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def main(request, response): headers = [("Content-type", "text/plain"), ("X-Request-Method", request.method), ("X-Request-Query", request.url_parts.query if request.url_parts.query else "NO"), ("X-Request-Content-Length", request.headers.get("Content-Length", "NO")), ...
12,202
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class TestPotatoCaptchaSerializer(TestCase): fixtures = fixture('user_999') def setUp(self): self.request = mock.Mock() self.request.META = {} self.request.user = mock.Mock() self.context = {'request': self.request} self.request.user.is_authenticated = lambda: False ...
12,203
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def setUp(self): self.SerializerClass.Meta = type('Meta', (self.Struct,), {'model': UserProfile, 'url_basename': self.url_basename}) self.request = RequestFactory().get('/') self.request.API_VERSION = 1 se...
12,204
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class TestURLSerializerMixin(TestCase): SerializerClass = type('Potato', (URLSerializerMixin, Serializer), {'Meta': None}) Struct = type('Struct', (object,), {}) url_basename = 'potato' def setUp(self): self.SerializerClass.Meta = type('Meta', (self.Struct,), ...
12,205
[ 0.020828835666179657, 0.038902852684259415, 0.006354052573442459, -0.0009581983322277665, 0.017606893554329872, 0.03768114745616913, -0.012720082886517048, -0.004665228072553873, 0.009174749255180359, 0.041609760373830795, -0.02795543521642685, -0.006168401800096035, 0.044771816581487656, ...
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class DragActionTest(tab_test_case.TabTestCase): def CheckWithinRange(self, value, expected, error_ratio): error_range = abs(expected * error_ratio) return abs(value - expected) <= error_range # https://github.com/catapult-project/catapult/issues/3099 (Android) # crbug.com/483212 (CrOS) @decorators.Dis...
12,206
[ 0.02217288501560688, 0.024294400587677956, 0.013650447130203247, 0.00569172715768218, -0.005094671621918678, 0.0013729246566072106, -0.034162487834692, -0.006764608900994062, -0.012583627365529537, 0.03753266856074333, -0.013432233594357967, 0.005243177991360426, 0.033144157379865646, -0.0...
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def testDragAction(self): self.Navigate('draggable.html') utils.InjectJavaScript(self._tab, 'gesture_common.js') div_width = self._tab.EvaluateJavaScript( '__GestureCommon_GetBoundingVisibleRect(document.body).width') div_height = self._tab.EvaluateJavaScript( '__GestureCommon_GetBound...
12,207
[ 0.05197771638631821, 0.020791087299585342, 0.10144936293363571, -0.0080751096829772, -0.018505459651350975, 0.01575574465095997, 0.010053278878331184, 0.02183528244495392, -0.011422335170209408, -0.025455158203840256, 0.029028626158833504, -0.024109307676553726, 0.020141365006566048, 0.021...
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def forwards(self, orm): # Changing field 'GroupObjectPermission.object_pk' db.alter_column('guardian_groupobjectpermission', 'object_pk', self.gf('django.db.models.fields.CharField')(max_length=255)) # Changing field 'UserObjectPermission.object_pk' db.alter_column('guardian_u...
12,208
[ 0.039544083178043365, 0.03192688152194023, 0.06068621575832367, -0.030217410996556282, -0.027276115491986275, 0.001305671175941825, -0.006036568898707628, -0.013902020640671253, -0.027628066018223763, -0.001574347261339426, 0.006586490664631128, -0.03333468362689018, -0.0011540499981492758, ...
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class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'GroupObjectPermission.object_pk' db.alter_column('guardian_groupobjectpermission', 'object_pk', self.gf('django.db.models.fields.CharField')(max_length=255)) # Changing field 'UserObjectPermission.object_...
12,209
[ 0.06750444322824478, 0.03977510333061218, 0.08475397527217865, -0.018947981297969818, -0.012148149311542511, 0.015346298925578594, -0.0012482419842854142, 0.014226043596863747, -0.020730754360556602, -0.026693405583500862, 0.022417161613702774, -0.01739407889544964, 0.012961238622665405, 0...
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def backwards(self, orm): # Changing field 'GroupObjectPermission.object_pk' db.alter_column('guardian_groupobjectpermission', 'object_pk', self.gf('django.db.models.fields.TextField')()) # Changing field 'UserObjectPermission.object_pk' db.alter_column('guardian_userobjectpermission',...
12,210
[ 0.016348261386156082, -0.00492154760286212, 0.049108002334833145, -0.011252864263951778, -0.014565505087375641, 0.01968619041144848, -0.002204739488661289, -0.032721810042858124, 0.01299768965691328, 0.05492408946156502, 0.010298267006874084, -0.015033320523798466, -0.004023847170174122, 0...
10
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def test_ovr_exceptions(): ovr = OneVsRestClassifier(LinearSVC(random_state=0)) # test predicting without fitting with pytest.raises(NotFittedError): ovr.predict([]) # Fail on multioutput data msg = "Multioutput target data is not supported with label binarization" with pytest.raises(V...
12,211
[ 0.00934682134538889, -0.014883158728480339, 0.0236918106675148, -0.005527058150619268, -0.007682827301323414, 0.029568370431661606, 0.003983688075095415, 0.0056043812073767185, -0.024223793298006058, 0.05587061122059822, 0.026500187814235687, -0.01685025915503502, -0.013794448226690292, 0....
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def test_ovr_partial_fit(): # Test if partial_fit is working as intended X, y = shuffle(iris.data, iris.target, random_state=0) ovr = OneVsRestClassifier(MultinomialNB()) ovr.partial_fit(X[:100], y[:100], np.unique(y)) ovr.partial_fit(X[100:], y[100:]) pred = ovr.predict(X) ovr2 = OneVsRestC...
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[ 0.017289824783802032, -0.015016661025583744, 0.021950960159301758, 0.007364822551608086, 0.023994512856006622, 0.016658391803503036, -0.0022114559542387724, -0.02144581265747547, 0.011325639672577381, 0.06938891857862473, -0.0022846448700875044, -0.03336270526051521, -0.009362451732158661, ...
9
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def test_ovr_fit_predict(): # A classifier which implements decision_function. ovr = OneVsRestClassifier(LinearSVC(random_state=0)) pred = ovr.fit(iris.data, iris.target).predict(iris.data) assert len(ovr.estimators_) == n_classes clf = LinearSVC(random_state=0) pred2 = clf.fit(iris.data, iris....
12,213
[ 0.049796734005212784, 0.013102438300848007, 0.01610119268298149, -0.0026005790568888187, 0.010246778838336468, 0.04616338759660721, 0.010308993980288506, -0.04046451300382614, 0.00726668955758214, 0.053504735231399536, 0.021227693185210228, -0.017805878072977066, 0.005761091131716967, 0.03...
9
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def test_ovr_ovo_regressor(): # test that ovr and ovo work on regressors which don't have a decision_ # function ovr = OneVsRestClassifier(DecisionTreeRegressor()) pred = ovr.fit(iris.data, iris.target).predict(iris.data) assert len(ovr.estimators_) == n_classes assert_array_equal(np.unique(pred...
12,214
[ -0.010275508277118206, -0.0009727271972224116, 0.04578866809606552, -0.02135300636291504, -0.035137224942445755, -0.0009351338958367705, 0.01617765985429287, 0.004645908251404762, -0.012957165017724037, 0.063908651471138, 0.020638734102249146, -0.027468187734484673, -0.0032831502612680197, ...
