Merge pull request #13 from guezoloic/jalon3

Jalon3

.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

mkdocs.yml

@@ -7,6 +7,7 @@ theme:
 plugins:
   - search
   - mkdocstrings
+  - mkdocs-jupyter
 
 extra:
   generator: false

pyproject.toml

@@ -6,6 +6,9 @@ dependencies = [
     "beautifulsoup4==4.14.3",
     "pandas==2.3.3",
     "tqdm==4.67.3",
+    "scikit-learn==1.7.2",
+    "matplotlib==3.10.8",
+    "seaborn==0.13.2"
 ]
 
 [tool.pytest.ini_options]
@@ -14,7 +17,12 @@ 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]"]
+doc = [
+    "mkdocs<2.0.0",
+    "mkdocs-material==9.6.23",
+    "mkdocstrings[python]",
+    "mkdocs-jupyter==0.26.1",
+]
 
 [build-system]
 requires = ["setuptools", "wheel"]

src/cleaning.py

@@ -92,18 +92,25 @@ 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]
+def main(filename: str | None = None) -> None:
-    cleaning: Cleaning = Cleaning(filename)
+    if not filename:
-    cleaning.drop_empty_appellation()   \
+        if len(argv) != 2:
-        .fill_missing_scores()          \
+            raise ValueError(f"Usage: {argv[0]} <filename.csv>")
-        .encode_appellation()           \
+        filename = argv[1]
-        .getVins()                      \
+
-        .to_csv("clean.csv", index=False)
+    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 +1,64 @@
+# 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()

src/scraper.py

@@ -490,11 +490,12 @@ class Scraper:
                 savestate((page, cache))
 
 
-def main() -> None:
+def main(filename: str | None = None, suburl: str | None = None) -> None:
-    if len(argv) != 3:
+    if filename is None or suburl is None:
-        raise ValueError(f"{argv[0]} <filename> <sous-url>")
+        if len(argv) != 3:
-    filename = argv[1]
+            raise ValueError(f"Usage: python {argv[0]} <filename> <sous-url>")
-    suburl = argv[2]
+        filename = argv[1]
+        suburl = argv[2]
 
     scraper: Scraper = Scraper()
     scraper.getvins(suburl, filename)