def create_event_tmap(tm_name: str, event_name: str, event_type: str):
tm = None
name = event_name.replace("|_", "")
if tm_name == f"{name}_start_date":
tm = TensorMap(
name=tm_name,
shape=(None, None), # type: ignore
interpretation=Interpretation.EVENT,
tensor_from_file=make_event_tensor_from_file(),
path_prefix=f"edw/*/{event_type}/{event_name}/start_date",
)
elif tm_name == f"{name}_end_date":
tm = TensorMap(
name=tm_name,
shape=(None, None), # type: ignore
interpretation=Interpretation.EVENT,
tensor_from_file=make_event_tensor_from_file(),
path_prefix=f"edw/*/{event_type}/{event_name}/end_date",
)
elif tm_name == f"{name}_double":
tm = TensorMap(
name=tm_name,
shape=(2,),
interpretation=Interpretation.CATEGORICAL,
tensor_from_file=make_event_outcome_tensor_from_file(
visit_tm=get_visit_tmap(f"{name}_first_visit"),
double=True,
),
channel_map={f"no_{name}": 0, name: 1},
path_prefix=f"edw/*/{event_type}/{event_name}/start_date",
time_series_limit=2,
)
elif tm_name == f"{name}_single":
tm = TensorMap(
name=tm_name,
shape=(1,),
interpretation=Interpretation.CATEGORICAL,
tensor_from_file=make_event_outcome_tensor_from_file(
visit_tm=get_visit_tmap(f"{name}_first_visit"),
double=False,
),
channel_map={f"no_{name}": 0, name: 1},
path_prefix=f"edw/*/{event_type}/{event_name}/start_date",
time_series_limit=2,
)
return tm
def create_arrest_tmap(tm_name: str):
arrest_list = ["code_start", "rapid_response_start"]
tm = None
if tm_name == "arrest_start_date":
tm = TensorMap(
name=tm_name,
shape=(None, None), # type: ignore
interpretation=Interpretation.EVENT,
tensor_from_file=make_general_event_tensor_from_subevents(arrest_list),
path_prefix="edw/*/events/{}/start_date",
)
if tm_name == "arrest_double":
tm = TensorMap(
name=tm_name,
shape=(2,),
interpretation=Interpretation.CATEGORICAL,
tensor_from_file=make_general_event_outcome_tensor_from_subevents(
visit_tm=get_visit_tmap("arrest_first_visit"),
events_names_list=arrest_list,
double=True,
),
channel_map={"no_arrest": 0, "arrest": 1},
path_prefix="edw/*/events/{}/start_date",
time_series_limit=2,
)
if tm_name == "arrest_single":
tm = TensorMap(
name=tm_name,
shape=(1,),
interpretation=Interpretation.CATEGORICAL,
tensor_from_file=make_general_event_outcome_tensor_from_subevents(
visit_tm=get_visit_tmap("arrest_first_visit"),
events_names_list=arrest_list,
double=False,
),
channel_map={"no_arrest": 0, "arrest": 1},
path_prefix="edw/*/events/{}/start_date",
time_series_limit=2,
)
return tm
def create_sliding_window_outcome_tmap(tmap_name: str) -> Optional[TensorMap]:
match = None
if not match:
pattern = re.compile(
r"(\d+)_hrs_sliding_window_(.*)_to_(.*)_(\d+)_hrs_step"
r"_(\d+)_hrs_prediction_(\d+)_hrs_gap$", )
match = pattern.findall(tmap_name)
if match:
window, event_proc_tm_1, event_proc_tm_2, step, prediction, gap = match[
0]
make_tensor_from_file = make_sliding_window_outcome_tensor_from_file
visit_tm = get_visit_tmap(
re.sub(r"(end_date|start_date)", "first_visit",
event_proc_tm_2), )
event_proc_tm_1 = get_signal_tmap(event_proc_tm_1)
event_proc_tm_2 = get_signal_tmap(event_proc_tm_2)
window = int(window)
step = int(step)
prediction = int(prediction)
gap = int(gap)
name = event_proc_tm_2.name
return TensorMap(
name=tmap_name,
shape=(2, ),
tensor_from_file=make_tensor_from_file(
window=window,
step=step,
prediction=prediction,
gap=gap,
event_tm_1=event_proc_tm_1,
event_tm_2=event_proc_tm_2,
visit_tm=visit_tm,
),
channel_map={
f"no_{name}": 0,
name: 1
},
path_prefix=event_proc_tm_2.path_prefix,
interpretation=Interpretation.CATEGORICAL,
validators=validator_no_nans,
time_series_limit=0,
)
return None
def set_tmap_params(self, signal_tm) -> bool:
pattern = re.compile(
r"^(i|ii|iii|v)_value_(\d+)_to_(\d+)_hrs_(pre|post)_(.*)$", )
match = pattern.findall(signal_tm.name)
