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How to use check_attributes method in autotest

Best Python code snippet using autotest_python

test_datasets.py

Source:test_datasets.py

...19            'test': (test_examples, num_features),20        }21    )22    23    check_attributes(24        data_dir, 'adult', {25            'task': 'classification',26            'num_classes': 2,27        }28    )29    30    validate_dataset_file(data_dir / 'adult.npz')31@pytest.mark.dataset32def test_amazon(data_dir):33    num_features = 934    train_examples = 26_21535    test_examples = 6_55436    37    check_split_sizes(38        data_dir, 'amazon', {39            'train': (train_examples, num_features),40            'test': (test_examples, num_features),41        }42    )43    44    check_attributes(45        data_dir, 'amazon', {46            'task': 'classification',47            'num_classes': 2,48        }49    )50    51    validate_dataset_file(data_dir / 'amazon.npz')52@pytest.mark.dataset53def test_arcene(data_dir):54    train_test_shape = (100, 10_000)55    56    check_split_sizes(57        data_dir, 'arcene', {58            'train': train_test_shape,59            'test': train_test_shape,  # arcene's "official" validation set used as test set60        }61    )62    63    check_attributes(64        data_dir, 'arcene', {65            'task': 'classification',66            'num_classes': 2,67        }68    )69    70    validate_dataset_file(data_dir / 'arcene.npz')71@pytest.mark.dataset72@pytest.mark.large73def test_cifar10(data_dir):74    num_features = 32 * 32 * 375    76    check_split_sizes(77        data_dir, 'cifar10', {78            'train': (50_000, num_features),79            'test': (10_000, num_features),80        }81    )82    83    check_attributes(84        data_dir, 'cifar10', {85            'task': 'classification',86            'num_classes': 10,87        }88    )89    90    validate_dataset_file(data_dir / 'cifar10.npz')91@pytest.mark.dataset92def test_covertype(data_dir):93    num_features = 5494    95    check_split_sizes(96        data_dir, 'covertype', {97            'train': (11_340, num_features),98            'valid': (3_780, num_features),99            'test': (565_892, num_features),100        }101    )102    103    check_attributes(104        data_dir, 'covertype', {105            'task': 'classification',106            'num_classes': 7,107        }108    )109    110    validate_dataset_file(data_dir / 'covertype.npz')111@pytest.mark.dataset112@pytest.mark.large113def test_duolingo_original(data_dir):114    num_features = 10115    116    check_split_sizes(117        data_dir, 'duolingo-original', {118            'train': (10_275_881, num_features),119            'test': (2_578_345, num_features),120        }121    )122    123    check_attributes(124        data_dir, 'duolingo-original', {125            'task': 'regression',126        }127    )128    129    validate_dataset_file(data_dir / 'duolingo-original.npz')130@pytest.mark.dataset131@pytest.mark.large132def test_duolingo_categorical(data_dir):133    num_features = 10134    135    check_split_sizes(136        data_dir, 'duolingo-categorical', {137            'train': (10_275_881, num_features),138            'test': (2_578_345, num_features),139        }140    )141    142    check_attributes(143        data_dir, 'duolingo-categorical', {144            'task': 'regression',145        }146    )147    148    validate_dataset_file(data_dir / 'duolingo-categorical.npz')149@pytest.mark.dataset150@pytest.mark.large151def test_higgs(data_dir):152    num_features = 28153    154    check_split_sizes(155        data_dir, 'higgs', {156            'train': (10_500_000, num_features),157            'test': (500_000, num_features),158        }159    )160    161    check_attributes(162        data_dir, 'higgs', {163            'task': 'classification',164            'num_classes': 2,165        }166    )167    168    validate_dataset_file(data_dir / 'higgs.npz')169@pytest.mark.dataset170def test_musk(data_dir):171    num_features = 166172    train_examples = 5548173    test_examples = 1050174    175    check_split_sizes(176        data_dir, 'musk', {177            'train': (train_examples, num_features),178            'test': (test_examples, num_features),179        }, )180    181    check_attributes(182        data_dir, 'musk', {183            'task': 'classification',184            'num_outputs': 1,185            'num_classes': 2,186        }187    )188    189    validate_dataset_file(data_dir / 'musk.npz')190@pytest.mark.dataset191def test_parkinsons(data_dir):192    num_features = 16193    num_outputs = 2194    train_examples = 4646195    test_examples = 1229196    197    check_split_sizes(198        data_dir, 'parkinsons', {199            'train': (train_examples, num_features, num_outputs),200            'test': (test_examples, num_features, num_outputs),201        }, )202    203    check_attributes(204        data_dir, 'parkinsons', {205            'task': 'regression',206            'num_outputs': 2,207        }208    )209    210    validate_dataset_file(data_dir / 'parkinsons.npz')211@pytest.mark.dataset212def test_poker(data_dir):213    num_features = 10214    215    check_split_sizes(216        data_dir, 'poker', {217            'train': (25_010, num_features),218            'test': (1_000_000, num_features),219        }220    )221    222    check_attributes(223        data_dir, 'poker', {224            'task': 'classification',225            'num_classes': 10,226        }227    )228    229    validate_dataset_file(data_dir / 'poker.npz')230@pytest.mark.dataset231def test_rossman(data_dir):232    num_features = 18233    train_examples = 814_688234    test_examples = 202_521235    236    check_split_sizes(237        data_dir, 'rossman', {238            'train': (train_examples, num_features),239            'test': (test_examples, num_features),240        }241    )242    243    check_attributes(244        data_dir, 'rossman', {245            'task': 'regression',246        }247    )248    ...

