[1]:
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
Conformance Checking with a DECLARE model¶
This tutorial explains how to perform the conformance checking of a DECLARE model and how to browse the results. We start by importing the classes for managing with DECLARE models: DeclareModel and DeclareModelTemplate:
[2]:
import os
from Declare4Py.ProcessModels.DeclareModel import DeclareModel
The next step is the parsing of the log and of the DECLARE model.
[3]:
from Declare4Py.D4PyEventLog import D4PyEventLog
log_path = os.path.join("../../../", "tests", "test_logs","Sepsis Cases.xes.gz")
event_log = D4PyEventLog(case_name="case:concept:name")
event_log.parse_xes_log(log_path)
model_path = os.path.join("../../../", "tests", "test_models","data_model.decl")
declare_model = DeclareModel().parse_from_file(model_path)
We retrieve the constraints of the model.
[4]:
model_constraints = declare_model.get_decl_model_constraints()
print("Model constraints:")
print("-----------------")
for idx, constr in enumerate(model_constraints):
print(idx, constr)
Model constraints:
-----------------
0 Existence2[Admission NC] | |
1 Chain Response[Admission NC, Release B] |A.org:group is K |T.org:group is E |
2 Chain Response[Admission NC, Release A] |A.org:group is I |T.org:group is E |133020,957701,s
3 Chain Precedence[IV Liquid, Admission NC] |A.org:group is I |T.org:group is A |92,14473,s
4 Chain Response[ER Registration, ER Triage] |(A.DiagnosticArtAstrup is false) AND (A.SIRSCritHeartRate is true) AND (A.org:group is A) AND (A.DiagnosticBlood is true) AND (A.DisfuncOrg is false) AND (A.DiagnosticECG is true) AND (A.Age >= 45) AND (A.InfectionSuspected is true) AND (A.DiagnosticLacticAcid is true) AND (A.DiagnosticSputum is true) AND (A.Hypoxie is false) AND (A.DiagnosticUrinaryCulture is true) AND (A.DiagnosticLiquor is false) AND (A.SIRSCritTemperature is true) AND (A.Infusion is true) AND (A.Hypotensie is false) AND (A.DiagnosticUrinarySediment is true) AND (A.Oligurie is false) AND (A.Age <= 80) AND (A.SIRSCritTachypnea is true) AND (A.DiagnosticOther is false) AND (A.SIRSCritLeucos is false) AND (A.DiagnosticIC is true) AND (A.SIRSCriteria2OrMore is true) AND (A.DiagnosticXthorax is true) |T.org:group is C |52,2154,s
5 Chain Precedence[Release A, Return ER] |A.org:group is ? |T.org:group is E |1121801,1121801,s
6 Chain Precedence[ER Sepsis Triage, IV Antibiotics] |A.org:group is L |T.org:group is L |15,11000,s
7 Chain Response[ER Sepsis Triage, IV Antibiotics] |A.org:group is L |T.org:group is L |15,11000,s
8 Chain Precedence[Admission IC, Admission NC] |A.org:group is J |T.org:group is J |
9 Chain Precedence[IV Antibiotics, Admission NC] |A.org:group is F |T.org:group is A |92,14459,s
10 Chain Precedence[Admission NC, Release B] |A.org:group is E |T.org:group is K |48225,48225,s
11 Chain Response[Admission IC, Admission NC] |A.org:group is J |T.org:group is J |61534,61534,s
12 Chain Response[LacticAcid, Leucocytes] |A.LacticAcid <= 0.8 |T.Leucocytes >= 13.8 |0,2778,m
13 Chain Precedence[ER Registration, ER Triage] |A.org:group is C |(T.InfectionSuspected is true) AND (T.SIRSCritTemperature is true) AND (T.DiagnosticLacticAcid is true) AND (T.DiagnosticBlood is true) AND (T.DiagnosticIC is true) AND (T.SIRSCriteria2OrMore is true) AND (T.DiagnosticECG is true) |52,2154,s
The class MPDeclareAnalyzer initializes the DECLARE conformance checking (Declare4Py implements the MP DECLARE analyzer algorithm). The MPDeclareAnalyzer constructor takes as an input the boolean parameter consider_vacuity=true that means that vacuously satisfied traces are considered as satisfied, violated otherwise. The constructor also needs the event_log and the declare_model objects. After this setting, the method run of the MPDeclareAnalyzer class will execute
the task.
[5]:
from Declare4Py.ProcessMiningTasks.ConformanceChecking.MPDeclareAnalyzer import MPDeclareAnalyzer
from Declare4Py.ProcessMiningTasks.ConformanceChecking.MPDeclareResultsBrowser import MPDeclareResultsBrowser
basic_checker = MPDeclareAnalyzer(log=event_log, declare_model=declare_model, consider_vacuity=False)
conf_check_res: MPDeclareResultsBrowser = basic_checker.run()
The result of the run method is a ResultsBrowser object that allows for the retrieval of the conformance checking results with the get_metric() method. This takes as input the metric parameter with values in num_pendings, num_activations, num_fulfillments, num_violations and state. This return a table whose rows are the results of each trace according to the DECLARE constraints in the model (as columns). For example, this execution
[6]:
conf_check_res.get_metric(metric="num_activations")
[6]:
| Existence2[Admission NC] | | | Chain Response[Admission NC, Release B] |A.org:group is K |T.org:group is E | | Chain Response[Admission NC, Release A] |A.org:group is I |T.org:group is E |133020,957701,s | Chain Precedence[IV Liquid, Admission NC] |A.org:group is I |T.org:group is A |92,14473,s | Chain Response[ER Registration, ER Triage] |(A.DiagnosticArtAstrup is false) AND (A.SIRSCritHeartRate is true) AND (A.org:group is A) AND (A.DiagnosticBlood is true) AND (A.DisfuncOrg is false) AND (A.DiagnosticECG is true) AND (A.Age >= 45) AND (A.InfectionSuspected is true) AND (A.DiagnosticLacticAcid is true) AND (A.DiagnosticSputum is true) AND (A.Hypoxie is false) AND (A.DiagnosticUrinaryCulture is true) AND (A.DiagnosticLiquor is false) AND (A.SIRSCritTemperature is true) AND (A.Infusion is true) AND (A.Hypotensie is false) AND (A.DiagnosticUrinarySediment is true) AND (A.Oligurie is false) AND (A.Age <= 80) AND (A.SIRSCritTachypnea is true) AND (A.DiagnosticOther is false) AND (A.SIRSCritLeucos is false) AND (A.DiagnosticIC is true) AND (A.SIRSCriteria2OrMore is true) AND (A.DiagnosticXthorax is true) |T.org:group is C |52,2154,s | Chain Precedence[Release A, Return ER] |A.org:group is ? |T.org:group is E |1121801,1121801,s | Chain Precedence[ER Sepsis Triage, IV Antibiotics] |A.org:group is L |T.org:group is L |15,11000,s | Chain Response[ER Sepsis Triage, IV Antibiotics] |A.org:group is L |T.org:group is L |15,11000,s | Chain Precedence[Admission IC, Admission NC] |A.org:group is J |T.org:group is J | | Chain Precedence[IV Antibiotics, Admission NC] |A.org:group is F |T.org:group is A |92,14459,s | Chain Precedence[Admission NC, Release B] |A.org:group is E |T.org:group is K |48225,48225,s | Chain Response[Admission IC, Admission NC] |A.org:group is J |T.org:group is J |61534,61534,s | Chain Response[LacticAcid, Leucocytes] |A.LacticAcid <= 0.8 |T.Leucocytes >= 13.8 |0,2778,m | Chain Precedence[ER Registration, ER Triage] |A.org:group is C |(T.InfectionSuspected is true) AND (T.SIRSCritTemperature is true) AND (T.DiagnosticLacticAcid is true) AND (T.DiagnosticBlood is true) AND (T.DiagnosticIC is true) AND (T.SIRSCriteria2OrMore is true) AND (T.DiagnosticECG is true) |52,2154,s | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 |
| 2 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 3 | None | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 4 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1045 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 1046 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1047 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1048 | None | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 1049 | None | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
1050 rows × 14 columns
returns the number of activations of each trace (rows) for each column. We can also specify a trace_id, a constraint_id or both (we see an example with the constraint Chain Response[LacticAcid, Leucocytes] |A.LacticAcid <= 0.8 |T.Leucocytes >= 13.8 |0,2778,m at index 13):
[7]:
# Number of activations for the second trace
print(conf_check_res.get_metric(trace_id=1, metric="num_activations"))
print("-------------------------------------------")
# Number of activations for the constraint with id=12
print(conf_check_res.get_metric(constr_id=12, metric="num_activations"))
print("-------------------------------------------")
# Number of activations for the second trace and for the constraint with id=12
conf_check_res.get_metric(trace_id=1, constr_id=12, metric="num_activations")
[None, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1]
-------------------------------------------
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 4, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 5, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0]
-------------------------------------------
[7]:
1
Similar retrievals can be done for the other metrics. For example, the state metric return 1 if the constraint is satisfied in the trace and 0 otherwise:
[8]:
# Truth values for the second trace
print(conf_check_res.get_metric(trace_id=1, metric="state"))
print("-------------------------------------------")
# Truth values for the constraint with id=12
print(conf_check_res.get_metric(constr_id=12, metric="state"))
print("-------------------------------------------")
# Truth value for the second trace and for the constraint with id=12
print(conf_check_res.get_metric(trace_id=1, constr_id=12, metric="state"))
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0]
-------------------------------------------
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-------------------------------------------
1