Feature: Client support for competitions

.gitignore

@@ -139,3 +139,6 @@ rever/
 # Generated requirements.txt and uv lock file
 requirements.txt
 uv.lock
+
+# OS-specific files
+.DS_store

docs/api/competition.evaluation.md

@@ -1,7 +1 @@
 ::: polaris.evaluate.CompetitionPredictions
-
----
-
-::: polaris.evaluate.CompetitionResults
-
----

docs/api/dataset.md

@@ -4,6 +4,12 @@
 
 --- 
 
+::: polaris.dataset.DatasetV2
+    options:
+        filters: ["!^_"]
+
+--- 
+
 ::: polaris.dataset._base.BaseDataset
     options:
         filters: ["!^_"]
@@ -20,4 +26,4 @@
     options:
         filters: ["!^_"]
 
----
+---

docs/tutorials/competition.participate.ipynb

@@ -98,7 +98,7 @@
     "The Polaris library is designed to make it easy to participate in a competition. In just a few lines of code, we can get the train and test partition, access the associated data in various ways and evaluate our predictions. There's two main API endpoints. \n",
     "\n",
     "- `get_train_test_split()`: For creating objects through which we can access the different dataset partitions.\n",
-    "- `evaluate()`: For evaluating a set of predictions in accordance with the competition protocol."
+    "- `submit_predictions()`: For submitting the predictions to an active competition."
    ]
   },
   {
@@ -116,7 +116,7 @@
    "id": "e78bf878",
    "metadata": {},
    "source": [
-    "The created test and train objects support various flavours to access the data."
+    "Similar to benchmarks, the created test and train objects support various flavours to access the data."
    ]
   },
   {
@@ -144,7 +144,7 @@
    "id": "5ec12825",
    "metadata": {},
    "source": [
-    "Now, let's create some predictions against the official Polaris `hello-world-competition`. We will train a simple random forest model on the ECFP representation through scikit-learn and datamol, and then we will submit our results for secure evaluation by the Polaris Hub."
+    "Now, let's create some predictions against the imaginary `hello-world-competition`. Let's assume we rain a simple random forest model on the ECFP representation through scikit-learn and datamol, and then we will submit our results for secure evaluation by the Polaris Hub."
    ]
   },
   {
@@ -204,7 +204,7 @@
    "id": "5ff06a9c",
    "metadata": {},
    "source": [
-    "Once your `CompetitionPredictions` object is created, you're ready to submit them for evaluation! This will automatically save your result to the Polaris Hub, but it will be private. You can choose to make it public through the Polaris web application. "
+    "Once your `CompetitionPredictions` object is created, you're ready to submit them for evaluation! This will automatically save your result to the Polaris Hub, but it will be private until the competition closes."
    ]
   },
   {
@@ -224,7 +224,7 @@
    "id": "44973556",
    "metadata": {},
    "source": [
-    "That's it! Just like that you have partaken in your first Polaris competition. Keep an eye on that leaderboard and best of luck in your future competitions!\n",
+    "That's it! Just like that you have partaken in your first Polaris competition. Keep an eye on that leaderboard when it goes public and best of luck in your future competitions!\n",
     "\n",
     "The End.\n",
     "\n",

mkdocs.yml

@@ -24,21 +24,16 @@ nav:
           - PDB Datasets: tutorials/dataset_pdb.ipynb
           - SDF Datasets: tutorials/dataset_sdf.ipynb
           - Optimization: tutorials/optimization.ipynb
-      # NOTE (cwognum): Competitions are currently gated.
-      # - Competitions:
-      #     - tutorials/competition.participate.ipynb
+      - Competitions:
+          - tutorials/competition.participate.ipynb
   - API Reference:
       - Load: api/load.md
       - Core:
           - Dataset: api/dataset.md
           - Benchmark: api/benchmark.md
+          - Competition: api/competition.md
           - Subset: api/subset.md
           - Evaluation: api/evaluation.md
-      # NOTE (cwognum): Competitions are currently gated.
-      # - Competitions:
-      #     - Competition Dataset: api/competition.dataset.md
-      #     - Competition: api/competition.md
-      #     - Competiton Evaluation: api/competition.evaluation.md
       - Hub:
           - Client: api/hub.client.md
           - External Auth Client: api/hub.external_client.md

polaris/__init__.py

@@ -4,9 +4,9 @@
 from loguru import logger
 
 from ._version import __version__
-from .loader import load_benchmark, load_dataset
+from .loader import load_benchmark, load_dataset, load_competition
 
-__all__ = ["load_dataset", "load_benchmark", "__version__"]
+__all__ = ["load_dataset", "load_benchmark", "load_competition", "__version__"]
 
 # Configure the default logging level
 os.environ["LOGURU_LEVEL"] = os.environ.get("LOGURU_LEVEL", "INFO")