convlab2.nlu.milu package¶

Subpackages¶

convlab2.nlu.milu.multiwoz package

Submodules¶

convlab2.nlu.milu.dai_f1_measure module¶

class convlab2.nlu.milu.dai_f1_measure.DialogActItemF1Measure¶

Bases: allennlp.training.metrics.metric.Metric

get_metric(reset: bool = False)¶

A Dict per label containing following the span based metrics: precision : float recall : float f1-measure : float

Additionally, an overall key is included, which provides the precision, recall and f1-measure for all spans.

reset()¶: Reset any accumulators or internal state.

convlab2.nlu.milu.dataset_reader module¶

class convlab2.nlu.milu.dataset_reader.MILUDatasetReader(context_size: int = 0, agent: Optional[str] = None, random_context_size: bool = True, token_delimiter: Optional[str] = None, token_indexers: Optional[Dict[str, allennlp.data.token_indexers.token_indexer.TokenIndexer]] = None, lazy: bool = False)¶

Bases: allennlp.data.dataset_readers.dataset_reader.DatasetReader

Reads instances from a pretokenised file where each line is in the following format:

WORD###TAG [TAB] WORD###TAG [TAB] …..

and converts it into a Dataset suitable for sequence tagging. You can also specify alternative delimiters in the constructor.

word_tag_delimiter: str, optional (default=``”###”``): The text that separates each WORD from its TAG.
token_delimiter: str, optional (default=``None``): The text that separates each WORD-TAG pair from the next pair. If None then the line will just be split on whitespace.
token_indexersDict[str, TokenIndexer], optional (default=``{“tokens”: SingleIdTokenIndexer()}``): We use this to define the input representation for the text. See TokenIndexer. Note that the output tags will always correspond to single token IDs based on how they are pre-tokenised in the data file.

text_to_instance(context_tokens: List[allennlp.data.tokenizers.token.Token], tokens: List[allennlp.data.tokenizers.token.Token], tags: Optional[List[str]] = None, intents: Optional[List[str]] = None, dialog_act: Optional[Dict[str, Any]] = None) → allennlp.data.instance.Instance¶: We take pre-tokenized input here, because we don’t have a tokenizer in this class.

convlab2.nlu.milu.evaluate module¶

The evaluate subcommand can be used to evaluate a trained model against a dataset and report any metrics calculated by the model.

convlab2.nlu.milu.evaluate.evaluate_from_args(args: argparse.Namespace) → Dict[str, Any]¶

convlab2.nlu.milu.model module¶

class convlab2.nlu.milu.model.MILU(vocab: allennlp.data.vocabulary.Vocabulary, text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, intent_encoder: Optional[allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder] = None, tag_encoder: Optional[allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder] = None, attention: Optional[allennlp.modules.attention.attention.Attention] = None, attention_function: Optional[allennlp.modules.similarity_functions.similarity_function.SimilarityFunction] = None, context_for_intent: bool = True, context_for_tag: bool = True, attention_for_intent: bool = True, attention_for_tag: bool = True, sequence_label_namespace: str = 'labels', intent_label_namespace: str = 'intent_labels', feedforward: Optional[allennlp.modules.feedforward.FeedForward] = None, label_encoding: Optional[str] = None, include_start_end_transitions: bool = True, crf_decoding: bool = False, constrain_crf_decoding: Optional[bool] = None, focal_loss_gamma: Optional[float] = None, nongeneral_intent_weight: float = 5.0, num_train_examples: Optional[float] = None, calculate_span_f1: Optional[bool] = None, dropout: Optional[float] = None, verbose_metrics: bool = False, initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object>, regularizer: Optional[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator] = None)¶

Bases: allennlp.models.model.Model

The MILU encodes a sequence of text with a Seq2SeqEncoder, then performs multi-label classification for closed-class dialog act items and sequence labeling to predict a tag for each token in the sequence.

initializerInitializerApplicator, optional (default=``InitializerApplicator()``): Used to initialize the model parameters.
regularizerRegularizerApplicator, optional (default=``None``): If provided, will be used to calculate the regularization penalty during training.

