hier_fix (#4061)

Roll out of panel datatype check changes to hierarchical datatype

sktime/datatypes/_hierarchical/_check.py

@@ -43,9 +43,8 @@
 __all__ = ["check_dict"]
 
 import numpy as np
-import pandas as pd
 
-from sktime.datatypes._series._check import check_pddataframe_series
+from sktime.datatypes._panel._check import check_pdmultiindex_panel
 
 
 def _list_all_equal(obj):
@@ -77,56 +76,11 @@ def _ret(valid, msg, metadata, return_metadata):
 
 def check_pdmultiindex_hierarchical(obj, return_metadata=False, var_name="obj"):
 
-    if not isinstance(obj, pd.DataFrame):
-        msg = f"{var_name} must be a pd.DataFrame, found {type(obj)}"
-        return _ret(False, msg, None, return_metadata)
-
-    if not isinstance(obj.index, pd.MultiIndex):
-        msg = f"{var_name} must have a MultiIndex, found {type(obj.index)}"
-        return _ret(False, msg, None, return_metadata)
-
-    # check that columns are unique
-    if not obj.columns.is_unique:
-        msg = f"{var_name} must have unique column indices, but found {obj.columns}"
-        return _ret(False, msg, None, return_metadata)
-
-    # check that there are 3 or more index levels
-    nlevels = obj.index.nlevels
-    if not nlevels > 2:
-        msg = (
-            f"{var_name} must have a MultiIndex with 3 or more levels, found {nlevels}"
-        )
-        return _ret(False, msg, None, return_metadata)
-
-    inst_inds = obj.index.droplevel(-1).unique()
-    panel_inds = inst_inds.droplevel(-1).unique()
-
-    check_res = [
-        check_pddataframe_series(obj.loc[i], return_metadata=True) for i in inst_inds
-    ]
-    bad_inds = [i[1] for i in enumerate(inst_inds) if not check_res[i[0]][0]]
-
-    if len(bad_inds) > 0:
-        msg = (
-            f"{var_name}.loc[i] must be Series of mtype pd.DataFrame,"
-            f" not at i={bad_inds}"
-        )
-        return _ret(False, msg, None, return_metadata)
-
-    metadata = dict()
-    metadata["is_univariate"] = np.all([res[2]["is_univariate"] for res in check_res])
-    metadata["is_equally_spaced"] = np.all(
-        [res[2]["is_equally_spaced"] for res in check_res]
+    ret = check_pdmultiindex_panel(
+        obj, return_metadata=return_metadata, var_name=var_name, panel=False
     )
-    metadata["is_empty"] = np.any([res[2]["is_empty"] for res in check_res])
-    metadata["n_instances"] = len(inst_inds)
-    metadata["n_panels"] = len(panel_inds)
-    metadata["is_one_series"] = len(inst_inds) == 1
-    metadata["is_one_panel"] = len(panel_inds) == 1
-    metadata["has_nans"] = obj.isna().values.any()
-    metadata["is_equal_length"] = _list_all_equal([len(obj.loc[i]) for i in inst_inds])
-
-    return _ret(True, None, metadata, return_metadata)
+
+    return ret
 
 
 check_dict[("pd_multiindex_hier", "Hierarchical")] = check_pdmultiindex_hierarchical

sktime/datatypes/_panel/_check.py

@@ -115,6 +115,9 @@ def check_dflist_panel(obj, return_metadata=False, var_name="obj"):
     metadata["is_empty"] = np.any([res[2]["is_empty"] for res in check_res])
     metadata["has_nans"] = np.any([res[2]["has_nans"] for res in check_res])
     metadata["is_one_series"] = n == 1
+    metadata["n_panels"] = 1
+    metadata["is_one_panel"] = True
+
     metadata["n_instances"] = n
 
     return _ret(True, None, metadata, return_metadata)
@@ -143,6 +146,8 @@ def check_numpy3d_panel(obj, return_metadata=False, var_name="obj"):
 
     metadata["n_instances"] = obj.shape[0]
     metadata["is_one_series"] = obj.shape[0] == 1
+    metadata["n_panels"] = 1
+    metadata["is_one_panel"] = True
 
