feat: add trainable document classifier

[LucasDedieu]

Description

add trainable document classifier

Checklist

• If this PR is a bug fix, the bug is documented in the test suite.
• Changes were documented in the changelog (pending section).
• If necessary, changes were made to the documentation (eg new pipeline).

[github-actions]

Docs preview URL

https://edsnlp-classifhead.vercel.app/

[github-actions]

Coverage Report

Name	Stmts	Miss	∆ Miss	Cover
edsnlp/pipes/trainable/doc_classifier/doc_classifier.py New missing coverage at lines 57-61 ! ): - super().__init__() - self.alpha = alpha - self.gamma = gamma - self.reduction = reduction New missing coverage at lines 66-80 ! """ - ce_loss = torch.nn.functional.cross_entropy( - inputs, targets, weight=self.alpha, reduction="none" - ) - - pt = torch.exp(-ce_loss) - - focal_loss = (1 - pt) ** self.gamma * ce_loss - - if self.reduction == "mean": - return focal_loss.mean() - elif self.reduction == "sum": - return focal_loss.sum() - else: - return focal_loss New missing coverage at lines 133-141 ! if self.hidden_size: - self.hidden_layer = torch.nn.Linear(self.embedding_size, self.hidden_size) - self.activation = {"relu": nn.ReLU(), "gelu": nn.GELU(), "silu": nn.SiLU()}[ - self.activation_mode - ] - if self.layer_norm: - self.norm = nn.LayerNorm(self.hidden_size) - self.dropout = nn.Dropout(self.dropout_rate) - self.classifier = torch.nn.Linear(self.hidden_size, self.num_classes) else: New missing coverage at lines 149-159 ! """ - total_samples = sum(freq_dict.values()) - - weights = torch.zeros(len(self.label2id)) - - for label, freq in freq_dict.items(): - if label in self.label2id: - weight = total_samples / (len(self.label2id) * freq) - weights[self.label2id[label]] = weight - - return weights New missing coverage at lines 166-199 ! def post_init(self, gold_data: Iterable[Doc], exclude: Set[str]): - print("post_init") - if not self.label2id: - if self.labels is not None: - labels = set(self.labels) - else: - labels = set() - for doc in gold_data: ... - self.loss_fn = torch.nn.CrossEntropyLoss(weight=weight_tensor) - elif self.loss == "focal": - self.loss_fn = FocalLoss(alpha=weight_tensor, gamma=2.0, reduction="mean") - else: - raise ValueError(f"Unknown loss: {self.loss}") - print("loss finie") - super().post_init(gold_data, exclude=exclude) New missing coverage at lines 205-216 ! """Preprocess document with target labels for training.""" - preps = self.preprocess(doc) - label = getattr(doc._, self.label_attr, None) - if label is None: - raise ValueError( - f"Document does not have a gold label in 'doc._.{self.label_attr}'" - ) - if isinstance(label, str) and self.label2id: - if label not in self.label2id: - raise ValueError(f"Label '{label}' not in label2id mapping.") - label = self.label2id[label] - return { **preps, New missing coverage at line 224 ! if "targets" in batch: - batch_input["targets"] = torch.stack(batch["targets"]) return batch_input New missing coverage at lines 235-240 ! if self.hidden_size: - x = self.hidden_layer(x) - x = self.activation(x) - if self.layer_norm: - x = self.norm(x) - x = self.dropout(x) logits = self.classifier(x) New missing coverage at lines 244-247 ! if "targets" in batch: - loss = self.loss_fn(logits, batch["targets"]) - output["loss"] = loss - output["labels"] = None else: New missing coverage at line 259 ! if self.id2label and isinstance(label, int): - label = self.id2label.get(label, label) setattr(doc._, self.label_attr, label) New missing coverage at lines 265-281 ! """Save classifier state to disk.""" - repr_id = object.__repr__(self) - if repr_id in exclude: - return - exclude.add(repr_id) - os.makedirs(path, exist_ok=True) - data_path = path / "label_attr.pkl" - with open(data_path, "wb") as f: - pickle.dump( - { - "label_attr": self.label_attr, - "label2id": self.label2id, - "id2label": self.id2label, - }, - f, - ) - return super().to_disk(path, exclude=exclude) New missing coverage at lines 285-293 ! """Load classifier from disk.""" - data_path = path / "label_attr.pkl" - with open(data_path, "rb") as f: - data = pickle.load(f) - obj = super().from_disk(path, **kwargs) - obj.label_attr = data.get("label_attr", "label") - obj.label2id = data.get("label2id", {}) - obj.id2label = data.get("id2label", {}) - return obj	161	87	114	45.96%
edsnlp/metrics/doc_classif.py New missing coverage at lines 35-133 ! """ - examples = make_examples(examples) - if filter_expr is not None: - filter_fn = eval(f"lambda doc: {filter_expr}") - examples = [eg for eg in examples if filter_fn(eg.reference)] - - pred_labels = [] - gold_labels = [] ... - "f": macro_f1, - "p": macro_precision, - "r": macro_recall, - "support": len(labels), - "classes": len(labels), - } - return results New missing coverage at lines 143-146 ! ): - self.label_attr = label_attr - self.micro_key = micro_key - self.filter_expr = filter_expr New missing coverage at line 148 ! def __call__(self, *examples): - return doc_classification_metric( examples,	52	42	102	19.23%
edsnlp/tune.py Was already missing at line 169 ) - except RuntimeError as e: if "zero total variance" in str(e): # pragma: no cover New missing coverage at lines 597-599 ! return n_trials - except ValueError: - return 0 Was already missing at line 684 else: - n_trials = compute_n_trials( gpu_hours, compute_time_per_trial(study, ema=True)	289	4	2	98.62%
TOTAL	12015	368	218	96.94%

