def test_panda():
#print(os.getcwd())
print('Start Panda run ...')
ppi ='tests/puma/ToyData/ToyPPIData.txt'
motif ='tests/puma/ToyData/ToyMotifData.txt'
expression_data='tests/puma/ToyData/ToyExpressionData.txt'
lioness_file =''
rm_missing = False
output_file ='travis_test_panda.txt'
gt_file ='tests/panda/union_test_panda.txt'
gt_file_inter ='tests/panda/inter_test_panda.txt'
#0. Intersection
panda_obj = Panda(expression_data, motif, ppi, save_tmp=False, remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file), modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res=pd.read_csv(output_file, sep=' ', header=None)
gt =pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,gt,check_less_precise=False,check_exact=False)
#0.5 Intersection via DataFrame
expression = pd.read_csv(expression_data, sep='\t', header=None, index_col=0)
motif_data = pd.read_csv(motif, sep='\t', names=['source','target','w'])
ppi_data = pd.read_csv(ppi, sep='\t', header=None)
panda_obj = Panda(expression, motif_data, ppi_data, save_tmp=False, remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file), modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res=pd.read_csv(output_file, sep=' ', header=None)
gt =pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,gt,check_less_precise=False,check_exact=False)
#1. Union
panda_obj = Panda(expression_data, motif, ppi, save_tmp=False, remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file), modeProcess='union')
panda_obj.save_panda_results(output_file)
res=pd.read_csv(output_file, sep=' ', header=None)
gt =pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,gt,check_less_precise=False,check_exact=False)
#2. In-degree and out-degree
panda_obj.return_panda_indegree()
panda_obj.return_panda_outdegree()
# Lazy test
assert (round(panda_obj.panda_indegree.iloc[0].loc['force'], 5) == 1.13971)
assert (round(panda_obj.panda_outdegree.iloc[0].loc['force'], 5) == 1030.06840)
#3. Legacy
panda_obj = Panda(expression_data, motif, ppi, save_tmp=True, remove_missing=rm_missing,
keep_expression_matrix=True, save_memory=True, modeProcess='legacy')
panda_obj.save_panda_results(output_file)
gt_file ='tests/panda/legacy_test_panda.txt'
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,gt,check_less_precise=False,check_exact=False)
print('Test panda passed was successful!')
def test_panda():
#print(os.getcwd())
print('Start Panda run ...')
ppi = 'tests/puma/ToyData/ToyPPIData.txt'
motif = 'tests/puma/ToyData/ToyMotifData.txt'
expression_data = 'tests/puma/ToyData/ToyExpressionData.txt'
lioness_file = ''
rm_missing = False
output_file = 'travis_test_panda.txt'
gt_file = 'tests/panda/union_test_panda.txt'
gt_file_inter = 'tests/panda/inter_test_panda.txt'
#0. Intersection
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#0.1 Intersection via DataFrame
expression = pd.read_csv(expression_data,
sep='\t',
header=None,
index_col=0)
motif_data = pd.read_csv(motif, sep='\t', names=['source', 'target', 'w'])
ppi_data = pd.read_csv(ppi, sep='\t', header=None)
panda_obj = Panda(expression,
motif_data,
ppi_data,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#0.2 Intersection with symmetric PPI
ppi_data = pd.read_csv(ppi, sep='\t', header=None)
new_df = pd.DataFrame(data={
0: ppi_data[0],
1: ppi_data[1],
2: ppi_data[2]
})
ppi_data_symm = pd.concat([ppi_data, new_df])
panda_obj = Panda(expression,
motif_data,
ppi_data_symm,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='intersection')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file_inter, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#1. Union
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='union')
panda_obj.save_panda_results(output_file)
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#2. In-degree and out-degree
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=False,
remove_missing=rm_missing,
keep_expression_matrix=bool(lioness_file),
modeProcess='union',
save_memory=False)
panda_obj.return_panda_indegree()
panda_obj.return_panda_outdegree()
# Lazy test
assert (np.round(panda_obj.panda_indegree.iloc[0].loc['force'],
5) == 1.13971)
assert (np.round(panda_obj.panda_outdegree.iloc[0].loc['force'],
5) == 1030.06840)
#3. Legacy
panda_obj = Panda(expression_data,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess='legacy')
panda_obj.save_panda_results(output_file)
gt_file = 'tests/panda/legacy_test_panda.txt'
res = pd.read_csv(output_file, sep=' ', header=None)
gt = pd.read_csv(gt_file, sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
print('Test panda passed was successful!')
#4. None Types
i = 0
gt_test_panda = 'gt_panda'
test_panda = 'test_panda'
for modeProcess in ['legacy', 'union', 'intersection']:
print(modeProcess)
#Motif
i = i + 1
panda_obj = Panda(expression_data,
None,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#PPI
i = i + 1
panda_obj = Panda(expression_data,
motif,
None,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#Expression
i = i + 1
panda_obj = Panda(None,
motif,
ppi,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)
#Expression and PPI
i = i + 1
panda_obj = Panda(None,
motif,
None,
save_tmp=True,
remove_missing=rm_missing,
keep_expression_matrix=True,
save_memory=True,
modeProcess=modeProcess)
panda_obj.save_panda_results(test_panda + str(i) + '.txt')
res = pd.read_csv(test_panda + str(i) + '.txt', sep=' ', header=None)
os.system(
'curl -O https://netzoo.s3.us-east-2.amazonaws.com/netZooPy/tutorial_datasets/'
+ gt_test_panda + str(i) + '.txt')
gt = pd.read_csv(gt_test_panda + str(i) + '.txt', sep=' ', header=None)
pd.testing.assert_frame_equal(res,
gt,
check_less_precise=False,
check_exact=False)