How to use dm_func method in hypothesis
Best Python code snippet using hypothesis
commons.py
Source:commons.py 
...151 errors = []152 for (image, recon) in zip(images, decoded):153 # compute the mean squared error between the ground-truth image154 # and the reconstructed image, then add it to our list of errors155 mse = dm_func(image, recon)156 errors.append(mse)157 errors_sorted = np.argsort(errors)[::-1]158 # loop over our number of output samples159 for y in range(0, samples):160 outputs = None161 for x in range(0, samples):162 i = y * samples + x163 if i >= gt.shape[0]:164 original = np.full(gt[0].shape, 0)165 recon = original166 i_sorted = 0167 else:168 # grab the original image and reconstructed image169 i_sorted = errors_sorted[i]170 original = (gt[i_sorted] * 255).astype("uint8")171 recon = (decoded[i_sorted] * 255).astype("uint8")172 # stack the original and reconstructed image side-by-side173 output = np.hstack([original, recon])174 v = "" if i >= gt.shape[0] else ' %0.6f' % errors[errors_sorted[i]]175 color = 255176 if marked_first_half and i_sorted < gt.shape[0]/2:177 color = 128178 text = np.expand_dims(draw_text(v, color, width=decoded.shape[1]*2), axis=-1)179 output = np.vstack([output, text])180 # if the outputs array is empty, initialize it as the current181 # side-by-side image display182 if outputs is None:183 outputs = output184 # otherwise, vertically stack the outputs185 else:186 outputs = np.vstack([outputs, output])187 if outputs2 is None:188 outputs2 = outputs189 # otherwise, horizontally stack the outputs190 else:191 outputs2 = np.hstack([outputs2, outputs])192 # return the output images193 return outputs2, errors194def prepare_dataset(args, augmentation=False):195 if args["kind"] == "mnist":196 from tensorflow.keras.datasets import mnist197 print("[INFO] loading MNIST dataset...")198 ((train_set, trainY), (unused_set, unused_set2)) = mnist.load_data()199 else:200 from dataset_loader import load_dataset201 print("[INFO] loading CREDO dataset...")202 train_set, trainY = load_dataset()203 # build our unsupervised dataset of images with a small amount of204 # contamination (i.e., anomalies) added into it205 print("[INFO] creating unsupervised dataset...")206 images, anomalies = build_unsupervised_dataset(train_set, trainY, kind=args["kind"])207 # add a channel dimension to every image in the dataset, then scale208 # the pixel intensities to the range [0, 1]209 images = np.expand_dims(images, axis=-1)210 images = images.astype("float32") / 255.0211 anomalies = np.expand_dims(anomalies, axis=-1)212 anomalies = anomalies.astype("float32") / 255.0213 # construct the training and testing split214 (train_set, test_set) = train_test_split(images, test_size=0.2)215 if augmentation:216 train_set = do_augmentation(train_set)217 (train_set, validation_set) = train_test_split(train_set, test_size=0.2)218 # prepare test set219 max_test = min(anomalies.shape[0], test_set.shape[0])220 test_set = np.vstack([anomalies[0:max_test], test_set[0:max_test]])221 return train_set, validation_set, test_set222def original_autoencoder(size=60, kl=False):223 from pyimagesearch.convautoencoder import ConvAutoencoder224 (encoder, decoder, autoencoder) = ConvAutoencoder.build(size, size, 1)225 opt = tf.keras.optimizers.Adam(learning_rate=INIT_LR, decay=INIT_LR / EPOCHS)226 autoencoder.compile(loss="mse", optimizer=opt, metrics=['kullback_leibler_divergence' if kl else 'accuracy'])227 return autoencoder228def train_or_cache(train_set, autoencoder, fncache=None, force_train=False, epochs=EPOCHS, batch_size=BS, shuffle=False, validation_set=None, kl=False):229 from os.path import exists230 from keras.models import load_model231 import matplotlib.pyplot as plt232 fn = fncache # 'cache/%s.h5' % str(fncache)233 if fncache is not None and exists(fn) and not force_train:234 print('Load from: %s' % fn)235 return load_model(fn)236 #(input_set, validation_set) = train_test_split(train_set, test_size=0.2)237 # train the convolutional autoencoder238 H = autoencoder.fit(239 train_set,240 train_set,241 shuffle=shuffle,242 validation_data=(validation_set, validation_set) if validation_set is not None else None,243 epochs=epochs,244 batch_size=batch_size245 )246 # r = autoencoder.evaluate(validation_set, validation_set)247 if fncache is not None:248 autoencoder.save(fn)249 print('Saved in: %s' % fn)250 N = np.arange(0, EPOCHS)251 plt.style.use("ggplot")252 plt.figure()253 plt.plot(N, H.history["loss"], label="train_loss")254 if validation_set is not None:255 plt.plot(N, H.history["val_loss"], label="val_loss")256 plt.title("Training Loss")257 plt.xlabel("Epoch #")258 plt.ylabel("Loss")259 plt.legend(loc="lower left")260 plt.savefig(fn.replace('.h5', '_loss.png'))261 if kl:262 N = np.arange(0, EPOCHS)263 plt.style.use("ggplot")264 plt.figure()265 plt.plot(N, H.history["kullback_leibler_divergence"], label="kullback_leibler_divergence")266 if validation_set is not None:267 plt.plot(N, H.history["val_kullback_leibler_divergence"], label="val_kullback_leibler_divergence")268 plt.title("Training Loss")269 plt.xlabel("Epoch #")270 plt.ylabel("Loss")271 plt.legend(loc="lower left")272 plt.savefig(fn.replace('.h5', '_kullback_leibler_divergence.png'))273 else:274 N = np.arange(0, EPOCHS)275 plt.style.use("ggplot")276 plt.figure()277 plt.plot(N, H.history["accuracy"], label="accuracy")278 if validation_set is not None:279 plt.plot(N, H.history["val_accuracy"], label="val_accuracy")280 plt.title("Training Loss")281 plt.xlabel("Epoch #")282 plt.ylabel("Loss")283 plt.legend(loc="lower left")284 plt.savefig(fn.replace('.h5', '_accuracy.png'))285 return autoencoder286def binarize_image(image, cutoff_qt_value=0):287 """288 Binaryzacja obrazka na podstawie podanego progu. DomyÅlnie, co nie jest caÅkiem czarne, jest biaÅe.289 :param image: źródlowy obrazek290 :param cutoff_qt_value: próg binaryzacji, 1 - wiÄksze od progu291 :return:292 """293 return np.where(image > cutoff_qt_value, 1, 0)294def cutoff_reconstruction_background(image, reconstruction):295 """296 Odcina tÅo od rekonstrukcji, która w oryginalnym obrazku byÅa tÅem.297 W oryginalnym obrazku mamy coÅ na tle czerni (0).298 :return: rekonstrukcja z obciÄtym tÅem299 """300 return binarize_image(image, 0) * reconstruction301def count_non_black_pixels(image):302 """303 Zwraca liczbÄ nieczarnych pikseli z obrazka.304 :param image: obrazek.305 :return: liczba nieczarnych pikseli.306 """307 return np.count_nonzero(image)308def compute_errors(image, recon, dm_func, normalize=True):309 """310 Obliczanie bÅÄdu.311 :param image: obrazek źródÅowy.312 :param recon: rekonstrukcja.313 :param dm_func: funkcja porównujÄ
ca, jako parametr przyjmuje (image, recon), zwraca skalar bÄdÄ
cy miarÄ
podobieÅstwa.314 :param normalize: jeÅli true, to dzieli wynik dm_func przez liczbÄ nieczarnych pikseli z image.315 :return: tablica316 """317 return dm_func(image, recon, normalize)318def prepare_for_histogram(images, reconstructions, dm_func, normalize=True, cutoff_background=False, binarize_for_compare=False):319 errors = []320 for (image, recon) in zip(images, reconstructions):321 try:322 if cutoff_background:323 recon = cutoff_reconstruction_background(image, recon)324 if binarize_for_compare:325 image = binarize_image(image)326 recon = binarize_image(recon)327 mse = compute_errors(image, recon, dm_func, normalize)328 errors.append(mse)329 except:330 errors.append(0)331 return errors...
