Logic

Elements of propositional logic: constraints, propositions, and conjunctions.

Overview

Details

class realkd.logic.Conjunction(props)

Conjunctive aggregation of propositions.

For example:

>>> old = KeyValueProposition('age', Constraint.greater_equals(60))
>>> male = KeyValueProposition('sex', Constraint.equals('male'))
>>> high_risk = Conjunction([male, old])
>>> stephanie = {'age': 30, 'sex': 'female'}
>>> erika = {'age': 72, 'sex': 'female'}
>>> ron = {'age': 67, 'sex': 'male'}
>>> high_risk(stephanie), high_risk(erika), high_risk(ron)
(False, False, True)

Elements can be accessed via index and are sorted lexicographically. >>> high_risk age>=60 & sex==male >>> high_risk[0] age>=60 >>> len(high_risk) 2

>>> high_risk2 = Conjunction([old, male])
>>> high_risk == high_risk2
True

>>> titanic = pd.read_csv("../datasets/titanic/train.csv")
>>> titanic.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'], inplace=True)
>>> male = KeyValueProposition('Sex', Constraint.equals('male'))
>>> third_class = KeyValueProposition('Pclass', Constraint.greater_equals(3))
>>> conj = Conjunction([male, third_class])
>>> titanic.loc[conj]
     Survived  Pclass   Sex   Age  SibSp  Parch     Fare Embarked
0           0       3  male  22.0      1      0   7.2500        S
4           0       3  male  35.0      0      0   8.0500        S
5           0       3  male   NaN      0      0   8.4583        Q
7           0       3  male   2.0      3      1  21.0750        S
12          0       3  male  20.0      0      0   8.0500        S
..        ...     ...   ...   ...    ...    ...      ...      ...
877         0       3  male  19.0      0      0   7.8958        S
878         0       3  male   NaN      0      0   7.8958        S
881         0       3  male  33.0      0      0   7.8958        S
884         0       3  male  25.0      0      0   7.0500        S
890         0       3  male  32.0      0      0   7.7500        Q

[347 rows x 8 columns]