pysal · mehak-sachdeva · Aug 1, 2019 · Aug 1, 2019 · Aug 8, 2019 · Aug 8, 2019
diff --git a/mgwr/gwr.py b/mgwr/gwr.py
@@ -1484,7 +1484,8 @@ def _chunk_compute_R(self, chunk_id=0):
 
         for i in range(n):
             wi = self._build_wi(i, self.bw_init).reshape(-1, 1)
-            if isinstance(self.family, Poisson):
+            if isinstance(self.family, (Poisson, Binomial)):
+            #if isinstance(self.family, Poisson):
                 wi=wi.reshape(-1,1)
                 rslt = iwls(self.y, self.X, self.family, self.offset, None, wi=wi)
                 inv_xtx_xt = rslt[5]
@@ -1510,7 +1511,9 @@ def _chunk_compute_R(self, chunk_id=0):
                     for i in range(len(chunk_index_Aj)):
                         index = chunk_index_Aj[i]
                         wi = self._build_wi(index, self.bws_history[iter_i, j])
-                        if isinstance(self.family, Poisson):
+
+                        if isinstance(self.family, (Poisson, Binomial)):
+                        #if isinstance(self.family, Poisson):
                             Xj = Xj.reshape(-1,1)
                             wi = wi.reshape(-1,1)
                             rslt = iwls(self.y, Xj, self.family, self.offset, None, wi=wi)
@@ -1559,7 +1562,9 @@ def fit(self, n_chunks=1, pool=None):
             predy = self.offset*(np.exp(np.sum(self.X * params, axis=1).reshape(-1, 1)))
 
         elif isinstance(self.family,Binomial):
-            predy = 1/(1+np.exp(-1*np.sum(self.X * params, axis=1).reshape(-1, 1)))
+            #XXB = np.multiply(param, X)
+            #p = np.exp(XXB) / ( 1 + np.exp (XXB))
+            predy = (np.exp(np.sum(self.X * params, axis=1).reshape(-1, 1)))/(1+np.exp(np.sum(self.X * params, axis=1).reshape(-1, 1)))
 
         else:
             predy = np.sum(self.X * params, axis=1).reshape(-1, 1)

diff --git a/mgwr/search.py b/mgwr/search.py
@@ -6,7 +6,6 @@
 from copy import deepcopy
 from spglm.family import Gaussian, Binomial, Poisson
 
-
 def golden_section(a, c, delta, function, tol, max_iter, int_score=False,
                    verbose=False):
     """
@@ -180,20 +179,17 @@ def multi_bw(init, y, X, n, k, family, offset, tol, max_iter, rss_score, gwr_fun
     bw_gwr = bw
     err = optim_model.resid_response.reshape((-1, 1))
     param = optim_model.params
+    old_param=param
+    ni = np.multiply(param, X)
+    p = np.exp(ni) / ( 1 + np.exp (ni))
+    w = (p)*(1-p)
 
     if isinstance(family, Poisson):
         XB = offset*np.exp(np.multiply(param,X))
+
     elif isinstance(family, Binomial):
-        #v = np.multiply(X, param)
-        #XB = 1 / (1 + np.exp(-1 * v))
-        #XB = v + ((1 / (mu * (1 - mu))) * (y - mu))
-        m = np.multiply(param, X)
-        XB = 1 / ( 1 + np.exp (-1 * m))
-        #print("XB "+str(XB.shape))
-        #mu = 1 / ( 1 + np.exp (-1 * np.multiply (param, X)))
-        #XB = np.multiply(param, X)
-        #print(XB.shape)
-        #XB=np.log(XB/(1-XB))
+        XB = ni + ((y - p)/(p*(1-p)))
+
     else:
         XB = np.multiply(param, X)
 
@@ -215,28 +211,25 @@ def tqdm(x, desc=''):  #otherwise, just passthrough the range
 
     for iters in tqdm(range(1, max_iter + 1), desc='Backfitting'):
         new_XB = np.zeros_like(X)
+        new_p = np.zeros_like(X)
+        new_ni = np.zeros_like(X)
+        new_w = np.zeros_like(X)
+        new_add = np.zeros_like(X)
 
         params = np.zeros_like(X)
 
         for j in range(k):
+            temp_y = XB[:,j].reshape((-1, 1))
+            if isinstance(family, (Poisson,Gaussian)):
+                temp_y = temp_y + err
 
-            if isinstance(family, Binomial):
-                ni_old = np.log((XB[:,j])/(1-XB[:,j]))
-                temp_y = (ni_old + ((optim_model.y.reshape(-1) - XB[:,j].reshape(-1))/XB[:,j].reshape(-1)*(1-XB[:,j].reshape(-1)))).reshape(-1,1)
-                print(temp_y.shape)
-            #temp_y = ((XB[:,j] + (y - mu[:,j]))/(mu[:,j] * ( 1 - mu[:,j] )))
-            #print(temp_y.shape)
-            else:
-                temp_y = XB[:, j].reshape((-1, 1))
-            #temp_y = (1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))+err))).reshape(-1))
-
-            #temp_y = y_new
-            #temp_y = temp_y + err
-            #print(min(err))
             temp_X = X[:, j].reshape((-1, 1))
+            temp_w = np.sqrt(w[:,j]).reshape((-1, 1))
+            temp_X_w = np.multiply(temp_X, temp_w)
+            temp_y_w = np.multiply(temp_y, temp_w)
 
             if isinstance(family, Binomial):
-                bw_class = bw_func_g(temp_y, temp_X)
+                bw_class = bw_func_g(temp_y_w, temp_X_w)
             else:
                 bw_class = bw_func(temp_y, temp_X)
 
@@ -251,41 +244,50 @@ def tqdm(x, desc=''):  #otherwise, just passthrough the range
                     bw_stable_counter = np.ones(k)
 
             if isinstance(family, Binomial):
-                optim_model = gwr_func_g(temp_y, temp_X, bw)
+                optim_model = gwr_func_g(temp_y_w, temp_X_w, bw)
 
             else:
                 optim_model = gwr_func(temp_y, temp_X, bw)
-            #err=(temp_y - optim_model.predy).reshape(-1,1)
-            #print(err.shape)
-            err = optim_model.resid_response.reshape((-1, 1))
-            #print(err.shape)
+
+            err = optim_model.resid_response.reshape((-1,1))
+            #Needs further investigation
+
             param = optim_model.params.reshape((-1, ))
-            #print(param.shape)
-            #print(temp_X.shape)
-            #print(err.shape)
-            #new_XB[:, j] = 1 / (1+np.exp(-1*np.multiply(temp_X ,param)+err)).reshape(-1)
-            new_XB[:, j] = 1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))))).reshape(-1)
-            #new_XB[:,j] =  optim_model.predy.reshape(-1)
-            #new_XB[:,j] = np.multiply(param, temp_X).reshape(-1)
-            #new_XB[:, j] = optim_model.predy.reshape(-1)
-            #neww_XB[:, j] = (1 / (1+np.exp((-1*(np.multiply(temp_X ,param.reshape(-1,1)))+err))).reshape(-1))
-            #new_XB[:, j] = np.log(new_XB[:,j]/(1-new_XB[:,j]))
-            params[:, j] = param
+
+            new_ni[:,j] = np.multiply(param.reshape(-1), temp_X_w.reshape(-1))
+            new_p[:,j] = np.exp(new_ni[:,j])/(1+(np.exp(new_ni[:,j])))
+
+            if isinstance(family, (Poisson, Gaussian)):
+                new_XB[:,j] = optim_model.predy.reshape(-1)
+            else:
+                new_XB[:,j] = new_ni[:,j] + ((y.reshape(-1) - new_p[:,j])/(new_p[:,j]*(1-new_p[:,j])))
+
+            new_w[:,j] = new_p[:,j]*(1-new_p[:,j])
+            params[:, j] = param*2
             bws[j] = bw
 
-        num = np.sum((new_XB - XB)**2) / n
-        den = np.sum(np.sum(new_XB, axis=1)**2)
-        score = (num / den)**0.5
+        if isinstance(family, Binomial):
+            num = np.sum((params - old_param)**2) / n
+            den = np.sum(np.sum(params, axis=1)**2)
+            score = (num / den)**0.5
+        else:
+            num = np.sum((new_XB - XB)**2) / n
+            den = np.sum(np.sum(new_XB, axis=1)**2)
+            score = (num / den)**0.5
+
         XB = new_XB
-        #mu = new_mu
-        #XXB = neww_XB
+        p = new_p
+        ni = new_ni
+        w = new_w
+        add = new_add
+        old_param = params
 
         if rss_score:
             if isinstance(family, Poisson):
                 predy = offset*(np.exp(np.sum(X * params, axis=1).reshape(-1, 1)))
 
             elif isinstance(family,Binomial):
-                predy = 1/(1+np.exp(-1*np.sum(X * params, axis=1).reshape(-1, 1)))
+                predy = (np.exp(np.sum(X * params, axis=1).reshape(-1, 1)))/(1+np.exp(np.sum(X * params, axis=1).reshape(-1, 1)))
 
             else:
                 predy = np.sum(np.multiply(params, X), axis=1).reshape((-1, 1))

diff --git a/notebooks/Binomial_MC_script_2/main_mc.py b/notebooks/Binomial_MC_script_2/main_mc.py
@@ -0,0 +1,24 @@
+from multiprocessing import Process, Queue
+import multiprocessing as mp
+
+
+import model_mc
+import warnings
+import pickle
+import sys
+warnings.filterwarnings("ignore")
+
+
+if __name__ == '__main__':
+    for i in range(int(sys.argv[1]),int(sys.argv[2]), 50):
+        start = i
+        end = i + 50
+
+        print("Starting iterations for indexes: {} to {}".format(str(start), str(end)))
+
+        pool = mp.Pool(processes=50)
+        result = pool.map(model_mc.models, range(start,end))
+
+        print("Completing iterations for indexes: {} to {}".format(str(start), str(end)))
+        with open("pkls/results-{}-{}.pkl".format(str(start), str(end)), 'wb') as out:
+           pickle.dump(result, out, pickle.HIGHEST_PROTOCOL)
diff --git a/notebooks/Binomial_MC_script_2/model_mc.py b/notebooks/Binomial_MC_script_2/model_mc.py
@@ -0,0 +1,117 @@
+import sys
+sys.path.append("C:/Users/msachde1/Downloads/Research/Development/mgwr")
+import pandas as pd
+import numpy as np
+
+from mgwr.gwr import GWR
+from spglm.family import Gaussian, Binomial, Poisson
+from mgwr.gwr import MGWR
+from mgwr.sel_bw import Sel_BW
+
+class stats(object):
+	def __init__(self):
+		self.gwr_bw = []
+		self.gwr_aicc = []
+		self.gwr_bic=[]
+		self.gwr_aic=[]
+		self.gwr_params = []
+		self.gwr_predy = []
+		self.gwr_rss = []
+
+		self.mgwr_bw = []
+		self.mgwr_aicc = []
+		self.mgwr_bic=[]
+		self.mgwr_aic=[]
+		self.mgwr_params = []
+		self.mgwr_predy = []
+		self.mgwr_rss = []
+
+def add(a,b):
+	return 1+((1/12)*(a+b))
+
+def con(u,v):
+	return (0*(u)*(v))+0.3
+
+def sp(u,v):
+	return 1+1/324*(36-(6-u/2)**2)*(36-(6-v/2)**2)
+
+def med(u,v):
+    B = np.zeros((25,25))
+    for i in range(25):
+        for j in range(25):
+
+            if u[i][j]<=8:
+                B[i][j]=0.2
+            elif u[i][j]>17:
+                B[i][j]=0.7
+            else:
+                B[i][j]=0.5
+    return B
+
+class foo(object):
+	def __init__(self):
+		self.x = []
+		self.name = ""
+		self.num = 0
+
+def fkj(name, output):
+	ff = foo()
+	ff.x = [1]
+	ff.name = name
+	output.put(ff)
+	return
+
+def models(output):
+	print("start of the function")
+	s = stats()
+	x = np.linspace(0, 25, 25)
+	y = np.linspace(25, 0, 25)
+	X, Y = np.meshgrid(x, y)
+	x1=np.random.normal(0,1,625).reshape(-1,1)
+	x2=np.random.normal(0,1,625).reshape(-1,1)
+	#x3=np.random.normal(0,1,625).reshape(-1,1)
+	error = np.random.normal(0,0.1,625)
+
+	B0=con(X,Y).reshape(-1,1)
+	#B1=add(X,Y).reshape(-1,1)
+	B2=sp(X,Y).reshape(-1,1)
+	B3=med(X,Y).reshape(-1,1)
+
+	lat=Y.reshape(-1,1)
+	lon=X.reshape(-1,1)
+
+	param = np.hstack([B0,B2,B3])
+	cons=np.ones_like(x1)
+	X=np.hstack([cons,x1,x2])
+	y_raw = ((np.exp(np.sum(X * param, axis=1)+error)/(1+(np.exp(np.sum(X * param, axis=1)+error))))).reshape(-1,1)
+	#y_raw=(np.exp((np.sum(X * param, axis=1)+error).reshape(-1, 1)))
+	#y_new = np.random.poisson(y_raw)
+	y_new = np.random.binomial(1,y_raw,(625,1))
+
+	coords = np.array(list(zip(lon,lat)))
+	y = np.array(y_new).reshape((-1,1))
+	X1=np.hstack([x1,x2])
+
+	bw=Sel_BW(coords,y,X1,family=Binomial())
+	bw=bw.search()
+	s.gwr_bw.append(bw)
+	gwr_model=GWR(coords,y,X1,bw,family=Binomial()).fit()
+	s.gwr_aicc.append(gwr_model.aicc)
+	s.gwr_bic.append(gwr_model.bic)
+	s.gwr_aic.append(gwr_model.aic)
+	s.gwr_params.append(gwr_model.params)