if not match:
return False
lead, time_1, time_2, period, event_proc_tm = match[0]
self.lead = lead
visit_tm = get_visit_tmap(
re.sub(r"(end_date|start_date)", "first_visit", event_proc_tm), )
self.visit = visit_tm.tensor_from_file(visit_tm, self)
event_tm = GET_SIGNAL_TMAP(event_proc_tm)
event_time = event_tm.tensor_from_file(event_tm,
self,
visits=self.visit)[0][0]
if period == "pre":
offset = event_time - time_1 * 60 * 60
else:
offset = event_time + time_1 * 60 * 60
signal_size = (time_2 - time_1) * 60 * 60
self.sampling_rate = ECG_TMAPS[f"{lead}_sample_freq"].tensor_from_file(
ECG_TMAPS[f"{lead}_sample_freq"],
self,
visit=self.visit,
)[0]
if self.sampling_rate.size == 1:
return True
last = -1
for k, _ in enumerate(self.sampling_rate):
self.sampling_rate[k][1] -= offset
if self.sampling_rate[k][1] < 0:
last_negative = k
if self.sampling_rate[k][1] > signal_size:
last = k
self.sampling_rate = self.sampling_rate[last_negative:last]
self.sampling_rate[0][1] = 0
self.update_sampling_rate()
return True
def create_static_around_tmap(tm_name: str):
pattern = re.compile(r"^age_(.*)_(single|double)$")
match = pattern.findall(tm_name)
if match:
event_proc, samples = match[0]
visit_tm = get_visit_tmap(
event_proc.replace("end_date", "first_visit").replace(
"start_date",
"first_visit",
), )
samples = 1 if samples == "single" else 2
return TensorMap(
name=tm_name,
shape=(samples, ),
interpretation=Interpretation.CONTINUOUS,
tensor_from_file=admin_age_event_visit_tensor_from_file(
visit_tm=visit_tm,
samples=samples,
),
path_prefix="edw/*",
)
pattern = re.compile(
r"^(age|length_of_stay)_(\d+)_hrs_sliding_window_(.*)"
r"_to_(.*)_(\d+)_hrs_step$", )
match = pattern.findall(tm_name)
if match:
signal, window, event_proc_tm_1, event_proc_tm_2, step = match[0]
visit_tm = get_visit_tmap(
event_proc_tm_1.replace("end_date", "first_visit").replace(
"start_date",
"first_visit",
), )
event_proc_tm_1 = get_signal_tmap(event_proc_tm_1)
event_proc_tm_2 = get_signal_tmap(event_proc_tm_2)
window = int(window)
step = int(step)
if signal == "age":
tensor_from_file = admin_age_event_visit_tensor_from_file
else:
tensor_from_file = length_of_stay_sliding_window_tensor_from_file
return TensorMap(
name=tm_name,
shape=(1, ),
interpretation=Interpretation.CONTINUOUS,
tensor_from_file=tensor_from_file(
visit_tm=visit_tm,
window=window,
step=step,
event_tm_1=event_proc_tm_1,
event_tm_2=event_proc_tm_2,
),
path_prefix="edw/*",
)
pattern = re.compile(r"^length_of_stay_(\d+)_hrs_(pre|post)_(.*)$")
match = pattern.findall(tm_name)
if match:
time, period, event_proc_tm = match[0]
visit_tm = get_visit_tmap(
event_proc_tm.replace("end_date", "first_visit").replace(
"start_date",
"first_visit",
), )
hrs_to_event = [int(time)]
periods = [period]
else:
pattern = re.compile(
r"^length_of_stay_(\d+)_hrs_(pre|post)_(.*)"
r"_(\d+)_hrs_(pre|post)_(.*)$", )
match = pattern.findall(tm_name)
if match:
time_1, period_1, time_2, period_2, event_proc_tm = match[0]
hrs_to_event = [int(time_1), int(time_2)]
periods = [period_1, period_2]
if match:
visit_tm = get_visit_tmap(
event_proc_tm.replace("end_date", "first_visit").replace(
"start_date",
"first_visit",
), )
event_proc_tm = get_signal_tmap(event_proc_tm)
return TensorMap(
name=tm_name,
shape=(1, ),
interpretation=Interpretation.CONTINUOUS,
tensor_from_file=length_of_stay_event_tensor_from_file(
visit_tm=visit_tm,
event_tm=event_proc_tm,
hrs_to_event=hrs_to_event,
periods=periods,
),
path_prefix="edw/*",
)
return None
def create_sliding_window_tmap(tmap_name: str) -> Optional[TensorMap]:
match = None
imputation_type = None
feature = "raw"
tmap_match_name = tmap_name
pattern = re.compile(r"^(.*)_(mean_imputation)$")
match = pattern.findall(tmap_match_name)
if match:
_, imputation_type = match[0]
tmap_match_name = tmap_match_name.replace(f"_{imputation_type}", "")
match = None
pattern = re.compile(fr"^(.*)_({FEATURES})$")
match = pattern.findall(tmap_match_name)
if match:
_, feature = match[0]
tmap_match_name = tmap_match_name.replace(f"_{feature}", "")
match = None
if not match:
pattern = re.compile(
r"^(.*)_(\d+)_hrs_sliding_window_(.*)_to_(.*)_(\d+)_hrs_step$", )
match = pattern.findall(tmap_match_name)
if match:
signal_tm, window, event_proc_tm_1, event_proc_tm_2, step = match[
0]
make_tensor_from_file = make_sliding_window_tensor_from_file
if not match:
return None
if feature == "raw":
shape = (None, None)
else:
shape = (None, )
if signal_tm.endswith("_timeseries"):
shape += (2, )
time_tm = get_time_tm(signal_tm)
visit_tm = get_visit_tmap(
re.sub(r"(end_date|start_date)", "first_visit", event_proc_tm_2), )
signal_tm = _get_tmap(signal_tm)
event_proc_tm_1 = get_signal_tmap(event_proc_tm_1)
event_proc_tm_2 = get_signal_tmap(event_proc_tm_2)
window = int(window)
step = int(step)
return TensorMap(
name=tmap_name,
shape=shape,
tensor_from_file=make_tensor_from_file(
window=window,
step=step,
event_tm_1=event_proc_tm_1,
event_tm_2=event_proc_tm_2,
visit_tm=visit_tm,
signal_tm=signal_tm,
signal_time_tm=time_tm,
feature=feature,
imputation_type=imputation_type,
),
channel_map=signal_tm.channel_map,
path_prefix=signal_tm.path_prefix,
interpretation=signal_tm.interpretation,
validators=validator_no_nans,
time_series_limit=0,
)
def create_around_tmap(tmap_name: str) -> Optional[TensorMap]:
match = None
time_2 = None
period_2 = None
event_proc_tm_2 = None
imputation_type = None
feature = "raw"
shape: Optional[Tuple[Axis, ...]] = None
make_tensor_from_file = None
tmap_match_name = tmap_name
if tmap_name.endswith("_explore"):
return None
pattern = re.compile(r"^(.*)_(mean_imputation|sample_and_hold)$")
match = pattern.findall(tmap_match_name)
if match:
_, imputation_type = match[0]
tmap_match_name = tmap_match_name.replace(f"_{imputation_type}", "")
match = None
pattern = re.compile(fr"^(.*)_({FEATURES})$")
match = pattern.findall(tmap_match_name)
if match:
_, feature = match[0]
tmap_match_name = tmap_match_name.replace(f"_{feature}", "")
match = None
if not match:
pattern = re.compile(
r"^(.*)_(\d+)_hrs_(pre|post)_(.*)_(\d+)_hrs_(pre|post)_(.*)"
r"_(\d+)_hrs_window$", )
match = pattern.findall(tmap_match_name)
if match:
(
signal_tm,
time_1,
period_1,
event_proc_tm,
time_2,
period_2,
event_proc_tm_2,
window,
) = match[0]
make_tensor_from_file = make_around_event_tensor_from_file
times = [int(time_1), int(time_2)]
periods = [period_1, period_2]
if not match:
pattern = re.compile(
r"^(.*)_(\d+)_hrs_(pre|post)_(.*)_(\d+)_hrs_window$", )
match = pattern.findall(tmap_match_name)
if match:
signal_tm, time, period, event_proc_tm, window = match[0]
make_tensor_from_file = make_around_event_tensor_from_file
times = [int(time)]
periods = [period]
if not match:
return None
if signal_tm.endswith("_timeseries"):
if feature == "raw":
shape = (None, 2)
else:
shape = (2, )
else:
if feature == "raw":
shape = (None, )
else:
shape = (1, )
time_tm = get_time_tm(signal_tm)
visit_tm = get_visit_tmap(
re.sub(r"(end_date|start_date)", "first_visit", event_proc_tm), )
window = int(window)
signal_tm = _get_tmap(signal_tm)
event_proc_tms = [get_signal_tmap(event_proc_tm)]
if event_proc_tm_2 is not None:
event_proc_tms.append(get_signal_tmap(event_proc_tm_2))
return TensorMap(
name=tmap_name,
shape=shape,
tensor_from_file=make_tensor_from_file(
times=times,
periods=periods,
window=window,
feature=feature,
event_tms=event_proc_tms,
visit_tm=visit_tm,
signal_tm=signal_tm,
signal_time_tm=time_tm,
imputation_type=imputation_type,
),
channel_map=signal_tm.channel_map,
path_prefix=signal_tm.path_prefix,
interpretation=signal_tm.interpretation,
validators=validator_no_nans,
)