update viash version

_viash.yaml

@@ -1,37 +1,38 @@
-viash_version: 0.9.0-RC6
+viash_version: 0.9.0-RC7
 
 # Step 1: Change the name of the task.
 # example: task_name_of_this_task
 name: task_template
 organization: openproblems-bio
 version: dev
-# Step 2: Update the description to a short description of the task.
-description: |
-  An OpenProblems benchmark task.
+
 license: MIT
-# Step 3: Add keywords to describe the task.
+# Step 2: Add keywords to describe the task.
 keywords: [single-cell, openproblems, benchmark]
-# Step 4: Update the `task_template` to the name of the task from step 1.
+# Step 3: Update the `task_template` to the name of the task from step 1.
 links:
   issue_tracker: https://github.com/openproblems-bio/task_template/issues
   repository: https://github.com/openproblems-bio/task_template
   docker_registry: ghcr.io
-# Step 5: Update the info fields to the text from the task issue. 
+
+
+# Step 4: Update the label, summary and description.
+# A unique, human-readable, short label. Used for creating summary tables and visualisations.
+label: Template
+summary: A one sentence summary of purpose and methodology. Used for creating an overview tables.
+description: |
+  Provide a clear and concise description of your task, detailing the specific problem it aims
+  to solve. Outline the input data types, the expected output, and any assumptions or constraints.
+  Be sure to explain any terminology or concepts that are essential for understanding the task.
+
+  Explain the motivation behind your proposed task. Describe the biological or computational 
+  problem you aim to address and why it's important. Discuss the current state of research in
+  this area and any gaps or challenges that your task could help address. This section 
+  should convince readers of the significance and relevance of your task.
+
 info:
-  # A unique, human-readable, short label. Used for creating summary tables and visualisations.
-  label: Template
-  description: |
-    Provide a clear and concise description of your task, detailing the specific problem it aims
-    to solve. Outline the input data types, the expected output, and any assumptions or constraints.
-    Be sure to explain any terminology or concepts that are essential for understanding the task.
-  summary: A one sentence summary of purpose and methodology. Used for creating an overview tables.
-  motivation: |
-    Explain the motivation behind your proposed task. Describe the biological or computational 
-    problem you aim to address and why it’s important. Discuss the current state of research in
-    this area and any gaps or challenges that your task could help address. This section 
-    should convince readers of the significance and relevance of your task.
   image: The name of the image file to use for the component on the website.
-  # Step 6: Replace the task_template to the name of the task in `info.name`.
+  # Step 5: Replace the task_template to the name of the task.
   test_resources:
     - type: s3
       path: s3://openproblems-data/resources_test/task_template/
@@ -40,10 +41,10 @@ info:
       path: s3://openproblems-data/resources_test/common/
       dest: resources_test/common
 
-# Step 7: Update the authors of the task.
+# Step 6: Update the authors of the task.
 authors: 
   # Full name of the author, usually in the name of FirstName MiddleName LastName.
-  - name: Kai Waldrant 
+  - name: John Doe
     # Role of the author. Possible values:
     # 
     # * `"author"`: Authors who have made substantial contributions to the component.
@@ -52,13 +53,14 @@ authors:
     roles: [ "author", "maintainer" ]
     # Additional information on the author
     info: 
-      github: KaiWaldrant
-      orcid: 0009-0003-8555-1361
-      email: ...
-      twitter: ...
-      linkedin: ...
-# Step 8: Remove all of the comments of the steps you completed
-# Step 9: High five yourself!
+      github: johndoe
+      orcid: 0000-0000-0000-0000
+      email: [email protected]
+      twitter: johndoe
+      linkedin: johndoe
+
+# Step 7: Remove all of the comments of the steps you completed
+# Step 8: High five yourself!
 
