How to use decorate_class method in autotest

Best Python code snippet using autotest_python

models.py

Source:models.py

...13from imblearn.pipeline import make_pipeline as make_pipeline_imb14from .factory import register_model15from ._models import sk_model_factory, nn_model_factory, cv_factory, gcv_factory16from ..helpers.utils import decorate_class17@decorate_class(staticmethod)18@decorate_class(register_model(sk_model_factory))19class SkLearnModels():20    def xgboost():21        return XGBClassifier()22    def randomforest():23        return RandomForestClassifier()24    def ridge():25        return make_pipeline(StandardScaler(), RidgeClassifier())26    def lr():27        return make_pipeline(StandardScaler(), LogisticRegression())28    def dummy():29        return DummyClassifier()30    def catboost():31        return CatBoostClassifier()32    def lightgbm():33        return LGBMClassifier()34    def rf_xgboost():35        return make_pipeline(36            SelectFromModel(RandomForestClassifier(random_state=17)),37            XGBClassifier()38        )39    def rf_lr():40        return make_pipeline(41            SelectFromModel(RandomForestClassifier(random_state=17)),42            LogisticRegression()43        )44    def columnestimator():45        return ColumnEstimator()46    def smote_randomforest():47        return make_pipeline_imb(48            SMOTE(), RandomForestClassifier()49        )50    def smote_xgboost():51        return make_pipeline_imb(52            SMOTE(), XGBClassifier()53        )54    def smote_ridge():55        return make_pipeline_imb(56            SMOTE(), StandardScaler(), RidgeClassifier()57        )58    59    def smote_lr():60        return make_pipeline_imb(61            SMOTE(), StandardScaler(), LogisticRegression()62        )63    def smote_catboost():64        return make_pipeline_imb(65            SMOTE(), CatBoostClassifier()66        )67    68    def smote_lightgbm():69        return make_pipeline_imb(70            SMOTE(), LGBMClassifier(),71        )72from tensorflow.keras.models import Sequential73from tensorflow.keras import layers as L74@decorate_class(staticmethod)75@decorate_class(register_model(nn_model_factory))76class KerasModels:77    def perceptron(input_shape: tuple, n_classes: int=3, units_array: list=[10], 78                    optimizer: str='adam') -> Sequential:79        model = Sequential([80            L.Input(shape=input_shape),81            *(L.Dense(units=units, activation='relu') for units in units_array),82            L.Dense(units=n_classes, activation='softmax')  83        ])84        model.compile(loss='categorical_crossentropy', optimizer=optimizer)85        return model86    def lstm(input_shape, n_classes, units_array, optimizer, ):87        88        model = Sequential([89            L.Input(shape=input_shape),90            *(L.LSTM(i, return_sequences=True, ) 91            for i in units_array['rnn'][:-1]),92            L.LSTM(units_array['rnn'][-1]),93            *(L.Dense(units=units, activation='relu') 94            for units in units_array['dense']),95            L.Dense(n_classes, activation='softmax')96        ], )97        98        model.compile(loss='categorical_crossentropy', optimizer=optimizer)99        return model100    def gru(input_shape, n_classes, units_array, optimizer, ):101        102        model = Sequential([103            L.Input(shape=input_shape),104            *(L.GRU(i, return_sequences=True, ) 105            for i in units_array['rnn'][:-1]),106            L.GRU(units_array['rnn'][-1]),107            *(L.Dense(units=units, activation='relu') 108            for units in units_array['dense']),109            L.Dense(n_classes, activation='softmax')110        ], )111        112        model.compile(loss='categorical_crossentropy', optimizer=optimizer)113        return model114        115    def bi_lstm(input_shape, n_classes, units_array, optimizer, ):116        117        model = Sequential([118            L.Input(shape=input_shape),119            *(L.Bidirectional(L.LSTM(i, return_sequences=True, ))120            for i in units_array['rnn'][:-1]),121            L.Bidirectional(L.LSTM(units_array['rnn'][-1])),122            *(L.Dense(units=units, activation='relu') 123            for units in units_array['dense']),124            L.Dense(n_classes, activation='softmax')125        ], )126        127        model.compile(loss='categorical_crossentropy', optimizer=optimizer)128        return model129    def bi_gru(input_shape, n_classes, units_array, optimizer, ):130        131        model = Sequential([132            L.Input(shape=input_shape),133            *(L.Bidirectional(L.GRU(i, return_sequences=True, ))134            for i in units_array['rnn'][:-1]),135            L.Bidirectional(L.GRU(units_array['rnn'][-1])),136            *(L.Dense(units=units, activation='relu') 137            for units in units_array['dense']),138            L.Dense(n_classes, activation='softmax')139        ], )140        141        model.compile(loss='categorical_crossentropy', optimizer=optimizer)142        return model143from sklearn.model_selection import StratifiedKFold, TimeSeriesSplit144@decorate_class(staticmethod)145@decorate_class(register_model(cv_factory))146class CV:147    def skf(**kwargs):148        return StratifiedKFold(**kwargs)149    150    def tss(**kwargs):151        return TimeSeriesSplit(**kwargs)152from sklearn.model_selection import GridSearchCV, RandomizedSearchCV153@decorate_class(staticmethod)154@decorate_class(register_model(gcv_factory))155class GCV:156    def gcv(**kwargs):157        return GridSearchCV(**kwargs)158    159    def rscv(**kwargs):160        if 'param_grid' in kwargs:161            kwargs['param_distributions'] = kwargs['param_grid']162            kwargs.pop('param_grid')163            ...

