diff --git a/pyproject.toml b/pyproject.toml
index 29a4814a..9a371c72 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,14 @@ smlp = "smlp.run_smlp:main2"
 where = ["."]
 exclude = ["*"]
 
+[tool.pytest]
+strict = true
+markers = [
+    "toy: Toy example tests (deselect with '-m \"not toy\"')",
+    "real: Tests on real world data (deselect with '-m \"not real\"')",
+    "test: ?? TODO: description (deselect with '-m \"not test\"')",
+]
+
 # ---------------------------------------------------------------------------
 # Build-time environment variables (all optional)
 # ---------------------------------------------------------------------------
diff --git a/regr_smlp/code/convert-csv-pytest.py b/regr_smlp/code/convert-csv-pytest.py
new file mode 100644
index 00000000..f8098ef9
--- /dev/null
+++ b/regr_smlp/code/convert-csv-pytest.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+
+import csv
+import sys
+import shlex
+
+from smlp_regr import (conf_identifier, get_conf_name)
+
+HEADER = '''\
+#!/usr/bin/env python3
+
+import pytest
+import subprocess
+
+from lib import *
+'''
+
+FOOTER = '''\
+'''
+
+def process(row : list[str]):
+	assert len(row) >= 5, repr(row)
+	nr = int(row[0])
+	data = row[1]
+	new_data = row[2]
+	switches = row[3]
+	comment = row[4:]
+
+	args = shlex.split(switches)
+
+	ignored_tests = []
+	is_toy = ((row[1].startswith('smlp_toy') or
+	           row[1].startswith('mlbt_toy') or
+	           row[2].startswith('smlp_toy') or
+	           row[2].startswith('mlbt_toy') or
+	           (conf_identifier(row[3]) and get_conf_name(row[3]).startswith('smlp_toy')) or
+	           (not conf_identifier(row[3]) and row[1] == '' and row[2] == '')) and
+	           (row[0] not in ignored_tests))
+
+	is_real = ((not (row[1].startswith('smlp_toy') or
+	                 row[1].startswith('mlbt_toy') or
+	                 row[2].startswith('smlp_toy') or
+	                 row[2].startswith('mlbt_toy') or
+	                 (conf_identifier(row[3]) and get_conf_name(row[3]).startswith('smlp_toy')))) and
+	                 (row[0] not in ignored_tests))
+
+	i_picks = ['36', '51', '60', '80', '95', '104', '120']
+	is_test = str(nr) in i_picks
+
+	failing = {
+		104: 'wrong spec due to singleton value',
+		107: 'contradictory eta contraints',
+		229: 'missing radius spec for p1/p2',
+	}
+
+	# keep in sync with /pyproject.toml: tool.pytest.ini_options.markers
+	if is_toy:
+		print('@pytest.mark.toy')
+	if is_real:
+		print('@pytest.mark.real')
+	if is_test:
+		print('@pytest.mark.test')
+
+	if nr in failing:
+		print(f'@pytest.mark.xfail(True, reason={failing[nr]!r},\n'
+		       '                   raises=subprocess.CalledProcessError, strict=True)')
+
+	print(f"class Test{nr}(CmdTestCase):\n\t'''")
+	for line in comment:
+		print(f'\t{line.strip()}')
+	print("\t'''")
+
+	print(f'''
+	nr = {nr}
+	data = {data!r}
+	new_data = {new_data!r}
+	args = {args!r}\n''')
+
+def main():
+	print(HEADER)
+	itr = csv.reader(sys.stdin)
+	next(itr)
+	for row in itr:
+		process(row)
+	print(FOOTER)
+
+if __name__ == '__main__':
+	main()
diff --git a/regr_smlp/code/csv_comparator.py b/regr_smlp/code/csv_comparator.py
index 25ba93b8..a89672bd 100644
--- a/regr_smlp/code/csv_comparator.py
+++ b/regr_smlp/code/csv_comparator.py
@@ -41,7 +41,7 @@ def check_values(csv1, csv2):
                 if np.isnan(val2):
                     return False
                 if isinstance(val1, np.float64) and isinstance(val2, np.float64):
-                    diff = abs(abs(val1) - abs(val2))
+                    diff = abs(val1 - val2)
                     max_val = max(abs(val1), abs(val2))
                     diff_ratio = diff / max_val
                     if diff_ratio >= THRESHOLD:
@@ -66,9 +66,13 @@ def compare_csv(csv1, csv2):
 
 
 def main():
-    pass
-    #return comapre_csv(path.join(old_path, file), path.join(new_path, file))
+    if compare_csv(sys.argv[1], sys.argv[2]):
+        print('OK')
+        return 0
+    else:
+        print('Failed')
+        return 1
 
 
 if __name__ == "__main__":
-    main()
+    sys.exit(main())
diff --git a/regr_smlp/code/lib.py b/regr_smlp/code/lib.py
new file mode 100644
index 00000000..0e111b69
--- /dev/null
+++ b/regr_smlp/code/lib.py
@@ -0,0 +1,491 @@
+
+import argparse
+import json
+import os
+import pytest
+import shlex
+import subprocess
+import sys
+import warnings
+
+import csv_comparator as csv_cmp
+
+from pathlib import Path
+
+files_to_ignore_from_diff = ['Test41_doe_two_levels_doe.csv', 'Test42_doe_two_levels_doe.csv']
+
+def _cwd_rel(path):
+	return Path(path).relative_to(Path('.').absolute(), walk_up=True)
+
+def _filter_out(lst, item, n):
+	i = 0
+	while i < len(lst):
+		it = lst[i]
+		if it == item:
+			i += n
+		else:
+			yield it
+		i += 1
+
+def _get_arg(lst, item):
+	i = 0
+	while i < len(lst):
+		if lst[i] == item:
+			return lst[i+1]
+		i += 1
+
+# --------------- adapted from regr_smlp.py ---------------
+
+def extract_smlp_error(error_string):
+	error_list = error_string.splitlines()
+	for line in error_list:
+		if line.startswith('Error:'):
+			error_msg = line[7:]
+			return [error_msg]
+	return []
+
+def get_file_from_list_underscore(prefix_list, list1):
+	outs_list = []
+	for file1 in list1:
+		for pref in prefix_list:
+			if file1.startswith(pref):
+				outs_list.append(file1)
+	return outs_list
+
+def smlp_txt_file(fname):
+	if 'config' in fname or 'error' in fname or 'mrmr_features_summary' in fname or '_formula' in fname:
+		return False
+	elif fname.endswith('.txt'):
+		return True
+	else:
+		return False
+
+def get_all_files_from_dir(dir_path):
+	return os.listdir(dir_path)
+
+def comapre_files(file1, file2):
+	if Path(file1).suffix == '.csv':
+		return csv_cmp.compare_csv(file1, file2)
+	f1 = open(file1, 'r')
+	f2 = open(file2, 'r')
+	lines1 = f1.readlines()
+	lines2 = f2.readlines()
+	len1 = len(lines1)
+	len2 = len(lines2)
+	if len1 != len2:
+		return False
+	for x in range(0, len1):
+		if lines1[x].startswith('<environment:'):
+			continue
+		if lines1[x] != lines2[x]:
+			return False
+	return True
+
+# --------------- end adapted from regr_smlp.py ---------------
+
+class CmdTestCase:
+	def _construct_args(self, regrdir, extdir, tmpdir):
+		pre = []
+
+		if self.data:
+			pre += ['-data', self.data + '.csv']
+
+		pre += ['-out_dir', str(tmpdir)]
+		pre += ['-pref', f'Test{self.nr}']
+		pre += self.args
+
+		if self.new_data:
+			pre += ['-new_dat', self.new_data + '.csv']
+
+		# resolve paths in special locations
+		special = {
+			# config
+			'-config'             : regrdir/'models',
+			'--load-configuration': regrdir/'models',
+			# data
+			'-data'               : regrdir/'data',
+			'--labeled_data'      : regrdir/'data',
+			# new data
+			'-new_dat'            : regrdir/'data',
+			'-new_data'           : regrdir/'data',
+			'--new_data'          : regrdir/'data',
+			# solver path
+			'-solver_path'        : extdir,
+			'--solver_path'       : extdir,
+			# spec
+			'-spec'               : regrdir/'specs',
+			'--spec'              : regrdir/'specs',
+		}
+		i = 0
+		args = []
+		while i < len(pre):
+			arg = pre[i]
+			loc = special.get(arg)
+			if loc:
+				path = Path(pre[i+1])
+				if path.is_absolute():
+					warnings.warn(
+						f'path given to {arg} in args is absolute: {path}')
+				else:
+					path = _cwd_rel(loc/path)
+				args += [arg, str(path)]
+				i += 2
+			else:
+				args.append(arg)
+				i += 1
+
+		args = self._apply_morespecial_logic(args, regrdir)
+
+		for o in ('-data', '--labeled_data', '-new_dat', '-new_data', '--new_data'):
+			datapath = _get_arg(args, o)
+			if datapath is not None and not Path(datapath).exists():
+				warnings.warn(f'path for option {o} does not exist: {datapath}')
+				pytest.skip(f'path for option {o} does not exist: {datapath}')
+				return None
+
+		return args
+
+	def _apply_morespecial_logic(self, pre, regrdir):
+		use_model = False
+		for o in ('-use_model', '--use_model'):
+			if o in pre:
+				use_model |= pre[pre.index(o) + 1].lower().startswith('t')
+		try:
+			cfgpath = pre[pre.index('-config') + 1]
+			with open(cfgpath, 'r') as f:
+				# TODO: get rid of double quotes in config file to make this a Boolean
+				use_model |= json.load(f)['use_model'] == 'true'
+		except ValueError:
+			pass
+
+		if use_model:
+			assert self.data
+			args = ['-model_name', str(_cwd_rel(regrdir/'models'/self.data))]
+			args += list(_filter_out(pre, '-data', 1))
+			return args
+
+		try:
+			is_doe = pre[pre.index('-mode') + 1] == 'doe'
+		except ValueError:
+			is_doe = False
+
+		if is_doe:
+			assert self.data
+			path = regrdir/'grids'/(self.data + '.csv')
+			if not path.exists():
+				warnings.warn(f'-doe_spec file {path} does not exist')
+				pytest.skip(f'-doe_spec file {path} does not exist')
+				return None
+			args = ['-doe_spec', str(_cwd_rel(path))]
+			args += list(_filter_out(pre, '-data', 1))
+			return args
+
+		return pre
+
+	def more_subproc_run_args(self) -> dict:
+		return {}
+
+	def test(self, tmp_path, pytestconfig):
+		assert self.nr > 0
+		projdir = pytestconfig.rootdir
+		regrdir = projdir/'regr_smlp'
+		extdir  = projdir/'..'/'external'
+		exepath = 'smlp' # projdir/'src'/'run_smlp.py'
+		args    = self._construct_args(regrdir, extdir, tmp_path)
+
+		print(f'Test {self.nr} using tmp-path: {tmp_path}', file=sys.stderr)
+
+		# write the meta-data for this test so results can be checked
+		# independently of the execution
+		meta = tmp_path/'.meta'
+		meta.mkdir()
+		with open(meta/'env', 'w') as f:
+			for k, v in os.environ.items():
+				print(f'{k}={v}', file=f)
+		with open(meta/'test-id', 'w') as f:
+			print(self.nr, file=f)
+		with open(meta/'cwd', 'w') as f:
+			print(Path().absolute(), file=f)
+		with open(meta/'project-dir', 'w') as f:
+			print(projdir, file=f)
+
+		if args is None:
+			print('', file=sys.stderr)
+		else:
+			cmd = [str(exepath)] + args
+			with open(meta/'cmd', 'w') as f:
+				print(shlex.join(cmd), file=f)
+
+			print(shlex.join(cmd), file=sys.stderr)
+
+			with (open(meta/'stdout', 'wb', buffering=0) as o,
+			      open(meta/'stderr', 'wb', buffering=0) as e):
+				pr = subprocess.run(cmd,
+				                    stdin=subprocess.DEVNULL,
+				                    stdout=o, stderr=e, check=True,
+				                    **self.more_subproc_run_args())
+
+			#check_outputs(str(self.nr), args, pr.stdout, pr.stderr, regrdir, tmp_path)
+			assert check_outputs(tmp_path), (
+				'checking against master failed, '
+				f'for details see summary in {meta/"test_log.txt"}'
+			)
+
+def check_outputs(tmpdir):
+	meta = tmpdir/'.meta'
+	try:
+		with open(meta/'cmd') as f:
+			cmd = shlex.split(f.read()[:-1])
+		args = cmd[1:]
+	except FileNotFoundError:
+		args = None
+	with open(meta/'cwd') as f:
+		os.chdir(f.read()[:-1])
+	with open(meta/'test-id') as f:
+		test_id = f.read()[:-1]
+	with open(meta/'project-dir') as f:
+		projdir = Path(f.read()[:-1])
+	with open(meta/'stdout') as f:
+		stdout = f.read()
+	with open(meta/'stderr') as f:
+		stderr = f.read()
+
+	return _check_outputs(test_id, args, stdout, stderr, projdir/'regr_smlp', tmpdir)
+
+def _check_outputs(test_id, smlp_args, stdout, stderr, regrdir, output_path):
+	# adapted from smlp_regr.py
+	diff = 'diff'
+
+	args = argparse.Namespace()
+	args.no_graphical_compare = True
+	args.default = 'n'
+	args.config_default = 'n'
+	args.fail_txt = False
+
+	log = True
+	log_file = output_path/'.meta'/'test_log.txt'
+	def write_to_log(line):
+		with open(log_file, 'a') as writefile:
+			writefile.write(line + '\n')
+
+	test_model = False
+	for o in ('-save_model', '--save_model'):
+		o = _get_arg(smlp_args, o)
+		if o and o.lower().startswith('t'):
+			test_model = True
+			break
+	if test_model:
+		test_model = _get_arg(smlp_args, '-model_name')
+		if not test_model:
+			warnings.warn(f'-save_model {o} specified in args but no -model_name given')
+
+	test_errors = extract_smlp_error(stderr)
+	test_prefix = 'Test' + test_id + '_'
+
+	data_path   = regrdir/'data'
+	models_path = regrdir/'models'
+	master_path = regrdir/'master'
+	files_in_master = get_all_files_from_dir(str(master_path))
+	files_in_output = get_all_files_from_dir(str(output_path))
+
+	output_prefixes = [test_prefix]
+	if test_model:
+		output_prefixes.append(test_model)
+	new_files = get_file_from_list_underscore(output_prefixes, files_in_output)
+	master_files = get_file_from_list_underscore(output_prefixes, files_in_master)
+	test_result = True
+	test_files_check = []
+	txt_index = -1
+	# print(new_files)
+	# while not smlp_txt_file(new_files[txt_index]):
+	#     txt_index += 1
+	for k in range(0, len(new_files)):
+		if smlp_txt_file(new_files[k]):
+			txt_index = k  # found the txt file
+	if txt_index != -1:
+		new_files_tmp = new_files[:]
+		new_files = [new_files_tmp.pop(txt_index)]
+		new_files_tmp.sort()
+		new_files = new_files + new_files_tmp
+
+	to_show = True
+	answer = None
+	for file in new_files:
+		new_file = str(output_path/file)
+		master_file = str(master_path/file)
+		if os.path.isdir(new_file):
+			if os.path.exists(master_file):
+				assert os.path.isdir(master_file)
+			if new_file.endswith('_plots'):
+				if os.path.exists(master_file):
+					assert master_file.endswith('_plots')
+					file_to_minitor =  'plotReport.html'
+					new_file = os.path.join(new_file,)
+					master_file = os.path.join(master_file, file_to_minitor)
+					#print('dropping from master_files',  file)
+					master_files.remove(file)
+					file =  os.path.join(file, file_to_minitor)
+					#print('appending to master files', file)
+					master_files.append(file)
+					#print('update new_file', new_file); print('updated master file', master_file);
+
+		file_name = file
+		config_file = 'config' in file_name
+		# model_file = 'model' in file_name  # if its a model file it needs to be replaced in data as well
+		model_file = file_name.startswith('test' + str(test_id) + '_model')
+		txt_file = False
+		if os.path.exists(master_file):
+			if Path(new_file).suffix == '.txt' and not config_file:
+				txt_file = True
+			# condition before, dropping from it h5 file checks because getting UnicodeDecodeError error on Sles 15, say on Test 13.
+			# (new_file.endswith('.csv') or new_file.endswith('.txt') or  new_file.endswith('.html') or new_file.endswith('.json') or new_file.endswith('.h5')) and not file_name in files_to_ignore_from_diff:
+			exclude_cond = file_name in files_to_ignore_from_diff
+			exclude_cond = file_name in files_to_ignore_from_diff or file_name.endswith('_model_term.json')
+			if (Path(new_file).suffix in ('.csv', '.txt', '.html', '.json')) and not exclude_cond:
+				print('comparing {file} to master'.format(file=file_name))
+				# XXX fb: Hack using sed to replace output_path in new log
+				#         file.
+				cmd = (
+					f'sed \'s,{output_path},.,g\' {new_file} | {diff} -B '
+					'-I \'Feature selection.*file .*\' '
+					'-I \'\\[-v-] Input.*\' '
+					'-I \'usage:.*\' '
+					'-I \'Seving model rerun configuration in file\' '
+					'-I \'Saving data bounds into file:\' '
+					f'- {master_file}'
+				)
+				p = subprocess.Popen(
+					cmd,
+					shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+				output, error = p.communicate()
+				if p.returncode == 0:
+					print("Passed!")
+					test_files_check.append((file_name, 'Passed'))
+				elif comapre_files(new_file, master_file):
+					print("Passed!")
+					test_files_check.append((file_name, 'Passed'))
+				else:
+					print("Failed!")
+					if not args.no_graphical_compare and to_show:
+						Popen('{diff} {l} {k}'.format(diff=DIFF, k=new_file, l=master_file), shell=True).wait()
+					if args.default or (args.config_default and config_file):
+						if args.config_default and config_file:
+							user_input = args.config_default
+						else:
+							user_input = args.default
+					elif not to_show:
+						print('answer is: ' + answer)
+						user_input = answer
+					else:
+						user_input = input(
+							'Do you wish to switch the new file with the master?\n(yes/no|y/n): ').lower()
+					while user_input not in {'yes', 'no', 'y', 'n'}:
+						user_input = input('(yes/no|y/n):').lower()
+					if user_input in {'yes', 'y'}:
+						if model_file or config_file:
+							copyfile(new_file, master_file)
+							copyfile(new_file, models_path/file_name)
+							print('Replacing Files both in master and data')
+
+						else:
+							copyfile(new_file, master_file)
+							print('Replacing Files...')
+							if path.exists(data_path/file_name):
+								if args.default:
+									user_input = args.default
+								else:
+									user_input = input(
+										'File exists also in data, switch there as well?\n(yes/no|y/n): ').lower()
+								while user_input not in {'yes', 'no', 'y', 'n'}:
+									user_input = input('(yes/no|y/n):').lower()
+								if user_input in {'yes', 'y'}:
+									copyfile(new_file, data_path/file_name)
+					test_result = False
+					test_files_check.append((file_name, 'Failed -> content diff'))
+					if txt_file and args.fail_txt:
+						to_show = False
+						answer = user_input
+			if model_file:
+				master_files.remove(file_name)
+				os.remove(new_file)
+				if file in master_files:
+					master_files.remove(file)
+			else:
+				if os.path.isfile(new_file):
+					master_files.remove(file)
+		else:
+			# not comparing directories; such as the range plots directory in mode subgroups
+			if os.path.isdir(new_file):
+				continue
+			print('File master {file} does not exist'.format(file=file))
+			test_files_check.append((file, 'Failed -> master file does not exist'))
+			if file.endswith("smlp_error.txt"):
+				to_print = 'Test number ' + test_id + ' Crashed!'