9
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def test_ovr_partial_fit_exceptions(): ovr = OneVsRestClassifier(MultinomialNB()) X = np.abs(np.random.randn(14, 2)) y = [1, 1, 1, 1, 2, 3, 3, 0, 0, 2, 3, 1, 2, 3] ovr.partial_fit(X[:7], y[:7], np.unique(y)) # If a new class that was not in the first call of partial fit is seen # it should raise...
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[ -0.016448242589831352, 0.04771518334746361, -0.0008275044965557754, -0.013876614160835743, 0.01972006820142269, 0.043717700988054276, 0.0359518863260746, -0.0335075668990612, 0.0268111452460289, 0.07944042980670929, 0.02118411473929882, -0.015289736911654472, 0.03666481375694275, 0.0088415...
10
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def test_ovr_always_present(): # Test that ovr works with classes that are always present or absent. # Note: tests is the case where _ConstantPredictor is utilised X = np.ones((10, 2)) X[:5, :] = 0 # Build an indicator matrix where two features are always on. # As list of lists, it would be: [[...
12,216
[ 0.01573321595788002, -0.03814977779984474, 0.000036163986806059256, -0.01893218420445919, 0.014876986853778362, 0.03398755192756653, -0.005309811793267727, -0.01850407011806965, -0.0007885932573117316, 0.049185626208782196, -0.027613399550318718, -0.030134519562125206, 0.008574186824262142, ...
11
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def test_ovr_fit_predict_sparse(): for sparse in [ sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix, sp.lil_matrix, ]: base_clf = MultinomialNB(alpha=1) X, Y = datasets.make_multilabel_classification( n_samples=100, n_fea...
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[ -0.006559597793966532, -0.007126165553927422, 0.041346870362758636, -0.03411998227238655, 0.006735863164067268, -0.0029414319433271885, 0.004069846589118242, -0.0031475997529923916, 0.007403154391795397, 0.06768598407506943, 0.017928728833794594, -0.04469591751694679, 0.022209463641047478, ...
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def test_ovr_binary(): # Toy dataset where features correspond directly to labels. X = np.array([[0, 0, 5], [0, 5, 0], [3, 0, 0], [0, 0, 6], [6, 0, 0]]) y = ["eggs", "spam", "spam", "eggs", "spam"] Y = np.array([[0, 1, 1, 0, 1]]).T classes = set("eggs spam".split()) def conduct_test(base_clf, ...
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[ -0.009025108069181442, -0.005554847419261932, 0.024990737438201904, -0.0385071262717247, 0.01750323921442032, -0.008769852109253407, 0.008241108618676662, -0.006484707351773977, 0.001079518347978592, 0.07925078272819519, 0.022109994664788246, -0.044074129313230515, 0.014646807685494423, 0....
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def test_ovr_multiclass(): # Toy dataset where features correspond directly to labels. X = np.array([[0, 0, 5], [0, 5, 0], [3, 0, 0], [0, 0, 6], [6, 0, 0]]) y = ["eggs", "spam", "ham", "eggs", "ham"] Y = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0]]) classes = set("ham eggs spam"...
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[ -0.002315969904884696, -0.008910209871828556, 0.03924599289894104, 0.013336792588233948, 0.0051339236088097095, 0.013918637298047543, -0.00628049997612834, -0.029594218358397484, -0.005827003158628941, 0.07269635796546936, 0.0017227166099473834, -0.044037654995918274, 0.016291651874780655, ...
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def conduct_test(base_clf, test_predict_proba=False): clf = OneVsRestClassifier(base_clf).fit(X, y) assert set(clf.classes_) == classes y_pred = clf.predict(np.array([[0, 0, 4]]))[0] assert_array_equal(y_pred, ["eggs"]) if hasattr(base_clf, "decision_function"): dec =...
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[ -0.01401066966354847, -0.005148680415004492, 0.043259747326374054, -0.02205115742981434, 0.002112433547154069, -0.013685679994523525, 0.002742853481322527, -0.00638544699177146, -0.03091013804078102, 0.06764601916074753, 0.0010216113878414035, -0.020642869174480438, 0.015960607677698135, 0...
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def test_ovr_multilabel(): # Toy dataset where features correspond directly to labels. X = np.array([[0, 4, 5], [0, 5, 0], [3, 3, 3], [4, 0, 6], [6, 0, 0]]) y = np.array([[0, 1, 1], [0, 1, 0], [1, 1, 1], [1, 0, 1], [1, 0, 0]]) for base_clf in ( MultinomialNB(), LinearSVC(random_state=0)...
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[ -0.003385675372555852, -0.019318604841828346, 0.03146073594689369, -0.01601974293589592, -0.010851523838937283, -0.009786627255380154, -0.0014389120042324066, -0.012616705149412155, 0.00415540998801589, 0.04150780290365219, -0.026113105937838554, -0.03900761157274246, 0.0040483418852090836, ...
11
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def test_ovr_multilabel_predict_proba(): base_clf = MultinomialNB(alpha=1) for au in (False, True): X, Y = datasets.make_multilabel_classification( n_samples=100, n_features=20, n_classes=5, n_labels=3, length=50, allow_unlabeled=au...
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[ 0.002387160202488303, 0.0009815338999032974, 0.05647572502493858, -0.016089284792542458, -0.04621588811278343, 0.002414850052446127, 0.002597020473331213, 0.00909977313131094, -0.029357116669416428, 0.05904068052768707, -0.01864258572459221, -0.04957365244626999, 0.0031333300285041332, 0.0...
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def test_ovr_multilabel_dataset(): base_clf = MultinomialNB(alpha=1) for au, prec, recall in zip((True, False), (0.51, 0.66), (0.51, 0.80)): X, Y = datasets.make_multilabel_classification( n_samples=100, n_features=20, n_classes=5, n_labels=2, ...
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[ 0.008081783540546894, -0.002505352720618248, 0.017962129786610603, -0.004317141603678465, 0.0000670420631649904, -0.007200133986771107, -0.006277341395616531, -0.01723330095410347, 0.003288551000878215, 0.056989796459674835, -0.019525587558746338, -0.041402239352464676, -0.007970107719302177...
11
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def test_ovr_multilabel_decision_function(): X, Y = datasets.make_multilabel_classification( n_samples=100, n_features=20, n_classes=5, n_labels=3, length=50, allow_unlabeled=True, random_state=0, ) X_train, Y_train = X[:80], Y[:80] X_test = X[80:]...
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[ 0.0023160807322710752, -0.012478917837142944, 0.04321543499827385, 0.010332240723073483, 0.0073941112495958805, -0.0011146730976179242, 0.012197030708193779, 0.0009317176300100982, -0.006580975838005543, 0.040244780480861664, -0.01946103945374489, -0.022724423557519913, 0.01766130141913891, ...
10
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def test_ovr_single_label_predict_proba(): base_clf = MultinomialNB(alpha=1) X, Y = iris.data, iris.target X_train, Y_train = X[:80], Y[:80] X_test = X[80:] clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train) # Decision function only estimator. decision_only = OneVsRestClassifier(svm....
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[ 0.015915019437670708, -0.013822793960571289, 0.04001087695360184, -0.019029850140213966, -0.018594948574900627, 0.015738708898425102, 0.0018806522712111473, -0.009179933927953243, -0.007675412110984325, 0.08157329261302948, 0.029338175430893898, -0.04259677231311798, 0.029949387535452843, ...
9
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def test_ovr_pipeline(): # Test with pipeline of length one # This test is needed because the multiclass estimators may fail to detect # the presence of predict_proba or decision_function. clf = Pipeline([("tree", DecisionTreeClassifier())]) ovr_pipe = OneVsRestClassifier(clf) ovr_pipe.fit(iris....
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[ 0.005819242913275957, -0.054080888628959656, 0.016620192676782608, 0.0039483834989368916, 0.0016410808311775327, 0.010648670606315136, -0.00944675225764513, -0.011279541999101639, 0.007010284345597029, 0.03643824905157089, 0.01976367086172104, -0.013183032162487507, 0.009354297071695328, 0...
10
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def test_ovo_fit_on_list(): # Test that OneVsOne fitting works with a list of targets and yields the # same output as predict from an array ovo = OneVsOneClassifier(LinearSVC(random_state=0)) prediction_from_array = ovo.fit(iris.data, iris.target).predict(iris.data) iris_data_list = [list(a) for a i...
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[ 0.02225678786635399, -0.026488449424505234, 0.022793548181653023, -0.027387209236621857, -0.005948042497038841, 0.02688789740204811, 0.008856529369950294, 0.004515644162893295, -0.014904433861374855, 0.03253011405467987, 0.019872577860951424, -0.032030802220106125, 0.006484801881015301, -0...
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def test_ovo_partial_fit_predict(): temp = datasets.load_iris() X, y = temp.data, temp.target ovo1 = OneVsOneClassifier(MultinomialNB()) ovo1.partial_fit(X[:100], y[:100], np.unique(y)) ovo1.partial_fit(X[100:], y[100:]) pred1 = ovo1.predict(X) ovo2 = OneVsOneClassifier(MultinomialNB()) ...
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[ 0.031326718628406525, -0.029777469113469124, 0.016096236184239388, -0.011961109936237335, 0.014615337364375591, 0.002884906018152833, 0.011915544047951698, -0.002772414591163397, 0.00183830875903368, 0.05723106488585472, 0.009443581104278564, -0.04048551246523857, -0.004536678083240986, 0....
9
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def test_ovo_fit_predict(): # A classifier which implements decision_function. ovo = OneVsOneClassifier(LinearSVC(random_state=0)) ovo.fit(iris.data, iris.target).predict(iris.data) assert len(ovo.estimators_) == n_classes * (n_classes - 1) / 2 # A classifier which implements predict_proba. ovo...
12,229
[ 0.010668661445379257, 0.008182819932699203, 0.02475922927260399, -0.02094058133661747, -0.011158390901982784, 0.010216127149760723, -0.010910427197813988, -0.020990174263715744, -0.0009701601229608059, 0.031342681497335434, 0.042054735124111176, -0.03526051715016365, -0.03959989175200462, ...
9
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def test_ovo_ties(): # Test that ties are broken using the decision function, # not defaulting to the smallest label X = np.array([[1, 2], [2, 1], [-2, 1], [-2, -1]]) y = np.array([2, 0, 1, 2]) multi_clf = OneVsOneClassifier(Perceptron(shuffle=False, max_iter=4, tol=None)) ovo_prediction = multi...
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[ 0.032507456839084625, -0.022444451227784157, 0.02999170497059822, -0.005281228572130203, 0.038130901753902435, 0.0054816254414618015, 0.0028533432632684708, -0.02459023892879486, -0.006338707637041807, 0.06057535111904144, 0.020680958405137062, -0.0574183315038681, -0.015131506137549877, 0...
11
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def test_ovo_decision_function(): n_samples = iris.data.shape[0] ovo_clf = OneVsOneClassifier(LinearSVC(random_state=0)) # first binary ovo_clf.fit(iris.data, iris.target == 0) decisions = ovo_clf.decision_function(iris.data) assert decisions.shape == (n_samples,) # then multi-class ov...
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[ 0.016830332577228546, -0.008825661614537239, 0.03415823355317116, -0.025998074561357498, -0.013011476024985313, 0.013011476024985313, 0.021544815972447395, 0.015648601576685905, -0.010380570776760578, 0.035874854773283005, 0.04876193776726723, -0.028585441410541534, -0.013621000573039055, ...
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def test_ovo_ties2(): # test that ties can not only be won by the first two labels X = np.array([[1, 2], [2, 1], [-2, 1], [-2, -1]]) y_ref = np.array([2, 0, 1, 2]) # cycle through labels so that each label wins once for i in range(3): y = (y_ref + i) % 3 multi_clf = OneVsOneClassifi...
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[ 0.04898228123784065, -0.006966873072087765, -0.01734789088368416, -0.01748647354543209, -0.027036001905798912, 0.021265970543026924, 0.02746434509754181, -0.004040912259370089, 0.0016677030362188816, 0.019716376438736916, 0.0413224995136261, -0.03152100369334221, -0.016088059172034264, 0.0...
8
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def test_ovo_string_y(): # Test that the OvO doesn't mess up the encoding of string labels X = np.eye(4) y = np.array(["a", "b", "c", "d"]) ovo = OneVsOneClassifier(LinearSVC()) ovo.fit(X, y) assert_array_equal(y, ovo.predict(X))
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[ 0.010704821906983852, -0.0041287560015916824, 0.03084186464548111, -0.02644246444106102, 0.02192789688706398, 0.0017001606756821275, -0.011988939717411995, -0.007111595012247562, 0.0119774229824543, 0.017309678718447685, 0.04367152601480484, -0.0377749502658844, 0.015443965792655945, 0.041...
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def test_ovo_one_class(): # Test error for OvO with one class X = np.eye(4) y = np.array(["a"] * 4) ovo = OneVsOneClassifier(LinearSVC()) msg = "when only one class" with pytest.raises(ValueError, match=msg): ovo.fit(X, y)
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[ 0.004069937858730555, 0.02560275048017502, 0.023390311747789383, -0.0160312969237566, -0.000019271939891041256, 0.003845144994556904, 0.018858956173062325, 0.0009368843748234212, 0.010257663205265999, 0.025531763210892677, 0.025555424392223358, 0.004963194951415062, -0.010689502581954002, ...
8
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def test_ovo_float_y(): # Test that the OvO errors on float targets X = iris.data y = iris.data[:, 0] ovo = OneVsOneClassifier(LinearSVC()) msg = "Unknown label type" with pytest.raises(ValueError, match=msg): ovo.fit(X, y)
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[ 0.027597855776548386, 0.03120623156428337, 0.0389704629778862, -0.01330122072249651, 0.023354902863502502, 0.040463581681251526, 0.03556117042899132, -0.004473142325878143, 0.03294820711016655, 0.01834050379693508, 0.03197767958045006, -0.018987523391842842, -0.031952790915966034, -0.00531...
8
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def test_ecoc_float_y(): # Test that the OCC errors on float targets X = iris.data y = iris.data[:, 0] ovo = OutputCodeClassifier(LinearSVC()) msg = "Unknown label type" with pytest.raises(ValueError, match=msg): ovo.fit(X, y) ovo = OutputCodeClassifier(LinearSVC(), code_size=-1) ...
