From eb446e8ff9c349bd6d594e14d349729707c75626 Mon Sep 17 00:00:00 2001
From: MainHHH <bmn0330@gmail.com>
Date: Fri, 22 Nov 2024 17:33:33 +0900
Subject: [PATCH] =?UTF-8?q?[delete]=20=ED=8C=8C=EC=9D=BC=20=EC=82=AD?=
 =?UTF-8?q?=EC=A0=9C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 accuracy.py      |  37 ----------------
 requirements.txt | 109 -----------------------------------------------
 2 files changed, 146 deletions(-)
 delete mode 100644 accuracy.py
 delete mode 100644 requirements.txt

diff --git a/accuracy.py b/accuracy.py
deleted file mode 100644
index 9319513..0000000
--- a/accuracy.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from filtering import *
-
-class RecommenderEvaluator:
-    def __init__(self, df):
-        # 데이터프레임 및 모델 초기화
-        self.df = df
-        self.recommender = FastSkincareRecommender(df)
-
-    def accuracy(self):
-        prediction = []
-
-        for i in range(len(self.df)):
-            new_data = self.df[i:i + 1]
-            recommendations = self.recommender.fit_and_recommend(new_data=new_data, n_recommendations=3)
-            recommend_product = [goodsName for goodsName, _ in recommendations]
-            prediction.append(self.hit_rate(new_data['goodsName'][i], recommend_product))
-
-        return prediction
-
-    def hit_rate(self, gt_item, pred_items):
-        if gt_item in pred_items:
-            return 1
-        return 0
-
-    def evaluate(self):
-        # 정확도를 계산하고 결과 출력
-        prediction = self.accuracy()
-        accuracy_score = sum(prediction) / len(prediction) if prediction else 0
-        print(f"Accuracy: {accuracy_score:.2f}")
-        return accuracy_score
-
-
-'''# 클래스 사용 예시
-if __name__ == "__main__":
-    # df는 데이터프레임으로 정의되어 있어야 함
-    evaluator = RecommenderEvaluator(df)
-    evaluator.evaluate()'''
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index a5a004c..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,109 +0,0 @@
-anyio==4.6.2.post1
-appnope==0.1.4
-argon2-cffi==23.1.0
-argon2-cffi-bindings==21.2.0
-arrow==1.3.0
-asttokens==2.4.1
-async-lru==2.0.4
-attrs==24.2.0
-babel==2.16.0
-beautifulsoup4==4.12.3
-bleach==6.2.0
-certifi==2024.8.30
-cffi==1.17.1
-charset-normalizer==3.4.0
-comm==0.2.2
-contourpy==1.3.0
-cycler==0.12.1
-debugpy==1.8.8
-decorator==5.1.1
-defusedxml==0.7.1
-executing==2.1.0
-fastjsonschema==2.20.0
-fonttools==4.54.1
-fqdn==1.5.1
-h11==0.14.0
-httpcore==1.0.6
-httpx==0.27.2
-idna==3.10
-ipykernel==6.29.5
-ipython==8.29.0
-isoduration==20.11.0
-jedi==0.19.1
-Jinja2==3.1.4
-joblib==1.4.2
-JPype1==1.5.0
-json5==0.9.25
-jsonpointer==3.0.0
-jsonschema==4.23.0
-jsonschema-specifications==2024.10.1
-jupyter-events==0.10.0
-jupyter-lsp==2.2.5
-jupyter_client==8.6.3
-jupyter_core==5.7.2
-jupyter_server==2.14.2
-jupyter_server_terminals==0.5.3
-jupyterlab==4.2.5
-jupyterlab_pygments==0.3.0
-jupyterlab_server==2.27.3
-kiwisolver==1.4.7
-konlpy==0.6.0
-lxml==5.3.0
-MarkupSafe==3.0.2
-matplotlib==3.9.2
-matplotlib-inline==0.1.7
-mistune==3.0.2
-nbclient==0.10.0
-nbconvert==7.16.4
-nbformat==5.10.4
-nest-asyncio==1.6.0
-notebook==7.2.2
-notebook_shim==0.2.4
-numpy==2.1.3
-overrides==7.7.0
-packaging==24.2
-pandas==2.2.3
-pandocfilters==1.5.1
-parso==0.8.4
-pexpect==4.9.0
-pillow==11.0.0
-platformdirs==4.3.6
-prometheus_client==0.21.0
-prompt_toolkit==3.0.48
-psutil==6.1.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-pycparser==2.22
-Pygments==2.18.0
-pyparsing==3.2.0
-python-dateutil==2.9.0.post0
-python-json-logger==2.0.7
-pytz==2024.2
-PyYAML==6.0.2
-pyzmq==26.2.0
-referencing==0.35.1
-requests==2.32.3
-rfc3339-validator==0.1.4
-rfc3986-validator==0.1.1
-rpds-py==0.21.0
-scikit-learn==1.5.2
-scipy==1.14.1
-Send2Trash==1.8.3
-six==1.16.0
-sniffio==1.3.1
-soupsieve==2.6
-stack-data==0.6.3
-terminado==0.18.1
-threadpoolctl==3.5.0
-tinycss2==1.4.0
-tornado==6.4.1
-traitlets==5.14.3
-types-python-dateutil==2.9.0.20241003
-typing_extensions==4.12.2
-tzdata==2024.2
-uri-template==1.3.0
-urllib3==2.2.3
-wcwidth==0.2.13
-webcolors==24.8.0
-webencodings==0.5.1
-websocket-client==1.8.0