+				print(to_print)
+				new_error_ids.append(test_id)
+				new_error_fns.append(file)
+			elif file.endswith("png"):
+				continue
+			else:
+				if not args.default:
+					user_input = input(
+						'What to do with the new file?\n1 - Nothing\n2 - Copy to master only\n3 - Copy to master and models\n4 - Remove from master only\n5 - Remove from master and models\nOption number: ')
+					while user_input not in {'1', '2', '3', '4', '5'}:
+						user_input = input('(1|2|3|4|5):')
+					if user_input == '1':
+						pass
+					elif user_input == '2':
+						if os.path.isdir(new_file):
+							copytree(new_file, master_file, dirs_exist_ok=True)
+						else:
+							copyfile(new_file, master_file)
+					elif user_input == '3':
+						copyfile(new_file, master_file)
+						copyfile(new_file, models_path/file_name)
+					elif user_input == '4':
+						os.remove(master_file)
+					elif user_input == '5':
+						os.remove(master_file)
+						os.remove(models_path/file_name)
+
+	for file in master_files:
+		new_file = str(output_path/file); #print('new_file', new_file)
+		master_file = str(master_path/file); #print(' master_file',  master_file)
+		file_name = file
+		print(f'File new {file} does not exist')
+		test_files_check.append((file, 'Failed -> new file does not exist'))
+		test_result = False
+		#  diff_errors.append('File new {file} does not exist'.format(file=file))
+		if not args.default:
+			user_input = input(
+				'What to do with the master file?\n'
+				'1 - Nothing\n'
+				'2 - Remove from master only\n'
+				'3 - Remove from master and models\n'
+				'Option number: ')
+			while user_input not in {'1', '2', '3',}:
+				user_input = input('(1|2|3):')
+			if user_input == '1':
+				pass
+			elif user_input == '2':
+				os.remove(master_file)
+			elif user_input == '3':
+				os.remove(master_file)
+				if os.path.exists(models_path/file_name):
+					os.remove(models_path/file_name)
+	if log:
+		if test_result:
+			write_to_log('Test ' + test_id + ' Passed:')
+		else:
+			write_to_log('Test ' + test_id + ' Failed:')
+		for file_check in test_files_check:
+			write_to_log(file_check[0] + ' ' + file_check[1])
+		write_to_log('')
+
+	return test_result
+
+def main():
+	return 0 if check_outputs(Path(sys.argv[1]).absolute()) else 1
+
+if __name__ == '__main__':
+	sys.exit(main())
diff --git a/regr_smlp/code/smlp_regr.py b/regr_smlp/code/smlp_regr.py
index 12e7d73a..941876d8 100755
--- a/regr_smlp/code/smlp_regr.py
+++ b/regr_smlp/code/smlp_regr.py
@@ -965,7 +965,7 @@ def get_id(l):
                             if file in master_files:
                                 master_files.remove(file)
                         else:
-                            if os.path.isfile(file):
+                            if os.path.isfile(new_file):
                                 master_files.remove(file)
                     else:
                         # not comparing directories; such as the range plots directory in mode subgroups 
diff --git a/regr_smlp/code/test_cmdline.py b/regr_smlp/code/test_cmdline.py
new file mode 100644
index 00000000..6219aa9c
--- /dev/null
+++ b/regr_smlp/code/test_cmdline.py
@@ -0,0 +1,2668 @@
+#!/usr/bin/env python3
+
+import pytest
+import subprocess
+
+from lib import *
+
+@pytest.mark.toy
+class Test1(CmdTestCase):
+	'''
+	basic dt_caret training and test on labeled data with single numeric response
+	'''
+
+	nr = 1
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'train', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test2(CmdTestCase):
+	'''
+	basic rf_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 2
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '15', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test3(CmdTestCase):
+	'''
+	basic poly_sklearn prediction test on labeled and new data with numeric response in training/test data only
+	'''
+
+	nr = 3
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_unlabeled'
+	args = ['-mode', 'predict', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'poly_sklearn', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test4(CmdTestCase):
+	'''
+	basic nn_keras prediction test on labeled and new data with numeric labels and one response
+	'''
+
+	nr = 4
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_weights_precision', '2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f']
+
+@pytest.mark.toy
+class Test5(CmdTestCase):
+	'''
+	basic dt_caret prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 5
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-save_model', 't', '-use_model', 'f', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test6(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 6
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test7(CmdTestCase):
+	'''
+	basic rf_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 7
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '15', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test8(CmdTestCase):
+	'''
+	basic nn_keras prediction test on labeled and new data with numeric labels and two responses
+	'''
+
+	nr = 8
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test9(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 9
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 't', '-model_name', 'test20_model', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-save_config', 't', '-save_model_config', 't']
+
+@pytest.mark.toy
+class Test10(CmdTestCase):
+	'''
+	basic et_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 10
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '15', '-et_sklearn_bootstrap', 'f', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test11(CmdTestCase):
+	'''
+	basic poly_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 11
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'poly_sklearn', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test12(CmdTestCase):
+	'''
+	EV-SI real life dt_sklearn predict test on labeled and new data with numeric labels
+	'''
+
+	nr = 12
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'train', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test13(CmdTestCase):
+	'''
+	EV-SI real life nn_keras prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 13
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'train', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test14(CmdTestCase):
+	'''
+	EV-SI real life poly_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 14
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'train', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'poly_sklearn', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test15(CmdTestCase):
+	'''
+	basic dt_caret prediction test from saved model on new data with numeric labels
+	'''
+
+	nr = 15
+	data = 'Test5_smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test16(CmdTestCase):
+	'''
+	basic nn_keras prediction test from saved model on new data with numeric labels and two responses
+	'''
+
+	nr = 16
+	data = 'Test8_smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test17(CmdTestCase):
+	'''
+	basic poly_sklearn prediction test from saved model on new data with numeric labels and two responses
+	'''
+
+	nr = 17
+	data = 'Test11_smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'poly_sklearn', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test18(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6
+	'''
+
+	nr = 18
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 't', '-use_model', 'f', '-model_name', 'test19_model', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test19(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test using a model saved under a name specified through model_name  option on new data with numeric labels
+	'''
+
+	nr = 19
+	data = 'test19_model'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test20(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels
+	'''
+
+	nr = 20
+	data = 'test20_model'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 'f', '-use_model', 't', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test21(CmdTestCase):
+	'''
+	test for illegal symbols in column names
+	'''
+
+	nr = 21
+	data = 'smlp_toy_num_metasymbol_mult_reg'
+	new_data = 'smlp_toy_num_metasymbol_mult_reg_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'PF ,|PF |', '-model', 'poly_sklearn', '-save_model', 't', '-use_model', 'f', '-model_name', 'test22_model', '-pred_plots', 't', '-resp_plots', 't', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test22(CmdTestCase):
+	'''
+	test for illegal symbols in column names
+	'''
+
+	nr = 22
+	data = 'test22_model'
+	new_data = 'smlp_toy_num_metasymbol_mult_reg_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'PF ,|PF |', '-model', 'poly_sklearn', '-save_model', 'f', '-use_model', 't', '-pred_plots', 't', '-resp_plots', 't', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test23(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6
+	'''
+
+	nr = 23
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 't', '-use_model', 'f', '-model_name', 'test24_model', '-model_per_response', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test24(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test using a model saved under a name specified through model_name  option on new data with numeric labels
+	'''
+
+	nr = 24
+	data = 'test24_model'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 'f', '-use_model', 't', '-model_per_response', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test25(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test on labeled and new data with numeric labels and saving model using name specified through model_name option - adapts Test6
+	'''
+
+	nr = 25
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 't', '-use_model', 'f', '-model_name', 'test26_model', '-mrmr_pred', '2', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test26(CmdTestCase):
+	'''
+	basic dt_sklearn prediction test using a model saved under a name specified through model_name  option on new data with numeric labels
+	'''
+
+	nr = 26
+	data = 'test26_model'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '2', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test27(CmdTestCase):
+	'''
+	checks nn_keras prediction with nn_keras_seq_api t
+	adapts test 4 which uses nn_keras_seq_api f
+	'''
+
+	nr = 27
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't']
+
+@pytest.mark.toy
+class Test28(CmdTestCase):
+	'''
+	checks nn_keras prediction with sw_coef 0.8 and functional API
+	adapts test 4
+	'''
+
+	nr = 28
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-sw_coef', '0.8']
+
+@pytest.mark.toy
+class Test29(CmdTestCase):
+	'''
+	basic  test for subgroup discovery for pass-fail responses
+	'''
+
+	nr = 29
+	data = 'smlp_toy_cls_metasymbol_colnames_mult'
+	new_data = ''
+	args = ['-mode', 'subgroups', '-psg_dim', '3', '-psg_top', '10', '-resp', 'PF 1,PF#', '-plots', 't', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test30(CmdTestCase):
+	'''
+	basic  test for subgroup discovery for numric responses
+	'''
+
+	nr = 30
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'subgroups', '-psg_dim', '3', '-psg_top', '10', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-plots', 't', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test31(CmdTestCase):
+	'''
+	testing resp2b in subgroup discovery mode
+	'''
+
+	nr = 31
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'subgroups', '-psg_dim', '3', '-psg_top', '10', '-resp', 'y1,y2', '-resp2b', 'y1<6;y2>6', '-feat', 'x,p1,p2', '-plots', 't', '-seed', '10', '-log_time', 'f', '-save_config', 't']
+
+@pytest.mark.toy
+class Test32(CmdTestCase):
+	'''
+	test reusing saved model by using configuration file
+	'''
+
+	nr = 32
+	data = 'test20_model'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-config', 'test20_model_rerun_model_config.json']
+
+@pytest.mark.toy
+class Test33(CmdTestCase):
+	'''
+	testing -config option with subgroups mode
+	'''
+
+	nr = 33
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-config', 'Test31_smlp_toy_num_resp_mult_args_config.json']
+
+@pytest.mark.real
+class Test34(CmdTestCase):
+	'''
+	doe test with four levels with full_factorial method
+	'''
+
+	nr = 34
+	data = 'doe_four_levels_real'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'full_factorial', '-log_time', 'f']
+
+@pytest.mark.real
+class Test35(CmdTestCase):
+	'''
+	doe test with four levels with plackett_burman
+	'''
+
+	nr = 35
+	data = 'doe_four_levels_real'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'plackett_burman', '-log_time', 'f']
+
+@pytest.mark.real
+@pytest.mark.test
+class Test36(CmdTestCase):
+	'''
+	doe test with four levels with sukharev_grid
+	'''
+
+	nr = 36
+	data = 'doe_four_levels_real'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'sukharev_grid', '-doe_samples', '125', '-log_time', 'f']
+
+@pytest.mark.real
+class Test37(CmdTestCase):
+	'''
+	doe test with four levels with box_behnken
+	'''
+
+	nr = 37
+	data = 'doe_three_levels_real_nan'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'box_behnken', '-log_time', 'f']
+
+@pytest.mark.real
+class Test38(CmdTestCase):
+	'''
+	doe test with four levels with box_wilson
+	'''
+
+	nr = 38
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'box_wilson', '-doe_cc_face', 'ccc', '-doe_cc_alpha', 'r', '-doe_cc_center', '2,3', '-log_time', 'f']
+
+@pytest.mark.real
+class Test39(CmdTestCase):
+	'''
+	doe test with four levels with latin_hypercube
+	'''
+
+	nr = 39
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'latin_hypercube', '-doe_prob_distr', 'Exponential', '-doe_samples', '30', '-log_time', 'f']
+
+@pytest.mark.real
+class Test40(CmdTestCase):
+	'''
+	doe test with four levels with latin_hypercube_space_filling
+	'''
+
+	nr = 40
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'latin_hypercube_sf', '-doe_samples', '20', '-log_time', 'f']
+
+@pytest.mark.real
+class Test41(CmdTestCase):
+	'''
+	doe test with four levels with random_k_means
+	'''
+
+	nr = 41
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'random_k_means', '-doe_samples', '20', '-log_time', 'f']
+
+@pytest.mark.real
+class Test42(CmdTestCase):
+	'''
+	doe test with four levels with maximin_reconstruction
+	'''
+
+	nr = 42
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'maximin_reconstruction', '-doe_samples', '20', '-log_time', 'f']
+
+@pytest.mark.real
+class Test43(CmdTestCase):
+	'''
+	doe test with four levels with halton_sequence
+	'''
+
+	nr = 43
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'halton_sequence', '-doe_samples', '20', '-log_time', 'f']
+
+@pytest.mark.real
+class Test44(CmdTestCase):
+	'''
+	doe test with four levels with uniform_random_matrix
+	'''
+
+	nr = 44
+	data = 'doe_two_levels'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'uniform_random_matrix', '-doe_samples', '20', '-log_time', 'f']
+
+@pytest.mark.real
+class Test45(CmdTestCase):
+	'''
+	doe test with four levels with fractional_factorial
+	'''
+
+	nr = 45
+	data = 'doe_two_levels_real'
+	new_data = ''
+	args = ['-mode', 'doe', '-doe_algo', 'fractional_factorial', '-doe_resolution', '5', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test46(CmdTestCase):
+	'''
+	tests options -pos_val and -neg_val
+	'''
+
+	nr = 46
+	data = 'smlp_toy_pf_mult'
+	new_data = 'smlp_toy_pf_mult'
+	args = ['-mode', 'predict', '-resp', 'PF,PF1', '-model', 'poly_sklearn', '-save_model', 't', '-save_model_config', 'f', '-use_model', 'f', '-model_name', 'test47_model', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test47(CmdTestCase):
+	'''
+	tests options -pos_val and -neg_val when re-using saved model
+	'''
+
+	nr = 47
+	data = 'test47_model'
+	new_data = 'smlp_toy_pf_mult'
+	args = ['-mode', 'predict', '-resp', 'PF,PF1', '-model', 'poly_sklearn', '-save_model', 'f', '-use_model', 't', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test48(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 48
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test49(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 49
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'quantile', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'category', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test50(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 50
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'kmeans', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'ordered', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+@pytest.mark.test
+class Test51(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 51
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'jenks', '-discr_bins', '6', '-discr_labels', 'f', '-discr_type', 'integer', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test52(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 52
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'jenks', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'ordered', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test53(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 53
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'ordinals', '-discr_bins', '6', '-discr_labels', 'f', '-discr_type', 'integer', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test54(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 54
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'ordinals', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test55(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 55
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'ranks', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'category', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test56(CmdTestCase):
+	'''
+	tests discretization options
+	'''
+
+	nr = 56
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'discretize', '-resp', 'PF,PF1', '-discr_algo', 'ranks', '-discr_bins', '6', '-discr_labels', 'f', '-discr_type', 'object', '-data_scaler', 'none', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test57(CmdTestCase):
+	'''
+	basic dt_sklearn assertion verfication test with numeric labels and integer grid as domain
+	'''
+
+	nr = 57
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'y1*2+x0<=5 and y1<=10;-2*y2-1<10-x2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test58(CmdTestCase):
+	'''
+	basic dt_sklearn optimization test with numeric labels and integer grid as domain and without scaling objectives
+	'''
+
+	nr = 58
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult.spec', '-objv_names', 'objv_y1,objv_y2', '-objv_exprs', 'y1;y2', '-epsilon', '0.01', '-delta_rel', '0.01', '-data_scaler', 'none', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test59(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test for functional nn_keras model
+	'''
+
+	nr = 59
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+@pytest.mark.test
+class Test60(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test for functional nn_keras model
+	'''
+
+	nr = 60
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test61(CmdTestCase):
+	'''
+	tests verificaion mode for NN with nn_keras_seq_api f
+	'''
+
+	nr = 61
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-nn_keras_epochs', '100', '-save_model_config', 'f', '--spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'not(x2<y1*2+x0<=(x1+1)>5 and y1<=10);-2*y2-1<10-x2 and x2>5 and x2<8', '-vacuity', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_seq_api', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test62(CmdTestCase):
+	'''
+	tests verificaion mode for NN with nn_keras_seq_api t
+	'''
+
+	nr = 62
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-nn_keras_epochs', '100', '-nn_keras_seq_api', 't', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'not(x2<y1*2+x0<=(x1+1)>5 and y1<=10);-2*y2-1<10-x2 and x2>5 and x2<8', '-vacuity', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test63(CmdTestCase):
+	'''
+	basic dt_sklearn assertion verification test on data with numeric labels
+	'''
+
+	nr = 63
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test63_model', '-spec', 'smlp_toy_num_resp_mult_y1_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x/2+y1>4.3;(y1+p2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.real
+class Test64(CmdTestCase):
+	'''
+	basic dt_sklearn  assertion verification test on data with one numeric response
+	'''
+
+	nr = 64
+	data = 'test63_model'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 't', '-spec', 'smlp_toy_num_resp_mult_y1_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x/2+y1>4.3;(y1+p2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test65(CmdTestCase):
+	'''
+	basic dt_sklearn assertion verification test on data with numeric labels
+	'''
+
+	nr = 65
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test65_model', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x0**2+y1>4.3;(y1+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.real
+class Test66(CmdTestCase):
+	'''
+	basic dt_sklearn  assertion verification test on data with one numeric response
+	'''
+
+	nr = 66
+	data = 'test65_model'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x0**2+y1>4.3;(y1+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test67(CmdTestCase):
+	'''
+	basic dt_sklearn assertion verification test on data with numeric labels
+	'''
+
+	nr = 67
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-model_per_response', 't', '-save_model', 't', '-use_model', 'f', '-model_name', 'test67_model', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x0**2+y1>4.3;(y1+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.real
+class Test68(CmdTestCase):
+	'''
+	basic dt_sklearn  assertion verification test on data with one numeric response
+	'''
+
+	nr = 68
+	data = 'test67_model'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-model_per_response', 't', '-save_model', 'f', '-use_model', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x0**2+y1>4.3;(y1+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test69(CmdTestCase):
+	'''
+	nn_keras verification test with model_per_response training
+	'''
+
+	nr = 69
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model', 't', '-use_model', 'f', '-model_name', 'test69_model', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '(y2**3+p2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.real
+class Test70(CmdTestCase):
+	'''
+	nn_keras verification test with re-using saved model_per_response trained model
+	'''
+
+	nr = 70
+	data = 'test69_model'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model', 'f', '-use_model', 't', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '(y2**3+p2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test71(CmdTestCase):
+	'''
+	nn_keras verification test with model_per_response training
+	'''
+
+	nr = 71
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model', 't', '-use_model', 'f', '-model_name', 'test71_model', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '(y1**3+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.real
+class Test72(CmdTestCase):
+	'''
+	nn_keras verification test with re-using saved model_per_response trained model
+	'''
+
+	nr = 72
+	data = 'test71_model'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model', 'f', '-use_model', 't', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '(y2**3+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test73(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	'''
+
+	nr = 73
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test73_model', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test74(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model. with separate model for each response
+	'''
+
+	nr = 74
+	data = 'test73_model'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test75(CmdTestCase):
+	'''
+	verification test run using model_rerun config covering the case when mrmr selcts only a subset of features specified through the command line or config file
+	'''
+
+	nr = 75
+	data = 'test73_model'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-config', 'test73_model_rerun_model_config.json']
+
+@pytest.mark.toy
+class Test76(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	'''
+
+	nr = 76
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test76_model', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.real
+class Test77(CmdTestCase):
+	'''
+	verification test run using model_rerun config covering the case when mrmr selcts only a subset of features specified through the command line or config file
+	'''
+
+	nr = 77
+	data = 'test76_model'
+	new_data = ''
+	args = ['-config', 'test76_model_rerun_model_config.json']
+
+@pytest.mark.toy
+class Test78(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	with one model for all responses
+	'''
+
+	nr = 78
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test78_model', '-mrmr_pred', '1', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'y1==9;y2>0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test79(CmdTestCase):
+	'''
+	basic test in query mode to test stability (theta) and guard (eta) constraint generation
+	'''
+
+	nr = 79
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'query', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult.spec', '-quer_names', 'query1,query2,query3', '-quer_exprs', '(y2**3+p2)/2<6;y1>=9;y2<0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.test
+class Test80(CmdTestCase):
+	'''
+	basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives
+	'''
+
+	nr = 80
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1', '-objv_exprs', '(y1+y2)/2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test81(CmdTestCase):
+	'''
+	basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives
+	'''
+
+	nr = 81
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1', '-objv_exprs', '(y1+y2)/2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test82(CmdTestCase):
+	'''
+	basic dt_sklearn single objective optimization test with numeric labels and integer grid as domain and with scaling objectives
+	'''
+
+	nr = 82
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1,objv2,objv3', '-objv_exprs', '(y1+y2)/2;y1;y2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test83(CmdTestCase):
+	'''
+	basic dt_sklearn multi objective pareto optimization test with numeric labels and integer grid as domain and with scaling objectives
+	'''
+
+	nr = 83
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps.spec', '-data_scaler', 'min_max', '-beta', 'y1>7 and y2>6', '-objv_names', 'obj1,objv2,objv3', '-objv_exprs', '(y1+y2)/2;y1/2-y2;y2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test84(CmdTestCase):
+	'''
+	tests global alpha constraints specified using option -alpha on inputs
+	'''
+
+	nr = 84
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-alpha', 'x2==7.0 and x0==0 and x1==2.5', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test85(CmdTestCase):
+	'''
+	tests alpha and eta constraints specified in command line in optimization mode
+	'''
+
+	nr = 85
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1,objv2', '-objv_exprs', '(y1+y2)/2;y1', '-alpha', 'p2<5 and x==10 and  x<12', '-eta', 'p1==4', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test86(CmdTestCase):
+	'''
+	tests alpha
+	beta and eta constraints specified in command line in optimization mode
+	'''
+
+	nr = 86
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1,objv2', '-objv_exprs', '(y1+y2)/2;y1', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+p2)/2<6;y1>=9;y2<0', '-alpha', 'p2<5 and x==10 and  x<12', '-eta', 'p1==4', '-epsilon', '0.05', '-delta_rel', '0.01', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test87(CmdTestCase):
+	'''
+	tests global alpha constraints and assertions specified in spec file
+	equivalent to test 84 where the same alpha and assertions are specified in command line
+	'''
+
+	nr = 87
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_alpha_asrt_verify.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test88(CmdTestCase):
+	'''
+	basic dt_sklearn multi objective pareto optimization test with beta and objectives specified in spec file
+	must give same results as test 83 where same beta and objectives is specified in command line
+	'''
+
+	nr = 88
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test89(CmdTestCase):
+	'''
+	basic test in query mode to test stability (theta) and guard (eta) constraint generation
+	'''
+
+	nr = 89
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'query', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_query.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test90(CmdTestCase):
+	'''
+	test to detect contradictory constraints in optsyn mode
+	'''
+
+	nr = 90
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn_vacuous.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test91(CmdTestCase):
+	'''
+	test to detect contradictory constraints in optimization mode due to contradictory alpha global and alpha bounds constraints on FMAX_xyx
+	'''
+
+	nr = 91
