plot report for multiclass fixed

.gitignore

@@ -14,6 +14,7 @@ tests/__pycache__/*
 tests/*/__pycache__/*
 tests/*/*/__pycache__/*
 htmlcov/*
+test*.py
 
 *.coverage
 .coverage
@@ -22,4 +23,4 @@ scp_sync.py
 
 out/*
 output/*
-!output/main/
+!output/main/

conf.yaml

@@ -415,6 +415,26 @@ kde_lr_gs_conf: &kde_lr_gs_conf
       - m3w_kde_lr_gs
     N_JOBS: -2
 
+  confs: 
+    - DATASET_NAME: twitter_gasp
+
+
+multiclass_conf: &multiclass_conf
+  global:
+    METRICS: 
+      - acc
+      - f1
+    OUT_DIR_NAME: output/multiclass
+    DATASET_N_PREVS: 5
+    COMP_ESTIMATORS:
+      - bin_sld_lr_gs
+      - mul_sld_lr_gs
+      - bin_kde_lr_gs
+      - mul_kde_lr_gs
+      - atc_mc
+      - doc
+    N_JOBS: -2
+
   confs: *main_confs
 
 timing_conf: &timing_conf
@@ -461,4 +481,4 @@ timing_gs_conf: &timing_gs_conf
 
   confs: *main_confs
 
-exec: *debug_conf
+exec: *multiclass_conf

quacc/evaluation/report.py

@@ -436,6 +436,16 @@ def _cr_data(cr: CompReport, metric=None, estimators=None):
     return cr.data(metric, estimators)
 
 
+def _key_reverse_delta_train(idx):
+    idx = idx.to_numpy()
+    sorted_idx = np.array(
+        sorted(list(idx), key=lambda x: x[-1]), dtype=("float," * len(idx[0]))[:-1]
+    )
+    # get sorting index
+    nparr = np.nonzero(idx[:, None] == sorted_idx)[1]
+    return nparr
+
+
 class DatasetReport:
     _default_dr_modes = [
         "delta_train",
@@ -457,6 +467,16 @@ class DatasetReport:
         self.name = name
         self.crs: List[CompReport] = [] if crs is None else crs
 
+    def sort_delta_train_index(self, data):
+        # data_ = data.sort_index(axis=0, level=0, ascending=True, sort_remaining=False)
+        data_ = data.sort_index(
+            axis=0,
+            level=0,
+            key=_key_reverse_delta_train,
+        )
+        print(data_.index)
+        return data_
+
     def join(self, other, estimators=None):
         _crs = [
             s_cr.join(o_cr, estimators=estimators)
@@ -542,6 +562,7 @@ class DatasetReport:
         _data.index = _idx
 
         _data = _data.sort_index(axis=0, level=0, ascending=False, sort_remaining=False)
+
         return _data
 
     def shift_data(
@@ -633,6 +654,8 @@ class DatasetReport:
             avg_on_train = _data.groupby(level=1, sort=False).mean()
             if avg_on_train.empty:
                 return None
+            # sort index in reverse order
+            avg_on_train = self.sort_delta_train_index(avg_on_train)
             prevs_on_train = avg_on_train.index.unique(0)
             return plot.plot_delta(
                 # base_prevs=np.around(