Merge branch 'main' of github.com:lanl/pyBASS

.github/workflows/Build.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-22.04, macos-latest, windows-latest]
-        python-version: [3.7, 3.8, 3.9, '3.10']
+        python-version: [3.9, '3.10', 3.11]
 
     steps:
     - uses: actions/checkout@v3

pyBASS/sobol.py

@@ -26,6 +26,7 @@
 from scipy import stats
 import re
 import time
+import math
 
 class sobolBasis:
     """
@@ -206,6 +207,14 @@ def decomp(self, int_order, prior=None, mcmc_use=None, nind=None, ncores=1):
         sob_comb_var = sob_comb_var[use,:]
         sob_comb = sob_comb[use,:]
 
+        # Calculate "Total Sobol' Index"
+        sob_comb_tot = np.zeros((p,nxfunc))
+        idx = 0
+        for i in range(int_order):
+            for j in range(len(u_list[i])):
+                sob_comb_tot[u_list[i][j],:] += sob_comb_var[idx]
+                idx += 1
+
         names_ind1 = []
         for i in range(len(u_list)):
             for j in range(len(u_list[i])):
@@ -226,36 +235,36 @@ def decomp(self, int_order, prior=None, mcmc_use=None, nind=None, ncores=1):
 
         self.S = sob_comb
         self.S_var = sob_comb_var
+        self.T_var = sob_comb_tot
         self.Var_tot = V_tot
         self.names_ind = names_ind2
         self.xx = np.linspace(0,1,nxfunc)
 
         return
 
-    def plot(self, text=False, labels=[], col='Paired', time=[]):
+    def plot(self, int_order=1, total_sobol=True, text=False, labels=[], col='Paired', time=[]):
         if len(time) == 0:
             time = self.xx
 
+        p = np.shape(self.mod.xx)[1]
+        ncomb = np.sum([math.comb(p, k) for k in range(1,int_order+1)])
+
         if len(labels) == 0:
-            labels1 = self.names_ind
-        else:
-            labels1 = self.names_ind
-            for i in range(len(labels)):
-                labels1[i] = labels[float(labels1[i])]
+            labels = self.names_ind[:ncomb] + [self.names_ind[-1]]
 
         map = cm.Paired(np.linspace(0,1,12))
-        map = np.resize(map, (len(labels1),4))
+        map = np.resize(map, (len(labels),4))
         rgb = np.ones((map.shape[0]+1,4))
         rgb[0:map.shape[0],:] = map
         rgb[-1,0:3] = np.array([153,153,153])/255
 
         ord = time.argsort()
-        x_mean = self.S
+        x_mean = np.vstack([self.S[:ncomb, :],
+                            np.sum(self.S[ncomb:, :],axis=0,keepdims=True)])
         sens = np.cumsum(x_mean,axis=0).T
-        fig, axs = plt.subplots(1, 2)
-        idx = np.where(np.sum(sens,axis=0)/sens.shape[0]>=.99999)[0][0]
+        fig, axs = plt.subplots(1, 2+total_sobol)
         cnt = 0
-        for i in range(idx+1):
+        for i in range(ncomb+1):
             x2 = np.concatenate((time[ord], np.flip(time[ord])))
             if i == 0:
                 inBetween = np.concatenate((np.zeros(time[ord].shape[0]), np.flip(sens[ord,i])))
@@ -282,10 +291,11 @@ def plot(self, text=False, labels=[], col='Paired', time=[]):
             cs_diff2 = cs_diff/2
             plt.text(time[lab_x], cs[ind] + cs_diff2[ind1], self.names_ind)
 
-        x_mean_var = self.S_var
+        x_mean_var = np.vstack([self.S_var[:ncomb, :],
+                            np.sum(self.S_var[ncomb:, :],axis=0,keepdims=True)])
         sens_var = np.cumsum(x_mean_var,axis=0).T
         cnt = 0
-        for i in range(idx+1):
+        for i in range(ncomb+1):
             x2 = np.concatenate((time[ord], np.flip(time[ord])))
             if i == 0:
                 inBetween = np.concatenate((np.zeros(time[ord].shape[0]), np.flip(sens_var[ord,i])))
@@ -300,7 +310,25 @@ def plot(self, text=False, labels=[], col='Paired', time=[]):
         axs[1].set(xlabel="x", ylabel="variance", title='Variance Decomposition', xlim=[time.min(), time.max()], ylim=[0, inBetween.max()+3])
 
         if not text:
-            plt.legend(labels1[0:(idx+1)],loc='upper left')
+            axs[1].legend(labels,loc='upper left')
+
+        if total_sobol:
+            x_mean_tot = self.T_var
+            sens_tot = np.cumsum(x_mean_tot,axis=0).T
+            cnt = 0
+            for i in range(p):
+                x2 = np.concatenate((time[ord], np.flip(time[ord])))
+                if i == 0:
+                    inBetween = np.concatenate((np.zeros(time[ord].shape[0]), np.flip(sens_tot[ord,i])))
+                else:
+                    inBetween = np.concatenate((sens_tot[ord,i-1], np.flip(sens_tot[ord,i])))
+                if (cnt % rgb.shape[0]+1) == 0:
+                    cnt = 0
+
+                axs[2].fill(x2, inBetween, color=rgb[cnt,:])
+                cnt += 1
+
+            axs[2].set(xlabel="x", ylabel="total variance", title="Total Sobol'", xlim=[time.min(), time.max()], ylim=[0, inBetween.max()+3])
 
         fig.tight_layout()
         return

tests/test_sobolBasis.py

@@ -0,0 +1,35 @@
+import pyBASS as pb
+import numpy as np
+
+# Get dataset
+# Friedman function with functional response
+def f2(x):
+    out = (10. * np.sin(np.pi * np.linspace(0, 1, 50) * x[1]) + 20. * (x[2] - .5) ** 2 + 10 * x[3] + 5. * x[4]) * x[5]
+    return out
+
+np.random.seed(0)
+tt = np.linspace(0, 1, 50) # functional variable grid
+n = 500 # sample size
+p = 9 # number of predictors other (only 4 are used)
+x = np.random.rand(n, p) # training inputs
+xx = np.random.rand(1000, p)
+e = np.random.normal(size=[n, len(tt)]) * .1 # noise
+y = np.apply_along_axis(f2, 1, x) + e # training response
+ftest = np.apply_along_axis(f2, 1, xx)
+
+# fit BASS model with RJMCMC
+mod = pb.bassPCA(x, y)
+
+sob = pb.sobolBasis(mod)
+sob.decomp(int_order=3)
+sob.plot()
+
+# Check that T_var is computed correctly
+S_var = sob.S_var
+T_var = sob.T_var
+np.all(T_var[0] == np.sum([S_var[i] for i in range(len(S_var)) if str(1) in sob.names_ind[i]], axis=0))
+np.all(T_var[1] == np.sum([S_var[i] for i in range(len(S_var)) if str(2) in sob.names_ind[i]], axis=0))
+np.all(T_var[2] == np.sum([S_var[i] for i in range(len(S_var)) if str(3) in sob.names_ind[i]], axis=0))
+np.all(T_var[3] == np.sum([S_var[i] for i in range(len(S_var)) if str(4) in sob.names_ind[i]], axis=0))
+### etc
+np.all(T_var[8] == np.sum([S_var[i] for i in range(len(S_var)) if str(9) in sob.names_ind[i]], axis=0))