Source code for pytext.metric_reporters.word_tagging_metric_reporter

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from collections import Counter
from typing import List

from pytext.common.constants import DatasetFieldName, Stage
from import CommonMetadata
from pytext.metrics.intent_slot_metrics import (
from pytext.utils.data_utils import parse_slot_string
from pytext.utils.test_utils import merge_token_labels_to_slot

from .channel import Channel, ConsoleChannel, FileChannel
from .metric_reporter import MetricReporter

[docs]def get_slots(word_names): slots = { Node(label=slot.label, span=Span(slot.start, slot.end)) for slot in parse_slot_string(word_names) } return Counter(slots)
[docs]class WordTaggingMetricReporter(MetricReporter): def __init__( self, label_names: List[str], use_bio_labels: bool, channels: List[Channel] ) -> None: super().__init__(channels) self.label_names = label_names self.use_bio_labels = use_bio_labels
[docs] @classmethod def from_config(cls, config, meta: CommonMetadata): return cls(,, [ConsoleChannel(), FileChannel((Stage.TEST,), config.output_path)], )
[docs] def calculate_loss(self): total_loss = n_words = pos = 0 for loss, batch_size in zip(self.all_loss, self.batch_size): num_words_in_batch = sum( self.all_context["seq_lens"][pos : pos + batch_size] ) pos = pos + batch_size total_loss += loss * num_words_in_batch n_words += num_words_in_batch return total_loss / float(n_words)
[docs] def process_pred(self, pred: List[int]) -> List[str]: """pred is a list of token label index """ return [self.label_names[p] for p in pred]
[docs] def calculate_metric(self): return compute_prf1_metrics( [ NodesPredictionPair( get_slots( merge_token_labels_to_slot( token_range, self.process_pred(pred[0:seq_len]), self.use_bio_labels, ) ), get_slots(slots_label), ) for pred, seq_len, token_range, slots_label in zip( self.all_preds, self.all_context[DatasetFieldName.SEQ_LENS], self.all_context[DatasetFieldName.TOKEN_RANGE], self.all_context[DatasetFieldName.RAW_WORD_LABEL], ) ] )[1]
[docs] @staticmethod def get_model_select_metric(metrics): return metrics.micro_scores.f1