Grants tagger is a machine learning powered tool that assigns biomedically related tags to grants proposals. Those tags can be custom to the organisation or based upon a preexisting ontology like MeSH.
The tool is current being developed internally at the Wellcome Trust for internal use but both the models and the code will be made available in a reusable manner.
This work started as a means to automate the tags of one funding division within Wellcome but currently it has expanded into the development and automation of a complete set of tags that can cover past and future directions for the organisation.
Science tags refer to the custom tags for the Science funding division. These tags are higly specific to the research Wellcome funds so it is not advisable to use them.
MeSH tags are subset of tags from the MeSH ontology that aim to tags grants according to:
- diseases
- themes of research Those tags are generic enough to be used by other biomedical funders but note that the selection of tags are highly specific to Wellcome at the moment.
Easiest way to install is with pip. The project is available in the PyPI repository at https://pypi.org/project/grants-tagger/.
pip install grants-tagger
Alternatively you can clone this repository and manually install with
pip install setup.py
You can download the mesh pretrained model using:
grants_tagger download model mesh
Or, you can download some pretrained models from the releases section of this repository. Unzip this in the models folder. This will contain the mesh model and the label-binarizer.
After that, you can predict a text's tag using the predict cli command.
Grants tagger comes with a nice CLI with the following commands
| Commands | |
|---|---|
| ⚙️ preprocess | preprocess data to use for training |
| 🔥 train | trains a new model |
| 📈 evaluate | evaluate performance of pretrained model |
| 🔖 predict | predict tags given a grant abstract using a pretrained model |
| 🎛 tune | tune params and threshold |
| 📚 pretrain | pretrains embeddings or language model using unlabeled data |
| ⬇️ download | download trained models and data from EPMC |
| 🔍 explain | importance of feature be it words or tfidf numbers |
| 🌐 visualize | creates a streamlit app to interactively tag grants |
in square brackets the commands that are not implemented yet
Preprocess creates a JSONL datafile with text, tags and meta as keys.
Text and tags are used for training whereas meta can be useful during annotation
or to analyse predictions and performance. Each dataset needs its own
preprocessing so the current preprocess works with the wellcome-science dataset and
the bioasq-mesh one. If you want to use a different dataset see section on bringing
your own data under development.
Usage: grants_tagger preprocess wellcome-science [OPTIONS] [INPUT_PATH]
[OUTPUT_PATH]
[LABEL_BINARIZER_PATH]
Arguments:
[INPUT_PATH] path to raw Excel file with tagged or untagged grant data
[TRAIN_OUTPUT_PATH] path to JSONL output file that will be generated for the train set
[LABEL_BINARIZER_PATH] path to pickle file that will contain the label binarizer
Options:
--test-output-path PATH path to JSONL output file that will be generated for the test set
--text-cols TEXT comma delimited column names to concatenate to text
--meta-cols TEXT comma delimited column names to include in the meta
--test-split FLOAT split percentage for test data. if None no split.
--config PATH path to config file that defines the arguments
--help Show this message and exit.
Usage: grants_tagger preprocess bioasq-mesh [OPTIONS] [INPUT_PATH]
[TRAIN_OUTPUT_PATH]
[LABEL_BINARIZER_PATH]
Arguments:
[INPUT_PATH] path to BioASQ JSON data
[TRAIN_OUTPUT_PATH] path to JSONL output file that will be generated for
the train set
[LABEL_BINARIZER_PATH] path to pickle file that will contain the label
binarizer
Options:
--test-output-path TEXT path to JSONL output file that will be generated
for the test set
--mesh-tags-path TEXT path to mesh tags to filter
--test-split FLOAT split percentage for test data. if None no split.
[default: 0.01]
--filter-years TEXT years to keep in form min_year,max_year with both
inclusive
--config PATH path to config files that defines arguments
--help Show this message and exit.
Train acts as the entry point command for training all models. You can control
which model you want to use with an --approach flag. This is convenient but
also not ideal as different models require different params and considerations.
This will change in the future and the approach will be a subcommand so each
model will trigger a different train and have different params.
Usage: grants_tagger train [OPTIONS] [DATA_PATH] [LABEL_BINARIZER_PATH]
[MODEL_PATH]
Arguments:
[DATA_PATH] path to processed JSON data to be used for training
[LABEL_BINARIZER_PATH] path to label binarizer
[MODEL_PATH] path to output model.pkl or dir to save model
Options:
--approach TEXT tfidf-svm, scibert, cnn, ... [default:
tfidf-svm]
--parameters TEXT model params in sklearn format e.g.
{'svm__kernel: linear'}
--threshold FLOAT threshold to assign a tag
--data-format TEXT format that will be used when loading the
data. One of list,generator [default: list]
--sparse-labels / --no-sparse-labels
flat about whether labels should be sparse
when binarized [default: False]
--cache-path PATH path to cache data transformartions
--config PATH
--cloud / --no-cloud flag to train using Sagemaker [default:
False]
--instance-type TEXT instance type to use when training with
Sagemaker [default: local]
--help Show this message and exit.
Label binarizer converts tags to a Y matrix with one column per tag. This is neccesary as this is a multilabel problem so multiple tags may be assigned to one grant.
Parameters is a JSON like dump (essentially a stringified dict) with all params that the model expects in an sklearn fashion.
There is a small functionality to notify a Slack channel after you train. For this,
you need to chmod +x /train-slack-notify.sh, set up the environement variables
SLACK_HOOK and SLACK_USER and, when running your training command, run it in
quotation marks like:
./train-slack-notify.sh 'grants_tagger train etc...' # Quotations needed!Evaluate enables evaluation of the performance of various approaches including human performance and other systems like MTI, SciSpacy and soon Dimensions. As such evaluate has the followin subcommands
Model is the generic entrypoint for model evaluation. Similar to train approach
controls which model will be evaluated. Approach which is a positional argument
in this command controls which model will be evaluated. Since the data in train
are sometimes split inside train, the same splitting is performed in evaluate.
Evaluate only supports some models, in particular those that have made it to
production. These are: tfidf-svm, scibert, science-ensemble, mesh-tfidf-svm
and mesh-cnn. Note that train also outputs evaluation scores so for models
not made into production this is the way to evaluate. The plan is to extend
evaluate to all models when train starts training explicit model approaches.
Usage: grants_tagger evaluate model [OPTIONS] APPROACH MODEL_PATH DATA_PATH
LABEL_BINARIZER_PATH
Arguments:
APPROACH model approach e.g.mesh-cnn [required]
MODEL_PATH comma separated paths to pretrained models [required]
DATA_PATH path to data that was used for training [required]
LABEL_BINARIZER_PATH path to label binarize [required]
Options:
--threshold TEXT threshold or comma separated thresholds used
to assign tags [default: 0.5]
--results-path TEXT path to save results
--mesh-tags-path TEXT path to mesh subset to evaluate
--split-data / --no-split-data flag on whether to split data in same way as
was done in train [default: True]
--grants / --no-grants flag on whether the data is grants data
instead of publications to evaluate MeSH
[default: False]
--config PATH path to config file that defines arguments
--help Show this message and exit.
Human evaluates human accuracy on the task in the case human annotations exists. Currently only works for wellcome-science that has two annotations per grant but it can be extended to support a more general format in the future.
Usage: grants_tagger evaluate human [OPTIONS] DATA_PATH LABEL_BINARIZER_PATH
Arguments:
DATA_PATH [required]
LABEL_BINARIZER_PATH [required]
Options:
--help Show this message and exit.
MTI is the automatic mesh indexer from NLM. https://ii.nlm.nih.gov/MTI/ To get MTI annotations you need to submit grants for tagging through an email service and get the results which you can use here for evaluation. The service is called Batch MetaMap and to use it you need to have an account https://uts.nlm.nih.gov/uts/
Usage: grants_tagger evaluate mti [OPTIONS] DATA_PATH LABEL_BINARIZER_PATH
MTI_OUTPUT_PATH
Arguments:
DATA_PATH path to sample JSONL mesh data [required]
LABEL_BINARIZER_PATH path to pickled mesh label binarizer [required]
MTI_OUTPUT_PATH path to mti output txt [required]
Options:
--help Show this message and exit.
