add warning when pool is passed

mgwr/gwr.py

@@ -5,6 +5,7 @@
 import copy
 import os
 from typing import Optional
+import warnings
 import numpy as np
 import numpy.linalg as la
 from scipy.stats import t
@@ -291,7 +292,7 @@ def _local_fit(self, i):
             return influ, resid, predy, betas.reshape(-1), w, Si, tr_STS_i, CCT
 
     def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls',
-            lite=False):
+            lite=False,pool=None):
         """
         Method that fits a model with a particular estimation routine.
 
@@ -318,6 +319,7 @@ def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls',
                         bandwidth selection (could speed up
                         bandwidth selection for GWR) or to estimate
                         a full GWR. Default is False.
+        pool          : None, deprecated and not used.
 
         Returns
         -------
@@ -333,6 +335,9 @@ def fit(self, ini_params=None, tol=1.0e-5, max_iter=20, solve='iwls',
         self.fit_params['solve'] = solve
         self.fit_params['lite'] = lite
 
+        if pool:
+            warnings.warn("The pool parameter is no longer used and will have no effect; parallelization is default and implemented using joblib instead.", RuntimeWarning)
+
         if solve.lower() == 'iwls':
 
             if self.points is None:
@@ -1604,7 +1609,7 @@ def _chunk_compute_R(self, chunk_id=0):
             return ENP_j, CCT, pR
         return ENP_j, CCT
 
-    def fit(self, n_chunks=1):
+    def fit(self, n_chunks=1,pool=None):
         """
         Compute MGWR inference by chunk to reduce memory footprint.
         See Li and Fotheringham, 2020, IJGIS.
@@ -1615,6 +1620,8 @@ def fit(self, n_chunks=1):
         n_chunks      : integer, optional
                         A number of chunks parameter to reduce memory usage.
                         e.g. n_chunks=2 should reduce overall memory usage by 2.
+        
+        pool          : None, deprecated and not used
 
         Returns
         -------
@@ -1631,6 +1638,9 @@ def tqdm(x, total=0,
                      desc=''):  #otherwise, just passthrough the range
                 return x
 
+        if pool:
+            warnings.warn("The pool parameter is no longer used and will have no effect; parallelization is default and implemented using joblib instead.", RuntimeWarning)
+
         if self.n_jobs == -1:
             max_processors = os.cpu_count()
             self.n_chunks = max_processors * n_chunks

mgwr/sel_bw.py

@@ -5,6 +5,7 @@
 __author__ = "Taylor Oshan [email protected]"
 
 import spreg.user_output as USER
+import warnings
 import numpy as np
 from scipy.spatial.distance import pdist
 from scipy.optimize import minimize_scalar
@@ -203,7 +204,7 @@ def search(self, search_method='golden_section', criterion='AICc',
                max_iter=200, init_multi=None, tol_multi=1.0e-5,
                rss_score=False, max_iter_multi=200, multi_bw_min=[None],
                multi_bw_max=[None
-                             ], bws_same_times=5, verbose=False):
+                             ], bws_same_times=5, verbose=False,pool=None):
         """
         Method to select one unique bandwidth for a gwr model or a
         bandwidth vector for a mgwr model.
@@ -251,6 +252,7 @@ def search(self, search_method='golden_section', criterion='AICc',
                          current set of bandwidths as final bandwidths.
         verbose        : Boolean
                          If true, bandwidth searching history is printed out; default is False.
+        pool          : None, deprecated and not used.
 
         Returns
         -------
@@ -290,6 +292,10 @@ def search(self, search_method='golden_section', criterion='AICc',
                 " a single entry or a list containing an entry for each of k"
                 " covariates including the intercept")
 
+        if pool:
+            warnings.warn("The pool parameter is no longer used and will have no effect; parallelization is default and implemented using joblib instead.", RuntimeWarning)
+
+
         self.interval = interval
         self.tol = tol
         self.max_iter = max_iter