initial resample function

tests/test_utils.py

@@ -334,20 +334,20 @@ def test_check_logger_exists():
 
 
 def test_class_stratify_check():
-    selection_frac = 0.9
+    train_frac = 0.9
     idx = np.arange(100)
     y = np.tile(np.arange(5), 20)
-    train, test = resample(idx, selection_frac=selection_frac, random_state=0, stratify=y)
+    train, test = resample(idx, train_frac=train_frac, random_state=0, stratify=y)
 
-    if int(np.ceil(len(idx) * selection_frac)) != len(train):
+    if int(np.ceil(len(idx) * train_frac)) != len(train):
         raise ValueError("Incorrect train size")
-    if (len(idx) - int(np.ceil(len(idx) * selection_frac))) != len(test):
+    if (len(idx) - int(np.ceil(len(idx) * train_frac))) != len(test):
         raise ValueError("Incorrect test size")
 
     classes, dist = np.unique(y, return_counts=True)
 
     for cl, di in zip(classes, dist):
-        if int(np.ceil(di * selection_frac)) != sum(y[train] == cl):
+        if int(np.ceil(di * train_frac)) != sum(y[train] == cl):
             raise ValueError(f"Incorrect train class size {cl}")
-        if di - int(np.ceil(di * selection_frac)) != sum(y[test] == cl):
+        if di - int(np.ceil(di * train_frac)) != sum(y[test] == cl):
             raise ValueError(f"Incorrect test class size {cl}")