Run RiskPaths Model from Python - openmpp/openmpp.github.io GitHub Wiki

OpenM++ integration with Python: using RiskPaths model

This example shows how Python can be used to automate modeling, using very general openM++ interfaces. These same interfaces can be used by platforms and applications other than Python with equivalent functionality.

Following Python script is running "RiskPaths" model to analyze childlessness by varying two parameters:

Age baseline for first union formation
Relative risks of union status on first pregnancy by following scale factor:

scaleStep = 0.02
scaleValues = [0.44 + i * scaleStep for i in range(1 + round((1.00 - 0.44) / scaleStep))]

Please keep in mind, scaling above result in 841 runs of RiskPaths model and task may take long time to be completed. If you want to get results faster scale values by 0.08 instead of 0.02.

How to run the script

Python example script is using openM++ web-service in order to run the model, modify parameters and read output values. OpenM++ web-service does not require any installation, just download latest release of openM++, unpack it into any directory, start oms.exe and run the script:

Windows:

cd C:\my-openmpp-release
bin\oms
py ompp-python\riskpaths_childlessness.py

Linux / MacOS:

cd ~/my-openmpp-release
bin/oms
python3 ompp-python/riskpaths_childlessness.py

As result oms web-service will start to listen incoming requests on http://localhost:4040 and Python script will do all actions using oms web-service API.

You may also need to install mathplotlib to display the chart and requests to communicate with web-service:

pip install -U matplotlib
pip install requests

Important:

This is an example script and error handling intentionally omitted. It is highly recommended to use try ... except in production code.

OpenM++ Run RiskPaths Model from Python

Python script

#
# Python integration example using RiskPaths model
#   to analyze contribution of delayed union formations 
#   versus decreased fertility on childlessness
#
# Input parameters:
#   AgeBaselineForm1: age baseline for first union formation
#   UnionStatusPreg1: relative risks of union status on first pregnancy
# Output value:
#   T05_CohortFertility: Cohort fertility, expression 1
#

# Prerequisite:
#
# download openM++ release from https://github.com/openmpp/main/releases/latest
# unpack it into any directory
# start oms web-service:
#   Windows:
#     cd C:\my-openmpp-release
#     bin\ompp_ui.bat
#   Linux:
#     cd ~/my-openmpp-release
#     bin/oms
#
# Script below is using openM++ web-service "oms"
# to run the model, modify parameters and read output values.

# Important:
#   Script below does not handle errors, please use try/except in production.

import time
import requests
import numpy as np
import matplotlib.pyplot as plt

# get default values for AgeBaselineForm1, UnionStatusPreg1 and SimulationCases parameters 
# by reading it from first model run results
# assuming first run of the model done with default set of parameters
#
rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/status/first')
rsp.raise_for_status()
firstRunStatus = rsp.json()
firstRunDigest = rsp.json()['RunDigest']

rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/' + firstRunDigest + '/parameter/AgeBaselineForm1/value/start/0/count/0')
rsp.raise_for_status()
ageFirstUnion = rsp.json()

rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/' + firstRunDigest + '/parameter/UnionStatusPreg1/value/start/0/count/0')
rsp.raise_for_status()
unionStatusPreg = rsp.json()

rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/' + firstRunDigest + '/parameter/SimulationCases/value/start/0/count/0')
rsp.raise_for_status()
simulationCases = rsp.json()

# create new input data for our modelling task
#

# set number of simulation cases
simulationCases[0]['Value'] = 1000

# for AgeBaselineForm1 and UnionStatusPreg1 parameter values
# apply scale in range from 0.44 to 1.0
#
scaleStep = 0.02
scaleValues = [0.44 + i * scaleStep for i in range(1 + round((1.00 - 0.44) / scaleStep))]

ageValues = [x['Value'] for x in ageFirstUnion]
unionValues = [x['Value'] for x in unionStatusPreg]

inpSetLst = []
for scaleAgeBy in scaleValues:
    #
    print("Scale age by:", scaleAgeBy)
    for i in range(len(ageFirstUnion)):
        ageFirstUnion[i]['Value'] = ageValues[i] * scaleAgeBy
    
    for scaleUnionBy in scaleValues:
        #
        # scale first two values of unionStatusPreg vector
        unionStatusPreg[0]['Value'] = unionValues[0] * scaleUnionBy
        unionStatusPreg[1]['Value'] = unionValues[1] * scaleUnionBy
        #
        # create new set of input parameters
        # automatically generate unique names for each input set
        #
        inpSetRq = {
            'ModelName': 'RiskPaths',
            'Name': '',
            'BaseRunDigest': firstRunDigest,
            'IsReadonly': True,
            'Txt': [{
                'LangCode': 'EN',
                'Descr': 'Scale age: ' + str(scaleAgeBy) + ' union status: ' + str(scaleUnionBy) 
            }],
            'Param': [
                {
                    'Name': 'AgeBaselineForm1',
                    'SubCount': 1,
                    'Value': ageFirstUnion,
                    'Txt': [{'LangCode': 'EN', 'Note': 'Age values scale by: ' + str(scaleAgeBy)}]
                },
                {
                    'Name': 'UnionStatusPreg1',
                    'SubCount': 1,
                    'Value': unionStatusPreg,
                    'Txt': [{'LangCode': 'EN', 'Note': 'Union Status values scale by: ' + str(scaleUnionBy)}]
                }
            ],
        }
        #
        # create new input set of model parameters
        # automatically generate unique name for that input set
        #
        rsp = requests.put('http://127.0.0.1:4040/api/workset-create', json=inpSetRq)
        rsp.raise_for_status()
        js = rsp.json()
        #
        inpSetName = js['Name']
        if inpSetName is None or inpSetName == '':
            raise Exception("Fail to create input set, scales:", scaleAgeBy, scaleUnionBy)
        #
        inpSetLst.append(inpSetName)

# create modeling task from all input sets
# automatically generate unique name for the task
#
inpLen = len(inpSetLst)
print("Create task from", inpLen, "input sets of parameters")

taskRq = {
    'ModelName': 'RiskPaths',
    'Name': '',
    'Set': inpSetLst,
    'Txt': [{
        'LangCode': 'EN',
        'Descr': 'Task to run RiskPaths ' + str(inpLen) + ' times',
        'Note': 'Task scales AgeBaselineForm1 and UnionStatusPreg1 parameters from 0.44 to 1.00 with step ' + str(scaleStep)
    }]
}
rsp = requests.put('http://127.0.0.1:4040/api/task-new', json=taskRq)
rsp.raise_for_status()
js = rsp.json()

taskName = js['Name']
if taskName is None or taskName == '':
    raise Exception("Error at create modeling task")

#
# submit request to web-service to run RiskPaths with modeling task
#
runModelRq = {
    'ModelName': 'RiskPaths',
    'Opts': {
        'OpenM.TaskName': taskName,
        'OpenM.ProgressPercent': '100'
    }
}
rsp = requests.post('http://127.0.0.1:4040/api/run', json=runModelRq)
rsp.raise_for_status()
js = rsp.json()
#
taskRunStamp = js['RunStamp']
if taskRunStamp is None or taskRunStamp == '':
    raise Exception('Model failed to start, task run stamp is empty')

print("Starting modeling task:", taskName)

# wait until modeling task completed
# and report the progress
#
# task status returned by web-service can be one of:
# i=initial p=in progress w=waiting s=success x=exit e=error(failed)
#
taskStatus = '' 

while taskStatus in '' 'i' 'p' 'w':
    #
    time.sleep(1)
    #
    rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/task/' + taskName + '/run-status/run/' + taskRunStamp)
    rsp.raise_for_status()
    js = rsp.json()
    taskStatus = js['Status']
    #
    # if model not started to run the task yet check again after short sleep
    #
    if taskStatus in '' 'i':
        #
        print("Waiting for modeling task to start...")
        continue
    #
    # if task completed successfully then get pairs of {model run, inpur set name}
    #
    if taskStatus == 's':
        rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/task/' + taskName + '/runs')
        rsp.raise_for_status()
        js = rsp.json()
        taskRuns = js['TaskRun'][0]['TaskRunSet']   # use index=0 because this is first run of our task
        break
    #
    # if task still in progress then count completed model runs
    #
    if taskStatus in 'i' 'p' 'w':
        rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/' + taskRunStamp + '/status/list')
        rsp.raise_for_status()
        trs = rsp.json()
        #
        n = 0
        for r in trs:
            if r['Status'] == 's': n += 1
        #
        print("Completed", n, "model runs out of", inpLen)
        continue
    #
    # any other task run status considered as failure
    #
    raise Exception("Model run failed, task run stamp:", taskRunStamp, "status:", taskStatus)
    #

print("Modeling task completed, retriving results...")

# for each age and union status retrive output:
#   childlessness value: T05_CohortFertility.Expr1
#
# organize results into 2-dimensional array to plot 3d chart
#
childlessnessVals = np.zeros((len(scaleValues), len(scaleValues)))
runIdx = 0

for ageIdx in range(len(scaleValues)):
    for unionIdx in range(len(scaleValues)):
        #
        runDigest = taskRuns[runIdx]['Run']['RunDigest']
        #
        rsp = requests.get('http://127.0.0.1:4040/api/model/RiskPaths/run/' + runDigest + '/table/T05_CohortFertility/expr')
        rsp.raise_for_status()
        js = rsp.json()
        #
        childlessnessVals[ageIdx][unionIdx] = js[1]['Value']
        runIdx += 1

# display the results
#
ageVals, unionVals = np.meshgrid(scaleValues, scaleValues)

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_wireframe(ageVals, unionVals, childlessnessVals, color='black')
ax.set_xlabel('Decreased union formation')
ax.set_ylabel('Decreased fertility')
ax.set_zlabel('Childlessness')
ax.view_init(elev=45)
plt.show()