comments and ci testing

.github/workflows/run_tests.yml

@@ -0,0 +1,30 @@
+name: Run Tests
+
+on: [push, pull_request]
+
+jobs:
+  test:
+
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        python-version: [3.11]
+
+    steps:
+    - name: Check out code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Run tests
+      run: |
+        python -m unittest discover -s tests -p '*test*.py'

README.md

@@ -57,6 +57,8 @@ Model is stored in memory; you restart the app, you start from scratch.
 
 ### Advanced Options
 
+- Use `-k <int>` to specify the number of relevant top-k results used for ranking.
+
 - Use `-q` to enable quantization of the model.
 In our experiments, this yielded a data reduction of 4x when transferring the model,
 however, at the cost of performance speed and accuracy.

p2p_ol2r/utils.py

@@ -58,7 +58,7 @@ def ndcg(expected: list[str], actual: list[str], form="linear"):
     if form == "linear":
         idcg = np.sum(rel_true[:p] * discount)
         dcg = np.sum(rel_pred[:p] * discount)
-    elif form == "exponential" or form == "exp":
+    elif form == "exp":
         idcg = np.sum([2**x - 1 for x in rel_true[:p]] * discount)
         dcg = np.sum([2**x - 1 for x in rel_pred[:p]] * discount)
     else: