Fix deprecations; support geographic coordinates; add some type hinting/formatting

requirements.txt

@@ -4,5 +4,4 @@ pandas
 geopandas
 shapely
 scikit-learn
-scipy
-pygeos
+scipy

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name="spacv",
-    version="0.0.21",
+    version="0.0.22",
     description="Spatial cross-validation in Python",
     long_description=long_description,
     long_description_content_type="text/markdown",

spacv/base_classes.py

@@ -1,28 +1,30 @@
-from abc import ABC, abstractmethod, ABCMeta
-import numpy as np
+from abc import ABCMeta, abstractmethod
+
 import geopandas as gpd
+import numpy as np
 from sklearn.model_selection import BaseCrossValidator
-from .utils import convert_geoseries, convert_geodataframe
+
+from .utils import convert_geodataframe, convert_geoseries
+
 
 class BaseSpatialCV(BaseCrossValidator, metaclass=ABCMeta):
     """
     Base class for partitioning-based spatial cross-validation approaches.
     """
-    def __init__(
-        self
-    ):
+
+    def __init__(self):
         self.buffer_radius = buffer_radius
-        
+
     def split(self, XYs):
         """
         Generate indices to split data into training and test set.
-        
+
         Parameters
         ----------
         XYs : GeoSeries
             GeoSeries containing shapely Points that identify Easting
             and Northing coordinates of data points.
-    
+
         Yields
         ------
         train : ndarray
@@ -32,77 +34,80 @@ def split(self, XYs):
         """
         XYs = convert_geoseries(XYs).reset_index(drop=True)
         minx, miny, maxx, maxy = XYs.total_bounds
-        
+
         buffer_radius = self.buffer_radius
-        if buffer_radius > maxx-minx or buffer_radius > maxy-miny:
+        if buffer_radius > maxx - minx or buffer_radius > maxy - miny:
             raise ValueError(
                 "buffer_radius too large and excludes all points. Given {}.".format(
                     self.buffer_radius
                 )
             )
-        num_samples = XYs.shape[0]
+        # num_samples = XYs.shape[0]
         indices = XYs.index.values
-
-        for test_indices, train_excluded in self._iter_test_indices(XYs):  
-            # Exclude the training indices within buffer
-            train_excluded = np.concatenate([test_indices, train_excluded])
+
+        for test_indices, train_excluded in self._iter_test_indices(XYs):
+            if train_excluded.ndim and train_excluded.size:
+                # Exclude the training indices within buffer
+                train_excluded = np.concatenate([test_indices, train_excluded])
+            else:
+                train_excluded = test_indices
             train_index = np.setdiff1d(
-                                np.union1d(
-                                    indices,
-                                    train_excluded
-                                ), np.intersect1d(indices, train_excluded)
-                            )
+                np.union1d(indices, train_excluded),
+                np.intersect1d(indices, train_excluded),
+            )
             if len(train_index) < 1:
                 raise ValueError(
                     "Training set is empty. Try lowering buffer_radius to include more training instances."
                 )
-            test_index = indices[test_indices]      
+            test_index = indices[test_indices]
             yield train_index, test_index
-
-    def _remove_buffered_indices(self, XYs, test_indices, buffer_radius, geometry_buffer):
+
+    def _remove_buffered_indices(
+        self, XYs, test_indices, buffer_radius, geometry_buffer
+    ):
         # Remove training points from dead zone buffer
-        if buffer_radius > 0:            
-            # Buffer grid and clip training instances            
-            candidate_deadzone = XYs.loc[~XYs.index.isin( test_indices )]            
+        if buffer_radius > 0:
+            # Buffer grid and clip training instances
+            candidate_deadzone = XYs.loc[~XYs.index.isin(test_indices)]
             candidate_deadzone = convert_geodataframe(candidate_deadzone)
             geometry_buffer = convert_geodataframe(geometry_buffer)
             deadzone_points = gpd.sjoin(candidate_deadzone, geometry_buffer)
-            train_exclude = deadzone_points.loc[~deadzone_points.index.isin(test_indices)].index.values
+            train_exclude = deadzone_points.loc[
+                ~deadzone_points.index.isin(test_indices)
+            ].index.values
             return test_indices, train_exclude
         else:
             # Yield empty array because no training data removed in dead zone when buffer is zero
-            _ = np.empty([], dtype=np.int)
+            _ = np.empty([], dtype=int)
             return test_indices, _
-        
+
     @abstractmethod
     def _iter_test_indices(self, XYs):
         """
-        Generates integer indices corresponding to test sets and 
+        Generates integer indices corresponding to test sets and
         training indices to be excluded from model training.
-        
+
         Parameters
         ----------
         X : GeoSeries
             GeoSeries containing shapely Points that identify Easting
             and Northing coordinates of data points.
-            
+
         Yields
         ------
         test_indices : array
             The testing set indices for that fold.
         train_exclude : array
             The training set indices to exclude for that fold.
-        """ 
-    
+        """
+
     def get_n_splits(self):
         """
         Returns the number of folds used in the cross-validation.
-        
+
         Returns
         -------
         n_splits : int
             Returns the number of folds in the cross-validator.
         """
         return self.n_splits
-
-