def usage(self, o):
return pacal.BinomialDistr(int(o * self.n), p=self.p)
def boostrap_info_process(item_id="38242"):
case_service = "Cardiac Surgery"
#item_id = "3824ns_info_state_rvs2"
info_granularity = 1
eps_trunk = 1e-3
elective_outdir = "scm_implementation/ns_info_state_rvs/elective"
emergency_outdir = "scm_implementation/ns_info_state_rvs/emergency"
analytics = ScmAnalytics.ScmAnalytics(lhs_config)
filters = [{
"dim": "case_service",
"op": "eq",
"val": case_service
}, {
"dim": "urgent_elective",
"op": "eq",
"val": "Elective"
}]
elective_filter = [{
"dim": "urgent_elective",
"op": "eq",
"val": "Elective"
}]
emergency_filter = [{
"dim": "urgent_elective",
"op": "eq",
"val": "Urgent"
}]
case_service_filter = [{
"dim": "case_service",
"op": "eq",
"val": case_service
}]
surgery_df = pre_process_columns(analytics.surgery_df)
surgery_df = surgery_df[surgery_df["start_date"].notna()]
surgery_df = surgery_df[
surgery_df["start_date"] > datetime.date(2016, 1, 1)]
surgery_df = Analytics.process_filters(surgery_df,
filters=elective_filter +
case_service_filter)
dist_df = surgeries_per_day_distribution(surgery_df,
day_group_by="is_weekday",
filters=[])
data = dist_df.set_index("is_weekday").loc[True]["data"]
bins = range(1 + int(max(data)))
binom_x = [x + 0.5 for x in bins]
n = int(max(data))
p = np.mean(data) / n
surgery_df = pre_process_columns(analytics.surgery_df)
surgery_df = surgery_df[surgery_df["start_date"].notna()]
surgery_df = surgery_df[
surgery_df["start_date"] > datetime.date(2016, 1, 1)]
surgery_df = Analytics.process_filters(surgery_df,
filters=emergency_filter +
case_service_filter)
dist_df = surgeries_per_day_distribution(surgery_df, filters=[])
emergency_surgeries_mean = np.mean(dist_df)
surgery_df = Analytics.process_filters(analytics.surgery_df,
filters=case_service_filter)
surgery_df["procedure_count"] = surgery_df["procedures"].apply(
lambda x: len(x))
procedure_count_df = surgery_df.groupby("procedure_count").agg({
"event_id":
"count"
}).reset_index()
procedure_count_df = procedure_count_df[
procedure_count_df["procedure_count"] != 6]
procedure_count_df["p"] = procedure_count_df["procedure_count"] / sum(
procedure_count_df["procedure_count"])
procedure_count_rv = pacal.DiscreteDistr(
procedure_count_df["procedure_count"], procedure_count_df["p"])
"""
Procedure weights
"""
usage_events = set(analytics.usage_df["event_id"])
surgery_df = analytics.surgery_df[analytics.surgery_df["event_id"].isin(
usage_events)]
surgery_df = Analytics.process_filters(surgery_df,
filters=case_service_filter)
surgery_df["procedures"] = surgery_df["procedures"].apply(
lambda x: set(e.replace(" ", "_") for e in x))
procedures = surgery_df["procedures"].apply(lambda x: list(x)).to_list()
procedures = pd \
.DataFrame({"procedure": [val for sublist in procedures for val in sublist],
"count": [1 for sublist in procedures for val in sublist]}) \
.groupby("procedure") \
.agg({"count": "count"}) \
.reset_index()
procedures["p"] = procedures["count"] / sum(procedures["count"])
def procedure_pick_rv(size):
return np.random.choice(procedures["procedure"],
p=procedures["p"],
replace=False,
size=size)
synthetic_surgeries = pd.DataFrame({"event_id": list(range(1000))})
synthetic_surgeries["procedure_count"] = procedure_count_rv.rand(1000)
synthetic_surgeries["procedures"] = synthetic_surgeries[
"procedure_count"].apply(lambda x: procedure_pick_rv(x))
synthetic_procedure_df = pd.concat(
[pd.Series(row['event_id'], row['procedures']) for _, row in synthetic_surgeries.iterrows()]) \
.reset_index() \
.rename(columns={"index": "procedure",
0: "event_id"}
)
synthetic_procedure_df["flag"] = 1
synthetic_surgeries_df = synthetic_procedure_df \
.pivot(index="event_id", columns="procedure", values="flag") \
.fillna(0) \
.reset_index()
feature_df = pd.read_csv(os.path.join("regression_results", item_id))
features = feature_df["feature"]
featured_procedures = list(
filter(lambda x: "." not in x, feature_df["feature"]))
if "other" in featured_procedures:
featured_procedures.remove("other")