 config_mods: |
   .runners[.type == "nextflow"].config.labels := { lowmem : "memory = 20.Gb", midmem : "memory = 50.Gb", highmem : "memory = 100.Gb", lowcpu : "cpus = 5", midcpu : "cpus = 15", highcpu : "cpus = 30", lowtime : "time = 1.h", midtime : "time = 4.h", hightime : "time = 8.h", veryhightime : "time = 24.h" }

src/control_methods/true_labels/config.vsh.yaml

@@ -5,23 +5,23 @@
 #   - A unit test
 __merge__: ../../api/comp_control_method.yaml
 
-
 # A unique identifier for your component (required).
 # Can contain only lowercase letters or underscores.
 name: true_labels
 
+# A relatively short label, used when rendering visualisations (required)
+label: True Labels
+# A one sentence summary of how this method works (required). Used when 
+# rendering summary tables.
+summary: "a positive control, solution labels are copied 1 to 1 to the predicted data."
+# A multi-line description of how this component works (required). Used
+# when rendering reference documentation.
+description: |
+  A positive control, where the solution labels are copied 1 to 1 to the predicted data.
+# Which normalisation method this component prefers to use (required).
+
 # Metadata for your component
 info:
-  # A relatively short label, used when rendering visualisations (required)
-  label: True Labels
-  # A one sentence summary of how this method works (required). Used when 
-  # rendering summary tables.
-  summary: "a positive control, solution labels are copied 1 to 1 to the predicted data."
-  # A multi-line description of how this component works (required). Used
-  # when rendering reference documentation.
-  description: |
-    A positive control, where the solution labels are copied 1 to 1 to the predicted data.
-  # Which normalisation method this component prefers to use (required).
   preferred_normalization: counts
 
 # Component-specific parameters (optional)

src/methods/logistic_regression/config.vsh.yaml

@@ -9,25 +9,34 @@ __merge__: ../../api/comp_method.yaml
 # A unique identifier for your component (required).
 # Can contain only lowercase letters or underscores.
 name: logistic_regression
-
+# A relatively short label, used when rendering visualisations (required)
+label: Logistic Regression
+# A one sentence summary of how this method works (required). Used when 
+# rendering summary tables.
+summary: "Logistic Regression with 100-dimensional PCA coordinates estimates parameters for multivariate classification by minimizing cross entropy loss over cell type classes."
+# A multi-line description of how this component works (required). Used
+# when rendering reference documentation.
+description: |
+  Logistic Regression estimates parameters of a logistic function for
+  multivariate classification tasks. Here, we use 100-dimensional whitened PCA
+  coordinates as independent variables, and the model minimises the cross
+  entropy loss over all cell type classes.
 # Metadata for your component
+# A reference key from the bibtex library at src/common/library.bib (required).
+references:
+  bibtex: 
+    - |
+      @book{hosmer2013applied,
+        title = {Applied logistic regression},
+        author = {Hosmer Jr, D.W. and Lemeshow, S. and Sturdivant, R.X.},
+        year = {2013},
+        publisher = {John Wiley \& Sons},
+        volume = {398}
+      }
 info:
-  # A relatively short label, used when rendering visualisations (required)
-  label: Logistic Regression
-  # A one sentence summary of how this method works (required). Used when 
-  # rendering summary tables.
-  summary: "Logistic Regression with 100-dimensional PCA coordinates estimates parameters for multivariate classification by minimizing cross entropy loss over cell type classes."
-  # A multi-line description of how this component works (required). Used
-  # when rendering reference documentation.
-  description: |
-    Logistic Regression estimates parameters of a logistic function for
-    multivariate classification tasks. Here, we use 100-dimensional whitened PCA
-    coordinates as independent variables, and the model minimises the cross
-    entropy loss over all cell type classes.
+
   # Which normalisation method this component prefers to use (required).
   preferred_normalization: log_cp10k
-  # A reference key from the bibtex library at src/common/library.bib (required).
-  reference: "hosmer2013applied"
   # URL to the documentation for this method (required).
   documentation_url: "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   # URL to the code repository for this method (required).