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'query', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_query_vacuous.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test92(CmdTestCase):
+	'''
+	test to detect contradictory constraints in verification mode
+	'''
+
+	nr = 92
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_verify_vacuous.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test93(CmdTestCase):
+	'''
+	basic test for mode optsyn
+	'''
+
+	nr = 93
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test94(CmdTestCase):
+	'''
+	basic test for rf_sklearn in model exploration mode optsyn
+	'''
+
+	nr = 94
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.test
+class Test95(CmdTestCase):
+	'''
+	basic test for dt_caret in model exploration mode optsyn
+	'''
+
+	nr = 95
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-save_model', 'f', '-use_model', 'f', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test96(CmdTestCase):
+	'''
+	basic test for rf_sklearn in model exploration mode optsyn
+	'''
+
+	nr = 96
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_caret', '-save_model', 'f', '-use_model', 'f', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test97(CmdTestCase):
+	'''
+	basic test for rf_sklearn in model exploration mode optsyn
+	'''
+
+	nr = 97
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'query', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_query.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test98(CmdTestCase):
+	'''
+	basic test for et_caret in model exploration mode optsyn
+	'''
+
+	nr = 98
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_caret', '-save_model', 'f', '-use_model', 'f', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test99(CmdTestCase):
+	'''
+	testing that the response and feature names can be taken from spec file in model exploration modes when the responses and/or features are not specified in the command line
+	'''
+
+	nr = 99
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test100(CmdTestCase):
+	'''
+	basic test for sat_threshold option enabing usage of objectve values in SAT assignments that prove optimization thresholds
+	'''
+
+	nr = 100
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test101(CmdTestCase):
+	'''
+	basic test in certify mode to test stability (theta) and guard (eta) constraint generation
+	'''
+
+	nr = 101
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test101_model', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_witness.spec', '-quer_names', 'query1,query2,query3', '-quer_exprs', '(y2**3+p2)/2<6;y1>=9;y2<20', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.real
+class Test102(CmdTestCase):
+	'''
+	basic test in certify mode to test stability (theta) and guard (eta) constraint generation
+	'''
+
+	nr = 102
+	data = 'test101_model'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 't', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_witness.spec', '-quer_names', 'query1,query2,query3', '-quer_exprs', '(y2**3+p2)/2<6;y1>=9;y2<20', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test103(CmdTestCase):
+	'''
+	
+	basic test in certify mode to test one valid witness and two conflicting witnesses for queries that are constant true
+	'''
+
+	nr = 103
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 't', '-use_model', 'f', '-model_name', 'test103_model', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_certify_witness.spec', '-quer_names', 'valid_candidate,grid_conflict,range_conflict', '-quer_exprs', 'True;True;True', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.test
+@pytest.mark.xfail(True, reason='wrong spec due to singleton value',
+                   raises=subprocess.CalledProcessError, strict=True)
+class Test104(CmdTestCase):
+	'''
+	assertion verfication test with wrong spec that does not assign a single value using a singleton grid or range with equal max and min
+	'''
+
+	nr = 104
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'y1*2+x<=5 and y1<=10;-2*y2-1<10-p2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test105(CmdTestCase):
+	'''
+	basic dt_sklearn assertion verfication test with numeric labels and integer grid as domain
+	'''
+
+	nr = 105
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_stable_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'y1*2+x<=5 and y1<=10;-2*y2-1<10-p2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test106(CmdTestCase):
+	'''
+	test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined
+	'''
+
+	nr = 106
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_unsat_eta_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'y1*2+x<=5 and y1<=10;-2*y2-1<10-p2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.xfail(True, reason='contradictory eta contraints',
+                   raises=subprocess.CalledProcessError, strict=True)
+class Test107(CmdTestCase):
+	'''
+	test for verification mode to check that eta contraints are not contradictory and as otherwise verification problem is not well defined
+	'''
+
+	nr = 107
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_beta_verify.spec', '-asrt_names', 'asrt_y1,asrt_y2', '-asrt_expr', 'y1*2+x<=5 and y1<=10;-2*y2-1<10-p2', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test108(CmdTestCase):
+	'''
+	basic test for dt_sklearn in model exploration mode synthesize where synthesis succeeds
+	'''
+
+	nr = 108
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'synthesize', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_synthesize.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test109(CmdTestCase):
+	'''
+	basic test for mode synthesize where synthesis fails
+	'''
+
+	nr = 109
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'synthesize', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_cannot_synthesize.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test110(CmdTestCase):
+	'''
+	smlp toy basic example for predict mode from SMLP user manual
+	'''
+
+	nr = 110
+	data = 'smlp_toy_basic'
+	new_data = 'smlp_toy_basic_pred_unlabeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'poly_sklearn', '-save_model', 't', '-model_name', 'test110_model', '-save_model_config', 't', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test111(CmdTestCase):
+	'''
+	smlp toy basic test to rerun saved model using the model rerun config file saved during model training
+	'''
+
+	nr = 111
+	data = 'test110_model'
+	new_data = 'smlp_toy_basic_pred_unlabeled'
+	args = ['-config', 'test110_model_rerun_model_config.json']
+
+@pytest.mark.toy
+class Test112(CmdTestCase):
+	'''
+	smlp toy basic test from SMLP manual
+	to rerun saved model without using the model rerun config file saved during model training and directly adding required options to command that match option values used during training
+	'''
+
+	nr = 112
+	data = 'test110_model'
+	new_data = 'smlp_toy_basic_pred_unlabeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'poly_sklearn', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-use_model', 't', '-save_model', 'f']
+
+@pytest.mark.toy
+class Test113(CmdTestCase):
+	'''
+	smlp toy basic test for mode optimize from SMLP manual
+	'''
+
+	nr = 113
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '0', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model', 't', '-model_name', 'test113_model', '-save_model_config', 't', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-spec', 'smlp_toy_basic.spec']
+
+@pytest.mark.toy
+class Test114(CmdTestCase):
+	'''
+	smlp toy basic test for mode optimize from SMLP manual without specifying resp and feat in command line
+	'''
+
+	nr = 114
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '0', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model', 'f', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-spec', 'smlp_toy_basic.spec']
+
+@pytest.mark.toy
+class Test115(CmdTestCase):
+	'''
+	basic test in certify mode
+	'''
+
+	nr = 115
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_system.spec', '-quer_names', 'query1,query2', '-quer_exprs', 'y1>0;y2<=0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test116(CmdTestCase):
+	'''
+	basic test in certify mode when system is specified and is used as the model; p2 rel-rad needs to be 0 or very close to it the witness to first query to be stable
+	'''
+
+	nr = 116
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_system.spec', '-quer_names', 'query1,query2', '-quer_exprs', 'y1>0;y2<=0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test117(CmdTestCase):
+	'''
+	certification test with knobs only where assertion is valid without stability and fails with stability
+	'''
+
+	nr = 117
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_certify.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test118(CmdTestCase):
+	'''
+	verification test with knobs only where assertion is valid without stability and fails with stability
+	'''
+
+	nr = 118
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'verify', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_verify.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test119(CmdTestCase):
+	'''
+	query test with knobs only where query is satisfiable without stability and fails with stability
+	'''
+
+	nr = 119
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'query', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_query.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.test
+class Test120(CmdTestCase):
+	'''
+	synthesis test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible
+	'''
+
+	nr = 120
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'synthesize', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_fail.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test121(CmdTestCase):
+	'''
+	synthesis test with constant knob and no inputs where synthesis is feasible
+	'''
+
+	nr = 121
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'synthesize', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test122(CmdTestCase):
+	'''
+	optimization test with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed
+	'''
+
+	nr = 122
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'lazy', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_fail.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test123(CmdTestCase):
+	'''
+	optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed
+	'''
+
+	nr = 123
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test124(CmdTestCase):
+	'''
+	optimized synthesis test with constant knob and no inputs where synthesis is not feasible because while beta constraint is feasible the assertion is not feasible therefore optimization is not performed
+	'''
+
+	nr = 124
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-pareto', 'f', '-opt_strategy', 'lazy', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_fail.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test125(CmdTestCase):
+	'''
+	optimized synthesis test with constant knob and no inputs where synthesis is feasible and optimization is performed
+	'''
+
+	nr = 125
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test126(CmdTestCase):
+	'''
+	verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability
+	'''
+
+	nr = 126
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'verify', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_verify.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test127(CmdTestCase):
+	'''
+	certification example with knobs only and fictitious inputs with values fixed through their ranges
+	where query is valid without stability and fails with stability
+	'''
+
+	nr = 127
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_certify.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test128(CmdTestCase):
+	'''
+	Basic regression test in certify mode covering all four possible outcomes when certifying a witness for a query: the witness is stable
+	the witness is valid but not stable
+	the witness is invalid
+	and the constraints are conflicting. The fifth query and witness capture a scenario where the polynomial model conversion to terms was missing the intercepts.
+	'''
+
+	nr = 128
+	data = 'smlp_toy_ctg_num_resp'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'poly_sklearn', '-dt_sklearn_max_depth', '15', '-save_model', 'f', '-use_model', 'f', '-model_per_response', 'f', '-spec', 'smlp_toy_witness_certify.spec', '-quer_names', 'query_stable_witness,query_grid_conflict,query_unstable_witness,query_infeasible_witness,query_poly_intercept_sensitive', '-quer_exprs', 'y2<=90;y1>=9;y1>=(-13);y1>9;y1>=(-10)', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test129(CmdTestCase):
+	'''
+	verification example with demonstrating all basic result scenarious for assertions
+	'''
+
+	nr = 129
+	data = 'smlp_toy_ctg_num_resp'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'poly_sklearn', '-save_model', 'f', '-use_model', 'f', '-model_per_response', 'f', '-spec', 'smlp_toy_configuration_verify.spec', '-asrt_names', 'assert_stable_config,assert_grid_conflict,assert_unstable_config,assert_infeasible', '-asrt_exprs', 'y2<=90;y1>=9;y1>=(-10);y1>20', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test130(CmdTestCase):
+	'''
+	captures scenario that interface constraints are consistent but model constraints are (because y2 is declared as int and not as real) -- constant input x1 is dropped as constant feature since it does not occur in constraints
+	'''
+
+	nr = 130
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x1,x2,p1,p2', '-resp', 'y1,y2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_const_input.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test131(CmdTestCase):
+	'''
+	test where input x1 has a constant range 0 and in data it is also constant and it is dropped before building the model because it does not occur in constraints alpha
+	eta
+	beta
+	'''
+
+	nr = 131
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x1,x2,p1,p2', '-resp', 'y1,y2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_const_input_const_range.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test132(CmdTestCase):
+	'''
+	test where input x1 has a non-constant range 0 to 1 and in data it is constant and it is not dropped before building the model because its range is constrained to constant value through alpha constraint
+	'''
+
+	nr = 132
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x1,x2,p1,p2', '-resp', 'y1,y2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_const_input_alpha.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test133(CmdTestCase):
+	'''
