Release

kerashypetune/kerashypetune.py

@@ -1,9 +1,10 @@
 import random
+import inspect
 import numpy as np
 from copy import deepcopy
 
 from .utils import (ParameterSampler, _check_param, _check_data,
-                    _clear_callbacks, _create_fold)
+                    _clear_callbacks, _create_fold, _is_multioutput)
 
 
 class _KerasSearch:
@@ -497,12 +498,25 @@ def _search(self,
                 "Expected cv as cross-validation object with split method to "
                 "generate indices to split data into training and test set "
                 "(like from sklearn.model_selection).")
+        else:
+            split_args = inspect.signature(self.cv.split).parameters
+            split_args = {k: v.default for k, v in split_args.items()}
+            split_need_y = split_args['y'] is not None
+
+        _x = _check_data(x)
+
+        if y is not None:
+            _y = _check_data(y, is_target=True)
+            if _is_multioutput(y) and split_need_y:
+                raise ValueError(
+                    "{} not supports multioutput.".format(self.cv))
+        else:
+            _y = None
 
-        _check_data(x)
-        if y is not None: _check_data(y)
-        if sample_weight is not None: _check_data(sample_weight)
+        if sample_weight is not None:
+            _ = _check_data(sample_weight)
 
-        for fold, (train_id, val_id) in enumerate(self.cv.split(x, y, groups)):
+        for fold, (train_id, val_id) in enumerate(self.cv.split(_x, _y, groups)):
 
             if self.tuner_verbose > 0:
                 print("\n{}\n{}  Fold {}  {}\n{}".format(

kerashypetune/utils.py

@@ -13,8 +13,10 @@ def _check_param(values):
 
     if isinstance(values, (list, tuple, np.ndarray)):
         return list(set(values))
+
     elif hasattr(values, 'rvs'):
         return values
+
     else:
         return [values]
 
@@ -44,7 +46,7 @@ def _create_fold(X, ids):
 
     Returns
     -------
-    array/list or arrays/dict of arrays containing fold data.
+    Data fold.
     """
 
     if isinstance(X, list):
@@ -57,31 +59,62 @@ def _create_fold(X, ids):
         return X[ids]
 
 
-def _check_data(X):
+def _check_data(X, is_target=False):
     """Data controls for cross validation."""
 
     if isinstance(X, list):
+        data_len = []
         for x in X:
             if not isinstance(x, np.ndarray):
                 raise ValueError(
                     "Received data in list format. Take care to cast each "
                     "value of the list to numpy array.")
+            data_len.append(len(x))
+
+        if len(set(data_len)) > 1:
+            raise ValueError("Data must have the same cardinality. "
+                             "Got {}".format(data_len))
 
     elif isinstance(X, dict):
+        data_len = []
         for x in X.values():
             if not isinstance(x, np.ndarray):
                 raise ValueError(
                     "Received data in dict format. Take care to cast each "
                     "value of the dict to numpy array.")
+            data_len.append(len(x))
+
+        if len(set(data_len)) > 1:
+            raise ValueError("Data must have the same cardinality. "
+                             "Got {}".format(data_len))
 
     elif isinstance(X, np.ndarray):
-        pass
+        x = X
+        data_len = [len(x)]
 
     else:
         raise ValueError(
             "Data format not appropriate for Keras CV search. "
             "Supported types are list, dict or numpy array.")
 
+    if not is_target:
+        x = np.zeros(data_len[0])
+
+    return x
+
+
+def _is_multioutput(y):
+    """Check if multioutput task."""
+
+    if isinstance(y, list):
+        return len(y) > 1
+
+    elif isinstance(y, dict):
+        return len(y) > 1
+
+    else:
+        return False
+
 
 class ParameterSampler(object):
     # modified from scikit-learn ParameterSampler

setup.py

@@ -3,7 +3,7 @@
 
 HERE = pathlib.Path(__file__).parent
 
-VERSION = '0.1.2'
+VERSION = '0.1.3'
 PACKAGE_NAME = 'keras-hypetune'
 AUTHOR = 'Marco Cerliani'
 AUTHOR_EMAIL = 'cerlymarco@gmail.com'