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[ 0.055766619741916656, -0.020530598238110542, 0.019356442615389824, -0.010219700634479523, 0.02384786866605282, 0.0339706726372242, 0.020804187282919884, 0.005557282827794552, 0.024987824261188507, 0.02574019320309162, -0.00859526451677084, -0.04787812754511833, -0.02765531837940216, 0.0075...
9
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def test_ecoc_fit_predict(): # A classifier which implements decision_function. ecoc = OutputCodeClassifier(LinearSVC(random_state=0), code_size=2, random_state=0) ecoc.fit(iris.data, iris.target).predict(iris.data) assert len(ecoc.estimators_) == n_classes * 2 # A classifier which implements predi...
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[ 0.0401332825422287, -0.020910507068037987, 0.022275583818554878, -0.002567276591435075, 0.001192891621030867, 0.04082822799682617, 0.010759293101727962, -0.009934041649103165, 0.017783237621188164, 0.023789579048752785, -0.01025049202144146, -0.02136966958642006, 0.01779564842581749, 0.001...
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def test_ecoc_delegate_sparse_base_estimator(): # Non-regression test for # https://github.com/scikit-learn/scikit-learn/issues/17218 X, y = iris.data, iris.target X_sp = sp.csc_matrix(X) # create an estimator that does not support sparse input base_estimator = CheckingClassifier( check...
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[ -0.010483958758413792, -0.01705465465784073, 0.027987124398350716, -0.0037899231538176537, 0.0045271641574800014, 0.011952834203839302, 0.024421457201242447, -0.0038403805810958147, -0.023031070828437805, 0.03433356434106827, -0.006957536563277245, -0.059652045369148254, 0.020317576825618744...
11
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def test_pairwise_indices(): clf_precomputed = svm.SVC(kernel="precomputed") X, y = iris.data, iris.target ovr_false = OneVsOneClassifier(clf_precomputed) linear_kernel = np.dot(X, X.T) ovr_false.fit(linear_kernel, y) n_estimators = len(ovr_false.estimators_) precomputed_indices = ovr_fals...
12,239
[ -0.008172761648893356, -0.018172236159443855, -0.005815234035253525, -0.02052086777985096, -0.017555423080921173, 0.02619079500436783, 0.05242903530597687, -0.028254743665456772, -0.005385244730859995, 0.02545536495745182, -0.014317159540951252, -0.07700663059949875, 0.015111899003386497, ...
9
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def test_pairwise_tag(MultiClassClassifier): clf_precomputed = svm.SVC(kernel="precomputed") clf_notprecomputed = svm.SVC() ovr_false = MultiClassClassifier(clf_notprecomputed) assert not ovr_false._get_tags()["pairwise"] ovr_true = MultiClassClassifier(clf_precomputed) assert ovr_true._get_ta...
12,240
[ 0.020743872970342636, 0.007488723378628492, 0.012437603436410427, -0.00016955568571574986, 0.007379717659205198, 0.013080740347504616, 0.044256504625082016, -0.016176514327526093, -0.00703089777380228, 0.023240115493535995, -0.038762591779232025, -0.0658833235502243, 0.009129266254603863, ...
7
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def test_pairwise_cross_val_score(MultiClassClassifier): clf_precomputed = svm.SVC(kernel="precomputed") clf_notprecomputed = svm.SVC(kernel="linear") X, y = iris.data, iris.target multiclass_clf_notprecomputed = MultiClassClassifier(clf_notprecomputed) multiclass_clf_precomputed = MultiClassClass...
12,241
[ 0.04334929957985878, 0.03196294605731964, 0.04069964587688446, -0.02621009387075901, -0.005514145363122225, -0.0021811905317008495, 0.02243851311504841, -0.030268123373389244, -0.005821481347084045, 0.036188069730997086, 0.04714474827051163, -0.025899773463606834, -0.00379545078612864, 0.0...
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def test_support_missing_values(MultiClassClassifier): # smoke test to check that pipeline OvR and OvO classifiers are letting # the validation of missing values to # the underlying pipeline or classifiers rng = np.random.RandomState(42) X, y = iris.data, iris.target X = np.copy(X) # Copy to av...
12,242
[ -0.027503956109285355, 0.023774605244398117, 0.026380913332104683, -0.04339607059955597, 0.04576929658651352, -0.06941676139831543, 0.023075353354215622, -0.025978313758969307, 0.0031280983239412308, -0.0024672511499375105, -0.047422073781490326, -0.008179142139852047, -0.02977123111486435, ...
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def bootstrap_find_resource(filename, use_minified=None, cdn='bootstrap'): # FIXME: get rid of this function and instead manipulate the flask routing # system config = current_app.config if None == use_minified: use_minified = confi...
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[ -0.014829814434051514, -0.00035974866477772593, 0.05655437707901001, -0.005422936286777258, 0.003418397856876254, -0.07057986408472061, -0.00936289131641388, -0.029760170727968216, -0.02316216006875038, 0.038331300020217896, -0.009890732355415821, -0.027699077501893044, -0.04207645729184151,...
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def init_app(self, app): app.config.setdefault('BOOTSTRAP_USE_MINIFIED', True) app.config.setdefault('BOOTSTRAP_JQUERY_VERSION', '1') app.config.setdefault('BOOTSTRAP_HTML5_SHIM', True) app.config.setdefault('BOOTSTRAP_GOOGLE_ANALYTICS_ACCOUNT', None) app.config.setdefault('BOOTS...
12,244
[ -0.01839223876595497, 0.015034559182822704, 0.017157627269625664, 0.0011906204745173454, 0.0015836479142308235, -0.0802612379193306, -0.004202868789434433, -0.047307513654232025, -0.03140757605433464, 0.02605374902486801, -0.03620755672454834, -0.022649914026260376, -0.02986142598092556, -...
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class Bootstrap(object): def __init__(self, app=None): if app is not None: self.init_app(app) def init_app(self, app): app.config.setdefault('BOOTSTRAP_USE_MINIFIED', True) app.config.setdefault('BOOTSTRAP_JQUERY_VERSION', '1') app.config.setdefault('BOOTSTRAP_HTML5_...
12,245
[ 0.027150707319378853, 0.014091615565121174, -0.009040458127856255, 0.01601586677134037, 0.004605294670909643, -0.00023668135690968484, 0.01802225038409233, 0.03548130393028259, 0.009304455481469631, 0.02094382420182228, 0.0021427820902317762, -0.011211106553673744, 0.06082509085536003, 0.0...
8
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def test_as_crispy_errors_form_without_non_field_errors(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ form|as_crispy_errors }} """ ) form = TestForm({"password1": "god", "password2": "god"}) form.is_valid() ...
12,246
[ 0.004065971355885267, 0.010156968608498573, -0.0042188032530248165, -0.008985255844891071, 0.0019231375772505999, 0.00011004709085682407, -0.0031696746591478586, 0.013754891231656075, 0.0034387228079140186, 0.02049224078655243, 0.015206796117126942, -0.03420892357826233, 0.027255062013864517...
12
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class TestBasicFunctionalityTags(TestCase): def setUp(self): pass def tearDown(self): pass def test_as_crispy_errors_form_without_non_field_errors(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ form|as_crispy_err...
