asreview/cloud-usage

ASReview: Active learning for Systematic Reviews

Systematically screening large amounts of textual data is time-consuming and often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for search tasks. A well-established approach to increasing efficiency is screening prioritization via Active Learning.

The Active learning for Systematic Reviews (ASReview) project, published in Nature Machine Intelligence implements different machine learning algorithms that interactively query the researcher. ASReview LAB is designed to accelerate the step of screening textual data with a minimum of records to be read by a human with no or very few false negatives. ASReview LAB will save time, increase the quality of output and strengthen the transparency of work when screening large amounts of textual data to retrieve relevant information. Active Learning will support decision-making in any discipline or industry.

ASReview software implements three different modes:

• Oracle Screen textual data in interaction with the active learning model. The reviewer is the 'oracle', making the labeling decisions.
• Exploration Explore or demonstrate ASReview LAB with a completely labeled dataset. This mode is suitable for teaching purposes.
• Simulation Evaluate the performance of active learning models on fully labeled data. Simulations can be run in ASReview LAB or via the command line interface with more advanced options.

Installation

The ASReview software requires Python 3.8 or later. Detailed step-by-step instructions to install Python and ASReview are available for Windows and macOS users.

pip install asreview

Upgrade ASReview with the following command:

pip install --upgrade asreview

To install ASReview LAB with Docker, see Install with Docker.

How it works

Getting started

Getting Started with ASReview LAB.

Citation

If you wish to cite the underlying methodology of the ASReview software, please use the following publication in Nature Machine Intelligence:

van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7

For citing the software, please refer to the specific release of the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the right can be used to find the citation format of prevalence.

For more scientific publications on the ASReview software, go to asreview.ai/papers.

Contact

For an overview of the team working on ASReview, see ASReview Research Team. ASReview LAB is maintained by Jonathan de Bruin and Yongchao Terry Ma.

The best resources to find an answer to your question or ways to get in contact with the team are:

• Documentation - asreview.readthedocs.io
• Newsletter - asreview.ai/newsletter/subscribe
• Quick tour - ASReview LAB quick tour
• Issues or feature requests - ASReview issue tracker
• FAQ - ASReview Discussions
• Donation - asreview.ai/donate
• Contact - [email protected]

License

The ASReview software has an Apache 2.0 LICENSE. The ASReview team accepts no responsibility or liability for the use of the ASReview tool or any direct or indirect damages arising out of the application of the tool.

cloud-usage's Issues

Move to asreview main organization

Is there a way to check to which worker the tasker sended the task?

Is there a way to check to which worker the tasker sended the task?

Change abelsiqueira to asreview

After #18

When I put more than 3 datasets into the K8s, it ends up with an error

When I put more than 3 datasets into the K8s, it ends up with an error

To Reproduce
I followed README.md with 4 datasets from Synergy-datasets in the data folder: van den Schoot, Lenaars, Hall, and Menon.
This leads to errors in all of the workers:

Received 'asreview simulate data/Hall_2012.csv -s output/simulation/Hall_2012/state_files/sim_Hall_2012_4905.asreview --prior_record_id 4905 231 5214 36 4214 788 8582 7303 3880 4806 7253 --seed 165\n'
Traceback (most recent call last):
File "/root/.local/bin/asreview", line 8, in
sys.exit(main())
File "/root/.local/lib/python3.8/site-packages/asreview/main.py", line 48, in main
entry.load()().execute(sys.argv[2:])
File "/root/.local/lib/python3.8/site-packages/asreview/entry_points/simulate.py", line 137, in execute
raise ProjectExistsError("Project already exists.")
asreview.project.ProjectExistsError: Project already exists.
Traceback (most recent call last):
File "/app/worker-receiver.py", line 53, in
worker = Worker()
File "/app/worker-receiver.py", line 37, in init
channel.start_consuming()
File "/usr/local/lib/python3.8/site-packages/pika/adapters/blocking_connection.py", line 1883, in start_consuming
self._process_data_events(time_limit=None)
File "/usr/local/lib/python3.8/site-packages/pika/adapters/blocking_connection.py", line 2044, in _process_data_events
self.connection.process_data_events(time_limit=time_limit)
File "/usr/local/lib/python3.8/site-packages/pika/adapters/blocking_connection.py", line 851, in process_data_events
self._dispatch_channel_events()
File "/usr/local/lib/python3.8/site-packages/pika/adapters/blocking_connection.py", line 567, in _dispatch_channel_events
impl_channel._get_cookie()._dispatch_events()
File "/usr/local/lib/python3.8/site-packages/pika/adapters/blocking_connection.py", line 1510, in _dispatch_events
consumer_info.on_message_callback(self, evt.method,
File "/app/worker-receiver.py", line 41, in callback
subprocess.run(
File "/usr/local/lib/python3.8/subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command 'asreview simulate data/Hall_2012.csv -s output/simulation/Hall_2012/state_files/sim_Hall_2012_4905.asreview --prior_record_id 4905 231 5214 36 4214 788 8582 7303 3880 4806 7253 --seed 165

Additional information

• OS: Mac M1
• ASReview version ..2
• CPU limit in worker.yml is 500
• Memory limit in worker.yml is 552

Fix limitation of one data file

Currently, you can only have one data file in the data folder (at least with Makita's ARFI template) because the mkdir of the second file happens after the simulate of the first file.

During minikube start there is an Error on Linux, should I worry or not?

After minikube start I experience this erorr, but everything works fine after this.

m/rabbitmq/cluster-operator/releases/latest/download/cluster-operator.yml"😄 minikube v1.30.1 on Ubuntu 20.04 (amd64)
✨ Automatically selected the docker driver. Other choices: ssh, none
📌 Using Docker driver with root privileges
👍 Starting control plane node minikube in cluster minikube
🚜 Pulling base image ...
🔥 Creating docker container (CPUs=2, Memory=16000MB) ...| E0528 08:15:17.810888 3192134 network_create.go:102] failed to find free subnet for docker network minikube after 20 attempts: no free private network subnets found with given parameters (start: "192.168.49.0", step: 9, tries: 20)

❗ Unable to create dedicated network, this might result in cluster IP change after restart: un-retryable: no free private network subnets found with given parameters (start: "192.168.49.0", step: 9, tries: 20)

🐳 Preparing Kubernetes v1.26.3 on Docker 23.0.2 ...| E0528 08:15:28.094721 3192134 start.go:131] Unable to get host IP: network inspect: docker network inspect minikube --format "{"Name": "{{.Name}}","Driver": "{{.Driver}}","Subnet": "{{range .IPAM.Config}}{{.Subnet}}{{end}}","Gateway": "{{range .IPAM.Config}}{{.Gateway}}{{end}}","MTU": {{if (index .Options "com.docker.network.driver.mtu")}}{{(index .Options "com.docker.network.driver.mtu")}}{{else}}0{{end}}, "ContainerIPs": [{{range $k,$v := .Containers }}"{{$v.IPv4Address}}",{{end}}]}": exit status 1
stdout:

stderr:
Error response from daemon: network minikube not found

Fix linter

Workers do not recieve messages from RabbitMQ, but all the pods are running

I have run kubeclt apply -f tasker.yml, and from logs of the takser it is evident that all of commands have been send to the workers. But all the workers show such output:

Logging as default_user_G08RHRdf8edvEBcNsM1
[*] Waiting for messages. CTRL+C to exit
^C

The volume of the dataset is not a problem for RabbitMQ heartbeat, because I have already run simulations via K8s on this data. I may have some difficulties with K8s setup, but I wondered I how I can follow the message from Tasker to Worker, so i can make sure that every step is alright.