def test_exceptions():
with pytest.raises(ValueError) as e_info:
ndl.ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, method='threading', weights=1)
assert e_info == 'weights need to be None or xarray.DataArray with method=threading'
with pytest.raises(ValueError) as e_info:
ndl.ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, method='magic')
assert e_info == 'method needs to be either "threading" or "openmp"'
with pytest.raises(ValueError) as e_info:
ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, weights=1)
assert e_info == 'weights needs to be either defaultdict or None'
with pytest.raises(ValueError) as e_info:
ndl.dict_ndl(FILE_PATH_MULTIPLE_CUES,
ALPHA,
BETAS,
remove_duplicates=None)
assert e_info == 'cues or outcomes needs to be unique: cues "a a"; outcomes "A"; use remove_duplicates=True'
with pytest.raises(ValueError) as e_info:
ndl.ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
method='threading',
len_sublists=-1)
assert e_info == "'len_sublists' must be larger then one"
with pytest.raises(ValueError) as e_info:
ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, make_data_array="magic")
assert e_info == "make_data_array must be True or False"
with pytest.raises(ValueError) as e_info:
ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, remove_duplicates="magic")
assert e_info == "remove_duplicates must be None, True or False"
with pytest.raises(ValueError) as e_info:
ndl.ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
method='threading',
remove_duplicates="magic")
assert e_info == "remove_duplicates must be None, True or False"
with pytest.raises(FileNotFoundError,
match="No such file or directory") as e_info:
ndl.ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
method='threading',
temporary_directory="./magic")
with pytest.raises(
ValueError,
match="events_per_file has to be larger than 1") as e_info:
ndl.ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
method='threading',
events_per_temporary_file=1)
def test_continue_learning_dict_ndl_data_array(result_dict_ndl,
result_dict_ndl_data_array):
continue_from_dict = ndl.dict_ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
weights=result_dict_ndl)
continue_from_data_array = ndl.dict_ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
weights=result_dict_ndl_data_array)
unequal, unequal_ratio = compare_arrays(FILE_PATH_SIMPLE,
continue_from_dict,
continue_from_data_array)
print(continue_from_data_array)
print('%.2f ratio unequal' % unequal_ratio)
assert len(unequal) == 0
def test_dict_ndl_data_array_vs_ndl_threading(result_ndl_threading):
result_dict_ndl = ndl.dict_ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
make_data_array=True)
unequal, unequal_ratio = compare_arrays(FILE_PATH_SIMPLE, result_dict_ndl,
result_ndl_threading)
print('%.2f ratio unequal' % unequal_ratio)
assert len(unequal) == 0
def test_continue_learning_dict():
events_simple = pd.read_csv(FILE_PATH_SIMPLE, sep="\t")
part_1 = events_simple.head(CONTINUE_SPLIT_POINT)
part_2 = events_simple.tail(len(events_simple) - CONTINUE_SPLIT_POINT)
assert len(part_1) > 0
assert len(part_2) > 0
part_path_1 = os.path.join(TMP_PATH, "event_file_simple_1.tab.gz")
part_path_2 = os.path.join(TMP_PATH, "event_file_simple_2.tab.gz")
part_1.to_csv(part_path_1,
header=True,
index=None,
sep='\t',
columns=["cues", "outcomes"],
compression='gzip')
part_2.to_csv(part_path_2,
header=True,
index=None,
sep='\t',
columns=["cues", "outcomes"],
compression='gzip')
del events_simple, part_1, part_2
result_part = ndl.dict_ndl(part_path_1, ALPHA, BETAS)
result_part_copy = copy.deepcopy(result_part)
result_inplace = ndl.dict_ndl(part_path_2,
ALPHA,
BETAS,
weights=result_part,
inplace=True)
assert result_part is result_inplace
assert result_part != result_part_copy
result_part = ndl.dict_ndl(part_path_1, ALPHA, BETAS)
result = ndl.dict_ndl(part_path_2, ALPHA, BETAS, weights=result_part)
assert result_part != result
def test_exceptions():
with pytest.raises(ValueError) as e_info:
wm = ndl.dict_ndl(FILE_PATH_SIMPLE,
ALPHA,
BETAS,
remove_duplicates=None)
activation(FILE_PATH_MULTIPLE_CUES, wm)
assert e_info == 'cues or outcomes needs to be unique: cues "a a"; outcomes "A"; use remove_duplicates=True'
with pytest.raises(ValueError) as e_info:
activation(FILE_PATH_MULTIPLE_CUES, "magic")
assert e_info == "Weights other than xarray.DataArray or dicts are not supported."
def test_multiple_cues_dict_ndl_vs_ndl_threading():
result_dict_ndl = ndl.dict_ndl(FILE_PATH_MULTIPLE_CUES,
ALPHA,
BETAS,
remove_duplicates=True)
result_ndl_threading = ndl.ndl(FILE_PATH_MULTIPLE_CUES,
ALPHA,
BETAS,
remove_duplicates=True,
method='threading')
unequal, unequal_ratio = compare_arrays(FILE_PATH_MULTIPLE_CUES,
result_dict_ndl,
result_ndl_threading)
print('%.2f ratio unequal' % unequal_ratio)
assert len(unequal) == 0
def test_multiple_cues_dict_ndl_vs_ndl2():
"""
Checks whether the output of the R learner implemented in ndl2 and the
python implementation of dict_ndl is equal.
R code to generate the results::
library(ndl2)
learner <- learnWeightsTabular('tests/resources/event_file_multiple_cues.tab.gz',
alpha=0.1, beta=0.1, lambda=1.0, removeDuplicates=FALSE)
wm <- learner$getWeights()
wm <- wm[order(rownames(wm)), order(colnames(wm))]
write.csv(wm, 'tests/reference/weights_event_file_multiple_cues_ndl2.csv')
"""
result_ndl2 = defaultdict(lambda: defaultdict(float))
with open(REFERENCE_PATH_MULTIPLE_CUES_NDL2, 'rt') as reference_file:
first_line = reference_file.readline().strip()
outcomes = first_line.split(',')[1:]
outcomes = [outcome.strip('"') for outcome in outcomes]
for line in reference_file:
cue, *cue_weights = line.strip().split(',')
cue = cue.strip('"')
for ii, outcome in enumerate(outcomes):
result_ndl2[outcome][cue] = float(cue_weights[ii])
result_python = ndl.dict_ndl(FILE_PATH_MULTIPLE_CUES,
ALPHA,
BETAS,
remove_duplicates=False)
unequal, unequal_ratio = compare_arrays(FILE_PATH_MULTIPLE_CUES,
result_ndl2, result_python)
print(set(outcome for outcome, *_ in unequal))
print('%.2f ratio unequal' % unequal_ratio)
assert len(unequal) == 0
def result_dict_ndl_data_array():
return ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS, make_data_array=True)
def result_dict_ndl_generator():
return ndl.dict_ndl(ndl.events_from_file(FILE_PATH_SIMPLE), ALPHA, BETAS)
def result_dict_ndl():
return ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS)
def test_dict_ndl_vs_ndl_openmp(result_dict_ndl, result_ndl_openmp):
result_dict_ndl = ndl.dict_ndl(FILE_PATH_SIMPLE, ALPHA, BETAS)
unequal, unequal_ratio = compare_arrays(FILE_PATH_SIMPLE, result_dict_ndl,
result_ndl_openmp)
print('%.2f ratio unequal' % unequal_ratio)
assert len(unequal) == 0
