A control method is used to test the relative performance of all other methods, and also as a quality control for the pipeline as a whole. A control method can either be a positive control or a negative control. The positive control and negative control methods set a maximum and minimum threshold for performance, so any new method should perform better than the negative control methods and worse than the positive control method.
This guide will show you how to create a new Viash component. In the following we will show examples for both Python and R. Note that the Task template repo is used throughout the guide, so make sure to replace any occurrences of "task_template" with your task of interest.
Make sure you have followed the “Getting started” guide.
Use the create_*_method.sh script found in the scripts repository to start creating a new control method. Open the script and update the name parameter to the desired name of the method and update the type to control_method.
scripts/create_component/create_python_method.sh
Check inputs
Check language
Check API file
Read API file
Create output dir
Create config
Create script
Done!This creates a new folder at src/control_methods/my_python_method containing a Viash config and a script.
tree src/tasks/label_projection/control_methods/my_python_method
├── script.py Script for running the method.
├── config.vsh.yaml Config file for method.
└── ... Optional additional resources.Check inputs
Check language
Check API file
Read API file
Create output dir
Create config
Create script
Done!scripts/create_component/create_r_method.sh
Check inputs
Check language
Check API file
Read API file
Create output dir
Create config
Create script
Done!This creates a new folder at src/control_methods/my_r_method containing a Viash config and a script.
tree src/tasks/label_projection/control_methods/my_r_method
├── script.R Script for running the method.
├── config.vsh.yaml Config file for method.
└── ... Optional additional resources.Change the --name to a unique name for your metric. It must match the regex [a-z][a-z0-9_]* (snakecase).
Change the --type to control_method.
Some tasks have multiple method subtypes (e.g. batch_integration), which will require you to use a different value for --type corresponding to the desired method subtype.
The Viash config contains metadata of your method, which script is used to run it, and the required dependencies.
This is what the config.vsh.yaml generated by the create_component component looks like:
config.vsh.yaml
# The API specifies which type of component this is.
# It contains specifications for:
# - The input/output files
# - Common parameters
# - A unit test
__merge__: ../../api/comp_control_method.yaml
# A unique identifier for your component (required).
# Can contain only lowercase letters or underscores.
name: my_python_method
# A relatively short label, used when rendering visualisations (required)
label: My Python Method
# A one sentence summary of how this method works (required). Used when
# rendering summary tables.
summary: "FILL IN: A one sentence summary of this method."
# A multi-line description of how this component works (required). Used
# when rendering reference documentation.
description: |
FILL IN: A (multi-line) description of how this method works.
# Metadata for your component
info:
# Which normalisation method this component prefers to use (required).
preferred_normalization: log_cp10k
# Component-specific parameters (optional)
# arguments:
# - name: "--n_neighbors"
# type: "integer"
# default: 5
# description: Number of neighbors to use.
# Resources required to run the component
resources:
# The script of your component (required)
- type: python_script
path: script.py
# Additional resources your script needs (optional)
# - type: file
# path: weights.pt
engines:
# Specifications for the Docker image for this component.
- type: docker
image: openproblems/base_python:1.0.0
# Add custom dependencies here (optional). For more information, see
# https://viash.io/reference/config/engines/docker/#setup .
# setup:
# - type: python
# packages: numpy<2
runners:
# This platform allows running the component natively
- type: executable
# Allows turning the component into a Nextflow module / pipeline.
- type: nextflow
directives:
label: [midtime,midmem,midcpu]config.vsh.yaml
# The API specifies which type of component this is.
# It contains specifications for:
# - The input/output files
# - Common parameters
# - A unit test
__merge__: ../../api/comp_control_method.yaml
# A unique identifier for your component (required).
# Can contain only lowercase letters or underscores.
name: my_r_method
# A relatively short label, used when rendering visualisations (required)
label: My R Method
# A one sentence summary of how this method works (required). Used when
# rendering summary tables.
summary: "FILL IN: A one sentence summary of this method."
# A multi-line description of how this component works (required). Used
# when rendering reference documentation.
description: |
FILL IN: A (multi-line) description of how this method works.
# Metadata for your component
info:
# Which normalisation method this component prefers to use (required).
preferred_normalization: log_cp10k
# Component-specific parameters (optional)
# arguments:
# - name: "--n_neighbors"
# type: "integer"
# default: 5
# description: Number of neighbors to use.
# Resources required to run the component
resources:
# The script of your component (required)
- type: r_script
path: script.R
# Additional resources your script needs (optional)
# - type: file
# path: weights.pt
engines:
# Specifications for the Docker image for this component.
- type: docker
image: openproblems/base_r:1.0.0
# Add custom dependencies here (optional). For more information, see
# https://viash.io/reference/config/engines/docker/#setup .
# setup:
# - type: r
# packages: tibble
runners:
# This platform allows running the component natively
- type: executable
# Allows turning the component into a Nextflow module / pipeline.
- type: nextflow
directives:
label: [midtime,midmem,midcpu]Please edit info section in the config file to fill in the necessary metadata.
.__merge__: The API specifies which type of component this is. It contains specifications for:
.name: A unique identifier. Can only contain lowercase letters, numbers or underscores.
.label: A unique, human-readable, short label. Used for creating summary tables and visualisations.
.summary: A one sentence summary of purpose and methodology. Used for creating an overview tables.
.description: A longer description (one or more paragraphs). Used for creating reference documentation and supplementary information.
Each component has it’s own set of dependencies, because different components might have conflicting dependencies.
For your convenience we have created several base images that can be used for python or R scripts. These images can be found in the OpenProblems Docker repository. Click on the packages to view the url you need to use. You are not required to use these images but install the required packages to make sure OpenProblems works properly.
openproblems/base_python Base image for python scripts.
openproblems/base_r Base image for R scripts.
openproblems/base_pytorch_nvidia Base image for scripts that use pytorch with nvidia gpu support.
openproblems/base_tensorflow_nvidia Base image for scripts that use tensorflow with nvidia gpu support.
Update the setup definition in the platforms section of the config file. This section describes the packages that need to be installed in the Docker image and are required for your method to run.
If you’re using a custom image use the following minimum setup:
platforms:
- type: docker
Image: your custom image
setup:
- type: apt
packages:
- procps
- libhdf5-dev
- libgeos-dev
- python3
- python3-pip
- python3-dev
- python-is-python3
- type: python
packages:
- rpy2
- anndata~=0.10.0
- scanpy~=1.10.0
- pyyaml
- requests
- jsonschema
github:
- "openproblems-bio/core#subdirectory=packages/python/openproblems"
- type: r
packages:
- anndata
- BiocManager
- reticulate
- bit64
github:
- openproblems-bio/core/packages/r/openproblemsPlease check out this guide for more information on how to add extra package dependencies.
Tip: After making changes to the components dependencies, you will need to rebuild the docker container as follows:
[notice] Building container 'ghcr.io/openproblems-bio/task_template/control_methods/my_python_method:dev' with Dockerfile[notice] Building container 'ghcr.io/openproblems-bio/task_template/control_methods/my_python_method:dev' with DockerfileA component’s script typically has five sections:
This is what the script generated by the create_component component looks like:
script.py
import anndata as ad
## VIASH START
# Note: this section is auto-generated by viash at runtime. To edit it, make changes
# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`.
par = {
'input_train': 'resources_test/.../train.h5ad',
'input_test': 'resources_test/.../test.h5ad',
'input_solution': 'resources_test/.../solution.h5ad',
'output': 'output.h5ad'
}
meta = {
'name': 'my_python_method'
}
## VIASH END
print('Reading input files', flush=True)
input_train = ad.read_h5ad(par['input_train'])
input_test = ad.read_h5ad(par['input_test'])
input_solution = ad.read_h5ad(par['input_solution'])
print('Preprocess data', flush=True)
# ... preprocessing ...
print('Train model', flush=True)
# ... train model ...
print('Generate predictions', flush=True)
# ... generate predictions ...
print("Write output AnnData to file", flush=True)
output = ad.AnnData(
)
output.write_h5ad(par['output'], compression='gzip')script.R
library(anndata)
## VIASH START
par <- list(
input_train = "resources_test/.../train.h5ad",
input_test = "resources_test/.../test.h5ad",
input_solution = "resources_test/.../solution.h5ad",
output = "output.h5ad"
)
meta <- list(
name = "my_r_method"
)
## VIASH END
cat("Reading input files\n")
input_train <- anndata::read_h5ad(par[["input_train"]])
input_test <- anndata::read_h5ad(par[["input_test"]])
input_solution <- anndata::read_h5ad(par[["input_solution"]])
cat("Preprocess data\n")
# ... preprocessing ...
cat("Train model\n")
# ... train model ...
cat("Generate predictions\n")
# ... generate predictions ...
cat("Write output AnnData to file\n")
output <- anndata::AnnData(
)
output$write_h5ad(par[["output"]], compression = "gzip")The required sections are explained here in more detail:
In the top section of the script you can define which packages/libraries the method needs. If you add a new or different package add the dependency to config.vsh.yaml in the setup field (see above).
