def parsetest(parseOption):
import sk_handler as skh
"""
ParseOptions:
-------------
COMMA_DL=0;
SPACE_DL=1;
LC_SINGLE=1
LC_SEPERATE=2
LC_SVM_A=3
LC_SVM_B=4
LC_SVM_C = 5
"""
dname = '{}/data/datasets/libras/movement_libras_10.data'.format(
os.getcwd())
#dname = '{}/data/init/adult/adult.data'.format(os.getcwd())
#pdb.set_trace()
data = fp.Parser(parseOption, dname, True, .25)
pdb.set_trace()
target_input = data.convert_file()
train_data, test_data = data.write_csv(target_input)
X_train, y_train = data.split_last_column(train_data)
X_test, y_test = data.split_last_column(test_data)
sk = skh.sk_handler(X_train, y_train, X_test, y_test)
duration, acc = sk.svm()
dur2, acc2 = sk.clustering()
pdb.set_trace()
def populate_data_from_init_folder(self):
for dirpath, dirname, filelist in os.walk('./data/init'):
for filename in filelist:
if re.search(r".*[.]data$", filename):
print("dirpath: {}, dirname: {}, filename: {}"
.format(dirpath, dirname, filename))
dpath = '{}/{}'.format(dirpath, filename)
data = fp.Parser(fp.LC_SINGLE, dpath, False, .25, ',')
full_set = data.convert_file()
pdb.set_trace()
repo.add_dset(filename,
dpath,
full_set,
self.session)
def populate_learning_curves(self):
"""Populate learning_curves database table with a curve for every dataset with every algorithm"""
import sk_handler as skh
from random import shuffle
try:
d_sets = self.session.query(repo.DatasetAll).all()
except AttributeError:
print('Repo metabases likely not defined, defining now')
repo.defineMeta()
d_sets = self.session.query(repo.DatasetAll).all()
algs = self.session.query(repo.Algorithm).all()
for d_set in d_sets:
print("Crafting Learning Curve for dataset: {}".format(d_set.data_name))
data_id = d_set.data_id
data = fp.Parser(fp.COMMA_DL, d_set.data_path,
fp.TP_TRUE, per=.25)
target_input = data.convert_file()
shuffle(target_input)
target_input = data.limit_size(target_input) # limiting size of datasets for sanity
percents = [0.1, 0.2, 0.3]
for alg in algs:
results = []
train_time = 0
alg_id = alg.alg_id
evstring = '{}()'.format(alg.alg_path)
for percent in percents:
shuffle(target_input)
train_data, test_data = data.partition(target_input, per=percent)
X_train, y_train = data.split_last_column(train_data)
X_test, y_test = data.split_last_column(test_data)
sk = skh.SkHandler(X_train, y_train, X_test, y_test)
print('{} evaluated at {} percent'.format(evstring, str(percent)))
try:
durr, acc = eval(evstring)
train_time += durr
results.append(acc)
except Exception as ex:
print("Could not train dataset {} with method {}: {}".format(d_set.data_path,
alg.alg_path,
ex))
durr, acc = [float('inf'), 0]
results.append(acc)
results.append(train_time)
repo.add_curve(data_id, alg_id, results, self.session)
def populate_data_all(self):
all_dict = {'className': 'DatasetAll',
'tableName': 'all_data'}
filelist = self.get_allowed_files()
for dpath, filename in filelist:
print("Adding set {} to all_sets at {}".format(filename, dpath))
data = fp.Parser(fp.LC_SINGLE, dpath, False, .25, ',')
target_input = data.convert_file()
target_input = data.limit_size(target_input) # limiting size of datasets for sanity
try:
repo.add_dset(all_dict['className'],
all_dict['tableName'],
filename,
dpath,
target_input,
self.session)
except Exception as ex:
print("Exception occured whilst trying to add dataset: {}".format(ex))
def populate_runs_all(self):
"""Populate runs_all database table with a run of every dataset with every algorithm"""
import sk_handler as skh
from random import shuffle
try:
d_sets = self.session.query(repo.DatasetAll).all()
except AttributeError:
print('Repo metabases likely not defined, defining now')
repo.defineMeta()
d_sets = self.session.query(repo.DatasetAll).all()
algs = self.session.query(repo.Algorithm).all()
for d_set in d_sets:
print("Analyzing dataset: {}".format(d_set.data_name))
data_id = d_set.data_id
data = fp.Parser(fp.COMMA_DL, d_set.data_path,
fp.TP_TRUE, per=.25)
target_input = data.convert_file()
shuffle(target_input) # keep commented while debugging
target_input = data.limit_size(target_input) # limiting size of datasets for sanity
train_data, test_data = data.partition(target_input)
X_train, y_train = data.split_last_column(train_data)
X_test, y_test = data.split_last_column(test_data)
sk = skh.SkHandler(X_train, y_train, X_test, y_test)
for alg in algs:
alg_id = alg.alg_id
evstring = '{}()'.format(alg.alg_path)
print(evstring)
try:
durr, acc = eval(evstring)
except Exception as ex:
print("Could not train dataset {} with method {}: {}".format(d_set.data_path,
alg.alg_path,
ex))
durr, acc = [float('inf'), 0]
pdb.set_trace()
repo.add_run(data_id, alg_id, durr, acc, self.session)
import fparser
import misc as appmisc
import sys
# make sure this file is being run directly
if __name__ == '__main__':
if len(sys.argv) == 1:
appmisc.usage(sys.argv[0])
exit(1)
else:
file = appmisc.file_open(sys.argv[1])
if (file != None):
appparser = fparser.Parser(file)
appparser.parse()
appmisc.file_close(file)
else:
appmisc.print_err("{}: no such file or directory".format(sys.argv[1]))
exit(1)
pass