Files without new missing coverage

Name	Stmts	Miss	∆ Miss	Cover
edsnlp/utils/torch.py Was already missing at line 102 def load_pruned_obj(obj, _): - return obj Was already missing at line 118 def save_align_devices_hook(pickler, obj): - pickler.save_reduce(load_align_devices_hook, (obj.__dict__,), obj=obj) Was already missing at lines 121-128 def load_align_devices_hook(state): - state["execution_device"] = MAP_LOCATION ... - AlignDevicesHook = None Was already missing at line 143 if torch.Tensor in copyreg.dispatch_table: - old_dispatch[torch.Tensor] = copyreg.dispatch_table[torch.Tensor] copyreg.pickle(torch.Tensor, reduce_empty)	83	9	0	89.16%
edsnlp/utils/span_getters.py Was already missing at lines 78-80 if span_getter is None: - yield doc[:], None - return if callable(span_getter): Was already missing at lines 81-83 if callable(span_getter): - yield from span_getter(doc) - return for key, span_filter in span_getter.items(): Was already missing at line 85 if key == "*": - candidates = ( (span, group) for group in doc.spans.values() for span in group Was already missing at lines 94-97 else: - for span, group in candidates: - if span.label_ in span_filter: - yield span, group Was already missing at line 101 if callable(span_setter): - span_setter(doc, matches) else: Was already missing at line 132 if callable(value): - return value if isinstance(value, str): Was already missing at line 181 elif isinstance(v, str): - new_value[k] = [v] elif isinstance(v, list) and all(isinstance(i, str) for i in v):	231	11	0	95.24%
edsnlp/utils/resources.py Was already missing at line 33 if not verbs: - return conjugated_verbs	24	1	0	95.83%
edsnlp/utils/numbers.py Was already missing at line 34 else: - string = s string = string.lower().strip() Was already missing at lines 38-41 return int(string) - except ValueError: - parsed = DIGITS_MAPPINGS.get(string, None) - return parsed	16	4	0	75.00%
edsnlp/utils/filter.py Was already missing at line 206 if isinstance(label, int): - return [span for span in spans if span.label == label] else:	74	1	0	98.65%
edsnlp/training/trainer.py Was already missing at line 57 if result is None: - result = {} if isinstance(x, dict): Was already missing at lines 365-371 if self.sub_batch_size and self.sub_batch_size[1] == "splits": - data = data.batchify( ... - data = data.map(lambda b: [nlp.collate(sb, device=device) for sb in b]) elif self.sub_batch_size: Was already missing at lines 885-892 raise - except Exception: ... - raise Was already missing at lines 919-921 ) > grad_max_dev * math.sqrt(grad_var): - spike = True - spikes += 1 else: Was already missing at line 928 if spike and grad_dev_policy == "clip_mean": - torch.nn.utils.clip_grad_norm_( grad_params, grad_mean, norm_type=2 Was already missing at line 932 elif spike and grad_dev_policy == "clip_threshold": - torch.nn.utils.clip_grad_norm_( grad_params,	335	12	0	96.42%
edsnlp/training/loggers.py Was already missing at line 109 if col not in values and col != "step": - row.append("") else: Was already missing at line 278 def tracker(self): - return self.printer Was already missing at lines 369-388 """ - env_logging_dir = os.environ.get("AIM_LOGGING_DIR", None) ... - accelerate.tracking.logger.debug( f"Initialized Aim run {self.writer.hash} in project {project_name}" Was already missing at lines 392-394 def log(self, values: dict, step: Optional[int], **kwargs): - values = flatten_dict(values) - return super().log(values, step, **kwargs)	140	11	0	92.14%
edsnlp/reducers.py Was already missing at line 115 if not hasattr(module, "__file__"): - return True if module.__file__ is None: Was already missing at line 117 if module.__file__ is None: - return False # Hack to avoid copying the full module dict	67	2	0	97.01%
edsnlp/processing/spark.py Was already missing at line 50 getActiveSession = SparkSession.getActiveSession - except AttributeError:	47	1	0	97.87%
edsnlp/processing/multiprocessing.py Was already missing at lines 393-398 self.on_stop() - except BaseException as e: ... - self.main_control_queue.put(e) finally: Was already missing at lines 402-404 pass - except StopSignal: - pass for name, queue in self.consumer_queues(stage): Was already missing at line 542 while schedule[task_idx] is None: - task_idx = (task_idx + 1) % len(schedule) Was already missing at lines 606-608 if isinstance(docs, StreamSentinel): - self.active_batches[stage].append([None, None, None, docs]) - continue batch_id = str(hash(tuple(id(x) for x in docs)))[-8:] + "-" + self.uid Was already missing at lines 1121-1127 if out[0].kind == requires_sentinel: - missing_sentinels -= 1 ... - missing_sentinels = len(self.cpu_worker_names) continue	626	14	0	97.76%
edsnlp/processing/deprecated_pipe.py Was already missing at lines 207-209 def converter(doc): - res = results_extractor(doc) - return ( [{"note_id": doc._.note_id, **row} for row in res]	57	2	0	96.49%
edsnlp/pipes/trainable/span_linker/span_linker.py Was already missing at lines 402-404 if self.reference_mode == "synonym": - embeds = embeds.to(new_lin.weight) - new_lin.weight.data = embeds else:	173	2	0	98.84%
edsnlp/pipes/trainable/span_classifier/span_classifier.py Was already missing at line 379 if not all(keep_bindings): - logger.warning( "Some attributes have no labels or values and have been removed:"	164	1	0	99.39%
edsnlp/pipes/trainable/ner_crf/ner_crf.py Was already missing at line 301 if self.labels is not None and not self.infer_span_setter: - return Was already missing at lines 309-311 if callable(self.target_span_getter): - for span in get_spans(doc, self.target_span_getter): - inferred_labels.add(span.label_) else:	173	3	0	98.27%
edsnlp/pipes/trainable/layers/crf.py Was already missing at line 21 # out: 2 * N * O - return (log_A.unsqueeze(-1) + log_B.unsqueeze(-3)).logsumexp(-2) Was already missing at line 29 # out: 2 * N * O - return (log_A.unsqueeze(-1) + log_B.unsqueeze(-3)).max(-2) Was already missing at line 98 if learnable_transitions: - self.transitions = torch.nn.Parameter( torch.zeros_like(forbidden_transitions, dtype=torch.float) Was already missing at line 108 if learnable_transitions and with_start_end_transitions: - self.start_transitions = torch.nn.Parameter( torch.zeros(num_tags, dtype=torch.float) Was already missing at line 117 if learnable_transitions and with_start_end_transitions: - self.end_transitions = torch.nn.Parameter( torch.zeros(num_tags, dtype=torch.float)	137	5	0	96.35%
edsnlp/pipes/trainable/embeddings/transformer/transformer.py Was already missing at line 166 if quantization is not None: - kwargs["quantization_config"] = quantization Was already missing at line 189 if self.cls_token_id is None: - [self.cls_token_id] = self.tokenizer.convert_tokens_to_ids( [self.tokenizer.special_tokens_map["bos_token"]] Was already missing at line 193 if self.sep_token_id is None: - [self.sep_token_id] = self.tokenizer.convert_tokens_to_ids( [self.tokenizer.special_tokens_map["eos_token"]]	168	3	0	98.21%
edsnlp/pipes/qualifiers/reported_speech/reported_speech.py Was already missing at lines 24-28 return "REPORTED" - elif token._.rspeech is False: - return "DIRECT" - else: - return None	100	3	0	97.00%
edsnlp/pipes/qualifiers/negation/negation.py Was already missing at line 28 else: - return None	101	1	0	99.01%
edsnlp/pipes/qualifiers/hypothesis/hypothesis.py Was already missing at line 27 else: - return None	98	1	0	98.98%
edsnlp/pipes/qualifiers/history/history.py Was already missing at lines 26-32 def history_getter(token: Union[Token, Span]) -> Optional[str]: - if token._.history is True: - return "ATCD" - elif token._.history is False: - return "CURRENT" - else: - return None Was already missing at lines 353-359 ) - except ValueError: ... - note_datetime = None Was already missing at lines 368-374 ) - except ValueError: ... - birth_datetime = None Was already missing at lines 440-443 ) - except ValueError as e: - absolute_date = None - logger.warning( "In doc {}, the following date {} raises this error: {}. "	180	14	0	92.22%
edsnlp/pipes/qualifiers/family/family.py Was already missing at line 27 else: - return None	83	1	0	98.80%
edsnlp/pipes/ner/tnm/model.py Was already missing at line 147 def __str__(self): - return self.norm() Was already missing at line 171 ) - exclude_unset = skip_defaults	112	2	0	98.21%
edsnlp/pipes/ner/scores/sofa/sofa.py Was already missing at line 32 if not assigned: - continue if assigned.get("method_max") is not None: Was already missing at line 40 else: - method = "Non précisée"	25	2	0	92.00%
edsnlp/pipes/ner/scores/elston_ellis/patterns.py Was already missing at line 26 if x <= 5: - return 1 Was already missing at lines 32-36 else: - return 3 - - except ValueError: - return None	21	4	0	80.95%
edsnlp/pipes/ner/scores/charlson/patterns.py Was already missing at lines 21-23 return int(extracted_score) - except ValueError: - return None	13	2	0	84.62%
edsnlp/pipes/ner/disorders/solid_tumor/solid_tumor.py Was already missing at lines 131-137 for span in spans: - span.label_ = "solid_tumor" ... - yield span	38	6	0	84.21%
edsnlp/pipes/ner/disorders/peripheral_vascular_disease/peripheral_vascular_disease.py Was already missing at line 108 if "peripheral" not in span._.assigned.keys(): - continue	16	1	0	93.75%
edsnlp/pipes/ner/disorders/diabetes/diabetes.py Was already missing at line 131 # Mostly FP - continue Was already missing at line 134 elif self.has_far_complications(span): - span._.status = 2 Was already missing at line 145 if next(iter(self.complication_matcher(context)), None) is not None: - return True return False	30	3	0	90.00%
edsnlp/pipes/ner/disorders/connective_tissue_disease/connective_tissue_disease.py Was already missing at line 104 # Huge change of FP / Title section - continue	15	1	0	93.33%
edsnlp/pipes/ner/disorders/ckd/ckd.py Was already missing at lines 121-124 dfg_value = float(dfg_span.text.replace(",", ".").strip()) - except ValueError: - logger.trace(f"DFG value couldn't be extracted from {dfg_span.text}") - return False	30	3	0	90.00%
edsnlp/pipes/ner/disorders/cerebrovascular_accident/cerebrovascular_accident.py Was already missing at lines 112-114 if span._.source == "ischemia": - if "brain" not in span._.assigned.keys(): - continue	18	2	0	88.89%
edsnlp/pipes/ner/adicap/models.py Was already missing at line 15 def norm(self) -> str: - return self.code Was already missing at line 18 def __str__(self): - return self.norm()	16	2	0	87.50%
edsnlp/pipes/misc/split/split.py Was already missing at lines 186-188 if max_length <= 0 and self.regex is None: - yield doc - return	73	2	0	97.26%
edsnlp/pipes/misc/sections/sections.py Was already missing at line 126 if sections is None: - sections = patterns.sections sections = dict(sections)	45	1	0	97.78%
edsnlp/pipes/misc/quantities/quantities.py Was already missing at lines 147-149 def __getitem__(self, item: int): - assert isinstance(item, int) - return [self][item] Was already missing at lines 160-163 def __eq__(self, other: Any): - if isinstance(other, SimpleQuantity): - return self.convert_to(other.unit) == other.value - return False Was already missing at line 166 if other.unit == self.unit: - return SimpleQuantity(self.value + other.value, self.unit, self.registry) return SimpleQuantity( Was already missing at line 193 return self.convert_to(other_unit) - except KeyError: raise AttributeError(f"Unit {other_unit} not found") Was already missing at line 198 def verify(cls, ent): - return True Was already missing at line 264 def __lt__(self, other: Union[SimpleQuantity, "RangeQuantity"]): - return max(self.convert_to(other.unit)) < min((part.value for part in