KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use __attrs_post_init__ method in hypothesis

Best Python code snippet using hypothesis

markov.py

Source:markov.py

...27    model = attr.ib(default=None)28    order = attr.ib(default=None)29    smoothing = attr.ib(default=None)30    settings = attr.ib(default=config.MarkovSettings())31    def __attrs_post_init__(self):32        if not isinstance(self.settings, config.MarkovSettings):33            self.settings = config.MarkovSettings()34        self.model = self.settings.model if self.model is None else self.model35        self.order = self.settings.order if self.order is None else self.order36        self.smoothing = (37            self.settings.smoothing if self.smoothing is None else self.smoothing38        )39@attr.s40class MarkovWord(Markov):41    """42    Use 2nd or 3rd order ngrams (1st or 2nd order dependency) to calculate word entropies.43    Notes44    -----45    Markov model is trained from list of tokens at time of construction.46    Entropy or entropy lists are calculated from the trained Markov model.47    Parameters48    ----------49    tokens : [[str]]50        list of tokens, each token a list of character segments51        tokens/words used to fit the Markov model.52    model : str, optional53        Markov word model option from nltk. The default is "kni".54        kni = Kneser Ney interpolated,55        wbi = Witten Bell interpolated56        lp = Laplace,57        ls = Lidstone,58        ml = Maximum likelihood without smoothing.59    order : int, optional60        ngram order of model which counts symbol emitted and number61        of conditioning symbols. The default is 3.62    smoothing : float, optional63        Smoothing quantity for use by 'kni' or 'ls'. The default is 0.5.64    """65    tokens = attr.ib(default=[], repr=False)66    def __attrs_post_init__(self):67        super().__attrs_post_init__()68        # NLTK recommended padding explicitly for training.69        train, vocab = pp.padded_everygram_pipeline(self.order, self.tokens)70        # Define the vocabulary.  Allow for out of vocabulary, thus cutoff=2.71        vocab = lm.Vocabulary(vocab, unk_cutoff=2)72        # Define and then train the language model.73        options = {74            "kni": (lm.KneserNeyInterpolated, {"discount": self.smoothing}),75            "wbi": (lm.WittenBellInterpolated, {}),76            "lp": (lm.Laplace, {}),77            "ls": (lm.Lidstone, {"gamma": self.smoothing}),78            "mle": (lm.MLE, {}),79        }80        self.lm = options[self.model][0](81            order=self.order, vocabulary=vocab, **options[self.model][1]82        )83        self.lm.fit(train)84    def calculate_entropies(self, tokens):85        """86        Calculate entropies for list of tokens.87        Parameters88        ----------89        tokens : [[str]]90            list of tokens, each token a list of characters.91        Returns92        -------93        entropies : [float]94            calclated token entropies.95        """96        # Verified by experiment - use order for left and order 2 for right.97        # Use of order 2 for right stops calculation of first pad symbol.98        # This is in order to be consistent with neural imlementation.99        padded_tokens = [100            list(101                pp.pad_both_ends(102                    pp.pad_both_ends(token, pad_right=False, n=self.order),103                    pad_left=False,104                    n=2,105                )106            )107            for token in tokens108        ]109        # Convert lists of padded tokens to lists of ngrams.110        ngrams_lst = [list(ngrams(seq, self.order)) for seq in padded_tokens]111        entropies = [self.lm.entropy(segs) for segs in ngrams_lst]112        return entropies113    def calculate_entropy(self, token):114        """115        Calculate per sound average entropy of single token.116        Parameters117        ----------118        token : [str]119            Token as list of character segments.120        Returns121        -------122        float123            calcalated token entropy.124        """125        return self.calculate_entropies([token])[0]126@attr.s127class DualMarkov(Markov):128    """129    Construct Markov models of native and loan distributions.130    Notes131    -----132    Fit dual Markov models - one to native data, another to loan data.133    Parameters134    ----------135    data : [[str, [str], int]]136        List of language tokens in format:137            identifier,138            token as list of character segments,139            binary (0, 1) indicator of borrowed word status.140    model : str, optional141        Model type from MarkovWord class. The default is 'kni'.142    order : int, optional143        Order from MarkovWord class. The default is 3.144    smoothing : float, optional145        smoothing from MarkovWord class. The default is 0.5.146    """147    def __attrs_post_init__(self):148        super().__attrs_post_init__()149        nativetokens = [token for _, token, status in self.data if status == 0]150        self.native = MarkovWord(151            tokens=nativetokens,152            data=self.data,153            model=self.model,154            order=self.order,155            smoothing=self.smoothing,156        )157        loantokens = [token for _, token, status in self.data if status == 1]158        self.loan = MarkovWord(159            tokens=loantokens,160            data=self.data,161            model=self.model,162            order=self.order,163            smoothing=self.smoothing,164        )165    def predict_tokens(self, tokens):166        """167        Predict loan word statuses for a list of tokens.168        Parameters169        ----------170        tokens : [[str]]171            List of tokens as list of character segments.172        Returns173        -------174        predictions : int (binary 0,1)175            List of loan word predictions corresponding to tokens.176        """177        nativeentropies = self.native.calculate_entropies(tokens)178        loanentropies = self.loan.calculate_entropies(tokens)179        predictions = [180            int(loanS < nativeS)181            for nativeS, loanS in zip(nativeentropies, loanentropies)182        ]183        return predictions184    def predict_data(self, data):185        ids = [row[0] for row in data]186        tokens = [row[1] for row in data]187        predictions = self.predict_tokens(tokens)188        return list(zip(ids, tokens, predictions))189    def predict(self, token):190        # Insert '' for identifer before invoking predict_data191        return self.predict_data([["", token]])[0]192@attr.s193class NativeMarkov(Markov):194    """195    Unsupervised language model approach.196    """197    p = attr.ib(default=None)198    def __attrs_post_init__(self):199        super().__attrs_post_init__()200        self.p = self.settings.p if self.p is None else self.p201        nativetokens = [token for _, token, status in self.data if not status]202        self.nativemodel = MarkovWord(203            tokens=nativetokens,204            data=self.data,205            model=self.model,206            order=self.order,207            smoothing=self.smoothing,208        )209        nativeentropies = self.nativemodel.calculate_entropies(nativetokens)210        entropies = sorted(nativeentropies)211        idx = min((len(entropies) - 1) * self.p, len(entropies) - 1)212        self.ref_limit = (entropies[math.floor(idx)] + entropies[math.ceil(idx)]) / 2213    def predict_tokens(self, tokens):...