for fp in featured_procedures:
if fp not in synthetic_surgeries_df:
print(procedures.set_index("procedure").loc[fp])
synthetic_surgeries_df[fp] = 0
all_procedures = set.union(*surgery_df["procedures"])
interactions = list(filter(lambda x: "." in x, feature_df["feature"]))
interactions = list(Interaction(i.split(".")) for i in interactions)
data, _ = SURegressionModel.extract_features_data(synthetic_surgeries_df,
featured_procedures, [],
interactions,
other=True)
for f in feature_df["feature"]:
if f not in data:
print(f)
data[f] = 0
synthetic_surgeries_df["feature_vector"] = data[features].values.tolist()
coeff = np.array(feature_df["estimate"])
synthetic_surgeries_df["expected_usage"] = synthetic_surgeries_df["feature_vector"] \
.apply(lambda x: np.exp(np.dot(x, coeff)))
"""
Information rv for empirical surgeries
"""
surgery_df = surgery_df.drop_duplicates("event_id", keep="last")
empirical_procedure_df = pd.concat(
[pd.Series(row['event_id'], row['procedures']) for _, row in surgery_df.iterrows()]) \
.reset_index() \
.rename(columns={"index": "procedure",
0: "event_id"}
)
empirical_procedure_df["flag"] = 1
empirical_surgeries_df = empirical_procedure_df \
.pivot(index="event_id", columns="procedure", values="flag") \
.fillna(0) \
.reset_index()
data, _ = SURegressionModel.extract_features_data(empirical_surgeries_df,
featured_procedures, [],
interactions,
other=True)
empirical_surgeries_df["feature_vector"] = data[features].values.tolist()
empirical_surgeries_df["expected_usage"] = empirical_surgeries_df["feature_vector"] \
.apply(lambda x: np.exp(np.dot(x, coeff)))
"""
Plotly histogram for per surgery info rv, empirical surgeries and synthetic using regression results
"""
s = 0
e = int(
max(max(empirical_surgeries_df["expected_usage"]),
max(synthetic_surgeries_df["expected_usage"])) + 1)
empirical_trace = go.Histogram(
x=empirical_surgeries_df["expected_usage"],
name='Empirical Surgery Info RV (mean={:0.2f})'.format(
np.mean(empirical_surgeries_df["expected_usage"])),
xbins=dict(start=s, end=e, size=info_granularity),
histnorm='probability density',
opacity=0.75)
synthetic_trace = go.Histogram(
x=synthetic_surgeries_df["expected_usage"],
name='Synthetic Surgery Info RV (mean={:0.2f})'.format(
np.mean(synthetic_surgeries_df["expected_usage"])),
xbins=dict(start=s, end=e, size=info_granularity),
histnorm='probability density',
opacity=0.75)
layout = go.Layout(title="Per Surgery Info R.V Item: {0}".format(item_id),
xaxis={'title': 'Info [Expected Usage]'},
yaxis={'title': 'Probability Density'})
figure = go.Figure(data=[empirical_trace, synthetic_trace], layout=layout)
plot(figure, filename="{0}_Per_Surgery_Info_Rv.html".format(item_id))
"""
Plotly histogram for per weekday elective surgery RV
"""
empirical_rv_df = empirical_surgeries_df.groupby(["expected_usage"]) \
.agg({"event_id": "count"}) \
.rename(columns={"event_id": "count"}) \
.reset_index()
empirical_rv_df["p"] = empirical_rv_df["count"] / sum(
empirical_rv_df["count"])
emp_surgery_rv = pacal.DiscreteDistr(empirical_rv_df["expected_usage"],
empirical_rv_df["p"])
surgery_demand_rv = pacal.BinomialDistr(n, p)
days = 100000
elective_samples = [
sum(emp_surgery_rv.rand(x)) for x in np.random.binomial(n, p, days)
]
elective_samples = [
round(sample / info_granularity) * info_granularity
for sample in elective_samples
]
weekday_elective_trace = go.Histogram(
x=elective_samples,
name='{} Elective Info RV (mean={:0.2f})'.format(
item_id, np.mean(elective_samples)),
xbins=dict(start=0, end=max(elective_samples), size=info_granularity),
histnorm='probability',
opacity=0.75)
"""
Plotly histogram for per day emergency surgery RV
"""
emergency_samples = [
sum(emp_surgery_rv.rand(x))
for x in np.random.poisson(emergency_surgeries_mean, days)
]
emergency_samples = [
round(sample / info_granularity) * info_granularity
for sample in emergency_samples
]
emergency_trace = go.Histogram(
x=emergency_samples,
name='{} Emergency Info RV (mean={:0.2f})'.format(
item_id, np.mean(emergency_samples)),
xbins=dict(start=0, end=max(emergency_samples), size=info_granularity),