12,247
[ 0.02325587533414364, -0.008860797621309757, -0.017257429659366608, -0.004605948459357023, 0.008961961604654789, 0.019542552530765533, -0.002508278237655759, 0.040489502251148224, 0.005843722727149725, 0.038942284882068634, 0.021744361147284508, -0.03253918141126633, 0.07102920114994049, 0....
8
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def test_crispy_filter_with_form(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ form|crispy }} """ ) c = Context({"form": TestForm()}) html = template.render(c) self.assertTrue("<td>" not in html) ...
12,248
[ 0.025002459064126015, 0.02215193584561348, 0.004478097427636385, 0.014131824485957623, -0.0006039240979589522, 0.007591326255351305, 0.015810733661055565, 0.038433730602264404, -0.010284828022122383, 0.029133299365639687, -0.0006903607281856239, -0.028456903994083405, 0.05749357491731644, ...
8
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def test_as_crispy_errors_form_with_non_field_errors(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ form|as_crispy_errors }} """ ) form = TestForm({"password1": "god", "password2": "wargame"}) form.is_valid() ...
12,249
[ 0.0006170088308863342, -0.013420871458947659, -0.024388594552874565, -0.013289878144860268, -0.001753525692038238, 0.016898151487112045, 0.008520526811480522, 0.0324387364089489, 0.013861485756933689, 0.03043810836970806, 0.027103731408715248, -0.05735130235552788, 0.048681922256946564, -0...
8
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def test_crispy_filter_with_formset(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ testFormset|crispy }} """ ) TestFormset = formset_factory(TestForm, extra=4) testFormset = TestFormset() c = Context(...
12,250
[ 0.007200862746685743, 0.0019209005404263735, -0.01499940361827612, -0.03606753051280975, 0.018160197883844376, 0.011861597187817097, -0.010763939470052719, 0.026987431570887566, 0.00001803762643248774, 0.02471166104078293, 0.021183066070079803, -0.03225158900022507, 0.051032453775405884, -...
11
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def test_classes_filter(self): template = get_template_from_string( u""" {% load crispy_forms_field %} {{ testField|classes }} """ ) test_form = TestForm() test_form.fields["email"].widget.attrs.update({"class": "email-fields"}) c = Co...
12,251
[ 0.021498514339327812, -0.0012887819902971387, 0.03466854989528656, -0.012016089633107185, 0.008014907129108906, -0.0247847530990839, 0.03399123251438141, 0.03534586727619171, 0.013834808953106403, 0.014336524531245232, 0.00677943229675293, -0.017271561548113823, 0.027820132672786713, -0.01...
8
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def test_inputs(self): form_helper = FormHelper() submit = Submit("my-submit", "Submit", css_class="button white") reset = Reset("my-reset", "Reset") hidden = Hidden("my-hidden", "Hidden") button = Button("my-button", "Button") form_helper.add_input(submit) form_h...
12,252
[ 0.002907327376306057, -0.0003032049862667918, 0.01633983664214611, -0.008507724851369858, 0.021019086241722107, 0.011998394504189491, 0.023371221497654915, 0.016039563342928886, 0.023108482360839844, 0.0305777657777071, 0.01740330271422863, -0.019405119121074677, 0.011923326179385185, 0.01...
11
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class TestFormHelpers(TestCase): urls = "crispy_forms.tests.urls" def setUp(self): pass def tearDown(self): pass def test_inputs(self): form_helper = FormHelper() submit = Submit("my-submit", "Submit", css_class="button white") reset = Reset("my-reset", "Reset"...
12,253
[ 0.04194851592183113, 0.022694703191518784, 0.028286151587963104, 0.01749541610479355, -0.004044944886118174, -0.015357508324086666, 0.012979244813323021, 0.039975062012672424, -0.0050949230790138245, 0.03476312384009361, 0.00028324892628006637, -0.012308777309954166, 0.04230272397398949, 0...
9
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def test_form_show_errors_non_field_errors(self): form = TestForm({"password1": "wargame", "password2": "god"}) form.helper = FormHelper() form.helper.form_show_errors = True form.is_valid() template = get_template_from_string( u""" {% load crispy_forms_t...
12,254
[ 0.015715384855866432, 0.005487912334501743, 0.034713014960289, -0.0014474696945399046, 0.01543967705219984, -0.016030481085181236, 0.021150780841708183, 0.014717582613229752, 0.004582013003528118, 0.044323425740003586, -0.0016427632654085755, -0.028831232339143753, 0.02888374961912632, 0.0...
9
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def test_form_with_helper_without_layout(self): form_helper = FormHelper() form_helper.form_id = "this-form-rocks" form_helper.form_class = "forms-that-rock" form_helper.form_method = "GET" form_helper.form_action = "simpleAction" form_helper.form_error_title = "ERRORS" ...
12,255
[ -0.008890458382666111, 0.02662946656346321, 0.04190788418054581, -0.016787100583314896, 0.04023156687617302, -0.003220920218154788, 0.01719420589506626, 0.0047745052725076675, 0.02128920517861843, 0.033885516226291656, 0.03017367608845234, -0.01834367960691452, 0.012201180681586266, -0.004...
8
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def test_html5_required(self): form = TestForm() form.helper = FormHelper() form.helper.html5_required = True html = render_crispy_form(form) # 6 out of 7 fields are required and an extra one for the SplitDateTimeWidget makes 7. self.assertEqual(html.count('required="requ...
12,256
[ 0.03151487186551094, 0.02653089165687561, 0.033176202327013016, 0.030734553933143616, -0.005043764133006334, -0.01490160170942545, 0.027588101103901863, 0.011264301836490631, 0.007815789431333542, 0.04347139596939087, 0.004187928978353739, -0.019054919481277466, 0.03393134847283363, 0.0166...
8
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def test_form_show_errors(self): form = TestForm( { "email": "invalidemail", "first_name": "first_name_too_long", "last_name": "last_name_too_long", "password1": "yes", "password2": "yes", } ) ...
12,257
[ -0.004134829621762037, -0.0028001137543469667, 0.014272211119532585, -0.012968329712748528, -0.0032009691931307316, -0.009720373898744583, 0.019640441983938217, 0.027064340189099312, 0.02001633495092392, 0.022342177107930183, 0.0001255425886483863, 0.017291106283664703, 0.04383859410881996, ...
8
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def test_template_context(self): helper = FormHelper() helper.attrs = { "id": "test-form", "class": "test-forms", "action": "submit/test/form", "autocomplete": "off", } node = CrispyFormNode("form", "helper") context = node.get_resp...
12,258
[ 0.020313424989581108, 0.008241642266511917, 0.012561949901282787, -0.008617816492915154, 0.005252194590866566, -0.018968315795063972, 0.009615249000489712, -0.0005464504938572645, 0.0222626943141222, -0.006805339362472296, 0.007643182761967182, -0.011148445308208466, 0.07117678225040436, -...
7
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def test_attrs(self): form = TestForm() form.helper = FormHelper() form.helper.attrs = {"id": "TestIdForm", "autocomplete": "off"} html = render_crispy_form(form) self.assertTrue('autocomplete="off"' in html) self.assertTrue('id="TestIdForm"' in html)
12,259
[ 0.020265523344278336, 0.003999477252364159, 0.013378632254898548, -0.03192807734012604, 0.015117289498448372, -0.0025755278766155243, 0.0263056643307209, 0.01171900425106287, 0.02558310516178608, 0.013175412081182003, 0.0003349950129631907, 0.014880199916660786, 0.0570821650326252, -0.0069...
8
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def test_template_context_using_form_attrs(self): helper = FormHelper() helper.form_id = "test-form" helper.form_class = "test-forms" helper.form_action = "submit/test/form" node = CrispyFormNode("form", "helper") context = node.get_response_dict(helper, {}, False) ...
12,260
[ -0.004438732750713825, 0.0036375506315380335, 0.0026782809291034937, -0.011345474980771542, 0.0017481731483712792, 0.007778526283800602, 0.022396260872483253, 0.017251506447792053, 0.025637825950980186, 0.026472773402929306, 0.0007716365507803857, -0.032145511358976364, 0.038112934678792953,...
8
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def test_without_helper(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form %} """ ) c = Context({"form": TestForm()}) html = template.render(c) # Lets make sure everything loads right s...
12,261
[ 0.019062159582972527, 0.01672222837805748, 0.0038792025297880173, -0.008745193481445312, -0.0016530188731849194, 0.010470597073435783, 0.011711468920111656, 0.018731260672211647, -0.006103909108787775, 0.03039545752108097, -0.016084065660834312, -0.032853566110134125, 0.029071860015392303, ...
10
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def test_invalid_helper(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) c = Context({"form": TestForm(), "form_helper": "invalid"}) settings.CRISPY_FAIL_SILENTLY = False ...
12,262
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def test_formset_with_helper_without_layout(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy testFormSet formset_helper %} """ ) form_helper = FormHelper() form_helper.form_id = "thisFormsetRocks" ...
12,263
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def test_CSRF_token_GET_form(self): form_helper = FormHelper() form_helper.form_method = "GET" template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) c = Context( { ...
12,264
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def test_CSRF_token_POST_form(self): form_helper = FormHelper() template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) # The middleware only initializes the CSRF token when processing a real ...
12,265
[ 0.028335604816675186, 0.02935558743774891, 0.009148743003606796, -0.0033802459947764874, -0.02985313907265663, -0.03059946745634079, 0.036918383091688156, 0.03467939794063568, 0.0021037133410573006, 0.03793836385011673, -0.03236578032374382, -0.007432187907397747, -0.009148743003606796, 0....
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def test_invalid_unicode_characters(self): # Adds a BooleanField that uses non valid unicode characters "ñ" form_helper = FormHelper() form_helper.add_layout(Layout("españa")) template = get_template_from_string( u""" {% load crispy_forms_tags %} {% c...
12,266
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class TestFormLayout(TestCase): urls = "crispy_forms.tests.urls" def test_invalid_unicode_characters(self): # Adds a BooleanField that uses non valid unicode characters "ñ" form_helper = FormHelper() form_helper.add_layout(Layout("españa")) template = get_template_from_string( ...
12,267
[ 0.04921744018793106, 0.05423678085207939, 0.05544514209032059, -0.02656068652868271, 0.0204026959836483, -0.0003569164837244898, 0.029604824259877205, 0.012234646826982498, -0.013803191483020782, 0.01481403037905693, 0.009010416455566883, -0.04608035087585449, 0.032741911709308624, -0.0122...
7
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def test_meta_extra_fields_with_missing_fields(self): form = FormWithMeta() # We remove email field on the go del form.fields["email"] form_helper = FormHelper() form_helper.layout = Layout("first_name") template = get_template_from_string( u""" ...
12,268
[ 0.002672584028914571, 0.025641854852437973, 0.021293453872203827, -0.01685783639550209, 0.00048553498345427215, -0.011356924660503864, 0.011736942455172539, -0.015412523411214352, -0.023685697466135025, -0.0029575973749160767, -0.034513089805841446, 0.01120740920305252, 0.03894870728254318, ...
9
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def test_layout_unresolved_field(self): form_helper = FormHelper() form_helper.add_layout(Layout("typo")) template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) c = Context({"form": T...
12,269
[ 0.04048651456832886, -0.018953556194901466, 0.0193961001932621, -0.005373756401240826, 0.0066571361385285854, 0.02077431045472622, 0.03171148523688316, 0.02612277865409851, 0.03993017226457596, 0.043116495013237, 0.025187114253640175, -0.051891524344682693, 0.0387922003865242, 0.0074410722...
8
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def test_context_pollution(self): form = ExampleForm() form2 = TestForm() template = get_template_from_string( u""" {% load crispy_forms_tags %} {{ form.as_ul }} {% crispy form2 %} {{ form.as_ul }} """ ) c = Con...
12,270
[ 0.05181777477264404, 0.0007099477224983275, 0.03400701656937599, -0.0023400646168738604, -0.00028890426619909704, 0.0110388258472085, 0.03141868859529495, 0.015837479382753372, -0.010071407072246075, -0.008527381345629692, -0.007854672148823738, -0.029752936214208603, 0.0448216050863266, -...
9
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def test_double_rendered_field(self): form_helper = FormHelper() form_helper.add_layout(Layout("is_company", "is_company")) template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) c = ...
12,271
[ 0.01033051684498787, -0.003484381129965186, 0.06608659774065018, -0.000036685076338471845, 0.018280290067195892, -0.022491455078125, 0.02792290411889553, 0.03545993193984032, -0.0015343240229412913, 0.0193689726293087, 0.002552720485255122, 0.003651870647445321, 0.010276680812239647, 0.021...
9
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def test_second_layout_multifield_column_buttonholder_submit_div(self): form_helper = FormHelper() form_helper.add_layout( Layout( MultiField( "Some company data", "is_company", "email", css_id="m...
12,272
[ 0.01702031120657921, -0.027178579941391945, 0.057347048074007034, -0.010011226870119572, -0.0083508575335145, -0.014496060088276863, 0.01483916211873293, 0.001066066906787455, 0.003718980588018894, 0.02012048289179802, -0.01363830454647541, -0.02225261554121971, 0.0315408781170845, -0.0039...
9
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def test_layout_fieldset_row_html_with_unicode_fieldnames(self): form_helper = FormHelper() form_helper.add_layout( Layout( Fieldset( u"Company Data", u"is_company", css_id="fieldset_company_data", ...
12,273
[ 0.0160279031842947, 0.00819846335798502, 0.0564851351082325, 0.002006563125178218, 0.017417889088392258, -0.003951622173190117, 0.02976786158978939, 0.011390512809157372, -0.0045359088107943535, 0.04044492915272713, -0.022018376737833023, -0.0038870431017130613, 0.03732053562998772, 0.0121...
9
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def test_layout_within_layout(self): form_helper = FormHelper() form_helper.add_layout( Layout( Layout( MultiField( "Some company data", "is_company", "email", ...
12,274
[ 0.044077515602111816, 0.021099166944622993, 0.013725162483751774, -0.02241935208439827, -0.016710445284843445, 0.013451610691845417, 0.015295112505555153, 0.005343179684132338, -0.0033183018676936626, -0.002356410725042224, 0.0030269098933786154, -0.02221716195344925, 0.04976263642311096, ...
10
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def test_change_layout_dynamically_delete_field(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form_helper %} """ ) form = TestForm() form_helper = FormHelper() form_helper.add_layout( ...
12,275
[ 0.010762267746031284, -0.034600187093019485, 0.005161110777407885, -0.02657877840101719, 0.008279151283204556, -0.003988702781498432, 0.01806703209877014, -0.006883576512336731, 0.01006448082625866, 0.015225591138005257, -0.008826064877212048, -0.049410879611968994, 0.03331776708364487, -0...
