WIP: add PCA benchmarks

.github/workflows/run_benchmark_results_file_sanity_checks.yaml

@@ -19,3 +19,4 @@ jobs:
     - name: Check sanity of benchmark files
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
+        python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv

.github/workflows/sync_benchmark_files_to_gsheet.yaml

@@ -26,3 +26,5 @@ jobs:
         echo "$GSPREAD_SERVICE_ACCOUNT_AUTH_KEY" > service_account.json
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
+        python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv \
+          --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json

.github/workflows/test_cpu_benchmarks.yaml

@@ -141,3 +141,5 @@ jobs:
       run: |
         cd benchmarks/kmeans
         PYTHONPATH=$PYTHONPATH:$(realpath ./kmeans_dpcpp/) benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=1000,n_features=14]
+        cd ../pca
+        benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]

README.md

@@ -17,6 +17,7 @@ hardware.
 
 Benchmarks are currently available for the following algorithms:
 - [k-means](https://github.com/soda-inria/sklearn-engine-benchmarks/tree/main/benchmarks/kmeans)
+- [PCA](https://github.com/soda-inria/sklearn-engine-benchmarks/tree/main/benchmarks/pca)
 
 Here is a (non-exhaustive) list of libraries that are compared in the benchmarks:
 - [scikit-learn](https://scikit-learn.org/stable/index.html)