The Viash code block is designed to facilitate prototyping, by enabling you to execute directly by running python script.py (or Rscript script.R for R users). Note that anything between “VIASH START” and “VIASH END” will be removed and replaced with a CLI argument parser when the components are being built by Viash.
Here, the par dictionary contains all the arguments defined in the config.vsh.yaml file (including those from the defined __merge__ file). When adding a argument in the par dict also add it to the config.vsh.yaml in the arguments section.
This section reads any input AnnData files passed to the component.
This is the most important section of your script, as it defines the core functionality provided by the component. It processes the input data to create results for the particular task at hand.
The output stored in a AnnData object and then written to an .h5ad file. The format is specified by the API file specified in the __merge__ field in the config file.
It is possible to add additional resources such as a file containing helper functions or other resources. Please visit this page for more information on how to do this.
Your component’s API file contains the necessary unit tests to check whether your component works and the output is in the correct format.
You can test your component by using the following command:
Running tests in temporary directory: '/tmp/viash_test_true_labels_347717401874301490'
====================================================================
+/tmp/viash_test_true_labels_347717401874301490/build_engine_environment/true_labels ---verbosity 6 ---setup cachedbuild ---engine docker
[notice] Building container 'ghcr.io/openproblems-bio/task_template/control_methods/true_labels:test' with Dockerfile
[info] docker build -t 'ghcr.io/openproblems-bio/task_template/control_methods/true_labels:test' '/tmp/viash_test_true_labels_347717401874301490/build_engine_environment' -f '/tmp/viash_test_true_labels_347717401874301490/build_engine_environment/tmp/dockerbuild-true_labels-nrrESb/Dockerfile'
#0 building with "default" instance using docker driver
#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 486B done
#1 DONE 0.0s
#2 [internal] load metadata for docker.io/openproblems/base_python:1.0.0
#2 DONE 0.1s
#3 [internal] load .dockerignore
#3 transferring context: 2B done
#3 DONE 0.0s
#4 [1/1] FROM docker.io/openproblems/base_python:1.0.0@sha256:6c094ecee24215a2aa6e268beec6a1473a80e86c18ea3468e4357c004f343e15
#4 CACHED
#5 exporting to image
#5 exporting layers done
#5 writing image sha256:2cae61a88c72eee51dde89d9fe5a8e90e6837c0458e608789731481a9f298672 done
#5 naming to ghcr.io/openproblems-bio/task_template/control_methods/true_labels:test done
#5 DONE 0.0s
====================================================================
+/tmp/viash_test_true_labels_347717401874301490/test_run_and_check_output/test_executable
>> Running test 'run'
>> Checking whether input files exist
>> Running script as test
Reading input files
Preprocess data
Train model
Generate predictions
Write output AnnData to file
>> Checking whether output file exists
>> Reading h5ad files and checking formats
Reading and checking output
AnnData object with n_obs × n_vars = 123 × 0
obs: 'label_pred'
uns: 'dataset_id', 'method_id', 'normalization_id'
All checks succeeded!
====================================================================
+/tmp/viash_test_true_labels_347717401874301490/test_check_config/test_executable
Load config data
Check .namespace
Check .info.type
Check component metadata
Checking contents of .info.preferred_normalization
Check Nextflow runner
All checks succeeded!
====================================================================
SUCCESS! All 2 out of 2 test scripts succeeded!
Cleaning up temporary directoryVisit “Run tests” for more information on running unit tests and how to interpret common error messages.
You can also run your component on local files using the viash run command. For example:
viash run src/tasks/label_projection/control_methods/my_python_method/config.vsh.yaml -- \
--input_train resources_test/task_template/cxg_mouse_pancreas_atlas/train.h5ad \
--input_test resources_test/task_template/cxg_mouse_pancreas_atlas/test.h5ad \
--input_solution resources_test/task_template/cxg_mouse_pancreas_atlas/solution.h5ad \
--output output.h5adIf your component works, please create a pull request.