How to use reporter_class method in Slash

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run_experiment.py

Source:run_experiment.py

...245  model_class = choose_model_class(args)246  regimen_class = regimen.ProbeRegimen247  task = task_class(args)248  expt_dataset = dataset_class(args, task)249  expt_reporter = reporter_class(args, expt_dataset)250  expt_probe = probe_class(args)251  expt_model = model_class(args)252  expt_regimen = regimen_class(args)253  expt_loss = loss_class(args)254  if train_probe:255    print('Training probe...')256    run_train_probe(args, expt_probe, expt_dataset, expt_model, expt_loss, expt_reporter, expt_regimen)257  if report_results:258    print('Reporting results of trained probe...')259    run_report_results(args, expt_probe, expt_dataset, expt_model, expt_loss, expt_reporter, expt_regimen)260def setup_new_experiment_dir(args, yaml_args, reuse_results_path, embeddings_path):261  """Constructs a directory in which results and params will be stored.262  If reuse_results_path is not None, then it is reused; no new263  directory is constrcted....

configuration.py

Source:configuration.py

...217                                       ext=extension)218                file_path = osp.join(self.work_dir, filename)219                file_paths.append(file_path)220            modes = [self.mode for i in range(len(file_paths))]221            reporter = reporter_class(file_paths=file_paths, modes=modes,222                                  **self.reporter_partial_kwargs[idx])223            reporters.append(reporter)224        return reporters225    # TODO: remove, not used226    def _gen_work_mapper(self):227        """ """228        work_mapper = self._work_mapper_class(n_workers=self._default_n_workers)229        return work_mapper230    @property231    def reporters(self):232        """ """233        return deepcopy(self._reporters)234    @property235    def work_mapper(self):...

