This is a python interface to Monument model serving.

Requirements:

• Monument gui version 1.14 or later

• python 3 with numpy (tested with python 3.8)

Method:

• create and save a Monument model

• select a csv file for model serving

• use the test.py script as a template for calling the server. You will need to specify:

  • maifile - the Monument file saved after running a model
  • csvfile - the data file to be served
  • algo - the algo to be called

These definitions must match the model in the maifile.

For example, run the Monument application, then menu Help|Demos|Insurers and save, for example to insure.mai.

Copy the insure2.csv file in the repo to the same directory. Note that this file and the insure demo data in Monument contain random values.

Copy test.py to a new script, e.g. demo.py. Update the new script with the directories used for the two files. Then run the script, using

   run.command       macos, linux
   run.cmd           windows

For example:

~/monument$ ./run.command demo.py
Ent-Boost(AIG):
[0, [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, 84.266975306, 83.456123554, 83.679003499, 83.922832468, 83.227100486, 82.136659608, 78.618343321, 79.206285358, 78.956238572, 77.3779471, 80.624277601, 77.35610506, 75.990184392, 77.118267567, 77.539357834, 83.413333175, 78.771760555, 87.582798411]]
-----------------
LightGBM(MET):
[0, [None, None, None, None, None, None, None, None, None, None, 70.347962976, 73.729574441, 74.177049298, 76.60359986, 78.540167867, 77.743111935, 76.62659741, 76.21243108, 76.004730501, 76.696084498, 72.799332191, 75.723403363, 77.695306181, 80.571822401, 78.51396662, 80.981662204, 78.29077038, 77.587885817, 79.87918814, 81.641283798, 81.863832669, 79.108968194, 77.755798659, 74.463372438, 72.033799787, 75.703130468, 75.215633618, 73.379521242, 70.60071591, 70.914619537]]