     # check whether there any nans; only if requested
     if return_metadata:
@@ -154,7 +159,7 @@ def check_numpy3d_panel(obj, return_metadata=False, var_name="obj"):
 check_dict[("numpy3D", "Panel")] = check_numpy3d_panel
 
 
-def check_pdmultiindex_panel(obj, return_metadata=False, var_name="obj"):
+def check_pdmultiindex_panel(obj, return_metadata=False, var_name="obj", panel=True):
 
     if not isinstance(obj, pd.DataFrame):
         msg = f"{var_name} must be a pd.DataFrame, found {type(obj)}"
@@ -172,9 +177,14 @@ def check_pdmultiindex_panel(obj, return_metadata=False, var_name="obj"):
 
     # check that there are precisely two index levels
     nlevels = obj.index.nlevels
-    if not nlevels == 2:
+    if panel is True and not nlevels == 2:
         msg = f"{var_name} must have a MultiIndex with 2 levels, found {nlevels}"
         return _ret(False, msg, None, return_metadata)
+    elif panel is False and not nlevels > 2:
+        msg = (
+            f"{var_name} must have a MultiIndex with 3 or more levels, found {nlevels}"
+        )
+        return _ret(False, msg, None, return_metadata)
 
     # check that no dtype is object
     if "object" in obj.dtypes.values:
@@ -225,10 +235,17 @@ def check_pdmultiindex_panel(obj, return_metadata=False, var_name="obj"):
         )
         return _ret(False, msg, None, return_metadata)
 
+    if panel is True:
+        panel_inds = [1]
+    else:
+        panel_inds = inst_inds.droplevel(-1).unique()
+
     metadata = dict()
     metadata["is_univariate"] = len(obj.columns) < 2
     metadata["is_equally_spaced"] = is_equally_spaced
     metadata["is_empty"] = len(obj.index) < 1 or len(obj.columns) < 1
+    metadata["n_panels"] = len(panel_inds)
+    metadata["is_one_panel"] = len(panel_inds) == 1
     metadata["n_instances"] = len(inst_inds)
     metadata["is_one_series"] = len(inst_inds) == 1
     metadata["has_nans"] = obj.isna().values.any()
@@ -360,6 +377,8 @@ def is_nested_dataframe(obj, return_metadata=False, var_name="obj"):
     metadata["is_univariate"] = obj.shape[1] < 2
     metadata["n_instances"] = len(obj)
     metadata["is_one_series"] = len(obj) == 1
+    metadata["n_panels"] = 1
+    metadata["is_one_panel"] = True
     if return_metadata:
         metadata["has_nans"] = _nested_dataframe_has_nans(obj)
         metadata["is_equal_length"] = not _nested_dataframe_has_unequal(obj)
@@ -394,6 +413,8 @@ def check_numpyflat_Panel(obj, return_metadata=False, var_name="obj"):
     metadata["is_equal_length"] = True
     metadata["n_instances"] = obj.shape[0]
     metadata["is_one_series"] = obj.shape[0] == 1
+    metadata["n_panels"] = 1
+    metadata["is_one_panel"] = True
 
     # check whether there any nans; only if requested
     if return_metadata:

sktime/datatypes/_panel/_examples.py

@@ -82,6 +82,8 @@
 example_dict_metadata[("Panel", 0)] = {
     "is_univariate": False,
     "is_one_series": False,
+    "n_panels": 1,
+    "is_one_panel": True,
     "is_equally_spaced": True,
     "is_equal_length": True,
     "is_empty": False,
@@ -140,6 +142,8 @@
 example_dict_metadata[("Panel", 1)] = {
     "is_univariate": True,
     "is_one_series": False,
+    "n_panels": 1,
+    "is_one_panel": True,
     "is_equally_spaced": True,
     "is_equal_length": True,
     "is_empty": False,
@@ -192,6 +196,8 @@
 example_dict_metadata[("Panel", 2)] = {
     "is_univariate": True,
     "is_one_series": True,
+    "n_panels": 1,
+    "is_one_panel": True,
     "is_equally_spaced": True,
     "is_equal_length": True,
     "is_empty": False,