def test_fourEeyesPrinciple_neg(self):
df = csv_import_adapter.import_dataframe_from_path(
os.path.join("input_data", "running-example.csv"))
filt_foureyes_neg = ltl_checker.four_eyes_principle(
df,
"check ticket",
"pay compensation",
parameters={ltl_checker.Parameters.POSITIVE: False})
def test_fourEeyesPrinciple_neg(self):
df = pd.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
filt_foureyes_neg = ltl_checker.four_eyes_principle(
df,
"check ticket",
"pay compensation",
parameters={ltl_checker.Parameters.POSITIVE: False})
def apply(dataframe, filter, parameters=None):
"""
Apply an LTL checker filter on the dataframe
Parameters
--------------
dataframe
Dataframe
filter
Content of the filter (dictionary including the type, the arguments and the parameters)
Returns
-------------
filtered_dataframe
Filtered dataframe
"""
if parameters is None:
parameters = {}
filter = filter[1]
args = filter["arguments"]
for param in filter["parameters"]:
parameters[param] = filter["parameters"][param]
if filter["type"] == "A_eventually_B":
return ltl_checker.A_eventually_B(dataframe,
args[0],
args[1],
parameters=parameters)
elif filter["type"] == "A_eventually_B_eventually_C":
return ltl_checker.A_eventually_B_eventually_C(dataframe,
args[0],
args[1],
args[2],
parameters=parameters)
elif filter["type"] == "A_eventually_B_eventually_C_eventually_D":
return ltl_checker.A_eventually_B_eventually_C_eventually_D(
dataframe,
args[0],
args[1],
args[2],
args[3],
parameters=parameters)
elif filter["type"] == "A_next_B_next_C":
return ltl_checker.A_next_B_next_C(dataframe,
args[0],
args[1],
args[2],
parameters=parameters)
elif filter["type"] == "four_eyes_principle":
return ltl_checker.four_eyes_principle(dataframe,
args[0],
args[1],
parameters=parameters)
elif filter["type"] == "attr_value_different_persons":
return ltl_checker.four_eyes_principle(dataframe,
args[0],
parameters=parameters)
return dataframe
def test_fourEeyesPrinciple_pos(self):
df = csv_import_adapter.import_dataframe_from_path(os.path.join("input_data", "running-example.csv"))
filt_foureyes_pos = ltl_checker.four_eyes_principle(df, "check ticket", "pay compensation",
parameters={"positive": True})
def execute_script():
# import the dataframe
df = csv_import_adapter.import_dataframe_from_path(
os.path.join("..", "tests", "input_data", "running-example.csv"))
# A eventually B positive: filter the cases of the log where each instance of A is eventually followed by an instance
# of B
filt_A_ev_B_pos = ltl_checker.A_eventually_B(df,
"check ticket",
"pay compensation",
parameters={"positive": True})
print("len(filt_A_ev_B_pos) = ",
len(filt_A_ev_B_pos.groupby("case:concept:name")))
# A eventually B negative: filter the cases of the log where A or B where not there, or cases where an instance of
# A is not followed by an instance of B
filt_A_ev_B_neg = ltl_checker.A_eventually_B(
df, "check ticket", "pay compensation", parameters={"positive": False})
print("len(filt_A_ev_B_neg) = ",
len(filt_A_ev_B_neg.groupby("case:concept:name")))
# A eventually B eventually C positive: filter the cases of the log where each instance of A is
# eventually followed by an instance of B that is eventually followed by an instance of C
filt_A_ev_B_ev_C_pos = ltl_checker.A_eventually_B_eventually_C(
df,
"check ticket",
"decide",
"pay compensation",
parameters={"positive": True})
print("len(filt_A_ev_B_ev_C_pos) = ",
len(filt_A_ev_B_ev_C_pos.groupby("case:concept:name")))
# A eventually B eventually C negative: filter the cases of the log where A,B,C are not all in the case,
# or an instance of A is not eventually followed by an instance of B that is not eventually followed by
# an instance of C
filt_A_ev_B_ev_C_neg = ltl_checker.A_eventually_B_eventually_C(
df,
"check ticket",
"decide",
"pay compensation",
parameters={"positive": False})
print("len(filt_A_ev_B_ev_C_neg) = ",
len(filt_A_ev_B_ev_C_neg.groupby("case:concept:name")))
# A next B next C positive: filter the cases of the log where an instance of A was followed by an
# instance of B, and the instance of B was directly followed by an instance of C
filt_A_next_B_next_C_pos = ltl_checker.A_next_B_next_C(
df,
"check ticket",
"decide",
"pay compensation",
parameters={"positive": True})
print("len(filt_A_next_B_net_C_pos) = ",
len(filt_A_next_B_next_C_pos.groupby("case:concept:name")))
# A next B next C negative: filter the cases of the log where none instance of A was followed directly
# by an instance of B, and the instance of B was directly followed by an instance of C
filt_A_next_B_next_C_neg = ltl_checker.A_next_B_next_C(
df,
"check ticket",
"decide",
"pay compensation",
parameters={"positive": False})
print("len(filt_A_next_B_next_C_neg) = ",
len(filt_A_next_B_next_C_neg.groupby("case:concept:name")))
# four eyes principle positive: filter the cases of the log where A and B are both there, and the resource doing A
# is NEVER the same resource doing B
filt_foureyes_pos = ltl_checker.four_eyes_principle(
df, "check ticket", "pay compensation", parameters={"positive": True})
print("len(filt_foureyes_pos) = ",
len(filt_foureyes_pos.groupby("case:concept:name")))
# four eyes principle negative: filter the cases of the log where A and B are both there, and there is a common
# resource doing A and B
filt_foureyes_neg = ltl_checker.four_eyes_principle(
df, "check ticket", "pay compensation", parameters={"positive": False})
print("len(filt_foureyes_neg) = ",
len(filt_foureyes_neg.groupby("case:concept:name")))
# attr_value_different_persons positive: filter the cases of the log in which A was done by two different resources
attr_value_different_persons_pos = ltl_checker.attr_value_different_persons(
df, "check ticket", parameters={"positive": True})
print("len(attr_value_different_persons_pos) = ",
len(attr_value_different_persons_pos.groupby("case:concept:name")))
# attr_value_different_persons negative: filter the cases of the log in which A was not there or was not executed
# by two different resources
attr_value_different_persons_neg = ltl_checker.attr_value_different_persons(
df, "check ticket", parameters={"positive": False})
print("len(attr_value_different_persons_neg) = ",
len(attr_value_different_persons_neg.groupby("case:concept:name")))
