diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 6c3a9dd..87f9eb4 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -12,7 +12,12 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.8', '3.9', '3.10', '3.11' ] + python-version: [ + '3.9', + '3.10', + '3.11', + '3.12', + ] max-parallel: 4 steps: @@ -20,11 +25,12 @@ jobs: - name: Install uv and set Python to ${{ matrix.python-version }} uses: astral-sh/setup-uv@v6 with: - version: "0.7.20" + version: "0.8.10" python-version: ${{ matrix.python-version }} - name: Install dependencies run: | - uv sync --group dev --group docs + uv sync --extra dev --extra docs --extra vllm + uv run python -m ensurepip - name: Check types run: | uv run mypy app diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 21038a8..3f5118b 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -17,11 +17,11 @@ jobs: - name: Install uv uses: astral-sh/setup-uv@v5 with: - version: "0.6.10" + version: "0.8.10" python-version: "3.10" - name: Install dependencies run: | - uv sync --group dev --group docs --group vllm + uv sync --extra dev --extra docs --extra vllm - name: Run unit tests run: | uv run pytest -v tests/app --cov --cov-report=html:coverage_reports #--random-order diff --git a/app/api/api.py b/app/api/api.py index 5dd1522..da91693 100644 --- a/app/api/api.py +++ b/app/api/api.py @@ -211,9 +211,9 @@ def _get_app( tags = TagsStreamable else: tags = Tags - tags_metadata = [{ # type: ignore - "name": tag.name, # type: ignore - "description": tag.value # type: ignore + tags_metadata = [{ + "name": tag.name, + "description": tag.value } for tag in tags] app = FastAPI( title="CogStack ModelServe", diff --git a/app/api/auth/db.py b/app/api/auth/db.py index 7df542a..1c093cf 100644 --- a/app/api/auth/db.py +++ b/app/api/auth/db.py @@ -52,5 +52,4 @@ async def get_user_db(session: AsyncSession = Depends(_get_async_session)) -> As SQLAlchemyUserDatabase: A database instance initialised with the given session and the User model. """ - # TODO: fix this type checking error yield SQLAlchemyUserDatabase(session, User) diff --git a/app/api/routers/generative.py b/app/api/routers/generative.py index b2d9454..78222d5 100644 --- a/app/api/routers/generative.py +++ b/app/api/routers/generative.py @@ -217,7 +217,8 @@ async def _stream(prompt: str, max_tokens: int, temperature: float) -> AsyncGene yield f"data: {json.dumps(data)}\n\n" yield "data: [DONE]\n\n" - prompt = get_prompt_from_messages(model_service.tokenizer, messages) # type: ignore + assert hasattr(model_service, "tokenizer"), "Model service doesn't have a tokenizer" + prompt = get_prompt_from_messages(model_service.tokenizer, messages) if stream: return StreamingResponse( _stream(prompt, max_tokens, temperature), diff --git a/app/api/routers/stream.py b/app/api/routers/stream.py index c1b9197..edba5e8 100644 --- a/app/api/routers/stream.py +++ b/app/api/routers/stream.py @@ -137,7 +137,7 @@ def __init__( self, content: Any, status_code: int = 200, - max_chunk_size: Optional[int] = 1024, + max_chunk_size: int = 1024, headers: Optional[Mapping[str, str]] = None, media_type: Optional[str] = None, background: Optional[BackgroundTask] = None, @@ -161,8 +161,8 @@ async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: }) response_started = True line_bytes = line.encode("utf-8") - for i in range(0, len(line_bytes), self.max_chunk_size): # type: ignore - chunk = line_bytes[i:i + self.max_chunk_size] # type: ignore + for i in range(0, len(line_bytes), self.max_chunk_size): + chunk = line_bytes[i:i + self.max_chunk_size] await send({ "type": "http.response.body", "body": chunk, diff --git a/app/api/utils.py b/app/api/utils.py index 87cea26..6cf38cd 100644 --- a/app/api/utils.py +++ b/app/api/utils.py @@ -367,7 +367,7 @@ async def generate_text( params = SamplingParams(max_tokens=max_tokens) conversation, _ = parse_chat_messages(messages, model_config, tokenizer, content_format="string") # type: ignore - prompt_tokens = apply_hf_chat_template( # type: ignore + prompt_tokens = apply_hf_chat_template( # type: ignore tokenizer, conversation=conversation, tools=None, diff --git a/app/config.py b/app/config.py index 09fba95..854123f 100644 --- a/app/config.py +++ b/app/config.py @@ -36,6 +36,7 @@ class Settings(BaseSettings): # type: ignore TRAINING_CACHE_DIR: str = os.path.join(os.path.abspath(os.path.dirname(__file__)), "cms_cache") # the directory to cache the intermediate files created during training HF_PIPELINE_AGGREGATION_STRATEGY: str = "simple" # the strategy used for aggregating the predictions of the Hugging Face NER model LOG_PER_CONCEPT_ACCURACIES: str = "false" # if "true", per-concept accuracies will be exposed to the metrics scrapper. Switch this on with caution due to the potentially high number of concepts + MEDCAT2_MAPPED_ONTOLOGIES: str = "" # the comma-separated names of ontologies for MedCAT2 to map to DEBUG: str = "false" # if "true", the debug mode is switched on class Config: diff --git a/app/envs/.env b/app/envs/.env index 0824551..3a3a12b 100644 --- a/app/envs/.env +++ b/app/envs/.env @@ -73,5 +73,8 @@ TRAINING_SAFE_MODEL_SERIALISATION=false # The strategy used for aggregating the predictions of the Hugging Face NER model HF_PIPELINE_AGGREGATION_STRATEGY=simple +# The comma-separated names of ontologies for MedCAT2 to map to +MEDCAT2_MAPPED_ONTOLOGIES=opcs4,icd10 + # If "true", the debug mode is switched on DEBUG=false diff --git a/app/management/tracker_client.py b/app/management/tracker_client.py index 9be24be..61686a2 100644 --- a/app/management/tracker_client.py +++ b/app/management/tracker_client.py @@ -1,3 +1,4 @@ +import ast import os import socket import mlflow @@ -114,7 +115,7 @@ def send_model_stats(stats: Dict, step: int) -> None: step (int): The current step in the training or evaluation process. """ - metrics = {key.replace(" ", "_").lower(): val for key, val in stats.items()} + metrics = {key.replace(" ", "_").lower(): val for key, val in stats.items() if isinstance(val, (int, float))} mlflow.log_metrics(metrics, step) @staticmethod @@ -563,6 +564,7 @@ def get_metrics_by_job_id(self, job_id: str) -> List[Dict[str, Any]]: metrics_history = {} for metric in run.data.metrics.keys(): metrics_history[metric] = [m.value for m in self.mlflow_client.get_metric_history(run_id=run.info.run_id, key=metric)] + metrics_history["concepts"] = ast.literal_eval(run.data.tags.get("training.entity.classes", "[]")) metrics.append(metrics_history) return metrics except MlflowException as e: diff --git a/app/model_services/medcat_model.py b/app/model_services/medcat_model.py index 9ab5235..0ef00d1 100644 --- a/app/model_services/medcat_model.py +++ b/app/model_services/medcat_model.py @@ -3,8 +3,9 @@ import pandas as pd from multiprocessing import cpu_count -from typing import Dict, List, Optional, TextIO, Tuple, Any +from typing import Dict, List, Optional, TextIO, Tuple, Any, Set, Union from medcat.cat import CAT +from medcat.data.entities import Entities, OnlyCUIEntities from app import __version__ as app_version from app.model_services.base import AbstractModelService from app.trainers.medcat_trainer import MedcatSupervisedTrainer, MedcatUnsupervisedTrainer @@ -46,7 +47,7 @@ def __init__( base_model_file (Optional[str]): The model package file name. Defaults to None. """ super().__init__(config) - self._model: CAT = None + self._model: Optional[CAT] = None self._config = config self._model_parent_dir = model_parent_dir or os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "model")) self._model_pack_path = os.path.join(self._model_parent_dir, base_model_file or config.BASE_MODEL_FILE) @@ -55,7 +56,7 @@ def __init__( self.model_name = model_name or "MedCAT model" @property - def model(self) -> CAT: + def model(self) -> Optional[CAT]: """Getter for the MedCAT model.""" return self._model @@ -113,7 +114,7 @@ def load_model(model_file_path: str, *args: Tuple, **kwargs: Dict[str, Any]) -> model_path = os.path.join(os.path.dirname(model_file_path), get_model_data_package_base_name(model_file_path)) if unpack_model_data_package(model_file_path, model_path): - cat = CAT.load_model_pack(model_file_path.replace(".tar.gz", ".zip"), *args, **kwargs) + cat = CAT.load_model_pack(model_file_path.replace(".tar.gz", ".zip"), **kwargs) logger.info("Model package loaded from %s", os.path.normpath(model_file_path)) return cat else: @@ -131,11 +132,10 @@ def init_model(self, *args: Any, **kwargs: Any) -> None: logger.warning("Model service is already initialised and can be initialised only once") else: if non_default_device_is_available(get_settings().DEVICE): - self._model = self.load_model( - self._model_pack_path, - meta_cat_config_dict={"general": {"device": get_settings().DEVICE}}, - ) - self._model.config.general["device"] = get_settings().DEVICE + self._model = self.load_model(self._model_pack_path) + for addon in self._model.get_addons(): + addon.config.general.device = get_settings().DEVICE # type: ignore + self._model.config.general.device = get_settings().DEVICE # type: ignore else: self._model = self.load_model(self._model_pack_path) self._set_tuis_filtering() @@ -143,6 +143,9 @@ def init_model(self, *args: Any, **kwargs: Any) -> None: self._supervised_trainer = MedcatSupervisedTrainer(self) self._unsupervised_trainer = MedcatUnsupervisedTrainer(self) self._metacat_trainer = MetacatTrainer(self) + self._model.config.general.map_to_other_ontologies = [ # type: ignore + tui.strip() for tui in self._config.MEDCAT2_MAPPED_ONTOLOGIES.split(",") + ] def info(self) -> ModelCard: """ @@ -168,10 +171,8 @@ def annotate(self, text: str) -> List[Annotation]: List[Annotation]: A list of annotations containing the extracted named entities. """ - doc = self.model.get_entities( - text, - addl_info=["cui2icd10", "cui2opcs4", "cui2ontologies", "cui2snomed", "cui2athena_ids"], - ) + assert self.model is not None, "Model is not initialised" + doc = self.model.get_entities(text) return [load_pydantic_object_from_dict(Annotation, record) for record in self.get_records_from_doc(doc)] def batch_annotate(self, texts: List[str]) -> List[List[Annotation]]: @@ -187,12 +188,12 @@ def batch_annotate(self, texts: List[str]) -> List[List[Annotation]]: batch_size_chars = 500000 - docs = self.model.multiprocessing_batch_char_size( - self._data_iterator(texts), + assert self.model is not None, "Model is not initialised" + docs = {i: result for i, (_, result) in enumerate(self.model.get_entities_multi_texts( + texts, batch_size_chars=batch_size_chars, - nproc=max(int(cpu_count() / 2), 1), - addl_info=["cui2icd10", "cui2opcs4", "cui2ontologies", "cui2snomed", "cui2athena_ids"], - ) + n_process=max(int(cpu_count() / 2), 1), + ))} docs = dict(sorted(docs.items(), key=lambda x: x[0])) annotations_list = [] for _, doc in docs.items(): @@ -342,12 +343,12 @@ def train_metacat( **hyperparams, ) - def get_records_from_doc(self, doc: Dict) -> List[Dict]: + def get_records_from_doc(self, doc: Union[Dict, Entities, OnlyCUIEntities]) -> List[Dict]: """ Extracts and formats entity records from a document dictionary. Args: - doc (Dict): The document dictionary containing extracted named entities. + doc (Union[Dict, Entities, OnlyCUIEntities]): The document dictionary containing extracted named entities. Returns: List[Dict]: A list of formatted entity records. @@ -362,9 +363,9 @@ def get_records_from_doc(self, doc: Dict) -> List[Dict]: if "athena_ids" in row and row["athena_ids"]: df.loc[idx, "athena_ids"] = [athena_id["code"] for athena_id in row["athena_ids"]] if self._config.INCLUDE_SPAN_TEXT == "true": - df.rename(columns={"pretty_name": "label_name", "cui": "label_id", "source_value": "text", "types": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) + df.rename(columns={"pretty_name": "label_name", "cui": "label_id", "source_value": "text", "type_ids": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) else: - df.rename(columns={"pretty_name": "label_name", "cui": "label_id", "types": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) + df.rename(columns={"pretty_name": "label_name", "cui": "label_id", "type_ids": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) df = self._retrieve_meta_annotations(df) records = df.to_dict("records") return records @@ -384,15 +385,19 @@ def _retrieve_meta_annotations(df: pd.DataFrame) -> pd.DataFrame: def _set_tuis_filtering(self) -> None: # this patching may not be needed after the base 1.4.x model is fixed in the future + assert self._model is not None, "Model is not initialised" if self._model.cdb.addl_info.get("type_id2name", {}) == {}: self._model.cdb.addl_info["type_id2name"] = TYPE_ID_TO_NAME_PATCH - tuis2cuis = self._model.cdb.addl_info.get("type_id2cuis") - model_tuis = set(tuis2cuis.keys()) + type_id2info = self._model.cdb.type_id2info + model_tuis = set(type_id2info.keys()) if self._whitelisted_tuis == {""}: return assert self._whitelisted_tuis.issubset(model_tuis), f"Unrecognisable Type Unique Identifier(s): {self._whitelisted_tuis - model_tuis}" - whitelisted_cuis = set() + whitelisted_cuis: Set = set() for tui in self._whitelisted_tuis: - whitelisted_cuis.update(tuis2cuis.get(tui, {})) - self._model.cdb.config.linking.filters = {"cuis": whitelisted_cuis} + type_info = type_id2info.get(tui) + if type_info is None: + continue + whitelisted_cuis.update(type_info.cuis) + self._model.config.components.linking.filters.cuis = whitelisted_cuis diff --git a/app/model_services/medcat_model_deid.py b/app/model_services/medcat_model_deid.py index 9ec1248..547f225 100644 --- a/app/model_services/medcat_model_deid.py +++ b/app/model_services/medcat_model_deid.py @@ -2,10 +2,11 @@ import inspect import threading import torch -from typing import Dict, List, TextIO, Tuple, Optional, Any, final, Callable +from typing import Dict, List, TextIO, Tuple, Optional, Any, final, Callable, cast from functools import partial from transformers import pipeline from medcat.cat import CAT +from medcat.components.types import CoreComponentType from app import __version__ as app_version from app.config import Settings from app.model_services.medcat_model import MedCATModel @@ -62,14 +63,15 @@ def info(self) -> ModelCard: ModelCard: A card containing information about the MedCAT De-Identification (AnonCAT) model. """ + assert self.model is not None, "Model is not initialised" model_card = self.model.get_model_card(as_dict=True) model_card["Basic CDB Stats"]["Average training examples per concept"] = 0 return ModelCard( model_description=self.model_name, model_type=ModelType.ANONCAT, api_version=self.api_version, - model_card=model_card, - labels=self.model.cdb.cui2preferred_name, + model_card=dict(model_card), + labels={cui: info['preferred_name'] for cui, info in self.model.cdb.cui2info.items()}, ) def annotate(self, text: str) -> List[Annotation]: @@ -83,10 +85,11 @@ def annotate(self, text: str) -> List[Annotation]: List[Annotation]: A list of annotations containing the extracted PII entities. """ + assert self.model is not None, "Model is not initialised" doc = self.model.get_entities(text) if doc["entities"]: for _, entity in doc["entities"].items(): - entity["types"] = ["PII"] + entity["type_ids"] = ["PII"] records = self.get_records_from_doc({"entities": doc["entities"]}) return [load_pydantic_object_from_dict(Annotation, record) for record in records] @@ -102,7 +105,8 @@ def annotate_with_local_chunking(self, text: str) -> List[Annotation]: List[Annotation]: A list of annotation containing the extracted PII entities. """ - tokenizer = self.model._addl_ner[0].tokenizer.hf_tokenizer + assert self.model is not None, "Model is not initialised" + tokenizer = self.model.pipe.get_component(CoreComponentType.ner)._component.tokenizer.hf_tokenizer # type: ignore leading_ws_len = len(text) - len(text.lstrip()) text = text.lstrip() tokenized = self._with_lock(tokenizer, text, return_offsets_mapping=True, add_special_tokens=False) @@ -134,7 +138,7 @@ def annotate_with_local_chunking(self, text: str) -> List[Annotation]: for entity in doc["entities"].values(): entity["start"] += processed_char_len entity["end"] += processed_char_len - entity["types"] = ["PII"] + entity["type_ids"] = ["PII"] aggregated_entities[ent_key] = entity ent_key += 1 processed_char_len = chunk[:window_overlap_start_idx][-1][1][1] + leading_ws_len + 1 @@ -146,7 +150,7 @@ def annotate_with_local_chunking(self, text: str) -> List[Annotation]: for entity in doc["entities"].values(): entity["start"] += processed_char_len entity["end"] += processed_char_len - entity["types"] = ["PII"] + entity["type_ids"] = ["PII"] aggregated_entities[ent_key] = entity ent_key += 1 processed_char_len += len(c_text) @@ -168,10 +172,12 @@ def batch_annotate(self, texts: List[str]) -> List[List[Annotation]]: """ annotations_list = [] + assert self.model is not None, "Model is not initialised" entities_list = self.model.get_entities_multi_texts(texts) - for entities in entities_list: + for _, entities in entities_list: for _, entity in entities["entities"].items(): - entity["types"] = ["PII"] + entity = cast(Dict[str, Any], entity) + entity["type_ids"] = ["PII"] annotations_list.append([ load_pydantic_object_from_dict(Annotation, record) for record in self.get_records_from_doc(entities) ]) @@ -190,26 +196,26 @@ def init_model(self, *args: Any, **kwargs: Any) -> None: logger.warning("Model service is already initialised and can be initialised only once") else: self._model = self.load_model(self._model_pack_path) - self._model._addl_ner[0].tokenizer.hf_tokenizer._in_target_context_manager = getattr(self._model._addl_ner[0].tokenizer.hf_tokenizer, "_in_target_context_manager", False) - self._model._addl_ner[0].tokenizer.hf_tokenizer.clean_up_tokenization_spaces = getattr(self._model._addl_ner[0].tokenizer.hf_tokenizer, "clean_up_tokenization_spaces", None) - self._model._addl_ner[0].tokenizer.hf_tokenizer.split_special_tokens = getattr(self._model._addl_ner[0].tokenizer.hf_tokenizer, "split_special_tokens", False) + ner = self._model.pipe.get_component(CoreComponentType.ner)._component # type: ignore + ner.tokenizer.hf_tokenizer._in_target_context_manager = getattr(ner.tokenizer.hf_tokenizer, "_in_target_context_manager", False) + ner.tokenizer.hf_tokenizer.clean_up_tokenization_spaces = getattr(ner.tokenizer.hf_tokenizer, "clean_up_tokenization_spaces", None) + ner.tokenizer.hf_tokenizer.split_special_tokens = getattr(ner.tokenizer.hf_tokenizer, "split_special_tokens", False) if non_default_device_is_available(self._config.DEVICE): - self._model.config.general["device"] = self._config.DEVICE - self._model._addl_ner[0].model.to(torch.device(self._config.DEVICE)) - self._model._addl_ner[0].ner_pipe = pipeline( - model=self._model._addl_ner[0].model, + ner.model.to(torch.device(self._config.DEVICE)) + ner.ner_pipe = pipeline( + model=ner.model, framework="pt", task="ner", - tokenizer=self._model._addl_ner[0].tokenizer.hf_tokenizer, + tokenizer=ner.tokenizer.hf_tokenizer, device=get_hf_pipeline_device_id(self._config.DEVICE), aggregation_strategy=self._config.HF_PIPELINE_AGGREGATION_STRATEGY, ) else: if self._config.DEVICE != "default": logger.warning("DEVICE is set to '%s' but it is not available. Using 'default' instead.", self._config.DEVICE) - _save_pretrained = self._model._addl_ner[0].model.save_pretrained + _save_pretrained = ner.model.save_pretrained if ("safe_serialization" in