+	captures scenario that interface constraints are consistent but model constraints are not (because y2 is declared as int and not as real) -- constant inut x1 is dropped explicitly using -feat option ; uses uses dt_sklearn
+	'''
+
+	nr = 133
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x2,p1,p2', '-resp', 'y1,y2', '-model', 'dt_sklearn', '-tree_encoding', 'nested', '-compress_rules', 'f', '-spec', 'smlp_toy_const_input_dropped.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test134(CmdTestCase):
+	'''
+	test that model cnstraints are consistent with interface constraints when system is used as the model and output y2 is declared as int -- the model constraints are consistent because y2 is defined as p1+p2-x2 and due to constraints all these variables can assume only integer values thus u2 can also only be an integer
+	'''
+
+	nr = 134
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x2,p1,p2', '-resp', 'y1,y2', '-model', 'system', '--spec', 'smlp_toy_consistent_system.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test135(CmdTestCase):
+	'''
+	captures scenario where interface constraints are consistent but model constraints are not (because y2 is declared as int and not as real) -- constant inut x1 is dropped explicitly using -feat option ; uses nn_keras model which is the only difference with test 133 but which model is used does not matter as the problem is in declaration of y2 as int
+	'''
+
+	nr = 135
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x2,p1,p2', '-resp', 'y1,y2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-nn_keras_epochs', '20', '-spec', 'smlp_toy_const_input_dropped.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test136(CmdTestCase):
+	'''
+	tests usage of compressed data files as well as data files without .csv suffix
+	'''
+
+	nr = 136
+	data = 'smlp_toy_num_resp_mult_compressed.csv.gz'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test137(CmdTestCase):
+	'''
+	tests usage of compressed data files as well as data files without .csv suffix
+	'''
+
+	nr = 137
+	data = 'smlp_toy_num_resp_mult_compressed'
+	new_data = 'smlp_toy_num_resp_mult_compressed.csv.bz2'
+	args = ['-mode', 'synthesize', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_synthesize.spec', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test138(CmdTestCase):
+	'''
+	adapts test 134 by changing the system function for y2 to p1+p2-x2+0.01 so while p1
+	p2 and x2 can only assume int values y2 can become non-integer which violates the declartion of y2 as integer -- hence the conflict of the system/model constraints with alpha and eta constraits and variable domain declarations
+	'''
+
+	nr = 138
+	data = 'smlp_toy_const_input'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-feat', 'x2,p1,p2', '-resp', 'y1,y2', '-model', 'system', '--spec', 'smlp_toy_inconsistent_system.spec', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test139(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	'''
+
+	nr = 139
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test140(CmdTestCase):
+	'''
+	verification example with knobs only and fictitious inputs that have no effect where proparty is valid without stability and fails with stability
+	'''
+
+	nr = 140
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'verify', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_verify.spec', '-trace_prec', '1', '-trace_anonym', 't', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test141(CmdTestCase):
+	'''
+	basic test for compress_rules option for dt_sklearn in optimization mode
+	'''
+
+	nr = 141
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-pareto', 'f', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 't', '-spec', 'smlp_toy_num_resp_mult.spec', '-objv_names', 'objv_y1,objv_y2', '-objv_exprs', 'y1;y2', '-epsilon', '0.01', '-delta_rel', '0.01', '-data_scaler', 'none', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test142(CmdTestCase):
+	'''
+	basic test for compress_rules option for rf_sklearn in optsin mode
+	'''
+
+	nr = 142
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test143(CmdTestCase):
+	'''
+	basic test for compress_rules for et_sklearn in mode query
+	'''
+
+	nr = 143
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'query', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'nested', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_query.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test144(CmdTestCase):
+	'''
+	basic test for compress_rules for dt_sklearn in mode verify and re-using saved model
+	'''
+
+	nr = 144
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-tree_encoding', 'nested', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2', '-asrt_exprs', 'x0**2+y1>4.3;(y1+x2)/2<6', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.real
+class Test145(CmdTestCase):
+	'''
+	optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed
+	'''
+
+	nr = 145
+	data = ''
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-doe_spec', '../grids/doe_two_levels_opt.csv', '-doe_algo', 'latin_hypercube', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.real
+class Test146(CmdTestCase):
+	'''
+	optimization test with constant knob and no inputs where synthesis is feasible and optimization is performed
+	'''
+
+	nr = 146
+	data = ''
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-model', 'poly_sklearn', '-resp', 'y1,y2', '-feat', 'p1,p2,x1,x2', '-save_model', 't', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-split', '1', '-spec', 'smlp_toy_system.spec', '-doe_spec', '../grids/explore_doe_two_levels.csv', '-doe_algo', 'latin_hypercube', '-epsilon', '0.99999999', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test147(CmdTestCase):
+	'''
+	checks nn_keras prediction with sw_coef 0.8 and sequential API
+	adapts test 28
+	'''
+
+	nr = 147
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-sw_coef', '0.8']
+
+@pytest.mark.toy
+class Test148(CmdTestCase):
+	'''
+	checks nn_keras prediction with sw_coef 0.8 and sequential API
+	adapts test 28 to have two responses
+	'''
+
+	nr = 148
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-sw_coef', '0.8']
+
+@pytest.mark.toy
+class Test149(CmdTestCase):
+	'''
+	tests the mae loss function MeanAbsoluteError and sample weoghts
+	'''
+
+	nr = 149
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'mae', '-sw_coef', '0.8']
+
+@pytest.mark.toy
+class Test150(CmdTestCase):
+	'''
+	tests the mape loss function MeanAbsolutePercentageError and sample weights
+	'''
+
+	nr = 150
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'mape', '-sw_coef', '0.8']
+
+@pytest.mark.toy
+class Test151(CmdTestCase):
+	'''
+	tests msle loss function MeanSquaredLogarithmicError and and sample weoghts
+	'''
+
+	nr = 151
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'msle', '-sw_coef', '3', '-sw_exp', '10', '-sw_int', '0']
+
+@pytest.mark.toy
+class Test152(CmdTestCase):
+	'''
+	tests the huber loss function Huber and sample weights
+	'''
+
+	nr = 152
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'huber', '-sw_coef', '8', '-sw_exp', '5', '-sw_int', '0.5']
+
+@pytest.mark.toy
+class Test153(CmdTestCase):
+	'''
+	tests the logcosh loss function LogCosh and sample weights
+	'''
+
+	nr = 153
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nn_keras_loss', 'logcosh', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'mse']
+
+@pytest.mark.toy
+class Test154(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test that uses keras tuner for functional model training
+	'''
+
+	nr = 154
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3,3,3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5']
+
+@pytest.mark.toy
+class Test155(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training
+	'''
+
+	nr = 155
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3,3,3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'mae']
+
+@pytest.mark.toy
+class Test156(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test that uses keras tuner for functional model training; adapts test 154 by consdering multiple responses
+	'''
+
+	nr = 156
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'rmse']
+
+@pytest.mark.toy
+class Test157(CmdTestCase):
+	'''
+	basic nn_keras assertion verification test that uses keras tuner with sequrntial models for model training; adapts test 155 by consdering multiple responses
+	'''
+
+	nr = 157
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '--model', 'nn_keras', '-nnet_encoding', 'nested', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'rmse,logcosh']
+
+@pytest.mark.toy
+class Test158(CmdTestCase):
+	'''
+	tests the mape loss function and sample weights with model_per_response t
+	adapts test 152
+	'''
+
+	nr = 158
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'mape', '-model_per_response', 't', '-sw_coef', '8', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'rmse']
+
+@pytest.mark.toy
+class Test159(CmdTestCase):
+	'''
+	tests the msle loss function and sample weights with model_per_response t
+	adapts test 153
+	'''
+
+	nr = 159
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nn_keras_loss', 'msle', '-model_per_response', 't', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'mae,cosine']
+
+@pytest.mark.toy
+class Test160(CmdTestCase):
+	'''
+	tests nn keras tuner bayesian
+	'''
+
+	nr = 160
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nn_keras_loss', 'mape', '-nn_keras_metrics', 'msle', '-nn_keras_tuner', 'bayesian', '-nn_keras_layers_grid', '2,3', '-nn_keras_losses_grid', 'mse,mae,huber', '-model_per_response', 'f', '-sw_coef', '8', '-sw_exp', '5', '-sw_int', '0.5']
+
+@pytest.mark.toy
+class Test161(CmdTestCase):
+	'''
+	tests nn keras tuner bayesian
+	'''
+
+	nr = 161
+	data = 'smlp_toy_num_resp_mult'
+	new_data = 'smlp_toy_num_resp_mult_pred_labeled'
+	args = ['-mode', 'predict', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nnet_encoding', 'nested', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nn_keras_loss', 'msle', '-nn_keras_metrics', 'mape,logcosh', '-nn_keras_tuner', 'random', '-nn_keras_lrates_grid', '0.01,0.001', '-nn_keras_batches_grid', '32,64', '-model_per_response', 'f', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5']
+
+@pytest.mark.toy
+class Test162(CmdTestCase):
+	'''
+	tests model term construction with flat_encoding of tress and model per reponse when mrmr_pred is activated and not all features are selected for training the model
+	adapts test 139
+	'''
+
+	nr = 162
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test163(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	adapts test 139
+	'''
+
+	nr = 163
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test164(CmdTestCase):
+	'''
+	basic flat tree encoding test for dt_sklearn multi objective pareto optimization
+	'''
+
+	nr = 164
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test165(CmdTestCase):
+	'''
+	basic flat tree encoding test for dt_caretin model exploration mode optsyn
+	model_per_response is forced to true for caret models
+	adapts test 95
+	'''
+
+	nr = 165
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-tree_encoding', 'flat', '-save_model', 'f', '-use_model', 'f', '-compress_rules', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test166(CmdTestCase):
+	'''
+	basic flat tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn
+	'''
+
+	nr = 166
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '4', '-rf_sklearn_n_estimators', '3', '-tree_encoding', 'flat', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-compress_rules', 't', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test167(CmdTestCase):
+	'''
+	basic flat tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn
+	adapts test 94 and test 166
+	'''
+
+	nr = 167
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '4', '-rf_sklearn_n_estimators', '3', '-tree_encoding', 'flat', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test168(CmdTestCase):
+	'''
+	basic test for rf_caret with flat tree_encoding and modelper_response in model exploration mode optimize
+	'''
+
+	nr = 168
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_caret', '-model_per_response', 't', '-compress_rules', 't', '-tree_encoding', 'flat', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test169(CmdTestCase):
+	'''
+	basic test for et_sklearn with flat tree_encoding and model_per_response t in model exploration mode optimize
+	'''
+
+	nr = 169
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-rf_sklearn_n_estimators', '3', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'flat', '-model_per_response', 't', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test170(CmdTestCase):
+	'''
+	basic test for et_sklearn with flat tree_encoding and model_per_response f in model exploration mode optimize
+	'''
+
+	nr = 170
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-rf_sklearn_n_estimators', '3', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'flat', '-model_per_response', 'f', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test171(CmdTestCase):
+	'''
+	basic test for et_caret with flat tree_encoding in model exploration mode optimize
+	'''
+
+	nr = 171
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_caret', '-tree_encoding', 'flat', '-model_per_response', 't', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test172(CmdTestCase):
+	'''
+	basic test for nn_keras flat encoding for functional api
+	i
+	one response variable
+	adapts test 154
+	'''
+
+	nr = 172
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nnet_encoding', 'layered', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3,3,3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test173(CmdTestCase):
+	'''
+	basic test for nn_keras flat encoding for sequential api
+	one response variable
+	adapts test 155
+	'''
+
+	nr = 173
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'verify', '-resp', 'y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nnet_encoding', 'layered', '-nn_keras_tuner', 'hyperband', '-nn_keras_layers_grid', '2,2;3,3,3', '-save_model_config', 'f', '-spec', 'smlp_toy_num_resp_mult_y2_verify.spec', '-asrt_names', 'asrt1', '-asrt_exprs', '2*y2>1', '-sw_coef', '4', '-sw_exp', '5', '-sw_int', '0.5', '-nn_keras_metrics', 'mae', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test174(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn
+	'''
+
+	nr = 174
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test175(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn
+	'''
+
+	nr = 175
