DAFNI Workflow Enhancements

.github/workflows/publish-to-dafni.yaml

@@ -38,14 +38,25 @@ jobs:
         with:
           python-version: '3.10'
 
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install causal-testing-framework -e .[dev]
+          python -m pip install dafni-cli
+
+      - name: Run tests before uploading
+        id: tests
+        run: |
+          python -m pytest ./dafni/tests 
+
       - name: Build the container
+        if: steps.tests.outcome == 'success'
         run: |
           docker build -t ctf:${{ env.VERSION }} -f ./dafni/Dockerfile .
           docker save ctf:${{ env.VERSION }} | gzip > ctf-dafni-${{ env.VERSION }}.tar.gz
 
-      - name: Install DAFNI-CLI and log in
+      - name: Log into DAFNI
         run: |
-          python -m pip install dafni-cli
           dafni login
 
       - name: Upload to DAFNI

dafni/Dockerfile

@@ -1,5 +1,5 @@
 # Define the Python version neded for CTF
-FROM python:3.10-slim
+FROM python:3.12-slim
 
 ## Prevents Python from writing pyc files
 ENV PYTHONDONTWRITEBYTECODE=1
@@ -8,26 +8,22 @@ ENV PYTHONDONTWRITEBYTECODE=1
 ## from crashing without emitting any logs due to buffering
 ENV PYTHONUNBUFFERED=1
 
-#Label maintainer
+# Label maintainer
 LABEL maintainer="Dr. Farhad Allian - The University of Sheffield"
 
 # Create a folder for the source code/outputs
 RUN mkdir -p ./causal_testing
 RUN mkdir -p ./data/outputs
 
-# Copy the source code and test files from build into the container
-COPY --chown=nobody  ../causal_testing ./causal_testing
-COPY --chown=nobody ./dafni/main_dafni.py ./
+# Copy the source code from local root and test files from build into the container
+COPY --chown=nobody  ./causal_testing ./causal_testing
 COPY --chown=nobody ./dafni/data/inputs ./data/inputs
 
 # Install core dependencies using PyPi
 RUN pip install causal-testing-framework --no-cache-dir
 
-#For local testing purposes
-ENV VARIABLES_PATH=./data/inputs/variables.json \
-    CAUSAL_TESTS=./data/inputs/causal_tests.json \
-    DATA_PATH=./data/inputs/runtime_data.csv \
-    DAG_PATH=./data/inputs/dag.dot
+# Set the PYTHONPATH environment variable to include the /src directory
+ENV PYTHONPATH="/causal_testing:${PYTHONPATH}"
 
 # Define the entrypoint/commands
-CMD python main_dafni.py --variables_path $VARIABLES_PATH --dag_path $DAG_PATH --data_path $DATA_PATH --tests_path $CAUSAL_TESTS
+CMD python -m causal_testing --dag_path $DAG_PATH --data_path $DATA_PATH --test_config $TESTS_PATH --output $OUTPUT

dafni/README.md

@@ -5,25 +5,29 @@ to upload the framework onto [DAFNI](https://www.dafni.ac.uk).
 - It is **not** recommended to install the causal testing framework using Docker, and should only be installed
   using [PyPI](https://pypi.org/project/causal-testing-framework/).
 
-### Folders
+### Directory Hierarchy
 
 - `data` contains two sub-folders (the structure is important for DAFNI).
   - `inputs` is a folder that contains the input files that are (separately) uploaded to DAFNI.
     - `causal_tests.json` is a JSON file that contains the causal tests.
-    - `variables.json` is a JSON file that contains the variables and constraints to be used.
-    - `dag.dot` is a dot file that contains the directed acyclc graph (dag) file.
-    - `runtime_data.csv` is a csv file that contains the runtime data.
+    - `dag.dot` is a dot file that contains the directed acyclic graph (dag). In this file, Causal Variables are defined as 
+       node metadata attributes as key-value pairs using the following syntax: 
+       `node [datatype="int", typestring="input"]`. The `datatype` key specifies the datatype of the causal variable
+       as a string (e.g. `"int"`, `"str"`) and the `typestring` key specifies its typestring, which is also a string 
+       representing the variable type (e.g. `"input"` or `"output"`).
+    - `runtime_data.csv` is the `.csv` file that contains the runtime data.
 
