Update Space (evaluate main: c447fc8e)

requirements.txt

@@ -1,4 +1,4 @@
-git+https://github.com/huggingface/evaluate@e4a2724377909fe2aeb4357e3971e5a569673b39
+git+https://github.com/huggingface/evaluate@c447fc8eda9c62af501bfdc6988919571050d950
 git+https://github.com/google-research/rl-reliability-metrics
 scipy
 tensorflow

rl_reliability.py

@@ -13,9 +13,6 @@
 # limitations under the License.
 """Computes the RL Reliability Metrics."""
 
-from dataclasses import dataclass
-from typing import List, Optional
-
 import datasets
 import numpy as np
 from rl_reliability_metrics.evaluation import eval_metrics
@@ -84,27 +81,11 @@ Examples:
 """
 
 
-@dataclass
-class RLReliabilityConfig(evaluate.info.Config):
-
-    name: str = "default"
-
-    baseline: str = "default"
-    freq_thresh: float = 0.01
-    window_size: int = 100000
-    window_size_trimmed: int = 99000
-    alpha: float = 0.05
-    eval_points: Optional[List] = None
-
-
 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
 class RLReliability(evaluate.Metric):
     """Computes the RL Reliability Metrics."""
 
-    CONFIG_CLASS = RLReliabilityConfig
-    ALLOWED_CONFIG_NAMES = ["online", "offline"]
-
-    def _info(self, config):
+    def _info(self):
         if self.config_name not in ["online", "offline"]:
             raise KeyError("""You should supply a configuration name selected in '["online", "offline"]'""")
 
@@ -113,7 +94,6 @@ class RLReliability(evaluate.Metric):
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
-            config=config,
             features=datasets.Features(
                 {
                     "timesteps": datasets.Sequence(datasets.Value("int64")),
@@ -127,19 +107,18 @@ class RLReliability(evaluate.Metric):
         self,
         timesteps,
         rewards,
+        baseline="default",
+        freq_thresh=0.01,
+        window_size=100000,
+        window_size_trimmed=99000,
+        alpha=0.05,
+        eval_points=None,
     ):
         if len(timesteps) < N_RUNS_RECOMMENDED:
             logger.warning(
                 f"For robust statistics it is recommended to use at least {N_RUNS_RECOMMENDED} runs whereas you provided {len(timesteps)}."
             )
 
-        baseline = self.config.baseline
-        freq_thresh = self.config.freq_thresh
-        window_size = self.config.window_size
-        window_size_trimmed = self.config.window_size_trimmed
-        alpha = self.config.alpha
-        eval_points = self.config.eval_points
-
         curves = []
         for timestep, reward in zip(timesteps, rewards):
             curves.append(np.stack([timestep, reward]))