Add benchmark CI job (#421)

.github/workflows/benchmarks.yml

@@ -0,0 +1,37 @@
+# This workflow will run the benchmark suite
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Benchmark
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.9"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements_dev.txt
+        pip install tabulate
+    - name: Install Surprise
+      run: |
+        python -m pip install -e .
+    - name: Run Benchmarks
+      run: |
+        yes | python examples/benchmark.py

examples/benchmark.py

@@ -27,17 +27,17 @@
 from tabulate import tabulate
 
 # The algorithms to cross-validate
-classes = (
-    SVD,
-    SVDpp,
-    NMF,
-    SlopeOne,
-    KNNBasic,
-    KNNWithMeans,
-    KNNBaseline,
-    CoClustering,
-    BaselineOnly,
-    NormalPredictor,
+algos = (
+    SVD(),
+    SVDpp(),
+    NMF(),
+    SlopeOne(),
+    KNNBasic(),
+    KNNWithMeans(),
+    KNNBaseline(),
+    CoClustering(),
+    BaselineOnly(),
+    NormalPredictor(),
 )
 
 # ugly dict to map algo names and datasets to their markdown links in the table
@@ -102,16 +102,16 @@
 np.random.seed(0)
 random.seed(0)
 
-dataset = "ml-1m"
+dataset = "ml-100k"
 data = Dataset.load_builtin(dataset)
 kf = KFold(random_state=0)  # folds will be the same for all algorithms.
 
 table = []
-for klass in classes:
+for algo in algos:
     start = time.time()
-    out = cross_validate(klass(), data, ["rmse", "mae"], kf)
+    out = cross_validate(algo, data, ["rmse", "mae"], kf)
     cv_time = str(datetime.timedelta(seconds=int(time.time() - start)))
-    link = LINK[klass.__name__]
+    link = LINK[algo.__class__.__name__]
     mean_rmse = "{:.3f}".format(np.mean(out["test_rmse"]))
     mean_mae = "{:.3f}".format(np.mean(out["test_mae"]))