decode(output_dict: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]¶: Converts the tag ids to the actual tags. output_dict["tags"] is a list of lists of tag_ids, so we use an ugly nested list comprehension.

forward(context_tokens: Dict[str, torch.LongTensor], tokens: Dict[str, torch.LongTensor], tags: Optional[torch.LongTensor] = None, intents: Optional[torch.LongTensor] = None, metadata: Optional[List[Dict[str, Any]]] = None, **kwargs) → Dict[str, torch.Tensor]¶

get_metrics(reset: bool = False) → Dict[str, float]¶: Returns a dictionary of metrics. This method will be called by allennlp.training.Trainer in order to compute and use model metrics for early stopping and model serialization. We return an empty dictionary here rather than raising as it is not required to implement metrics for a new model. A boolean reset parameter is passed, as frequently a metric accumulator will have some state which should be reset between epochs. This is also compatible with Metrics should be populated during the call to ``forward`, with the Metric handling the accumulation of the metric until this method is called.

get_predicted_tags(sequence_logits: torch.Tensor) → torch.Tensor¶: Does a simple position-wise argmax over each token, converts indices to string labels, and adds a "tags" key to the dictionary with the result.

convlab2.nlu.milu.multilabel_f1_measure module¶

class convlab2.nlu.milu.multilabel_f1_measure.MultiLabelF1Measure(vocabulary: allennlp.data.vocabulary.Vocabulary, namespace: str = 'intent_labels', ignore_classes: Optional[List[str]] = None, coarse: bool = True)¶

Bases: allennlp.training.metrics.metric.Metric

get_metric(reset: bool = False)¶

A Dict per label containing following the span based metrics: precision : float recall : float f1-measure : float

Additionally, an overall key is included, which provides the precision, recall and f1-measure for all spans.

reset()¶: Reset any accumulators or internal state.

convlab2.nlu.milu.train module¶

The train subcommand can be used to train a model. It requires a configuration file and a directory in which to write the results.

convlab2.nlu.milu.train.train_model(params: allennlp.common.params.Params, serialization_dir: str, file_friendly_logging: bool = False, recover: bool = False, force: bool = False) → allennlp.models.model.Model¶

Trains the model specified in the given Params object, using the data and training parameters also specified in that object, and saves the results in serialization_dir.

paramsParams: A parameter object specifying an AllenNLP Experiment.
serialization_dirstr: The directory in which to save results and logs.
file_friendly_loggingbool, optional (default=False): If True, we add newlines to tqdm output, even on an interactive terminal, and we slow down tqdm’s output to only once every 10 seconds.
recoverbool, optional (default=False): If True, we will try to recover a training run from an existing serialization directory. This is only intended for use when something actually crashed during the middle of a run. For continuing training a model on new data, see the fine-tune command.
forcebool, optional (default=False): If True, we will overwrite the serialization directory if it already exists.

best_model: Model: The model with the best epoch weights.

convlab2.nlu.milu.train.train_model_from_args(args: argparse.Namespace)¶: Just converts from an argparse.Namespace object to string paths.

convlab2.nlu.milu.train.train_model_from_file(parameter_filename: str, serialization_dir: str, overrides: str = '', file_friendly_logging: bool = False, recover: bool = False, force: bool = False) → allennlp.models.model.Model¶

A wrapper around train_model() which loads the params from a file.

parameter_filenamestr: A json parameter file specifying an AllenNLP experiment.
serialization_dirstr: The directory in which to save results and logs. We just pass this along to train_model().
overridesstr: A JSON string that we will use to override values in the input parameter file.
file_friendly_loggingbool, optional (default=False): If True, we make our output more friendly to saved model files. We just pass this along to train_model().
recover``bool`, optional (default=False): If True, we will try to recover a training run from an existing serialization directory. This is only intended for use when something actually crashed during the middle of a run. For continuing training a model on new data, see the fine-tune command.
forcebool, optional (default=False): If True, we will overwrite the serialization directory if it already exists.