def test_add_true_cps(example_detector):
det = example_detector
det.run()
true_cp_index = 50
true_cp_model_index = 2
# Add CPs to the EvT before setting it up with the detector:
# Set up the EvT
evt = EvaluationTool()
assert evt.has_true_CPs is False
evt.add_true_CPs(true_cp_index, true_cp_model_index)
assert evt.has_true_CPs is True
# Build the evt using the detector, then check that true CP is stored in results
evt.build_EvaluationTool_via_run_detector(det)
assert evt.results[evt.results[0].index("has true CPs")] is True
assert evt.results[evt.results[0].index(
"true CP locations")] == true_cp_index
assert evt.results[evt.results[0].index(
"true CP model index")] == true_cp_model_index
assert evt.results[evt.results[0].index("true CP model labels")] is None
# Add CPs to the EvT after setting it up with the detector:
# Set up the EvT
evt2 = EvaluationTool()
evt2.build_EvaluationTool_via_run_detector(det)
evt2.add_true_CPs(true_cp_index, true_cp_model_index)
# Check that CPs are still stored correctly if added after the detector has been run
assert evt2.has_true_CPs is True
assert evt2.results[evt.results[0].index("has true CPs")] is True
assert evt2.results[evt.results[0].index(
"true CP locations")] == true_cp_index
assert evt2.results[evt.results[0].index(
"true CP model index")] == true_cp_model_index
assert evt2.results[evt.results[0].index("true CP model labels")] is None
model_prior = model_prior,
cp_model = cp_model, S1 = S1, S2 = S2, T = T,
store_rl=True, store_mrl=True,
trim_type="keep_K", threshold = 200,
notifications = 100,
save_performance_indicators = True,
training_period = 250)
detector.run()
"""STEP 7: give some results/pictures/summaries"""
"""Store results + real CPs into EvaluationTool obj"""
EvT = EvaluationTool()
EvT.add_true_CPs(true_CP_location=true_CP_location,
true_CP_model_index=true_CP_location,
true_CP_model_label = -1)
EvT.build_EvaluationTool_via_run_detector(detector)
print("convergence diagnostics for on-line hyperparameter opt:")
plt.plot(np.linspace(1,len(detector.model_universe[0].a_list),
len(detector.model_universe[0].a_list)),
np.array(detector.model_universe[0].a_list))
plt.plot(np.linspace(1,len(detector.model_universe[0].b_list),
len(detector.model_universe[0].b_list)),
np.array(detector.model_universe[0].b_list))
fig = EvT.plot_run_length_distr(
time_range = np.linspace(1,
T-max(upper_VAR, upper_AR)-1,
T-max(upper_VAR, upper_AR)-1,