viash run src/common/create_component/config.vsh.yaml -- \
--task label_projection \
--type method \
--name my_method_py \
--language python
Add a method
A method is a specific technique used to solve the task problem and is compared to the baseline methods and other methods to determine the best approach for the task depending on the type of dataset.
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 Label Projection task is used throughout the guide, so make sure to replace any occurrences of "label_projection"
with your task of interest.
Make sure you have followed the “Getting started” guide.
Step 1: Create a new component
Use the create_component
component to start creating a new method.
This will create a new folder at src/label_projection/methods/my_method_py
containing a Viash config and a script.
src/label_projection/methods/my_method_py
├── script.py Script for running the method.
├── config.vsh.yaml Config file for method.
└── ... Optional additional resources.
viash run src/common/create_component/config.vsh.yaml -- \
--task label_projection \
--type method \
--name my_method_r \
--language r
This will create a new folder at src/label_projection/methods/my_method_r
containing a Viash config and a script.
src/label_projection/methods/my_method_r
├── 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 method. It must match the regex [a-z][a-z0-9_]*
(snakecase).
- A config file contains metadata of the component and the dependencies required to run it. In steps 2 and 3 we will fill in the required information.
- A script contains the code to run the method. In step 4 we will edit the script.
Use the command viash run src/common/create_component/config.vsh.yaml -- --help
to get information on all of the parameters if the create_component
component.
Step 2: Fill in metadata
The Viash config contains metadata of your method, which script is used to run it, and the required dependencies.
Generated config file
This is what the config.vsh.yaml
generated by the create_component
component looks like:
Contents of 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_method.yaml
functionality:
name: my_method_py
# Metadata for your component (required)
info:
pretty_name: My Method Py
summary: 'FILL IN: A one sentence summary of this method.'
description: 'FILL IN: A (multiline) description of how this method works.'
reference: bibtex_reference_key
documentation_url: https://url.to/the/documentation
repository_url: https://github.com/organisation/repository
preferred_normalization: log_cpm
# 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
- type: python_script
path: script.py
platforms:
- type: docker
image: python:3.10
# Add custom dependencies here
setup:
- type: python
pypi: anndata~=0.8.0
- type: nextflow
directives:
label: [midmem, midcpu]
Contents of 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_method.yaml
functionality:
name: my_method_r
# Metadata for your component (required)
info:
pretty_name: My Method R
summary: 'FILL IN: A one sentence summary of this method.'
description: 'FILL IN: A (multiline) description of how this method works.'
reference: bibtex_reference_key
documentation_url: https://url.to/the/documentation
repository_url: https://github.com/organisation/repository
preferred_normalization: log_cpm
# 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
- type: r_script
path: script.R
platforms:
- type: docker
image: eddelbuettel/r2u:22.04
# Add custom dependencies here
setup:
- type: apt
packages:
- libhdf5-dev
- libgeos-dev
- python3
- python3-pip
- python3-dev
- python-is-python3
- type: python
pypi: anndata~=0.8.0
- type: r
cran: anndata
- type: nextflow
directives:
label: [midmem, midcpu]
Required metadata fields
Please edit functionality.info
section in the config file to fill in the necessary metadata.
functionality.name
A unique identifier for the method. Must be written in snake case. Example: my_new_method
.
functionality.info.pretty_name
A label for the method used for visualisations and documentation. Example: "My new method"
.
functionality.info.summary
A one sentence summary of the method. Used for creating short overviews of the components in a task.
functionality.info.description
An explanation for how the method works. Used for creating reference documentation of a task.
functionality.info.reference
A bibtex reference key to the paper where the method is described.
functionality.info.documentation_url
The url to the documentation of the method.
functionality.info.repository_url
The repository url for the method.
functionality.info.preferred_normalization
Which normalization method a component prefers. Possible values are l1_sqrt
, log_cpm
, log_scran_pooling
, sqrt_cpm
. Each value corresponds to a normalization component in the directory src/datasets/normalization
.
__merge__
The file specified in this field contains information regarding the input and output arguments of the component, as well as a unit test to ensure that the component is functioning properly. Normally you don’t need to change this if you gave the right arguments to the create_component
component.
Step 3: Add dependencies
Each component has it’s own set of dependencies, because different components might have conflicting dependencies.
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. Note that both anndata~=0.8.0
and pyyaml
are necessary Python package dependencies.
Please 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:
viash run src/label_projection/methods/my_method_py/config.vsh.yaml -- \
---setup cachedbuild
output
[notice] Building container 'ghcr.io/openproblems-bio/label_projection/methods/my_method_py:dev' with Dockerfile
Step 4: Edit script
A component’s script typically has five sections:
- Imports and libraries
- Argument values
- Read input data
- Generate results
- Write output data to file
This is what the script generated by the create_component
component looks like:
Contents of script.py
import anndata as ad
## VIASH START
= {
par 'input_train': 'resources_test/label_projection/pancreas/train.h5ad',
'input_test': 'resources_test/label_projection/pancreas/test.h5ad',
'output': 'output.h5ad'
}= {
meta 'functionality_name': 'my_method_py'
}## VIASH END
print('Reading input files', flush=True)
= ad.read_h5ad(par['input_train'])
input_train = ad.read_h5ad(par['input_test'])
input_test
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)
= ad.AnnData(
output ={
obs'label_pred': obs_label_pred
},={
uns'dataset_id': input_train.uns['dataset_id'],
'normalization_id': input_train.uns['normalization_id'],
'method_id': meta['functionality_name']
}
)'output'], compression='gzip') output.write_h5ad(par[
Contents of script.R
library(anndata)
## VIASH START
<- list(
par input_train = "resources_test/label_projection/pancreas/train.h5ad",
input_test = "resources_test/label_projection/pancreas/test.h5ad",
output = "output.h5ad"
)<- list(
meta functionality_name = "my_method_r"
)## VIASH END
cat("Reading input files\n")
<- anndata::read_h5ad(par[["input_train"]])
input_train <- anndata::read_h5ad(par[["input_test"]])
input_test
cat("Preprocess data\n")