9
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def test_formset_layout(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy testFormSet formset_helper %} """ ) form_helper = FormHelper() form_helper.form_id = "thisFormsetRocks" form_helper.form_cl...
12,276
[ 0.01925414800643921, 0.007400483824312687, 0.04736778512597084, -0.019066646695137024, -0.015175972133874893, -0.009539182297885418, 0.0228518508374691, 0.0006848230841569602, -0.013441575691103935, 0.03637545928359032, -0.011929837986826897, -0.021820588037371635, 0.04516463354229927, -0....
9
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def test_multiwidget_field(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form %} """ ) test_form = TestForm() test_form.helper = FormHelper() test_form.helper.layout = Layout( Multi...
12,277
[ 0.008807404898107052, -0.0061421701684594154, 0.0200778990983963, -0.039583563804626465, 0.025700705125927925, -0.01143221091479063, 0.003669752273708582, -0.016619624570012093, -0.020413774996995926, -0.011245613917708397, -0.015960311517119408, -0.020214736461639404, 0.05483480542898178, ...
9
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class TestLayoutObjects(TestCase): def test_field_type_hidden(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy test_form %} """ ) test_form = TestForm() test_form.helper = FormHelper() test_fo...
12,278
[ 0.02192007564008236, -0.0016115937614813447, 0.007004580460488796, -0.028522508218884468, 0.025713473558425903, -0.005170905031263828, 0.028354445472359657, 0.007298688869923353, -0.008805244229733944, 0.012268519960343838, -0.02097172662615776, -0.005867161322385073, 0.02205212414264679, ...
8
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def test_i18n(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy form form.helper %} """ ) form = TestForm() form_helper = FormHelper() form_helper.layout = Layout( HTML(_("i18n text")), ...
12,279
[ 0.01948689855635166, 0.008925480768084526, 0.0011886106804013252, -0.023360220715403557, 0.015649665147066116, 0.0005589702632278204, 0.020894287154078484, 0.017189370468258858, -0.00829396117478609, -0.028989769518375397, -0.0013622785918414593, -0.011421488597989082, 0.07684093713760376, ...
8
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def test_field_type_hidden(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy test_form %} """ ) test_form = TestForm() test_form.helper = FormHelper() test_form.helper.layout = Layout( ...
12,280
[ 0.040722355246543884, -0.00635969452559948, 0.03132879361510277, -0.010650266893208027, -0.028891546651721, -0.015677090734243393, 0.026200419291853905, 0.011411907151341438, 0.010307529009878635, 0.01504239160567522, -0.013798379339277744, -0.027393653988838196, 0.03117646649479866, -0.00...
8
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def test_appended_prepended_text(self): template = get_template_from_string( u""" {% load crispy_forms_tags %} {% crispy test_form %} """ ) test_form = TestForm() test_form.helper = FormHelper() test_form.helper.layout = Layout( ...
12,281
[ 0.013676934875547886, 0.009867018088698387, 0.04017069563269615, -0.016099775210022926, -0.003603975288569927, -0.011175352148711681, -0.012647227384150028, -0.02185402251780033, -0.004727567546069622, 0.023489439859986305, -0.015881719067692757, -0.013628478161990643, 0.020012663677334785, ...
12
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class TestDynamicLayouts(TestCase): def test_wrap_all_fields(self): helper = FormHelper() layout = Layout("email", "password1", "password2") helper.layout = layout helper.all().wrap(Field, css_class="test-class") for field in layout.fields: self.assertTrue(isinst...
12,282
[ 0.029675975441932678, -0.002699385629966855, 0.06460107862949371, -0.0034504940267652273, 0.0009583106730133295, -0.03140266239643097, 0.0026662908494472504, -0.001720930333249271, 0.002431749366223812, 0.03059687465429306, -0.0041037569753825665, 0.015793420374393463, 0.0219749566167593, ...
9
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def test_wrap_all_fields(self): helper = FormHelper() layout = Layout("email", "password1", "password2") helper.layout = layout helper.all().wrap(Field, css_class="test-class") for field in layout.fields: self.assertTrue(isinstance(field, Field)) self.ass...
12,283
[ 0.033154506236314774, -0.0027481901925057173, 0.07802490144968033, -0.02011389657855034, 0.016686635091900826, -0.02950848452746868, -0.0029426447581499815, -0.01432887278497219, -0.020600032061338425, 0.017427992075681686, -0.022544579580426216, 0.004095699638128281, 0.013721201568841934, ...
9
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def test_wrap_selected_fields(self): helper = FormHelper() layout = Layout("email", "password1", "password2") helper.layout = layout helper[1:3].wrap(Field, css_class="test-class") self.assertFalse(isinstance(layout.fields[0], Field)) self.assertTrue(isinstance(layout.fi...
12,284
[ 0.020523490384221077, 0.024063345044851303, 0.04401176795363426, -0.007533373311161995, 0.01527121476829052, -0.02060016617178917, 0.00830012932419777, 0.01171858049929142, -0.01714976504445076, 0.05134706199169159, 0.010830421932041645, 0.005868874955922365, 0.007335294969379902, 0.022798...
11
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def test_wrap_filtered_fields(self): helper = FormHelper() layout = Layout("email", Div("password1"), "password2") helper.layout = layout helper.filter(basestring).wrap(Field, css_class="test-class") self.assertTrue(isinstance(layout.fields[0], Field)) self.assertTrue(is...
12,285
[ 0.034759312868118286, -0.0052507235668599606, 0.07317750155925751, -0.04763665422797203, 0.004912158939987421, -0.015075040981173515, -0.006741596385836601, -0.0562136285007, -0.020777184516191483, 0.013994010165333748, -0.009265982545912266, -0.004027138464152813, 0.0015963030746206641, 0...
8
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def test_get_field_names(self): layout_1 = Div("field_name") self.assertEqual(layout_1.get_field_names(), [[0], "field_name"]) layout_2 = Div(Div("field_name")) self.assertEqual(layout_2.get_field_names(), [[0, 0], "field_name"]) layout_3 = Div(Div("field_name"), "password") ...
12,286
[ 0.03852936252951622, -0.0174995306879282, 0.0741596668958664, -0.06485464423894882, 0.0063475253991782665, -0.01968551054596901, -0.01586296781897545, -0.03628493472933769, -0.031164830550551414, 0.005839021876454353, -0.021333763375878334, 0.017429392784833908, 0.009205665439367294, 0.015...
9
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def test_layout_get_field_names(self): layout_1 = Layout(Div("field_name"), "password") self.assertEqual( layout_1.get_field_names(), [[[0, 0], "field_name"], [[1], "password"]] ) layout_2 = Layout( Div("field_name"), "password", Fieldset("legend", "extra_field")...
12,287
[ 0.01454832125455141, -0.014703530818223953, -0.0190700963139534, 0.03164208307862282, 0.00934362318366766, -0.04734930023550987, 0.04813569784164429, 0.038492001593112946, 0.04267231747508049, 0.005877273622900248, -0.03348390385508537, 0.035532671958208084, -0.021398242563009262, -0.03309...
