Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use _set_ids method in autotest

Best Python code snippet using autotest_python

dataset.py

Source:dataset.py

1'''2Script containing functions related to dataset preparation3Author Courtesy: @ysbecca4Ref : https://github.com/ysbecca/Domain-Adaptation/blob/master/MNIST_USPS_Dataset/dataset.py5'''6import time7import mnist_usps_script as mnus8from datetime import timedelta9import numpy as np10class DataSet(object):11  def __init__(self, images, cls, set_ids):12    """ set_id = the dataset which the images belong to. 0 = MNIST; 1 = USPS """13    self._num_images = images.shape[0]14    # Convert shape from [num examples, rows, columns, depth]15    # to [num examples, rows*columns] (assuming depth == 1)16    # Convert from [0, 255] -> [0.0, 1.0].17    # images = images.astype(np.uint8)18    # images = np.multiply(images, 1.0 / 255.0)19    self._images = images20    self._cls = cls21    self._set_ids = set_ids22    # Set the labels based on cls. 23    labels = np.zeros((self._num_images, 10))24    for i, cls_ in enumerate(cls):25    	labels[i][cls_] = 126    self._labels = labels27    self._epochs_completed = 028    self._index_in_epoch = 029  @property30  def images(self):31    return self._images32  @property33  def cls(self):34    return self._cls35  @property36  def labels(self):37    return self._labels38  @property39  def set_ids(self):40    return self._set_ids41  @property42  def num_images(self):43    return self._num_images44  @property45  def epochs_completed(self):46    return self._epochs_completed47  def set_images(self, images):48    self._images = images49    50  def remove_from_set(self, selected):51  	''' Removes the images at selected indices from the dataset. '''52  	if(len(selected) != self._num_images):53  		print("ERROR: number of selected indices does not match dataset size.")54  		return55  	num_removed = np.count_nonzero(selected)56  	# Building new arrays instead of altering old ones for efficiency.57  	new_images, new_cls, new_set_ids, new_labels = [], [], [], []58  	for i, s in enumerate(selected):59  		if s == 0:60  			new_images.append(self._images[i])61  			new_cls.append(self._cls[i])62  			new_set_ids.append(self._set_ids[i])63  			new_labels.append(self._labels[i])64  	self._images = np.array(new_images)65  	self._cls = np.array(new_cls)66  	self._set_ids = np.array(new_set_ids)67  	self._labels = np.array(new_labels)68  	self._num_images -= num_removed69  def add_to_set(self, selected, dataset, preds):70  	''' Adds the images at the selected indices to the dataset and updates the params.'''71  	num_added = np.count_nonzero(selected)72  	max_preds = np.argmax(preds, axis=1) # Indices of the winning prediction (preds[max_preds[i]])73  	images_, cls_, set_ids_, labels_ = [], [], [], []74  	for i, s in enumerate(selected):75  		if s > 0:76  			images_.append(dataset.images[i])77  			cls_.append(max_preds[i]) # Add class as PREDICTED by the CNN78  			set_ids_.append(dataset.set_ids[i])79  			new_label = np.zeros(10)80  			new_label[max_preds[i]] = 181  			labels_.append(new_label)82  	# Add all the data.83  	self._images = np.concatenate((self._images, images_))84  	self._cls = np.concatenate((self._cls, cls_))85  	self._set_ids = np.concatenate((self._set_ids, set_ids_))86  	self._labels = np.concatenate((self._labels, labels_))87  	self._num_images += num_added88  	89  	# Reshuffle everything the same way.90  	perm = np.arange(self._num_images)91  	np.random.shuffle(perm)92  	self._images = self._images[perm]93  	self._cls = self._cls[perm]94  	self._set_ids = self._set_ids[perm]95  	self._labels = self._labels[perm]96  def next_batch(self, batch_size):97    """Return the next `batch_size` examples from this data set."""98    start = self._index_in_epoch99    self._index_in_epoch += batch_size100    if self._index_in_epoch > self._num_images:101      # Finished epoch102      self._epochs_completed += 1103      # # Shuffle the data (maybe)104      # perm = np.arange(self._num_images)105      # np.random.shuffle(perm)106      # self._images = self._images[perm]107      # self._labels = self._labels[perm]108      # Start next epoch109      start = 0110      self._index_in_epoch = batch_size111      assert batch_size <= self._num_images112    end = self._index_in_epoch113    return self._images[start:end], self._labels[start:end] # self._cls[start:end], self._set_ids[start:end]114def generate_combined_dataset(mnist_dataset, usps_dataset):115	class DataSets(object):116		pass117	dataset = DataSets()118	# Complete MNIST119	dataset.train = DataSet(np.concatenate( \120		(mnist_dataset.train.images, mnist_dataset.valid.images, mnist_dataset.test.images)), \121		np.concatenate((mnist_dataset.train.cls, mnist_dataset.valid.cls, mnist_dataset.test.cls)), \122		np.concatenate((mnist_dataset.train.set_ids, mnist_dataset.valid.set_ids, mnist_dataset.test.set_ids)), \123		)124	dataset.test = DataSet(usps_dataset.test.images, usps_dataset.test.cls, usps_dataset.test.set_ids)125	# Only used to keep track of remaining USPS images not yet added in bootstrap iterations.126	dataset.usps_train = DataSet(usps_dataset.train.images, usps_dataset.train.cls, usps_dataset.train.set_ids)127	return dataset128def read_datasets():129	class DataSets(object):130		pass131	mnist_datasets = DataSets()132	usps_datasets = DataSets()133	start_time = time.time()134	# mnist_x[i]; 0 = train, 1 = valid, 2 = test135	mnist_x, usps_x, mnist_y, usps_y = mnus.dataset(normalisation=True, store=False)136	mnist_datasets.train = DataSet(mnist_x[0], mnist_y[0], np.zeros((len(mnist_y[0]))))137	mnist_datasets.valid = DataSet(mnist_x[1], mnist_y[1], np.zeros((len(mnist_y[1]))))138	mnist_datasets.test = DataSet(mnist_x[2], mnist_y[2], np.zeros((len(mnist_y[2]))))139	# For now, the USPS train includes the validation set, so:140	# usps_x[i]; 0 = train, 1 = test141	usps_datasets.train = DataSet(usps_x[0], usps_y[0], np.ones((len(usps_y[0]))))142	usps_datasets.test = DataSet(usps_x[1], usps_y[1], np.ones((len(usps_y[1]))))143	end_time = time.time()144	time_dif = end_time - start_time145	# print("Time elapsed: " + str(timedelta(seconds=int(round(time_dif)))))...

identity_list.py

Source:identity_list.py

...34    def ids(self, ids: Union[List[str], bytes], sep: str = "\n"):35        if isinstance(ids, bytes):36            self._set_ids_from_bytes(ids, sep=sep)37        else:38            self._set_ids(ids)39    def _set_ids(self, ids: list):40        if ids:41            ids = [x for x in ids if x != ""]  # drop trailing empties42            self.collector.save({"name": self.key, "count": len(ids), "ids": ids})43            logger.info(44                "Saved %s ids to %s/%s", len(ids), self.model.__name__, self.key45            )46    def _set_ids_from_bytes(self, ids: bytes, sep: str = "\n"):47        self._set_ids(ids=ids.decode().split(sep))48    def delete(self):49        self.model.objects(name=self.key).delete()50        self.ids = []51class WellList(IdentityList):52    def __init__(self, key: str, hole_direction: str):53        if hole_direction == HoleDirection.H.name:54            self.model = WellMasterHorizontal55        elif hole_direction == HoleDirection.V.name:56            self.model = WellMasterVertical57        else:58            raise ValueError(59                f"Invalid value for hole_direction ({hole_direction}) -- Valid options are {HoleDirection.member_names()}"  # noqa60            )61        super().__init__(self.model, key)...

sets.py

Source:sets.py

1from abc import ABC, abstractmethod2import numpy as np3class SetGenerator(ABC):4    def __init__(self, n_instances):5        """6        Generates a training set for the classifiers.7        :param n_instances: <int> Amount of instances to consider.8        """9        self._n_instances = n_instances10        self._set_ids = None11    @abstractmethod12    def training_ids(self):13        pass14    @abstractmethod15    def oob_ids(self):16        pass17    def clear(self):18        """19        Clears the set ids.20        """21        self._set_ids = None22class SimpleSet(SetGenerator):23    def training_ids(self):24        """25        Generates the ids of the training instances.26        :return: <numpy array>27        """28        if self._set_ids is None:29            self._set_ids = np.array(range(self._n_instances))30        return self._set_ids31    def oob_ids(self):32        """33        Returns an empty array. No out-of-bag instances for SimpleSet.34        :return: <numpy array>35        """36        return np.array([])37class BaggingSet(SetGenerator):38    def training_ids(self):39        """40        Generates the ids of the training instances.41        :return: <numpy array>42        """43        if self._set_ids is None:44            self._set_ids = np.random.choice(self._n_instances, replace=True, size=self._n_instances)45        return self._set_ids46    def oob_ids(self):47        """48        Returns the ids for the out-of-bag set.49        :return: <numpy array>50        """...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.