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...

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