   - `outputs` is a folder where the `causal_tests_results.json` output file is created.
 
 ### Docker files
-- `main_dafni.py` is the entry-point to the causal testing framework that is used by Docker.
 - `model_definition.yaml` is the model metadata that is required to be uploaded to DAFNI.
-- `.env` is an example of a configuration file containing the environment variables. This is only required
-    if using `docker-compose` to build the image. 
-- `Dockerfile` is the main blueprint that builds the image.
-- `.dockerignore` tells the Dockerfile which files to not include in the image.
+- `Dockerfile` is the main blueprint that builds the image. The main command calls the `causal_testing` module, 
+   with specified paths for the DAG, input runtime data, test configurations, and the output filename as defined above. 
+   This command is identical to that referenced in the main [README.md](../README.md) file.
 - `docker-compose.yaml` is another method of building the image and running the container in one line. 
    Note: the `.env` file that contains the environment variables for `main_dafni.py` is only used here.
+- `.dockerignore` tells the Dockerfile which files to not include in the image.
+- `.env` is an example of a configuration file containing the environment variables. This is only required
+    if using `docker-compose` to build the image. 
 
 

dafni/data/inputs/causal_tests.json

@@ -1,58 +1,11 @@
 {
   "tests": [
-    {
-      "name": "max_doses _||_ cum_vaccinations",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_vaccinations": "NoEffect"
-      },
-      "formula": "cum_vaccinations ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
-    {
-      "name": "max_doses _||_ cum_vaccinated",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_vaccinated": "NoEffect"
-      },
-      "formula": "cum_vaccinated ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
-    {
-      "name": "max_doses _||_ cum_infections",
-      "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
-      "effect": "direct",
-      "mutations": [
-        "max_doses"
-      ],
-      "expected_effect": {
-        "cum_infections": "NoEffect"
-      },
-      "formula": "cum_infections ~ max_doses",
-      "alpha": 0.05,
-      "skip": false
-    },
     {
       "name": "vaccine --> cum_vaccinations",
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_vaccinations": "SomeEffect"
       },
@@ -64,9 +17,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_vaccinated": "SomeEffect"
       },
@@ -78,9 +29,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "vaccine"
-      ],
+      "treatment_variable": "vaccine",
       "expected_effect": {
         "cum_infections": "SomeEffect"
       },
@@ -92,9 +41,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinations"
-      ],
+      "treatment_variable": "cum_vaccinations",
       "expected_effect": {
         "cum_vaccinated": "NoEffect"
       },
@@ -107,9 +54,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinations"
-      ],
+      "treatment_variable": "cum_vaccinations",
       "expected_effect": {
         "cum_infections": "NoEffect"
       },
@@ -122,9 +67,7 @@
       "estimator": "LinearRegressionEstimator",
       "estimate_type": "coefficient",
       "effect": "direct",
-      "mutations": [
-        "cum_vaccinated"
-      ],
+      "treatment_variable": "cum_vaccinated",
       "expected_effect": {
         "cum_infections": "NoEffect"
       },

dafni/data/inputs/dag.dot

@@ -1,7 +1,12 @@
 digraph CausalDAG {
     rankdir=LR;
-    "vaccine" -> "cum_vaccinations";
-    "vaccine" -> "cum_vaccinated";
-    "vaccine" -> "cum_infections";
-    "max_doses";
+    vaccine [datatype="int", typestring="input"];
+    cum_vaccinations [datatype="int", typestring="output"];
+    cum_vaccinated [datatype="int", typestring="output"];
+    cum_infections [datatype="int", typestring="output"];
+    max_doses [datatype="int", typestring="output"];
+    vaccine -> cum_vaccinations;
+    vaccine -> cum_vaccinated;
+    vaccine -> cum_infections;
+    max_doses;
 }