__init__.py

Source:__init__.py

1from argparse import ArgumentParser2import os3import random4from datetime import datetime5import shutil6import yaml7from tqdm import tqdm8import torch9import random10import numpy as np11from . import data, model, probe, regimen, reporter, task, loss12def choose_task_classes(args):13  """Chooses which task class to use based on config.14  Args:15    args: the global config dictionary built by yaml.16  Returns:17    A class to be instantiated as a task specification.18  """19  if args['probe']['task_name'] == 'parse-distance':20    task_class = task.ParseDistanceTask21    reporter_class = reporter.WordPairReporter22    if args['probe_training']['loss'] == 'L1':23      loss_class = loss.L1DistanceLoss24    else:25      raise ValueError("Unknown loss type for given probe type: {}".format(26        args['probe_training']['loss']))27  elif args['probe']['task_name'] == 'parse-depth':28    task_class = task.ParseDepthTask29    reporter_class = reporter.WordReporter30    if args['probe_training']['loss'] == 'L1':31      loss_class = loss.L1DepthLoss32    else:33      raise ValueError("Unknown loss type for given probe type: {}".format(34        args['probe_training']['loss']))35  elif args['probe']['task_name'] == 'part-of-speech':36    task_class = task.PartOfSpeechLabelTask37    reporter_class = reporter.WordReporter38    if args['probe_training']['loss'] == 'cross-entropy':39      loss_class = loss.CrossEntropyLoss40    else:41      raise ValueError("Unknown loss type for given probe type: {}".format(42        args['probe_training']['loss']))43  elif args['probe']['task_name'] == 'rand-prefix-label':44    task_class = task.RandomPrefixLabelTask45    reporter_class = reporter.WordReporter46    if args['probe_training']['loss'] == 'cross-entropy':47      loss_class = loss.CrossEntropyLoss48    else:49      raise ValueError("Unknown loss type for given probe type: {}".format(50        args['probe_training']['loss']))51  elif args['probe']['task_name'] == 'rand-word-label':52    task_class = task.RandomWordLabelTask53    reporter_class = reporter.WordReporter54    if args['probe_training']['loss'] == 'cross-entropy':55      loss_class = loss.CrossEntropyLoss56    else:57      raise ValueError("Unknown loss type for given probe type: {}".format(58        args['probe_training']['loss']))59  elif args['probe']['task_name'] == 'corrupted-part-of-speech':60    task_class = task.CorruptedPartOfSpeechLabelTask61    reporter_class = reporter.WordReporter62    if args['probe_training']['loss'] == 'cross-entropy':63      loss_class = loss.CrossEntropyLoss64    else:65      raise ValueError("Unknown loss type for given probe type: {}".format(66        args['probe_training']['loss']))67  elif args['probe']['task_name'] == 'corrupted-edge-labels':68    task_class = task.CorruptedEdgePositionTask69    reporter_class = reporter.WordPairLabelReporter70    if args['probe_training']['loss'] == 'cross-entropy':71      loss_class = loss.CrossEntropyLoss72    else:73      raise ValueError("Unknown loss type for given probe type: {}".format(74        args['probe_training']['loss']))75      #elif args['probe']['task_name'] == 'corrupted-parse-distance':76      #  task_class = task.CorruptedParseDistanceTask77      #  reporter_class = reporter.WordPairReporter78      #  if args['probe_training']['loss'] == 'L1':79      #    loss_class = loss.L1DistanceLoss80      #  else:81      #    raise ValueError("Unknown loss type for given probe type: {}".format(82      #      args['probe_training']['loss']))83  elif args['probe']['task_name'] == 'rand-parse-depth':84    task_class = task.RandomParseDepthTask85    reporter_class = reporter.WordReporter86    if args['probe_training']['loss'] == 'L1':87      loss_class = loss.L1DepthLoss88    else:89      raise ValueError("Unknown loss type for given probe type: {}".format(90        args['probe_training']['loss']))91  elif args['probe']['task_name'] == 'rand-linear-parse-depth':92    task_class = task.RandomLinearParseDepthTask93    reporter_class = reporter.WordReporter94    if args['probe_training']['loss'] == 'L1':95      loss_class = loss.L1DepthLoss96    else:97      raise ValueError("Unknown loss type for given probe type: {}".format(98        args['probe_training']['loss']))99  elif args['probe']['task_name'] == 'balanced-distance':100    task_class = task.BalancedBinaryTreeDistanceTask101    reporter_class = reporter.WordPairReporter102    if args['probe_training']['loss'] == 'L1':103      loss_class = loss.L1DistanceLoss104    else:105      raise ValueError("Unknown loss type for given probe type: {}".format(106        args['probe_training']['loss']))107  elif args['probe']['task_name'] == 'corrupted-parse-depth':108    task_class = task.CorruptedParseDepthTask109    reporter_class = reporter.WordReporter110    if args['probe_training']['loss'] == 'L1':111      loss_class = loss.L1DepthLoss112    else:113      raise ValueError("Unknown loss type for given probe type: {}".format(114        args['probe_training']['loss']))115  else:116    raise ValueError("Unknown probing task type: {}".format(117      args['probe']['task_name']))118  return task_class, reporter_class, loss_class119def choose_dataset_class(args):120  """Chooses which dataset class to use based on config.121  Args:122    args: the global config dictionary built by yaml.123  Returns:124    A class to be instantiated as a dataset.125  """126  if args['model']['model_type'] in {'ELMo-disk', 'ELMo-random-projection', 