile(outdatapath+'x_train.npy'):
157		print('Data previously pre-processed and np-files saved to ' +
158		outdatapath)
159		else:
160		datadir = targetdir + '/PAMAP2_Dataset/Protocol'
161		filenames = listdir(datadir)
162		print('Start pre-processing all ' + str(len(filenames)) + ' files...')
163		# load the files and put them in a list of pandas dataframes:
164		datasets = [pd.read_csv(datadir+'/'+fn, header=None, sep=' ') \
165		for fn in filenames]
166		datasets = addheader(datasets) # add headers to the datasets
167		#Interpolate dataset to get same sample rate between channels
168		datasets_filled = [d.interpolate() for d in datasets]
169		# Create mapping for class labels
170		ysetall = [set(np.array(data.activityID)) - set([0]) \
171		for data in datasets_filled]
172		classlabels = list(set.union(*[set(y) for y in ysetall]))
173		nr_classes = len(classlabels)
174		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)
		0 ignored issues – show Coding Style Naming introduced 2016-09-02 09:06 UTC by Report Bug Copy Issue Report The name `Xlists` does not conform to the variable naming conventions (`[a-z_][a-z0-9_]{1,30}$`). This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
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		y_testlist = [y for ylist in ybinarylists[test_range] for y in ylist]
192
193		# Take sliding-window frames. Target is label of last time step
194		# Data is 100 Hz
195		frame_length = int(5.12 * 100)
196		step = 1 * 100
197		x_train = []
198		y_train = []
199		x_val = []
200		y_val = []
201		x_test = []
202		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		y_testlist, x_testlist)
208		numpify_and_store(x_train, y_train, 'X_train', 'y_train', \
209		outdatapath, shuffle=True)
210		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		shuffle=False)
214		print('Processed data succesfully stored in ' + outdatapath)
215		return outdatapath
216
217	1	def load_data(outputpath):
218		ext = '.npy'
219		x_train = np.load(outputpath+'X_train'+ext)
220		y_train_binary = np.load(outputpath+'y_train'+ext)
221		x_val = np.load(outputpath+'X_val'+ext)
222		y_val_binary = np.load(outputpath+'y_val'+ext)
223		x_test = np.load(outputpath+'X_test'+ext)
224		y_test_binary = np.load(outputpath+'y_test'+ext)
225		return x_train, y_train_binary, x_val, y_val_binary, x_test, y_test_binary
226

NLeSC / mcfly

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