模型模拟相关 API#
SimulationResult#
- class SimulationResult#
import polars as pl
import numpy as np
from mas.model import *
class WarfarinPkPd(OdeModule):
def __init__(self) -> None:
super().__init__(odeint.DVERK(tol=1e-3))
self.dose_cmt = compartment(default_dose=True)
self.central_cmt = compartment(default_obs=True)
self.effect_cmt = compartment()
self.theta_cl = theta(0.132, bounds=(0.01, 1))
self.theta_v = theta(7.978, bounds=(0.01, 20))
self.theta_ka = theta(1.359, bounds=(0.01, 5))
self.theta_alag = theta(0.828, bounds=(0.01, 24))
self.theta_e0 = theta(100, bounds=(0.01, 500))
self.theta_emax = theta(-250, bounds=(-500, 0))
self.theta_ec50 = theta(10, bounds=(0.01, 20))
self.theta_ke0 = theta(0.2, bounds=(0.001, 3))
self.eta_V = omega(0.08)
self.eta_Cl = omega(0.08)
self.eta_ka = omega(0.16)
self.eta_alag = omega(0.15)
self.eta_e0 = omega(0.09)
self.eta_emax = omega(0, fixed=True)
self.eta_ec50 = omega(0.4)
self.eta_ke0 = omega(0.3)
self.eps_prop_pk = sigma(0.01)
self.eps_add_pk = sigma(0.06)
self.eps_add_pd = sigma(15)
self.dvid = column("DVID")
def pred(self):
# PD 参数
cl = self.theta_cl * exp(self.eta_Cl)
v = self.theta_v * exp(self.eta_V)
ka = self.theta_ka * exp(self.eta_ka)
alag = self.theta_alag * exp(self.eta_alag)
k = cl / v
# PD 参数
e0 = self.theta_e0 * exp(self.eta_e0)
emax = self.theta_emax * exp(self.eta_emax)
ec50 = self.theta_ec50 * exp(self.eta_ec50)
ke0 = self.theta_ke0 * exp(self.eta_ke0)
# 定义微分方程组
conc = self.central_cmt.A / v
ce = self.effect_cmt.A
self.dose_cmt.alag = alag
self.dose_cmt.dAdt = -ka * self.dose_cmt.A
self.central_cmt.dAdt = ka * self.dose_cmt.A - k * self.central_cmt.A
self.effect_cmt.dAdt = ke0 * (conc - ce)
eff = e0 + (emax * ce) / (ec50 + ce)
if self.dvid == 1: # PK 预测值
y = conc * (1 + self.eps_prop_pk) + self.eps_add_pk
else: # PD 预测值
y = eff + self.eps_add_pd
return y
scenario = (
EventTable()
.add_dosing(
time=0,
amt=100,
)
.add_sampling(time=np.arange(0, 121))
.with_covariates(DVID=1)
.with_ids(np.arange(0, 1001))
.merge(
EventTable()
.add_sampling(
time=np.arange(0, 121),
)
.with_covariates(DVID=2)
.with_ids(np.arange(0, 1001))
)
)
pop_model = PopModel(mod=WarfarinPkPd, data=scenario)
res = pop_model.simulate()
res
➡️ Since build cache already exists, skip compile
[WARNING] Casting DVID to numeric dtype
Summary
| N Subject | N Obs | Seed | Simulation Time |
|---|---|---|---|
| 1001 | 243243 | 1234 | 0:00:03.41 |
output#
- property output
模拟得到的数据集。
类型：
DataFrame
示例：
res.output.head(10)
shape: (10, 29)
| ID | TIME | DV | AMT | CMT | EVID | MDV | SS | RATE | DUR | ADDL | II | DVID | _IPRED_ | _PRED_ | _Y_ | alag | ce | cl | conc | e0 | ec50 | eff | emax | k | ka | ke0 | v | y |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| i64 | f64 | f64 | f64 | i64 | i64 | i64 | i64 | f64 | f64 | i64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 0 | 0.0 | null | 100.0 | 1 | 1 | 1 | null | null | null | null | null | 1.0 | 0.0 | 0.0 | -0.144564 | 1.027434 | 0.0 | 0.131631 | 0.0 | 117.382865 | 9.965346 | 117.382865 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | -0.144564 |
