benchmark working

Author: vm-aifluence-jro

examples/benchmark.md

@@ -73,15 +73,15 @@ cols_to_impute = ["TEMP", "PRES"]
 
 The dataset `Artificial` is designed to have a sum of a periodical signal, a white noise and some outliers.
 
-```python tags=[]
-df_data
-```
-
 ```python
 # df_data = data.get_data_corrupted("Artificial", ratio_masked=.2, mean_size=10)
 # cols_to_impute = ["signal"]
 ```
 
+```python tags=[]
+df_data
+```
+
 Let's take a look at variables to impute. We only consider a station, Aotizhongxin.
 Time series display seasonalities (roughly 12 months).
 
@@ -164,7 +164,7 @@ dict_imputers = {
 }
 n_imputers = len(dict_imputers)
 
-search_params = {
+dict_config_opti = {
     "RPCA_opti": {
         "tau": {"min": .5, "max": 5, "type":"Real"},
         "lam": {"min": .1, "max": 1, "type":"Real"},
@@ -195,7 +195,7 @@ comparison = comparator.Comparator(
     generator_holes = generator_holes,
     metrics=["mae", "wmape", "KL_columnwise", "ks_test", "energy"],
     n_calls_opt=10,
-    search_params=search_params,
+    dict_config_opti=dict_config_opti,
 )
 results = comparison.compare(df_data)
 results
@@ -343,7 +343,7 @@ comparison = comparator.Comparator(
     df_data.columns,
     generator_holes = generator_holes,
     n_calls_opt=10,
-    search_params=search_params,
+    dict_config_opti=dict_config_opti,
 )
 results = comparison.compare(df_data)
 results

qolmat/benchmark/comparator.py

@@ -1,5 +1,5 @@
 from functools import partial
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 import numpy as np
 import pandas as pd
@@ -28,7 +28,7 @@ class Comparator:
         10.
     """
 
-    dict_metrics: Dict[str, Any] = {
+    dict_metrics: Dict[str, Callable] = {
         "mse": metrics.mean_squared_error,
         "rmse": metrics.root_mean_squared_error,
         "mae": metrics.mean_absolute_error,
@@ -101,7 +101,7 @@ def evaluate_errors_sample(
             imputation model
         df : pd.DataFrame
             dataframe to impute
-        search_space : Dict
+        dict_config_opti_imputer : Dict
             search space for tested_model's hyperparameters
 
         Returns