Metadata-Version: 1.0
Name: pypred
Version: 0.2.2
Summary: A Python library for simple evaluation of natural language predicates
Home-page: https://github.com/armon/pypred/
Author: Armon Dadgar
Author-email: armon@kiip.me
License: MIT License
Description: PyPred [![Build Status](https://travis-ci.org/armon/pypred.png)](https://travis-ci.org/armon/pypred)
        ======
        
        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.
        
        Grammar
        =======
        
        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
        
        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.
        
        API
        ===
        
        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 a dictionary 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 info dict.
        
        * 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, info = p.analyze({'server': 'east-web-001', 'errors': [], 'environment': 'prod'})
            assert res == False
            pprint.pprint(info)
        
            {'failed': ["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'],
             'literals': {'"CPU load"': 'CPU load',
                          'errors': [],
                          'server': 'east-web-001'}}
        
        
Keywords: python,predicate,natural language
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries
