PKDocClassifier| Data | Reproduce our results | Make new predictions | Citing
This repository contains custom pipes and models to classify scientific publications from PubMed depending on whether they estimate pharmacokinetic (PK) parameters from in vivo studies. The final pipeline retrieved more than 121K PK publications and runs weekly updates available at https://app.pkpdai.com/.
The labels assigned to each publication can be found in the labels folder. These labels are available under the terms of the Creative Commons Zero "No rights reserved" data (CC0 1.0 Public domain dedication).
You will need an environment with Python 3.7+. We strongly recommend that you use an isolated Python environment (such as virtualenv or conda) to install the packages related to this project. Our default option will be to create a virtual environment with conda:
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If you don't have anaconda installed follow the instructions here
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Create conda environment for this project and activate it
conda create -n PKDocClassifier python=3.7 conda activate PKDocClassifier
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Clone and access this repository on your local machine
git clone https://github.com/fgh95/PKDocClassifier cd PKDocClassifierIf you are on MacOSX install LLVM's OpenMP runtime library, e.g.
brew install libomp
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Install all project dependencies
pip install .
If you would like to reproduce the steps taken for data retrieval and parsing you will need to download the whole MEDLINE dataset and store it into a spark dataframe.
However, you can also skip this step and use the parsed data available at data/subsets/. Alternatively, follow the steps at pubmed_parser wiki and place the resulting medline_lastview.parquet file at data/medline_lastview.parquet. Then, change the spark config file to your spark configuration and run:
python scripts/getready.pyThis should generate the files at data/subsets/.
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To generate the features run (~30min):
python scripts/features_bow.py
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Bootstrap field analysis (~3h on 12 threads, requires at least 16GB of RAM, set overwrite to False if you want to skip this step)
python scripts/bootstrap_bow.py \ --input-dir data/encoded/fields \ --output-dir data/results/fields \ --output-dir-bootstrap data/results/fields/bootstrap \ --path-labels data/labels/training_labels.csv \ --overwrite True
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Bootstrap n-grams (set overwrite to False if you want to skip this step)
python scripts/bootstrap_bow.py \ --input-dir data/encoded/ngrams \ --output-dir data/results/ngrams \ --output-dir-bootstrap data/results/ngrams/bootstrap \ --path-labels data/labels/training_labels.csv \ --overwrite True
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Display results
python scripts/display_results.py \ --input-dir data/results/fields\ --output-dir data/final/fields
python scripts/display_results.py \ --input-dir data/results/ngrams\ --output-dir data/final/ngrams
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Encode using SPECTER. To generate the features with specter you can preprocess the data running:
python preprocess_specter.py
This will generate the following input data as .ids and .json files at
data/encoded/specter/. Finally, to generate the input features you will need to clone the SPECTER repo and follow the instructions on how to use the pretrained model. After cloning and installing SPECTER dependencies we ran the following command from the specter directory to encode the documents:python scripts/embed.py \ --ids ../data/encoded/specter/training_ids.ids --metadata ../data/encoded/specter/training_meta.json \ --model ./model.tar.gz \ --output-file ../data/encoded/specter/training_specter.jsonl \ --vocab-dir data/vocab/ \ --batch-size 16 \ --cuda-device -1
python scripts/embed.py \ --ids ../data/encoded/specter/test_ids.ids --metadata ../data/encoded/specter/test_meta.json \ --model ./model.tar.gz \ --output-file ../data/encoded/specter/test_specter.jsonl \ --vocab-dir data/vocab/ \ --batch-size 16 \ --cuda-device -1
This should output two files in the data directory:
/data/encoded/specter/training_specter.jsonlanddata/encoded/specter/test_specter.jsonl -
Generate BioBERT representations:
python scripts/features_dist.py
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Run bootstrap iterations for distributed representations (set overwrite to False if you want to skip this step):
python scripts/bootstrap_dist.py \ --is-specter True \ --use-bow False \ --input-dir data/encoded/specter \ --output-dir data/results/distributional \ --output-dir-bootstrap data/results/distributional/bootstrap \ --path-labels data/labels/training_labels.csv \ --path-optimal-bow data/encoded/ngrams/training_unigrams.parquet \ --overwrite True
python scripts/bootstrap_dist.py \ --is-specter False \ --use-bow False \ --input-dir data/encoded/biobert \ --output-dir data/results/distributional \ --output-dir-bootstrap data/results/distributional/bootstrap \ --path-labels data/labels/training_labels.csv \ --path-optimal-bow data/encoded/ngrams/training_unigrams.parquet \ --overwrite True
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Display results
python scripts/display_results.py \ --input-dir data/results/distributional \ --output-dir data/final/distributional \ --convert-latex
python scripts/display_results.py \ --input-dir data/results/distributional/bow_and_distributional \ --output-dir data/final/distributional/bow_and_distributional \ --convert-latex
From these plots we can see that the best-performing architecture on the training data, on average, is the one using average embeddings from BioBERT and unigram features.
Run the cross-validation analyses:
python scripts/cross_validate.py \
--training-embeddings data/encoded/biobert/training_biobert_avg.parquet \
--training-optimal-bow data/encoded/ngrams/training_unigrams.parquet \
--training-labels data/labels/training_labels.csv\
--output-dir data/results/final-pipeline Train the final pipeline (preprocessing, encoding, decoding) from scratch with optimal hyperparameters and apply it to the test set:
python scripts/train_test_final.py \
--path-train data/subsets/training_subset.parquet \
--train-labels data/labels/training_labels.csv \
--path-test data/subsets/test_subset.parquet \
--test-labels data/labels/test_labels.csv \
--cv-dir data/results/final-pipeline \
--output-dir data/results/final-pipeline \
--train-pipeline True Final results on the test set should be printed on the terminal.
You can make new predictions in three simple steps:
import pandas as pd
import joblib
# 1. Import data
data = pd.read_csv('data/examples/to_classify.csv').reset_index(drop=True)
data['pmid'] = data['pmid'].fillna(0).astype(int).fillna('')
data.head()
#>>> abstract mesh_terms ... title pmid
#>>> 0 Rituximab, an anti-CD20 monoclonal antibody, i... NaN ... Pharmacokinetics, efficacy and safety of the r... 28766389
#>>> 1 Background: Biosimilars are highly similar to ... NaN ... A Randomized, Double-Blind, Efficacy and Safet... 31820339
#>>> 2 AIMS: Rituximab is standard care in a number o... NaN ... Pharmacokinetics, exposure, efficacy and safet... 31050355
#>>> 3 BACKGROUND: Studies in patients with rheumatoi... NaN ... Efficacy, pharmacokinetics, and safety of the ... 28712940
#>>> 4 Rituximab, a chimeric monoclonal antibody targ... NaN ... Rituximab (monoclonal anti-CD20 antibody): mec... 14576843
# 2. Import trained model
pipeline_trained = joblib.load("data/results/final-pipeline/optimal_pipeline.pkl")
# 3. Make predictions
pred_test = pipeline_trained.predict(data)
print(pred_test)
#>>> array(['Not Relevant', 'Not Relevant', 'Relevant', 'Relevant',
# 'Not Relevant'], dtype=object)You can reproduce this example on our jupyter notebook: here.
Cite the repository:
