class TestStatsFunctions(TestCase):
def setUp(self):
selected_attributes = {
"Age": "C",
"workclass": "D",
"fnlwgt": "C",
"education": "D",
"education_num": "D",
"marital_status": "D",
"occupation": "D",
"relationship": "D",
"race": "D",
"sex": "D",
"capital_gain": "C",
"capital_loss": "C",
"hours_per_week": "C",
"native_country": "D",
"salary_class": "D"
}
self.data = DataUtils(c.TEST_ORIGIN_DATA_PATH,
selected_attrs=selected_attributes)
self.data.data_coarsilize()
self.stats_funcs = StatsFunctions()
def test_histogram(self):
cliques = [['capital_loss', 'salary_class'], ['fnlwgt'],
['workclass', 'occupation'],
['education', 'education_num', 'salary_class'],
['sex', 'hours_per_week', 'salary_class'],
['occupation', 'sex', 'capital_gain', 'salary_class'],
['marital_status', 'relationship', 'sex', 'salary_class'],
['race', 'native_country']]
self.stats_funcs.histogramdd_batch(self.data, cliques)
class TestStatsFunctions(TestCase):
def setUp(self):
selected_attributes = {
"Age":"C",
"workclass":"D",
"fnlwgt":"C",
"education":"D",
"education_num":"D",
"marital_status":"D",
"occupation":"D",
"relationship":"D",
"race":"D",
"sex":"D",
"capital_gain":"C",
"capital_loss":"C",
"hours_per_week":"C",
"native_country":"D",
"salary_class":"D"
}
self.data = DataUtils(c.TEST_ORIGIN_DATA_PATH, selected_attrs = selected_attributes)
self.data.data_coarsilize()
self.stats_funcs = StatsFunctions()
def test_histogram(self):
cliques = [['capital_loss', 'salary_class'], ['fnlwgt'], ['workclass', 'occupation'], ['education', 'education_num', 'salary_class'], ['sex', 'hours_per_week', 'salary_class'], ['occupation', 'sex', 'capital_gain', 'salary_class'], ['marital_status', 'relationship', 'sex', 'salary_class'], ['race', 'native_country']]
self.stats_funcs.histogramdd_batch(self.data, cliques)
class Preprocess(Base):
def __init__(self, request):
self.chunk_size = request['chunk_size'] if 'chunk_size' in request.keys() else -1
self.coarse_data_path = None
self.data = None
self.data_path = request['data_path']
self.date_format = request['selected_attrs']['date_format'] if 'date_format' in request['selected_attrs'].keys() else None
self.dep_graph = None # original edges
self.domain = None
self.edges = None
self.eps1_val = request['eps1_val'] if 'eps1_val' in request.keys() else c.EPSILON_1
self.eps1_level = request['eps1_level'] if 'eps1_level' in request.keys() else 1
self.jtree_strct = None
self.jtree_file_path = None
self.names = request['names'] if 'names' in request.keys() else None
self.nodes = None
self.opted_cluster = None
self.selected_attrs = self.convert_selected_attrs(request['selected_attrs'])
self.specified_c_domain = request['selected_attrs']['specified_c_domain'] if 'specified_c_domain' in request['selected_attrs'].keys() else None
self.task_id = request['task_id']
self.task_folder = self.create_task_folder(self.task_id)
self.valbin_map = None
self.white_list = self.get_white_list(request)
def read_data(self):
self.data = DataUtils(
file_path = self.data_path,
selected_attrs = self.selected_attrs,
names = self.names,
specified_c_domain = self.specified_c_domain,
chunk_size = self.chunk_size,
date_format = self.date_format
)
def coarse(self):
self.data.data_coarsilize()
self.domain = self.data.get_domain()
self.nodes = self.data.get_nodes_name()
self.valbin_map = str(self.data.get_valbin_maps())
def build_dep_graph(self):
# dependency graph
dep_graph_obj = DependencyGraph(self.data, eps1_val = self.eps1_val)
self.edges = dep_graph_obj.get_dep_edges(display = True)
self.cust_edges = dep_graph_obj.set_white_list(self.white_list) \
.get_dep_edges(display = True)
self.dep_graph = str(self.edges)
def get_white_list(self, request):
white_list = request['white_list'] if 'white_list' in request.keys() and len(request['white_list']) > 0 else "[]"
if not isinstance(white_list, list):
white_list = ast.literal_eval(white_list)
return white_list
def build_jtree(self):
# junction tree
jtree = JunctionTree(
self.cust_edges,
self.nodes,
self.get_jtree_file_path(self.task_id, self.eps1_level), # the path to save junction tree file
)
# optimize marginal
var_reduce = VarianceReduce(self.domain, jtree.get_jtree()['cliques'], 0.2)
self.opted_cluster = var_reduce.main()
self.jtree_strct = jtree.get_jtree()['cliques']
self.jtree_file_path = self.save_merged_jtree(self.task_id, self.eps1_level, self.jtree_strct)
def save_coarse_data(self):
# TODO: to deal with failure
file_path = os.path.join(self.task_folder,c.COARSE_DATA_NAME)
if self.data is not None:
self.data.save(file_path)
self.coarse_data_path = file_path
def update_instance(self, status, is_celery):
if not is_celery:
return
instance = get_object_or_404(Task, pk = self.task_id)
instance.eps1_val = self.eps1_val
instance.eps1_level = self.eps1_level
instance.dep_graph = str(self.edges)
instance.valbin_map = str(self.valbin_map)
instance.domain = str(self.domain.items()) if self.domain is not None else None
instance.white_list = self.white_list
instance.jtree_strct = str(self.jtree_strct)
instance.opted_cluster = str(self.opted_cluster)
instance.status = ProcessStatus.get_code(status)
instance.end_time = datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ')
instance.save()
class DataUtilitiesTests(TestCase):