test_models.py

Source:test_models.py

...5nd = 2006X = np.random.uniform(low=0, high=30, size=(nx, nd)).astype(np.float32)7sf = np.ones(nx)8cond = np.random.randint(3, size=nx).astype(np.float32)9def check_attributes(model, ce=False, cd=False, c=False, sf=False):10    assert model._conditional_encoder() == ce11    assert model._conditional_decoder() == cd12    assert model._use_conditions() == c13    assert model._use_sf() == sf14def test_autoencoder():15    lat_dim = 1816    ae = Autoencoder(x_dim=X.shape[1], latent_dim=lat_dim)17    ae.compile(optimizer="adam", loss="mse", run_eagerly=False)18    check_attributes(ae)19    ae.fit(X, batch_size=50, epochs=1)20    lat = ae.transform(X)21    assert X.shape[0] == lat.shape[0]22    assert lat.shape[1] == lat_dim23    rec = ae.predict(X)24    assert rec.shape == X.shape25def test_conditional_autoencoder():26    ae = Autoencoder(x_dim=X.shape[1], conditional="all")27    ae.compile(optimizer="adam", loss="mse", run_eagerly=True)28    check_attributes(ae, ce=True, cd=True, c=True)29    ae.fit([X, cond], batch_size=50, epochs=1)30    lat = ae.transform([X, cond])31    assert X.shape[0] == lat.shape[0]32    rec = ae.predict([X, cond])33    assert rec.shape == X.shape34    ae = Autoencoder(x_dim=X.shape[1], conditional="first")35    ae.compile(optimizer="adam", loss="mse", run_eagerly=False)36    check_attributes(ae, ce=True, cd=True, c=True)37    ae.fit([X, cond], batch_size=50, epochs=1)38    lat = ae.transform([X, cond])39    assert X.shape[0] == lat.shape[0]40    rec = ae.predict([X, cond])41    assert rec.shape == X.shape42def test_poisson_autoencoder():43    ae = PoissonAutoencoder(x_dim=X.shape[1])44    ae.compile(optimizer="adam", loss="mse", run_eagerly=False)45    check_attributes(ae, sf=True)46    ae.fit([X, sf], batch_size=50, epochs=1)47    lat = ae.transform(X)48    assert X.shape[0] == lat.shape[0]49    rec = ae.predict([X, sf])50    assert rec.shape == X.shape51def test_nb_autoencoder():52    ae = NegativeBinomialAutoencoder(x_dim=X.shape[1], dispersion="gene")53    ae.compile(optimizer="adam", run_eagerly=False)54    check_attributes(ae, sf=True)55    ae.fit([X, sf], batch_size=50, epochs=1)56    lat = ae.transform(X)57    assert X.shape[0] == lat.shape[0]58    rec = ae.predict([X, sf])59    assert rec.shape == X.shape60def test_conditional_zinb_autoencoder():61    ae = NegativeBinomialAutoencoder(62        x_dim=X.shape[1], dispersion="constant", conditional="all"63    )64    ae.compile(optimizer="adam", run_eagerly=True)65    check_attributes(ae, ce=True, cd=True, c=True, sf=True)66    ae.fit([X, cond, sf], batch_size=50, epochs=1)67    lat = ae.transform([X, cond])68    assert X.shape[0] == lat.shape[0]69    rec = ae.predict([X, cond, sf])70    assert rec.shape == X.shape71def test_topological_autoencoder():72    ae = TopologicalAutoencoder(x_dim=X.shape[1])73    ae.compile(optimizer="adam", run_eagerly=True)74    check_attributes(ae)75    ae.fit(X, batch_size=50, epochs=1)76    lat = ae.transform(X)77    assert X.shape[0] == lat.shape[0]78    rec = ae.predict(X)79    assert rec.shape == X.shape80def test_variational_autoencoder():81    ae = VariationalAutoencoder(x_dim=X.shape[1])82    ae.compile(optimizer="adam", run_eagerly=False)83    check_attributes(ae)84    ae.fit(X, batch_size=50, epochs=1)85    lat = ae.transform(X)86    assert X.shape[0] == lat.shape[0]87    rec = ae.predict(X)88    assert rec.shape == X.shape89    ae = VariationalAutoencoder(x_dim=X.shape[1], latent_dist="multivariate")90    ae.compile(optimizer="adam", run_eagerly=False)91    check_attributes(ae)92    ae.fit(X, batch_size=50, epochs=1)93    lat = ae.transform(X)94    assert X.shape[0] == lat.shape[0]95    rec = ae.predict(X)96    assert rec.shape == X.shape97    ae = VariationalAutoencoder(x_dim=X.shape[1], prior="iaf", iaf_units=[128, 128])98    ae.compile(optimizer="adam", run_eagerly=False)99    check_attributes(ae)100    ae.fit(X, batch_size=50, epochs=1)101    lat = ae.transform(X)102    assert X.shape[0] == lat.shape[0]103    rec = ae.predict(X)104    assert rec.shape == X.shape105    ae = VariationalAutoencoder(x_dim=X.shape[1], prior="vamp", n_pseudoinputs=30)106    ae.compile(optimizer="adam", run_eagerly=False)107    check_attributes(ae)108    ae.fit(X, batch_size=50, epochs=1)109    lat = ae.transform(X)110    assert X.shape[0] == lat.shape[0]111    rec = ae.predict(X)112    assert rec.shape == X.shape113def test_conditional_variational_autoencoder():114    ae = VariationalAutoencoder(x_dim=X.shape[1], conditional="all")115    ae.compile(optimizer="adam", run_eagerly=True)116    check_attributes(ae, ce=True, cd=True, c=True)117    ae.fit([X, cond], batch_size=50, epochs=1)118    lat = ae.transform([X, cond])119    assert X.shape[0] == lat.shape[0]120    rec = ae.predict([X, cond])121    assert rec.shape == X.shape122def test_negative_binomial_variational_autoencoder():123    ae = NegativeBinomialVAE(x_dim=X.shape[1], dispersion="cell-gene")124    ae.compile(optimizer="adam", run_eagerly=False)125    check_attributes(ae, sf=True)126    ae.fit([X, sf], batch_size=50, epochs=1)127    lat = ae.transform(X)128    assert X.shape[0] == lat.shape[0]129    rec = ae.predict([X, sf])...

