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        """...

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