wilcoxon statistic added

qcpanel/util.py

@@ -6,7 +6,7 @@ import panel as pn
 
 from quacc.evaluation.estimators import CE
 from quacc.evaluation.report import CompReport, DatasetReport
-from quacc.evaluation.stats import ttest_rel
+from quacc.evaluation.stats import wilcoxon
 
 _plot_sizing_mode = "stretch_both"
 valid_plot_modes = defaultdict(lambda: CompReport._default_modes)
@@ -44,7 +44,7 @@ def create_plots(
             )
             return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
         case ("avg", "stats_table"):
-            _data = ttest_rel(dr, metric=metric, estimators=estimators)
+            _data = wilcoxon(dr, metric=metric, estimators=estimators)
             return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
         case ("avg", _ as plot_mode):
             _plot = dr.get_plots(
@@ -80,6 +80,10 @@ def create_plots(
                 .mean()
             )
             return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
+        case (_, "stats_table"):
+            cr = dr.crs[_prevs.index(int(plot_view))]
+            _data = wilcoxon(cr, metric=metric, estimators=estimators)
+            return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
         case (_, _ as plot_mode):
             cr = dr.crs[_prevs.index(int(plot_view))]
             _plot = cr.get_plots(

quacc/evaluation/report.py

@@ -66,6 +66,7 @@ class CompReport:
         "shift",
         "shift_table",
         "diagonal",
+        "stats_table",
     ]
 
     def __init__(

quacc/evaluation/stats.py

@@ -4,34 +4,37 @@ import numpy as np
 import pandas as pd
 from scipy import stats as sp_stats
 
-from quacc.evaluation.estimators import CE
-from quacc.evaluation.report import DatasetReport
+# from quacc.evaluation.estimators import CE
+from quacc.evaluation.report import CompReport, DatasetReport
 
 
-def ttest_rel(
-    dr: DatasetReport, metric: str = None, estimators: List[str] = None
+def shapiro(
+    r: DatasetReport | CompReport, metric: str = None, estimators: List[str] = None
 ) -> pd.DataFrame:
-    _data = dr.data(metric, estimators)
-
+    _data = r.data(metric, estimators)
     shapiro_data = np.array(
         [sp_stats.shapiro(_data.loc[:, e]) for e in _data.columns.unique(0)]
     ).T
+    dr_index = ["shapiro_W", "shapiro_p"]
+    dr_columns = _data.columns.unique(0)
+    return pd.DataFrame(shapiro_data, columns=dr_columns, index=dr_index)
 
-    _ttest_rel = {}
-    for bs in np.intersect1d(CE.name.baselines, _data.columns.unique(0)):
-        _ttest_rel[f"ttr_{bs}"] = [
-            sp_stats.ttest_rel(_data.loc[:, bs], _data.loc[:, e]).statistic
-            if e not in CE.name.baselines
-            else np.nan
+
+def wilcoxon(
+    r: DatasetReport | CompReport, metric: str = None, estimators: List[str] = None
+) -> pd.DataFrame:
+    _data = r.data(metric, estimators)
+
+    _wilcoxon = {}
+    for est in _data.columns.unique(0):
+        _wilcoxon[est] = [
+            sp_stats.wilcoxon(_data.loc[:, est], _data.loc[:, e]).pvalue
+            if e != est
+            else 1.0
             for e in _data.columns.unique(0)
         ]
-    ttr_data = np.array(list(_ttest_rel.values()))
+    wilcoxon_data = np.array(list(_wilcoxon.values()))
 
-    dr_index = ["shapiro_W", "shapiro_p"] + list(_ttest_rel.keys())
+    dr_index = list(_wilcoxon.keys())
     dr_columns = _data.columns.unique(0)
-    dr_data = (
-        np.concatenate([shapiro_data, ttr_data], axis=0)
-        if ttr_data.shape[0] > 0
-        else shapiro_data
-    )
-    return pd.DataFrame(dr_data, columns=dr_columns, index=dr_index)
+    return pd.DataFrame(wilcoxon_data, columns=dr_columns, index=dr_index)