# TODO: The Data Coarse and Generalize step should seperate, to simulate a more real case.
def setUp(self):
self.selected_attrs = dict({
'Age':'C',
'workclass':'D',
'fnlwgt':'C',
'education':'D',
'education_num':'D',
'marital_status':'D',
'occupation':'D',
'relationship':'D',
'race':'D',
'sex':'D',
'capital_gain':'C',
'capital_loss':'C',
'hours_per_week':'C',
'native_country':'D',
'salary_class':'D'
})
self.data = DataUtils(file_path = TESTING_FILE, selected_attrs = self.selected_attrs)
self.data.data_coarsilize()
self.base = Base()
def test_data_preview(self):
data = DataUtils(file_path = TESTING_FILE)
preview = data.data_preview()
self.assertEqual(len(preview.values[0]) > 0, True)
def test_read_data_by_three_selected_column(self):
"""
Test the read data by user specified columns
"""
self.assertEqual(len(self.data.get_nodes_name()) == len(self.selected_attrs), True)
def test_data_domain_keep_original_order(self):
"""
Test the order in domain object is in same order with
original raw data.
"""
df = self.data.get_pandas_df()
domain = self.data.get_domain()
cols = domain.keys()
self.assertEqual(cols == list(df.columns.values), True)
def test_data_coarsilization(self):
print self.data.get_pandas_df()[:5]
def test_data_generalization(self):
self.data.data_generalize()
print self.data.get_pandas_df()[:5]
def test_is_skip_pre_processing_with_create(self):
create_flag = True
request = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
instance = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(request, request, create_flag)
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_with_data_path_change(self):
create_flag = False
request = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
instance = {
'data_path':'/path/to/dummy/file22222222.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(request, instance, create_flag)
print skip_pre_process
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_with_selected_attr_change(self):
create_flag = False
request = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
instance = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['D', 'D', 'D', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(request, instance, create_flag)
print skip_pre_process
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_without_change(self):
create_flag = False
request = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
instance = {
'data_path':'/path/to/dummy/file.csv',
'selected_attrs':{
'names':['A', 'D', 'C', 'B'],
'types':['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(request, instance, create_flag)
self.assertEqual(skip_pre_process == True, True)
class DataUtilitiesTests(TestCase):
# TODO: The Data Coarse and Generalize step should seperate, to simulate a more real case.
def setUp(self):
self.selected_attrs = dict({
'Age': 'C',
'workclass': 'D',
'fnlwgt': 'C',
'education': 'D',
'education_num': 'D',
'marital_status': 'D',
'occupation': 'D',
'relationship': 'D',
'race': 'D',
'sex': 'D',
'capital_gain': 'C',
'capital_loss': 'C',
'hours_per_week': 'C',
'native_country': 'D',
'salary_class': 'D'
})
self.data = DataUtils(file_path=TESTING_FILE,
selected_attrs=self.selected_attrs)
self.data.data_coarsilize()
self.base = Base()
def test_data_preview(self):
data = DataUtils(file_path=TESTING_FILE)
preview = data.data_preview()
self.assertEqual(len(preview.values[0]) > 0, True)
def test_read_data_by_three_selected_column(self):
"""
Test the read data by user specified columns
"""
self.assertEqual(
len(self.data.get_nodes_name()) == len(self.selected_attrs), True)
def test_data_domain_keep_original_order(self):
"""
Test the order in domain object is in same order with
original raw data.