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test176(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api f for nn_keras in model exploration mode optsyn
+	'''
+
+	nr = 176
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test177(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn
+	'''
+
+	nr = 177
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test178(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response t nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features are not scaled adapts test 177
+	'''
+
+	nr = 178
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-scale_feat', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test179(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api f for nn_keras in model exploration mode optsyn when resposes are not scaled adapts test 174
+	'''
+
+	nr = 179
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 'f', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 'f', '-scale_resp', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test180(CmdTestCase):
+	'''
+	basic layered nn_keras encoding test with model_per_response f nn_keras_seq_api t for nn_keras in model exploration mode optsyn when features and responses are not scaled adapts test 175
+	'''
+
+	nr = 180
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'nn_keras', '-nn_keras_epochs', '20', '-nn_keras_seq_api', 't', '-nnet_encoding', 'layered', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 'f', '-scale_feat', 'f', '-scale_resp', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test181(CmdTestCase):
+	'''
+	basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164
+	'''
+
+	nr = 181
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-scale_feat', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test182(CmdTestCase):
+	'''
+	basic flat tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164
+	'''
+
+	nr = 182
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-scale_resp', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test183(CmdTestCase):
+	'''
+	basic flat tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164
+	'''
+
+	nr = 183
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'flat', '-scale_resp', 'f', '-scale_feat', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test184(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model adapts test 139
+	'''
+
+	nr = 184
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test185(CmdTestCase):
+	'''
+	tests model term construction with branched_encoding of tress and model per reponse when mrmr_pred is activated and not all features are selected for training the model
+	adapts test 162
+	'''
+
+	nr = 185
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test186(CmdTestCase):
+	'''
+	tests model term formation when mrmr_pred is activated and not all features are selected for training the model
+	adapts test 163
+	'''
+
+	nr = 186
+	data = 'smlp_toy_num_resp_noknobs'
+	new_data = 'smlp_toy_num_resp_noknobs_pred_labeled'
+	args = ['-mode', 'verify', '-resp', 'y1,y2', '-feat', 'x0,x1,x2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_noknobs_verify.spec', '-asrt_names', 'asrt1,asrt2,asrt3', '-asrt_exprs', '(y2**3+x2)/2<6;y1>=9;y2<0', '-trace_anonym', 't', '-trace_prec', '3', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test187(CmdTestCase):
+	'''
+	basic branched tree encoding test for dt_sklearn multi objective pareto optimization adapts test 164
+	'''
+
+	nr = 187
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test188(CmdTestCase):
+	'''
+	basic branched tree encoding test for dt_caretin model exploration mode optsyn
+	model_per_response is forced to true for caret models
+	adapts test 165
+	'''
+
+	nr = 188
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_caret', '-tree_encoding', 'branched', '-save_model', 'f', '-use_model', 'f', '-compress_rules', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test189(CmdTestCase):
+	'''
+	basic branched tree encoding test with model_per_response f for rf_sklearn in model exploration mode optsyn adapts test 166
+	'''
+
+	nr = 189
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '4', '-rf_sklearn_n_estimators', '3', '-tree_encoding', 'branched', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-compress_rules', 't', '-mrmr_pred', '2', '-model_per_response', 'f', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test190(CmdTestCase):
+	'''
+	basic test for rf_caret with branched tree_encoding and modelper_response in model exploration mode optimize adapts test 168
+	'''
+
+	nr = 190
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_caret', '-model_per_response', 't', '-compress_rules', 't', '-tree_encoding', 'branched', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test191(CmdTestCase):
+	'''
+	basic test for et_sklearn with branched tree_encoding and model_per_response t in model exploration mode optimize adapts test 169
+	'''
+
+	nr = 191
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-et_sklearn_n_estimators', '3', '-et_sklearn_bootstrap', 't', '-tree_encoding', 'branched', '-model_per_response', 't', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test192(CmdTestCase):
+	'''
+	basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results
+	cvc5 faild with incomparable ite tipes for if and else branches)
+	'''
+
+	nr = 192
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-et_sklearn_n_estimators', '100', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'branched', '-model_per_response', 'f', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test193(CmdTestCase):
+	'''
+	basic test for et_caret with branched tree_encoding in model exploration mode optimize adapts test 171
+	'''
+
+	nr = 193
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_caret', '-tree_encoding', 'branched', '-model_per_response', 't', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test194(CmdTestCase):
+	'''
+	basic branched tree encoding test with model_per_response t for rf_sklearn in model exploration mode optsyn
+	adapts test 94 and test 167
+	'''
+
+	nr = 194
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'rf_sklearn', '-rf_sklearn_max_depth', '4', '-rf_sklearn_n_estimators', '3', '-tree_encoding', 'branched', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test195(CmdTestCase):
+	'''
+	basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 192 by setting n_estimators 3 and then discrepancy between z3
+	mathsat and yices results disappear
+	'''
+
+	nr = 195
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-et_sklearn_n_estimators', '3', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'branched', '-model_per_response', 'f', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test196(CmdTestCase):
+	'''
+	basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features are not scaled modifies test 164 and test 181
+	'''
+
+	nr = 196
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-scale_feat', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test197(CmdTestCase):
+	'''
+	basic branched tree encoding test for dt_sklearn multi objective pareto optimization when responses are not scaled modifies test 164 and test 182
+	'''
+
+	nr = 197
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-scale_resp', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test198(CmdTestCase):
+	'''
+	basic branched tree encoding test for dt_sklearn multi objective pareto optimization when features and responses are not scaled modifies test 164 and test 183
+	'''
+
+	nr = 198
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-tree_encoding', 'branched', '-scale_resp', 'f', '-scale_feat', 'f', '-spec', 'smlp_toy_num_resp_mult_free_inps_beta_objv.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat']
+
+@pytest.mark.toy
+class Test199(CmdTestCase):
+	'''
+	test to demonstrate that in pareto optimization and optsyn modes with multiple objectives when beta constraints are not present SMLP results are not consistent when different solvers are used; this is due to fact that when a subset of objectoves are exemined in pareto algo
+	outputs not covered by the active objectives become don't cares (there are no contraints on then except model constraints) and this situation is likely not modeled in SMLP accurately; modifies test 192 to use z3 instead of mathsat
+	'''
+
+	nr = 199
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-et_sklearn_n_estimators', '100', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'branched', '-model_per_response', 'f', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0.05', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test200(CmdTestCase):
+	'''
+	basic test for et_sklearn with branched tree_encoding and model_per_response f in model exploration mode optimize adapts test 170 !!!!!!!!! in this test z3 result differs from mathsat and yices results (the latter two give sma results
+	cvc5 faild with incomparable ite tipes for if and else branches)
+	'''
+
+	nr = 200
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-opt_strategy', 'lazy', '-resp', 'y1,y2', '-feat', 'x,p1,p2', '-model', 'et_sklearn', '-et_sklearn_max_depth', '2', '-et_sklearn_n_estimators', '100', '-et_sklearn_bootstrap', 'f', '-tree_encoding', 'branched', '-model_per_response', 'f', '-compress_rules', 't', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-spec', 'smlp_toy_num_resp_mult_optsyn.spec', '-epsilon', '0.1', '-delta_rel', '0', '-solver_path', 'mathsat-5.6.8-linux-x86_64-reentrant/bin/mathsat', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test201(CmdTestCase):
+	'''
+	basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and there are beta constraints
+	'''
+
+	nr = 201
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'eager', '-resp', 'y1,y2', '-feat', 'p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_no_input_beta.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1', '-objv_exprs', '(y1+y2)/2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test202(CmdTestCase):
+	'''
+	basic dt_sklearn single objective optimization with the eager algorithm when there are no inputs and no beta constraints
+	'''
+
+	nr = 202
+	data = 'smlp_toy_num_resp_mult'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'eager', '-resp', 'y1,y2', '-feat', 'p1,p2', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_num_resp_mult_no_input.spec', '-data_scaler', 'min_max', '-objv_names', 'obj1', '-objv_exprs', '(y1+y2)/2', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test203(CmdTestCase):
+	'''
+	optimization test with eager strategy and with constant knob and no inputs where synthesis is not feasible because the assertion is not feasible but beta constraint is feasible therefore optimization is performed adapts test 122
+	'''
+
+	nr = 203
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 'f', '-opt_strategy', 'eager', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_fail.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test204(CmdTestCase):
+	'''
+	optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 123
+	'''
+
+	nr = 204
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'eager', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.real
+class Test205(CmdTestCase):
+	'''
+	optimization test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 145
+	'''
+
+	nr = 205
+	data = ''
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-opt_strategy', 'eager', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-doe_spec', '../grids/doe_two_levels_opt.csv', '-doe_algo', 'latin_hypercube', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test206(CmdTestCase):
+	'''
+	optimized synthesis test with eager strategy and with constant knob and no inputs where synthesis is feasible and optimization is performed adapts test 125
+	'''
+
+	nr = 206
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'optsyn', '-pareto', 't', '-opt_strategy', 'eager', '-model', 'system', '-resp', 'y1,y2', '-feat', 'p1,p2', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '0', '-model_per_response', 't', '-spec', 'smlp_toy_system_stable_constant_synth_feasible.spec', '-epsilon', '0.00000001', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test207(CmdTestCase):
+	'''
+	basic test for mode frontier -- selecting pareto frontier directly from data without building a model
+	'''
+
+	nr = 207
+	data = 'smlp_toy_frontier_beta'
+	new_data = ''
+	args = ['-mode', 'frontier', '-pareto', 't', '-resp', 'y', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_beta.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test208(CmdTestCase):
+	'''
+	testing unbounded knob and input domains for mode frontier when knob and input are of type real -- bounds inf and minus inf are specified in the spec file as null
+	'''
+
+	nr = 208
+	data = 'smlp_toy_frontier_beta'
+	new_data = ''
+	args = ['-mode', 'frontier', '-pareto', 't', '-resp', 'y', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_real.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test209(CmdTestCase):
+	'''
+	testing unbounded knob and input domains for mode frontier when knob and input are of typ eint -- bounds inf and minus inf are specified in the spec file as null
+	'''
+
+	nr = 209
+	data = 'smlp_toy_frontier_null_bounds_int'
+	new_data = ''
+	args = ['-mode', 'frontier', '-pareto', 't', '-resp', 'y', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_int.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test210(CmdTestCase):
+	'''
+	test for the frontier mode on data and spec such that no data points satisfy eta constrints
+	as a result the pareto frontier is empty
+	'''
+
+	nr = 210
+	data = 'smlp_toy_frontier_null_bounds_empty'
+	new_data = ''
+	args = ['-mode', 'frontier', '-pareto', 't', '-resp', 'y1,y2', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_empty_eta.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test211(CmdTestCase):
+	'''
+	test for the frontier mode on data and spec such that no data points satisfy eta constrints
+	as a result the pareto frontier is empty
+	'''
+
+	nr = 211
+	data = 'smlp_toy_frontier_null_bounds_empty'
+	new_data = ''
+	args = ['-mode', 'frontier', '-pareto', 't', '-resp', 'y1,y2', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_empty_alpha.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test212(CmdTestCase):
+	'''
+	testing unbounded knob and input domains for optimization mode -- bounds inf and minus inf are specified in the spec file as null
+	'''
+
+	nr = 212
