[dssutils] init prepare/apply train valid test

used to split dss object in train valid and test according to event number
first map prepare:
  1. duplicates data ([012]->[01201])
  2. saves correct chunk starts and stops:
    split 0: test=[0], valid=[1], train=[2]
    split 1: test=[1], valid=[2], train=[0]
    split 2: test=[2], valid=[0], train=[1]
then map apply

dssutils.py

 import numpy as np
 
+from .data import DSS
+
 
 def nan_to_zero(x):
     return x.map(lambda y: np.nan_to_num(y, nan=0, posinf=0, neginf=0))
@@ -52,3 +54,44 @@ def splitting(split, n_splits, identifier):
         valid = (test + 1) % n
         func = splitting_function(identifier, n, test=test, valid=valid)
         return func
+
+
+def get_prepare_train_valid_test(n_splits, identifier):
+    def prepare_train_valid_test(x):
+        masks = ((x[identifier] % n_splits == i).flatten() for i in range(n_splits))
+        chunks = [mask(x, _mask) for _mask in masks]
+        chunks = chunks + chunks[:-1]
+
+        lenghts = [chunk.blen for chunk in chunks]
+        stops = np.cumsum(lenghts).tolist()
+        starts = [0] + stops[:-1]
+
+        train, valid, test = [], [], []
+        for i in range(n_splits):
+            test.append((starts[i], stops[i]))
+            valid.append((starts[i + 1], stops[i + 1]))
+            train.append((starts[i + 2], stops[i + (n_splits - 1)]))
+
+        data = DSS.concatenate(*chunks)
+
+        data["test"] = test
+        data["valid"] = valid
+        data["train"] = train
+
+        return data
+
+    return prepare_train_valid_test
+
+
+def get_apply_train_valid_test(split):
+    def apply_train_valid_test(x):
+        train_start, train_stop = x["train"][split][0], x["train"][split][1]
+        valid_start, valid_stop = x["valid"][split][0], x["valid"][split][1]
+        test_start, test_stop = x["test"][split][0], x["test"][split][1]
+        return {
+            "train": x.map(lambda y: y[train_start:train_stop]),
+            "valid": x.map(lambda y: y[valid_start:valid_stop]),
+            "test": x.map(lambda y: y[test_start:test_stop]),
+        }
+
+    return apply_train_valid_test