Merge branch 'master' of https://github.com/optuna/optuna into feat-s…

…uggest-api

README.md

@@ -24,6 +24,9 @@ Optuna can dynamically construct the search spaces for the hyperparameters.
 ## News
 
 Help us create the next version of Optuna!
+
+Optuna 3.0 Roadmap published for review. Please take a look at the [planned improvements to Optuna](https://github.com/optuna/optuna/wiki/Optuna-V3-Roadmap), and share your feedback in the github issues. PR contributions also welcome!
+
 Please take a few minutes to fill in this survey, and let us know how you use Optuna now and what improvements you'd like.🤔
 
 All questions optional. 🙇‍♂️

optuna/trial/_trial.py

@@ -506,6 +506,11 @@ def report(self, value: float, step: int) -> None:
             function internally. Thus, it accepts all float-like types (e.g., ``numpy.float32``).
             If the conversion fails, a ``TypeError`` is raised.
 
+        .. note::
+            If this method is called multiple times at the same ``step`` in a trial,
+            the reported ``value`` only the first time is stored and the reported values
+            from the second time are ignored.
+
         Example:
 
             Report intermediate scores of `SGDClassifier <https://scikit-learn.org/stable/modules/

tests/integration_tests/test_lightgbm.py

@@ -1,6 +1,7 @@
 from functools import partial
 
 import lightgbm as lgb
+import numpy as np
 import pytest
 
 import optuna
@@ -116,8 +117,8 @@ def objective(
     cv: bool = False,
 ) -> float:
 
-    dtrain = lgb.Dataset([[1.0], [2.0], [3.0]], label=[1.0, 0.0, 1.0])
-    dtest = lgb.Dataset([[1.0]], label=[1.0])
+    dtrain = lgb.Dataset(np.asarray([[1.0], [2.0], [3.0]]), label=[1.0, 0.0, 1.0])
+    dtest = lgb.Dataset(np.asarray([[1.0]]), label=[1.0])
 
     if force_default_valid_names:
         valid_names = None

tests/integration_tests/test_pytorch_lightning.py

@@ -45,6 +45,8 @@ def validation_step(  # type: ignore
         return {"validation_accuracy": accuracy}
 
     def validation_epoch_end(self, outputs: List[Dict[str, torch.Tensor]]) -> None:
+        if not len(outputs):
+            return
 
         accuracy = sum(x["validation_accuracy"] for x in outputs) / len(outputs)
         self.log("accuracy", accuracy)
@@ -69,24 +71,10 @@ def _generate_dummy_dataset(self) -> torch.utils.data.DataLoader:
         return torch.utils.data.DataLoader(dataset, batch_size=1)
 
 
-class ModelDDP(pl.LightningModule):
+class ModelDDP(Model):
     def __init__(self) -> None:
 
         super().__init__()
-        self._model = nn.Sequential(nn.Linear(4, 8))
-
-    def forward(self, data: torch.Tensor) -> torch.Tensor:  # type: ignore
-
-        return self._model(data)
-
-    def training_step(  # type: ignore
-        self, batch: List[torch.Tensor], batch_nb: int
-    ) -> Dict[str, torch.Tensor]:
-
-        data, target = batch
-        output = self.forward(data)
-        loss = F.nll_loss(output, target)
-        return {"loss": loss}
 
     def validation_step(  # type: ignore
         self, batch: List[torch.Tensor], batch_nb: int
@@ -104,25 +92,6 @@ def validation_step(  # type: ignore
 
         self.log("accuracy", accuracy, sync_dist=True)
 
-    def configure_optimizers(self) -> torch.optim.Optimizer:
-
-        return torch.optim.SGD(self._model.parameters(), lr=1e-2)
-
-    def train_dataloader(self) -> torch.utils.data.DataLoader:
-
-        return self._generate_dummy_dataset()
-
-    def val_dataloader(self) -> torch.utils.data.DataLoader:
-
-        return self._generate_dummy_dataset()
-
-    def _generate_dummy_dataset(self) -> torch.utils.data.DataLoader:
-
-        data = torch.zeros(3, 4, dtype=torch.float32)
-        target = torch.zeros(3, dtype=torch.int64)
-        dataset = torch.utils.data.TensorDataset(data, target)
-        return torch.utils.data.DataLoader(dataset, batch_size=1)
-
 
 def test_pytorch_lightning_pruning_callback() -> None:
     def objective(trial: optuna.trial.Trial) -> float: