fix error when sklearnex has more parameters than sklearn

Author: david-cortes-intel

sklbench/report/implementation.py

@@ -16,8 +16,10 @@
 
 import argparse
 import json
+from functools import reduce
 from typing import Dict, List
 
+import numpy as np
 import openpyxl as xl
 import pandas as pd
 from openpyxl.formatting.rule import ColorScaleRule
@@ -165,7 +167,12 @@ def select_comparison(i, j, diffs_selection):
     df = input_df.set_index(index_columns)
     unique_indices = df.index.unique()
     splitted_dfs = split_df_by_columns(input_df, diff_columns)
-    splitted_dfs = {key: df.set_index(index_columns) for key, df in splitted_dfs.items()}
+    common_cols = reduce(np.intersect1d, [df.columns for df in splitted_dfs.values()])
+    df_specific_cols = np.setdiff1d(index_columns, common_cols)
+    splitted_dfs = {
+        key: df.assign(**{col: None for col in df_specific_cols}).set_index(index_columns)
+        for key, df in splitted_dfs.items()
+    }
 
     # drop results with duplicated indices (keep first entry only)
     for key, splitted_df in splitted_dfs.items():
@@ -181,6 +188,9 @@ def select_comparison(i, j, diffs_selection):
     # compared values
     for i, (key_ith, df_ith) in enumerate(splitted_dfs.items()):
         for j, (key_jth, df_jth) in enumerate(splitted_dfs.items()):
+            common_indexes = np.intersect1d(df_ith.index, df_jth.index)
+            df_ith = df_ith.loc[common_indexes]
+            df_jth = df_jth.loc[common_indexes]
             if select_comparison(i, j, diffs_selection):
                 comparison_name = f"{key_jth} vs {key_ith}"
                 for column in df_ith.columns:
@@ -196,7 +206,9 @@ def select_comparison(i, j, diffs_selection):
                         df[f"{comparison_name}\n{column} is equal"] = (
                             df_ith[column] == df_jth[column]
                         )
-    df = df.reset_index()
+    if len(df_specific_cols):
+        df.index = df.index.droplevel(list(df_specific_cols))
+    df = df.dropna(axis=0, how="all", ignore_index=False).reset_index()
     # move to multi-index
     df = df[reorder_columns(list(df.columns))]
     df.columns = [