+	data = 'smlp_toy_frontier_null_bounds_int'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-resp', 'y', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_int.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test213(CmdTestCase):
+	'''
+	test optimize mode on data and spec such that no data points satisfy eta constrints
+	'''
+
+	nr = 213
+	data = 'smlp_toy_frontier_null_bounds_empty'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-resp', 'y1,y2', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_empty_eta.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test214(CmdTestCase):
+	'''
+	test for the frontier mode on data and spec such that no data points satisfy eta constrints
+	as a result the pareto frontier is empty
+	'''
+
+	nr = 214
+	data = 'smlp_toy_frontier_null_bounds_empty'
+	new_data = ''
+	args = ['-mode', 'optimize', '-pareto', 't', '-resp', 'y1,y2', '-feat', 'x,p', '-model', 'dt_sklearn', '-dt_sklearn_max_depth', '15', '-compress_rules', 'f', '-spec', 'smlp_toy_frontier_null_bounds_empty_alpha.spec', '-data_scaler', 'min_max', '-epsilon', '0.05', '-delta_rel', '0.01', '-save_model_config', 'f', '-mrmr_pred', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test215(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type object
+	'''
+
+	nr = 215
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test216(CmdTestCase):
+	'''
+	basic test for correlate mode
+	'''
+
+	nr = 216
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test217(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type category
+	adapts test 215
+	'''
+
+	nr = 217
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'category', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test218(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type ordered
+	adapts test 215
+	'''
+
+	nr = 218
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'ordered', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test219(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features casted to integer
+	adapts test 215
+	'''
+
+	nr = 219
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'integer', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test220(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type object and tests the normalized mutual information
+	adapts test 215
+	'''
+
+	nr = 220
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'normalized', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test221(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type object and tests the Shannon mutual information
+	adapts test 215
+	'''
+
+	nr = 221
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'shannon', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test222(CmdTestCase):
+	'''
+	basic test for correlate mode
+	contains correlations for categorical features of type object and tests the adjusted mutual information
+	adapts test 215
+	'''
+
+	nr = 222
+	data = 'smlp_toy_mult_discr'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'PF,PF1', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'adjusted', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f', '-pos_val', 'fail', '-neg_val', 'pass']
+
+@pytest.mark.toy
+class Test223(CmdTestCase):
+	'''
+	basic test for correlate mode and tests the normalized mutual information
+	adapts test 216
+	'''
+
+	nr = 223
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'normalized', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test224(CmdTestCase):
+	'''
+	basic test for correlate mode and tests the Shannon mutual information
+	adapts test 216
+	'''
+
+	nr = 224
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'shannon', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test225(CmdTestCase):
+	'''
+	basic test for correlate mode and tests the adjusted mutual information
+	adapts test 216
+	'''
+
+	nr = 225
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'adjusted', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test226(CmdTestCase):
+	'''
+	basic test for correlate mode
+	'''
+
+	nr = 226
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discret_num', 't', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'correlation', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test227(CmdTestCase):
+	'''
+	basic test for correlate mode and tests the normalized mutual information
+	adapts test 216 and 223
+	'''
+
+	nr = 227
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'correlate', '-resp', 'y1,y2', '-discr_algo', 'uniform', '-discret_num', 't', '-discr_bins', '6', '-discr_labels', 't', '-discr_type', 'object', '-data_scaler', 'none', '-cont_est', 'pearson,spearman,kendall', '-mi_method', 'normalized', '-mrmr_pred', '0', '-plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test228(CmdTestCase):
+	'''
+	test that radii specified in command line properly override the radii specified in the spec file. Here we override both ansolute and relative radii and one can observe that the certification results also change compared to test 116
+	'''
+
+	nr = 228
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_system_radii_update_certify.spec', '-rad_rel', '0.005', '-rad_abs', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+@pytest.mark.xfail(True, reason='missing radius spec for p1/p2',
+                   raises=subprocess.CalledProcessError, strict=True)
+class Test229(CmdTestCase):
+	'''
+	basic test for checking that each knob must have either absolute or relative radius specified in the spec file (even if radii are specified in the command line)
+	'''
+
+	nr = 229
+	data = 'smlp_toy_basic'
+	new_data = ''
+	args = ['-mode', 'certify', '-resp', 'y1,y2', '-feat', 'x1,x2,p1,p2', '-model', 'system', '-save_model', 'f', '-use_model', 'f', '-mrmr_pred', '2', '-model_per_response', 't', '-spec', 'smlp_toy_missing_radii.spec', '-rad_rel', '0.005', '-rad_abs', '0', '-plots', 'f', '-pred_plots', 'f', '-resp_plots', 'f', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test230(CmdTestCase):
+	'''
+	tests that outputs in system specificaation might depend on different inuts (knobs and free inputs)
+	'''
+
+	nr = 230
+	data = 'smlp_toy_monotone_basic.csv'
+	new_data = ''
+	args = ['-mode', 'verify', '-spec', 'smlp_toy_system_monotone_knob.05_verify.spec', '-model', 'system', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test231(CmdTestCase):
+	'''
+	certification test with monotonicity query with a knob with a grid point
+	'''
+
+	nr = 231
+	data = 'smlp_toy_monotone_basic.csv'
+	new_data = ''
+	args = ['-mode', 'certify', '-spec', 'smlp_toy_system_decreasing_knob.05_certify.spec', '-model', 'system', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test232(CmdTestCase):
+	'''
+	running example from smlp manual
+	contains for witnesses to a query
+	covering all possible witness scenarios -- stable witness
+	unstable witness
+	and not a witness cases
+	'''
+
+	nr = 232
+	data = 'smlp_toy_system_running_example_certify'
+	new_data = ''
+	args = ['-mode', 'certify', '-spec', 'smlp_toy_system_running_example_certify.spec', '-model', 'system', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test233(CmdTestCase):
+	'''
+	tests subgroup discovery mode when the response has string values
+	e
+	g
+	yes/no
+	pass/fail
+	'''
+
+	nr = 233
+	data = 'smlp_toy_string_response'
+	new_data = ''
+	args = ['-mode', 'subgroups', '-resp', 'str_resp1', '-feat', 'num,int,str', '-pos_val', 'no', '-neg_val', 'yes', '-seed', '10', '-log_time', 'f']
+
+@pytest.mark.toy
+class Test234(CmdTestCase):
+	'''
+	tests subgroup discovery mode when there are two responses with string values
+	e
+	g
+	yes/no
+	pass/fail
+	'''
+
+	nr = 234
+	data = 'smlp_toy_string_response'
+	new_data = ''
+	args = ['-mode', 'subgroups', '-resp', 'str_resp1,str_resp2', '-feat', 'num,int,str', '-pos_val', 'no', '-neg_val', 'yes', '-seed', '10', '-log_time', 'f']
+
+
diff --git a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_args_config.json b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_args_config.json
index f0b0b4fe..8592e30b 100644
--- a/regr_smlp/master/Test31_smlp_toy_num_resp_mult_args_config.json
+++ b/regr_smlp/master/Test31_smlp_toy_num_resp_mult_args_config.json
@@ -103,7 +103,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json b/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json
index 1206555c..335e50d7 100644
--- a/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json
+++ b/regr_smlp/master/Test32_test20_model_smlp_toy_num_resp_mult_pred_labeled_args_config.json
@@ -103,7 +103,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/Test87_smlp_toy_num_resp_mult_rerun_model_config.json b/regr_smlp/master/Test87_smlp_toy_num_resp_mult_rerun_model_config.json
index 73b8fa90..c5fbfc72 100644
--- a/regr_smlp/master/Test87_smlp_toy_num_resp_mult_rerun_model_config.json
+++ b/regr_smlp/master/Test87_smlp_toy_num_resp_mult_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/Test92_smlp_toy_num_resp_mult_rerun_model_config.json b/regr_smlp/master/Test92_smlp_toy_num_resp_mult_rerun_model_config.json
index 682d6c96..79a6c350 100644
--- a/regr_smlp/master/Test92_smlp_toy_num_resp_mult_rerun_model_config.json
+++ b/regr_smlp/master/Test92_smlp_toy_num_resp_mult_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/Test9_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_args_config.json b/regr_smlp/master/Test9_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_args_config.json
index 40207e62..4bd17843 100644
--- a/regr_smlp/master/Test9_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_args_config.json
+++ b/regr_smlp/master/Test9_smlp_toy_num_resp_mult_smlp_toy_num_resp_mult_pred_labeled_args_config.json
@@ -103,7 +103,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test101_model_rerun_model_config.json b/regr_smlp/master/test101_model_rerun_model_config.json
index 6bed1abf..876af4da 100644
--- a/regr_smlp/master/test101_model_rerun_model_config.json
+++ b/regr_smlp/master/test101_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test103_model_rerun_model_config.json b/regr_smlp/master/test103_model_rerun_model_config.json
index 2a2dfb28..438e88b5 100644
--- a/regr_smlp/master/test103_model_rerun_model_config.json
+++ b/regr_smlp/master/test103_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test110_model_rerun_model_config.json b/regr_smlp/master/test110_model_rerun_model_config.json
index b47e5ed9..3f55e02f 100644
--- a/regr_smlp/master/test110_model_rerun_model_config.json
+++ b/regr_smlp/master/test110_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test113_model_rerun_model_config.json b/regr_smlp/master/test113_model_rerun_model_config.json
index 4bbeae29..b76c3ada 100644
--- a/regr_smlp/master/test113_model_rerun_model_config.json
+++ b/regr_smlp/master/test113_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "lazy",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test19_model_rerun_model_config.json b/regr_smlp/master/test19_model_rerun_model_config.json
index 4116c06e..9e539c43 100644
--- a/regr_smlp/master/test19_model_rerun_model_config.json
+++ b/regr_smlp/master/test19_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test20_model_rerun_model_config.json b/regr_smlp/master/test20_model_rerun_model_config.json
index a10f0f15..2bb6ca19 100644
--- a/regr_smlp/master/test20_model_rerun_model_config.json
+++ b/regr_smlp/master/test20_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test22_model_rerun_model_config.json b/regr_smlp/master/test22_model_rerun_model_config.json
index 49b994df..2228a953 100644
--- a/regr_smlp/master/test22_model_rerun_model_config.json
+++ b/regr_smlp/master/test22_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test24_model_rerun_model_config.json b/regr_smlp/master/test24_model_rerun_model_config.json
index 0c0bfa5d..21adf622 100644
--- a/regr_smlp/master/test24_model_rerun_model_config.json
+++ b/regr_smlp/master/test24_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test26_model_rerun_model_config.json b/regr_smlp/master/test26_model_rerun_model_config.json
index aecc78d3..05d9851a 100644
--- a/regr_smlp/master/test26_model_rerun_model_config.json
+++ b/regr_smlp/master/test26_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test63_model_rerun_model_config.json b/regr_smlp/master/test63_model_rerun_model_config.json
index 238d29a4..063c160b 100644
--- a/regr_smlp/master/test63_model_rerun_model_config.json
+++ b/regr_smlp/master/test63_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test65_model_rerun_model_config.json b/regr_smlp/master/test65_model_rerun_model_config.json
index 467742e1..465dd630 100644
--- a/regr_smlp/master/test65_model_rerun_model_config.json
+++ b/regr_smlp/master/test65_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test67_model_rerun_model_config.json b/regr_smlp/master/test67_model_rerun_model_config.json
index bc7414f0..8b12212e 100644
--- a/regr_smlp/master/test67_model_rerun_model_config.json
+++ b/regr_smlp/master/test67_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test69_model_rerun_model_config.json b/regr_smlp/master/test69_model_rerun_model_config.json
index def0da63..31b0ea5f 100644
--- a/regr_smlp/master/test69_model_rerun_model_config.json
+++ b/regr_smlp/master/test69_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test71_model_rerun_model_config.json b/regr_smlp/master/test71_model_rerun_model_config.json
index af460e0c..ac11f59b 100644
--- a/regr_smlp/master/test71_model_rerun_model_config.json
+++ b/regr_smlp/master/test71_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test73_model_rerun_model_config.json b/regr_smlp/master/test73_model_rerun_model_config.json
index f7d85b10..7e252305 100644
--- a/regr_smlp/master/test73_model_rerun_model_config.json
+++ b/regr_smlp/master/test73_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test76_model_rerun_model_config.json b/regr_smlp/master/test76_model_rerun_model_config.json
index 9fa306ff..6e8d23f0 100644
--- a/regr_smlp/master/test76_model_rerun_model_config.json
+++ b/regr_smlp/master/test76_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,
diff --git a/regr_smlp/master/test78_model_rerun_model_config.json b/regr_smlp/master/test78_model_rerun_model_config.json
index 86758504..d19edf78 100644
--- a/regr_smlp/master/test78_model_rerun_model_config.json
+++ b/regr_smlp/master/test78_model_rerun_model_config.json
@@ -104,7 +104,7 @@
     "objectives_names": "None",
     "optimization_strategy": "eager",
     "optimize_pareto": true,
-    "output_directory": "./",
+    "output_directory": ".",
     "poly_sklearn_copy_X": true,
     "poly_sklearn_degree": 2,
     "poly_sklearn_fit_intercept": true,