updates

aeon/visualisation/results/_mcm.py

@@ -27,6 +27,7 @@ def create_multi_comparison_matrix(
     order_win_tie_loss="higher",
     include_pvalue=True,
     pvalue_test="wilcoxon",
+    pvalue_test_params=None,
     pvalue_correction=None,
     pvalue_threshold=0.05,
     use_mean="mean-difference",
@@ -89,16 +90,33 @@ def create_multi_comparison_matrix(
     pvalue_test: str, default = 'wilcoxon'
         The statistical test to produce the pvalue stats. Currently only wilcoxon is
         supported.
+    pvalue_test_params: dict, default = None,
+        The default parameter set for the pvalue_test used. If pvalue_test is set
+        to Wilcoxon, one should check the scipy.stats.wilcoxon parameters,
+        in the case Wilcoxon is set and this parameter is None, then the default setup
+        is {"zero_method": "pratt", "alternative": "greater"}.
     pvalue_correction: str, default = None
         Correction to use for the pvalue significant test, None or "Holm".
     pvalue_threshold: float, default = 0.05
         Threshold for considering a comparison is significant or not. If pvalue <
         pvalue_threshhold -> comparison is significant.
     use_mean: str, default = 'mean-difference'
-        The mean used to compare two estimators.
+        The mean used to compare two estimators. The only option available
+        is 'mean-difference' which is the difference between arithmetic mean
+        over all datasets.
     order_stats: str, default = 'average-statistic'
         The way to order the used_statistic, default setup orders by average
         statistic over all datasets.
+        The options are:
+        ===============================================================
+        method                               what it does
+        ===============================================================
+        average-statistic      average used_statistic over all datasets
+        average-rank           average rank over all datasets
+        max-wins               maximum number of wins over all datasets
+        amean-amean            average over difference of use_mean
+        pvalue                 average pvalue over all comparates
+        ================================================================
     order_better: str, default = 'decreasing'
         By which order to sort stats, from best to worse.
     dataset_column: str, default = 'dataset_name'
@@ -175,6 +193,7 @@ def create_multi_comparison_matrix(
         order_win_tie_loss=order_win_tie_loss,
         include_pvalue=include_pvalue,
         pvalue_test=pvalue_test,
+        pvalue_test_params=pvalue_test_params,
         pvalue_correction=pvalue_correction,
         pvalue_threshhold=pvalue_threshold,
         use_mean=use_mean,
@@ -221,6 +240,7 @@ def _get_analysis(
     order_win_tie_loss="higher",
     include_pvalue=True,
     pvalue_test="wilcoxon",
+    pvalue_test_params=None,
     pvalue_correction=None,
     pvalue_threshhold=0.05,
     use_mean="mean-difference",
@@ -277,7 +297,16 @@ def _plot_1v1(
         ax.set_xlabel(name_y, fontsize=fontsize)
         ax.set_ylabel(name_x, fontsize=fontsize)
 
-        p_value = round(wilcoxon(x=x, y=y, zero_method="pratt")[1], 4)
+        if pvalue_test == "wilcoxon":
+            _pvalue_test_params = {}
+            if pvalue_test_params is None:
+                _pvalue_test_params = {"zero_method": "pratt", "alternative": "greater"}
+            else:
+                _pvalue_test_params = pvalue_test_params
+            p_value = round(wilcoxon(x=x, y=y, **_pvalue_test_params)[1], precision)
+        else:
+            raise ValueError("The test " + pvalue_test + " is not yet supported.")
+
         legend_elements = [
             mpl.lines.Line2D(
                 [], [], marker="o", color="blue", label=f"Win {win_x}", linestyle="None"
@@ -354,8 +383,10 @@ def _plot_1v1(
                 pairwise_content = _get_pairwise_content(
                     x=x,
                     y=y,
+                    order_WinTieLoss=order_win_tie_loss,
                     include_pvalue=include_pvalue,
                     pvalue_test=pvalue_test,
+                    pvalue_test_params=pvalue_test_params,
                     pvalue_threshhold=pvalue_threshhold,
                     use_mean=use_mean,
                 )
@@ -672,7 +703,10 @@ def _draw(
                 latex_cell = "\\rule{0em}{3ex} " + df_annotations_np[i, j].replace(
                     "\n", " \\\\ "
                 )
-                r = [str(round(_, 4)) for _ in cm(cm_norm(pairwise_matrix[i, j]))[:-1]]
+                r = [
+                    str(round(_, precision))
+                    for _ in cm(cm_norm(pairwise_matrix[i, j]))[:-1]
+                ]
                 latex_row.append(
                     f"\\cellcolor[rgb]{{{','.join(r)}}}\\shortstack{{{latex_cell}}}"
                 )
@@ -697,7 +731,10 @@ def _draw(
                 "\n", " \\\\ "
             )
             s1 = f"{latex_bold}\\cellcolor[rgb]"
-            s2 = [str(round(_, 4)) for _ in cm(cm_norm(pairwise_matrix[i, j]))[:-1]]
+            s2 = [
+                str(round(_, precision))
+                for _ in cm(cm_norm(pairwise_matrix[i, j]))[:-1]
+            ]
             s = f"{s1}{{{','.join(s2)}}}\\shortstack{{{latex_cell}}}"
             latex_row.append(s)
 
@@ -915,12 +952,11 @@ def _get_pairwise_content(
     x,
     y,
     order_WinTieLoss="higher",
-    includeProbaWinTieLoss=False,
     include_pvalue=True,
     pvalue_test="wilcoxon",
+    pvalue_test_params=None,
     pvalue_threshhold=0.05,
     use_mean="mean-difference",
-    bayesian_rope=0.01,
 ):
     content = {}
 
@@ -940,7 +976,12 @@ def _get_pairwise_content(
 
     if include_pvalue:
         if pvalue_test == "wilcoxon":
-            pvalue = wilcoxon(x=x, y=y, zero_method="pratt", alternative="greater")[1]
+            _pvalue_test_params = {}
+            if pvalue_test_params is None:
+                _pvalue_test_params = {"zero_method": "pratt", "alternative": "greater"}
+            else:
+                _pvalue_test_params = pvalue_test_params
+            pvalue = wilcoxon(x=x, y=y, **_pvalue_test_params)[1]
             content["pvalue"] = pvalue
 
             if pvalue_test == "wilcoxon":