other)) Was already missing at line 275 return self.convert_to(other.unit) == other.value - return False Was already missing at line 289 def verify(cls, ent): - return True Was already missing at line 888 if snippet.end != last and doclike.doc[last: snippet.end].text.strip() == "": - pseudo.append("w") pseudo = "".join(pseudo) Was already missing at line 1069 if start_line is None: - continue Was already missing at lines 1100-1102 unit_norm = self.unit_followers[unit_before.label_] - except (KeyError, AttributeError, IndexError): - pass Was already missing at line 1145 ): - ent = doc[unit_text.start: number.end] else: Was already missing at lines 1152-1154 dims = self.unit_registry.parse_unit(unit_norm)[0] - except KeyError: - continue Was already missing at lines 1260-1262 last._.set(last.label_, new_value) - except (AttributeError, TypeError): - merged.append(ent) else:	440	20	0	95.45%
edsnlp/pipes/misc/dates/models.py Was already missing at line 165 else: - d["month"] = note_datetime.month if self.day is None: Was already missing at lines 169-175 else: - if self.year is None: ... - d["day"] = default_day Was already missing at lines 183-185 return dt - except ValueError: - return None Was already missing at line 201 else: - return None Was already missing at line 217 if self.second: - norm += f"{self.second:02}s"	206	11	0	94.66%
edsnlp/pipes/misc/dates/dates.py Was already missing at line 249 if isinstance(absolute, str): - absolute = [absolute] if isinstance(relative, str): Was already missing at line 251 if isinstance(relative, str): - relative = [relative] if isinstance(duration, str): Was already missing at line 253 if isinstance(duration, str): - relative = [duration] if isinstance(false_positive, str): Was already missing at lines 357-366 if self.merge_mode == "align": - alignments = align_spans(matches, spans, sort_by_overlap=True) ... - matches.append(span) Was already missing at lines 462-464 if v1.mode == Mode.DURATION: - m1 = Bound.FROM if v2.bound == Bound.UNTIL else Bound.UNTIL - m2 = v2.mode or Bound.FROM elif v2.mode == Mode.DURATION:	153	14	0	90.85%
edsnlp/pipes/misc/consultation_dates/consultation_dates.py Was already missing at line 131 else: - self.date_matcher = None Was already missing at line 134 if not consultation_mention: - consultation_mention = [] elif consultation_mention is True:	48	2	0	95.83%
edsnlp/pipes/core/normalizer/__init__.py Was already missing at line 7 def excluded_or_space_getter(t): - return t.is_space or t.tag_ == "EXCLUDED"	5	1	0	80.00%
edsnlp/pipes/core/endlines/endlines.py Was already missing at lines 160-164 if end_lines_model is None: - path = build_path(__file__, "base_model.pkl") - - with open(path, "rb") as inp: - self.model = pickle.load(inp) elif isinstance(end_lines_model, str): Was already missing at lines 167-169 self.model = pickle.load(inp) - elif isinstance(end_lines_model, EndLinesModel): - self.model = end_lines_model else: Was already missing at line 200 ): - return "ENUMERATION" Was already missing at line 287 if np.isnan(sigma): - sigma = 1	89	7	0	92.13%
edsnlp/pipes/core/contextual_matcher/contextual_matcher.py Was already missing at lines 241-243 ): - to_keep = False - break	130	2	0	98.46%
edsnlp/patch_spacy.py Was already missing at lines 67-69 # if module is reloaded. - existing_func = registry.factories.get(internal_name) - if not util.is_same_func(factory_func, existing_func): raise ValueError(	31	2	0	93.55%
edsnlp/package.py Was already missing at lines 474-476 version = version or pyproject["project"]["version"] - except (KeyError, TypeError): - version = "0.1.0" name = name or pyproject["project"]["name"] Was already missing at line 480 else: - main_package = None model_package = snake_case(name.lower())	214	3	0	98.60%
edsnlp/metrics/span_attribute.py Was already missing at lines 67-69 ) - assert attributes is None - attributes = kwargs.pop("qualifiers") if attributes is None:	73	2	0	97.26%
