centralised logging

prompt_selection.ipynb

@@ -347,13 +347,6 @@
     "rnd_chain.metrics.to_pandas()['score'].plot(label=\"slates\")\n",
     "plt.legend()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -372,7 +365,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.8.10"
   },
   "orig_nbformat": 4
  },

rl_chain.ipynb

@@ -75,6 +75,10 @@
     "import rl_chain\n",
     "from langchain.prompts.prompt import PromptTemplate\n",
     "\n",
+    "import logging\n",
+    "logger = logging.getLogger(\"rl_chain\")\n",
+    "logger.setLevel(logging.INFO)\n",
+    "\n",
     "_PROMPT_TEMPLATE = \"\"\"Here is the description of a meal: {meal}.\n",
     "\n",
     "You have to embed this into the given text where it makes sense. Here is the given text: {text_to_personalize}.\n",
@@ -341,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.8.10"
   },
   "orig_nbformat": 4
  },

rl_chain/__init__.py

@@ -14,3 +14,20 @@
     Policy,
     VwPolicy,
 )
+
+import logging
+
+
+def configure_logger():
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.INFO)
+    ch = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
+    ch.setFormatter(formatter)
+    ch.setLevel(logging.INFO)
+    logger.addHandler(ch)
+
+
+configure_logger()

rl_chain/metrics.py

@@ -1,6 +1,7 @@
 import pandas as pd
 from typing import Optional
 
+
 class MetricsTracker:
     def __init__(self, step: int):
         self._history = []
@@ -20,7 +21,7 @@ def on_feedback(self, score: Optional[float]) -> None:
         self._num += score or 0
         self._i += 1
         if self._step > 0 and self._i % self._step == 0:
-            self._history.append({'step': self._i, 'score': self.score})
+            self._history.append({"step": self._i, "score": self.score})
 
     def to_pandas(self) -> pd.DataFrame:
-        return pd.DataFrame(self._history)
+        return pd.DataFrame(self._history)

rl_chain/model_repository.py

@@ -7,12 +7,13 @@
 import glob
 import logging
 
+logger = logging.getLogger(__name__)
+
 
 class ModelRepository:
     def __init__(
         self,
         folder: Union[str, os.PathLike],
-        logger: logging.Logger,
         with_history: bool = True,
         reset: bool = False,
     ):
@@ -27,7 +28,6 @@ def __init__(
                 os.remove(self.model_path)
 
         self.folder.mkdir(parents=True, exist_ok=True)
-        self.logger = logger
 
     def get_tag(self) -> str:
         return datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
@@ -37,7 +37,7 @@ def has_history(self) -> bool:
 
     def save(self, workspace: vw.Workspace) -> None:
         with open(self.model_path, "wb") as f:
-            self.logger.info(f"storing rl_chain model in: {self.model_path}")
+            logger.info(f"storing rl_chain model in: {self.model_path}")
             f.write(workspace.serialize())
         if self.with_history:  # write history
             shutil.copyfile(self.model_path, self.folder / f"model-{self.get_tag()}.vw")
@@ -48,6 +48,6 @@ def load(self, commandline: Sequence[str]) -> vw.Workspace:
             with open(self.model_path, "rb") as f:
                 model_data = f.read()
         if model_data:
-            self.logger.info(f"rl_chain model is loaded from: {self.model_path}")
+            logger.info(f"rl_chain model is loaded from: {self.model_path}")
             return vw.Workspace(commandline, model_data=model_data)
         return vw.Workspace(commandline)

rl_chain/pick_best_chain.py

@@ -13,6 +13,10 @@
 from langchain.chains.llm import LLMChain
 from sentence_transformers import SentenceTransformer
 
+import logging
+
+logger = logging.getLogger(__name__)
+
 # sentinel object used to distinguish between user didn't supply anything or user explicitly supplied None
 SENTINEL = object()
 

rl_chain/rl_chain_base.py

@@ -23,12 +23,6 @@
 )
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-ch = logging.StreamHandler()
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
-ch.setFormatter(formatter)
-ch.setLevel(logging.INFO)
-logger.addHandler(ch)
 
 
 class _BasedOn:
@@ -168,14 +162,14 @@ def __init__(
         model_repo: ModelRepository,
         vw_cmd: Sequence[str],
         feature_embedder: Embedder,
-        logger: VwLogger,
-        *_,
-        **__,
+        vw_logger: VwLogger,
+        *args,
+        **kwargs,
     ):
         self.model_repo = model_repo
         self.workspace = self.model_repo.load(vw_cmd)
         self.feature_embedder = feature_embedder
-        self.logger = logger
+        self.vw_logger = vw_logger
 
     def predict(self, event: Event) -> Any:
         text_parser = vw.TextFormatParser(self.workspace)
@@ -191,9 +185,9 @@ def learn(self, event: Event):
         self.workspace.learn_one(multi_ex)
 
     def log(self, event: Event):
-        if self.logger.logging_enabled():
+        if self.vw_logger.logging_enabled():
             vw_ex = self.feature_embedder.format(event)
-            self.logger.log(vw_ex)
+            self.vw_logger.log(vw_ex)
 
     def save(self):
         self.model_repo.save()
@@ -284,7 +278,7 @@ def __init__(
         vw_cmd=None,
         policy=VwPolicy,
         vw_logs: Optional[Union[str, os.PathLike]] = None,
-        metrics_step = -1,
+        metrics_step=-1,
         *args,
         **kwargs,
     ):
@@ -295,11 +289,11 @@ def __init__(
             )
         self.policy = policy(
             model_repo=ModelRepository(
-                model_save_dir, logger, with_history=True, reset=reset_model
+                model_save_dir, with_history=True, reset=reset_model
             ),
             vw_cmd=vw_cmd or [],
             feature_embedder=feature_embedder,
-            logger=VwLogger(vw_logs),
+            vw_logger=VwLogger(vw_logs),
         )
         self.metrics = MetricsTracker(step=metrics_step)
 
@@ -426,9 +420,7 @@ def _call(
                 f"The LLM was not able to rank and the chain was not able to adjust to this response, error: {e}"
             )
         self.metrics.on_feedback(score)
-        event = self._call_after_scoring_before_learning(
-            score=score, event=event
-        )
+        event = self._call_after_scoring_before_learning(score=score, event=event)
         self.policy.learn(event=event)
         self.policy.log(event=event)