user_config_reader.py

Source:user_config_reader.py

...23        empty_string_check(self._config_dict['@id'])24        25        # Query Generator26        empty_string_check(self._config_dict['queryGenerator']['@class'])27        check_attributes(self._config_dict['queryGenerator'])28        29        # Snippet Classifier30        empty_string_check(self._config_dict['textClassifiers']['snippetClassifier']['@class'])31        check_attributes(self._config_dict['textClassifiers']['snippetClassifier'])32        33        # Document Classifier34        empty_string_check(self._config_dict['textClassifiers']['documentClassifier']['@class'])35        check_attributes(self._config_dict['textClassifiers']['documentClassifier'])36        37        # Stopping Decision Maker38        empty_string_check(self._config_dict['stoppingDecisionMaker']['@class'])39        check_attributes(self._config_dict['stoppingDecisionMaker'])40        41        # Logger42        empty_string_check(self._config_dict['logger']['@class'])43        check_attributes(self._config_dict['logger'])44        45        # Search Context46        empty_string_check(self._config_dict['searchContext']['@class'])47        check_attributes(self._config_dict['searchContext'])48        49        # SERP Impression50        empty_string_check(self._config_dict['serpImpression']['@class'])...

validation_test.py

Source:validation_test.py

...5import re6class Test_check_attributes:7    def test_None_inputs(self):8        with pytest.raises(ValueError, match=none_arg_msg):9            check_attributes(None, None)10        with pytest.raises(ValueError, match=none_arg_msg):11            check_attributes([], None)12    def test_incompatible_dims(self):13        X = np.array([[1, 2, 3], [11, 21, 31]])14        with pytest.raises(15            ValueError,16            match=re.escape(17                f"Incompatible dimension for X and e matrices. X and e should have the same feature dimension: X.shape[0] = {X.shape[0]} while e.shape[0] = {X.T.shape[0]}."18            ),19        ):20            check_attributes(X, X.T)21    def test_invalid_iterations(self):22        X = np.array([[1, 2, 3], [None, 21, 31]])23        str_input = "cake"24        neg_input = -125        with pytest.raises(26            ValueError,27            match=f"iterations has incorrect type or less than 2. iterations: {str_input}",28        ):29            check_attributes(X, X, str_input)30        with pytest.raises(31            ValueError,32            match=f"iterations has incorrect type or less than 2. iterations: {neg_input}",33        ):34            check_attributes(X, X, neg_input)35    def test_invalid_n_jobs(self):36        X = np.array([[1, 2, 3], [11, 21, 31]])37        str_input = "cake"38        neg_input = -139        with pytest.raises(40            ValueError,41            match=f"n_jobs is incorrect type or less than 1. n_jobs: {str_input}",42        ):43            check_attributes(X, X, n_jobs=str_input)44        with pytest.raises(45            ValueError,46            match=f"n_jobs is incorrect type or less than 1. n_jobs: {neg_input}",47        ):...

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