minor adjust to lambdify test code
Kai Staats
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@ -1,3 +1,8 @@
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2016 09/19
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Minor adjustment to the lambdify test code in 'fx_eval_subs()' method. Testing now ...
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2016 09/16
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With the 09/14 update I failed to upload the new coefficients.csv file to the files/ directory. While not yet engaged,
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@ -17,8 +22,7 @@ I welcome any assistance with these, if anyone has experience and time.
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2016 09/14 - version 0.9.2.0
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In karoo_gp_base_class.py
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- Merged 2 instances of 'algo_sym.subs(data)' into a single, new method 'fx_eval_subs'
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- Removed redundant lines in the method 'fx_karoo_data_load'
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- Removed redundant lines in the method 'fx_karoo_data_load()'
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- Added support for the Sympy 'lambdify' function in 'fx_karoo_data_load' (see explanation below)
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- Added a draft means of catching divide-by-zero errors in the new 'lambdify' function
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- Discovered the prior 'fx_eval_subs' incorrected applied a value of 1 to the variable 'result' as a means to
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@ -26,12 +30,9 @@ In karoo_gp_base_class.py
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Classification and Regression runs. My apology for not catching this sooner.
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"While attending the CHEAPR 2016 workshop hosted by the Center for Cosmology and Astro-Particle Physics, The Ohio State
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University, Erik Hemberg of MIT suggested that I could improve the performance by combining what were to Sympy.subs
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calls into one. This was successfully completed and the new method 'fx_eval_subs' was created.
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Michael Zevin of Northwestern University proposed that Karoo GP *should* be able to process trees far faster than what
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we were seeing. I looked into the Sympy functions I was at that time using. Indeed, '.subs' is noted as easy to use,
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but terribly slow as it relies upon an internal, Python mathematical library. I therefore replaced '.subs' with
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University, Michael Zevin of Northwestern University proposed that Karoo GP *should* be able to process trees far faster
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than what we were seeing. I looked into the Sympy functions I was at that time using. Indeed, '.subs' is noted as easy
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to use, but terribly slow as it relies upon an internal, Python mathematical library. I therefore replaced '.subs' with
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'.lambdify' which calls upon the C-based Numpy maths library. It is slated to be 500x faster than '.subs', but I am
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seeing only a 2x performance increase. Clearly, there are yet other barriers to remove.
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@ -49,7 +50,7 @@ I'll keep you informed ..." --kai
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2016 08/08 - version 0.9.1.9
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In karoo_gp_base_class.py
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- Created a new method 'fx_eval_subs' which conducts the SymPy subs function for both train and test data.
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- Created a new method 'fx_eval_subs()' which conducts the SymPy subs function for both train and test data.
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- Consolidated duplicate SymPy sub calls in both Train and Test methods, at Erik H. suggestion --thank you!
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- Consolidated duplicate lines into single lines in both Train and Test methods.
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- Fixed bug in which Ramped 50/50 trees would not print in Play mode; removed Ramped 50/50 option from Play mode as
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@ -2,7 +2,7 @@
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# Define the methods and global variables used by Karoo GP
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# by Kai Staats, MSc UCT / AIMS; see LICENSE.md
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# Much thanks to Emmanuel Dufourq and Arun Kumar for their support, guidance, and free psychotherapy sessions
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# version 0.9.2.0
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# version 0.9.2.0b
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'''
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A NOTE TO THE NEWBIE, EXPERT, AND BRAVE
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@ -1198,16 +1198,18 @@ class Base_GP(object):
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'''
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### OLD .subs method ###
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#result = self.algo_sym.subs(data) # process the expression against the data
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#if str(result) == 'zoo': result = 1 # TEST & DEBUG: print 'divide by zero', result; self.fx_karoo_pause(0)
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#else: result = round(float(result), self.precision) # force 'result' to the set number of floating points
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subs = self.algo_sym.subs(data) # process the expression against the data
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if str(subs) == 'zoo': pass # TEST & DEBUG: print 'divide by zero', subs; self.fx_karoo_pause(0)
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else: result = round(float(subs), self.precision) # force 'result' to the set number of floating points
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result = round(float(subs), self.precision) # force 'result' to the set number of floating points
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### NEW .lambdify method ###
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f = sp.lambdify(self.algo_ops, self.algo_sym, "numpy") # define the function
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with np.errstate(divide = 'ignore'): # do not raise 'divide by zero' errors
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result = f(*sp.flatten(data.values())) # execute the function against the given data row; which currently remains a dictionary
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# if str(subs) == 'inf' or str(subs) == '-inf': print subs; self.fx_karoo_pause(0) # TEST & DEBUG catch divide by zero
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result = round(float(result), self.precision) # force 'result' to the set number of floating points
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# f = sp.lambdify(self.algo_ops, self.algo_sym, "numpy") # define the function
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# with np.errstate(divide = 'ignore', invalid = 'ignore'): # do not raise 'divide by zero' errors
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# lamb = f(*sp.flatten(data.values())) # execute the function against the given data row; which currently remains a dictionary
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# 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)
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# MAY NOT BE NEEDED - else: result = round(float(lamb), self.precision) # force 'result' to the set number of floating points
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# result = round(float(lamb), self.precision) # force 'result' to the set number of floating points
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return result
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