Merge pull request #207 from daisybio/development

v1.3.0

Author: JudithBernett

.github/workflows/labeler.yml

@@ -13,6 +13,6 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Run Labeler
-        uses: crazy-max/ghaction-github-labeler@v5.2.0
+        uses: crazy-max/ghaction-github-labeler@v5.3.0
         with:
           skip-delete: true

.github/workflows/publish_docs.yml

@@ -13,7 +13,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.12"
 
       - name: Install pip
         run: |

.github/workflows/run_tests.yml

@@ -16,14 +16,12 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - { python-version: "3.11", os: ubuntu-latest, session: "pre-commit" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "safety" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "mypy" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "tests" }
-          - { python-version: "3.11", os: windows-latest, session: "tests" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "typeguard" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "xdoctest" }
-          - { python-version: "3.11", os: ubuntu-latest, session: "docs-build" }
+          - { python-version: "3.12", os: ubuntu-latest, session: "pre-commit" }
+          - { python-version: "3.12", os: ubuntu-latest, session: "mypy" }
+          - { python-version: "3.12", os: ubuntu-latest, session: "tests" }
+          - { python-version: "3.12", os: windows-latest, session: "typeguard" }
+          - { python-version: "3.12", os: ubuntu-latest, session: "xdoctest" }
+          - { python-version: "3.12", os: ubuntu-latest, session: "docs-build" }
 
     env:
       NOXSESSION: ${{ matrix.session }}
@@ -66,7 +64,7 @@ jobs:
           print("::set-output name=result::{}".format(result))
 
       - name: Restore pre-commit cache
-        uses: actions/[email protected].2
+        uses: actions/[email protected].3
         if: matrix.session == 'pre-commit'
         with:
           path: ~/.cache/pre-commit
@@ -99,10 +97,10 @@ jobs:
       - name: Check out the repository
         uses: actions/checkout@v4
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Install Poetry
         run: |
@@ -129,6 +127,6 @@ jobs:
         run: nox --force-color --session=coverage -- xml -i
 
       - name: Upload coverage report
-        uses: codecov/[email protected].0
+        uses: codecov/[email protected].2
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/safety_scan.yml

@@ -0,0 +1,15 @@
+name: Safety Action
+
+on:
+  push: # Run on every push to any branch
+  pull_request: # Run on new pull requests
+
+jobs:
+  security:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@main
+      - name: Run Safety CLI to check for vulnerabilities
+        uses: pyupio/safety-action@v1
+        with:
+          api-key: ${{ secrets.SAFETY_API_KEY }}

README.md

@@ -16,7 +16,7 @@ Focus on Innovating Your Models — DrEval Handles the Rest!
 
 By contributing your model to the DrEval catalog, you can increase your work's exposure, reusability, and transferability.
 
-![DrEval](assets/dreval.png)
+![DrEval](docs/_static/img/overview.png)
 
 Use DrEval to Build Drug Response Models That Have an Impact
 
@@ -82,7 +82,7 @@ results/my_first_run/LCO
 You can visualize them using
 
 ```bash
-python create_report.py --run_id my_first_run
+python create_report.py --run_id my_first_run --dataset GDSC2
 ```
 
 This will create an index.html file which you can open in your webbrowser.
@@ -91,7 +91,7 @@ You can also run a drug response experiment using Python:
 
 ```python
 
-from drevalpy import drug_response_experiment
+from drevalpy.experiment import drug_response_experiment
 
 drug_response_experiment(
             models=["MultiOmicsNeuralNetwork"],
@@ -106,9 +106,13 @@ drug_response_experiment(
 
 We recommend the use of our nextflow pipeline for computational demanding runs and for improved reproducibility. No knowledge of nextflow is required to run it. The nextflow pipeline is available here: [nf-core-drugresponseeval](https://github.com/JudithBernett/nf-core-drugresponseeval).
 
+## Example Report
+
+[Browse our benchmark results here.](https://dilis-lab.github.io/drevalpy-report/)
+
 ## Contact
 
 Main developers:
 
 - [Judith Bernett](mailto:[email protected]), [Data Science in Systems Biology](https://www.mls.ls.tum.de/daisybio/startseite/), TUM
-- [Pascal Iversen](mailto:[email protected]), [Data Integration in the Life Sciences](https://www.mi.fu-berlin.de/inf/groups/ag-dilis/index.html), FU Berlin, Hasso Plattner Institute
+- [Pascal Iversen](mailto:[email protected]), [Data Integration in the Life Sciences](https://www.mi.fu-berlin.de/w/DILIS/WebHome), FU Berlin, Hasso Plattner Institute

README.rst

@@ -31,6 +31,15 @@ DrEvalPy: Python Cancer Cell Line Drug Response Prediction Suite
    :target: https://github.com/psf/black
    :alt: Black
 
+.. image:: _static/img/overview.png
+   :align: center
+   :width: 80%
+   :alt: Overview of the DrEval framework. Via input options, implemented state-of-the-art models can be compared against baselines of varying complexity. We address obstacles to progress in the field at each point in our pipeline: Our framework is available on PyPI and nf-core and we follow FAIReR standards for optimal reproducibility. DrEval is easily extendable as demonstrated here with an implementation of a proteomics-based random forest. Custom viability data can be preprocessed with CurveCurator, leading to more consistent data and metrics. DrEval supports five widely used datasets with application-aware train/test splits that enable detecting weak generalization. Models are free to use provided cell line- and drug features or custom ones. The pipeline supports randomization-based ablation studies and performs robust hyperparameter tuning for all models. Evaluation is conducted using meaningful, bias-resistant metrics to avoid inflated results from artifacts such as Simpson’s paradox. All results are compiled into an interactive HTML report.
+
+
+Overview
+=======
+
 Focus on Innovating Your Models — DrEval Handles the Rest!
 -  DrEval is a toolkit that ensures drug response prediction evaluations are statistically sound, biologically meaningful, and reproducible.
 -  Focus on model innovation while using our automated standardized evaluation protocols and preprocessing workflows.