├── .gitignore ├── LICENSE ├── README.md ├── data ├── pubmed_causal_language_use.csv └── sample_new_sentences.csv └── main.py /.gitignore: -------------------------------------------------------------------------------- 1 | model 2 | 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # C extensions 9 | *.so 10 | 11 | # Distribution / packaging 12 | .Python 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | .hypothesis/ 50 | .pytest_cache/ 51 | 52 | # Translations 53 | *.mo 54 | *.pot 55 | 56 | # Django stuff: 57 | *.log 58 | local_settings.py 59 | db.sqlite3 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # Jupyter Notebook 75 | .ipynb_checkpoints 76 | 77 | # pyenv 78 | .python-version 79 | 80 | # celery beat schedule file 81 | celerybeat-schedule 82 | 83 | # SageMath parsed files 84 | *.sage.py 85 | 86 | # Environments 87 | .env 88 | .venv 89 | env/ 90 | venv/ 91 | ENV/ 92 | env.bak/ 93 | venv.bak/ 94 | 95 | # Spyder project settings 96 | .spyderproject 97 | .spyproject 98 | 99 | # Rope project settings 100 | .ropeproject 101 | 102 | # mkdocs documentation 103 | /site 104 | 105 | # mypy 106 | .mypy_cache/ 107 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Detecting Causal Language Use in Science Findings 2 | 3 | This is a collaborative project with the School of Information at Syracuse University. The goal of the project is to develop an automated NLP model that can identify causal language use in science findings, and to further study whether causal language use differs by countries and languages. We have developed an annotated corpus and trained a BioBert model with 0.88 macro-F1 score to categorize conclusion sentences into direct causal, conditional causal, correlational, and no relationships. We then applied this model to the observational studies in PubMed, and observed different levels of causal language use by authors from different countries and language backgrounds. This result challenges the notion of a shared consensus on causal language use in global science community. 4 | 5 | ### How to cite ### 6 | Yu, B., Li, Y. and Wang, J. (2019). Detecting Causal Language Use in Science Findings. EMNLP 2019, pages 4656–4666, Hong Kong, China, November 3–7, 2019. [PDF](https://www.aclweb.org/anthology/D19-1473.pdf) 7 | 8 | ``` 9 | @inproceedings{yu2019EMNLPCausalLanguage, 10 | title={Detecting Causal Language Use in Science Findings}, 11 | author={Yu, Bei and Li, Yingya and Wang, Jun}, 12 | booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, 13 | pages={4656-4666}, 14 | year={2019}, 15 | url={https://www.aclweb.org/anthology/D19-1473.pdf} 16 | } 17 | ``` 18 | ## About the data 19 | ``` 20 | Label = 0 : No relationship (1356 cases) 21 | Label = 1 : Direct causal (494 cases) 22 | Label = 2 : Conditional causal (213 cases) 23 | Label = 3 : Correlational (998 cases) 24 | ``` 25 | 26 | ## Usage 27 | 28 | STEP 1: Install bert-sklearn from [https://github.com/junwang4/bert-sklearn-with-class-weight](https://github.com/junwang4/bert-sklearn-with-class-weight) (for handling imbalanced classes) 29 | 30 | 31 | STEP 2: Clone this repo and run 32 | 33 | git clone https://github.com/junwang4/causal-language-use-in-science 34 | cd causal-language-use-in-science 35 | python3 main.py 36 | 37 | 38 | ## Performance 39 | On Ubuntu 16.04 with a GPU of 1080TI (your performance numbers may be different but should be similar) 40 | 41 | 5-fold; 5 epochs; BioBERT 42 | 43 | ``` 44 | Acc F1 F1_0 F1_1 F1_2 F1_3 P P_0 ... P_3 R R_0 R_1 R_2 R_3 size weight 45 | 0 0.876 0.859 0.885 0.853 0.804 0.893 0.848 0.911 ... 0.868 0.876 0.860 0.818 0.907 0.920 614 0.201 46 | 1 0.905 0.896 0.905 0.892 0.864 0.921 0.887 0.930 ... 0.908 0.905 0.882 0.919 0.884 0.935 613 0.200 47 | 2 0.902 0.889 0.902 0.892 0.843 0.919 0.897 0.901 ... 0.907 0.882 0.904 0.879 0.814 0.930 613 0.200 48 | 3 0.935 0.914 0.945 0.907 0.854 0.952 0.915 0.945 ... 0.964 0.914 0.945 0.939 0.833 0.940 611 0.200 49 | 4 0.880 0.855 0.907 0.821 0.804 0.889 0.849 0.893 ... 0.915 0.866 0.923 0.796 0.881 0.864 610 0.199 50 | avg 0.900 0.883 0.909 0.873 0.834 0.915 0.879 0.916 ... 0.912 0.889 0.903 0.870 0.864 0.918 612 0.200 51 | time used: 916s 52 | ``` 53 | -------------------------------------------------------------------------------- /data/sample_new_sentences.csv: -------------------------------------------------------------------------------- 1 | pmid,sentence 2 | 31064345,"Five hundred twenty-five patients were included, 60% (range: 43.1-78.8%) suffered from severe IBS-C." 3 | 31064345,"During follow-up 11.1-24.0% of patients had a hospitalisation/emergency room (ER) visit, median stay range: 1.5-12.0 days and 41.1-90.4% took prescription drugs for IBS-C." 4 | 31064345,21.4-50.8% of employed patients took sick leave (mean: 11.6-64.1 days). 5 | 31064345,The mean annual direct cost to the healthcare systems was €937.1- €2108.0. 6 | 31064345,The total direct cost (combined costs to healthcare systems and patient) for IBS-C was €1421.7-€2487.1. 7 | 31046705,"Of the 3451 AP patients, 2478 (71.8%) had serum D-dimer levels measured within 24 h of hospital admission; 1273 of these patients had D-dimer levels ≤2.5 mg/L, and 1205 had D-dimer levels > 2.5 mg/L (934 patients had mild AP (MAP); 1086, moderately severe AP (MSAP); and 458, severe AP (SAP))." 8 | 31046705,"Patients with D-dimer levels > 2.5 mg/L (n = 1205) had higher incidences of SAP (75.5% vs. 24.5%), acute peripancreatic fluid collection (APFC) (53.3% vs. 46.7%), acute necrotic collection (ANC) (72.4% vs. 27.6%), pancreatic necrosis (PN) (65.2% vs. 34.8%), infected pancreatic necrosis (IPN) (77.7% vs. 22.8%), organ failure (OF) (68.5% vs. 31.5%), persistent organ failure (POF) (75.5% vs. 24.5%), ICU requirement (70.2% vs. 29.8%), and mortality (79.2% vs. 20.8%) than did patients with D-dimer levels ≤2.5 mg/L (n = 1273)." 9 | 31046705,The multivariate analysis showed that patients with higher serum D-dimer levels had poorer prognoses that worsened over time. 10 | 31039775,Twenty-five eyes of 25 trabeculotomy patients and 20 eyes of 20 trabeculectomy patients with a mean postoperative follow-up period of 8.0 years were selected. 11 | 31039775,"The Kaplan-Meier analysis estimated that the success probability defined as intraocular pressure < 16 mmHg was 44.0 ± 9.9% and 75.0 ± 9.7% at 6 years for trabeculotomy and trabeculectomy, respectively." 12 | 31039775,The final mean deviation significantly progressed in trabeculotomy cases in Central 30-2 programs of the Humphrey Field Analyzer (P = 0.025). 13 | 31039775,Patient characteristics and postoperative clinical data were analyzed by Mann-Whitney's U test and Wilcoxon signed-rank test. 14 | 31032569,A sample of 111 patients (n=55 in the telemedicine group; n=56 in the face-to-face group) with 593 visits was analyzed. 15 | 31032569,"No significant difference in healing of LEU (78.2% in telemedicine vs. 75.0% in face-to-face) was detected, P = 0.823." 16 | 31032569,"A reduced number of visits in telemedicine (4.36 ± 2.36) compared to the face-to-face care (6.32 ± 4.17) was shown, P = 0.003." 17 | 31032569,Non-inferiority of telemedicine demonstrated within the Δ = 15% range limits and 80% statistical power was demonstrated. 18 | 31032564,HS were older than NHS and 51% of them were older than 85 years. 19 | 31032564,The prevalence rate of ischemic heart disease (IHD) was significantly higher among HS. 20 | 31032564,"HS underwent significantly more cardiac interventions (20% vs. 15.7%, P < 0.05)." 21 | 31032564,HS status was an independent risk factor for increased IHD and for more coronary interventions. 22 | 30989999,"The mean age was 49±11 years, and mean BMI was 24.4±4.6 kg/m2." 23 | 30989999,"We reported effective relief on both static and dynamic pain all along the perioperative period, with good effect on patient's rehabilitation." 24 | 30989999,"Postoperative nausea and vomiting is the most common adverse effect, but the incidence was strongly decreased with medical prophylaxis." 25 | 30989999,"SSTS was easy to prepare, use and manage by both patients and care providers." 26 | 30985679,The in-hospital mortality rate was 16.8% (26/155). 27 | 30985679,"Of 25 pre-procedure variables analyzed, Child-Pugh classification C, creatinine level ≥6.93 μmol/L, and quality-of-life score (≤30) were found to be significant in univariate and multivariate analyses." 28 | 30985679,"Increased mortality was observed in patients with 2 or more risk factors, which was significantly different from patients with no risk factors or one risk factor (P < .01)." 29 | 30985679,None of the intra-procedure factors were important in identifying patients at risk of death. 30 | 30985679,Multivariate analysis indicated post-PTBD cholangitis and unsuccessful drainage as post-procedure risk factors that correlated with in-hospital death. 31 | 30975960,"IOP was higher in the obese group compared to the control group (P = 0.008), whereas the average RNFL was lower in the obese group (P = 0.035)." 32 | 30975960,There was a negative correlation between the average RNFL and BMI-SDS (P = -0.044) and waist-hip ratio (P = 0.015). 33 | 30975960,"There was no statistically significant difference between the RGC, C/D, CMT, and CT of the obese and control groups." 34 | 30975960,"IOP was negatively correlated with HOMA-IR, body fat mass, body fat percentage, and diastolic blood pressure." 35 | 30971261,"From baseline to week 8, mean increases (95% CI) were estimated to be 0.086 (0.019-0.153), 0.154 (0.112-0.196) and 0.132 (0.063-0.201) for EQ-5D-5 L, AQL-5D (preference-based measure derived from AQLQ), and TTO respectively over 8 weeks (p < 0.01)." 36 | 30952765,A total of 704 patients were included in the study. 37 | 30952765,"Disease-free survival was not different between the two groups [hazard ratio (HR)=0.98, 95% confidence intervaI (CI)=0.77-1.25, p=0.88]." 38 | 30952765,"Both recurrence-free survival (HR=1.02, 95% CI=0.74-1.39, p=0.92) and all-cause mortality (HR=0.92, 95% CI=0.68-1.23, p=0.56) were similar for the two groups." 39 | 30952763,"Among patients with auto-PBSCT, grade two or more leukopenia occurred in five." 40 | 30952763,The incidence of grade two or more leukopenia was significantly higher in patients with auto-PBSCT than in those without (p=0.014). 41 | 30952762,"Fewer grade 3-4 complications were observed in the AC group compared to the non-AC group (38.6 vs. 61.2%, p=0.03)." 42 | 30952762,"In the non-AC group, complications were the most frequent reason for omitting AC (n=21, including 13 with biliary fistula)." 43 | 30952762,"In the AC group, the therapy completion rate was 56.1% and the main reason for discontinuation was adverse events (n=12, including six with cholangitis)." 44 | 30952762,"AC was not associated with survival benefits (median survival: 50.4 vs. 37.3 months, p=0.916)." 45 | 30952759,"Positive Notch3 protein expression was an unfavorable prognostic factor for disease-free survival (DFS) and overall survival (OS) (HR=2.43, p=0.024 and HR=2.56, p=0.028, respectively)." 46 | 30952759,"Activation of the Shh pathway showed univariately longer DFS (HR=0.49, p=0.032)." 47 | 30952759,Possible crosstalk between the two pathways was indicated. 48 | 30952759,No further associations between pathway activation and outcome were evident. 49 | 30952560,One hundred and thirty-six patients were consecutively included in the study. 50 | 30952560,The median follow-up was 13months. 51 | 30952560,"Median total dose prescribed, fractionation and overall treatment time were respectively 45Gy, three fractions and 5 days." 52 | 30952560,"Overall survival, progression-free survival and local control rates at 1year and 2years were 79.8 % and 63.5 %, 61.3 % and 39.4 %; 94.5 % and 91 %." 53 | 30952560,Two grade 3 acute toxicity events and two grade 4 late toxicity events corresponding to a duodenal ulcer have been reported. 54 | 30952560,Seven patients underwent classic radiation-induced hepatitis and 13 patients showed non-classical radiation-induced hepatitis. 55 | 30952560,"Barcelona Clinic Liver Cancer stage, World Health Organisation grade and planning target volume were correlated with overall survival in univariate Cox analysis." 56 | 30943922,"Seven eyes from six patients (five male, one female) were included." 57 | 30943922,"The median age was 29 (range, 20-38) years." 58 | 30943922,"Preoperatively, six eyes were pseudophakic, two eyes had a history of previous vitreoretinal surgery, and one had uveitis." 59 | 30943922,"Postoperatively, best-corrected visual acuity improved in two eyes, worsened in one, and remained similar in four." 60 | 30943922,Retinal attachment was maintained in all eyes at 12 months. 61 | 30943922,"The major complication was an increase in postoperative intraocular pressure in six eyes, requiring either medical or surgical treatment." 62 | 30943922,"During the extended follow-up period (15-37 months), retinas remained attached in all eyes and stable visual acuity was maintained in five." 63 | 30942649,"PTX group was younger (40.9 vs. 49.3 years, p = .03), had higher PTH levels (2578 vs. 1937 pg/ml, p = .01) and higher CaxP product (62 vs. 47.5, p = .02)." 64 | 30942649,"There were no percentage differences between groups of fractures, calciphylaxis and other complications due to SHPT." 65 | 30942649,"Patients who were not operated had a worst overall survival (5 y 62.2% vs. 96.7%, p = .04) with a HR for death of 8.08 (p = .07, PTX as a TVC)." 66 | 30942649,"Other variables associated with decreased survival included a history of previous myocardial infarction (HR: 10.4, p = .01) and age per additional year (HR: 1.09, p = .02)." 67 | 30942122,Multivariable linear regression revealed that HbA1c levels and history of diabetes were positively related with sCysC (all p < .05). 68 | 30942122,"Although stratification for above-mentioned glycemic status displayed no significant difference between AUC of sCysC (all p > .05), sCysC yielded the highest AUCs for detecting AKI in diabetic patients." 69 | 30942122,"Moreover, higher optimal cutoff values of sCysC to detect AKI were observed in patients with versus without diabetes." 70 | 30940181,Six hundred fifty-three individuals (13-26 years old) were screened; 95.6% were below 18 years old (624/653). 71 | 30940181,"Six 'definite RHD' were identified at screening, yielding a detection rate of 9.2‰ (95% CI 4.1-20.3‰), while 285 (436.4‰) were defined as 'borderline' (95% CI 398.8-474.9‰)." 72 | 30940181,"Out of 172 ""non-negative borderline"" cases available for being retested (113 ""non-negative borderline"" lost in follow-up), additional 11 were categorized as 'definite RHD', for a total of 17 'definite RHD', yielding a final prevalence of 26.0‰ (95% CI 16.2-41.5‰) (17/653), and 122 (122/653) were confirmed as 'borderline' (final prevalence of 186.8‰, 95% CI 158.7-218.7)." 73 | 30940181,"In multivariate logistic regression analysis the presence of systolic murmur was a strong predictor for both 'borderline' (OR 4.3 [2.8-6.5]) and 'definite RHD' (OR 5.2 [1.7-15.2]), while no specific country/geographic area of origin was statistically associated with an increased risk of latent, asymptomatic RHD." 74 | 30940127,"A total of 10,452 and 5168 children under 5 years of age were enrolled in the 2011 DHS and 2015 IMASIDA, respectively." 75 | 30940127,Care-seeking for fever in public and private sectors remained stable during this period (62.6%; 835/1432 in 2011 and 63.7%; 974/1529 in 2015). 76 | 30940127,The main place where care was sought in both surveys was public hospitals (86.2%; 773/897 in 2011 and 86.7%; 844/974 in 2015). 77 | 30940127,Prescription of anti-malarial drugs increased from 42.9% (385/897) in 2011 to 53.8% (524/974) in 2015. 78 | 30940127,Artemether-lumefantrine was the most used anti-malarial drug for febrile children in both surveys and its use increased from 59.0% (219/373) in 2011 to 89.3% (457/512) in 2015. 79 | 30940127,Data from 2015 elucidated that care-seeking was more common in children whose mothers had a secondary level of education (AOR = 2.27 [95% CI 1.15-4.49]) and among those in poorer quintile (AOR = 1.46 [95% CI 0.83-1.90]). 80 | 30940127,Mothers with higher education level (AOR = 0.16 [95% CI 0.34-0.78]) were less likely to seek out care. 81 | 30940127,"People from Manica (AOR = 2.49 [1.03-6.01]), Sofala ([AOR = 2.91 [1.03-8.24]), Inhambane (AOR = 3.95 [1.25-12.45]), Gaza (AOR = 3.25 [1.22-8.65]) and Maputo Province (AOR = 2.65 [1.10-6.41]) were more likely to seek care than people from Maputo City." 82 | 30940119,"According to physician's judgment, 51% of patients were considered to have APD." 83 | 30940119,There was a moderate correlation between physician's judgment and Delphi criteria for APD (K = 0.430; 95% CI 0.406-0.473). 84 | 30940119,"Activities of daily living, motor symptom severity, dyskinesia duration/disability, ""Off"" time duration, non-motor symptoms, and quality-of-life scores were worse among APD vs. non-APD patients (p < 0.0001 for all)." 85 | 30940119,APD patients (assessed by physicians) had higher disease burden by motor and non-motor symptoms compared with non-APD patients and a negative impact on activities of daily living and quality of life. 86 | 30933962,No difference in symptoms was found between children with SLE and healthy children (CDEA score 6.4 ± 5.4 vs. 3.8 ± 3.2; P = 0.09). 87 | 30933962,"Corneal staining was more prevalent in children with SLE than in healthy children (58.8% vs. 20.0%; P = 0.01), and children with SLE had higher mean corneal fluorescein staining scores (1.7 ± 1.7 vs. 0.2 ± 0.4; P = 0.002)." 88 | 30933962,"No statistically significant differences in tear osmolarity, inter-eye differences in tear osmolarity, tear film break-up time, Schirmer test 1, or lissamine green staining scores were observed between the 2 groups." 89 | 30933962,"In healthy children, CDEA scores weakly correlated with corneal fluorescein staining score (r = 0.53, P = 0.04)." 90 | 30933962,"In children with SLE, no correlation between CDEA score and any of the diagnostic test outcomes was found." 91 | 30929652,Mean patient age was 60.2 ± 21.0 years. 92 | 30929652,"Aspiration was identified in 10 patients when assessed by fibre-optic endoscopic evaluation of swallowing; of these, 1 had aspiration when evaluated by modified Evans blue dye test." 93 | 30929652,The dye test had a sensitivity of 10.0 per cent and specificity of 100.0 per cent for detecting aspiration. 94 | 30929652,"Fibre-optic endoscopic evaluation of swallowing revealed no statistically significant associations between aspiration presence and: speech and language therapy duration, intubation time, or tracheostomy plus mechanical ventilation duration." 95 | 30929494,"Following exclusions and losses to follow-up, 334 medial robotic-arm assisted UKAs were assessed at a mean follow-up of 30.0 months (8.0 to 54.9)." 96 | 30929494,None of the measured parameters were associated with overall KOOS outcome. 97 | 30929494,"Correlations were described between specific KOOS subscales and intraoperative, post-implantation robotic data, and between FJS-12 and femoral component sagittal alignment." 98 | 30929494,"Three UKAs were revised, resulting in 99.0% survival at two years (95% confidence interval (CI) 97.9 to 100.0)." 99 | 30929479,"Of 34 639 operations, 2.7% (95% confidence interval (CI) 2.6 to 2.9) had a surgical complication, 2.1% (95% CI 2.0 to 2.3) had repeat surgery within 90 days, 2.4% (95% CI 2.2 to 2.5) had a non-surgical readmission within 90 days, and 6.7% (95% CI 6.4 to 6.9) experienced at least one of these unfavourable events." 100 | 30929479,Unfavourable events were found to be associated with advanced age and comorbidity. 101 | 30922350,There were 1629 treatment-naïve patients for the pro re nata (PRN) treatment pattern and 226 treatment-naive patients for the three-injections (3Inj) treatment pattern. 102 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import os, sys, time, re, json 2 | import numpy as np 3 | import pandas as pd 4 | from IPython.display import display 5 | from sklearn.metrics import accuracy_score, classification_report, precision_recall_fscore_support, confusion_matrix 6 | from sklearn.model_selection import train_test_split, StratifiedKFold 7 | from sklearn.utils.class_weight import compute_class_weight 8 | from bert_sklearn import BertClassifier, load_model 9 | 10 | NVIDIA_TITAN_XP = "0" 11 | NVIDIA_1080TI = "1" 12 | os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" 13 | os.environ["CUDA_VISIBLE_DEVICES"] = NVIDIA_TITAN_XP 14 | os.environ["CUDA_VISIBLE_DEVICES"] = NVIDIA_1080TI 15 | 16 | pd.options.display.max_colwidth = 500 17 | pd.options.display.width = 1000 18 | pd.options.display.precision = 3 19 | np.set_printoptions(precision=3) 20 | 21 | 22 | BERT_NAME_2_MODEL = {'bert' : 'bert-base-cased', 23 | 'biobert' : 'biobert-base-cased' 24 | } 25 | 26 | BERT_MODEL_NAME = 'bert' 27 | BERT_MODEL_NAME = 'biobert' 28 | 29 | BERT_MODEL = BERT_NAME_2_MODEL[BERT_MODEL_NAME] 30 | 31 | PRJ = 'EMNLP' 32 | label_name = {0:'none', 1:'causal', 2:'cond', 3:'corr'} 33 | NUM_CLASSES = len(label_name) 34 | 35 | K = 5 36 | EPOCHS = 5 37 | 38 | DATA_DIR = 'data' 39 | MODEL_DIR = f'model/{PRJ}_{BERT_MODEL_NAME}' 40 | 41 | RANDOM_STATE = 0 42 | 43 | 44 | ################################## 45 | # 46 | # functions 47 | # 48 | def get_train_data_csv_fpath(): return f'{DATA_DIR}/pubmed_causal_language_use.csv' 49 | def read_train_data(): return pd.read_csv(get_train_data_csv_fpath(), usecols=['sentence', 'label'], encoding = 'utf8', keep_default_na=False) 50 | def clean_str(s): return s.strip() # BioBert or cased-Bert works better with cased letters, so don't use s.lower() 51 | 52 | def get_class_weight(labels): 53 | class_weight = [x for x in compute_class_weight("balanced", range(len(set(labels))), labels)] 54 | print('- auto-computed class weight:', class_weight) 55 | return class_weight 56 | 57 | def get_model_bin_file(fold=0): 58 | if not os.path.exists(MODEL_DIR): 59 | os.makedirs(MODEL_DIR) 60 | print(f'\ncreate a new folder for storing BERT model: "{MODEL_DIR}"\n') 61 | if fold>=0: 62 | return f'{MODEL_DIR}/K{K}_epochs{EPOCHS}_{fold}.bin' 63 | elif fold==-1: 64 | return f'{MODEL_DIR}/full_epochs{EPOCHS}.bin' 65 | else: 66 | print('Wrong value of fold:', fold) 67 | sys.exit() 68 | 69 | def get_pred_csv_file(mode='train'): 70 | pred_folder = f'./pred/{PRJ}_{BERT_MODEL_NAME}_{mode}' 71 | if not os.path.exists(pred_folder): 72 | os.makedirs(pred_folder) 73 | print('\ncreate new folder for prediction results:', pred_folder, '\n') 74 | if mode == 'train': 75 | return f'{pred_folder}/K{K}_epochs{EPOCHS}.csv' 76 | elif mode == 'apply': 77 | return f'{pred_folder}/epochs{EPOCHS}.csv' 78 | else: 79 | print('- wrong mode:', mode, '\n') 80 | 81 | def get_train_test_data(df, fold=0): 82 | df['sentence'] = df.sentence.apply(clean_str) 83 | skf = StratifiedKFold(n_splits=K, shuffle=True, random_state=RANDOM_STATE) 84 | for i, (train_index, test_index) in enumerate(skf.split(df.sentence, df.label)): 85 | if i == fold: 86 | break 87 | train = df.iloc[train_index] 88 | test = df.iloc[test_index] 89 | 90 | print(f"ALL: {len(df)} TRAIN: {len(train)} TEST: {len(test)}") 91 | label_list = np.unique(train.label) 92 | return train, test, label_list 93 | 94 | def train_model(train, model_file_to_save, epochs=3, val_frac=0.1, class_weight=None): 95 | X_train = train['sentence'] 96 | y_train = train['label'] 97 | 98 | max_seq_length, train_batch_size, lr = 128, 32, 2e-5 99 | 100 | model = BertClassifier(bert_model=BERT_MODEL, random_state=RANDOM_STATE, \ 101 | class_weight=class_weight, max_seq_length=max_seq_length, \ 102 | train_batch_size=train_batch_size, learning_rate=lr, \ 103 | epochs=epochs, validation_fraction=val_frac) 104 | print(model) 105 | model.fit(X_train, y_train) 106 | model.save(model_file_to_save) 107 | print(f'\n- model saved to: {model_file_to_save}\n') 108 | return model 109 | 110 | 111 | def train_one_full_model(): 112 | df_train = read_train_data() 113 | class_weight = get_class_weight(df_train['label']) 114 | 115 | model_file_to_save = get_model_bin_file(fold=-1) # -1: for one full model 116 | train_model(df_train, model_file_to_save, epochs=EPOCHS, val_frac=0.15, class_weight=None) 117 | 118 | 119 | def train_KFold_model(): 120 | df = read_train_data() 121 | print('- label value counts:') 122 | print(df.label.value_counts()) 123 | 124 | y_test_all, y_pred_all = [], [] 125 | results = [] 126 | df_out_proba = None 127 | for fold in range(K): 128 | train_data, test_data, label_list = get_train_test_data(df, fold) 129 | 130 | model_file = get_model_bin_file(fold) 131 | use_class_weight_for_unbalanced_data = True 132 | class_weight = get_class_weight(df['label']) if use_class_weight_for_unbalanced_data else None 133 | 134 | val_frac = 0.05 135 | model = train_model(train_data, model_file, epochs=EPOCHS, val_frac=val_frac, class_weight=class_weight) 136 | 137 | X_test = test_data['sentence'] 138 | y_test = test_data['label'] 139 | y_test_all += y_test.tolist() 140 | 141 | y_pred = model.predict(X_test) 142 | y_proba = model.predict_proba(X_test) 143 | del model 144 | 145 | y_pred_all += y_pred.tolist() 146 | 147 | tmp = pd.DataFrame(data=y_proba, columns=[f'c{i}' for i in range(NUM_CLASSES)]) 148 | tmp['confidence'] = tmp.max(axis=1) 149 | tmp['winner'] = tmp.idxmax(axis=1) 150 | tmp['sentence'] = X_test.tolist() 151 | tmp['label'] = y_test.tolist() 152 | df_out_proba = tmp if df_out_proba is None else pd.concat((df_out_proba, tmp)) 153 | 154 | acc = accuracy_score(y_pred, y_test) 155 | res = precision_recall_fscore_support(y_test, y_pred, average='macro') 156 | print(f'\nAcc: {acc:.3f} F1:{res[2]:.3f} P: {res[0]:.3f} R: {res[1]:.3f} \n') 157 | 158 | item = {'Acc': acc, 'weight': len(test_data)/len(df), 'size': len(test_data)} 159 | item.update({'P':res[0], 'R':res[1], 'F1':res[2]}) 160 | for cls in np.unique(y_test): 161 | res = precision_recall_fscore_support(y_test, y_pred, average=None, labels=[cls]) 162 | for i, scoring in enumerate('P R F1'.split()): 163 | item['{}_{}'.format(scoring, cls)] = res[i][0] 164 | results.append(item) 165 | 166 | acc_all = np.mean(np.array(y_pred_all) == np.array(y_test_all)) 167 | res = precision_recall_fscore_support(y_test_all, y_pred_all, average='macro') 168 | print( f'\nAVG of {fold+1} folds | Acc: {acc_all:.3f} F1:{res[2]:.3f} P: {res[0]:.3f} R: {res[1]:.3f} \n') 169 | 170 | # show an overview of the performance 171 | df_2 = pd.DataFrame(list(results)).transpose() 172 | df_2['avg'] = df_2.mean(axis=1) 173 | df_2 = df_2.transpose() 174 | df_2['size'] = df_2['size'].astype(int) 175 | display(df_2) 176 | 177 | # put together the results of all 5-fold tests and save 178 | output_pred_csv_file_train = get_pred_csv_file(mode='train') 179 | df_out_proba.to_csv(output_pred_csv_file_train, index=False, float_format="%.3f") 180 | print(f'\noutput all {K}-fold test results to: "{output_pred_csv_file_train}"\n') 181 | 182 | 183 | def apply_one_full_model_to_new_sentences(): 184 | fpath_unseen_data = "data/sample_new_sentences.csv" 185 | columns = ['pmid', 'sentence'] 186 | df = pd.read_csv(fpath_unseen_data, usecols=columns) 187 | 188 | print(f'all: {len(df):,} unique sentences: {len(df.sentence.unique()):,} papers: {len(df.pmid.unique()):,}') 189 | 190 | output_pred_file = get_pred_csv_file('apply') 191 | print(output_pred_file) 192 | 193 | model_file = get_model_bin_file(fold=-1) # -1: indicating this is the model trained on all data 194 | print(f'\n- use trained model: {model_file}\n') 195 | 196 | model = load_model(model_file) 197 | 198 | model.eval_batch_size = 32 199 | y_prob = model.predict_proba(df.sentence) 200 | 201 | df_out = pd.DataFrame(data=y_prob, columns=[f'c{i}' for i in range(NUM_CLASSES)]) 202 | df_out['confidence'] = df_out.max(axis=1) 203 | df_out['winner'] = df_out.idxmax(axis=1) 204 | for col in columns: 205 | df_out[col] = df[col] 206 | 207 | df_out.to_csv(output_pred_file, index=False, float_format="%.3f") 208 | print(f'\n- output prediction to: {output_pred_file}\n') 209 | 210 | 211 | def evaluate_and_error_analysis(): 212 | df = pd.read_csv(get_pred_csv_file(mode='train')) # -2: a flag indicating putting together the results on all folds 213 | df['pred'] = df['winner'].apply(lambda x:int(x[1])) # from c0->0, c1->1, c2->2, c3->3 214 | 215 | print('\nConfusion Matrix:\n') 216 | cm = confusion_matrix(df.label, df.pred) 217 | print(cm) 218 | 219 | print('\n\nClassification Report:\n') 220 | print(classification_report(df.label, df.pred)) 221 | 222 | out = [""" 223 | 229 | 230 | """] 231 | 232 | 233 | row = f'\n{'''' 240 | out.append(row + "
Predicted
' 234 | for i in range(NUM_CLASSES): 235 | row += f"{label_name[i]}" 236 | for i in range(NUM_CLASSES): 237 | row += f'''\n
Actual' if i==0 else ''}{label_name[i]}''' 238 | for j in range(NUM_CLASSES): 239 | row += f'''{cm[i][j]}
") 241 | 242 | for i in range(NUM_CLASSES): 243 | for j in range(NUM_CLASSES): 244 | row = f"

{label_name[i]} => {label_name[j]}

" 245 | row += f'' 246 | out.append(row) 247 | 248 | df_ = df[(df.label==i) & (df.pred==j)] 249 | df_ = df_.sort_values('confidence', ascending=False) 250 | 251 | cnt = 0 252 | for c0, c1, c2, c3, sentence, label, pred in zip(df_.c0, df_.c1, df_.c2, df_.c3, df_.sentence, df_.label, df_.pred): 253 | cnt += 1 254 | mark = "" if label == pred else "oops" 255 | item = f"""""" 263 | out.append(item) 264 | out.append('
SentenceLabel{label_name[0]}{label_name[1]}{label_name[2]}{label_name[3]}mark
{cnt}. 256 | {sentence} 257 | {label_name[label]} 258 | max(c1,c2,c3))}>{c0:.2f} 259 | max(c0,c2,c3))}>{c1:.2f} 260 | max(c0,c1,c3))}>{c2:.2f} 261 | max(c0,c1,c2))}>{c3:.2f} 262 | {mark}
') 265 | 266 | html_file_output = '/var/www/html/a.html' 267 | html_file_output = '/tmp/a.html' 268 | with open(html_file_output, 'w') as fout: 269 | fout.write('\n'.join(out)) 270 | print(f'\n- HTML file output to: "{html_file_output}"\n') 271 | 272 | 273 | def main(): 274 | task_func = { 275 | 'train_kfold': train_KFold_model, 276 | 'train_one_full_model': train_one_full_model, 277 | 'evaluate_and_error_analysis': evaluate_and_error_analysis, 278 | 'apply_one_full_model_to_new_sentences': apply_one_full_model_to_new_sentences 279 | } 280 | 281 | task = 'evaluate_and_error_analysis' # error analysis: outpu an HTML file for checking errors 282 | task = 'apply_one_full_model_to_new_sentences' 283 | task = 'train_one_full_model' # use all data (with about 10 percent used as validation) to train a model, which will be used to new sentences 284 | task = 'train_kfold' 285 | 286 | task_func[task]() 287 | 288 | 289 | if __name__ == "__main__": 290 | tic = time.time() 291 | main() 292 | print(f'time used: {time.time()-tic:.0f} seconds') 293 | --------------------------------------------------------------------------------