test_joyent.py
Source:test_joyent.py
1from datetime import (2 datetime,3 timedelta,4)5import json6from mock import patch7from unittest import TestCase8from joyent import (9 Client,10 ISO_8601_FORMAT,11 parse_args,12)13def make_machine(state='running', hours=2):14 then = datetime.utcnow() - timedelta(hours=hours)15 return {16 'id': 'id',17 'state': state,18 'created': then.strftime(ISO_8601_FORMAT)}19def fake_list_machines(machine):20 def list_machines(machine_id=None):21 if machine_id:22 return machine23 else:24 return [machine]25 return list_machines26class JoyentTestCase(TestCase):27 def test_parse_args(self):28 args = parse_args(29 ['-d', '-v', '-u', 'sdc_url', '-a', 'account', '-k', 'key_id',30 '-p', 'key/path', 'list-machines'])31 self.assertEqual('sdc_url', args.sdc_url)32 self.assertEqual('account', args.account)33 self.assertEqual('key_id', args.key_id)34 self.assertEqual('key/path', args.key_path)35 self.assertTrue(args.dry_run)36 self.assertTrue(args.verbose)37class ClientTestCase(TestCase):38 def test_init(self):39 client = Client(40 'sdc_url', 'account', 'key_id', './key', 'manta_url',41 dry_run=True, verbose=True)42 self.assertEqual('sdc_url', client.sdc_url)43 self.assertEqual('account', client.account)44 self.assertEqual('key_id', client.key_id)45 self.assertEqual('./key', client.key_path)46 self.assertEqual(3, client.pause)47 self.assertTrue(client.dry_run)48 self.assertTrue(client.verbose)49 def test_list_machine_tags(self):50 client = Client(51 'sdc_url', 'account', 'key_id', './key', 'manta_url', pause=0)52 headers = {}53 content = json.dumps({'env': 'foo'})54 with patch.object(client, '_request', autospec=True,55 return_value=(headers, content)) as mock:56 tags = client._list_machine_tags('bar')57 mock.assert_called_once_with('/machines/bar/tags')58 self.assertEqual({'env': 'foo'}, tags)59 def test_delete_old_machines(self):60 machine = make_machine('stopped')61 client = Client(62 'sdc_url', 'account', 'key_id', './key', 'manta_url', pause=0)63 with patch.object(client, '_list_machines',64 side_effect=fake_list_machines(machine)) as lm_mock:65 with patch.object(client, '_list_machine_tags', autospec=True,66 return_value={}) as lmt_mock:67 with patch.object(client, '_delete_running_machine',68 autospec=True) as drm_mock:69 with patch.object(client, 'attempt_deletion',70 autospec=True) as rd_mock:71 client.delete_old_machines(1)72 lm_mock.assert_called_once_with()73 lmt_mock.assert_called_once_with('id')74 drm_mock.assert_called_once_with('id')75 self.assertEqual(0, rd_mock.call_count)76 def test_delete_old_machines_stuck_provisioning(self):77 machine = make_machine('provisioning')78 client = Client(79 'sdc_url', 'account', 'key_id', './key', 'manta_url', pause=0)80 with patch.object(client, '_list_machines', autospec=True,81 side_effect=fake_list_machines(machine)):82 with patch.object(client, '_list_machine_tags', autospec=True):83 with patch.object(client, '_delete_running_machine',84 autospec=True) as drm_mock:85 with patch.object(client, 'attempt_deletion',86 autospec=True) as rd_mock:87 client.delete_old_machines(1)88 self.assertEqual(0, drm_mock.call_count)89 rd_mock.assert_called_once_with([machine])90 def test_delete_old_machines_permanent(self):91 machine = make_machine('provisioning')92 client = Client(93 'sdc_url', 'account', 'key_id', './key', 'manta_url', pause=0)94 with patch.object(client, '_list_machines', autospec=True,95 side_effect=fake_list_machines(machine)):96 with patch.object(client, '_list_machine_tags', autospec=True,97 return_value={'permanent': 'true'}) as lmt_mock:98 with patch.object(client, '_delete_running_machine',99 autospec=True) as drm_mock:100 with patch.object(client, 'attempt_deletion',101 autospec=True) as rd_mock:102 client.delete_old_machines(1)103 lmt_mock.assert_called_once_with('id')104 self.assertEqual(0, drm_mock.call_count)105 self.assertEqual(0, rd_mock.call_count)106 def test_attempt_deletion(self):107 client = Client(108 'sdc_url', 'account', 'key_id', './key', 'manta_url', pause=0)109 with patch.object(client, 'delete_machine', autospec=True) as dm_func:110 all_success = client.attempt_deletion(['a', 'b'])111 self.assertIs(True, all_success)112 dm_func.assert_any_call('a')113 dm_func.assert_any_call('b')114 with patch.object(client, 'delete_machine', autospec=True,115 side_effect=[Exception, None]) as dm_func:116 all_success = client.attempt_deletion(['a', 'b'])117 self.assertIs(False, all_success)118 dm_func.assert_any_call('a')...
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