minor adjust to lambdify test code

RELEASE_NOTES.txt

@@ -1,3 +1,8 @@
+2016 09/19
+
+Minor adjustment to the lambdify test code in 'fx_eval_subs()' method. Testing now ...
+
+
 2016 09/16
 
 With the 09/14 update I failed to upload the new coefficients.csv file to the files/ directory. While not yet engaged, 
@@ -17,8 +22,7 @@ I welcome any assistance with these, if anyone has experience and time.
 2016 09/14 - version 0.9.2.0
 
 In karoo_gp_base_class.py
- - Merged 2 instances of 'algo_sym.subs(data)' into a single, new method 'fx_eval_subs'
+ - Removed redundant lines in the method 'fx_karoo_data_load()'
- - Removed redundant lines in the method 'fx_karoo_data_load'
  - Added support for the Sympy 'lambdify' function in 'fx_karoo_data_load' (see explanation below)
  - Added a draft means of catching divide-by-zero errors in the new 'lambdify' function
  - Discovered the prior 'fx_eval_subs' incorrected applied a value of 1 to the variable 'result' as a means to
@@ -26,12 +30,9 @@ In karoo_gp_base_class.py
  	Classification and Regression runs. My apology for not catching this sooner.
 
 "While attending the CHEAPR 2016 workshop hosted by the Center for Cosmology and Astro-Particle Physics, The Ohio State
-University, Erik Hemberg of MIT suggested that I could improve the performance by combining what were to Sympy.subs
+University, Michael Zevin of Northwestern University proposed that Karoo GP *should* be able to process trees far faster 
-calls into one. This was successfully completed and the new method 'fx_eval_subs' was created.
+than what we were seeing. I looked into the Sympy functions I was at that time using. Indeed, '.subs' is noted as easy 
-
+to use, but terribly slow as it relies upon an internal, Python mathematical library. I therefore replaced '.subs' with
-Michael Zevin of Northwestern University proposed that Karoo GP *should* be able to process trees far faster than what 
-we were seeing. I looked into the Sympy functions I was at that time using. Indeed, '.subs' is noted as easy to use, 
-but terribly slow as it relies upon an internal, Python mathematical library. I therefore replaced '.subs' with
 '.lambdify' which calls upon the C-based Numpy maths library. It is slated to be 500x faster than '.subs', but I am 
 seeing only a 2x performance increase. Clearly, there are yet other barriers to remove.
 
@@ -49,7 +50,7 @@ I'll keep you informed ..." --kai
 2016 08/08 - version 0.9.1.9
 
 In karoo_gp_base_class.py
- - Created a new method 'fx_eval_subs' which conducts the SymPy subs function for both train and test data.
+ - Created a new method 'fx_eval_subs()' which conducts the SymPy subs function for both train and test data.
  - Consolidated duplicate SymPy sub calls in both Train and Test methods, at Erik H. suggestion --thank you!
  - Consolidated duplicate lines into single lines in both Train and Test methods.
  - Fixed bug in which Ramped 50/50 trees would not print in Play mode; removed Ramped 50/50 option from Play mode as 

karoo_gp_base_class.py

@@ -2,7 +2,7 @@
 # Define the methods and global variables used by Karoo GP
 # by Kai Staats, MSc UCT / AIMS; see LICENSE.md
 # Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
-# version 0.9.2.0
+# version 0.9.2.0b
 
 '''
 A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
@@ -1198,16 +1198,18 @@ class Base_GP(object):
 		'''
 		
 		### OLD .subs method ###
-		#result = self.algo_sym.subs(data) # process the expression against the data
+		subs = self.algo_sym.subs(data) # process the expression against the data
-		#if str(result) == 'zoo': result = 1 # TEST & DEBUG: print 'divide by zero', result; self.fx_karoo_pause(0)
+		if str(subs) == 'zoo': pass # TEST & DEBUG: print 'divide by zero', subs; self.fx_karoo_pause(0)
-		#else: result = round(float(result), self.precision) # force 'result' to the set number of floating points
+		else: result = round(float(subs), self.precision) # force 'result' to the set number of floating points
+		result = round(float(subs), self.precision) # force 'result' to the set number of floating points
 		
 		### NEW .lambdify method ###
-		f = sp.lambdify(self.algo_ops, self.algo_sym, "numpy") # define the function		
+		# f = sp.lambdify(self.algo_ops, self.algo_sym, "numpy") # define the function		
-		with np.errstate(divide = 'ignore'): # do not raise 'divide by zero' errors
+		# with np.errstate(divide = 'ignore', invalid = 'ignore'): # do not raise 'divide by zero' errors
-			result = f(*sp.flatten(data.values())) # execute the function against the given data row; which currently remains a dictionary
+		#	lamb = f(*sp.flatten(data.values())) # execute the function against the given data row; which currently remains a dictionary
-		# if str(subs) == 'inf' or str(subs) == '-inf': print subs; self.fx_karoo_pause(0) # TEST & DEBUG catch divide by zero
+		# MAY NOT BE NEEDED - if str(lamb) == 'inf' or str(lamb) == '-inf': pass # TEST & DEBUG: print 'divide by zero', subs; self.fx_karoo_pause(0)
-		result = round(float(result), self.precision) # force 'result' to the set number of floating points
+		# MAY NOT BE NEEDED - else: result = round(float(lamb), self.precision) # force 'result' to the set number of floating points
+		# result = round(float(lamb), self.precision) # force 'result' to the set number of floating points
 		
 		return result