inspect.signature(_save_pretrained).parameters): - self._model._addl_ner[0].model.save_pretrained = partial(_save_pretrained, safe_serialization=(self._config.TRAINING_SAFE_MODEL_SERIALISATION == "true")) + ner.model.save_pretrained = partial(_save_pretrained, safe_serialization=(self._config.TRAINING_SAFE_MODEL_SERIALISATION == "true")) if self._enable_trainer: self._supervised_trainer = MedcatDeIdentificationSupervisedTrainer(self) diff --git a/app/model_services/medcat_model_icd10.py b/app/model_services/medcat_model_icd10.py index b74b43c..c24601c 100644 --- a/app/model_services/medcat_model_icd10.py +++ b/app/model_services/medcat_model_icd10.py @@ -1,6 +1,7 @@ import logging import pandas as pd -from typing import Dict, Optional, final, List +from typing import Dict, Optional, final, List, Union +from medcat.data.entities import Entities, OnlyCUIEntities from app import __version__ as app_version from app.model_services.medcat_model import MedCATModel from app.config import Settings @@ -57,19 +58,20 @@ def info(self) -> ModelCard: ModelCard: A card containing information about the MedCAT ICD-10 model. """ + assert self.model is not None, "Model is not initialised" return ModelCard( model_description=self.model_name, model_type=ModelType.MEDCAT_ICD10, api_version=self.api_version, - model_card=self.model.get_model_card(as_dict=True), + model_card=dict(self.model.get_model_card(as_dict=True)), ) - def get_records_from_doc(self, doc: Dict) -> List[Dict]: + def get_records_from_doc(self, doc: Union[Dict, Entities, OnlyCUIEntities]) -> List[Dict]: """ Extracts and formats entity records from a document dictionary. Args: - doc (Dict): The document dictionary containing extracted named entities. + doc (Union[Dict, Entities, OnlyCUIEntities]): The document dictionary containing extracted named entities. Returns: List[Dict]: A list of formatted entity records. @@ -101,7 +103,7 @@ def get_records_from_doc(self, doc: Dict) -> List[Dict]: new_rows.append(output_row) if new_rows: df = pd.DataFrame(new_rows) - df.rename(columns={"pretty_name": "label_name", self.ICD10_KEY: "label_id", "types": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) + df.rename(columns={"pretty_name": "label_name", self.ICD10_KEY: "label_id", "type_ids": "categories", "acc": "accuracy", "athena_ids": "athena_ids"}, inplace=True) df = self._retrieve_meta_annotations(df) else: df = pd.DataFrame(columns=["label_name", "label_id", "start", "end", "accuracy"]) diff --git a/app/model_services/medcat_model_opcs4.py b/app/model_services/medcat_model_opcs4.py index a8a8e9c..10e0aa4 100644 --- a/app/model_services/medcat_model_opcs4.py +++ b/app/model_services/medcat_model_opcs4.py @@ -1,7 +1,7 @@ import logging import pandas as pd -from typing import Dict, Optional, final, List - +from typing import Dict, Optional, final, List, Union +from medcat.data.entities import Entities, OnlyCUIEntities from app import __version__ as app_version from app.model_services.medcat_model import MedCATModel from app.config import Settings @@ -58,19 +58,20 @@ def info(self) -> ModelCard: ModelCard: A card containing information about the MedCAT OPCS-4 model. """ + assert self.model is not None, "Model is not initialised" return ModelCard( model_description=self.model_name, model_type=ModelType.MEDCAT_OPCS4, api_version=self.api_version, - model_card=self.model.get_model_card(as_dict=True), + model_card=dict(self.model.get_model_card(as_dict=True)), ) - def get_records_from_doc(self, doc: Dict) -> List[Dict]: + def get_records_from_doc(self, doc: Union[Dict, Entities, OnlyCUIEntities]) -> List[Dict]: """ Extracts and formats entity records from a document dictionary. Args: - doc (Dict): The document dictionary containing extracted named entities. + doc (Union[Dict, Entities, OnlyCUIEntities]): The document dictionary containing extracted named entities. Returns: List[Dict]: A list of formatted entity records. diff --git a/app/model_services/medcat_model_snomed.py b/app/model_services/medcat_model_snomed.py index 94e4bc1..0b0fc10 100644 --- a/app/model_services/medcat_model_snomed.py +++ b/app/model_services/medcat_model_snomed.py @@ -55,9 +55,10 @@ def info(self) -> ModelCard: ModelCard: A card containing information about the MedCAT SNOMED model. """ + assert self.model is not None, "Model is not initialised" return ModelCard( model_description=self.model_name, model_type=ModelType.MEDCAT_SNOMED, api_version=self.api_version, - model_card=self.model.get_model_card(as_dict=True), + model_card=dict(self.model.get_model_card(as_dict=True)), ) diff --git a/app/model_services/medcat_model_umls.py b/app/model_services/medcat_model_umls.py index 114f93d..24c450f 100644 --- a/app/model_services/medcat_model_umls.py +++ b/app/model_services/medcat_model_umls.py @@ -52,9 +52,10 @@ def info(self) -> ModelCard: ModelCard: A card containing information about the MedCAT UMLS model. """ + assert self.model is not None, "Model is not initialised" return ModelCard( model_description=self.model_name, model_type=ModelType.MEDCAT_UMLS, api_version=self.api_version, - model_card=self.model.get_model_card(as_dict=True), + model_card=dict(self.model.get_model_card(as_dict=True)), ) diff --git a/app/model_services/trf_model_deid.py b/app/model_services/trf_model_deid.py index fbf6290..a8fa859 100644 --- a/app/model_services/trf_model_deid.py +++ b/app/model_services/trf_model_deid.py @@ -6,7 +6,7 @@ from typing import Tuple, List, Dict, Iterable, Optional, final, Any from scipy.special import softmax from transformers import AutoModelForTokenClassification, PreTrainedModel -from medcat.tokenizers.transformers_ner import TransformersTokenizerNER +from medcat.components.ner.trf.tokenizer import TransformersTokenizer from app import __version__ as app_version from app.model_services.base import AbstractModelService from app.domain import ModelCard, ModelType, Annotation @@ -41,7 +41,7 @@ def __init__( self._device = torch.device(config.DEVICE) self.model_name = model_name or "De-identification model" self._model: PreTrainedModel - self._tokenizer: TransformersTokenizerNER + self._tokenizer: TransformersTokenizer self._id2cui: Dict[str, str] @property @@ -73,14 +73,14 @@ def load_model( model_file_path: str, *args: Tuple, **kwargs: Dict[str, Any], - ) -> Tuple[TransformersTokenizerNER, PreTrainedModel]: + ) -> Tuple[TransformersTokenizer, PreTrainedModel]: model_file_dir = os.path.dirname(model_file_path) model_file_name = os.path.basename(model_file_path).replace(".zip", "") unpacked_model_dir = os.path.join(model_file_dir, model_file_name) if not os.path.isdir(unpacked_model_dir): shutil.unpack_archive(model_file_path, extract_dir=unpacked_model_dir) tokenizer_path = os.path.join(unpacked_model_dir, "tokenizer.dat") - tokenizer = TransformersTokenizerNER.load(tokenizer_path) + tokenizer = TransformersTokenizer.load(tokenizer_path) logger.info("Tokenizer loaded from %s", tokenizer_path) model = AutoModelForTokenClassification.from_pretrained(unpacked_model_dir) logger.info("Model loaded from %s", unpacked_model_dir) @@ -91,7 +91,7 @@ def init_model(self, *args: Any, **kwargs: Any) -> None: logger.warning("Model service is already initialised and can be initialised only once") else: self._tokenizer, self._model = self.load_model(self._model_file_path) - self._id2cui = {cui_id: cui for cui, cui_id in self._tokenizer.label_map.items()} + self._id2cui = {str(cui_id): cui for cui, cui_id in self._tokenizer.label_map.items()} self._model.to(self._device) def annotate(self, text: str) -> List[Annotation]: @@ -123,13 +123,13 @@ def _get_annotations(self, text: str) -> List[Annotation]: input_ids = dataset["input_ids"] for t_idx, cur_cui_id in enumerate(predictions): if cur_cui_id not in [0, -100]: - t_text = self._tokenizer.hf_tokenizer.decode(input_ids[t_idx]) + t_text = self._tokenizer.hf_tokenizer.decode(input_ids[t_idx]) # type: ignore if t_text.strip() in ["", "[PAD]"]: continue annotation = load_pydantic_object_from_dict( Annotation, { - "label_name": self._tokenizer.cui2name.get(self._id2cui[cur_cui_id]), + "label_name": self._tokenizer.cui2name.get(self._id2cui[cur_cui_id]), # type: ignore "label_id": self._id2cui[cur_cui_id], "start": offset_mappings[t_idx][0], "end": offset_mappings[t_idx][1], @@ -138,9 +138,9 @@ def _get_annotations(self, text: str) -> List[Annotation]: if ist: annotation.text = t_text if annotations: - token_type = self._tokenizer.id2type.get(input_ids[t_idx]) + token_type = self._tokenizer.id2type.get(input_ids[t_idx]) # type: ignore if any([ - self._should_expand_with_partial(cur_cui_id, token_type, annotation, annotations), + self._should_expand_with_partial(cur_cui_id, token_type, annotation, annotations), # type: ignore self._should_expand_with_whole(cas, annotation, annotations), ]): annotations[-1].end = annotation.end @@ -159,9 +159,9 @@ def _get_annotations(self, text: str) -> List[Annotation]: return annotations def _get_chunked_tokens(self, text: str) -> Iterable[Tuple[Dict, List[Tuple]]]: - tokens = self._tokenizer.hf_tokenizer(text, return_offsets_mapping=True, add_special_tokens=False) + tokens = self._tokenizer.hf_tokenizer(text, return_offsets_mapping=True, add_special_tokens=False) # type: ignore model_max_length = self._tokenizer.max_len - pad_token_id = self._tokenizer.hf_tokenizer.pad_token_id + pad_token_id = self._tokenizer.hf_tokenizer.pad_token_id # type: ignore partial = len(tokens["input_ids"]) % model_max_length for i in range(0, len(tokens["input_ids"]) - partial, model_max_length): dataset = { diff --git a/app/trainers/medcat_deid_trainer.py b/app/trainers/medcat_deid_trainer.py index 94d7103..65ac7be 100644 --- a/app/trainers/medcat_deid_trainer.py +++ b/app/trainers/medcat_deid_trainer.py @@ -15,10 +15,18 @@ from evaluate.visualization import radar_plot from transformers import pipeline from medcat import __version__ as medcat_version -from medcat.ner.transformers_ner import TransformersNER +from medcat.components.types import CoreComponentType +from medcat.components.ner.trf.transformers_ner import TransformersNERComponent +import json from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl, PreTrainedModel, Trainer from app.domain import TrainerBackend -from app.utils import get_settings, non_default_device_is_available, get_hf_pipeline_device_id, get_model_data_package_extension +from app.utils import ( + get_settings, + non_default_device_is_available, + get_hf_pipeline_device_id, + get_model_data_package_extension, + dump_pydantic_object_to_dict, +) from app.management.tracker_client import TrackerClient from app.trainers.medcat_trainer import MedcatSupervisedTrainer from app.processors.metrics_collector import get_stats_from_trainer_export @@ -62,14 +70,14 @@ def run( logger.info("Loading a new model copy for training...") copied_model_pack_path = self._make_model_file_copy(self._model_pack_path, run_id) model = self._model_service.load_model(copied_model_pack_path) - ner = model._addl_ner[0] + ner = model.pipe.get_component(CoreComponentType.ner)._component # type: ignore ner.tokenizer.hf_tokenizer._in_target_context_manager = getattr(ner.tokenizer.hf_tokenizer, "_in_target_context_manager", False) ner.tokenizer.hf_tokenizer.clean_up_tokenization_spaces = getattr(ner.tokenizer.hf_tokenizer, "clean_up_tokenization_spaces", None) ner.tokenizer.hf_tokenizer.split_special_tokens = getattr(ner.tokenizer.hf_tokenizer, "split_special_tokens", False) _save_pretrained = ner.model.save_pretrained if ("safe_serialization" in inspect.signature(_save_pretrained).parameters): ner.model.save_pretrained = partial(_save_pretrained, safe_serialization=(self._config.TRAINING_SAFE_MODEL_SERIALISATION == "true")) - ner_config = {f"transformers.cat_config.{arg}": str(val) for arg, val in ner.config.general.dict().items()} + ner_config = {f"transformers.cat_config.{arg}": str(val) for arg, val in dump_pydantic_object_to_dict(ner.config.general).items()} ner_config.update({f"transformers.training.{arg}": str(val) for arg, val in ner.training_arguments.to_dict().items()}) for key, val in ner_config.items(): ner_config[key] = "" if val == "" else val @@ -90,7 +98,7 @@ def run( # ner.training_arguments.evaluation_strategy = "steps" # ner.training_arguments.eval_steps = 1 logger.info("Performing supervised training...") - model.config.version.description = description or model.config.version.description + model.config.meta.description = description or model.config.meta.description ner.config.general.description = description or ner.config.general.description dataset = None @@ -145,7 +153,7 @@ def run( "per_concept_p_merged": row["p_merged"] if row["p_merged"] is not None else 0.0, "per_concept_r_merged": row["r_merged"] if row["r_merged"] is not None else 0.0, }) - cui2names[row["cui"]] = model.cdb.get_name(row["cui"]) + cui2names[row["cui"]] = model.cdb.get_name(row["cui"]) if model.cdb is not None else "" MedcatDeIdentificationSupervisedTrainer._save_metrics_plot( aggregated_metrics, list(cui2names.values()), @@ -175,7 +183,8 @@ def run( get_model_data_package_extension(model_pack_path), "_config.json", ) - model.cdb.config.save(cdb_config_path) + with open(cdb_config_path, "w") as f: + json.dump(dump_pydantic_object_to_dict(model.config), f) model_uri = self._tracker_client.save_model(model_pack_path, self._model_name, self._model_manager) logger.info("Retrained model saved: %s", model_uri) self._tracker_client.save_model_artifact(cdb_config_path, self._model_name) @@ -215,9 +224,10 @@ def run( else: try: logger.info("Evaluating the running model...") - self._tracker_client.log_model_config(self.get_flattened_config(self._model_service._model)) + if self._model_service._model is not None: + self._tracker_client.log_model_config(self.get_flattened_config(self._model_service._model)) self._tracker_client.log_trainer_version(TrainerBackend.MEDCAT, medcat_version) - ner = self._model_service._model._addl_ner[0] + ner = self._model.pipe.get_component(CoreComponentType.ner)._component # type: ignore ner.tokenizer.hf_tokenizer._in_target_context_manager = getattr(ner.tokenizer.hf_tokenizer, "_in_target_context_manager", False) ner.tokenizer.hf_tokenizer.clean_up_tokenization_spaces = getattr(ner.tokenizer.hf_tokenizer, "clean_up_tokenization_spaces", None) ner.tokenizer.hf_tokenizer.split_special_tokens = getattr(ner.tokenizer.hf_tokenizer, "split_special_tokens", False) @@ -236,7 +246,7 @@ def run( "per_concept_p_merged": row["p_merged"] if row["p_merged"] is not None else 0.0, "per_concept_r_merged": row["r_merged"] if row["r_merged"] is not None else 0.0, }) - cui2names[row["cui"]] = self._model_service._model.cdb.get_name(row["cui"]) + cui2names[row["cui"]] = self._model_service._model.cdb.get_name(row["cui"]) if self._model_service._model and self._model_service._model.cdb else "" self._tracker_client.send_batched_model_stats(aggregated_metrics, run_id) self._save_examples(examples, ["tp", "tn"]) self._tracker_client.log_classes_and_names(cui2names) @@ -273,7 +283,7 @@ def _save_metrics_plot( logger.exception(e) @staticmethod - def _customise_training_device(ner: TransformersNER, device_name: str) -> TransformersNER: + def _customise_training_device(ner: TransformersNERComponent, device_name: str) -> TransformersNERComponent: if non_default_device_is_available(device_name): ner.model.to(torch.device(device_name)) ner.ner_pipe = pipeline( diff --git a/app/trainers/medcat_trainer.py b/app/trainers/medcat_trainer.py index 02be50c..e49068f 100644 --- a/app/trainers/medcat_trainer.py +++ b/app/trainers/medcat_trainer.py @@ -1,16 +1,17 @@ import gc +import json import logging import os import re import tempfile import ijson import datasets +import pandas as pd from contextlib import redirect_stdout from typing import TextIO, Dict, Optional, Set, List, Union, final, TYPE_CHECKING - -import pandas as pd from medcat import __version__ as medcat_version from medcat.cat import CAT +from medcat.stats.stats import get_stats from app.management.log_captor import LogCaptor from app.management.model_manager import ModelManager from app.trainers.base import SupervisedTrainer, UnsupervisedTrainer @@ -21,6 +22,7 @@ non_default_device_is_available, get_model_data_package_extension, create_model_data_package, + dump_pydantic_object_to_dict, ) from app.domain import DatasetSplit, TrainerBackend from app.exception import TrainingCancelledException @@ -36,21 +38,18 @@ class _MedcatTrainerCommon(object): def get_flattened_config(model: CAT, prefix: Optional[str] = None) -> Dict: params = {} prefix = "" if prefix is None else f"{prefix}." - for key, val in model.cdb.config.general.__dict__.items(): + for key, val in model.config.general.__dict__.items(): params[f"{prefix}general.{key}"] = str(val) - for key, val in model.cdb.config.cdb_maker.__dict__.items(): + for key, val in model.config.cdb_maker.__dict__.items(): params[f"{prefix}cdb_maker.{key}"] = str(val) - for key, val in model.cdb.config.annotation_output.__dict__.items(): + for key, val in model.config.annotation_output.__dict__.items(): params[f"{prefix}annotation_output.{key}"] = str(val) - for key, val in model.cdb.config.preprocessing.__dict__.items(): + for key, val in model.config.preprocessing.__dict__.items(): params[f"{prefix}preprocessing.{key}"] = str(val) - for key, val in model.cdb.config.ner.__dict__.items(): - params[f"{prefix}ner.{key}"] = str(val) - for key, val in model.cdb.config.linking.__dict__.items(): - params[f"{prefix}linking.{key}"] = str(val) - params[f"{prefix}word_skipper"] = str(model.cdb.config.word_skipper) - params[f"{prefix}punct_checker"] = str(model.cdb.config.punct_checker) - params.pop(f"{prefix}linking.filters", None) # deal with the length value in the older model + for key, val in model.config.components.ner.__dict__.items(): + params[f"{prefix}components.ner.{key}"] = str(val) + for key, val in model.config.components.linking.__dict__.items(): + params[f"{prefix}components.linking.