Merge pull request #60 from Quantmetry/review_dcor

Review dcor

Author: JulienRoussel77

.github/workflows/test.yml

@@ -1,6 +1,12 @@
 name: Unit test Qolmat
 
-on: [push, pull_request, workflow_dispatch]
+on:
+  push:
+    branches:
+      -dev
+      -main
+    pull_request:
+    workflow_dispatch:
 
 jobs:
   build-linux:

examples/benchmark.md

@@ -51,7 +51,6 @@ from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, HistGra
 
 import sys
 from qolmat.benchmark import comparator, missing_patterns, hyperparameters
-from qolmat.benchmark.metrics import kl_divergence
 from qolmat.imputations import imputers
 from qolmat.utils import data, utils, plot
 
@@ -153,7 +152,7 @@ imputer_regressor = imputers.ImputerRegressor(groups=("station",), estimator=Lin
 ```
 
 ```python
-generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=2, groups=("station",), subset=cols_to_impute, ratio_masked=ratio_masked)
+generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=1, groups=("station",), subset=cols_to_impute, ratio_masked=ratio_masked)
 ```
 
 ```python
@@ -163,13 +162,13 @@ dict_imputers = {
     # "mode": imputer_mode,
     "interpolation": imputer_interpol,
     # "spline": imputer_spline,
-    "shuffle": imputer_shuffle,
-    # "residuals": imputer_residuals,
+    # "shuffle": imputer_shuffle,
+    "residuals": imputer_residuals,
     # "OU": imputer_ou,
     "TSOU": imputer_tsou,
     "TSMLE": imputer_tsmle,
-    "RPCA": imputer_rpca,
-    "RPCA_opti": imputer_rpca_opti,
+    # "RPCA": imputer_rpca,
+    # "RPCA_opti": imputer_rpca,
     # "RPCA_opticw": imputer_rpca_opti2,
     # "locf": imputer_locf,
     # "nocb": imputer_nocb,
@@ -193,11 +192,13 @@ Concretely, the comparator takes as input a dataframe to impute, a proportion of
 Note these metrics compute reconstruction errors; it tells nothing about the distances between the "true" and "imputed" distributions.
 
 ```python
+metrics = ["mae", "wmape", "KL_columnwise", "KL_forest", "ks_test", "dist_corr_pattern"]
+# metrics = ["KL_forest"]
 comparison = comparator.Comparator(
     dict_imputers,
     cols_to_impute,
     generator_holes = generator_holes,
-    metrics=["mae", "wmape", "KL_columnwise", "ks_test", "dist_corr_pattern"],
+    metrics=metrics,
     max_evals=10,
     dict_config_opti=dict_config_opti,
 )
@@ -206,28 +207,13 @@ results
 ```
 
 ```python
-df_plot = results.loc["KL_columnwise",'TEMP']
-plt.barh(df_plot.index, df_plot, color=tab10(0))
-plt.title('TEMP')
-plt.xlabel("KL")
-plt.show()
-
-df_plot = results.loc["KL_columnwise",'PRES']
-plt.barh(df_plot.index, df_plot, color=tab10(0))
-plt.title('PRES')
-plt.xlabel("KL")
-plt.show()
-```
-
-```python
-fig = plt.figure(figsize=(24, 8))
-fig.add_subplot(2, 1, 1)
-plot.multibar(results.loc["mae"], decimals=1)
-plt.ylabel("mae")
-
-fig.add_subplot(2, 1, 2)
-plot.multibar(results.loc["dist_corr_pattern"], decimals=2)
-plt.ylabel("dist_corr_pattern")
+n_metrics = len(metrics)
+fig = plt.figure(figsize=(24, 4 * n_metrics))
+for i, metric in enumerate(metrics):
+    fig.add_subplot(n_metrics, 1, i + 1)
+    df = results.loc[metric]
+    plot.multibar(df, decimals=2)
+    plt.ylabel(metric)
 
 plt.savefig("figures/imputations_benchmark_errors.png")
 plt.show()

qolmat/benchmark/comparator.py

@@ -67,9 +67,8 @@ def get_errors(
         """
         dict_errors = {}
         for name_metric in self.metrics:
-            dict_errors[name_metric] = metrics.get_metric(name_metric)(
-                df_origin, df_imputed, df_mask
-            )
+            fun_metric = metrics.get_metric(name_metric)
+            dict_errors[name_metric] = fun_metric(df_origin, df_imputed, df_mask)
         errors = pd.concat(dict_errors.values(), keys=dict_errors.keys())
         return errors