histnorm='probability',
opacity=0.75)
layout = go.Layout(
title="Weekday Elective Info R.V Item: {0}".format(item_id),
xaxis={'title': 'Info State (Poisson Usage)]'},
yaxis={'title': 'Probability'})
figure = go.Figure(data=[weekday_elective_trace, emergency_trace],
layout=layout)
plot(figure, filename="{0}_Weekday_Elective_Info_Rv.html".format(item_id))
elective_info_df = pd.DataFrame({"info": elective_samples, "count": [1] * len(elective_samples)}) \
.groupby(["info"]) \
.agg({"count": "count"}) \
.reset_index()
elective_info_df["p"] = elective_info_df["count"] / sum(
elective_info_df["count"])
elective_info_rv = pacal.DiscreteDistr(elective_info_df["info"],
elective_info_df["p"])
emergency_info_df = pd.DataFrame({"info": emergency_samples, "count": [1] * len(emergency_samples)}) \
.groupby(["info"]) \
.agg({"count": "count"}) \
.reset_index()
emergency_info_df["p"] = emergency_info_df["count"] / sum(
emergency_info_df["count"])
emergency_info_rv = pacal.DiscreteDistr(emergency_info_df["info"],
emergency_info_df["p"])
max_v = 999
for d in elective_info_rv.get_piecewise_pdf().getDiracs():
if 1 - elective_info_rv.cdf(d.a) < eps_trunk:
max_v = d.a
break
diracs = (pacal.CondLtDistr(elective_info_rv, max_v)) \
.get_piecewise_pdf().getDiracs()
diracs = list(filter(lambda d: d.f > 0, diracs))
elective_info_rv = pacal.DiscreteDistr([d.a for d in diracs],
[d.f for d in diracs])
max_v = 999
for d in emergency_info_rv.get_piecewise_pdf().getDiracs():
if 1 - emergency_info_rv.cdf(d.a) < eps_trunk:
max_v = d.a
break
diracs = (pacal.CondLtDistr(emergency_info_rv, max_v)) \
.get_piecewise_pdf().getDiracs()
diracs = list(filter(lambda d: d.f > 0, diracs))
emergency_info_rv = pacal.DiscreteDistr([d.a for d in diracs],
[d.f for d in diracs])
with open(os.path.join(elective_outdir, "{0}.pickle".format(item_id)),
"wb") as f:
pickle.dump(elective_info_rv, f)
with open(os.path.join(emergency_outdir, "{0}.pickle".format(item_id)),
"wb") as f:
pickle.dump(emergency_info_rv, f)
return emergency_trace, weekday_elective_trace
configs = []
i = 0
for demand_n in [1, 2, 3, 4]:
for usage_n in [1, 2, 3, 4]:
for p in [0.25, 0.5, 0.75]:
for q in [0.25, 0.5, 0.75]:
for horizon in [0, 1, 2, 3, 4]:
for b in [0.001, 0.05, 0.1, 0.5, 1, 2, 4, 100]:
configs.append(
ModelConfig(
gamma=1,
lead_time=0,
info_state_rvs=None,
holding_cost=1,
backlogging_cost=b,
setup_cost=0,
unit_price=0,
usage_model=BinomUsageModel(n=usage_n, p=p),
increments=1,
horizon=horizon,
info_rv=pacal.BinomialDistr(demand_n, q),
label="numerical_experiments_binomial_model",
label_index=i))
i += 1
if __name__ == "__main__":
xs = list(range(0, 20))
ts = list(range(0, 21))
run_configs(configs, ts, xs, pools=8)
from scm_optimization.model import ModelConfig, run_configs, PoissonUsageModel, BinomUsageModel, DeterministUsageModel
import pacal
from decimal import *
configs = []
i = 0
for horizon in [0, 1, 2, 3, 4]:
for b in [10, 1000]:
for d in [1, 5, 10, 20]:
configs.append(
ModelConfig(gamma=1,
lead_time=0,
info_state_rvs=None,
holding_cost=1,
backlogging_cost=b,
setup_cost=0,
unit_price=0,
usage_model=PoissonUsageModel(1),
horizon=horizon,
info_rv=pacal.BinomialDistr(d, 0.5),
label="demand_scale_experiment",
label_index=i))
i += 1
configs = [configs[35], configs[39]]
if __name__ == "__main__":
xs = list(range(0, 1))
ts = list(range(0, 21))
run_configs(configs, ts, xs, pools=8)
from multiprocessing import Pool
from datetime import date, datetime
configs = []
i = 0
binomial_usage_models = [
BinomUsageModel(n=2, p=0.5),
BinomUsageModel(n=3, p=0.3333),
BinomUsageModel(n=4, p=0.25),
BinomUsageModel(n=5, p=0.2),
BinomUsageModel(n=10, p=0.1),
]
b = 1000
poisson_usage_model = PoissonUsageModel(1, trunk=1e-10)
booking_model = pacal.BinomialDistr(10, 0.5)
for horizon in [0, 1, 2, 3]:
configs.append(ModelConfig(
gamma=1,
lead_time=0,
info_state_rvs=None,
holding_cost=1,
backlogging_cost=b,
setup_cost=0,
unit_price=0,
usage_model=poisson_usage_model,
horizon=horizon,
info_rv=booking_model,
label="poisson_usage_policy",
label_index=i)