decorators.py

Source:decorators.py

...7    """8    Custom decorator for mocking the course_catalog_api_client property of siteconfiguration9    to return a new instance of EdxRestApiClient with a dummy jwt value.10    """11    def decorate_class(klass):12        for attr in dir(klass):13            # Decorate only callable unit tests.14            if not attr.startswith('test_'):15                continue16            attr_value = getattr(klass, attr)17            if not hasattr(attr_value, "__call__"):18                continue19            setattr(klass, attr, decorate_callable(attr_value))20        return klass21    def decorate_callable(test):22        @functools.wraps(test)23        def wrapper(*args, **kw):24            with mock.patch(25                'ecommerce.core.models.SiteConfiguration.course_catalog_api_client',26                mock.PropertyMock(return_value=EdxRestApiClient(27                    settings.COURSE_CATALOG_API_URL,28                    jwt='auth-token'29                ))30            ):31                return test(*args, **kw)32        return wrapper33    if isinstance(test, type):34        return decorate_class(test)35    return decorate_callable(test)36def mock_enterprise_api_client(test):37    """38    Custom decorator for mocking the property "enterprise_api_client" of39    siteconfiguration to construct a new instance of EdxRestApiClient with a40    dummy jwt value.41    """42    def decorate_class(klass):43        for attr in dir(klass):44            # Decorate only callable unit tests.45            if not attr.startswith('test_'):46                continue47            attr_value = getattr(klass, attr)48            if not hasattr(attr_value, '__call__'):49                continue50            setattr(klass, attr, decorate_callable(attr_value))51        return klass52    def decorate_callable(test):53        @functools.wraps(test)54        def wrapper(*args, **kw):55            with mock.patch(56                'ecommerce.core.models.SiteConfiguration.enterprise_api_client',57                mock.PropertyMock(58                    return_value=EdxRestApiClient(59                        settings.ENTERPRISE_API_URL,60                        jwt='auth-token'61                    )62                )63            ):64                return test(*args, **kw)65        return wrapper66    if isinstance(test, type):67        return decorate_class(test)...

__init__.py

Source:__init__.py

...16        return callable(x)17    if mode == "function":18        wrapped_func = decorate_function(orig_func, func_name_str)19    elif mode == "class":20        wrapped_func = decorate_class(orig_func, func_name_str)21    else:22        if is_class(orig_func):23            wrapped_func = decorate_class(orig_func, func_name_str)24        elif is_callable(orig_func):25            wrapped_func = decorate_function(orig_func, func_name_str)26        else:27            wrapped_func = orig_func28    setattr(module_obj, func_name, wrapped_func)29with open(__file__.replace("__init__.py", "torch.txt"), "r") as f1:30    lines = f1.readlines()31    skipped = ["enable_grad", "get_default_dtype", "load", "tensor", "no_grad", "jit"]32    for l in lines:33        l = l.strip()34        if l not in skipped:35            hijack(torch, l, mode="function")36with open(__file__.replace("__init__.py", "torch.nn.txt"), "r") as f2:37    lines = f2.readlines()...

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