+	s.gwr_predy.append(gwr_model.predy)
+	s.gwr_rss.append(gwr_model.resid_ss)
+
+	selector=Sel_BW(coords,y,X1,multi=True,family=Binomial())
+	selector.search()
+	s.mgwr_bw.append(selector.bw[0])
+	mgwr_model=MGWR(coords,y,X1,selector,family=Binomial()).fit()
+	s.mgwr_aicc.append(mgwr_model.aicc)
+	s.mgwr_bic.append(mgwr_model.bic)
+	s.mgwr_aic.append(mgwr_model.aic)
+	s.mgwr_params.append(mgwr_model.params)
+	s.mgwr_predy.append(mgwr_model.predy)
+	s.mgwr_rss.append(mgwr_model.resid_ss)
+
+	return s
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-0-50.pkl b/notebooks/Binomial_MC_script_2/pkls/results-0-50.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-100-150.pkl b/notebooks/Binomial_MC_script_2/pkls/results-100-150.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-150-200.pkl b/notebooks/Binomial_MC_script_2/pkls/results-150-200.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-200-250.pkl b/notebooks/Binomial_MC_script_2/pkls/results-200-250.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-250-300.pkl b/notebooks/Binomial_MC_script_2/pkls/results-250-300.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-300-350.pkl b/notebooks/Binomial_MC_script_2/pkls/results-300-350.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-350-400.pkl b/notebooks/Binomial_MC_script_2/pkls/results-350-400.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-400-450.pkl b/notebooks/Binomial_MC_script_2/pkls/results-400-450.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-450-500.pkl b/notebooks/Binomial_MC_script_2/pkls/results-450-500.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-50-100.pkl b/notebooks/Binomial_MC_script_2/pkls/results-50-100.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-500-550.pkl b/notebooks/Binomial_MC_script_2/pkls/results-500-550.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-550-600.pkl b/notebooks/Binomial_MC_script_2/pkls/results-550-600.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-600-650.pkl b/notebooks/Binomial_MC_script_2/pkls/results-600-650.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-650-700.pkl b/notebooks/Binomial_MC_script_2/pkls/results-650-700.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-700-750.pkl b/notebooks/Binomial_MC_script_2/pkls/results-700-750.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-750-800.pkl b/notebooks/Binomial_MC_script_2/pkls/results-750-800.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-800-850.pkl b/notebooks/Binomial_MC_script_2/pkls/results-800-850.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-850-900.pkl b/notebooks/Binomial_MC_script_2/pkls/results-850-900.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-900-950.pkl b/notebooks/Binomial_MC_script_2/pkls/results-900-950.pkl
diff --git a/notebooks/Binomial_MC_script_2/pkls/results-950-1000.pkl b/notebooks/Binomial_MC_script_2/pkls/results-950-1000.pkl
diff --git a/notebooks/Binomial_MC_script_we/main_mc.py b/notebooks/Binomial_MC_script_we/main_mc.py
@@ -0,0 +1,24 @@
+from multiprocessing import Process, Queue
+import multiprocessing as mp
+
+
+import model_mc
+import warnings
+import pickle
+import sys
+warnings.filterwarnings("ignore")
+
+
+if __name__ == '__main__':
+    for i in range(int(sys.argv[1]),int(sys.argv[2]), 10):
+        start = i
+        end = i + 10
+
+        print("Starting iterations for indexes: {} to {}".format(str(start), str(end)))
+
+        pool = mp.Pool(processes=10)
+        result = pool.map(model_mc.models, range(start,end))
+
+        print("Completing iterations for indexes: {} to {}".format(str(start), str(end)))
+        with open("pkls/results-{}-{}.pkl".format(str(start), str(end)), 'wb') as out:
+           pickle.dump(result, out, pickle.HIGHEST_PROTOCOL)