From a1dc4e7374ba18d18fac310e14f05a28dd104fc1 Mon Sep 17 00:00:00 2001
From: davidberenstein1957 <david.m.berenstein@gmail.com>
Date: Mon, 19 Jun 2023 14:29:22 +0200
Subject: [PATCH] chore: removed print statement for logging

---
 spacy_setfit/models.py | 46 ++++++++++++++++++++++++++++++------------
 1 file changed, 33 insertions(+), 13 deletions(-)

diff --git a/spacy_setfit/models.py b/spacy_setfit/models.py
index 2a1484f..3e8168d 100644
--- a/spacy_setfit/models.py
+++ b/spacy_setfit/models.py
@@ -1,3 +1,4 @@
+import logging
 import types
 
 from setfit import SetFitModel, SetFitTrainer
@@ -7,9 +8,11 @@
 
 from spacy_setfit.schemas import SetFitTrainerArgs
 
+__LOGGER__ = logging.getLogger(__name__)
+
 
 class SpacySetFit:
-    def __init__(self, nlp: Language, model: SetFitModel, labels = None):
+    def __init__(self, nlp: Language, model: SetFitModel, labels=None):
         self.nlp = nlp
         self.model = model
         self.multi_label = self._check_multi_label(model)
@@ -27,7 +30,9 @@ def _check_multi_label(model: SetFitModel):
             return False
 
     @staticmethod
-    def _from_pretrained(pretrained_model_name_or_path: str, setfit_from_pretrained_args: dict = None):
+    def _from_pretrained(
+        pretrained_model_name_or_path: str, setfit_from_pretrained_args: dict = None
+    ):
         if setfit_from_pretrained_args is None:
             setfit_from_pretrained_args = {}
         model = SetFitModel.from_pretrained(
@@ -36,32 +41,47 @@ def _from_pretrained(pretrained_model_name_or_path: str, setfit_from_pretrained_
         return model
 
     @classmethod
-    def from_pretrained(cls, nlp: Language, pretrained_model_name_or_path: str, setfit_from_pretrained_args: dict = None):
-        model = cls._from_pretrained(pretrained_model_name_or_path, setfit_from_pretrained_args)
+    def from_pretrained(
+        cls,
+        nlp: Language,
+        pretrained_model_name_or_path: str,
+        setfit_from_pretrained_args: dict = None,
+    ):
+        model = cls._from_pretrained(
+            pretrained_model_name_or_path, setfit_from_pretrained_args
+        )
         return cls(nlp, model)
 
     @classmethod
-    def from_trained(cls, nlp: Language, pretrained_model_name_or_path: str, setfit_trainer_args: SetFitTrainerArgs, setfit_from_pretrained_args: dict = None):
-
-        setfit_from_pretrained_args["multi_target_strategy"] = setfit_from_pretrained_args.get("multi_target_strategy")
+    def from_trained(
+        cls,
+        nlp: Language,
+        pretrained_model_name_or_path: str,
+        setfit_trainer_args: SetFitTrainerArgs,
+        setfit_from_pretrained_args: dict = None,
+    ):
+        setfit_from_pretrained_args[
+            "multi_target_strategy"
+        ] = setfit_from_pretrained_args.get("multi_target_strategy")
         if setfit_trainer_args.multi_label:
             setfit_from_pretrained_args["multi_target_strategy"] = "one-vs-rest"
 
-        model = cls._from_pretrained(pretrained_model_name_or_path, setfit_from_pretrained_args)
-        trainer = SetFitTrainer(
-            model=model,
-            **setfit_trainer_args.dict()
+        model = cls._from_pretrained(
+            pretrained_model_name_or_path, setfit_from_pretrained_args
         )
+        trainer = SetFitTrainer(model=model, **setfit_trainer_args.dict())
         trainer.train()
         if setfit_trainer_args.eval_dataset:
             evaluation = trainer.evaluate()
-            print(evaluation)
+            __LOGGER__.info(f"evaluation: {evaluation}")
 
         return cls(nlp, model, labels=setfit_trainer_args.labels)
 
     def _assign_labels(self, doc, prediction: list):
         if self.id2label:
-            doc.cats = {self.id2label[idx]: float(score) for idx, score in enumerate(prediction)}
+            doc.cats = {
+                self.id2label[idx]: float(score) for idx, score in enumerate(prediction)
+            }
         else:
             doc.cats = {idx: float(score) for idx, score in enumerate(prediction)}
         return doc