Decision Engine

⚠️ this is alpha software and there are known bugs due to decision engine trying to dynamically evaluate things

A configuration driven decision engine written in Rust with Python bindings.

Python usage:

import pandas as pd
from decision_engine.engine import DecisionEngine

decision_engine = DecisionEngine.model_validate(
    {
        "config": {
            "key": "action",
            "default_action": "high",
            "rules": [
                ["score < 80", "low"],
                ["score <= 99", "medium"],
                ["score > 99", "high"],
            ],
        },
    }
)


dataframe = pd.DataFrame({"score": [0.0, 85, 100]})
print(decision_engine.transform(dataframe))
#    action
# 0     low
# 1  medium
# 2    high

This also works with multiple conditions, whereby it takes the first one which is "true".

import pandas as pd
from decision_engine.engine import DecisionEngine

decision_engine = DecisionEngine.model_validate(
    {
        "config": {
            "key": "action",
            "default_action": "high",
            "rules": [
                ["override", "'override'"],
                ["not override and score < 100", "low"],
                ["score >= 100", "high"],
            ],
        },
    }
)


dataframe = pd.DataFrame({"score": [0.0, 85, 100], "override": [True, False, False]})
print(decision_engine.transform(dataframe))
#         action
#  0  'override'
#  1         low
#  2        high

The outputs can also target a variable, and does not need to be a constant:

import pandas as pd
from decision_engine.engine import DecisionEngine

decision_engine = DecisionEngine.model_validate(
    {
        "config": {
            "key": "action",
            "default_action": "high",
            "rules": [
                ["override", "model_a"],
                ["not override and score < 100", "model_b"],
                ["score >= 100", "model_a"],
            ],
        },
    }
)

dataframe = pd.DataFrame(
  {
    "score": [0.0, 85, 100],
    "override": [True, False, False],
    "model_a": [1.0, 2.0, 3.0],
    "model_b": [4.0, 5.0, 6.0],
  }
)
print(decision_engine.transform(dataframe))
#    action
#  0    1.0
#  1    5.0
#  2    3.0

Developer Notes

From a development perspective the Rust and Python bindings are kept in two separate crates:

• decision_engine
• decision_engine_py

This allows the Rust crate to remain "pure" and the Python bindings to be developed separately.

The decision engine is configuration driven which can easily be exported in toml or json format and parsed in Rust or Python.

For example, in Rust, we may define a decision engine as follows:

key = "action"

[rule_config]
default_action = "'high'"
rules = [
    ["score <= 80", "low"],
    ["score <=100", "medium"],
    ["score > 100", "high"],
]

Which would have the corresponding json setup as

{
  "config": {
    "key": "action",
    "default_action": "high",
    "rules": [
      [
        "score < 80",
        "low"
      ],
      [
        "score <= 99",
        "medium"
      ],
      [
        "score > 99",
        "high"
      ]
    ]
  }
}

Tests and Formatting

We use just for running tests and auto formatters. This can be installed via brew install just

$ just test
$ just format
$ just lint