feat(learning.ipynb): ajout meilleur algo

Configure Dependabot for Python package updates.

.github/dependabot.yml

@@ -0,0 +1,18 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+      day: "saturday"
+    open-pull-requests-limit: 5
+    groups:
+      python-dependencies:
+        patterns:
+          - "*"
+      

.github/workflows/python-app.yml

@@ -19,15 +19,15 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python 3.10
+      - name: Set up Python 3.x
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.x"
 
       - name: install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install ".[test,doc]"
+          pip install ".[test]"
 
       - name: Lint with flake8
         run: |

.github/workflows/static.yml

@@ -32,15 +32,14 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
-      - name: Set up Python 3.10
+      - name: Set up Python 3.x
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.x'
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          # Installe le projet en mode éditable avec les extras de doc
           pip install -e ".[doc]"
 
       - name: Setup Pages

README.md

@@ -1 +1,37 @@
-# millesima_projetS6
+# Millesima AI Engine 🍷
+
+> A **University of Paris-Est Créteil (UPEC)** Semester 6 project.
+
+## Documentation
+- 🇫🇷 [Version Française](https://guezoloic.github.io/millesima-ai-engine)
+> note: only french version enabled for now.
+---
+
+## Installation
+> Make sure you have **Python 3.10+** installed.
+
+1. **Clone the repository:**
+    ```bash
+    git clone https://github.com/votre-pseudo/millesima-ai-engine.git
+    cd millesima-ai-engine
+    ```
+
+2. **Set up a virtual environment:**
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # Windows: .venv\Scripts\activate
+    ```
+
+3. **Install dependencies:**
+    ```bash
+    pip install -e .
+    ```
+
+## Usage
+
+### 1. Data Extraction (Scraping)
+To fetch the latest wine data from Millesima:
+```bash
+python3 src/scraper.py
+```
+> Note: that will take some time to fetch all data depending on the catalog size.

docs/index.md

@@ -1,3 +1,16 @@
 # Millesima
 
-L’objectif de ce projet est d’étudier, en utilisant des méthodes d’apprentissage automatique, l’impact de différents critères (notes des critiques, appelation) sur le prix d’un vin. Pour ce faire, on s’appuiera sur le site Millesima (https://www.millesima.fr/), qui a l’avantage de ne pas posséder de protection contre les bots. Par respect pour l’hébergeur du site, on veillera à limiter au maximum le nombre de requêtes. En particulier, on s’assurera d’avoir un code fonctionnel avant de scraper l’intégralité du site, pour éviter les répétitions.
+L’objectif de ce projet est d’étudier, en utilisant des méthodes d’apprentissage automatique, l’impact de différents critères (notes des critiques, appelation) sur le prix d’un vin. Pour ce faire, on s’appuiera sur le site Millesima (https://www.millesima.fr/), qui a l’avantage de ne pas posséder de protection contre les bots. Par respect pour l’hébergeur du site, on veillera à limiter au maximum le nombre de requêtes. En particulier, on s’assurera d’avoir un code fonctionnel avant de scraper l’intégralité du site, pour éviter les répétitions.
+
+## projet
+<div style="text-align: center;">
+    <object
+        data="/millesima-ai-engine/projet.pdf"
+        type="application/pdf"
+        width="100%"
+        height="1000px"
+    >
+        <p>Votre navigateur ne peut pas afficher ce PDF. 
+        <a href="/millesima-ai-engine/projet.pdf">Cliquez ici pour le télécharger.</a></p>
+    </object>
+</div>

mkdocs.yml

@@ -1,4 +1,5 @@
 site_name: "Projet Millesima S6"
+site_url: "https://github.guezoloic.com/millesima-ai-engine/"
 
 theme:
   name: "material"
@@ -7,6 +8,11 @@ plugins:
   - search
   - mkdocstrings
 
+extra:
+  generator: false
+
+copyright: "Loïc GUEZO & Chahrazad DAHMANI – UPEC S6 – 2026"
+
 markdown_extensions:
   - admonition
   - pymdownx.details

pyproject.toml

@@ -6,8 +6,14 @@ dependencies = [
     "beautifulsoup4==4.14.3",
     "pandas==2.3.3",
     "tqdm==4.67.3",
+    "scikit-learn==1.7.2",
+    "matplotlib==3.10.8"
 ]
 
+[tool.pytest.ini_options]
+pythonpath = "src"
+testpaths = ["tests"]
+
 [project.optional-dependencies]
 test = ["pytest==8.4.2", "requests-mock==1.12.1", "flake8==7.3.0"]
 doc = ["mkdocs<2.0.0", "mkdocs-material==9.6.23", "mkdocstrings[python]"]

src/cleaning.py

@@ -92,14 +92,24 @@ class Cleaning:
         self._vins = self._vins.join(appellation_dummies)
         return self
 
+    def drop_empty_price(self) -> "Cleaning":
+        self._vins = self._vins.dropna(subset=["Prix"])
+        return self
+
 
 def main() -> None:
     if len(argv) != 2:
         raise ValueError(f"Usage: {argv[0]} <filename.csv>")
 
     filename = argv[1]
-    cleaning: Cleaning = Cleaning(filename)
-    _ = cleaning.drop_empty_appellation().fill_missing_scores().encode_appellation()
+    cleaning: Cleaning = (
+        Cleaning(filename)
+        .drop_empty_appellation()