{key}"] = str(val) for key, val in params.items(): if val == "": params[key] = "" @@ -61,9 +60,10 @@ def deploy_model( model_service: "MedCATModel", model: CAT, skip_save_model: bool, + description: Optional[str] = None, ) -> None: if skip_save_model: - model._versioning() + model._versioning(change_description=description) if hasattr(model_service, "model"): del model_service.model gc.collect() @@ -73,8 +73,8 @@ def deploy_model( @staticmethod def save_model_pack(model: CAT, model_dir: str, base_model_file: str, description: Optional[str] = None) -> str: logger.info("Saving retrained model to %s...", model_dir) - model.config.version.description = description or model.config.version.description - model_pack_name = model.create_model_pack(model_dir, "model") + model.config.meta.description = description or model.config.meta.description + model_pack_name = model.save_model_pack(model_dir, "model") if get_model_data_package_extension(base_model_file) == ".tar.gz": model_pack_path = f"{os.path.join(model_dir, model_pack_name)}.tar.gz" create_model_data_package(model_dir, model_pack_path) @@ -137,11 +137,8 @@ def run( copied_model_pack_path = self._make_model_file_copy(self._model_pack_path, run_id) if non_default_device_is_available(self._config.DEVICE): - model = self._model_service.load_model( - copied_model_pack_path, - meta_cat_config_dict={"general": {"device": self._config.DEVICE}}, - ) - model.config.general["device"] = self._config.DEVICE + model = self._model_service.load_model(copied_model_pack_path) + model.config.general.device = self._config.DEVICE # type: ignore else: model = self._model_service.load_model(copied_model_pack_path) self._tracker_client.log_model_config(self.get_flattened_config(model)) @@ -150,12 +147,18 @@ def run( self._tracker_client.log_document_size(num_of_docs) training_params.update({"extra_cui_filter": self._get_concept_filter(cui_counts, model)}) logger.info("Performing supervised training...") - train_supervised_params = get_func_params_as_dict(model.train_supervised_from_json) + with open(data_file.name, "r") as f: + training_data = json.load(f) + train_supervised_params = get_func_params_as_dict(model.trainer.train_supervised_raw) train_supervised_params = {p_key: training_params[p_key] if p_key in training_params else p_val for p_key, p_val in train_supervised_params.items()} - model.config.version.description = description or model.config.version.description + model.config.meta.description = description or model.config.meta.description with redirect_stdout(LogCaptor(self._glean_and_log_metrics)): # type: ignore - fps, fns, tps, p, r, f1, cc, examples = model.train_supervised_from_json(data_file.name, **train_supervised_params) + fps, fns, tps, p, r, f1, cc, examples = model.trainer.train_supervised_raw(training_data, **train_supervised_params) + + # This can be removed after the returned values are fixed in medcat trainer's train_supervised_raw() + fps, fns, tps, p, r, f1, cc, examples = get_stats(model, training_data, training_params["nepochs"]) + self._save_examples(examples, ["tp", "tn"]) del examples gc.collect() @@ -171,12 +174,13 @@ def run( fn_accumulated += fns.get(cui, 0) tp_accumulated += tps.get(cui, 0) cc_accumulated += cc.get(cui, 0) + cui_info = model.cdb.cui2info.get(cui) aggregated_metrics.append({ "per_concept_fp": fps.get(cui, 0), "per_concept_fn": fns.get(cui, 0), "per_concept_tp": tps.get(cui, 0), "per_concept_counts": cc.get(cui, 0), - "per_concept_count_train": model.cdb.cui2count_train.get(cui, 0), + "per_concept_count_train": cui_info.get("count_train", 0) if cui_info is not None else 0, "per_concept_acc_fp": fp_accumulated, "per_concept_acc_fn": fn_accumulated, "per_concept_acc_tp": tp_accumulated, @@ -205,14 +209,15 @@ def run( get_model_data_package_extension(model_pack_path), "_config.json", ) - model.cdb.config.save(cdb_config_path) + with open(cdb_config_path, "w") as f: + json.dump(dump_pydantic_object_to_dict(model.config), f) model_uri = self._tracker_client.save_model(model_pack_path, self._model_name, self._model_manager) logger.info("Retrained model saved: %s", model_uri) self._tracker_client.save_model_artifact(cdb_config_path, self._model_name) else: logger.info("Skipped saving on the retrained model") if redeploy: - self.deploy_model(self._model_service, model, skip_save_model) + self.deploy_model(self._model_service, model, skip_save_model, description) else: del model gc.collect() @@ -240,7 +245,8 @@ def run( else: try: logger.info("Evaluating the running model...") - self._tracker_client.log_model_config(self.get_flattened_config(self._model_service._model)) + if self._model_service._model is not None: + self._tracker_client.log_model_config(self.get_flattened_config(self._model_service._model)) self._tracker_client.log_trainer_version(TrainerBackend.MEDCAT, medcat_version) cui_counts, cui_unique_counts, cui_ignorance_counts, num_of_docs = get_stats_from_trainer_export(data_file.name) self._tracker_client.log_document_size(num_of_docs) @@ -258,11 +264,14 @@ def run( @staticmethod def _get_concept_filter(training_concepts: Dict, model: CAT) -> Set[str]: - return set(training_concepts.keys()).intersection(set(model.cdb.cui2names.keys())) + return set(training_concepts.keys()).intersection(set(model.cdb.cui2info.keys())) def _glean_and_log_metrics(self, log: str) -> None: - metric_lines = re.findall(r"Epoch: (\d+), Prec: (\d+\.\d+), Rec: (\d+\.\d+), F1: (\d+\.\d+)", log, - re.IGNORECASE) + metric_lines = re.findall( + r"Epoch: (\d+), Prec: (\d+\.\d+), Rec: (\d+\.\d+), F1: (\d+\.\d+)", + log, + re.IGNORECASE + ) for step, metric in enumerate(metric_lines): metrics = { "precision": float(metric[1]), @@ -282,7 +291,7 @@ def _save_trained_concepts( model: CAT, ) -> None: if len(training_concepts.keys()) != 0: - unknown_concepts = set(training_concepts.keys()) - set(model.cdb.cui2names.keys()) + unknown_concepts = set(training_concepts.keys()) - set(model.cdb.cui2info.keys()) unknown_concept_pct = round(len(unknown_concepts) / len(training_concepts.keys()) * 100, 2) self._tracker_client.send_model_stats({ "unknown_concept_count": len(unknown_concepts), @@ -301,7 +310,8 @@ def _save_trained_concepts( annotation_ignorance_count = [] concepts = list(training_concepts.keys()) for c in concepts: - train_count.append(model.cdb.cui2count_train[c] if c in model.cdb.cui2count_train else 0) + cui_info = model.cdb.cui2info.get(c) + train_count.append(cui_info.get("count_train", 0) if cui_info is not None else 0) concept_names.append(model.cdb.get_name(c)) annotation_count.append(training_concepts[c]) annotation_unique_count.append(training_unique_concepts[c]) @@ -404,21 +414,18 @@ def run( logger.info("Loading a new model copy for training...") copied_model_pack_path = self._make_model_file_copy(self._model_pack_path, run_id) if non_default_device_is_available(self._config.DEVICE): - model = self._model_service.load_model( - copied_model_pack_path, - meta_cat_config_dict={"general": {"device": self._config.DEVICE}}, - ) - model.config.general["device"] = self._config.DEVICE + model = self._model_service.load_model(copied_model_pack_path) + model.config.general.device = self._config.DEVICE # type: ignore else: model = self._model_service.load_model(copied_model_pack_path) self._tracker_client.log_model_config(self.get_flattened_config(model)) - self._tracker_client.log_trainer_version(TrainerBackend.TRANSFORMERS, medcat_version) + self._tracker_client.log_trainer_version(TrainerBackend.MEDCAT, medcat_version) logger.info("Performing unsupervised training...") step = 0 - self._tracker_client.send_model_stats(model.cdb.make_stats(), step) - before_cui2count_train = dict(model.cdb.cui2count_train) + self._tracker_client.send_model_stats(dict(model.cdb.get_basic_info()), step) + before_cui2count_train = model.cdb.get_cui2count_train() num_of_docs = 0 - train_unsupervised_params = get_func_params_as_dict(model.train) + train_unsupervised_params = get_func_params_as_dict(model.trainer.train_unsupervised) train_unsupervised_params = {p_key: training_params[p_key] if p_key in training_params else p_val for p_key, p_val in train_unsupervised_params.items()} for batch in mini_batch(texts, batch_size=log_frequency): @@ -426,19 +433,20 @@ def run( self._cancel_event.clear() raise TrainingCancelledException("Training cancelled by the user") step += 1 - model.train(batch, **train_unsupervised_params) + model.trainer.train_unsupervised(batch, **train_unsupervised_params) num_of_docs += len(batch) - self._tracker_client.send_model_stats(model.cdb.make_stats(), step) + self._tracker_client.send_model_stats(dict(model.cdb.get_basic_info()), step) self._tracker_client.log_document_size(num_of_docs) after_cui2count_train = { c: ct for c, ct in sorted( - model.cdb.cui2count_train.items(), + model.cdb.get_cui2count_train().items(), key=lambda item: item[1], reverse=True, ) } + aggregated_metrics = [] cui_step = 0 for cui, train_count in after_cui2count_train.items(): @@ -462,14 +470,15 @@ def run( get_model_data_package_extension(model_pack_path), "_config.json", ) - model.cdb.config.save(cdb_config_path) + with open(cdb_config_path, "w") as f: + json.dump(dump_pydantic_object_to_dict(model.config), f) model_uri = self._tracker_client.save_model(model_pack_path, self._model_name, self._model_manager) logger.info(f"Retrained model saved: {model_uri}") self._tracker_client.save_model_artifact(cdb_config_path, self._model_name) else: logger.info("Skipped saving on the retrained model") if redeploy: - self.deploy_model(self._model_service, model, skip_save_model) + self.deploy_model(self._model_service, model, skip_save_model, description) else: del model gc.collect() diff --git a/app/trainers/metacat_trainer.py b/app/trainers/metacat_trainer.py index 8f79843..5cce3b9 100644 --- a/app/trainers/metacat_trainer.py +++ b/app/trainers/metacat_trainer.py @@ -1,16 +1,16 @@ import os +import json import logging import shutil import gc -from typing import Dict, TextIO, Optional, List - import pandas as pd +from typing import Dict, TextIO, Optional, List from medcat import __version__ as medcat_version -from medcat.meta_cat import MetaCAT +from medcat.components.addons.meta_cat.meta_cat import MetaCAT, MetaCATAddon from app.domain import TrainerBackend from app.trainers.medcat_trainer import MedcatSupervisedTrainer from app.exception import TrainingFailedException, TrainingCancelledException -from app.utils import non_default_device_is_available, get_model_data_package_extension +from app.utils import non_default_device_is_available, get_model_data_package_extension, dump_pydantic_object_to_dict logger = logging.getLogger("cms") @@ -24,7 +24,7 @@ class MetacatTrainer(MedcatSupervisedTrainer): """ @staticmethod - def get_flattened_config(model: MetaCAT, prefix: Optional[str] = None) -> Dict: + def get_flattened_metacat_config(model: MetaCAT, prefix: Optional[str] = None) -> Dict: """ Flattens the configuration of a MetaCAT model into a dictionary with string values. @@ -80,32 +80,45 @@ def run( logger.info("Loading a new model copy for training...") copied_model_pack_path = self._make_model_file_copy(self._model_pack_path, run_id) if non_default_device_is_available(self._config.DEVICE): - model = self._model_service.load_model( - copied_model_pack_path, - meta_cat_config_dict={"general": {"device": self._config.DEVICE}}, - ) - model.config.general["device"] = self._config.DEVICE + model = self._model_service.load_model(copied_model_pack_path) + model.config.general.device = self._config.DEVICE # type: ignore else: model = self._model_service.load_model(copied_model_pack_path) is_retrained = False - model.config.version.description = description or model.config.version.description - for meta_cat in model._meta_cats: + model.config.meta.description = description or model.config.meta.description + meta_cat_addons = model.get_addons_of_type(MetaCATAddon) + for meta_cat_addon in meta_cat_addons: if self._cancel_event.is_set(): self._cancel_event.clear() raise TrainingCancelledException("Training was cancelled by the user") - category_name = meta_cat.config.general["category_name"] + meta_cat = meta_cat_addon.mc + category_name = meta_cat.config.general.category_name + assert category_name is not None, "Category name should not be None" + if meta_cat.config.general.alternative_class_names == [[]]: + class_name_mapping = { + "Temporality": [["Past"], ["Recent", "Present"], ["Future"]], + "Time": [["Past"], ["Recent", "Present"], ["Future"]], + "Experiencer": [["Family"], ["Other"], ["Patient"]], + "Subject": [["Family"], ["Other"], ["Patient"]], + "Presence": [["Hypothetical (N/A)", "Hypothetical"], ["Not present (False)", "False"], ["Present (True)", "True"]], + "Status": [["Affirmed", "Confirmed"], ["Other"]], + } + meta_cat.config.general.alternative_class_names = class_name_mapping[category_name] + if training_params.get("lr_override") is not None: meta_cat.config.train.lr = training_params["lr_override"] if training_params.get("test_size") is not None: meta_cat.config.train.test_size = training_params["test_size"] meta_cat.config.train.nepochs = training_params["nepochs"] - self._tracker_client.log_model_config(self.get_flattened_config(meta_cat, category_name)) + self._tracker_client.log_model_config(self.get_flattened_metacat_config(meta_cat, category_name)) self._tracker_client.log_trainer_version(TrainerBackend.MEDCAT, medcat_version) logger.info('Performing supervised training on category "%s"...', category_name) try: mp_ext = get_model_data_package_extension(copied_model_pack_path) + if non_default_device_is_available(self._config.DEVICE): + meta_cat.config.general.device = self._config.DEVICE winner_report = meta_cat.train_from_json( data_file.name, os.path.join(copied_model_pack_path.replace(mp_ext, ""),f"meta_{category_name}"), @@ -144,7 +157,8 @@ def run( get_model_data_package_extension(model_pack_path), "_config.json", ) - model.cdb.config.save(cdb_config_path) + with open(cdb_config_path, "w") as f: + json.dump(dump_pydantic_object_to_dict(model.config), f) model_uri = self._tracker_client.save_model(model_pack_path, self._model_name, self._model_manager) logger.info("Retrained model saved: %s", model_uri) self._tracker_client.save_model_artifact(cdb_config_path, self._model_name) @@ -185,9 +199,12 @@ def run( try: logger.info("Evaluating the running model...") metrics: List[Dict] = [] - for meta_cat in self._model_service._model._meta_cats: - category_name = meta_cat.config.general["category_name"] - self._tracker_client.log_model_config(self.get_flattened_config(meta_cat, category_name)) + assert self._model_service.model is not None, "Model should not be None" + meta_cat_addons = self._model_service.model.get_addons_of_type(MetaCATAddon) + for meta_cat_addon in meta_cat_addons: + meta_cat = meta_cat_addon.mc + category_name = meta_cat.config.general.category_name + self._tracker_client.log_model_config(self.get_flattened_metacat_config(meta_cat, category_name)) self._tracker_client.log_trainer_version(TrainerBackend.MEDCAT, medcat_version) result = meta_cat.eval(data_file.name) metrics.append({"precision": result.get("precision"), "recall": result.get("recall"), "f1": result.get("f1")}) diff --git a/app/utils.py b/app/utils.py index 47faefa..6979f55 100644 --- a/app/utils.py +++ b/app/utils.py @@ -8,7 +8,7 @@ import copy import functools import warnings -import requests # type: ignore +import requests import time import torch import tarfile @@ -671,9 +671,9 @@ def load_pydantic_object_from_dict(model: Type[T], obj: Dict) -> T: """ if hasattr(model, "parse_obj"): - return model.parse_obj(obj) # type: ignore + return model.parse_obj(obj) elif hasattr(model, "model_validate"): - return model.model_validate(obj) # type: ignore + return model.model_validate(obj) else: raise TypeError("Model must have a known method for parsing objects.") @@ -690,9 +690,9 @@ def dump_pydantic_object_to_dict(model: BaseModel) -> Dict: """ if hasattr(model, "model_dump"): - return model.model_dump(mode="json") # type: ignore + return model.model_dump(mode="json") elif hasattr(model, "dict"): - return model.dict() # type: ignore + return model.dict() else: raise TypeError("Model must have a known method for dumping objects.") diff --git a/docker/Dockerfile b/docker/Dockerfile index aea6e1c..678702c 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -30,12 +30,14 @@ RUN addgroup --gid $CMS_GID cms || true && \ COPY app /app COPY pyproject.toml /pyproject.toml COPY uv.lock /uv.lock +COPY uv-requirements.txt /uv-requirements.txt COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir uv==0.7.20 && \ +RUN pip install --require-hashes -r /uv-requirements.txt --no-cache-dir && \ uv sync --locked && \ + /.venv/bin/python -m ensurepip && \ chown -R $CMS_UID:$CMS_GID /.venv && \ chmod +x /app/entrypoint.sh diff --git a/docker/huggingface-ner/Dockerfile b/docker/huggingface-ner/Dockerfile index 37eeefa..84b0093 100644 --- a/docker/huggingface-ner/Dockerfile +++ b/docker/huggingface-ner/Dockerfile @@ -31,9 +31,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/huggingface-ner/requirements.txt b/docker/huggingface-ner/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/huggingface-ner/requirements.txt +++ b/docker/huggingface-ner/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/docker/medcat-deid/Dockerfile b/docker/medcat-deid/Dockerfile index 1ca16f3..c3b7ed2 100644 --- a/docker/medcat-deid/Dockerfile +++ b/docker/medcat-deid/Dockerfile @@ -31,10 +31,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt && \ - python -m spacy download en_core_web_md -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/medcat-deid/requirements.txt b/docker/medcat-deid/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/medcat-deid/requirements.txt +++ b/docker/medcat-deid/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/docker/medcat-icd10/.env b/docker/medcat-icd10/.env index e95279d..951c987 100644 --- a/docker/medcat-icd10/.env +++ b/docker/medcat-icd10/.env @@ -1,3 +1,4 @@ ENABLE_TRAINING_APIS=true ENABLE_EVALUATION_APIS=true ENABLE_PREVIEWS_APIS=true +MEDCAT2_MAPPED_ONTOLOGIES=icd10 \ No newline at end of file diff --git a/docker/medcat-icd10/Dockerfile b/docker/medcat-icd10/Dockerfile index c1a8c93..f9e83ad 100644 --- a/docker/medcat-icd10/Dockerfile +++ b/docker/medcat-icd10/Dockerfile @@ -31,10 +31,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt && \ - python -m spacy download en_core_web_md -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/medcat-icd10/requirements.txt b/docker/medcat-icd10/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/medcat-icd10/requirements.txt +++ b/docker/medcat-icd10/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/docker/medcat-opcs4/.env b/docker/medcat-opcs4/.env index e95279d..c952059 100644 --- a/docker/medcat-opcs4/.env +++ b/docker/medcat-opcs4/.env @@ -1,3 +1,4 @@ ENABLE_TRAINING_APIS=true ENABLE_EVALUATION_APIS=true ENABLE_PREVIEWS_APIS=true +MEDCAT2_MAPPED_ONTOLOGIES=opcs4 \ No newline at end of file diff --git a/docker/medcat-opcs4/Dockerfile b/docker/medcat-opcs4/Dockerfile index fac1dfa..beb8841 100644 --- a/docker/medcat-opcs4/Dockerfile +++ b/docker/medcat-opcs4/Dockerfile @@ -31,10 +31,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt && \ - python -m spacy download en_core_web_md -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/medcat-opcs4/requirements.txt b/docker/medcat-opcs4/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/medcat-opcs4/requirements.txt +++ b/docker/medcat-opcs4/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/docker/medcat-snomed/Dockerfile b/docker/medcat-snomed/Dockerfile index a73bbf6..3bd10b7 100644 --- a/docker/medcat-snomed/Dockerfile +++ b/docker/medcat-snomed/Dockerfile @@ -32,10 +32,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt && \ - python -m spacy download en_core_web_md -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/medcat-snomed/requirements.txt b/docker/medcat-snomed/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/medcat-snomed/requirements.txt +++ b/docker/medcat-snomed/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/docker/medcat-umls/Dockerfile b/docker/medcat-umls/Dockerfile index 67045c9..2f806ab 100644 --- a/docker/medcat-umls/Dockerfile +++ b/docker/medcat-umls/Dockerfile @@ -31,10 +31,11 @@ COPY docker/entrypoint/serve.sh /app/entrypoint.sh RUN mkdir -p /app/model/model && \ mkdir -p /app/model/retrained && \ chown -R $CMS_UID:$CMS_GID /app -RUN pip install --no-cache-dir -U pip &&\ - pip install --no-cache-dir -r /app/requirements.txt && \ - python -m spacy download en_core_web_md -RUN chmod +x /app/entrypoint.sh +RUN python -m venv .venv && \ + /.venv/bin/pip install --no-cache-dir -U pip &&\ + /.venv/bin/pip install --no-cache-dir -r /app/requirements.txt &&\ + chown -R $CMS_UID:$CMS_GID /.venv && \ + chmod +x /app/entrypoint.sh WORKDIR /app EXPOSE 8000 diff --git a/docker/medcat-umls/requirements.txt b/docker/medcat-umls/requirements.txt index 6420bb6..9cfc7b4 100644 --- a/docker/medcat-umls/requirements.txt +++ b/docker/medcat-umls/requirements.txt @@ -1,20 +1,20 @@ -medcat~=1.14.1 -transformers~=4.43.0 -blis<1.0.0 -fastapi~=0.102.0 +medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0 +datasets>=2.21.0 +fastapi~=0.115.0 uvicorn~=0.29.0 -python-multipart~=0.0.5 +python-multipart~=0.0.7 ijson~=3.1.4 python-dotenv~=0.20.0 mlflow~=2.16.2 psycopg2-binary~=2.9.4 boto3~=1.28.84 -typer~=0.7.0 -prometheus-fastapi-instrumentator~=5.11.2 +click<8.2.0 +typer~=0.15.1 +prometheus-fastapi-instrumentator~=7.0.0 sentencepiece~=0.2.0 slowapi~=0.1.7 graypy~=2.1.0 -fastapi-users~=11.0.0 +fastapi-users~=13.0.0 fastapi-users-db-sqlalchemy~=5.0.0 asyncpg~=0.27.0 aiosqlite~=0.19.0 @@ -23,5 +23,6 @@ websockets~=12.0 pynvml~=11.5.3 toml~=0.10.2 peft<0.14.0 +huggingface-hub~=0.33.0 setuptools wheel \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0aaaaea..20cc8e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,14 +6,12 @@ build-backend = "setuptools.build_meta" name = "cogstack-model-serve" authors = [{name = "Xi Bai", email = "baixiac@gmail.com"}] description = "A model serving and governance system for CogStack NLP solutions" -requires-python = ">=3.8,<3.12" +requires-python = ">3.9,<3.13" dynamic = ["version"] dependencies = [ - "medcat~=1.16.0; python_version >= '3.9'", - "medcat~=1.13.1; python_version < '3.9'", - "torch<2.5.0; python_version < '3.9'", + "medcat[spacy,meta-cat,deid,rel-cat]~=2.2.0; python_version>='3.10'", + "medcat[spacy,meta-cat,deid,rel-cat]~=2.1.0; python_version<'3.10'", "datasets>=2.21.0", - "blis<1.0.0", "fastapi~=0.115.0", "uvicorn~=0.29.0", "python-multipart~=0.0.7", @@ -30,7 +28,7 @@ dependencies = [ "graypy~=2.1.0", "fastapi-users~=13.0.0", "fastapi-users-db-sqlalchemy~=5.0.0", - "asyncpg~=0.27.0", + "asyncpg~=0.29.0", "aiosqlite~=0.19.0", "evaluate~=0.4.1", "websockets~=12.0", @@ -45,7 +43,6 @@ license = {text = "Elastic License 2.0"} classifiers=[ "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", @@ -71,6 +68,7 @@ dev = [ "locust<2.32.0", "typer-cli~=0.15.1", "types-toml==0.10.8.20240310", + "types-requests>=2.31.0.6", "openai>=1.84.0", ] docs = [ @@ -81,7 +79,7 @@ docs = [ ] vllm = [ - "vllm~=0.8.5; python_version >= '3.9'", + "vllm~=0.8.5", "trl>=0.11.4", "bitsandbytes>=0.45.5", ] @@ -101,6 +99,7 @@ dev = [ "ruff==0.6.9", "locust<2.32.0", "typer-cli~=0.15.1", + "types-requests>=2.31.0.6", "types-toml==0.10.8.20240310", "openai>=1.84.0", ] @@ -112,7 +111,7 @@ docs = [ ] vllm = [ - "vllm~=0.8.5; python_version >= '3.9'", + "vllm~=0.8.5", "trl>=0.11.4", "bitsandbytes>=0.45.5", ] diff --git a/tests/app/conftest.py b/tests/app/conftest.py index ee9c3c5..087ed07 100644 --- a/tests/app/conftest.py +++ b/tests/app/conftest.py @@ -22,7 +22,7 @@ def mlflow_fixture(mocker): active_run.info.run_id = "run_id" active_run.data = MagicMock() active_run.data.metrics = {"precision": 0.9973285610540512, "recall": 0.9896606632947247, "f1": 0.9934285636532457} - active_run.data.tags = {"tag": "tag"} + active_run.data.tags = {"training.entity.classes": "['concept_1', 'concept_2']"} mocker.patch("mlflow.set_tracking_uri") mocker.patch("mlflow.get_tracking_uri", return_value="http://localhost:5000") mocker.patch("mlflow.get_experiment_by_name", return_value=None) @@ -49,7 +49,7 @@ def mlflow_fixture(mocker): def medcat_snomed_model(): config = Settings() config.BASE_MODEL_FILE = "snomed_model.zip" - config.TYPE_UNIQUE_ID_WHITELIST = "T-9,T-11,T-18,T-39,T-40,T-45" + config.TYPE_UNIQUE_ID_WHITELIST = "91776366,81102976,28321150,67667581,9090192,27603525" return MedCATModelSnomed(config, MODEL_PARENT_DIR, True) @@ -57,7 +57,7 @@ def medcat_snomed_model(): def medcat_icd10_model(): config = Settings() config.BASE_MODEL_FILE = "icd10_model.zip" - config.TYPE_UNIQUE_ID_WHITELIST = "T-9,T-11,T-18,T-39,T-40,T-45" + config.TYPE_UNIQUE_ID_WHITELIST = "91776366,81102976,28321150,67667581,9090192,27603525" return MedCATModelIcd10(config, MODEL_PARENT_DIR, True) diff --git a/tests/app/model_services/test_medcat_model_icd10.py b/tests/app/model_services/test_medcat_model_icd10.py index a24c732..b1f6bcd 100644 --- a/tests/app/model_services/test_medcat_model_icd10.py +++ b/tests/app/model_services/test_medcat_model_icd10.py @@ -30,7 +30,7 @@ def test_get_records_from_doc(medcat_icd10_model): "0": { "pretty_name": "pretty_name", "cui": "cui", - "types": ["type"], + "type_ids": ["type"], "icd10": [{"code": "code", "name": "name"}], "athena_ids": [{"name": "name_1", "code": "code_1"}, {"name": "name_2", "code": "code_2"}], "acc": 1.0, @@ -57,7 +57,7 @@ def test_init_model_with_no_tui_filter(medcat_icd10_model): medcat_icd10_model._whitelisted_tuis = set([""]) medcat_icd10_model.init_model() assert medcat_icd10_model.model is not None - assert medcat_icd10_model.model.cdb.config.linking.filters.get("cuis") == original.cdb.config.linking.filters.get("cuis") + assert medcat_icd10_model.model.config.components.linking.filters.cuis == original.config.components.linking.filters.cuis @pytest.mark.skipif( @@ -69,7 +69,7 @@ def test_init_model(medcat_icd10_model): target_tuis = medcat_icd10_model._config.TYPE_UNIQUE_ID_WHITELIST.split(",") target_cuis = {cui for tui in target_tuis for cui in medcat_icd10_model.model.cdb.addl_info.get("type_id2cuis").get(tui, {})} assert medcat_icd10_model.model is not None - assert medcat_icd10_model.model.cdb.config.linking.filters.get("cuis") == target_cuis + assert medcat_icd10_model.model.config.components.linking.filters.cuis == target_cuis @pytest.mark.skipif( diff --git a/tests/app/model_services/test_medcat_model_snomed.py b/tests/app/model_services/test_medcat_model_snomed.py index 8d147a1..4928660 100644 --- a/tests/app/model_services/test_medcat_model_snomed.py +++ b/tests/app/model_services/test_medcat_model_snomed.py @@ -29,7 +29,7 @@ def test_get_records_from_doc(medcat_snomed_model): "0": { "pretty_name": "pretty_name", "cui": "cui", - "types": ["type"], + "type_ids": ["type"], "athena_ids": [{"name": "name_1", "code": "code_1"}, {"name": "name_2", "code": "code_2"}], "meta_anns": {} } @@ -51,8 +51,10 @@ def test_init_model(medcat_snomed_model): medcat_snomed_model.init_model() target_tuis = medcat_snomed_model._config.TYPE_UNIQUE_ID_WHITELIST.split(",") target_cuis = {cui for tui in target_tuis for cui in medcat_snomed_model.model.cdb.addl_info.get("type_id2cuis").get(tui, {})} + mapped_ontologies = medcat_snomed_model._config.MEDCAT2_MAPPED_ONTOLOGIES.split(",") assert medcat_snomed_model.model is not None - assert medcat_snomed_model.model.cdb.config.linking.filters.get("cuis") == target_cuis + assert medcat_snomed_model.model.config.components.linking.filters.cuis == target_cuis + assert medcat_snomed_model.model.config.general.map_to_other_ontologies == mapped_ontologies @pytest.mark.skipif( @@ -64,7 +66,7 @@ def test_init_model_with_no_tui_filter(medcat_snomed_model): medcat_snomed_model._whitelisted_tuis = set([""]) medcat_snomed_model.init_model() assert medcat_snomed_model.model is not None - assert medcat_snomed_model.model.cdb.config.linking.filters.get("cuis") == original.cdb.config.linking.filters.get("cuis") + assert medcat_snomed_model.model.config.components.linking.filters.cuis == original.config.components.linking.filters.cuis @pytest.mark.skipif( diff --git a/tests/app/model_services/test_trf_model_deid.py b/tests/app/model_services/test_trf_model_deid.py index afa635c..c93cc0c 100644 --- a/tests/app/model_services/test_trf_model_deid.py +++ b/tests/app/model_services/test_trf_model_deid.py @@ -2,7 +2,7 @@ import pytest from tests.app.conftest import MODEL_PARENT_DIR from transformers.models.bert.modeling_bert import BertForTokenClassification -from medcat.tokenizers.transformers_ner import TransformersTokenizerNER +from medcat.components.ner.trf.tokenizer import TransformersTokenizer from app import __version__ from app.domain import ModelType from app.model_services.trf_model_deid import TransformersModelDeIdentification @@ -31,7 +31,7 @@ def test_init_model(trf_model): ) def test_load_model(trf_model): tokenizer, model = TransformersModelDeIdentification.load_model(os.path.join(MODEL_PARENT_DIR, "trf_deid_model.zip")) - assert type(tokenizer) is TransformersTokenizerNER + assert type(tokenizer) is TransformersTokenizer assert type(model) is BertForTokenClassification diff --git a/tests/app/monitoring/test_tracker_client.py b/tests/app/monitoring/test_tracker_client.py index 201529a..edcbaee 100644 --- a/tests/app/monitoring/test_tracker_client.py +++ b/tests/app/monitoring/test_tracker_client.py @@ -347,7 +347,7 @@ def test_get_info_by_job_id(mlflow_fixture): output_format="list", ) assert len(job_info) == 1 - assert job_info[0]["tags"] == {"tag": "tag"} + assert job_info[0]["tags"] == {"training.entity.classes": "['concept_1', 'concept_2']"} def test_get_metrics_by_job_id(mlflow_fixture): @@ -384,4 +384,5 @@ def test_get_metrics_by_job_id(mlflow_fixture): "precision": [0.9973285610540512, 0.9973285610540512], "recall": [0.9896606632947247, 0.9896606632947247], "f1": [0.9934285636532457, 0.9934285636532457], + "concepts": ["concept_1", "concept_2"], }] diff --git a/tests/app/trainers/test_hf_llm_trainer.py b/tests/app/trainers/test_hf_llm_trainer.py new file mode 100644 index 0000000..65e9d3b --- /dev/null +++ b/tests/app/trainers/test_hf_llm_trainer.py @@ -0,0 +1,56 @@ +import os +from unittest import skipIf +from unittest.mock import create_autospec, patch, Mock +from transformers import