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solarPOT/Solar/eval_logs_to_dict.py
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eval_logs_to_dict.py
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import pandas as pd
import os
import xarray as xr
import pickle
import sys
path = sys.argv[1]
dataset = sys.argv[2]
type_to_merge = sys.argv[3]
if len(sys.argv) > 4:
prefix = str(sys.argv[4]) + '_'
else:
prefix = ''
file_link = { 'train' : prefix + 'log_train.csv' ,
'train_pred' : prefix + 'log_pred_tr.csv' ,
'val_pred' : prefix + 'log_pred_val.csv',
'test_pred' : prefix + 'log_pred_te.csv'
}
dirpath = os.path.join( path, 'datasets', dataset )
subdirs = os.listdir( dirpath )
# decalre merged log
merged_log = {}
for directory in subdirs:
if directory[:3] !='ELM': continue
log_list = []
# load all files:
for file_label in file_link:
try:
logfile_name = file_link[file_label]
logfile_dir = os.path.join( dirpath, directory, type_to_merge, logfile_name)
# load file and drop the cputime (not relevant for ELM as equal to cputime)
current_log = pd.read_csv( logfile_dir, index_col = 0).drop('cputime', axis = 1)
# rename all columnsby appending the file label (for later identification)
for column_label in current_log.columns:
current_log = current_log.rename({ column_label : column_label +'_'+file_label }, axis = 1)
# go through the list of collected variables again and rename them accoding to the model
for column_label in current_log.columns:
entry = current_log[column_label].to_frame().rename({ column_label : directory }, axis = 1)
# merge with the merged_log ( and set if the variable does not yet exist)
if column_label in merged_log:
merged_log[ column_label ] = merged_log[ column_label ].merge(entry,
left_index = True,
right_index = True,
how = 'outer')
else:
merged_log[ column_label ] = entry
except Exception as e: print(e)
logfile_out = os.path.join( dirpath, type_to_merge + '_merged_log.pickle' )
with open(logfile_out, 'wb') as handle:
pickle.dump(merged_log, handle, protocol=pickle.HIGHEST_PROTOCOL)
print('Created file %s' %logfile_out)
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