add model functions

src/diamonds/model.py

@@ -1,5 +1,14 @@
-from sklearn.base import BaseEstimator
-from sklearn.pipeline import Pipeline
+from sklearn.base import BaseEstimator, Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.impute import SimpleImputer
+from sklearn.impute import KNNImputer
+from sklearn.linear_model import Ridge
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, mean_absolute_percentage_error
+import pandas as pd
 
 
 def create_model(model_name: str) -> BaseEstimator:
@@ -16,23 +25,73 @@ def create_model(model_name: str) -> BaseEstimator:
     BaseEstimator
         The model ready to be fitted
     """
-    pass
 
-def create_preproc() -> Pipeline:
+    models = {
+        "ridge": Ridge(alpha=1.0),
+        "random_forest": RandomForestRegressor(
+            n_estimators=200,
+            max_depth=10,
+            random_state=42
+        ),
+        "knn": KNeighborsRegressor(n_neighbors=5),
+        "linear": LinearRegression(fit_intercept=True),
+    }
+
+    if model_name not in models:
+        raise ValueError(f"Unknown model: {model_name}")
+
+    return models[model_name]
+
+def create_preproc(num_cols: list[str], cat_cols: list[str]) -> Pipeline:
     """
     Create a preprocessing pipeline.
     """
-    pass
+
+    # pipeline numérique
+    num_pipeline = Pipeline([
+        ("imputer", KNNImputer(n_neighbors=5)),
+        ("scaler", StandardScaler())
+    ])
+
+    # pipeline catégorique
+    cat_pipeline = Pipeline([
+        ("imputer", SimpleImputer(strategy="most_frequent")),
+        ("encoder", OneHotEncoder(handle_unknown="ignore"))
+    ])
+
+    # combiner les deux
+    preprocessing = ColumnTransformer([
+        ("num", num_pipeline, num_cols),
+        ("cat", cat_pipeline, cat_cols)
+    ])
+
+    return preprocessing
 
 def train_model(model, X_train, y_train):
-    pass
+    model.fit(X_train, y_train)
+    return model
 
 def evaluate_model(model, X_test, y_test) -> dict[str, float]:
     # NB : mae, mse, r2_score, mape
     # Only print the metrics for now
-    pass
+    y_pred = model.predict(X_test)
+
+    mae = mean_absolute_error(y_test, y_pred)
+    mse = mean_squared_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+    mape = mean_absolute_percentage_error(y_test, y_pred)
+
+    metrics = {
+        "mae": mae,
+        "mse": mse,
+        "r2": r2,
+        "mape": mape
+    }
 
-def predict(model, X):
+    for name, value in metrics.items():
+        print(f"{name}: {value:.4f}")
+
+def predict(model, X: pd.DataFrame) -> pd.Series:
     """
     Make predictions using the trained model.
 
@@ -48,4 +107,7 @@ def predict(model, X):
     pd.Series
         The predicted values
     """
+    y_pred = model.predict(X)
+
+    return pd.Series(y_pred, index=X.index, name="prediction")