| 0 | 0.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 1.0 | 0.0 | 0.0 | -0.201869 | 1.027434 | 0.0 | 0.131631 | 0.0 | 117.382865 | 9.965346 | 117.382865 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | -0.201869 |
| 0 | 0.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 2.0 | 117.382865 | 100.0 | 122.995336 | 1.027434 | 0.0 | 0.131631 | 0.0 | 117.382865 | 9.965346 | 117.382865 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 122.995336 |
| 0 | 1.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 1.0 | 0.0 | 2.60881 | -0.317276 | 1.027434 | 0.0 | 0.131631 | 0.0 | 117.382865 | 9.965346 | 117.382865 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | -0.317276 |
| 0 | 1.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 2.0 | 117.382865 | 98.852754 | 108.755962 | 1.027434 | 0.0 | 0.131631 | 0.0 | 117.382865 | 9.965346 | 117.382865 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 108.755962 |
| 0 | 2.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 1.0 | 7.621264 | 9.864767 | 8.258265 | 1.027434 | 0.851663 | 0.131631 | 7.621264 | 117.382865 | 9.965346 | 97.69945 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 8.258265 |
| 0 | 2.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 2.0 | 97.69945 | 70.495973 | 95.865509 | 1.027434 | 0.851663 | 0.131631 | 7.621264 | 117.382865 | 9.965346 | 97.69945 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 95.865509 |
| 0 | 3.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 1.0 | 8.331094 | 11.578074 | 9.121094 | 1.027434 | 2.060493 | 0.131631 | 8.331094 | 117.382865 | 9.965346 | 74.548165 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 9.121094 |
| 0 | 3.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 2.0 | 74.548165 | 41.10057 | 73.872453 | 1.027434 | 2.060493 | 0.131631 | 8.331094 | 117.382865 | 9.965346 | 74.548165 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 73.872453 |
| 0 | 4.0 | null | null | 2 | 0 | 1 | null | null | null | null | null | 1.0 | 8.315001 | 11.869852 | 8.120068 | 1.027434 | 3.102918 | 0.131631 | 8.315001 | 117.382865 | 9.965346 | 58.023066 | -250.0 | 0.011275 | 2.330835 | 0.181635 | 11.674763 | 8.120068 |
data#
- property data
用于模拟的原始数据集。
类型：
DataFrame
seed#
- property seed
设定的随机种子号。
类型：
int
elapsed_time#
- property elapsed_time
模拟耗时。
类型：
timedelta
plot_ind()#
- plot_ind(filter=None, idv_name=None, y_name='_IPRED_') Plot
绘制个体模拟数据折线图。
参数：
filter (
Expr, optional): 筛选条件，对于部分数据进行个体模拟图绘制。默认值为None。idv_name (
str, optional)：因变量名。若为None，则自动推断。默认值为None。y_name (
str, optional)：自变量名。默认值为"_IPRED_"。
返回值：
绘制完成的个体模拟数据折线图。
返回类型：
Plot
示例：
res.plot_ind().with_facet_wrap("DVID", shared_y_axis=False)
[WARNING] Sampling data for better performance...
plot_interval()#
- plot_interval(filter=None, idv_name=None, y_name='_IPRED_') Plot
绘制模拟数据区间图（5% 分位数至 95% 分位数）。
参数：
filter (
Expr, optional): 筛选条件，对于部分数据进行个体模拟图绘制。默认值为None。idv_name (
str, optional)：因变量名。若为None，则自动推断。默认值为None。y_name (
str, optional)：自变量名。默认值为"_IPRED_"。
示例：
绘制在 100 mg 给药时华法林的血药浓度个体预测值 95% 置信区间图：
from mas.plotting import GridPlot
GridPlot(
[
res.plot_interval(filter=pl.col("DVID") == 1),
res.plot_interval(filter=pl.col("DVID") == 2),
],
n_cols=2,
shared_x_axis=True,
shared_y_axis=False,
)