+        .fill_missing_scores()
+        .encode_appellation()
+        .drop_empty_price()
+    )
+    cleaning.getVins().to_csv("clean.csv", index=False)
 
 
 if __name__ == "__main__":

src/learning.py

@@ -0,0 +1,93 @@
+
+
+from typing import Any, Callable
+from pandas import DataFrame
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+import matplotlib.pyplot as plt
+
+from cleaning import Cleaning
+
+
+class Learning:
+    def __init__(self, vins: DataFrame, target: str) -> None:
+        self.X = vins.drop(target, axis=1)
+        self.y = vins[target]
+
+        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
+            self.X, self.y, test_size=0.25, random_state=49
+        )
+
+    def evaluate(
+        self,
+        estimator,
+        pretreatment=None,
+        fn_score=lambda m, xt, yt: m.score(xt, yt),
+    ):
+
+        pipeline = make_pipeline(pretreatment, estimator) if pretreatment else estimator
+        pipeline.fit(self.X_train, self.y_train)
+        score = fn_score(pipeline, self.X_test, self.y_test)
+        prediction = pipeline.predict(self.X_test)
+
+        return score, prediction
+
+    def draw(self, predictions, y_actual):
+        plt.figure(figsize=(8, 6))
+
+        plt.scatter(
+            predictions,
+            y_actual,
+            alpha=0.5,
+            c="royalblue",
+            edgecolors="k",
+            label="Vins",
+        )
+
+        mn = min(predictions.min(), y_actual.min())
+        mx = max(predictions.max(), y_actual.max())
+        plt.plot(
+            [mn, mx],
+            [mn, mx],
+            color="red",
+            linestyle="--",
+            lw=2,
+            label="Prédiction Parfaite",
+        )
+
+        plt.xlabel("Prix estimés (estim_LR)")
+        plt.ylabel("Prix réels (y_test)")
+        plt.title("titre")
+        plt.legend()
+        plt.grid(True, linestyle=":", alpha=0.6)
+
+        plt.show()
+
+
+df_vins = (
+    Cleaning("data.csv")
+    .drop_empty_appellation()
+    .fill_missing_scores()
+    .encode_appellation()
+    .drop_empty_price()
+    .getVins()
+)
+
+etude = Learning(df_vins, target="Prix")
+
+print("--- Question 16 & 17 ---")
+score_simple, estim_simple = etude.evaluate(LinearRegression())
+print(f"Score R² (LR Simple) : {score_simple:.4f}")
+
+etude.draw(estim_simple, etude.y_test)
+
+
+print("\n--- Question 18 ---")
+score_std, estim_std = etude.evaluate(
+    estimator=LinearRegression(), pretreatment=StandardScaler()
+)
+print(f"Score R² (Standardisation + LR) : {score_std:.4f}")
+
+etude.draw(estim_std, etude.y_test)

src/scraper.py

@@ -377,9 +377,6 @@ class Scraper:
         try:
             data: dict[str, object] = self.getjsondata(subdir).getdata()
 
-            for element in ["initialReduxState", "categ", "content"]:
-                data = cast(dict[str, object], data.get(element))
-
             products: list[dict[str, Any]] = cast(
                 list[dict[str, Any]], data.get("products")
             )

tests/test_scraper.py

@@ -185,17 +185,11 @@ def mock_site():
                         {dumps({
                             "props": {
                                 "pageProps": {
-                                    "initialReduxState": {
-                                        "categ": {
-                                            "content": {
-                                                "products": [
-                                                    {"seoKeyword": "/nino-negri-5-stelle-sfursat-2022.html",},
-                                                    {"seoKeyword": "/poubelle",},
-                                                    {"seoKeyword": "/",}
-                                                ]
-                                            }
-                                        }
-                                    }
+                                    "products": [
+                                        {"seoKeyword": "/nino-negri-5-stelle-sfursat-2022.html",},
+                                        {"seoKeyword": "/poubelle",},
+                                        {"seoKeyword": "/",}
+                                    ]
                                 }
                             }
                         }
@@ -213,14 +207,8 @@ def mock_site():
                         {dumps({
                             "props": {
                                 "pageProps": {
-                                    "initialReduxState": {
-                                        "categ": {
-                                            "content": {
-                                                "products": [
-                                                ]
-                                            }
-                                        }
-                                    }
+                                    "products": [
+                                    ]
                                 }
                             }
                         }