def reload_data(LOGFILE = None, PICKLE_DATA = True,
root_folder = 'Shared Sepsis Data', csv_filename = 'Sepsis_JCM.csv'):
'''
Reloads raw_data from folders.
IN:
LOGFILE - fileobj - an open text file where logs are written
PICKLE_DATA - bool - whether to pickle data once loaded
root_folder - str - relative path to top level folder for all data and csv_file
csv_filename - str - name of csv file containing the trial labels and locations
'''
csv_file = os.path.join(root_folder, csv_filename)
X, y, used_column_headers, df, df_raw = load_data(root_folder, csv_file,
verbose=False, LOGFILE=LOGFILE)
# pickle data for later loading efficiency
if PICKLE_DATA:
start = time.time()
ptf( '\n>> Pickling data ...\n', LOGFILE)
for z, zname in izip([X,y,used_column_headers], PICKLE_NAMES):
my_pickle(z, zname)
end = time.time()
ptf( 'Data pickled in %d seconds (%d total trials)' % ((end-start),
len(X)), LOGFILE)
return X, y, used_column_headers, df, df_raw
def reload_data(LOGFILE=None,
PICKLE_DATA=True,
root_folder='Shared Sepsis Data',
csv_filename='Sepsis_JCM.csv'):
'''
Reloads raw_data from folders.
IN:
LOGFILE - fileobj - an open text file where logs are written
PICKLE_DATA - bool - whether to pickle data once loaded
root_folder - str - relative path to top level folder for all data and
csv_file
csv_filename - str - name of csv file containing the trial labels and
locations
OUT:
X - pd Series - Series of features. Each row is a trial (index) and a
number of features + 1 X number of times numpy array (data)
y - pd DataFrame - labels data frame. Each row is a trial (index) and
the labels of each class the columns
used_column_headers - list of str -
df - pd DataFrame - DataFrame containing all trial data after elmination
of extraneous spots, trials
df_raw - pd DataFrame - DataFrame containing all trial data (before pruning)
'''
csv_file = os.path.join(root_folder, csv_filename)
X, y, used_column_headers, df, df_raw = load_data(root_folder,
csv_file,
verbose=False,
LOGFILE=LOGFILE)
# pickle data for later loading efficiency
if PICKLE_DATA:
start = time.time()
ptf('\n>> Pickling data ...\n', LOGFILE)
for z, zname in izip([X, y, used_column_headers], PICKLE_NAMES):
my_pickle(z, zname)
end = time.time()
ptf(
'Data pickled in %d seconds (%d total trials)' %
((end - start), len(X)), LOGFILE)
return X, y, used_column_headers, df, df_raw
def reload_data(LOGFILE = None, PICKLE_DATA = True,
root_folder = 'Shared Sepsis Data', csv_filename = 'Sepsis_JCM.csv'):
'''
Reloads raw_data from folders.
IN:
LOGFILE - fileobj - an open text file where logs are written
PICKLE_DATA - bool - whether to pickle data once loaded
root_folder - str - relative path to top level folder for all data and
csv_file
csv_filename - str - name of csv file containing the trial labels and
locations
OUT:
X - pd Series - Series of features. Each row is a trial (index) and a
number of features + 1 X number of times numpy array (data)
y - pd DataFrame - labels data frame. Each row is a trial (index) and
the labels of each class the columns
used_column_headers - list of str -
df - pd DataFrame - DataFrame containing all trial data after elmination
of extraneous spots, trials
df_raw - pd DataFrame - DataFrame containing all trial data (before pruning)
'''
csv_file = os.path.join(root_folder, csv_filename)
X, y, used_column_headers, df, df_raw = load_data(root_folder, csv_file,
verbose=False, LOGFILE=LOGFILE)
# pickle data for later loading efficiency
if PICKLE_DATA:
start = time.time()
ptf( '\n>> Pickling data ...\n', LOGFILE)
for z, zname in izip([X,y,used_column_headers], PICKLE_NAMES):
my_pickle(z, zname)
end = time.time()
ptf( 'Data pickled in %d seconds (%d total trials)' % ((end-start),
len(X)), LOGFILE)
return X, y, used_column_headers, df, df_raw