├── sensitive_variables.py ├── .gitignore ├── images ├── syrcover.png └── OpenAlex-logo-5.2de7053c.png ├── requirements.txt ├── LICENSE ├── parameter_space.json ├── parameter_space_multilabel.json ├── cv_data ├── 12 │ ├── outer_results_5_splits.json │ ├── parameter_combinations.json │ ├── inner_results_3_from_5_splits.json │ ├── inner_results_0_from_5_splits.json │ ├── outer_param_search_5_splits.json │ ├── inner_results_1_from_5_splits.json │ ├── inner_results_4_from_5_splits.json │ └── inner_results_2_from_5_splits.json └── INCLUDE │ ├── outer_results_5_splits.json │ ├── outer_param_search_5_splits.json │ └── parameter_combinations.json ├── I_conclusion.md ├── README.md ├── 0_introduction.md └── D_run_cv_experiments.py /sensitive_variables.py: -------------------------------------------------------------------------------- 1 | email = None -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/* 2 | .ipynb_checkpoints/* 3 | __pycache__/* 4 | OpenAlex_data/* 5 | data/* 6 | -------------------------------------------------------------------------------- /images/syrcover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mcallaghan/NLP-climate-science-tutorial-CCAI/HEAD/images/syrcover.png -------------------------------------------------------------------------------- /images/OpenAlex-logo-5.2de7053c.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mcallaghan/NLP-climate-science-tutorial-CCAI/HEAD/images/OpenAlex-logo-5.2de7053c.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Cartopy==0.19.0.post1 2 | geopandas==0.10.2 3 | numpy==1.20.3 4 | pandas==1.3.4 5 | scikit-learn==1.0.1 6 | transformers==4.21.0 7 | datasets==2.4.0 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 2 | -------------------------------------------------------------------------------- /parameter_space.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "vect__max_df": [0.5, 0.8], 4 | "vect__min_df": [5, 15], 5 | "vect__ngram_range": [[1, 1], [1, 2]], 6 | "clf__kernel": ["rbf"], 7 | "clf__gamma": [1e-3, 1e-4], 8 | "clf__C": [1, 1e1, 1e2] 9 | }, 10 | { 11 | "vect__max_df": [0.5,0.8], 12 | "vect__min_df": [5, 15], 13 | "vect__ngram_range": [[1, 1], [1, 2]], 14 | "clf__kernel": ["linear"], 15 | "clf__C": [1, 1e1, 1e2] 16 | } 17 | ] -------------------------------------------------------------------------------- /parameter_space_multilabel.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "vect__max_df": [0.5, 0.8], 4 | "vect__min_df": [5, 15], 5 | "vect__ngram_range": [[1, 1], [1, 2]], 6 | "clf__estimator__kernel": ["rbf"], 7 | "clf__estimator__gamma": [1e-3, 1e-4], 8 | "clf__estimator__C": [1, 1e1, 1e2] 9 | }, 10 | { 11 | "vect__max_df": [0.5,0.8], 12 | "vect__min_df": [5, 15], 13 | "vect__ngram_range": [[1, 1], [1, 2]], 14 | "clf__estimator__kernel": ["linear"], 15 | "clf__estimator__C": [1, 1e1, 1e2] 16 | } 17 | ] -------------------------------------------------------------------------------- /cv_data/INCLUDE/outer_results_5_splits.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "f1_score": 0.8159722222222223, 4 | "precision_score": 0.8047945205479452, 5 | "recall_score": 0.8274647887323944 6 | }, 7 | { 8 | "f1_score": 0.8291746641074856, 9 | "precision_score": 0.8181818181818182, 10 | "recall_score": 0.8404669260700389 11 | }, 12 | { 13 | "f1_score": 0.8226415094339623, 14 | "precision_score": 0.7898550724637681, 15 | "recall_score": 0.8582677165354331 16 | }, 17 | { 18 | "f1_score": 0.8160000000000001, 19 | "precision_score": 0.8095238095238095, 20 | "recall_score": 0.8225806451612904 21 | }, 22 | { 23 | "f1_score": 0.8522727272727274, 24 | "precision_score": 0.8490566037735849, 25 | "recall_score": 0.8555133079847909 26 | } 27 | ] -------------------------------------------------------------------------------- /I_conclusion.md: -------------------------------------------------------------------------------- 1 | # Conclusion 2 | 3 | To recap, in this tutorial we have learned 4 | 5 | - how to get bibliographic data from the Open Source database OpenAlex 6 | - How to set up simple machine learning pipelines for texts using Support vector machines with `scikitlearn` 7 | - How to classify texts by fine-tuning language models from `transformers` 8 | - How to evaluate models and choose hyperparameters 9 | - How to extract structured geographical information from texts and plot this on a grid cell level. 10 | 11 | 12 | ### What is this useful for? 13 | 14 | The number of scientific publications on climate change has reached levels where traditional methods of synthesis are 15 | challenging. Using machine learning, we are able to more efficiently identify and classify relevant literature. 16 | 17 | This work of identification and classification is similar in nature to the production of [systematic maps](https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-016-0059-6). 18 | These can be used to identify where we have lots of evidence, or where we have evidence gaps, and the maps can provide starting 19 | points towards more detailed systematic reviews. 20 | 21 | It is important to note that machine learning is a **complement** - not a **substitute** - for expert knowledge. 22 | We could not, and indeed would not want to, automate the process of the IPCC. 23 | However given scarce resources and the massive task of keeping on top of climate science literature, 24 | it does make sense to use machine learning in order to deploy those resources more effectively. 25 | Especially if it means we can 26 | 27 | - Assess a wider pool of evidence 28 | - Be as transparent about the selection process (from query to model performance) as possible 29 | - Update this efficiently 30 | 31 | It is important to remember, we are already "using" machine learning when we search for studies in google scholar. 32 | 33 | ## Some more things to try 34 | 35 | Using NLP to look at scientific texts is a large research area, and there are many other things we can try. 36 | 37 | #### Unsupervised learning 38 | 39 | Where we do not have a specific set of categories into which we want to classify documents, we may find it interesting to use **unsupervised learning** to classify studies. 40 | Some examples in climate literature are on [climate and health](https://apsis.mcc-berlin.net/climate-health/), 41 | and on [the literature as a whole](https://www.nature.com/articles/s41558-019-0684-5). 42 | 43 | #### Researcher in the Loop for systematic reviews 44 | 45 | Instead of simply screening documents and then making predictions, we can use prediction in order to prioritise which documents we screen. 46 | This process is sometimes called "researcher in the loop": 47 | [link](https://www.nature.com/articles/s42256-020-00287-7), 48 | [link](https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/2046-4053-4-5). 49 | However, it is important to think more carefully about [stopping criteria](https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-020-01521-4) than has often been done when presenting such systems 50 | 51 | -------------------------------------------------------------------------------- /cv_data/INCLUDE/outer_param_search_5_splits.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "f1_score": 0.7012987012987013, 4 | "precision_score": 0.54, 5 | "recall_score": 1.0, 6 | "param_id": 0 7 | }, 8 | { 9 | "f1_score": 0.7012987012987013, 10 | "precision_score": 0.54, 11 | "recall_score": 1.0, 12 | "param_id": 1 13 | }, 14 | { 15 | "f1_score": 0.7766990291262137, 16 | "precision_score": 0.8163265306122449, 17 | "recall_score": 0.7407407407407407, 18 | "param_id": 2 19 | }, 20 | { 21 | "f1_score": 0.7012987012987013, 22 | "precision_score": 0.54, 23 | "recall_score": 1.0, 24 | "param_id": 3 25 | }, 26 | { 27 | "f1_score": 0.6851851851851852, 28 | "precision_score": 0.6851851851851852, 29 | "recall_score": 0.6851851851851852, 30 | "param_id": 4 31 | }, 32 | { 33 | "f1_score": 0.717948717948718, 34 | "precision_score": 0.56, 35 | "recall_score": 1.0, 36 | "param_id": 0 37 | }, 38 | { 39 | "f1_score": 0.717948717948718, 40 | "precision_score": 0.56, 41 | "recall_score": 1.0, 42 | "param_id": 1 43 | }, 44 | { 45 | "f1_score": 0.7889908256880735, 46 | "precision_score": 0.8113207547169812, 47 | "recall_score": 0.7678571428571429, 48 | "param_id": 2 49 | }, 50 | { 51 | "f1_score": 0.717948717948718, 52 | "precision_score": 0.56, 53 | "recall_score": 1.0, 54 | "param_id": 3 55 | }, 56 | { 57 | "f1_score": 0.7924528301886793, 58 | "precision_score": 0.84, 59 | "recall_score": 0.75, 60 | "param_id": 4 61 | }, 62 | { 63 | "f1_score": 0.6394557823129251, 64 | "precision_score": 0.47, 65 | "recall_score": 1.0, 66 | "param_id": 0 67 | }, 68 | { 69 | "f1_score": 0.6394557823129251, 70 | "precision_score": 0.47, 71 | "recall_score": 1.0, 72 | "param_id": 1 73 | }, 74 | { 75 | "f1_score": 0.7843137254901961, 76 | "precision_score": 0.7272727272727273, 77 | "recall_score": 0.851063829787234, 78 | "param_id": 2 79 | }, 80 | { 81 | "f1_score": 0.6394557823129251, 82 | "precision_score": 0.47, 83 | "recall_score": 1.0, 84 | "param_id": 3 85 | }, 86 | { 87 | "f1_score": 0.737864077669903, 88 | "precision_score": 0.6785714285714286, 89 | "recall_score": 0.8085106382978723, 90 | "param_id": 4 91 | }, 92 | { 93 | "f1_score": 0.6666666666666666, 94 | "precision_score": 0.5, 95 | "recall_score": 1.0, 96 | "param_id": 0 97 | }, 98 | { 99 | "f1_score": 0.6666666666666666, 100 | "precision_score": 0.5, 101 | "recall_score": 1.0, 102 | "param_id": 1 103 | }, 104 | { 105 | "f1_score": 0.7446808510638298, 106 | "precision_score": 0.7954545454545454, 107 | "recall_score": 0.7, 108 | "param_id": 2 109 | }, 110 | { 111 | "f1_score": 0.0, 112 | "precision_score": 0.0, 113 | "recall_score": 0.0, 114 | "param_id": 3 115 | }, 116 | { 117 | "f1_score": 0.7184466019417476, 118 | "precision_score": 0.6981132075471698, 119 | "recall_score": 0.74, 120 | "param_id": 4 121 | }, 122 | { 123 | "f1_score": 0.7577639751552795, 124 | "precision_score": 0.61, 125 | "recall_score": 1.0, 126 | "param_id": 0 127 | }, 128 | { 129 | "f1_score": 0.7577639751552795, 130 | "precision_score": 0.61, 131 | "recall_score": 1.0, 132 | "param_id": 1 133 | }, 134 | { 135 | "f1_score": 0.8032786885245902, 136 | "precision_score": 0.8032786885245902, 137 | "recall_score": 0.8032786885245902, 138 | "param_id": 2 139 | }, 140 | { 141 | "f1_score": 0.7577639751552795, 142 | "precision_score": 0.61, 143 | "recall_score": 1.0, 144 | "param_id": 3 145 | }, 146 | { 147 | "f1_score": 0.7777777777777778, 148 | "precision_score": 0.7538461538461538, 149 | "recall_score": 0.8032786885245902, 150 | "param_id": 4 151 | } 152 | ] -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NLP for evidence synthesis in climate change research 2 | 3 | This repository contains materials for a tutorial on using NLP in research synthesis in the field of climate change. The following gives an outline of the tutorial. 4 | 5 | # Contents 6 | 7 | - [0_introduction.md](0_introduction.md) gives an introduction to the topic, motivating why we do this work, and setting out the kinds of methods available 8 | - [A_obtaining_data.ipynb](A_obtaining_data.ipynb) takes us through getting scientific texts using the open source OpenAlex API 9 | - [B_model_pipeline.ipynb](B_model_pipeline.ipynb) shows us how we can build a pipeline to learn from texts and labels 10 | - [C_evaluation.ipynb](C_evaluation.ipynb) shows us how we can evaluate the results of a model, and how we can choose and evaluate model hyperparameters 11 | - [D_run_cv_experiments.py](D_run_cv_experiments.py) is an example script showing how we can run cross-validation experiments to select a model and estimate its generalisation performance. 12 | - [E_viz_cv_experiments.ipynb](E_viz_cv_experiments.ipynb) contains a couple of example plots to visualize the results of our cv experiments 13 | - [F_geoparse_texts.ipynb](F_geoparse_texts.ipynb) shows how to run a geoparser to extract place mentions from our texts and resolve these to structured geographic information 14 | - [G_match_studies_gridcells.ipynb](G_match_studies_gridcells.ipynb) shows how to match place names to grid cells 15 | - [H_mapping_results.ipynb](H_mapping_results.ipynb) shows us how to plot these results on a map 16 | - [I_conclusion.md](I_conclusion.md) concludes the tutorial and gives an outlook on what these methods can be used for. 17 | # Setup 18 | 19 | The best way to run these notebooks in Colab is to connect to 20 | google drive and clone the repository. 21 | To do this open a new Colab notebook and enter the following: 22 | 23 | ``` 24 | from google.colab import drive 25 | import os 26 | drive.mount('/content/drive') 27 | os.chdir("/content/drive/MyDrive") 28 | ! git clone https://github.com/mcallaghan/NLP-climate-science-tutorial-CCAI 29 | ``` 30 | 31 | 32 | You can also run the notebooks locally. To do that you will need to clone the repository, set up a virtual environment, and install the packages specified in the requirements.txt file 33 | 34 | ``` 35 | git clone https://github.com/mcallaghan/NLP-climate-science-tutorial-CCAI 36 | python3 -m venv venv 37 | source venv/bin/activate 38 | pip install requirements.txt 39 | ``` 40 | 41 | # Synopsis 42 | 43 | Understanding climate change, and developing climate solutions, is predicated on our collective understanding of complex physical, biological, and social systems and their interplay. The scientific literature about climate change comprises hundreds of thousands of articles across disciplines, and to advance our knowledge of climate change, we need to be able to learn from these studies by synthesising the literature. This is done at a grand scale in global environmental assessments like the IPCC - whose assessment of the science of climate change are vital inputs into international climate policy - as well as in individual evidence synthesis projects like systematic maps and reviews. Producing evidence synthesis that is systematic, i.e. transparent about the selection of studies, and as comprehensive as is feasible, is challenged by the amount of literature published. In this tutorial we will explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature. 44 | 45 | # Learning objectives 46 | 47 | During the course of the tutorial participants will gain an understanding of the broad range of ways that NLP can assist research synthesis. In particular you will gain hands on experience replicating an ML-assisted evidence map of climate impacts research, learning the following skills 48 | 49 | - Obtaining abstracts and meta-data from research studies by searching bibliographic databases 50 | - Using supervised learning to replicate human annotations of studies 51 | - Choosing hyperparameters and evaluating models 52 | - Mapping research 53 | -------------------------------------------------------------------------------- /0_introduction.md: -------------------------------------------------------------------------------- 1 | ## The IPCC 2 | 3 | ![Cover of 5th IPCC report](images/syrcover.png) 4 | 5 | It is the job of the [IPCC](https://www.ipcc.ch/) to provide comprehensive, objective and transparent assessments of the scientific literature on climate change. The reports, which are published every 6-7, years serve as trusted sources of information across climate science, impacts & adaptation, and mitigation. Because the summaries for policymakers are approved by national governments, the reports are also vital in international climate negotiations and are key tools for holding governments accountable. 6 | 7 | The three working groups of the IPCC 8 | - WGI: The Physical Science Basis 9 | - WGII: Impacts, Adaptation and Vulnerability 10 | - WGIII: Mitigation of Climate Change 11 | 12 | have all recently published their sixth assessment report, with a synthesis report to published in late 2022 or early 2023 13 | 14 | ### Comprehensive assessments, an increasingly intractable task 15 | 16 | ![Graph showing growth in studies on climate change](images/literature_growth.svg) 17 | 18 | Yet whereas early IPCC reports had hundreds or thousands of scientific studies on climate change to contend with, during the most recent assessment report more than 300,000 studies were published on climate change ([Callaghan et al., 2020](https://www.nature.com/articles/s41558-019-0684-5)). Although the reports have got bigger - involving more authors, comprising more pages, and containing more references - this has not kept pace with the growth in the literature. With the IPCC citing an increasingly small proportion of available relevant literature, the question of what they cite and how this is selected becomes ever more important. 19 | 20 | ## Evidence synthesis methods 21 | 22 | Evidence synthesis ([overview](https://www.lshtm.ac.uk/research/centres/centre-evaluation/evidence-synthesis)) describes a suite of formal methods for selecting and assessing evidence on a research question. These are most frequently used in the medical domain, where important decisions with life-or-death outcomes are rarely made based on single studies, or on "normal" literature reviews (where a single expert or experts select and summarise a literature with no formal procedures for how evidence is selected or assessed). 23 | 24 | Evidence synthesis methods prioritise comprehensiveness in the evidence base assessed, as well as transparency in how this is identified, selected and assessed. 25 | 26 | ## Machine-learning assisted evidence synthesis 27 | 28 | Many processes in evidence synthesis, such as screening potentially relevant studies, or coding studies according to the type of evidence they provide, are repetitive and time consuming. As the number of papers grows, these processes present larger and larger challenges to the goal of producing comprehensive assessments. 29 | 30 | Repetitive and time consuming tasks are exactly those where machine-learning :computer: :mortar_board: can be helpful. Research is mostly recorded in written texts, so that means the ML tools we are looking for come from the area of Natural Language Processing (NLP). 31 | 32 | So far the uses of ML in evidence synthesis are: 33 | 34 | - **Identifying relevant studies**: A simple binary classifier can be trained on human screened data (studies labelled with 0, or 1 for relevant) to predict whether documents without labels will be relevant or not 35 | - **Classifying studies**: Machine learning can be used to give useful content labels to studies, either using 36 | - **Unsupervised** methods like topic modelling, which need no human input but cannot assign to documents to predefined categories; or 37 | - **Supervised** methods, where models predict the labels of unseen documents, based on the *predefined* labels assigned by humans. 38 | 39 | 40 | In this tutorial we will be exploring how we can use supervised methods to identify and classify evidence on climate impacts, following a simplified version of a pipeline used in a recent [publication](https://www.nature.com/articles/s41558-021-01168-6.epdf?sharing_token=7Sw0J3PwoQNKriW6TbOuX9RgN0jAjWel9jnR3ZoTv0PwAcRfhcoupIk0A95eY8_-YUKjuCHE46BRE8RXMy_HNaRNTl48Ji0i6-pYLEd3WbQwXQpTp1LscjrMM5Am67huate7yo35BOmH8Yq08b_CuYEhHpq1-SwJh0A5XD6kKvE%3D). 41 | -------------------------------------------------------------------------------- /cv_data/12/outer_results_5_splits.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "f1_score_micro": 0.7169811320754716, 4 | "f1_score_macro": 0.7007080610021786, 5 | "f1_score_weighted": 0.7123699838358282, 6 | "f1_score_0": 0.5, 7 | "f1_score_1": 0.8148148148148148, 8 | "f1_score_2": 0.8750000000000001, 9 | "f1_score_3": 0.6666666666666666, 10 | "f1_score_4": 0.6470588235294118, 11 | "precision_score_micro": 0.8636363636363636, 12 | "precision_score_macro": 0.8965034965034965, 13 | "precision_score_weighted": 0.8892397924655989, 14 | "precision_score_0": 1.0, 15 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"precision_score_2": 1.0, 69 | "precision_score_3": 0.7692307692307693, 70 | "precision_score_4": 0.6666666666666666, 71 | "recall_score_micro": 0.671875, 72 | "recall_score_macro": 0.6637405731523379, 73 | "recall_score_weighted": 0.671875, 74 | "recall_score_0": 0.3333333333333333, 75 | "recall_score_1": 0.8461538461538461, 76 | "recall_score_2": 0.6, 77 | "recall_score_3": 0.8333333333333334, 78 | "recall_score_4": 0.7058823529411765 79 | }, 80 | { 81 | "f1_score_micro": 0.7307692307692307, 82 | "f1_score_macro": 0.7275, 83 | "f1_score_weighted": 0.7292361111111111, 84 | "f1_score_0": 0.6666666666666666, 85 | "f1_score_1": 0.7000000000000001, 86 | "f1_score_2": 0.7499999999999999, 87 | "f1_score_3": 0.8333333333333333, 88 | "f1_score_4": 0.6875, 89 | "precision_score_micro": 0.8636363636363636, 90 | "precision_score_macro": 0.9034848484848486, 91 | "precision_score_weighted": 0.8797474747474747, 92 | "precision_score_0": 1.0, 93 | "precision_score_1": 0.875, 94 | "precision_score_2": 1.0, 95 | "precision_score_3": 0.9090909090909091, 96 | "precision_score_4": 0.7333333333333333, 97 | "recall_score_micro": 0.6333333333333333, 98 | "recall_score_macro": 0.6199245852187029, 99 | "recall_score_weighted": 0.6333333333333333, 100 | "recall_score_0": 0.5, 101 | "recall_score_1": 0.5833333333333334, 102 | "recall_score_2": 0.6, 103 | "recall_score_3": 0.7692307692307693, 104 | "recall_score_4": 0.6470588235294118 105 | }, 106 | { 107 | "f1_score_micro": 0.8461538461538461, 108 | "f1_score_macro": 0.7285470085470085, 109 | "f1_score_weighted": 0.8361626878868258, 110 | "f1_score_0": 0.5, 111 | "f1_score_1": 0.888888888888889, 112 | "f1_score_2": 0.5, 113 | "f1_score_3": 0.9333333333333333, 114 | "f1_score_4": 0.8205128205128205, 115 | "precision_score_micro": 0.9565217391304348, 116 | "precision_score_macro": 0.9777777777777779, 117 | "precision_score_weighted": 0.9597701149425287, 118 | "precision_score_0": 1.0, 119 | "precision_score_1": 1.0, 120 | "precision_score_2": 1.0, 121 | "precision_score_3": 1.0, 122 | "precision_score_4": 0.8888888888888888, 123 | "recall_score_micro": 0.7586206896551724, 124 | "recall_score_macro": 0.6207142857142858, 125 | "recall_score_weighted": 0.7586206896551724, 126 | "recall_score_0": 0.3333333333333333, 127 | "recall_score_1": 0.8, 128 | "recall_score_2": 0.3333333333333333, 129 | "recall_score_3": 0.875, 130 | "recall_score_4": 0.7619047619047619 131 | } 132 | ] -------------------------------------------------------------------------------- /D_run_cv_experiments.py: -------------------------------------------------------------------------------- 1 | ######################################### 2 | # WARNING! This script is optimized # 3 | # for readability not for performance # 4 | # consider parallelising the many loops # 5 | # and saving intermediate results # 6 | ######################################### 7 | 8 | import argparse 9 | import pandas as pd 10 | import numpy as np 11 | import re 12 | import random 13 | import os 14 | import json 15 | 16 | def load_data(test): 17 | labelled_data = pd.read_csv("data/labelled_data.csv") 18 | 19 | # We create a lower case version of the title without spaces and punctuation, to allow for merging 20 | labelled_data["title_lcase"] = labelled_data["title"].apply(lambda x: re.sub(r"\W", "", x).lower()) 21 | # We also want to get rid of documents without abstracts, as we can't use these for learning 22 | labelled_data = labelled_data.dropna(subset=["abstract"]) 23 | 24 | # Now we load the Open alex data, and create the same title variable for merging 25 | 26 | oa_data = pd.read_csv("data/openalex_data.csv").rename(columns={"id": "OA_id"}) 27 | oa_data = oa_data.dropna(subset=["title"]) 28 | oa_data["title_lcase"] = oa_data["title"].astype(str).apply(lambda x: re.sub(r"\W", "", x).lower()) 29 | 30 | # Get only those rows which don't match the labels 31 | oa_data = oa_data[~oa_data["title_lcase"].isin(labelled_data["title_lcase"])] 32 | oa_data = oa_data.dropna(subset=["abstract"]) # Drop rows without abstracts 33 | oa_data["seen"] = 0 34 | 35 | if test: 36 | labelled_data = labelled_data.sample(500) # just take a sample of each 37 | oa_data = oa_data.sample(100) 38 | 39 | # Now we add the OpenAlex rows which are not in the labelled data to the labelled_data 40 | df = pd.concat([ 41 | labelled_data, 42 | oa_data 43 | ]).sample(frac=1).reset_index(drop=True) 44 | return df 45 | 46 | 47 | def fit_eval_model(pipeline, param_set, x, y, train_index, val_index, metrics): 48 | clf = pipeline.set_params(**param_set) 49 | clf.fit(x[train_index], y[train_index]) 50 | y_pred = clf.predict(x[val_index]) 51 | if y.ndim==1: 52 | scores = {metric.__name__: metric(y[val_index], y_pred) for metric in metrics} 53 | elif y.ndim==2: # For multilabel we need to evaluate differently 54 | scores = {} 55 | for metric in metrics: 56 | for average in ["micro", "macro", "weighted"]: # We can get different types of average scores 57 | scores[f"{metric.__name__}_{average}"] = metric(y[val_index], y_pred, average=average) 58 | for c in np.arange(y.shape[1]): # And we can also calculate each score for each class 59 | scores[f"{metric.__name__}_{c}"] = metric(y[val_index,c], y_pred[:,c]) 60 | return scores 61 | 62 | 63 | def get_best_param(scores, scorer): 64 | return (pd.DataFrame(scores) 65 | .groupby("param_id")[scorer] 66 | .mean() 67 | .sort_values(ascending=False).index[0] 68 | ) 69 | 70 | 71 | def main(y_prefix, n_splits, test): 72 | from sklearn.svm import SVC 73 | from sklearn.multiclass import OneVsRestClassifier 74 | from sklearn.pipeline import Pipeline 75 | from sklearn.feature_extraction.text import TfidfVectorizer 76 | from sklearn.model_selection import ParameterGrid, KFold 77 | from sklearn.metrics import f1_score, recall_score, precision_score 78 | 79 | # Get our data 80 | df = load_data(test) 81 | 82 | X = df["abstract"].values 83 | cols = [x for x in df.columns if re.match(f"^{y_prefix}", x)] 84 | 85 | if len(cols)==1: 86 | cols = cols[0] 87 | # We need to use a pipeline for a binary classifier 88 | pipeline = Pipeline(steps=[ 89 | ("vect", TfidfVectorizer()), 90 | ("clf", SVC(probability=True, class_weight="balanced")) 91 | ]) 92 | # And we'll get our binary parameter space 93 | with open("parameter_space.json", "r") as fp: 94 | parameters = json.load(fp) 95 | # We'll rank models by the simple f1 score 96 | scorer = "f1_score" 97 | # For these purposes all documents that have been labelled at all are counted as labelled 98 | labelled_index = df[df["seen"] == 1].index 99 | elif len(cols)==0: 100 | print("Not enough columns match the given y_prefix, exiting...") 101 | return 1 102 | else: 103 | # In a multilabel setting we have a slightly different pipeline 104 | pipeline = Pipeline(steps=[ 105 | ("vect", TfidfVectorizer()), 106 | ("clf", OneVsRestClassifier(SVC(probability=True, class_weight="balanced"))) 107 | ]) 108 | # And a slightly differently defined parameter space 109 | with open("parameter_space_multilabel.json", "r") as fp: 110 | parameters = json.load(fp) 111 | # Here we'll rank scores by the macro f1 score 112 | scorer = "f1_score_macro" 113 | # In this setting only those labelled documents which were marked as INCLUDE are considered 114 | labelled_index = df[df["INCLUDE"] == 1].index 115 | 116 | unlabelled_index = df[df["seen"] == 0].index 117 | y = df[cols].values 118 | 119 | 120 | 121 | # Create a directory to store our results if it does not already exist, and change into it if it does 122 | data_path = f"cv_data/{y_prefix}" 123 | if not os.path.isdir(data_path): 124 | os.makedirs(data_path) 125 | os.chdir(data_path) 126 | 127 | parameter_combinations = list(ParameterGrid(parameters)) 128 | if test: 129 | parameter_combinations = random.choices(parameter_combinations, k=5) 130 | 131 | with open("parameter_combinations.json", "w") as fp: 132 | json.dump(parameter_combinations, fp, indent=2) 133 | 134 | metrics = [f1_score, precision_score, recall_score] 135 | 136 | ###################################### 137 | # Now we can start the cv experiments 138 | 139 | inner_fold = KFold(n_splits=n_splits) 140 | outer_fold = KFold(n_splits=n_splits) 141 | 142 | if os.path.exists(f"outer_param_search_{n_splits}_splits.json"): 143 | with open(f"outer_param_search_{n_splits}_splits.json", "r") as fp: 144 | outer_search_results = json.load(fp) 145 | else: 146 | outer_results = [] 147 | outer_search_results = [] 148 | 149 | # Do the nested loop 150 | for i, (o_train_index, test_index) in enumerate(outer_fold.split(labelled_index)): 151 | # If we have already done this before, load the results 152 | if os.path.exists(f"inner_results_{i}_from_{n_splits}_splits.json"): 153 | with open(f"inner_results_{i}_from_{n_splits}_splits.json", "r") as fp: 154 | inner_results = json.load(fp) 155 | else: 156 | ######################### 157 | # TUNE PARAMETERS 158 | inner_results = [] 159 | for j, (i_train_index, i_val_index) in enumerate(inner_fold.split(o_train_index)): 160 | for k, param_set in enumerate(parameter_combinations): 161 | scores = fit_eval_model( 162 | pipeline, param_set, X[labelled_index[o_train_index]], y[labelled_index[o_train_index]], 163 | i_train_index, i_val_index, metrics 164 | ) 165 | scores["param_id"] = k 166 | inner_results.append(scores) 167 | 168 | with open(f"inner_results_{i}_from_{n_splits}_splits.json", "w") as fp: 169 | json.dump(inner_results, fp, indent=2) 170 | 171 | ####################### 172 | # EVALUATE MODELS 173 | # fit a model with the best parameters on inner cv, and get the score 174 | best_param_id = get_best_param(inner_results, scorer) 175 | scores = fit_eval_model( 176 | pipeline, parameter_combinations[best_param_id], X[labelled_index], y[labelled_index], 177 | o_train_index, test_index, metrics 178 | ) 179 | outer_results.append(scores) 180 | 181 | ###################### 182 | # SEARCH PARAMETERS 183 | # Now do parameter search on the outer data 184 | for k, param_set in enumerate(parameter_combinations): 185 | scores = fit_eval_model( 186 | pipeline, param_set, X[labelled_index], y[labelled_index], 187 | o_train_index, test_index, metrics 188 | ) 189 | scores["param_id"] = k 190 | outer_search_results.append(scores) 191 | 192 | with open(f"outer_results_{n_splits}_splits.json", "w") as fp: 193 | json.dump(outer_results, fp, indent=2) 194 | with open(f"outer_param_search_{n_splits}_splits.json", "w") as fp: 195 | json.dump(outer_search_results, fp, indent=2) 196 | 197 | ###################### 198 | # MAKE PREDICTIONS 199 | # Get parameter_id with best results 200 | best_param_id = get_best_param(outer_search_results, scorer) 201 | clf = pipeline.set_params(**parameter_combinations[best_param_id]) 202 | clf.fit(X[labelled_index], y[labelled_index]) 203 | 204 | # These are our final results! 205 | y_pred = clf.predict_proba(X[unlabelled_index]) 206 | df = pd.DataFrame.from_dict({"OA_id": df.iloc[unlabelled_index]["OA_id"]}) 207 | if y.ndim==1: 208 | df[f"{cols}_prediction"] = y_pred[:,1] 209 | else : 210 | for i, col in enumerate(cols): 211 | df[f"{col}_prediction"] = y_pred[:,i] 212 | df.to_csv(f"predictions_{n_splits}_splits.csv", index=False) 213 | return "Success! Tuned parameters, evaluated our models and made predictions" 214 | 215 | 216 | if __name__ == "__main__": 217 | parser = argparse.ArgumentParser(description="Run a cross validation experiment on our labelled data") 218 | parser.add_argument( 219 | "--y_prefix", type=str, 220 | help="Variables in the data starting with this prefix will become our target variable" 221 | ) 222 | parser.add_argument( 223 | "--n_splits", type=int, 224 | ) 225 | parser.add_argument( 226 | "--test", action='store_true' 227 | ) 228 | args = parser.parse_args() 229 | main(args.y_prefix, args.n_splits, args.test) 230 | -------------------------------------------------------------------------------- /cv_data/INCLUDE/parameter_combinations.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "clf__C": 1, 4 | "clf__gamma": 0.001, 5 | "clf__kernel": "rbf", 6 | "vect__max_df": 0.5, 7 | "vect__min_df": 5, 8 | "vect__ngram_range": [ 9 | 1, 10 | 1 11 | ] 12 | }, 13 | { 14 | "clf__C": 1, 15 | "clf__gamma": 0.001, 16 | "clf__kernel": "rbf", 17 | "vect__max_df": 0.5, 18 | "vect__min_df": 5, 19 | "vect__ngram_range": [ 20 | 1, 21 | 2 22 | ] 23 | }, 24 | { 25 | "clf__C": 1, 26 | "clf__gamma": 0.001, 27 | "clf__kernel": "rbf", 28 | "vect__max_df": 0.5, 29 | "vect__min_df": 15, 30 | "vect__ngram_range": [ 31 | 1, 32 | 1 33 | ] 34 | }, 35 | { 36 | "clf__C": 1, 37 | "clf__gamma": 0.001, 38 | "clf__kernel": "rbf", 39 | "vect__max_df": 0.5, 40 | "vect__min_df": 15, 41 | "vect__ngram_range": [ 42 | 1, 43 | 2 44 | ] 45 | }, 46 | { 47 | "clf__C": 1, 48 | "clf__gamma": 0.001, 49 | "clf__kernel": "rbf", 50 | "vect__max_df": 0.8, 51 | "vect__min_df": 5, 52 | "vect__ngram_range": [ 53 | 1, 54 | 1 55 | ] 56 | }, 57 | { 58 | "clf__C": 1, 59 | "clf__gamma": 0.001, 60 | "clf__kernel": "rbf", 61 | "vect__max_df": 0.8, 62 | "vect__min_df": 5, 63 | "vect__ngram_range": [ 64 | 1, 65 | 2 66 | ] 67 | }, 68 | { 69 | "clf__C": 1, 70 | "clf__gamma": 0.001, 71 | "clf__kernel": "rbf", 72 | "vect__max_df": 0.8, 73 | "vect__min_df": 15, 74 | "vect__ngram_range": [ 75 | 1, 76 | 1 77 | ] 78 | }, 79 | { 80 | "clf__C": 1, 81 | "clf__gamma": 0.001, 82 | "clf__kernel": "rbf", 83 | "vect__max_df": 0.8, 84 | "vect__min_df": 15, 85 | "vect__ngram_range": [ 86 | 1, 87 | 2 88 | ] 89 | }, 90 | { 91 | "clf__C": 1, 92 | "clf__gamma": 0.0001, 93 | "clf__kernel": "rbf", 94 | "vect__max_df": 0.5, 95 | "vect__min_df": 5, 96 | "vect__ngram_range": [ 97 | 1, 98 | 1 99 | ] 100 | }, 101 | { 102 | "clf__C": 1, 103 | "clf__gamma": 0.0001, 104 | "clf__kernel": "rbf", 105 | "vect__max_df": 0.5, 106 | "vect__min_df": 5, 107 | "vect__ngram_range": [ 108 | 1, 109 | 2 110 | ] 111 | }, 112 | { 113 | "clf__C": 1, 114 | "clf__gamma": 0.0001, 115 | "clf__kernel": "rbf", 116 | "vect__max_df": 0.5, 117 | "vect__min_df": 15, 118 | "vect__ngram_range": [ 119 | 1, 120 | 1 121 | ] 122 | }, 123 | { 124 | "clf__C": 1, 125 | "clf__gamma": 0.0001, 126 | "clf__kernel": "rbf", 127 | "vect__max_df": 0.5, 128 | "vect__min_df": 15, 129 | "vect__ngram_range": [ 130 | 1, 131 | 2 132 | ] 133 | }, 134 | { 135 | "clf__C": 1, 136 | "clf__gamma": 0.0001, 137 | "clf__kernel": "rbf", 138 | "vect__max_df": 0.8, 139 | "vect__min_df": 5, 140 | "vect__ngram_range": [ 141 | 1, 142 | 1 143 | ] 144 | }, 145 | { 146 | "clf__C": 1, 147 | "clf__gamma": 0.0001, 148 | "clf__kernel": "rbf", 149 | "vect__max_df": 0.8, 150 | "vect__min_df": 5, 151 | "vect__ngram_range": [ 152 | 1, 153 | 2 154 | ] 155 | }, 156 | { 157 | "clf__C": 1, 158 | "clf__gamma": 0.0001, 159 | "clf__kernel": "rbf", 160 | "vect__max_df": 0.8, 161 | "vect__min_df": 15, 162 | "vect__ngram_range": [ 163 | 1, 164 | 1 165 | ] 166 | }, 167 | { 168 | "clf__C": 1, 169 | "clf__gamma": 0.0001, 170 | "clf__kernel": "rbf", 171 | "vect__max_df": 0.8, 172 | "vect__min_df": 15, 173 | "vect__ngram_range": [ 174 | 1, 175 | 2 176 | ] 177 | }, 178 | { 179 | "clf__C": 10.0, 180 | "clf__gamma": 0.001, 181 | "clf__kernel": "rbf", 182 | "vect__max_df": 0.5, 183 | "vect__min_df": 5, 184 | "vect__ngram_range": [ 185 | 1, 186 | 1 187 | ] 188 | }, 189 | { 190 | "clf__C": 10.0, 191 | "clf__gamma": 0.001, 192 | "clf__kernel": "rbf", 193 | "vect__max_df": 0.5, 194 | "vect__min_df": 5, 195 | "vect__ngram_range": [ 196 | 1, 197 | 2 198 | ] 199 | }, 200 | { 201 | "clf__C": 10.0, 202 | "clf__gamma": 0.001, 203 | "clf__kernel": "rbf", 204 | "vect__max_df": 0.5, 205 | "vect__min_df": 15, 206 | "vect__ngram_range": [ 207 | 1, 208 | 1 209 | ] 210 | }, 211 | { 212 | "clf__C": 10.0, 213 | "clf__gamma": 0.001, 214 | "clf__kernel": "rbf", 215 | "vect__max_df": 0.5, 216 | "vect__min_df": 15, 217 | "vect__ngram_range": [ 218 | 1, 219 | 2 220 | ] 221 | }, 222 | { 223 | "clf__C": 10.0, 224 | "clf__gamma": 0.001, 225 | "clf__kernel": "rbf", 226 | "vect__max_df": 0.8, 227 | "vect__min_df": 5, 228 | "vect__ngram_range": [ 229 | 1, 230 | 1 231 | ] 232 | }, 233 | { 234 | "clf__C": 10.0, 235 | "clf__gamma": 0.001, 236 | "clf__kernel": "rbf", 237 | "vect__max_df": 0.8, 238 | "vect__min_df": 5, 239 | "vect__ngram_range": [ 240 | 1, 241 | 2 242 | ] 243 | }, 244 | { 245 | "clf__C": 10.0, 246 | "clf__gamma": 0.001, 247 | "clf__kernel": "rbf", 248 | "vect__max_df": 0.8, 249 | "vect__min_df": 15, 250 | "vect__ngram_range": [ 251 | 1, 252 | 1 253 | ] 254 | }, 255 | { 256 | "clf__C": 10.0, 257 | "clf__gamma": 0.001, 258 | "clf__kernel": "rbf", 259 | "vect__max_df": 0.8, 260 | "vect__min_df": 15, 261 | "vect__ngram_range": [ 262 | 1, 263 | 2 264 | ] 265 | }, 266 | { 267 | "clf__C": 10.0, 268 | "clf__gamma": 0.0001, 269 | "clf__kernel": "rbf", 270 | "vect__max_df": 0.5, 271 | "vect__min_df": 5, 272 | "vect__ngram_range": [ 273 | 1, 274 | 1 275 | ] 276 | }, 277 | { 278 | "clf__C": 10.0, 279 | "clf__gamma": 0.0001, 280 | "clf__kernel": "rbf", 281 | "vect__max_df": 0.5, 282 | "vect__min_df": 5, 283 | "vect__ngram_range": [ 284 | 1, 285 | 2 286 | ] 287 | }, 288 | { 289 | "clf__C": 10.0, 290 | "clf__gamma": 0.0001, 291 | "clf__kernel": "rbf", 292 | "vect__max_df": 0.5, 293 | "vect__min_df": 15, 294 | "vect__ngram_range": [ 295 | 1, 296 | 1 297 | ] 298 | }, 299 | { 300 | "clf__C": 10.0, 301 | "clf__gamma": 0.0001, 302 | "clf__kernel": "rbf", 303 | "vect__max_df": 0.5, 304 | "vect__min_df": 15, 305 | "vect__ngram_range": [ 306 | 1, 307 | 2 308 | ] 309 | }, 310 | { 311 | "clf__C": 10.0, 312 | "clf__gamma": 0.0001, 313 | "clf__kernel": "rbf", 314 | "vect__max_df": 0.8, 315 | "vect__min_df": 5, 316 | "vect__ngram_range": [ 317 | 1, 318 | 1 319 | ] 320 | }, 321 | { 322 | "clf__C": 10.0, 323 | "clf__gamma": 0.0001, 324 | "clf__kernel": "rbf", 325 | "vect__max_df": 0.8, 326 | "vect__min_df": 5, 327 | "vect__ngram_range": [ 328 | 1, 329 | 2 330 | ] 331 | }, 332 | { 333 | "clf__C": 10.0, 334 | "clf__gamma": 0.0001, 335 | "clf__kernel": "rbf", 336 | "vect__max_df": 0.8, 337 | "vect__min_df": 15, 338 | "vect__ngram_range": [ 339 | 1, 340 | 1 341 | ] 342 | }, 343 | { 344 | "clf__C": 10.0, 345 | "clf__gamma": 0.0001, 346 | "clf__kernel": "rbf", 347 | "vect__max_df": 0.8, 348 | "vect__min_df": 15, 349 | "vect__ngram_range": [ 350 | 1, 351 | 2 352 | ] 353 | }, 354 | { 355 | "clf__C": 100.0, 356 | "clf__gamma": 0.001, 357 | "clf__kernel": "rbf", 358 | "vect__max_df": 0.5, 359 | "vect__min_df": 5, 360 | "vect__ngram_range": [ 361 | 1, 362 | 1 363 | ] 364 | }, 365 | { 366 | "clf__C": 100.0, 367 | "clf__gamma": 0.001, 368 | "clf__kernel": "rbf", 369 | "vect__max_df": 0.5, 370 | "vect__min_df": 5, 371 | "vect__ngram_range": [ 372 | 1, 373 | 2 374 | ] 375 | }, 376 | { 377 | "clf__C": 100.0, 378 | "clf__gamma": 0.001, 379 | "clf__kernel": "rbf", 380 | "vect__max_df": 0.5, 381 | "vect__min_df": 15, 382 | "vect__ngram_range": [ 383 | 1, 384 | 1 385 | ] 386 | }, 387 | { 388 | "clf__C": 100.0, 389 | "clf__gamma": 0.001, 390 | "clf__kernel": "rbf", 391 | "vect__max_df": 0.5, 392 | "vect__min_df": 15, 393 | "vect__ngram_range": [ 394 | 1, 395 | 2 396 | ] 397 | }, 398 | { 399 | "clf__C": 100.0, 400 | "clf__gamma": 0.001, 401 | "clf__kernel": "rbf", 402 | "vect__max_df": 0.8, 403 | "vect__min_df": 5, 404 | "vect__ngram_range": [ 405 | 1, 406 | 1 407 | ] 408 | }, 409 | { 410 | "clf__C": 100.0, 411 | "clf__gamma": 0.001, 412 | "clf__kernel": "rbf", 413 | "vect__max_df": 0.8, 414 | "vect__min_df": 5, 415 | "vect__ngram_range": [ 416 | 1, 417 | 2 418 | ] 419 | }, 420 | { 421 | "clf__C": 100.0, 422 | "clf__gamma": 0.001, 423 | "clf__kernel": "rbf", 424 | "vect__max_df": 0.8, 425 | "vect__min_df": 15, 426 | "vect__ngram_range": [ 427 | 1, 428 | 1 429 | ] 430 | }, 431 | { 432 | "clf__C": 100.0, 433 | "clf__gamma": 0.001, 434 | "clf__kernel": "rbf", 435 | "vect__max_df": 0.8, 436 | "vect__min_df": 15, 437 | "vect__ngram_range": [ 438 | 1, 439 | 2 440 | ] 441 | }, 442 | { 443 | "clf__C": 100.0, 444 | "clf__gamma": 0.0001, 445 | "clf__kernel": "rbf", 446 | "vect__max_df": 0.5, 447 | "vect__min_df": 5, 448 | "vect__ngram_range": [ 449 | 1, 450 | 1 451 | ] 452 | }, 453 | { 454 | "clf__C": 100.0, 455 | "clf__gamma": 0.0001, 456 | 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