changes removed on benchmark

Author: vm-aifluence-jro

.gitignore

@@ -57,7 +57,7 @@ qolmat/.converge
 examples/*.ipynb
 examples/figures/*
 examples/data/*
-examples/*.ipynb
+examples/local
 
 
 # VSCode

examples/benchmark.md

@@ -145,20 +145,20 @@ imputer_regressor = imputers.ImputerRegressor(groups=["station"], estimator=Line
 
 dict_imputers = {
     "mean": imputer_mean,
-    "median": imputer_median,
+    # "median": imputer_median,
     # "mode": imputer_mode,
     "interpolation": imputer_interpol,
     # "spline": imputer_spline,
-    "shuffle": imputer_shuffle,
+    # "shuffle": imputer_shuffle,
     # "residuals": imputer_residuals,
     # "OU": imputer_ou,
-    "TSOU": imputer_tsou,
-    "TSMLE": imputer_tsmle,
+    # "TSOU": imputer_tsou,
+    # "TSMLE": imputer_tsmle,
     # "RPCA": imputer_rpca,
-    "RPCA_opti": imputer_rpca_opti,
+    # "RPCA_opti": imputer_rpca_opti,
     # "locf": imputer_locf,
     # "nocb": imputer_nocb,
-    "knn": imputer_knn,
+    # "knn": imputer_knn,
     "ols": imputer_regressor,
     # "mice_ols": imputer_mice,
 }
@@ -207,21 +207,6 @@ plt.bar(df_plot.index, df_plot, color=tab10(0))
 plt.show()
 ```
 
-```python
-fig = plt.figure(figsize=(16, 6))
-fig.add_subplot(1, 2, 1)
-df_plot = results.loc["mae"].mean().sort_values(ascending=False)
-plt.barh(df_plot.index, df_plot, color=[tab10(0) if i<n_imputers-1 else "red" for i in range(n_imputers)])
-plt.xlabel("Erreur MAE")
-# plt.show()
-
-fig.add_subplot(1, 2, 2)
-df_plot = results.loc["energy"].mean().sort_values(ascending=False)
-plt.barh(df_plot.index, df_plot, color=[tab10(0) if i<n_imputers-1 else "red" for i in range(n_imputers)])
-plt.xlabel("Erreur énergétique")
-plt.show()
-```
-
 ```python
 fig = plt.figure(figsize=(24, 8))
 fig.add_subplot(2, 1, 1)
@@ -262,9 +247,8 @@ for col in cols_to_impute:
     values_orig = df_station[col]
 
     plt.plot(values_orig, ".", color='black', label="original")
+
     for ind, (name, model) in enumerate(list(dict_imputers.items())):
-        if name not in ["mean", "TSMLE"]:
-            continue
         values_imp = dfs_imputed_station[name][col].copy()
         values_imp[values_orig.notna()] = np.nan
         plt.plot(values_imp, ".", color=tab10(ind), label=name, alpha=1)