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A Python library for simple evaluation of natural language predicates

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PyPred is a package to do predicate evaluation in Python. It uses a PLY (Lex/Yacc for Python) to parse inputs into an AST tree which it then evaluates. The PyPred provides simple APIs to do the evaluation is most sitations, but allows for customized evaluation techniques for more complex situations.

Additionally, PyPred supports the notion of predicate “sets”. This is a collection of predicates that are all simultaneously evaluated against a single input document. For example, in a Pub/Sub system, each subscription can be modeled as a predicate. When a new event arrives, the predicate set of all subscriptions can be evaluated to find all matching subscriptions.

PyPred provides a PredicateSet model as well as an OptimizedPredicateSet. The optimized variant trades memory for speed. It extracts common sub-expressions into a branch, and conditionally executes different sets of predicates to prune the predicates that will not match most efficiently. The parameters of the optimization can be tweaked to find a speed/memory balance.


The grammar that PyPred understands is limited to simple comparisons and boolean logic.

It supports the following:

  • Logical operators not, and, or

  • Comparison operators >, >=, <, <=, =, !=, ‘is’, ‘is not’

  • Parenthesis to disambiguate

  • The subset check operator contains

  • The regular expression matcher matches

  • String literals, quoted if they include spaces

  • Numeric literals

  • Constants true, false, undefined, null, empty

  • Set literal, containing string literals, numeric and constant values

Grammar Examples

To demonstate the capabilities of the pypred grammar, the following examples are provided.

name is ‘Jack’ and friend_name is ‘Jill’

This predicate checks that the input document has a field name equal to “Jack”, and a field friend_name equal to “Jill”

event is “Record Score” and ((score >= 500 and highest_score_wins) or (score < 10 and lowest_score_wins))

This is a slightly more advanced predicate. It checks that this is a “Record Score” event, and that the score is either greater than or equal to 500 in the case that a high score is desireable, or that the score is less than 10 if a low score is desirable.

server matches “east-web-([d]+)” and errors contains “CPU load” and environment != test

This checks for any webserver hostname matching a numeric suffix, such as “east-web-001”, with “CPU load” being reported as an error in a non-test environment.

Literal sets can be used to check for multiple clauses:

{“WARN” “ERR” “CRIT”} contains error_level or {500 501 503} contains status_code

This provides two literal sets which are used to check against the dynamic values of error_level and status_code.


Predicates themselves have a single interface, which is the Predicate class. It is instantiated with a string predicate.

The main API’s for it are: * Predicate(Pred) : Creates a new predicate object

  • Predicate.description(): Returns a human readable version of the tree if valid

  • Predicate.is_valid() : Returns if the predicate is valid

  • Predicate.errors(): If not valid, returns a list of tokenization, syntax, and semantic errors

  • Predicate.evaluate(document) : Evaluates the given document against the predicate

  • Predicate.analyze(document) : Evaluates the given document against the predicate, returns the results, as well as the evaluation context that includes more information about the evaluation, including the failure reasons. This is generally much slower than evaluate in the failure cases.

One of the critical aspects of evaluating a predicate is the resolution of literals. When the AST needs a value to substitute a variable, it calls the resolve_identifier method of the Predicate. The default behavior is flexible, and support string literals, dictionary lookups, nested dictionaries, and call back resolution via set_resolver. However, if a client wants to customize the resolution of identifier, they can simply override this method.

Predicate Sets have two main interfaces, either the PredicateSet or OptimizedPredicateSet.

Both share part a subset of their calls:

  • Set(preds=None) : Instantiate the set, optionally with a list of predicates

  • Set.add(predicate) : Adds a predicate to the set

  • Set.update(predicates) : Extends to include a list of predicates

  • Set.evaluate(document) : Evaluates the document against the predicates and returns a list of matches

The OptimizedPredicateSet supports an extended set of API’s:

  • OptSet.description() : Returns ahuman readable version of the optimized tree

  • OptSet.analyze(document) : Like Predicate.analyze(), but returns a boolean, a list, and the evaluation context.

  • OptSet.compile_ast() : Forces compilation of the interal AST

  • OptSet.finalize() : Prunes the AST of sub-predicates, and removes any instance data that is not used as part of the evaluation of the optimized set. Not usually needed, but can reduce the total memory footprint, and is useful if the object is going to be pickled.

The standard PredicateSet relies on the underlying predicates to do resolution of literals, however the OptimizedPredicateSet implements resolve_identifier to do so. Thus if custom behavior is wanted, the optimized set must be sub-classed.

Human Readable Outputs

PyPred tries to make it possible to provide human readable output of both predicates as well as any error messages that are encountered. Here is an example of a human readable description of:

p = Predicate(‘server matches “east-web-([d]+)” and errors contains “CPU load” and environment != test’) print p.description()

AND operator at line: 1, col 34
MatchOperator at line: 1, col 7

Literal server at line: 1, col 0 Regex ‘east-web-([\d]+)’ at line: 1, col 15

AND operator at line: 1, col 65
ContainsOperator at line: 1, col 45

Literal errors at line: 1, col 38 Literal “CPU load” at line: 1, col 54

!= comparison at line: 1, col 81

Literal environment at line: 1, col 69 Literal test at line: 1, col 84

Here is an example of the output during a failed evaluation:

p = Predicate(‘server matches “east-web-([d]+)” and errors contains “CPU load” and environment != test’) res, ctx = p.analyze({‘server’: ‘east-web-001’, ‘errors’: [], ‘environment’: ‘prod’}) assert res == False

[“Right side: ‘CPU load’ not in left side: [] for ContainsOperator at line: 1, col 45”,

‘Left hand side of AND operator at line: 1, col 65 failed’, ‘Right hand side of AND operator at line: 1, col 34 failed’]

{‘“CPU load”’: ‘CPU load’,

‘errors’: [], ‘server’: ‘east-web-001’}

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