PreTrainedTokenizerFast +from app.model_services.huggingface_llm_model import HuggingFaceLlmModel +from app.trainers.huggingface_llm_trainer import HuggingFaceLlmSupervisedTrainer +from app.config import Settings + +model_parent_dir = os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fixture") +config = Settings() +config.MLFLOW_TRACKING_URI = "http://localhost:5000" +config.TRAINING_CACHE_DIR = "/tmp/test_cache" +config.REDEPLOY_TRAINED_MODEL = "false" +config.SKIP_SAVE_MODEL = "false" +config.DEVICE = "cpu" +config.TRAINING_SAFE_MODEL_SERIALISATION = "true" + +model_service = create_autospec( + HuggingFaceLlmModel, + _config=config, + _model_parent_dir=model_parent_dir, + _enable_trainer=True, + _model_pack_path=os.path.join(model_parent_dir, "model.zip"), +) +model_service.tokenizer = Mock(spec=PreTrainedTokenizerFast) +model_service.model = Mock() +model_service.model.config.max_position_embeddings = 512 +model_service.model_name = "llm_test_model" + +supervised_trainer = HuggingFaceLlmSupervisedTrainer(model_service) +supervised_trainer.model_name = "supervised_trainer" + +data_dir = os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fixture") + +def test_deploy_model(): + model = Mock() + tokenizer = Mock() + HuggingFaceLlmSupervisedTrainer.deploy_model(model_service, model, tokenizer) + assert model_service.model == model + assert model_service.tokenizer == tokenizer + + +def test_huggingface_llm_supervised_trainer(mlflow_fixture): + with patch.object(supervised_trainer, "run", wraps=supervised_trainer.run) as run: + supervised_trainer._tracker_client = Mock() + supervised_trainer._tracker_client.start_tracking = Mock(return_value=("experiment_id", "run_id")) + with open(os.path.join(data_dir, "trainer_export.json"), "r") as f: + supervised_trainer.train(f, 1, 1, "training_id", "input_file_name") + supervised_trainer._tracker_client.start_tracking.assert_called_once() + run.assert_called_once() + + +@skipIf(config.DEVICE != "cuda", "This requires a CUDA device to run") +def test_huggingface_llm_supervised_run(mlflow_fixture): + with open(os.path.join(data_dir, "trainer_export.json"), "r") as data_file: + HuggingFaceLlmSupervisedTrainer.run(supervised_trainer, {"nepochs": 1, "print_stats": 1}, data_file, 1, "run_id") diff --git a/tests/app/trainers/test_medcat_trainer.py b/tests/app/trainers/test_medcat_trainer.py index dc80ab0..82bf2c3 100644 --- a/tests/app/trainers/test_medcat_trainer.py +++ b/tests/app/trainers/test_medcat_trainer.py @@ -1,6 +1,6 @@ import os from unittest.mock import create_autospec, patch, Mock -from medcat.config import General +from medcat.config.config import General from app.config import Settings from app.model_services.medcat_model import MedCATModel from app.trainers.medcat_trainer import MedcatSupervisedTrainer, MedcatUnsupervisedTrainer @@ -26,9 +26,7 @@ def test_get_flattened_config(): model = Mock() model.cdb.config.general = General() config = supervised_trainer.get_flattened_config(model) - assert "word_skipper" in config - assert "punct_checker" in config - assert "linking.filters" not in config + assert len(config) > 0 def test_deploy_model(): @@ -40,15 +38,15 @@ def test_deploy_model(): def test_save_model_pack(): model = Mock() - model.create_model_pack.return_value = "model_pack_name" + model.save_model_pack.return_value = "model_pack_name" supervised_trainer.save_model_pack( model, "retrained_models_dir", "model.zip", "model description", ) - model.create_model_pack.called_once_with("retrained_models_dir", "model") - assert model.config.version.description == "model description" + model.save_model_pack.assert_called_once_with("retrained_models_dir", "model") + assert model.config.meta.description == "model description" def test_medcat_supervised_trainer(mlflow_fixture): diff --git a/tests/app/trainers/test_metacat_trainer.py b/tests/app/trainers/test_metacat_trainer.py index b9718ae..5ab023e 100644 --- a/tests/app/trainers/test_metacat_trainer.py +++ b/tests/app/trainers/test_metacat_trainer.py @@ -1,6 +1,6 @@ import os from unittest.mock import create_autospec, patch, Mock -from medcat.config_meta_cat import General, Model, Train +from medcat.config.config_meta_cat import General, Model, Train from app.config import Settings from app.model_services.medcat_model import MedCATModel from app.trainers.metacat_trainer import MetacatTrainer @@ -19,12 +19,12 @@ data_dir = os.path.join(os.path.dirname(__file__), "..", "..", "resources", "fixture") -def test_get_flattened_config(): +def test_get_flattened_metacat_config(): model = Mock() model.config.general = General() model.config.model = Model() model.config.train = Train() - config = metacat_trainer.get_flattened_config(model, "prefix") + config = metacat_trainer.get_flattened_metacat_config(model, "prefix") for key, val in config.items(): assert "prefix.general." in key or "prefix.model." in key or "prefix.train" in key @@ -38,15 +38,15 @@ def test_deploy_model(): def test_save_model_pack(): model = Mock() - model.create_model_pack.return_value = "model_pack_name" + model.save_model_pack.return_value = "model_pack_name" metacat_trainer.save_model_pack( model, "retrained_models_dir", "model.zip", "model description", ) - model.create_model_pack.called_once_with("retrained_models_dir", "model") - assert model.config.version.description == "model description" + model.save_model_pack.assert_called_once_with("retrained_models_dir", "model") + assert model.config.meta.description == "model description" def test_metacat_trainer(mlflow_fixture): diff --git a/tests/integration/steps/test_steps.py b/tests/integration/steps/test_steps.py index bf6d868..a302cba 100644 --- a/tests/integration/steps/test_steps.py +++ b/tests/integration/steps/test_steps.py @@ -210,12 +210,16 @@ def check_response_evaluation_metrics(context): assert context["response"].headers["Content-Type"] == "application/json" response_json = context["response"].json() assert len(response_json) == 1 - assert "precision" in response_json[0] - assert "recall" in response_json[0] - assert "f1" in response_json[0] + assert "per_concept_counts" in response_json[0] + assert "per_concept_count_train" in response_json[0] + assert "per_concept_acc_fp" in response_json[0] + assert "per_concept_acc_fn" in response_json[0] + assert "per_concept_acc_tp" in response_json[0] + assert "per_concept_acc_cc" in response_json[0] assert "per_concept_precision" in response_json[0] assert "per_concept_recall" in response_json[0] assert "per_concept_f1" in response_json[0] + assert "concepts" in response_json[0] context["response"].close() @then("the response should contain the unsupervised evaluation metrics") diff --git a/uv-requirements.in b/uv-requirements.in new file mode 100644 index 0000000..97394e0 --- /dev/null +++ b/uv-requirements.in @@ -0,0 +1 @@ +uv==0.8.10 \ No newline at end of file diff --git a/uv-requirements.txt b/uv-requirements.txt new file mode 100644 index 0000000..f1288cd --- /dev/null +++ b/uv-requirements.txt @@ -0,0 +1,23 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile --generate-hashes --output-file=uv-requirements.txt --strip-extras --python=3.10 uv-requirements.in +uv==0.8.10 \ + --hash=sha256:31e4fc37ee94b94c032384a0957ad32ba7dce4ce6c04b4880fd3e31e25e51a82 \ + --hash=sha256:36a5ce708d52388c37043e7335f9eb3fea5a19a56166a2cc6adb365179a1cd77 \ + --hash=sha256:38286d230daad82388469c8dc7a1d2f5dc279c11178319c886d1a88d7938e513 \ + --hash=sha256:3e190cee3bb2b4f574a419eef87ae8e33f713e9cd6f856b83277ece70ad9ca9b \ + --hash=sha256:3fdf89fc40af9902141c39ed943bcfca15664623363335eb032a44f22001e2b4 \ + --hash=sha256:4cc190d403a89e46d13cec83b6f8e8d7d07aaf1e5a996eac9a3f0c2a8cd92537 \ + --hash=sha256:57b71dc79eff25a5419d3fe4a563d3b9397f55d789f685ef27f43f033b31f482 \ + --hash=sha256:86fe044c2be43977566a0d184a487edd7aace2febb757fd95927684b629ef50b \ + --hash=sha256:88df34c32555064fae459cce665757619fd1af7deb2dc393352b15d909d2d131 \ + --hash=sha256:9ad21eeaa4156a1bf5ed85903f80db06e2c02badd3a587ba98d3171517960555 \ + --hash=sha256:a5495b5a6e3111c03cf5e4dbdd598bc8fd1da887e3920d58cd5a2d4c8bc9a473 \ + --hash=sha256:ab072cd3bf2f9dc264659a1ff48ad91a910ac4830bcfe965e2d3f89c86646f46 \ + --hash=sha256:af8a5526b0e331775a264fa0dbccfd53c183cb974f269a208af136d7561f9eb2 \ + --hash=sha256:b00637c63d5dfc9f879281c5c91db2bb909ab1f9ab275dab015e7fb6cac6be5b \ + --hash=sha256:b3ff3c451fcd23ea78356d8c18e802d0e423cbe655273601e3ec039a51b33286 \ + --hash=sha256:c4a493cd4b15b3aef11523531aff96a77a586666a63e842fa437966b7b7ee62d \ + --hash=sha256:defc50bb319be2d58be74a680710cd4b7697e88d5f79974eacd354df95f0b6b0 \ + --hash=sha256:e0a02bcec766eb0862b7082ab746b204add7d9fcaa62322502d159b5a7ccc54a \ + --hash=sha256:eb79a46d8099f563ef58237bf4e9009f876a40145e757ea883a92b24b724d01e + # via -r uv-requirements.in diff --git a/uv.lock b/uv.lock index 255a329..3f3f975 100644 --- a/uv.lock +++ b/uv.lock @@ -1,523 +1,214 @@ version = 1 -revision = 1 -requires-python = ">=3.8, <3.12" -resolution-markers = [ - "python_full_version > '3.11' and sys_platform == 'darwin'", - "python_full_version > '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version > '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version > '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", +revision = 3 +requires-python = ">3.9, <3.13" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform == 'darwin'", + "python_full_version > '3.11' and python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version > '3.11' and python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version > '3.11' and python_full_version < '3.12' and sys_platform != 'darwin' and 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