SciSpacy is a tool developed by AllenAI that identifies mainly entities in biomedical text using Spacy with almost state of the art accuracy. These entities are linked to various ontologies some of which is MeSH. Even though NER combined with Entity linking and Multilabel text classification are not the same problem, this command evaluate how SciSpacy would perform on our problem.
Usage: grants_tagger evaluate scispacy [OPTIONS] DATA_PATH
LABEL_BINARIZER_PATH MESH_METADATA_PATH
Arguments:
DATA_PATH JSONL of mesh data that contains text, tags and meta
per line [required]
LABEL_BINARIZER_PATH label binarizer that transforms mesh names to
binarized format [required]
MESH_METADATA_PATH csv that contains metadata about mesh such as UI, Name
etc [required]
Options:
--help Show this message and exit.
Predict assigns tags on a given abstract text that you can pass as argument. It is not meant to be used for tagging multiple grants, tag command is reserved for that. Similar to evaluate and train an approach is needed to know which model will be used. Similar to evaluate it also works on models that have made it to production, see list in evaluate.
Usage: grants_tagger predict [OPTIONS] TEXT MODEL_PATH LABEL_BINARIZER_PATH
APPROACH
Arguments:
TEXT [required]
MODEL_PATH [required]
LABEL_BINARIZER_PATH [required]
APPROACH [required]
Options:
--probabilities / --no-probabilities
[default: False]
--threshold FLOAT [default: 0.5]
--help Show this message and exit.
Tune optimises params of choice or the threshold that is used to assign tags. Parameter optimisation makes use of sklearn GridSearch and currently works on a predefined set of approaches and params. At a later point it will accept the parameter search space and will perform the optimisation either locally or in SageMaker. Threshold optimises the individual thresholds that assign tags. Each tag can have an individual threshold that together maximises f1 score.
Usage: grants_tagger tune params [OPTIONS] DATA_PATH LABEL_BINARIZER_PATH
APPROACH
Arguments:
DATA_PATH [required]
LABEL_BINARIZER_PATH [required]
APPROACH [required]
Options:
--params TEXT
--help Show this message and exit.
Usage: grants_tagger tune threshold [OPTIONS] APPROACH DATA_PATH MODEL_PATH
LABEL_BINARIZER_PATH THRESHOLDS_PATH
Arguments:
APPROACH modelling approach e.g. mesh-cnn [required]
DATA_PATH path to data in jsonl to train and test model
[required]
MODEL_PATH path to data in jsonl to train and test model
[required]
LABEL_BINARIZER_PATH path to label binarizer [required]
THRESHOLDS_PATH path to save threshold values [required]
Options:
--val-size FLOAT validation size of text data to use for
tuning [default: 0.8]
--nb-thresholds INTEGER number of thresholds to be tried divided
evenly between 0 and 1
--init-threshold FLOAT value to initialise threshold values
--split-data / --no-split-data flag on whether to split data as was done
for train [default: True]
--help Show this message and exit.
Pretrain pretrains an embedding based model like Doc2Vec and in the future a language model like BERT from unlabeled training examples.
It allows us to leverage transfer learning in domains with limited data. It applies more to wellcome-science than bioasq-mesh since the latter contains enough examples to not being in need of transfer learning.
Usage: grants_tagger pretrain [OPTIONS] DATA_PATH MODEL_PATH
Arguments:
DATA_PATH data to pretrain model on [required]
MODEL_PATH path to save mode [required]
Options:
--model-name TEXT name of model to pretrain [default: doc2vec]
--config PATH config file with arguments for pretrain
--help Show this message and exit.
This commands enables you to download mesh data from EPMC and pre trained models.
Usage: grants_tagger download [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
epmc-mesh
model
Available models:
- mesh
This command produces shap values for global or local expanations for a model (currently only mesh-cnn) and a given label. It saves the explanations into an html to visualise them.
