simpleruleengine 2.0.2

A lightweight rule engine that allows declarative specification of business rules.

simple-rule-engine

A lightweight yet powerful rule engine that allows declarative specification of business rules and saves tons of repeated development work.

• This library has been utilized in authoring & evaluation of a number of complex credit decisioning, upgrade/downgrade, and lender evaulation criteria rules at FundsCorner
• This library can also be considered as a Policy Framework for validating IaC (Infrastructure as Code).

At a glance

simple-rule-engine is a Python library that enables declarative specification of decision or scoring rules.

Example Decision matrix

Bureau Score	Marital Status	Decision
between 650 and 800	in [Married, Unspecified]	GO

Rule specification

from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.between import Between
from simpleruleengine.operator.string_in import In
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken

if __name__ == "__main__":
    cibil_score_between_650_800 = Expression(
        NumericToken("cibil_score"),
        Between(floor=650, ceiling=800)
    )
    marital_status_in_married_unspecified = Expression(
        StringToken("marital_status"),
        In("Married", "Unspecified")
    )

    rule_row_decision_go = RuleRowDecision(
        WhenAll(
            cibil_score_between_650_800,
            marital_status_in_married_unspecified
        ),
        "GO"
    )
    rule_set_decision = RuleSetDecision(rule_row_decision_go)

    fact = dict(
        cibil_score=700,
        marital_status="Married"
    )
    assert rule_set_decision.evaluate(fact) == "GO"

Key Features

1. Ability to declaratively author both Scoring and Decision Rules.
2. The library offers composable functional syntax that can be extended with various format adapters. See here for an example of such an extension.
3. Ability to version control rule declarations thus enabling auditing of rule changes over a period of time.
4. Ability to author chained rules. Evaluation of one rule can refer to the result of another rule, thus enabling modular, hierarchical rules.

Installation

pypi repository

pip install simpleruleengine==2.0.2

Source Code

simple-rule-engine GitHub Repository

Table of Contents

• Why Rule Engine
• Concepts
• Grammar
• Examples

Why Rule Engine?

Decision making has always been at the heart of any business. In certain industries (such as Lending), some of the decisions made are so dynamic & at a flux that programming these decisions by hand is counter-productive. See here for additional context.

Take the example of the decision of giving someone a loan. It primarily involves ascertaining two fundamental factors:

• Ability to repay the loan.
• Intent to repay the loan.

When you start assessing a borrower based on above, you typically get all facts required to make a decision (such as Bureau score, bank statements etc.) and you will pass these facts through a decision matrix to arrive at

• A composite score on a scale that gives an indication of whether the borrower will repay the loan (intent)
• A recommendation of how much loan should be given to the borrower. (ability)

The aforementioned decisions involve evaluation of multiple parameters. You simply cannot write a program to solve such complex scoring or decision problems:

• The evaluations and/or scores will always change over a period of time to adjust to business needs
• The rules will also change based on the nature of the business product.

The simple-rule-engine solves such dynamic decision making problems by abstracting the scoring or decision making into a framework and providing a standard rule template to author the rules.

The rules can be authored through a separate UI and stored as rule template in a database. The rule engine then can be treated as a service and just by passing all the facts (a.k.a inputs), we get the the corresponding decisions or scores (output).

The caller of the rule engine is agnostic of the business logic behind rule evaluation.

Benefits

• Declarative authoring of rules. This can be done by a business analyst, and can be facilitated through a UI.
• Once composed, rules can be serialized as a json and persisted into database for repeated use.
• The developer just focuses on extracting the facts that are required to be passed into the engine.
• Ability to version a rule and audit rule changes over a period of time.
• Segregate business logic from the data.

Concepts

The simple-rule-engine is composed of two parts:

• A Rule template which enables one to declaratively specify a rule, which can either be a Decision (or) a Score. The rule template is uniquely identified by a name.
• A parser engine which when passed with a rule name & facts, parses the rule template against the facts given and provides an output. The output can either be a score (numeric) or a decision (anything).

The simple-rule-engine allows the rules to be “chained”. I.e. you can build a small portion of the score as an independent rule and “use” this rule in another rule.

Rule Declaration Language

• At the heart of simple-rule-engine is the rule declaration language.
• The rule declaration language supports two types of rules: Decision rule or a Score rule.

Score rule:

• A Score rule is composed of one or many rule sets.
• Each rule-set computes a sub-score and is applied a weight.
• The total score then would be the sum of all the individual scores of all the rule sets belonging to a rule.
• A rule set is composed of one or many rule rows.
• You can ‘roughly’ think of each Rule Row as a Conditional evaluation of the facts (a.k.a antecedent) & a score based on these conditions (a.k.a consequent).

Decision rule:

• A Decision rule is always composed of only one rule set.
• A rule set is composed of one or many rule rows.
• You can ‘roughly’ think of each Rule Row as a Conditional evaluation of the facts (a.k.a antecedent) & a decision based on these conditions (a.k.a consequent).
• A decision rule always arrives at a single decision at the end of parsing.
• The decision can be anything (a numeric, a string such as YES/NO or even a JSON)
• Once a Rule Row evaluates to True, the corresponding decision is returned immediately.

Antecedent and Consequent

• An antecedent at the core is an evaluator. It evaluates one or many facts through an operator.
• For evaluating numeric facts, a numeric operator is used. It can be one of (<=, <, >, >=, ==, <>, between, is_none)
• For evaluating string facts, a string operator is used. It can be one of (in_list, contains, is_none, equals)
• You can mix evaluation of more than one fact & combine the result with an “and” or “or” condition.
• You can perform complex evaluations involving multiple facts combining AND and OR conditions recursively in the antecedent. See Examples.
• A rule can be part of another rule. See Examples

Grammar

Token:

An entity that is representative of a fact, and also guides clients on the data type of fact to be supplied.

Types of Tokens:

• NumericToken
• StringToken
• BooleanToken
• RuleToken: A rule itself, composed as a Token

Example:

• StringToken("pet") represents a token pet of type String.
• NumericToken("age") represents a token age of type Numeric.

A rule can be a Token too. A RuleToken implements Token and composes a Rule. When asked for value, a RuleToken executes the rule it composes and provides the value.

Operator:

An Operator composes a base value and evaluates against a value supplied.

Example: Gte(35).evaulate(15) returns False. Gte(35.0).evaulate(40.0) returns True.

Expression:

An Expression composes a Token on the left hand side (LHS), Operator in the middle and the data to be evaulated on the right hand side (RHS).

Example: Expression(NumericToken("cibil_score"), Between(floor=650, ceiling=800)) represents an evaulation that compares whether the fact cibil_score is between 650 and 800 or not. Specified in SQL terms, this translates to WHERE cibil_score between 650 and 800.

Expression(NumericToken("cibil_score"), Between(floor=650, ceiling=800)).evaluate(dict(cibil_score=700)) evaluates to True.

Conditional:

• A Conditional composes a list of Expressions.
• WhenAll evaluates to True when all expressions evaluate to True.
• WhenAny evaluates to True when any expression evaluates to True.
• A Conditional can compose a Conditional - this enables clients to express complex conditions.
• A Conditional can be further extended to implement NotWhenAll or NotWhenAny etc. as well.

Example:

cibil_score_between_650_800 = Expression(NumericToken("cibil_score"), Between(floor=650, ceiling=800))
marital_status_in_married_unspecified = Expression(StringToken("marital_status"), In("Married", "Unspecified"))
business_owned_by_self_family = Expression(StringToken("business_ownership"), In("Owned by Self", "Owned by Family"))

WhenAll(
    cibil_score_between_650_800,
    marital_status_in_married_unspecified,
    business_owned_by_self_family
)

# A Conditional composing another Conditional. The below statement is equivalent of 
# WHERE applicant_age >= 35 AND ( business_ownership in ('SELF', 'FAMILY') OR applicant_ownership in ('SELF', 'FAMILY') )
WhenAll(
    applicant_age_gte_35,
    WhenAny(
        business_owned_by_self_family,
        applicant_owned_by_self_family
    )
),

RuleRowDecision/RuleRowScore:

• A RuleRow composes a Conditional (as an antecedent) and specifies a consequent (result) when antecedent evaluates to True.
• For a Score, consequent must be a float.
• For a Decision, consequent can be anything.