"""
df = self.data.get_pandas_df()
domain = self.data.get_domain()
cols = domain.keys()
self.assertEqual(cols == list(df.columns.values), True)
def test_data_coarsilization(self):
print self.data.get_pandas_df()[:5]
def test_data_generalization(self):
self.data.data_generalize()
print self.data.get_pandas_df()[:5]
def test_is_skip_pre_processing_with_create(self):
create_flag = True
request = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
instance = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(
request, request, create_flag)
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_with_data_path_change(self):
create_flag = False
request = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
instance = {
'data_path': '/path/to/dummy/file22222222.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(
request, instance, create_flag)
print skip_pre_process
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_with_selected_attr_change(self):
create_flag = False
request = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
instance = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['D', 'D', 'D', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(
request, instance, create_flag)
print skip_pre_process
self.assertEqual(skip_pre_process == False, True)
def test_is_skip_pre_processing_without_change(self):
create_flag = False
request = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
instance = {
'data_path': '/path/to/dummy/file.csv',
'selected_attrs': {
'names': ['A', 'D', 'C', 'B'],
'types': ['C', 'C', 'C', 'D']
}
}
skip_pre_process = self.base.is_pre_process_skip(
request, instance, create_flag)
self.assertEqual(skip_pre_process == True, True)
class Preprocess(Base):
def __init__(self, request):
self.chunk_size = request[
'chunk_size'] if 'chunk_size' in request.keys() else -1
self.coarse_data_path = None
self.data = None
self.data_path = request['data_path']
self.date_format = request['selected_attrs'][
'date_format'] if 'date_format' in request['selected_attrs'].keys(
) else None
self.dep_graph = None # original edges
self.domain = None
self.edges = None
self.eps1_val = request['eps1_val'] if 'eps1_val' in request.keys(
) else c.EPSILON_1
self.eps1_level = request[
'eps1_level'] if 'eps1_level' in request.keys() else 1
self.jtree_strct = None
self.jtree_file_path = None
self.names = request['names'] if 'names' in request.keys() else None
self.nodes = None
self.opted_cluster = None
self.selected_attrs = self.convert_selected_attrs(
request['selected_attrs'])
self.specified_c_domain = request['selected_attrs'][
'specified_c_domain'] if 'specified_c_domain' in request[
'selected_attrs'].keys() else None
self.task_id = request['task_id']
self.task_folder = self.create_task_folder(self.task_id)
self.valbin_map = None
self.white_list = self.get_white_list(request)
def read_data(self):
self.data = DataUtils(file_path=self.data_path,
selected_attrs=self.selected_attrs,
names=self.names,
specified_c_domain=self.specified_c_domain,
chunk_size=self.chunk_size,
date_format=self.date_format)
def coarse(self):
self.data.data_coarsilize()
self.domain = self.data.get_domain()
self.nodes = self.data.get_nodes_name()
self.valbin_map = str(self.data.get_valbin_maps())
def build_dep_graph(self):
# dependency graph
dep_graph_obj = DependencyGraph(self.data, eps1_val=self.eps1_val)
self.edges = dep_graph_obj.get_dep_edges(display=True)
self.cust_edges = dep_graph_obj.set_white_list(self.white_list) \
.get_dep_edges(display = True)
self.dep_graph = str(self.edges)
def get_white_list(self, request):
white_list = request['white_list'] if 'white_list' in request.keys(
) and len(request['white_list']) > 0 else "[]"
if not isinstance(white_list, list):
white_list = ast.literal_eval(white_list)
return white_list
def build_jtree(self):
# junction tree
jtree = JunctionTree(
self.cust_edges,
self.nodes,
self.get_jtree_file_path(
self.task_id,
self.eps1_level), # the path to save junction tree file
)
# optimize marginal
var_reduce = VarianceReduce(self.domain,
jtree.get_jtree()['cliques'], 0.2)
self.opted_cluster = var_reduce.main()
self.jtree_strct = jtree.get_jtree()['cliques']
self.jtree_file_path = self.save_merged_jtree(self.task_id,
self.eps1_level,
self.jtree_strct)
def save_coarse_data(self):
# TODO: to deal with failure
file_path = os.path.join(self.task_folder, c.COARSE_DATA_NAME)
if self.data is not None:
self.data.save(file_path)
self.coarse_data_path = file_path
def update_instance(self, status, is_celery):
if not is_celery:
return
instance = get_object_or_404(Task, pk=self.task_id)
instance.eps1_val = self.eps1_val
instance.eps1_level = self.eps1_level
instance.dep_graph = str(self.edges)
instance.valbin_map = str(self.valbin_map)
instance.domain = str(
self.domain.items()) if self.domain is not None else None
instance.white_list = self.white_list
instance.jtree_strct = str(self.jtree_strct)
instance.opted_cluster = str(self.opted_cluster)
instance.status = ProcessStatus.get_code(status)
instance.end_time = datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ')
instance.save()