8
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def api_wekan_webhook( request: HttpRequest, user_profile: UserProfile, payload: Dict[str, Any] = REQ(argument_type="body"), ) -> HttpResponse: topic = "Wekan Notification" body = get_message_body(payload, payload["description"]) check_send_webhook_message(request, user_profile, topic, body) ...
12,288
[ 0.005717935971915722, 0.011225699447095394, 0.039339400827884674, -0.008765441365540028, 0.011621318757534027, -0.036026086658239365, -0.005118325352668762, -0.014724458567798138, 0.0018730104202404618, -0.0032360428012907505, -0.028583498671650887, -0.015070625580847263, 0.04710343107581138...
10
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def test_getitem_layout_object(self): layout = Layout(Div(Div(Div("email")), Div("password1"), "password2")) self.assertTrue(isinstance(layout[0], Div)) self.assertTrue(isinstance(layout[0][0], Div)) self.assertTrue(isinstance(layout[0][0][0], Div)) self.assertTrue(isinstance(lay...
12,289
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class ResUsersLogin(orm.Model): _name = 'res.users.login' _columns = { 'user_id': fields.many2one('res.users', 'User', required=True), 'login_dt': fields.date('Latest connection'), } _sql_constraints = [ ('user_id_unique', 'unique(user_id)', 'The user can only...
12,290
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12
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def handle_starttag(self, tag, raw_attrs): attrs = dict(raw_attrs) if tag == 'a': # Handle special cases for href's that: start with a space, contain # just a '.' (period), contain python templating code, are an absolute # url, are a zip file, or execute javascript on the page. href = a...
12,291
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class ResUsers(orm.Model): _inherit = 'res.users' # Function to retrieve the login date from the res.users object # (used in some functions, and the user state) def _get_login_date(self, cr, uid, ids, name, args, context=None): res = {} user_login_obj = self.pool['res.users.login'] ...
12,292
[ 0.01251798402518034, 0.026563040912151337, 0.05296533554792404, -0.0029612164944410324, 0.046816855669021606, 0.0056561995297670364, 0.05107658728957176, -0.018093811348080635, -0.030521376058459282, 0.015331014059484005, -0.008564671501517296, 0.015471665188670158, -0.013150915503501892, ...
9
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def cron_sync_login_date(self, cr, uid, context=None): # Simple SQL query to update the original login_date column. try: cr.execute("UPDATE res_users SET login_date = " "(SELECT login_dt FROM res_users_login " "WHERE res_users_login.user_id = res...
12,293
[ -0.015516499988734722, 0.020677324384450912, 0.02840154804289341, -0.008591922000050545, 0.03983475640416145, -0.030329767614603043, 0.0015156377339735627, -0.004301632288843393, 0.017342638224363327, 0.015459788031876087, 0.0156866367906332, -0.028015904128551483, 0.01994006335735321, 0.0...
9
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def _get_login_date(self, cr, uid, ids, name, args, context=None): res = {} user_login_obj = self.pool['res.users.login'] for user_id in ids: login_ids = user_login_obj.search( cr, uid, [('user_id', '=', user_id)], limit=1, context=context) ...
12,294
[ 0.01674777641892433, 0.03260352835059166, 0.04785716161131859, 0.0021757057402282953, 0.040854763239622116, 0.020427381619811058, 0.03735356405377388, 0.04941820725798607, -0.007905574515461922, 0.04078786075115204, 0.004822512157261372, -0.0024307691492140293, 0.03405306860804558, 0.02228...
12
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def _login(self, db, login, password): if not password: return False user_id = False cr = self.pool.cursor() try: # check if user exists res = self.search(cr, SUPERUSER_ID, [('login', '=', login)]) if res: user_id = res[0] ...
12,295
[ 0.06275279819965363, 0.0532703772187233, 0.08693049848079681, -0.022477317601442337, -0.013702351599931717, 0.015824727714061737, 0.025257505476474762, 0.0017701974138617516, 0.015725435689091682, 0.038326866924762726, 0.014521514065563679, 0.00743451900780201, 0.03040829859673977, -0.0443...
11
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def main_run(args): with common.temporary_file() as tempfile_path: rc = common.run_command([ os.path.join(common.SRC_DIR, 'tools', 'checkperms', 'checkperms.py'), '--root', args.paths['checkout'], '--json', tempfile_path ]) with open(tempfile_path) as f: checkperms_results =...
12,296
[ -0.008742940612137318, -0.01220734603703022, 0.06849221885204315, -0.018937185406684875, -0.054166439920663834, 0.002203291282057762, 0.018550951033830643, 0.00033685777452774346, -0.0007878302712924778, 0.0021389189641922712, 0.009462741203606129, -0.007256523706018925, -0.02199194952845573...
12
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def setUp(self): super(BaseLimitTestSuite, self).setUp() self.time = 0.0 self.stubs.Set(limits.Limit, "_get_time", self._get_time) self.absolute_limits = {} def stub_get_project_quotas(context, project_id, usages=True): return dict((k, dict(limit=v)) ...
12,297
[ -0.01534536387771368, -0.007861046120524406, 0.07208051532506943, -0.039933107793331146, -0.04613655060529709, 0.02978658117353916, -0.0030970133375376463, -0.029610775411128998, 0.015282575972378254, 0.023168737068772316, 0.02591884694993496, -0.020669778808951378, -0.006203445140272379, ...
12
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class LimitsViewBuilderTest(test.TestCase): def setUp(self): super(LimitsViewBuilderTest, self).setUp() self.view_builder = views.limits.ViewBuilder() self.rate_limits = [{"URI": "*", "regex": ".*", "value": 10, ...
12,298
[ 0.01723780855536461, -0.033405594527721405, 0.02442784234881401, -0.042748723179101944, -0.0004094142932444811, 0.008905984461307526, -0.012403132393956184, 0.025158589705824852, 0.03687664493918419, -0.0038690464571118355, -0.03228337690234184, -0.01653316058218479, -0.015671921893954277, ...
7
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def test_response_to_delays_usernames(self): delay = self._request("GET", "/delayed", "user1") self.assertEqual(delay, None) delay = self._request("GET", "/delayed", "user2") self.assertEqual(delay, None) delay = self._request("GET", "/delayed", "user1") self.assertEqua...
12,299
[ 0.03871278464794159, 0.019429851323366165, 0.0806087851524353, -0.04277750477194786, -0.02720423974096775, 0.0544084794819355, -0.014104087837040424, -0.01927069015800953, 0.06268483400344849, 0.008282478898763657, 0.027938827872276306, -0.03388898819684982, 0.004692180547863245, -0.016871...
17
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class LimitsXMLSerializationTest(test.TestCase): def test_xml_declaration(self): serializer = limits.LimitsTemplate() fixture = {"limits": { "rate": [], "absolute": {}}} output = serializer.serialize(fixture) has_dec = output.startswith("<?xml ...
12,300
[ -0.011362893506884575, -0.03499545902013779, 0.056432995945215225, -0.021162372082471848, -0.06223638728260994, 0.01709749549627304, 0.00568770058453083, -0.03422000631690025, -0.0031377719715237617, 0.008980250917375088, 0.03419499099254608, 0.007685743737965822, -0.008342377841472626, -0...
8
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def setUp(self): super(LimitsViewBuilderTest, self).setUp() self.view_builder = views.limits.ViewBuilder() self.rate_limits = [{"URI": "*", "regex": ".*", "value": 10, "verb": "POST", ...