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How to use suppress_output method in tox

Best Python code snippet using tox_python

__init__.py

Source:__init__.py

1"""2Contains possible interactions with the Apollo Organisms Module3"""4import json5from apollo.client import Client6from apollo.decorators import raise_error_decorator7class OrganismsClient(Client):8    CLIENT_BASE = '/organism/'9    @raise_error_decorator10    def add_organism(self, common_name, directory, blatdb=None, genus=None,11                     species=None, public=False, metadata=None, suppress_output=False):12        """13        Add an organism14        :type common_name: str15        :param common_name: Organism common name16        :type directory: str17        :param directory: Server-side directory18        :type blatdb: str19        :param blatdb: Server-side path to 2bit index of the genome for Blat20        :type genus: str21        :param genus: Genus22        :type species: str23        :param species: Species24        :type public: bool25        :param public: Should the organism be public or not26        :type metadata: str27        :param metadata: JSON formatted arbitrary metadata28        :type suppress_output: bool29        :param suppress_output: Suppress output of all organisms (true / false) (default false)30        :rtype: dict31        :return: a dictionary with information about the new organism32        """33        data = {34            'commonName': common_name,35            'directory': directory,36            'publicMode': public,37        }38        if blatdb is not None:39            data['blatdb'] = blatdb40        if genus is not None:41            data['genus'] = genus42        if species is not None:43            data['species'] = species44        if metadata is not None:45            if isinstance(metadata, dict):46                # Apollo wants a string47                metadata = json.dumps(metadata)48            data['metadata'] = metadata49        if suppress_output is not None and suppress_output is True:50            data['returnAllOrganisms'] = False51        response = self.post('addOrganism', data)52        # Apollo decides here that it would be nice to return information about53        # EVERY organism. LMAO.54        if type(response) is not list:55            return response56        if len(response) > 0:57            return [x for x in response if x['commonName'] == common_name][0]58        else:59            return data60    def update_organism(self, organism_id, common_name, directory, blatdb=None, species=None, genus=None, public=False,61                        no_reload_sequences=False, suppress_output=False):62        """63        Update an organism64        :type organism_id: str65        :param organism_id: Organism ID Number66        :type common_name: str67        :param common_name: Organism common name68        :type directory: str69        :param directory: Server-side directory70        :type blatdb: str71        :param blatdb: Server-side Blat directory for the organism72        :type genus: str73        :param genus: Genus74        :type species: str75        :param species: Species76        :type public: bool77        :param public: User's email78        :type no_reload_sequences: bool79        :param no_reload_sequences: Set this if you don't want Apollo to reload genome sequences (no change in genome sequence)80        :type suppress_output: bool81        :param suppress_output: Suppress output of all organisms (true / false) (default false)82        :rtype: dict83        :return: a dictionary with information about the updated organism84        """85        data = {86            'id': organism_id,87            'name': common_name,88            'directory': directory,89            'publicMode': public,90            'noReloadSequences': no_reload_sequences,91        }92        if blatdb is not None:93            data['blatdb'] = blatdb94        if genus is not None:95            data['genus'] = genus96        if species is not None:97            data['species'] = species98        if suppress_output is not None and suppress_output is True:99            data['returnAllOrganisms'] = False100        response = self.post('updateOrganismInfo', data)101        if type(response) is not list:102            return response103        if len(response) > 0:104            return [x for x in response if x['commonName'] == common_name][0]105        else:106            return self.show_organism(common_name)107    def get_organisms(self, common_name=None):108        """109        Get all organisms110        :type common_name: str111        :param common_name: Optionally filter on common name112        :rtype: list113        :return: Organism information114        """115        if common_name is None:116            orgs = self.post('findAllOrganisms', data={})117        else:118            orgs = self.post('findAllOrganisms', {'organism': common_name})119        return orgs120    def show_organism(self, common_name):121        """122        Get information about a specific organism.123        :type common_name: str124        :param common_name: Organism Common Name125        :rtype: dict126        :return: a dictionary containing the organism's information127        """128        orgs = self.get_organisms(common_name=common_name)129        if isinstance(orgs, list) and len(orgs) > 0:130            orgs = orgs[0]131        return orgs132    def delete_organism(self, organism_id, suppress_output=False):133        """134        Delete an organism135        :type organism_id: str136        :param organism_id: Organism ID Number137        :type suppress_output: bool138        :param suppress_output: Suppress return of all organisms (true / false) (default false)139        :rtype: list140        :return: A list of all remaining organisms141        """142        data = {143            'id': organism_id,144        }145        if suppress_output is not None and suppress_output is not False:146            data['returnAllOrganisms'] = False147        return self.post('deleteOrganism', data)148    def delete_features(self, organism_id):149        """150        Remove features of an organism151        :type organism_id: str152        :param organism_id: Organism ID Number153        :rtype: dict154        :return: an empty dictionary155        """156        return self.post('deleteOrganismFeatures', {'organism': organism_id})157    def get_sequences(self, organism_id):158        """159        Get the sequences for an organism160        :type organism_id: str161        :param organism_id: Organism ID Number162        :rtype: list of dict163        :return: The set of sequences associated with an organism164        """165        return self.post('getSequencesForOrganism', {'organism': organism_id})166    def get_organism_creator(self, organism_id):167        """168        Get the creator of an organism169        :type organism_id: str170        :param organism_id: Organism ID Number171        :rtype: dict172        :return: a dictionary containing user information173        """174        return self.post('getOrganismCreator', {'organism': organism_id})175    def update_metadata(self, organism_id, metadata):176        """177        Update the metadata for an existing organism.178        :type organism_id: str179        :param organism_id: Organism ID Number180        :type metadata: str181        :param metadata: Organism metadata. (Recommendation: use a structured format like JSON)182        :rtype: dict183        :return: An empty, useless dictionary184        """...

utils.py

Source:utils.py

1import torch2import torch.nn.functional as F3import torch.optim as optim4from torch.utils.data import random_split, DataLoader5from torchvision import datasets, transforms6torch.manual_seed(0)7data_dir = "data/mnist"8transforms_0 = transforms.Compose([transforms.ToTensor()])9train_dat_0_all = datasets.MNIST(10    data_dir, download=True, train=True, transform=transforms_011)12train_dat_0, val_dat_0 = random_split(train_dat_0_all, [54000, 6000])13test_dat_0 = datasets.MNIST(data_dir, train=False, transform=transforms_0)14train_loader_0 = DataLoader(train_dat_0, batch_size=64, shuffle=True)15val_loader_0 = DataLoader(val_dat_0, batch_size=1000, shuffle=False)16test_loader_0 = DataLoader(test_dat_0, batch_size=1000, shuffle=False)17transforms_1 = transforms.Compose(18    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]19)20train_dat_1_all = datasets.MNIST(data_dir, train=True, transform=transforms_1)21train_dat_1, val_dat_1 = random_split(train_dat_1_all, [54000, 6000])22test_dat_1 = datasets.MNIST(data_dir, train=False, transform=transforms_1)23train_loader_1 = DataLoader(train_dat_1, batch_size=64, shuffle=True)24val_loader_1 = DataLoader(val_dat_1, batch_size=1000, shuffle=False)25test_loader_1 = DataLoader(test_dat_1, batch_size=1000, shuffle=False)26tmp = iter(train_loader_1)27sample_inputs, sample_targets = next(tmp)28def train(29    model,30    dataloader,31    optimizer,32    l1_penalty_coef,33    l2_penalty_coef,34    suppress_output,35):36    total_loss = 0.037    correct = 038    for inputs, targets in dataloader:39        optimizer.zero_grad()40        outputs = model(inputs)41        loss = F.nll_loss(outputs, targets)42        total_loss += len(inputs) * loss.item()43        if l1_penalty_coef != 0.0:44            loss += l1_penalty_coef * model.l1_weight_penalty()45        if l2_penalty_coef != 0.0:46            loss += l2_penalty_coef * model.l2_weight_penalty()47        loss.backward()48        optimizer.step()49        with torch.no_grad():50            pred = outputs.argmax(dim=1, keepdim=True)51            correct += pred.eq(targets.view_as(pred)).sum().item()52    train_loss = total_loss / len(dataloader.dataset)53    train_accuracy = correct / len(dataloader.dataset)54    if not suppress_output:55        print(56            "Train set:\tAverage loss: {:.4f}, Accuracy: {:.4f}".format(57                train_loss, train_accuracy58            )59        )60    return train_loss, train_accuracy61def evaluate(model, dataloader, eval_type, suppress_output=True):62    total_loss = 0.063    correct = 064    with torch.no_grad():65        for inputs, targets in dataloader:66            outputs = model(inputs)67            total_loss += F.nll_loss(outputs, targets, reduction="sum")68            pred = outputs.argmax(dim=1, keepdim=True)69            correct += pred.eq(targets.view_as(pred)).sum().item()70    loss = total_loss / len(dataloader.dataset)71    accuracy = correct / len(dataloader.dataset)72    if not suppress_output:73        print(74            "{} set:\tAverage loss: {:.4f}, Accuracy: {:.4f}\n".format(75                eval_type, loss, accuracy76            )77        )78    return loss, accuracy79def run_experiment(80    model,81    optimizer,82    train_loader,83    val_loader,84    test_loader,85    n_epochs,86    l1_penalty_coef,87    l2_penalty_coef,88    suppress_output=True,89):90    train_losses = []91    train_accuracies = []92    val_losses = []93    val_accuracies = []94    for epoch in range(n_epochs):95        if not suppress_output:96            print("Epoch {}: training...".format(epoch))97        train_loss, train_accuracy = train(98            model=model,99            dataloader=train_loader,100            optimizer=optimizer,101            l1_penalty_coef=l1_penalty_coef,102            l2_penalty_coef=l2_penalty_coef,103            suppress_output=suppress_output,104        )105        val_loss, val_accuracy = evaluate(106            model=model,107            dataloader=val_loader,108            eval_type="Validation",109            suppress_output=suppress_output,110        )111        train_losses.append(train_loss)112        train_accuracies.append(train_accuracy)113        val_losses.append(val_loss)114        val_accuracies.append(val_accuracy)115    final_test_loss, final_test_accuracy = evaluate(116        model=model,117        dataloader=test_loader,118        eval_type="Test",119        suppress_output=suppress_output,120    )121    return {122        "train_losses": train_losses,123        "train_accuracies": train_accuracies,124        "val_losses": val_losses,125        "val_accuracies": val_accuracies,126        "final_test_loss": final_test_loss,127        "final_test_accuracy": final_test_accuracy,...

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