Note that global explanations take time to compute so passing a txt with 100 examples take a bit less than 1 hour. Take care to ask for explanations for a label that is predicted in the txt examples provided otherwise explanations might be less useful
Usage: grants_tagger explain [OPTIONS] TEXTS_PATH LABEL APPROACH MODEL_PATH
LABEL_BINARIZER_PATH EXPLANATIONS_PATH
Arguments:
TEXTS_PATH path to txt file with one text in every line
[required]
LABEL label to explain with local or global explanations
[required]
APPROACH model approach e.g. mesh-cnn [required]
MODEL_PATH path to model to explain [required]
LABEL_BINARIZER_PATH path to label binarizer associated with mode
[required]
EXPLANATIONS_PATH path to save explanations html [required]
Options:
--global-explanations / --no-global-explanations
flag on whether global or local explanations
should be produced [default: True]
--help Show this message and exit.
This command uses streamlit to create a web app in which you can interactively tag grants while choosing the threshold and the model to use. It currently works only with Wellcome trained models.
If you want to visualise
make build-streamlit-dockerAnd then
docker run -p 8501:8501 streamlitappAnd when you're really happy with it you can push the tag
make -f Makefile.streamlit docker-pushIf you work for Wellcome and have access to our AWS account,
you easily download the raw data by typing make sync_data.
This will give you access to both the custom science tags
dataset and the MeSH data. Note that the mesh data is 50+GB.
If you want to download only the science or mesh data, you
can do so with make sync_science_data and make sync_mesh_data
respectively.
The MeSH data can be downloaded from various places like EPMC. Grants tagger currently uses a sample provided from the BioASQ competition that contains tags for approx 14M publications from PubMed.
To create and setup the development environment
make virtualenv
This will create a new virtualenv and install requirements for tests and development. It will also install grants tagger in editable mode.
If you want to add additional dependencies, add the library to
unpinned_requirements.txt and run make update-requirements. This
will ensure that all requirements in the development enviroment are pinned
to exact versions which ensures the code will continue running as
expected in the future when newer versions will have been published.
You need to set the following variables for sagemaker or sync to work. If you want to participate to BIOASQ competition you need to also set some variables.
| Variable | Required for | Description |
|---|---|---|
| PROJECTS_BUCKET | sagemaker, sync | s3 bucket for data and models e.g. datalabs-data |
| PROJECT_NAME | sagemaker, sync | s3 prefix for specific project e.g.grants_tagger |
| AWS_ACCOUNT_ID | sagemaker | aws organisational account id, ask aws adminstrator |
| ECR_IMAGE | sagemaker | ecr image with dependencies to run grants tagger |
| SAGEMAKER_ROLE | sagemaker | aws sagemaker role, ask aws administrator |
| AWS_ACCESS_KEY_ID | sagemaker, sync | aws access key, for aws cli to work |
| AWS_SECRET_ACCESS_KEY | sagemaker, sync | aws secret key, for aws cli to work |
| BIOASQ_USERNAME | bioasq | username with which registered in BioASQ |
| BIOASQ_PASSWORD | bioasq | password --//-- |
There is a .envrc.template with the env variables needed. If you
use direnv then you can use it to populate
your .envrc which will export the variables automatically, otherwise
ensure you export every time or include in your bash profile.
Note that aws keys are not included as there are various ways to
setup aws, for example using aws configure or AWS_PROFILE and
.aws/credentials
To reproduce production models we use DVC. DVC defines a directed
acyclic graph (DAG) of steps that need to run to reproduce a model
or result. You can see all steps with dvc dag. You can reproduce
all steps with dvc repro. You can reproduce any step of the DAG
with dvc repro STEP_NAME for example dvc repro train_tfidf_svm.
Note that mesh models require a GPU to train and depending on the
parameters it might take from 1 to several days.
You can reproduce individual experiments using one of the configs in
the dedicated /configs folder. You can run all steps of the pipeline
using ./scripts/run_DATASET_config.sh path_to_config where DATASET
can be one of science or mesh. You can also run individual steps
with the CLI commands e.g. grants_tagger preprocess wellcome-science --config path_to_config
and grants_tagger train --config path_to_config.