edsnlp/matchers/simstring.py Was already missing at line 280 if custom: - attr = attr[1:].lower() Was already missing at line 295 if custom: - token_text = getattr(token._, attr) else:	146	2	0	98.63%
edsnlp/language.py Was already missing at line 103 if last != begin: - logger.warning( "Missed some characters during"	51	1	0	98.04%
edsnlp/data/standoff.py Was already missing at line 38 def __init__(self, ann_file, line): - super().__init__(f"File {ann_file}, unrecognized Brat line {line}") Was already missing at line 192 ) - except Exception: raise Exception(	186	2	0	98.92%
edsnlp/data/polars.py Was already missing at line 36 if hasattr(data, "collect"): - data = data.collect() assert isinstance(data, pl.DataFrame)	55	1	0	98.18%
edsnlp/data/json.py Was already missing at line 81 return records - except Exception as e: raise Exception(f"Cannot read {file}: {e}")	112	1	0	99.11%
edsnlp/data/converters.py Was already missing at line 428 elif key == "XPOS": - word.tag_ = value elif key == "FEATS": Was already missing at line 822 for attr in bool_attributes: - self.default_attributes[attr] = False self.opener = opener or self.PRESETS[preset]["opener"] Was already missing at line 830 if self.keep_raw_attribute_values: - return value try: Was already missing at lines 873-876 ) - except StopIteration: - warnings.warn(f"Unmatched closing tag for '{sep.group()}'") - continue start, start_label, start_attrs = starts.pop(idx) Was already missing at line 904 ): - if not Span.has_extension(dst): Span.set_extension(dst, default=None) Was already missing at line 911 if span is None: - continue for k, v in attrs.items(): Was already missing at lines 926-929 for attr, value in self.default_attributes.items(): - for span in spans: - if span._.get(attr) is None: - span._.set(attr, value) Was already missing at line 964 if isinstance(converter, type): - return converter(**kwargs), {} return converter, validate_kwargs(converter, kwargs)	317	11	0	96.53%
edsnlp/data/conll.py Was already missing at lines 81-83 ) - except StopIteration: - cols = DEFAULT_COLUMNS warnings.warn( Was already missing at lines 92-96 if not line: - if doc["words"]: - yield doc - doc = {"words": []} - continue if line.startswith("#"):	76	6	0	92.11%
edsnlp/core/torch_component.py Was already missing at line 405 if hasattr(self, "compiled"): - res = self.compiled(batch) else: Was already missing at line 451 """ - return self.preprocess(doc)	189	2	0	98.94%
edsnlp/core/stream.py Was already missing at lines 190-192 if isinstance(batch, StreamSentinel): - yield batch - continue results = [] Was already missing at lines 1007-1009 elif op.batch_fn is None: - batch_size = op.size - batch_fn = batchify else:	356	4	0	98.88%
edsnlp/core/pipeline.py Was already missing at line 605 if name in exclude: - continue if name not in components: Was already missing at lines 716-719 """ - res = Stream.ensure_stream(docs) - res = res.map(functools.partial(self.preprocess, supervision=supervision)) - return res	446	4	0	99.10%
edsnlp/connectors/omop.py Was already missing at line 69 if not isinstance(row.ents, list): - continue Was already missing at line 87 else: - doc.spans[span.label_].append(span) Was already missing at line 127 if df.note_id.isna().any(): - df["note_id"] = range(len(df)) Was already missing at line 171 if i > 0: - df.term_modifiers += ";" df.term_modifiers += ext + "=" + df[ext].astype(str)	84	4	0	95.24%

283 files skipped due to complete coverage.

Coverage failure: total of 96.94% is less than 97.98% ❌

[sonarqubecloud]

Quality Gate passed

Issues
6 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[sonarqubecloud]

Quality Gate passed

Issues
16 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
4.6% Duplication on New Code

See analysis details on SonarQube Cloud