jblitzar/github-python-corpus · Data Studio

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def extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths):
src_eq_tree = sympy2zss_module(src_eq.evalf())
edit_dists = compute_distances([src_eq_tree] * len(dst_eq_trees), dst_eq_trees, normalizes=True)
edit_dists = np.array(edit_dists)
complete_matches = edit_dists == 0.0
if not np.any(complete_matches):
return None
return np.array(dst_tabular_dataset_file_paths)[complete_matches].tolist()


def extract_min_max_input_variables(tabular_dataset):
# The last column is output, thus skipped
sample_min_values = tabular_dataset[:, :-1].min(0)
sample_max_values = tabular_dataset[:, :-1].max(0)
return sample_min_values, sample_max_values


def find_overlapped_wrt_domains(src_tabular_dataset_file_path, src_delimiter,
dst_tabular_dataset_file_paths, dst_delimiter):
src_tabular_dataset = load_tabular_dataset(src_tabular_dataset_file_path, src_delimiter)
src_sample_min_values, src_sample_max_values = extract_min_max_input_variables(src_tabular_dataset)
overlapped_list = list()
for dst_tabular_dataset_file_path in dst_tabular_dataset_file_paths:
dst_tabular_dataset = load_tabular_dataset(dst_tabular_dataset_file_path, dst_delimiter)
dst_sample_min_values, dst_sample_max_values = extract_min_max_input_variables(dst_tabular_dataset)
overlapped = (src_sample_min_values <= dst_sample_min_values) * (dst_sample_min_values <= src_sample_max_values)
overlapped += \
(src_sample_min_values <= dst_sample_max_values) * (dst_sample_max_values <= src_sample_max_values)
overlapped += \
(dst_sample_min_values <= src_sample_min_values) * (src_sample_max_values <= dst_sample_max_values)
if np.all(overlapped):
overlapped_list.append(dst_tabular_dataset_file_path)

num_overlapped_datasets = len(overlapped_list)
if num_overlapped_datasets > 0:
print(f'{src_tabular_dataset_file_path}: {num_overlapped_datasets} overlapped datasets')
return overlapped_list


def check_if_overlapped(src_tabular_dataset_file_path, src_eq, src_delimiter,
dst_eq_trees, dst_tabular_dataset_file_paths, dst_delimiter, overlapped_dict):
filtered_dst_file_paths = \
extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths)
if filtered_dst_file_paths is None or len(filtered_dst_file_paths) == 0:
return

overlapped_dst_tabular_dataset_file_paths = \
find_overlapped_wrt_domains(src_tabular_dataset_file_path, src_delimiter,
filtered_dst_file_paths, dst_delimiter)
overlapped_dict[src_tabular_dataset_file_path] = overlapped_dst_tabular_dataset_file_paths


def compute_intersections(src_sample_min_values, src_sample_max_values, dst_sample_min_values, dst_sample_max_values):
numerator = \
np.max((src_sample_min_values, dst_sample_min_values), axis=0) \
- np.min((src_sample_max_values, dst_sample_max_values), axis=0)
denominator = \
np.min((src_sample_min_values, dst_sample_min_values), axis=0) \
- np.max((src_sample_max_values, dst_sample_max_values), axis=0)
return numerator / denominator


def compute_domain_intersections(src_tabular_dataset_file_path, src_delimiter,
dst_tabular_dataset_file_paths, dst_delimiter):
src_tabular_dataset = load_tabular_dataset(src_tabular_dataset_file_path, src_delimiter)
src_sample_min_values, src_sample_max_values = extract_min_max_input_variables(src_tabular_dataset)
intersections_list = list()
for dst_tabular_dataset_file_path in dst_tabular_dataset_file_paths:
dst_tabular_dataset = load_tabular_dataset(dst_tabular_dataset_file_path, dst_delimiter)
if dst_tabular_dataset.shape[1] != src_tabular_dataset.shape[1]:
continue

dst_sample_min_values, dst_sample_max_values = extract_min_max_input_variables(dst_tabular_dataset)
domain_intersections = compute_intersections(src_sample_min_values, src_sample_max_values,
dst_sample_min_values, dst_sample_max_values)
intersections_list.append(domain_intersections)
return np.array(intersections_list).mean(axis=0) if len(intersections_list) > 0 else None


def assess_domain_intersections_if_overlapped(src_tabular_dataset_file_path, src_eq, src_delimiter,
dst_eq_trees, dst_tabular_dataset_file_paths, dst_delimiter, result_dict):
filtered_dst_file_paths = \
extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths)
if filtered_dst_file_paths is None or len(filtered_dst_file_paths) == 0:
return

intersection_mat = compute_domain_intersections(src_tabular_dataset_file_path, src_delimiter,
dst_tabular_dataset_file_paths, dst_delimiter)
if intersection_mat is not None:
result_dict[src_tabular_dataset_file_path] = intersection_mat


def compare_datasets(src_eqs, src_tabular_dataset_file_paths, src_delimiter,
dst_eqs, dst_tabular_dataset_file_paths, dst_delimiter, mode):
compute_stats(src_eqs, '[Source Equations]')
compute_stats(dst_eqs, '[Target Equations]')
dst_eq_trees = [sympy2zss_module(dst_eq.evalf()) for dst_eq in dst_eqs]
process_list = list()
manager = Manager()
result_dict = manager.dict()

def extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths):

src_eq_tree = sympy2zss_module(src_eq.evalf())

edit_dists = compute_distances([src_eq_tree] * len(dst_eq_trees), dst_eq_trees, normalizes=True)

edit_dists = np.array(edit_dists)

complete_matches = edit_dists == 0.0

if not np.any(complete_matches):

return None

return np.array(dst_tabular_dataset_file_paths)[complete_matches].tolist()

def extract_min_max_input_variables(tabular_dataset):