To use grants_tagger with your own data the main thing you need to
implement is a new preprocess function that creates a JSONL with the
fields text, tags and meta. Meta can be even left empty if you
do not plan to use it. You can easily plug the new preprocess into the
cli by importing your function to grants_tagger/cli.py and
define the subcommand name for your preprocess. For example if the
function was preprocessing EPMC data for MESH it could be
@preprocess_app.command()
def epmc_mesh(...)
and you would be able to run grants_tagger preprocess epmc_mesh ...
To use grants_tagger with your own model you need to define a class
for your model that adheres to the sklearn api so implements a
fit, predict, predict_proba and set_params but also a save
and load. Each custom model is defined in their own python script inside
the grants_tagger/models folder.
Then you need to import you class to grants_tagger/models/create_model.py and add it in
create_model as a separate approach with a name. Assuming your new approach
is a bilstm with attention
from grants_tagger.models.bilstm_attention import BiLSTMAttention
...
def create_model(approach, params)
...
elif approach == 'bilstm-attention':
model = BiLSTMAttention()
...
To make our experiments reproducible we use a config system (not DVC).
As such you need to create a new config that describes all parameters
for the various steps and run each step with the config or use
./scripts/run_science_config.sh or ./scripts/run_mesh_config.sh
depending on whether you want to reproduce a wellcome science or mesh
model.
We record the results of experiments in docs/results.md for wellcome
science and docs/mesh_results.md for bioasq mesh.
To package the code run make build. This will create a wheel in
dist that you can distribute and pip install. make deploy pushes
that wheel in a public Wellcome bucket that you can use if you have
access to write into it.
Packaged models are produced through dvc by running dvc repro and
stored in s3 by running make sync_artifacts. This might soon change to
a separate command that pushes models to a public bucket so others
can download.
Thus the recommended way for producing models that are released is
through DVC repro and as such you need to define the params that
produce it in the params.yaml as well as the pipeline that produces
it in dvc.yaml. The params.yaml is the equivalent of a config file and
dvc.yaml is the equivalent of run_config.sh
Run tests with make test. If you want to write some additional tests,
they should go in the subfolde tests/
A Makefile is being used to automate various operations. You can
get all make commands by running make. All the commands have been
mentioned in previous sections.
Usage: make [task]
task help
------ ----
sync_data Sync data to and from s3
sync_artifacts Sync processed data and models to and from s3
virtualenv Creates virtualenv
update-requirements Updates requirement
test Run tests
build Create wheel distribution
deploy Deploy wheel to public s3 bucket
clean Clean hidden and compiled files
help Show help message
Additional scripts, mostly related to Wellcome Trust-specific code can be
found in /scripts. Please refer to the readme therein for more info
on how to run those.
docker build -f Dockerfile.xlinear -t xlinear .Then run mounting the model volumes
docker run -v $(PWD)/models/:/code/models -d -t xlinearGet your image number, and ssh into the container to debug
predict_tags(['This is a malaria grant'], model_path='models/xlinear-0.2.3', label_binarizer_path='models/label_binarizer.pkl', probabilities=True, threshold=0.01)To train a xlinear model, you can use the following functionality:
from grants_tagger.slim.mesh_xlinear import train_and_evaluate
parameters={'ngram_range': (1, 1), 'beam_size': 30, 'only_topk': 200, 'min_weight_value': 0.1, 'max_features': 400_000}
train_and_evaluate(train_data_path='data/processed/train_mesh2021_toy.jsonl',
test_data_path='data/processed/test_mesh_2021_toy.jsonl',
label_binarizer_path='models/label_binarizer_toy.pkl',
model_path='models/xlinear-toy',
results_path='results/mesh_xlinear_toy.json',
full_report_path='results/mesh_xlinear_toy_full_report.json')If you want to change the parameters, you can either pass it as a dictionary to train_and_evaluate, as above,
or create a config file (check configs/mesh/2022.3.0.ini).
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