RuleSetDecision/RuleSetScore:

• A Rule Set composes a set of RuleRows.
• For a score, each rule set carries a weight and the total weight of all rule sets must be equal to 1.
• For a decision, there must be only one rule set.

RuleDecision/RuleScore:

• A rule composes one or many Rule sets.
• For a score, the total score is calculated as sum(rule set score * weight).
• A rule exposes get_token_dict_structure function that returns a dictionary of all facts required for the rule to be executed successfully.

Examples

A simple decision tree involving facts

Decision matrix

Bureau Score	Marital Status	Business Ownership	Decision
between 650 and 800	in [Married, Unspecified]	in [Owned by Self, Owned by Family]	GO

Rule specification

from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.between import Between
from simpleruleengine.operator.string_in import In
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken

if __name__ == "__main__":
    cibil_score_between_650_800 = Expression(
        NumericToken("cibil_score"),
        Between(floor=650, ceiling=800)
    )
    marital_status_in_married_unspecified = Expression(
        StringToken("marital_status"),
        In("Married", "Unspecified")
    )
    business_owned_by_self_family = Expression(
        StringToken("business_ownership"),
        In("Owned by Self", "Owned by Family")
    )

    rule_row_decision_go = RuleRowDecision(
        WhenAll(
            cibil_score_between_650_800,
            marital_status_in_married_unspecified,
            business_owned_by_self_family
        ),
        "GO"
    )
    rule_set_decision = RuleSetDecision(rule_row_decision_go)

    fact = dict(
        cibil_score=700,
        marital_status="Married",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact) == "GO"

A complex decision tree involving multiple AND and OR conditions

Decision matrix

Applicant Age	Applicant Ownership	Business Ownership	Decision
>=35	in [Owned by Self, Owned by Family]	in [Owned by Self, Owned by Family]	GO
>=35	in [Owned by Self, Owned by Family]	in [Rented]	GO
>=35	in [Rented]	in [Owned by Self, Owned by Family]	GO
>=35	in [Rented]	in [Rented]	NO GO
<35	in [Rented]	in [Rented]	NO GO
<35	in [Owned by Self, Owned by Family]	in [Rented]	NO GO
<35	in [Rented]	in [Owned by Self, Owned by Family]	NO GO
<35	in [Owned by Self, Owned by Family]	in [Owned by Self, Owned by Family]	GO

• when the applicant age is >=35, either of applicant ownership or business ownership must be Owned.
• When the applicant age is <35, both the applicant ownership and business ownership must be Owned.

Rule specification

from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.conditional.when_any import WhenAny
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.string_in import In
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken

if __name__ == "__main__":
    applicant_age_gte_35 = Expression(
        NumericToken("applicant_age"),
        Gte(35)
    )
    business_owned_by_self_family = Expression(
        StringToken("business_ownership"),
        In("Owned by Self", "Owned by Family")
    )
    applicant_owned_by_self_family = Expression(
        StringToken("applicant_ownership"),
        In("Owned by Self", "Owned by Family")
    )

    rule_row_decision_go = RuleRowDecision(
        WhenAll(
            applicant_age_gte_35,
            WhenAny(
                business_owned_by_self_family,
                applicant_owned_by_self_family
            )
        ),
        "GO"
    )
    rule_set_decision = RuleSetDecision(rule_row_decision_go)

    fact_go = dict(
        applicant_age=42,
        applicant_ownership="Not Owned",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact_go) == "GO"

    fact_no_go_1 = dict(
        applicant_age=42,
        applicant_ownership="Not Owned",
        business_ownership="Not Owned"
    )
    assert rule_set_decision.evaluate(fact_no_go_1) != "GO"

    fact_no_go_2 = dict(
        applicant_age=25,
        applicant_ownership="Owned by Self",
        business_ownership="Owned by Self"
    )
    assert rule_set_decision.evaluate(fact_no_go_2) != "GO"

A scoring rule involving multiple parameters

Rule set Name	Weightage
no_of_running_bl_pl	0.5
last_loan_drawn_in_months	0.5

no_of_running_bl_pl

Condition	Score
no_of_running_bl_pl >= 7	-100
no_of_running_bl_pl >= 4	-40
no_of_running_bl_pl >= 2	30
no_of_running_bl_pl >= 0	100
no_of_running_bl_pl is none	100

last_loan_drawn_in_months

Condition	Score
last_loan_drawn_in_months == 0	30
last_loan_drawn_in_months <3	-30
last_loan_drawn_in_months <= 12	40
last_loan_drawn_in_months >12	100
last_loan_drawn_in_months is none	100

Rule Specification

from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.greater_than import Gt
from simpleruleengine.operator.equal import Eq
from simpleruleengine.operator.less_than import Lt
from simpleruleengine.operator.less_than_equal import Lte
from simpleruleengine.rulerow.rule_row_score import RuleRowScore
from simpleruleengine.ruleset.rule_set_score import RuleSetScore
from simpleruleengine.rule.rule_score import RuleScore
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.expression.expression import Expression

if __name__ == "__main__":
    no_run_bl_pl_gte_7_score_minus_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(7))),
        -100
    )
    no_run_bl_pl_gte_4_score_minus_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(4))),
        -40
    )
    no_run_bl_pl_gte_2_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(2))),
        30
    )
    no_run_bl_pl_gte_0_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(0))),
        100
    )

    no_of_run_bl_pl_rule_set = RuleSetScore(
        no_run_bl_pl_gte_7_score_minus_100,
        no_run_bl_pl_gte_4_score_minus_40,
        no_run_bl_pl_gte_2_score_30,
        no_run_bl_pl_gte_0_score_100,
        weight=0.5
    )

    fact_no_run_bl_pl_2 = dict(no_of_running_bl_pl=2)
    assert no_of_run_bl_pl_rule_set.evaluate(fact_no_run_bl_pl_2) == 15.0

    last_loan_drawn_in_months_eq_0_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Eq(0))),
        30
    )
    last_loan_drawn_in_months_lt_3_score_minus_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lt(3))),
        -30
    )
    last_loan_drawn_in_months_lte_12_score_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lte(12))),
        40
    )
    last_loan_drawn_in_months_gt_12_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Gt(12))),
        100
    )

    last_loan_drawn_in_months_rule_set = RuleSetScore(
        last_loan_drawn_in_months_eq_0_score_30,
        last_loan_drawn_in_months_lt_3_score_minus_30,
        last_loan_drawn_in_months_lte_12_score_40,
        last_loan_drawn_in_months_gt_12_score_100,
        weight=0.5
    )

    fact_last_loan_drawn_in_months_lte_12 = dict(last_loan_drawn_in_months=6)
    assert last_loan_drawn_in_months_rule_set.evaluate(
        fact_last_loan_drawn_in_months_lte_12) == 20.0

    fact_rule_score = dict(last_loan_drawn_in_months=6, no_of_running_bl_pl=2)
    rule_score = RuleScore(
        no_of_run_bl_pl_rule_set,
        last_loan_drawn_in_months_rule_set
    )
    assert rule_score.execute(fact_rule_score) == 35.0

    no_run_bl_pl_gte_7_score_minus_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(7))), -100)
    no_run_bl_pl_gte_4_score_minus_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(4))), -40)
    no_run_bl_pl_gte_2_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(2))), 30)
    no_run_bl_pl_gte_0_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("no_of_running_bl_pl"), Gte(0))), 100)

    no_of_run_bl_pl_rule_set = RuleSetScore(
        no_run_bl_pl_gte_7_score_minus_100,
        no_run_bl_pl_gte_4_score_minus_40,
        no_run_bl_pl_gte_2_score_30,
        no_run_bl_pl_gte_0_score_100,
        weight=0.5
    )

    fact_no_run_bl_pl_2 = dict(no_of_running_bl_pl=2)
    assert no_of_run_bl_pl_rule_set.evaluate(fact_no_run_bl_pl_2) == 15.0

    last_loan_drawn_in_months_eq_0_score_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Eq(0))),
        30
    )
    last_loan_drawn_in_months_lt_3_score_minus_30 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lt(3))),
        -30
    )
    last_loan_drawn_in_months_lte_12_score_40 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Lte(12))),
        40
    )
    last_loan_drawn_in_months_gt_12_score_100 = RuleRowScore(
        WhenAll(Expression(NumericToken("last_loan_drawn_in_months"), Gt(12))),
        100
    )

    last_loan_drawn_in_months_rule_set = RuleSetScore(
        last_loan_drawn_in_months_eq_0_score_30,
        last_loan_drawn_in_months_lt_3_score_minus_30,
        last_loan_drawn_in_months_lte_12_score_40,
        last_loan_drawn_in_months_gt_12_score_100,
        weight=0.5
    )

    fact_last_loan_drawn_in_months_lte_12 = dict(last_loan_drawn_in_months=6)
    assert last_loan_drawn_in_months_rule_set.evaluate(
        fact_last_loan_drawn_in_months_lte_12) == 20.0

    fact_rule_score = dict(last_loan_drawn_in_months=6, no_of_running_bl_pl=2)
    rule_score = RuleScore(
        no_of_run_bl_pl_rule_set,
        last_loan_drawn_in_months_rule_set
    )
    assert rule_score.execute(fact_rule_score) == 35.0

A nested rule that involves another rule for evaulation

Rule

• If cibil score is between 650 and 800, score is 100
• If cibil score is less than 650, score is 0
• Decide GO if pet in [dog, cat] and cibil score is greater than 0

Rule Specification

from simpleruleengine.conditional.when_all import WhenAll
from simpleruleengine.expression.expression import Expression
from simpleruleengine.operator.between import Between
from simpleruleengine.operator.greater_than import Gt
from simpleruleengine.operator.greater_than_equal import Gte
from simpleruleengine.operator.string_in import In
from simpleruleengine.operator.less_than_equal import Lte
from simpleruleengine.operator.less_than import Lt
from simpleruleengine.operator.string_not_in import NotIn
from simpleruleengine.rulerow.rule_row_decision import RuleRowDecision
from simpleruleengine.ruleset.rule_set_decision import RuleSetDecision
from simpleruleengine.rulerow.rule_row_score import RuleRowScore
from simpleruleengine.ruleset.rule_set_score import RuleSetScore
from simpleruleengine.token.numeric_token import NumericToken
from simpleruleengine.token.string_token import StringToken
from simpleruleengine.token.rule_token import RuleToken
from simpleruleengine.rule.rule_score import RuleScore

if __name__ == "__main__":
    cibil_score_between_650_800 = Expression(
        NumericToken("cibil_score"),
        Between(floor=650, ceiling=800)
    )

    cibil_score_lt_650 = Expression(
        NumericToken("cibil_score"),
        Lt(650)
    )

    rule_row_between_650_800 = RuleRowScore(
        antecedent=WhenAll(cibil_score_between_650_800),
        consequent=100
    )

    rule_row_between_lt_650 = RuleRowScore(
        antecedent=WhenAll(cibil_score_lt_650),
        consequent=0
    )

    rule_set_cibil_score = RuleSetScore(
        rule_row_between_lt_650,
        rule_row_between_650_800,
        weight=1
    )

    rule_cibil_score = RuleScore(rule_set_cibil_score)

    fact = dict(cibil_score=350)
    assert rule_cibil_score.execute(fact) == 0

    expression_pet_in_dog_cat = Expression(
        StringToken(name="pet"),
        In("dog", "cat")
    )

    expression_cibil_score_gt_0 = Expression(
        RuleToken(name="cibil_rule", rule=rule_cibil_score),
        Gt(0)
    )

    when_all_cibil_and_pet = WhenAll(
        expression_cibil_score_gt_0,
        expression_pet_in_dog_cat
    )

    rule_row_go_cibil_and_pet = RuleRowDecision(
        antecedent=when_all_cibil_and_pet,
        consequent="GO"
    )

    rule_set_decision_cibil_score_and_pet = RuleSetDecision(
        rule_row_go_cibil_and_pet)

    fact = dict(cibil_score=350, pet="dog")
    assert rule_set_decision_cibil_score_and_pet.evaluate(fact) != "GO"

    fact = dict(cibil_score=725, pet="dog")
    assert rule_set_decision_cibil_score_and_pet.evaluate(fact) == "GO"