# The last column is output, thus skipped

sample_min_values = tabular_dataset[:, :-1].min(0)

sample_max_values = tabular_dataset[:, :-1].max(0)

return sample_min_values, sample_max_values

def find_overlapped_wrt_domains(src_tabular_dataset_file_path, src_delimiter,

dst_tabular_dataset_file_paths, dst_delimiter):

src_tabular_dataset = load_tabular_dataset(src_tabular_dataset_file_path, src_delimiter)

src_sample_min_values, src_sample_max_values = extract_min_max_input_variables(src_tabular_dataset)

overlapped_list = list()

for dst_tabular_dataset_file_path in dst_tabular_dataset_file_paths:

dst_tabular_dataset = load_tabular_dataset(dst_tabular_dataset_file_path, dst_delimiter)

dst_sample_min_values, dst_sample_max_values = extract_min_max_input_variables(dst_tabular_dataset)

overlapped = (src_sample_min_values <= dst_sample_min_values) * (dst_sample_min_values <= src_sample_max_values)

overlapped += \

(src_sample_min_values <= dst_sample_max_values) * (dst_sample_max_values <= src_sample_max_values)

overlapped += \

(dst_sample_min_values <= src_sample_min_values) * (src_sample_max_values <= dst_sample_max_values)

if np.all(overlapped):

overlapped_list.append(dst_tabular_dataset_file_path)

num_overlapped_datasets = len(overlapped_list)

if num_overlapped_datasets > 0:

print(f'{src_tabular_dataset_file_path}: {num_overlapped_datasets} overlapped datasets')

return overlapped_list

def check_if_overlapped(src_tabular_dataset_file_path, src_eq, src_delimiter,

dst_eq_trees, dst_tabular_dataset_file_paths, dst_delimiter, overlapped_dict):

filtered_dst_file_paths = \

extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths)

if filtered_dst_file_paths is None or len(filtered_dst_file_paths) == 0:

return

overlapped_dst_tabular_dataset_file_paths = \

find_overlapped_wrt_domains(src_tabular_dataset_file_path, src_delimiter,

filtered_dst_file_paths, dst_delimiter)

overlapped_dict[src_tabular_dataset_file_path] = overlapped_dst_tabular_dataset_file_paths

def compute_intersections(src_sample_min_values, src_sample_max_values, dst_sample_min_values, dst_sample_max_values):

numerator = \

np.max((src_sample_min_values, dst_sample_min_values), axis=0) \

- np.min((src_sample_max_values, dst_sample_max_values), axis=0)

denominator = \

np.min((src_sample_min_values, dst_sample_min_values), axis=0) \

- np.max((src_sample_max_values, dst_sample_max_values), axis=0)

return numerator / denominator

def compute_domain_intersections(src_tabular_dataset_file_path, src_delimiter,

dst_tabular_dataset_file_paths, dst_delimiter):

src_tabular_dataset = load_tabular_dataset(src_tabular_dataset_file_path, src_delimiter)

src_sample_min_values, src_sample_max_values = extract_min_max_input_variables(src_tabular_dataset)

intersections_list = list()

for dst_tabular_dataset_file_path in dst_tabular_dataset_file_paths:

dst_tabular_dataset = load_tabular_dataset(dst_tabular_dataset_file_path, dst_delimiter)

if dst_tabular_dataset.shape[1] != src_tabular_dataset.shape[1]:

continue

dst_sample_min_values, dst_sample_max_values = extract_min_max_input_variables(dst_tabular_dataset)

domain_intersections = compute_intersections(src_sample_min_values, src_sample_max_values,

dst_sample_min_values, dst_sample_max_values)

intersections_list.append(domain_intersections)

return np.array(intersections_list).mean(axis=0) if len(intersections_list) > 0 else None

def assess_domain_intersections_if_overlapped(src_tabular_dataset_file_path, src_eq, src_delimiter,

dst_eq_trees, dst_tabular_dataset_file_paths, dst_delimiter, result_dict):

filtered_dst_file_paths = \

extract_dataset_file_paths_w_identical_equation(src_eq, dst_eq_trees, dst_tabular_dataset_file_paths)

if filtered_dst_file_paths is None or len(filtered_dst_file_paths) == 0:

return

intersection_mat = compute_domain_intersections(src_tabular_dataset_file_path, src_delimiter,

dst_tabular_dataset_file_paths, dst_delimiter)

if intersection_mat is not None:

result_dict[src_tabular_dataset_file_path] = intersection_mat

def compare_datasets(src_eqs, src_tabular_dataset_file_paths, src_delimiter,

dst_eqs, dst_tabular_dataset_file_paths, dst_delimiter, mode):

compute_stats(src_eqs, '[Source Equations]')

compute_stats(dst_eqs, '[Target Equations]')

dst_eq_trees = [sympy2zss_module(dst_eq.evalf()) for dst_eq in dst_eqs]

process_list = list()

manager = Manager()

result_dict = manager.dict()