nodes.py

Source:nodes.py

...30@attr.s31class Literal(Expression):32    type: Optional[str] = attr.ib()33    value: Union[str, list] = attr.ib()34    def __attrs_post_init__(self):35        self.value = escaped_name(self.value)36@attr.s37class Value(Expression):38    value: str = attr.ib()39    def __attrs_post_init__(self):40        self.value = escaped_name(self.value)41@attr.s42class LiteralList(Expression):43    type: str = attr.ib()44    value: List[Value] = attr.ib(factory=list)45@attr.s46class ExtendedAttribute(Ast):47    name: str = attr.ib()48    arguments: List[str] = attr.ib(factory=list)49    rhs: Optional[Union[Literal, LiteralList]] = attr.ib(default=None)50    type: str = attr.ib(default='extended-attribute')51@attr.s52class IdlType(Ast):53    idl_type: Union[List['IdlType'], 'IdlType', str] = attr.ib()54    type: Optional[str] = attr.ib(default=None)55    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)56    nullable: bool = attr.ib(default=False)57    union: bool = attr.ib(default=False)58    generic: str = attr.ib(default='')59    def __attrs_post_init__(self):60        self.idl_type = escaped_name(self.idl_type)61@attr.s62class Argument(Ast):63    name: str = attr.ib()64    idl_type: IdlType = attr.ib()65    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)66    default: Optional[Union[Literal, Value]] = attr.ib(default=None)67    type: str = attr.ib(default='argument')68    optional: bool = attr.ib(default=False)69    variadic: bool = attr.ib(default=False)70    def __attrs_post_init__(self):71        self.name = escaped_name(self.name)72@attr.s73class Operation(Member):74    name: str = attr.ib()75    idl_type: Optional[IdlType] = attr.ib()76    arguments: List[Argument] = attr.ib(factory=list)77    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)78    type: str = attr.ib(default='operation')79    special: str = attr.ib(default='')80@attr.s81class Iterable_(Member):  # pylint: disable=invalid-name82    idl_type: List[IdlType] = attr.ib()83    arguments: List[Argument] = attr.ib()84    async_: bool = attr.ib()85    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)86    type: str = attr.ib(default='iterable')87    readonly: bool = attr.ib(default=False)88@attr.s89class Attribute(Member):90    idl_type: IdlType = attr.ib()91    name: str = attr.ib()92    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)93    type: str = attr.ib(default='attribute')94    readonly: bool = attr.ib(default=False)95    special: str = attr.ib(default='')96    def __attrs_post_init__(self):97        self.name = escaped_name(self.name)98@attr.s99class Const(Member):100    name: str = attr.ib()101    value: Expression = attr.ib()102    idl_type: IdlType = attr.ib()103    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)104    type: str = attr.ib(default='const')105@attr.s106class Constructor(Member):107    arguments: List[Argument] = attr.ib(factory=list)108    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)109    type: str = attr.ib(default='constructor')110@attr.s111class MapLike(Member):112    readonly: bool = attr.ib()113    idl_type: List[IdlType] = attr.ib()114    arguments: List[Argument] = attr.ib(factory=list)115    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)116    type: str = attr.ib(default='maplike')117    async_: bool = attr.ib(default=False)118@attr.s119class SetLike(Member):120    readonly: bool = attr.ib()121    idl_type: List[IdlType] = attr.ib()122    arguments: List[Argument] = attr.ib(factory=list)123    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)124    type: str = attr.ib(default='setlike')125    async_: bool = attr.ib(default=False)126@attr.s127class Field(Member):128    idl_type: IdlType = attr.ib()129    name: str = attr.ib()130    default: Optional[Literal] = attr.ib()131    required: bool = attr.ib(default=False)132    type: str = attr.ib(default='field')133    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)134@attr.s135class CallbackInterface(Definition):136    name: str = attr.ib()137    members: List[Member] = attr.ib()138    inheritance: Optional[str] = attr.ib()139    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)140    type: str = attr.ib(default='callback interface')141    partial: bool = attr.ib(default=False)142@attr.s143class Callback(Definition):144    name: str = attr.ib()145    idl_type: IdlType = attr.ib()146    arguments: List[Argument] = attr.ib(factory=list)147    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)148    type: str = attr.ib(default='callback')149@attr.s150class Interface(Definition):151    members: List[Member] = attr.ib()152    name: str = attr.ib()153    inheritance: Optional[str] = attr.ib(default=None)154    partial: bool = attr.ib(default=False)155    type: str = attr.ib(default='interface')156    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)157    def __attrs_post_init__(self):158        self.name = escaped_name(self.name)159        self.inheritance = escaped_name(self.inheritance)160@attr.s161class InterfaceMixin(Definition):162    members: List[Member] = attr.ib()163    name: str = attr.ib()164    inheritance: Optional[str] = attr.ib(default=None)165    partial: bool = attr.ib(default=False)166    type: str = attr.ib(default='interface mixin')167    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)168    def __attrs_post_init__(self):169        self.name = escaped_name(self.name)170        self.inheritance = escaped_name(self.inheritance)171@attr.s172class Enum(Definition):173    name: str = attr.ib()174    type: str = attr.ib(default='enum')175    values: List[Literal] = attr.ib(factory=list)176    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)177@attr.s178class Dictionary(Definition):179    name: str = attr.ib()180    inheritance: Optional[str] = attr.ib(default=None)181    members: List[Member] = attr.ib(factory=list)182    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)183    type: str = attr.ib(default='dictionary')184    partial: bool = attr.ib(default=False)185    def __attrs_post_init__(self):186        self.name = escaped_name(self.name)187@attr.s188class Includes(Definition):189    target: str = attr.ib()190    includes: str = attr.ib()191    type: str = attr.ib(default='includes')192    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)193    def __attrs_post_init__(self):194        self.target = escaped_name(self.target)195        self.includes = escaped_name(self.includes)196@attr.s197class Typedef(Definition):198    idl_type: IdlType = attr.ib()199    name: str = attr.ib()200    type: str = attr.ib(default='typedef')201    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)202    def __attrs_post_init__(self):203        self.name = escaped_name(self.name)204@attr.s205class Namespace(Definition):206    members: List[Member] = attr.ib()207    name: str = attr.ib()208    inheritance: Optional[str] = attr.ib(default=None)209    partial: bool = attr.ib(default=False)210    type: str = attr.ib(default='namespace')211    ext_attrs: List[ExtendedAttribute] = attr.ib(factory=list)212    def __attrs_post_init__(self):...

interface.py

Source:interface.py

...14    # add project class field15    link_set:str=None16    link_id:str=None17    # with @attr.s, use   __attrs_post_init__ instead of  __init__18    def __attrs_post_init__(self):19        super(ProjDataInfoObject, self).__attrs_post_init__()20        21@attr.s(auto_attribs=True)22class ProjResearchDataObject(un_v1_2.ResearchArrayDataObject):23    # mapping field to extended...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.