# ... preprocessing ...
cat("Train model\n")
# ... train model ...
cat("Generate predictions\n")
# ... generate predictions ...
cat("Write output AnnData to file\n")
<- anndata::AnnData(
output obs = list(
label_pred = obs_label_pred
),uns = list(
dataset_id = input_train$uns[["dataset_id"]],
normalization_id = input_train$uns[["normalization_id"]],
method_id = meta[["functionality_name"]]
)
)$write_h5ad(par[["output"]], compression = "gzip") output
The required sections are explained here in more detail:
a. Imports and libraries
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).
b. Argument block
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.
c. Read input data
This section reads any input AnnData files passed to the component.
d. Generate results
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.
e. Write output data to file
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.
Step 5: Add resources (optional)
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.
Step 6: Try component
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:
viash test src/label_projection/methods/my_method_py/config.vsh.yaml
Output
Running tests in temporary directory: '/tmp/viash_test_knn7724992926334110594'
====================================================================
+/tmp/viash_test_knn7724992926334110594/build_executable/knn ---verbosity 6 ---setup cachedbuild
[notice] Building container 'ghcr.io/openproblems-bio/label_projection/methods/knn:test' with Dockerfile
[info] Running 'docker build -t ghcr.io/openproblems-bio/label_projection/methods/knn:test /tmp/viash_test_knn7724992926334110594/build_executable -f /tmp/viash_test_knn7724992926334110594/build_executable/tmp/dockerbuild-knn-RUQLSY/Dockerfile'
Sending build context to Docker daemon 39.94kB
Step 1/7 : FROM python:3.10
---> fc98d03e6037
Step 2/7 : RUN pip install --upgrade pip && pip install --upgrade --no-cache-dir "scikit-learn" "pyyaml" "anndata~=0.8.0"
---> Using cache
---> 1d35b64eb218
Step 3/7 : LABEL org.opencontainers.image.description="Companion container for running component label_projection/methods knn"
---> Using cache
---> f9833a51c1bc
Step 4/7 : LABEL org.opencontainers.image.created="2023-05-06T00:08:35Z"
---> Running in dd24ef37ae9c
Removing intermediate container dd24ef37ae9c
---> b283cb6e633d
Step 5/7 : LABEL org.opencontainers.image.source="https://github.com/openproblems-bio/openproblems-v2"
---> Running in 904956427efb
Removing intermediate container 904956427efb
---> 1133431e5786
Step 6/7 : LABEL org.opencontainers.image.revision="9438b8ad0cdd9cd2ed3ba6a01d0b4f075c059d64"
---> Running in a0fa67f1e5ef
Removing intermediate container a0fa67f1e5ef
---> fcd16d92a287
Step 7/7 : LABEL org.opencontainers.image.version="test"
---> Running in bd63b0e7032b
Removing intermediate container bd63b0e7032b
---> bdea220a417f
Successfully built bdea220a417f
Successfully tagged ghcr.io/openproblems-bio/label_projection/methods/knn:test
====================================================================
+/tmp/viash_test_knn7724992926334110594/test_check_method_config/test_executable
Load config data
Check general fields
Check info fields
All checks succeeded!
====================================================================
+/tmp/viash_test_knn7724992926334110594/test_run_and_check_adata/test_executable
>> Checking whether input files exist
>> Running script as test
Load input data
Fit to train data
Predict on test data
Write output to file
>> Checking whether output file exists
>> Reading h5ad files and checking formats
Reading and checking input_train
AnnData object with n_obs × n_vars = 346 × 419
obs: 'label', 'batch'
var: 'hvg', 'hvg_score'
uns: 'dataset_id', 'normalization_id'
obsm: 'X_pca'
layers: 'counts', 'normalized'
Reading and checking input_test
AnnData object with n_obs × n_vars = 154 × 419
obs: 'batch'
var: 'hvg', 'hvg_score'
uns: 'dataset_id', 'normalization_id'
obsm: 'X_pca'
layers: 'counts', 'normalized'
Reading and checking output
AnnData object with n_obs × n_vars = 154 × 419
obs: 'batch', 'label_pred'
var: 'hvg', 'hvg_score'
uns: 'dataset_id', 'method_id', 'normalization_id'
obsm: 'X_pca'
layers: 'counts', 'normalized'
All checks succeeded!
====================================================================
[32mSUCCESS! All 2 out of 2 test scripts succeeded![0m
Cleaning up temporary directory
Visit “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/label_projection/methods/my_method_py/config.vsh.yaml -- \
--input_train resources_test/label_projection/pancreas/train.h5ad \
--input_test resources_test/label_projection/pancreas/test.h5ad \
--output output.h5ad
Next steps
If your component works, please create a pull request.