'ELMo-decay'}:127    dataset_class = data.ELMoDataset128  elif args['model']['model_type'] == 'BERT-disk':129    dataset_class = data.BERTDataset130  else:131    raise ValueError("Unknown model type for datasets: {}".format(132      args['model']['model_type']))133  return dataset_class134def choose_probe_class(args):135  """Chooses which probe and reporter classes to use based on config.136  Args:137    args: the global config dictionary built by yaml.138  Returns:139    A probe_class to be instantiated.140  """141  if args['probe']['task_signature'] == 'word':142    if args['probe']['psd_parameters']:143      return probe.OneWordPSDProbe144    elif 'probe_spec' not in args['probe'] or args['probe']['probe_spec']['probe_hidden_layers'] == 0:145      return probe.OneWordNonPSDProbe146    else:147      return probe.OneWordNNDepthProbe148  elif args['probe']['task_signature'] == 'word_pair':149    if args['probe']['psd_parameters']:150      return probe.TwoWordPSDProbe151    else:152      return probe.TwoWordNonPSDProbe153  elif args['probe']['task_signature'] == 'word_label':154    if 'probe_spec' not in args['probe'] or args['probe']['probe_spec']['probe_hidden_layers'] == 0:155      return probe.OneWordLinearLabelProbe156    else:157      if 'type' not in args['probe'] or args['probe']['type'] == 'none':158        return probe.OneWordNNLabelProbe159      elif args['probe'] or args['probe']['type'] == 'bayes':160        return probe.OneWordNNLabelProbeBayesCompression161      else:162        raise ValueError("Unknown Probe type: not 'none' and not 'bayesian'")163  elif args['probe']['task_signature'] == 'word_pair_label':164    if args['probe']['probe_spec']['probe_type'] == 'linear':165      return probe.TwoWordLinearLabelProbe166    if args['probe']['probe_spec']['probe_type'] == 'bilinear':167      return probe.TwoWordBilinearLabelProbe168    if args['probe']['probe_spec']['probe_type'] == 'MLP':169      return probe.TwoWordNNLabelProbe170    else:171      raise ValueError("Unknown TwoWordLabel type")172  else:173    raise ValueError("Unknown probe type (probe function signature): {}".format(174      args['probe']['task_signature']))175def choose_model_class(args):176  """Chooses which reporesentation learner class to use based on config.177  Args:178    args: the global config dictionary built by yaml.179  Returns:180    A class to be instantiated as a model to supply word representations.181  """182  if args['model']['model_type'] == 'ELMo-disk':183    return model.DiskModel184  elif args['model']['model_type'] == 'BERT-disk':185    return model.DiskModel186  elif args['model']['model_type'] == 'ELMo-random-projection':187    return model.ProjectionModel188  elif args['model']['model_type'] == 'ELMo-decay':189    return model.DecayModel190  elif args['model']['model_type'] == 'pytorch_model':191    raise ValueError("Using pytorch models for embeddings not yet supported...")192  else:193    raise ValueError("Unknown model type: {}".format(194      args['model']['model_type']))195def run_train_probe(args, probe, dataset, model, loss, reporter, regimen):196  """Trains a structural probe according to args.197  Args:198    args: the global config dictionary built by yaml.199          Describes experiment settings.200    probe: An instance of probe.Probe or subclass.201          Maps hidden states to linguistic quantities.202    dataset: An instance of data.SimpleDataset or subclass.203          Provides access to DataLoaders of corpora. 204    model: An instance of model.Model205          Provides word representations.206    reporter: An instance of reporter.Reporter207          Implements evaluation and visualization scripts.208  Returns:209    None; causes probe parameters to be written to disk.210  """211  regimen.train_until_convergence(probe, model, loss,212      dataset.get_train_dataloader(), dataset.get_dev_dataloader())213def run_report_results(args, probe, dataset, model, loss, reporter, regimen):214  """215  Reports results from a structural probe according to args.216  By default, does so only for dev set.217  Requires a simple code change to run on the test set.218  """219  probe_params_path = os.path.join(args['reporting']['root'],args['probe']['params_path'])220  probe.load_state_dict(torch.load(probe_params_path))221  probe.eval()222  dev_dataloader = dataset.get_dev_dataloader()223  dev_predictions = regimen.predict(probe, model, dev_dataloader)...

reporter.py

Source:reporter.py

...57        """58        [reporter_name, run_type] = extract_embedded_run_type(reporter_name, run_type)59        reporter_config = self.get_reporter_config(reporter_name, scan_type, run_type)60        reporter_class: ReporterMeta = self.reporters[reporter_name]61        reporter_instance = reporter_class(reporter_config)62        return reporter_instance63    def _add_reporters(self, reporters: dict):64        """adds new tools to tools registry"""65        for reporter_name in reporters:66            reporter = reporters[reporter_name]67            if issubclass(reporter, ReporterMeta):68                if not hasattr(self.reporters, reporter_name):69                    log_debug(f"-- installing reporter '{reporter_name}'")70                    self.reporters[reporter_name] = reporter71                else:72                    log_debug(f"-- skipping '{reporter_name}' already defined")73                    continue74            # TODO: else check public functions75            else:...

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